A Qualitative Insight into User Experiences of an Intelligent Tutoring System to Learn Sketching Skills.

Abstract: Abstract Sketching is a valuable skill for many engineering students to support the development of various auxiliary skills such as refined spatial visualization, problem-solving, idea generation, and communication. As the students engage in the engineering design process, sketching skills, along with the auxiliary skills, become valuable tools in future courses and continually in their careers. Explicitly teaching students to sketch is challenging given the instructor-to-student ratio. Hence, intelligent tutoring systems (ITS) are highly beneficial to students in this context to develop these skills actively rather than expect students to develop these skills independently through the needs of other courses. An ITS was introduced through this study to teach sketching skills to students in mechanical engineering courses. The basics of Two-point perspective sketching were the focus of the instruction material facilitated by this ITS. The tutoring platform provides individualized automatic feedback to students immediately after they complete a sketch to inform them of their performance and ultimately to enhance their sketching skill development. This study aims to understand the experiences of graduate and undergraduate mechanical engineering students from three institutions learning sketching through the ITS environment. Our study is guided by the following research questions: 1. What was the engineering student's experience in learning to sketch in an intelligent tutoring platform? 2. What are the strengths, weaknesses, and suggestions for improving the intelligent tutoring system? 3. What are the impacts of the intelligent tutoring System on the sketching self-efficacy of engineering students? In this study, researchers collected data through surveys and semi-structured interviews. The participants were students enrolled in undergraduate and graduate Mechanical Engineering courses at three different institutions where they learned and practiced sketching using the ITS. This study helps us to understand the strengths and weaknesses of the ITS, along with suggestions for how to improve the software from an engineering student perspective. The user experiences of mechanical engineering students were valuable to understand if and how students are finding this particular ITS helpful in their academic lives. The results of the study will be useful to researchers and the engineering education community working to develop educational software tools, to better understand student expectations, and educators interested in identifying a way to incorporate sketching.


Affine medical image registration with fusion feature mapping in local and global

Abstract: Abstract Objective . Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships of the features due to inductive biases, such as weight sharing and locality. This is not conducive to affine registration tasks. Therefore, the evolution of real-time and high-accuracy affine medical image registration algorithms is necessary for registration applications. Approach . In this paper, we propose a deep learning-based coarse-to-fine global and local feature fusion architecture for fast affine registration, and we use an unsupervised approach for end-to-end training. We use multiscale convolutional kernels as our elemental convolutional blocks to enhance feature extraction. Then, to learn the long-range spatial relationships of the features, we propose a new affine registration framework with weighted global positional attention that fuses global feature mapping and local feature mapping. Moreover, the fusion regressor is designed to generate the affine parameters. Main results . The additive fusion method can be adaptive to global mapping and local mapping, which improves affine registration accuracy without the center of mass initialization. In addition, the max pooling layer and the multiscale convolutional kernel coding module increase the ability of the model in affine registration. Significance . We validate the effectiveness of our method on the OASIS dataset with 414 3D MRI brain maps. Comprehensive results demonstrate that our method achieves state-of-the-art affine registration accuracy and very efficient runtimes.


Board 109: BYOE: Laboratory Exercise using Augmented Reality and Virtual Reality for Environmental Engineering Curriculum

Abstract: is a professor of environmental engineering in the Department of Civil, Environmental and Architectural Engineering (CEAE) at the


Expression of model ion channel currents generated using neuron in silico software in real time using dynamic clamp

Abstract: NEURON is the leading in silico software for generating computational models of excitable cells (www.neuron.yale/edu/neuron/). We have previously demonstrated the combining of our real-time, dynamic clamp system with NEURON allowing for interfacing of living cells with NEURON models during electrophysiological recording. In the past, we have shown that real ion channel currents expressed in living cells can be input into NEURON models in real-time. We have now added the ability to input individual NEURON ion channel model currents into living cells. We performed electrophysiological recordings of hiPSC GABA neurons (hiPSC-GNs) (Fujifilm cellular dynamics) using the whole-cell ruptured patch clamp configuration. Insufficient background K+ current expression typically observed in hiPSC-GNs results in depolarized resting membrane potentials (RMP = −29.6 + 0.9 mV, n = 40) and the inability to record stable action potentials (APs). To restore the RMP to physiological values in hiPSC-GNs, we virtually expressed two K+ channel currents, Kv1 and Kv4, from a NEURON model of a Purkinje neuron (Akemann and Knopfel, 2006). Virtual expression of Kv1 or Kv4 hyperpolarized the RMP to −54.6 ± 0.8 (n = 4) and −60 ± 1.6 (n = 4), respectively and physiological AP morphology was restored (Kv1: amplitude = 80.2 ± 4.2 mV; AHP amplitude = 25.9 ± 2.8, APD90 = 2.3 ± 0.1, n = 4; Kv4: amplitude = 85.5 ± 9.1 mV; AHP amplitude = 15 ± 3.2, APD90 = 2.7 ± 0.1, n = 4). Given the vast NEURON model database (ModelDB, https://modeldb.science/), the ability to virtually express a wide variety of ion channel models in living cells can easily be accomplished with our dynamic clamp system allowing electrophysiologists to expand their research capabilities.


Movie Recommendation using Multiple Data Sources

Abstract:
Most of the recommender systems are built for the content or item providers. For example, Netflix recommends movies or TV shows, Amazon recommends books or other items being sold on Amazon, Facebook or Twitter recommends popular posts or tweets, Pinterest recommends related pins, YouTube recommends videos, etc. Most of these recommender systems are designed based on the usage data collected on their own websites.

However, sometimes it could be helpful if we could get information about recommended items from multiple data sources, providing multiple perspectives for users to make their decisions. In this research work, we study different approaches to integrate multiple data sources and the effect on the recommendation results when multiple data sources are used to recommend items. We propose a multiple data source-based movie recommender system that uses MovieLens rating data, YouTube movie trailer data, Netflix rating data, and tweets from Twitter. The user feedback data such as likes, dislikes, comments on movie trailers posted on YouTube can be helpful side information for movie recommender systems. In this research work, we study the effect of adding these side information to the movie rating data. Our proposed recommendation framework can integrate the trailer and rating data adopting various integration strategies: integrating all the trailer data as movie features, using sentiment scores derived from the trailer comments as a rating matrix to integrate with the movie rating matrix, and treating others as the movie features, or only integrating the sentiment score based rating matrix with the movie rating matrix. Our experiment shows that if we include the movie trailer data, recommendation accuracy is improved. We also find that the most accurate result is achieved if all the trailer feedback data is integrated as movie features. We use both Matrix Factorization (MF) and Deep Neural Network (DNN) Models to design our system. We find that the DNN model performs better than the MF model. Our results show that when we include multiple data sources to recommend items using the DNN model, the recommendation accuracy (F1 score) is increased by 41% on average comparing to the case when only one data source is used.


News Recommender System Considering Temporal Dynamics and Accuracy-Diversity Tradeoff

Abstract:
News recommender systems aim to personalize users experience for online news readers and help them discover relevant and interesting news items from a broad and diverse search space. However, recommending news is a challenging task. There are hundreds of news articles published every day, many of which quickly become obsolete and irrelevant to the readers. Readers’ preferences (interests) also exhibit dynamic behavior and the relevance of readers’ preferences strongly depend on the context. Some of the readers’ preferences are long-term, reflecting the personality or behavior, whereas others are short-term, showing their current interests. External events, such as breaking news and trends, also influence readers’ interests. Although the high recommendation accuracy is appreciated, focusing on it too much sacrifices diversity and limits readers’ options.

The main contribution of this research is to design a recommender system to tackle the specific challenges of news recommendations. To address these challenges, we propose a recommendation strategy that seamlessly integrates readers’ long-term and short-term preferences when recommending news items. In addition to high accuracy, we focus on promoting reasonable diversity in news recommendations. To achieve a balanced objective (high accuracy and reasonable diversity), we formulate a combined optimization strategy that includes both of these aspects in the recommendation process.

More specifically, to achieve high accuracy, we propose novel latent factor models such as matrix factorization and a generalized linear model. In addition, we propose a regularized latent factor model to achieve reasonable diversity while maintaining high accuracy. Then, for the same purpose, we propose a deep learning-based framework composed of network components of news modelling and reader modelling. We use different regularization terms in the latent factor model to achieve accuracy-diversity balance, and the neural attention mechanism in deep neural networks for the same purpose.

We also introduce a new evaluation metric to measure the tradeoff performance with respect to accuracy and diversity. Experiments on real-world data have demonstrated the effectiveness of our proposed approach on quality factors such as accuracy, diversity as well as this tradeoff metric. Our model has achieved an improvement on this new tradeoff metric by 10-30% when compared to traditional and state-of-the-art recommendation algorithms.


Smart HomeKit-Enabled Peephole Camera

Abstract: It can be difficult to satisfy the need for smart home convenience devices without invasive procedures that would go against living contracts in an apartment, dormitory, or any rented vacant living area. Creating a device that allows for convenience and security is imperative. The objective of this project is to develop and test a Smart Peephole Door Camera to identify who is at the door. A small, portable 3D printed enclosure that houses a Raspberry Pi, portable battery, and camera fit over a door's peephole on the inside of the door through a non-invasive installation process via 3M Command Strips. This allows the resident to conceal the camera from any outside visitors. The device connects to the user's home network via Wi-Fi to allow access to Apple's HomeKit API to enable remote viewing from a compatible iOS device. In addition, an open-source platform provided by Apple on GitHub with the addition of the HomeBridge lightweight NodeJS server allows seamless integration between the smart peephole camera and the user's iOS device. 3D CAD files created on SolidWorks and Shapr3D were printed and tested through multiple design iterations to ensure that the design of the enclosure and the mounting methods meet the fundamental constraints of this project.


Ensemble memory of a scene interacts with current perception regardless of attentional requirements.

Abstract: How do we maintain a rich and stable perceptual experience across the entire visual scene, even when we are focusing on a subset of visual inputs? The current study explored this question by investigating whether the visual system processes summary statistics of multiple features regardless of task relevance, and how they interact with subsequent perception. To test the processing of multifeature summary statistics under different attentional requirements, we presented multiple Gabor patches with heterogeneous orientations/colors and asked participants to attend to a single feature dimension (Experiments 1 and 3) or a single item (Experiment 2) for the memory task. During the memory maintenance period (before memory response), we asked the participants to perform a discrimination task (Experiments 1 and 2) or a boundary localization task (Experiment 3) to test how the memory of the ensemble representation alters the subsequent perceptual experience. We found evidence for obligatory processing of scene summary statistics presented for the memory task, which interacted with the subsequent perceptual sensitivity. Specifically, not only summary statistics relevant but also those of task-irrelevant feature (Experiments 1 and 3) and outside the focus of attention (Experiment 2) were encoded and bidirectionally interacted with subsequent perception. These results suggest obligatory processing of summary statistics of a scene, which may allow rich and stable visual experience by integrating temporally adjacent visual inputs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Hemodialysis Wastewater Treatment via Electrocoagulation and Electro‐Oxidation: Modular Pilot‐Level Modeling and Simulation

Abstract: Abstract Hemodialysis treatment in specialized clinics within the same hospital significantly impacts environmental water health due to contaminated wastewater. The issues observed included changes in electrical conductivity, the presence of dangerous bacterial loads, toxicity from heavy metals, total cyanide content, and helminth parasite eggs. The level of damage is dependent on the patient's health under treatment. This research will use a modular system that employs electrocoagulation and electro‐oxidation processes at the laboratory and pilot levels to treat hemodialysis wastewater using synthetically prepared and real samples extracted from local clinics. The results showed that these hybrid systems improved various physicochemical parameters. Specifically, decreases in electrical conductivity of 49 %, total suspended solids of 27–100 %, chemical oxygen demand of 49 %, biochemical oxygen demand of 49 %, and cation and anion loading were observed at 96–100 % and pH 8.13 UpH in accordance with the established standards. With these results and the experimental conditions used, the proposed treatment system was modeled using the GPS‐X program, and it was concluded that the modular system used and the electrocoagulation/electro‐oxidation/activated carbon configuration is suitable for treating wastewater from hemodialysis and that scaling up this process to facilities that have dialysate machines more advanced than those considered in this work is possible.


Bulletin WKU

Abstract: Distance education is an urgent problem in the world field of education and science. The abolition of the correspondence form in our country has created a problem for citizens who, while working or due to health, family, social status, set themselves the goal of improving the level of professional education. In the course of the research, the directions of flexible transformation of Kazakhstani and foreign best practices are considered, the purpose of which is to solve the problem of distance learning in the higher education system through the introduction of part-time study. The authors study the experience of distance learning in the USA and European countries and present it with quantitative and qualitative data. The research uses methods of analysis of foreign experience historical, chronological, social, pedagogical scientific works, academic reports. Pedagogical conditions aimed at introducing the best of foreign experience in part-time study into the Kazakh higher education have been formulated. The article clearly presents trends in the transformation of foreign digital education experience into the higher education system in Kazakhstan. An exploration focused on the development of the part-time teaching methodology is still ongoing and offers a rational methodology in the future.


Built Custom Alexa Skills using AWS

Abstract: Abstract:The creation of unique Alexa skills using the Amazon Web Services (AWS) infrastructure is the focus of this project. Alexa skills are voice- activated features that improve Alexa, the virtual assistant from Amazon, in terms of functionality. The project's main goal is to demonstrate the flexibility and adaptability of AWS in voice- activated interactions by developing custom skills that are suited to certain use cases or applications. During the development process, the bespoke Alexa skills are hosted and executed using AWS Lambda, an event-driven computing service. With AWS Lambda, code can be seamlessly integrated without requiring server management, guaranteeing a scalable and effective solution. The stability and dependability of the custom Alexa skills are further enhanced by the extensive suite of tools and services offered by AWS, including the Alexa Skills Kit (ASK), DynamoDB for data storage, and IAM (Identity and Access Management) for security. The complexities of voice-activated technologies, AWS infrastructure, and Alexa skill development are investigated through this research. With the help of the Alexa ecosystem and customized skills, companies, developers, and enthusiasts can use AWS's power to create unique and interesting voice experiences. The project's main ideas are summarized in this abstract, which highlights the use of AWS technology to develop customized and inventive Alexa skills. Keywords:Custom Alexa skills, AWS, Voice- activated technology, Alexa Skills Kit, AWS Lambda, DynamoDB, IAM.


A novel demonstration of preparation in pop-out search.

Abstract: There is an ongoing debate among visual attention researchers about whether top-down processes contribute to pop-out search. In the present study, we describe a new method to orthogonally manipulate top-down preparation and feature priming in a pop-out search task. On each trial, participants viewed a single-item (randomly blue or orange) followed by a pop-out search display (randomly blue target with orange distractors, or vice versa). Preparation was induced by instructing participants to respond to the single-item if it was a particular colour and to ignore it otherwise-but to respond to the odd-coloured target in all following pop-out search displays. This method allowed us to examine whether top-down preparation for the single-item influenced subsequent pop-out search. Our results revealed a large effect of preparation for the single-item on subsequent search response times. We discuss this result in relation to the interplay between top-down control and selection history effects in pop-out visual search. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Optical bistability and flip-flop function in feedback Fano laser

Abstract: Optical bistability has the potential to emulate the capabilities of electrical flip-flops, offering plenty of applications in optical signal processing. Conventional optical bistable devices operate by altering the susceptibility of a nonlinear medium. This method, however, often results in drawbacks such as large device size, high energy consumption, or long switching times. This work proposes an optical bistable device incorporating strong optical feedback into a Fano laser. This leads to multiple stable states and introduces a region of bistability between the inherent Fano mode and a feedback-induced Fabry-Perot mode. Unlike conventional bistable devices, the Fano system exploits strong field localization in a nanocavity to control the properties of one of the laser mirrors. This configuration means that switching states can be achieved by modulating the mirror's loss rather than changing the susceptibility of the active medium. Importantly, modulation can be implemented locally on a nanocavity, bypassing the need to adjust the entire laser system. This leads to fast flip-flop actions with low energy consumption. The feedback Fano laser can be embodied in a compact microscopic structure, thus providing a promising approach towards integrated all-optical computation and on-chip signal processing.


Sentiment Analysis

Abstract: Abstract Project Members • Ravin Jajistar • Sagar Sathynarayanan • Michael Shiferaw Sentiment Analysis: JSS Soultions LLC Sentiment Analysis, is a computer program that assesses data from user experience, and categorizes them as positive, neutral, and negative in order to improve a service or product. In this project, individuals will develop a program that will grab tweets from twitter feeds, and run these tweets against a dictionary and categorize the tweet as positive, negative, and neutral. The program will then generate a report of the feedback, and if properly analyzed and assessed correctly, companies will be able to improve on their services, or products. Part of the project is establishing a small consultant business, mainly a LLC under the name JSS Solutions LLC and then going out and signing clients in order for us to help provide to improve their service or product. The product will be first tested among the Morgan state community on the safety around campus, hence contributing towards the community.


Heuristic Planning for Continuous Systems in Hybrid Temporal Situation Calculus

Abstract: Given a description of domain and its dynamics, temporal numeric planning attempts to find a sequence of actions that satisfies a given set of constraints for a dynamical system. Current planners operate on grounded transition systems and discretized representations of the domain which lead to poor scalability. Furthermore, given the problem’s difficulty, most modern planners restrict their capabilities to a subset of hybrid domains, e.g. support for only polynomial evolution of numeric state variables and linear action conditions. To address these concerns, we present a lifted planner, NEAT (Non-linEAr Temporal) Planner, that utilizes a logical description of the domain described in Hybrid Temporal Situation Calculus. Furthermore, we develop AMPLEX, an interface to AMPL and several non-linear programming solvers, which allows us handle several non-linear functions. We also present a novel non-linear programming based heuristic to improve scalability. Lastly, we perform a detailed comparison between current state-of-the-art solvers and our planner.


Intelligent deep learning-based disease monitoring system in 5G network using multi-disease big data

Abstract: Recently, real-world disease monitoring techniques designed based on wearable medical equipment efficiently minimize the mortality rate. Initially, the data are manually collected from the patients to predict five diseases using 5 G frameworks. Then, the collected data are pre-processed to obtain high-quality data using the techniques like contrast enhancement, median filtering, fill empty space, remove repeated value and stemming. The pre-processed data are taken for extracting the features using a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the deep features. The parameters like hidden neuron count and epoch are tuned by the proposed Modified Predator Presence Probability-based Squirrel Search-Glowworm Swarm Optimization (MPPP-SSGSO) algorithm to enhance the variance. Then, the extracted features acquired using the 1D-CNN are given to the ensemble boosting-based models for predicting the score, which is combined by comprising approaches like Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost) and Category Boosting (CatBoost). Further, the predicted scores obtained from such models are concatenated and passed to the Ensemble Boosting Scores-based Fuzzy Classifier (EBS-FC) for classifying the five different diseases. Here, the membership function of the fuzzy is optimized by the same developed MPPP-SSGSO algorithm for enhancing accuracy. Experiments are conducted, and validation is performed, which showcased that the recommended framework achieved a better outcome rate than the conventional techniques. Finally, the suggested strategy outperforms the current state-of-the-art methods with an accuracy rate of 91.34%.


The teaching electromagnetism using digital technologies

Abstract: The Physics of Electromagnetism began in ancient Greece and after centuries the studies of Electricity and Magnetism became unified. Although Electromagnetism is present in the curricular components of Brazilian education and in the daily lives of students, the difficulties that students have in assimilating the basic concepts related to this topic are notable. In this context, a didactic and practical mini-course was proposed where Digital Information and Communication Technologies (DICT) were used, conveniently to teach students some concepts of Electromagnetism. During the activity, two questionnaires were administered, one before the mini-course and the other after it. The first composed of seven questions (objective and discursive) aimed to obtain students' prior knowledge in relation to Electromagnetism. The second questionnaire consisted of four questions, three of which were paired with questions from the first questionnaire. It was applied with the aim of evaluating whether the mini-course carried out favored student learning, thus revealing possible signs of significant learning. The post-questionnaire also presented a discursive question that asked students' opinions about the mini-course developed based on DICT. This research can be characterized as qualitative and quantitative and from the data analysis, it was noticed that the activities developed with high school students favored their learning as observed in the best results revealed in the post-questionnaire. It is concluded that the use of DICT should be encouraged in schools, as they constitute tools that alleviate difficulties and help students learn.


Quantitative Forecasting of Malaria Parasite Using Machine Learning Models: MLR, ANN, ANFIS and Random Forest

Abstract: Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.


Advancing weakly supervised semantic segmentation using class activation map augmented with graph attention module

Abstract: In the field of computer vision, Weakly Supervised Semantic Segmentation (WSSS) at the image level poses a significant research challenge. Existing WSSS methods primarily rely on class activation maps (CAM). Due to the disparity between fully-supervised and weakly-supervised approaches, CAM often yields imprecise and coarse semantic information in generating target masks. To address the issues of semantic coarseness and detail loss in image-level WSSS, this paper introduces a novel approach that utilizes pixel-level relationships to optimize and refine the features extracted by the network. Maintaining the foundation of image-level class label weak supervision for semantic segmentation, this study forgoes extensive modifications to the model structure in favor of incorporating a Graph Attention Module (GAM). This module, by constructing a weighted undirected graph with pixels as vertices and pixel affinity as edges, simulates the interactions among neighboring pixels in graph representation learning. Such an arrangement allows for the effective propagation and integration of relational information between pixels, thereby enhancing the overall representational quality of the image. By employing graph constraints to facilitate semantic dissemination, our method optimizes the network's localization map, resulting in more precise CAMs and more accurate pseudo-labels for subsequent semantic segmentation network training. To evaluate our approach, we conducted quantity and quality experiments on the PASCAL VOC 2012 and MS COCO 2014 datasets. Compared to existing advanced weakly-supervised semantic segmentation methods, our approach shows notable improvements in performance.


Blockchain Applications in Retail Cybersecurity: Enhancing Supply Chain Integrity, Secure Transactions, and Data Protection

Abstract: Blockchain technology has proven a powerful tool for reinforcing cybersecurity in the retail sector. This research offers an extensive overview of the applications of blockchain in retail cybersecurity, particularly, underscoring supply chain integrity, data protection, and transaction security. The research explored how blockchain can facilitate provenance and traceability well as prevent counterfeiting and enhance vendor management in the supply chain. It also explores how blockchain-based payment frameworks and fraud detection systems can boost transaction security. Moreover, the study assesses the capability of blockchain to safeguard data via privacy and consent management, and secure and immutable data storage. The findings outline the capability of blockchain technology to diminish risks, enhance transparency, and affirm trust in retail cybersecurity. While challenges are inevitable, such as regulatory and scalability considerations, the research infers that blockchain technology presents noteworthy opportunities for innovation and advancement in the retail industry's cybersecurity landscape.


Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Abstract: Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed. Keywords: Machine Learning, Supervised, Classification, Regression, Decision Tree.


A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR)

Abstract: The limited transparency of the inner decision-making mechanism in deep neural networks (DNN) and other machine learning (ML) models has hindered their application in several domains. In order to tackle this issue, feature attribution methods have been developed to identify the crucial features that heavily influence decisions made by these black box models. However, many feature attribution methods have inherent downsides. For example, one category of feature attribution methods suffers from the artifacts problem, which feeds out-of-distribution masked inputs directly through the classifier that was originally trained on natural data points. Another category of feature attribution method finds explanations by using jointly trained feature selectors and predictors. While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features. As a result, the credibility of attribution results is undermined by these downsides. In this research, we introduce the Double-sided Remove and Reconstruct (DoRaR) feature attribution method based on several improvement methods that addresses these issues. By conducting thorough testing on MNIST, CIFAR10 and our own synthetic dataset, we demonstrate that the DoRaR feature attribution method can effectively bypass the above issues and can aid in training a feature selector that outperforms other state-of-the-art feature attribution methods. Our code is available at https://github.com/dxq21/DoRaR.


Longitudinal attacks against iterative data collection with local differential privacy

Abstract: Local differential privacy (LDP) has recently emerged as an accepted standard for privacy-preserving collection of users' data from smartphones and IoT devices. In many practical scenarios, users' data needs to be collected repeatedly across multiple iterations. In such cases, although each collection satisfies LDP individually by itself, a longitudinal collection of multiple responses from the same user degrades that user's privacy. To demonstrate this claim, in this paper, we propose longitudinal attacks against iterative data collection with LDP. We formulate a general Bayesian adversary model, and then individually show the application of this adversary model on six popular LDP protocols: GRR, BLH, OLR, RAPPOR, OUE, and SS. We experimentally demonstrate the effectiveness of our attacks using two metrics, three datasets, and various privacy and domain parameters. The effectiveness of our attacks highlights the privacy risks associated with longitudinal data collection in a practical and quantifiable manner and motivates the need for appropriate countermeasures.


Interpretable Artificial Intelligence in Cardiovascular Health: An In-depth Analysis of Heart Disease Data

Abstract: With the escalating abundance of structured and unstructured data and the rapid advancements in analytical techniques, Artificial Intelligence (AI) is catalyzing a revolution in the healthcare industry. However, as AI becomes increasingly indispensable in healthcare, concerns are mounting regarding the lack of transparency, explainability, and potential bias in model predictions. Addressing these issues, Explainable Artificial Intelligence (XAI) emerges as a pivotal solution. XAI plays a crucial role in fostering trust among medical practitioners and AI researchers, thereby paving the way for the broader integration of AI in healthcare. This paper aims to introduce diverse interpretability techniques, shedding light on the comprehensibility and interpretability of XAI systems. These techniques, when applied judiciously, offer significant advantages in the healthcare domain. Given that medical diagnosis models directly impact human life, it is imperative to instill confidence in treating patients based on instructionsfrom seemingly opaque models. The content of this paper includes illustrations grounded in the heart disease dataset, demonstrating how explainability techniques should be prioritized to establish trustworthiness when utilizing AI systems in healthcare. Keywords: Explainable AI, Healthcare, Heart disease, Programming frame- works, LIME, SHAP, Example-based Techniques, Feature-based Techniques.


Analisis Kesalahan Tata Tulis Bahasa Jawa Pada Siswa Kelas VII MTs AL Muslihun Tlogo Kanigoro

Abstract: The purpose of this research is to describe grammatical errors in writing Javanese for grade 7 students and to describe what are the factors that cause errors in writing Javanese letters. This study uses a type of qualitative descriptive research. Test and interview methods were used to collect data. Data analysis in this study used descriptive data analysis and the results showed that there were several grammatical errors in the Javanese language in the form of errors in the use of vowels. Factors causing errors in writing are due to a lack of learning and understanding of Javanese vowels


A Hybrid Cryptosystem Incorporating a New Algorithm for Improved Entropy

Abstract: Today, safeguarding sensitive content through encryption is crucial. This work presents a hybrid cryptosystem for images that employs both asymmetric and symmetric encryption. The asymmetric component involves applying the Diffie-Hellman protocol and the ElGamal cryptosystem to securely transmit two constants. These constants are necessary for the symmetrical aspect to generate dynamic permutations, substitution boxes, and round keys. Following an encryption process with fourteen rounds, the encrypted images are processed by an algorithm proposed to enhance entropy, a critical metric for assessing encryption quality. It increases the frequencies of the basic colors to achieve a histogram closely resembling a uniform distribution, but it increases the image size by approximately 8%. This improves the entropy values achieved by the hybrid cryptosystem, bringing them remarkably close to the ideal value of 8.0. In specific instances, the entropy values were elevated from 7.99926 to 8.0. The proposed method exhibits resilience against various attacks, including differential, linear, brute force, and algebraic attacks, as evaluated through the entropy, correlation, goodness of fit, Discrete Fourier Transform (DFT), Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Avalanche Criteria (AC), contrast, energy, and homogeneity. Further, encrypted images are subjected to noise attacks ranging from 20% to 50% noise, including additive, multiplicative, occlusion noise, as well as the newly introduced χ2 noise. The noise damage is quantified using the proposed Similarity Parameter (SP), and a 3 × 3 median filter is employed to enhance the visual quality.


INSANet: INtra-INter Spectral Attention Network for Effective Feature Fusion of Multispectral Pedestrian Detection

Abstract: Pedestrian detection is a critical task for safety-critical systems, but detecting pedestrians is challenging in low-light and adverse weather conditions. Thermal images can be used to improve robustness by providing complementary information to RGB images. Previous studies have shown that multi-modal feature fusion using convolution operation can be effective, but such methods rely solely on local feature correlations, which can degrade the performance capabilities. To address this issue, we propose an attention-based novel fusion network, referred to as INSANet (INtra-INter Spectral Attention Network), that captures global intra- and inter-information. It consists of intra- and inter-spectral attention blocks that allow the model to learn mutual spectral relationships. Additionally, we identified an imbalance in the multispectral dataset caused by several factors and designed an augmentation strategy that mitigates concentrated distributions and enables the model to learn the diverse locations of pedestrians. Extensive experiments demonstrate the effectiveness of the proposed methods, which achieve state-of-the-art performance on the KAIST dataset and LLVIP dataset. Finally, we conduct a regional performance evaluation to demonstrate the effectiveness of our proposed network in various regions.


Merge-and-Split Graph Convolutional Network for Skeleton-Based Interaction Recognition

Abstract: We introduce an innovative approach to address a significant challenge in interaction recognition, specifically the capture of correlation features between different interaction body parts. These features are often overlooked by traditional graph convolution networks commonly used in interaction recognition tasks. Our solution, the Merge-and-Split Graph Convolutional Network, takes a unique perspective, treating interaction recognition as a global problem. It leverages a Merge-and-Split Graph structure to effectively capture dependencies between interaction body parts. To extract the essential interaction features, we introduce the Merge-and-Split Graph Convolution module, which seamlessly combines the Merge-and-Split Graph with Graph Convolutional Networks. This fusion enables the extraction of rich semantic information between adjacent joint points. In addition, we introduce a Short-term Dependence module designed to extract joint and motion characteristics specific to each type of interaction. Furthermore, to extract correlation features between different hierarchical sets, we present the Hierarchical Guided Attention Module. This module plays a crucial role in highlighting the relevant hierarchical sets that contain essential interaction information. The effectiveness of our proposed model is demonstrated by achieving state-of-the-art performance on 2 widely recognized datasets, namely, the NTU60 and NTU120 interaction datasets. Our model's efficacy is rigorously validated through extensive experiments, and we have made the code available for the research community at https://github.com/wanghq05/MS-GCN/.


Motivations for Artificial Intelligence, for Deep Learning, for ALife: Mortality and Existential Risk

Abstract: We survey the general trajectory of artificial intelligence (AI) over the last century, in the context of influences from Artificial Life. With a broad brush, we can divide technical approaches to solving AI problems into two camps: GOFAIstic (or computationally inspired) or cybernetic (or ALife inspired). The latter approach has enabled advances in deep learning and the astonishing AI advances we see today-bringing immense benefits but also societal risks. There is a similar divide, regrettably unrecognized, over the very way that such AI problems have been framed. To date, this has been overwhelmingly GOFAIstic, meaning that tools for humans to use have been developed; they have no agency or motivations of their own. We explore the implications of this for concerns about existential risk for humans of the "robots taking over." The risks may be blamed exclusively on human users-the robots could not care less.


Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

Abstract: Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg−1) for Cu and 23.4 (mg kg−1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.


Extended Reality—New Opportunity for People With Disability? Practical and Ethical Considerations

Abstract: Since the introduction of virtual environments in the 70s, technologies have moved through virtual reality, mixed reality, and augmented reality into extended reality (XR). This development is promising for various groups. Previous research has shown people with disability benefiting from using technology in social and professional settings. Technology has offered people with disability the opportunity to communicate, interact, participate, and build new relationships. However, we do not know what impact XR has or will have and whether it will offer new opportunities for people with disability. This paper aims to indicate potential opportunities and challenges afforded by XR to people with disability. We offer reflections on the opportunities as well as the ethical considerations needed when introducing immersive technologies to a marginalized group.


REDISCOVER PARADISE: ELEVATING BALI’S MARINE TOURISM THROUGH SUSTAINABLE SOLID WASTE MANAGEMENT (SSWM)

Abstract: As a global tourist destination, Bali’s marine ecosystem holds significant value for the leisure industry. However, along with the enormous growth of the industry, the island also faces an imminent waste crisis. As of 2022, 1,085,507 metric tonnes of waste are produced by the island, which 18% of it or 190,726 metric tonnes of waste left unmanaged. Coupled with monsoon and wet season weather that delivers trash ashore, effective waste management holds a pivotal role to preserve the marine ecosystem of the island and its tourism industry. By conducting a qualitative descriptive method, this research proposes sustainable solid waste management (SSWM) as an attempt to combat the waste crisis on the island. This research aims to depict the feasibility – cost, and benefit – of implementing the SSWM. The findings of this research conclude that the implementation of SSWM will benefit the marine ecosystem and further sustain tourism in Bali.


INVESTIGATING THE IMPACT OF INNOVATION TECHNOLOGIES IN LOGISTICS (CROSSING TRANSPORTATION MODEL: CASE STUDY OF THE LANGARA - KENDARI ROUTE)

Abstract: Konawe Islands is a regency in the Southeast Sulawesi Province. This area heavily relies on Kendari City, the provincial capital, to support daily necessities, recreational activities, and higher education pursuits for the people of Konawe Islands. Wooden boats and ferries serve as the primary means of transportation for the locals. However, during harsh weather, only ferries is operational. Yet, there have been loading violations on ferries, increasing the risk to maritime safety. This research aims to formulate ways to address loading issues on ro-ro ferries along the Langara-Kendari route, ensuring compliance with laws and facilitating smooth cargo distribution. The distribution system is structured based on the method persona user and empathy mapping. Research findings indicate that distribution service users require a freight forwarder for large-scale cargo and consolidation for smaller-scale loads. Therefore, the author recommends the use of applications like Uber, Grab, and Uber to resolve this issue.


College Chatbot

Abstract: Creating a chatbot to be used by the students to get their queries responded easily from the college website. The College Chatbot has the capacity to make friendly conversations; respond the course and faculty details; give the link for the academic calendar; answer the frequently asked questions; calculate the fees based on the student's input; and give the timings, address, contacts, and events information of the departments like Union and Library. To build the chatbot, Microsoft Azure bot service as well as Microsoft cognitive services, namely, Text Analytics, LUIS, and QnA Maker are used. In order to address these problems, the College Enquiry Chatbot extends the implementation of the current chatbots by adding sentiment analysis and active learning


Children’s Accent-Based Preferences and Stereotypes in Media Contexts

Abstract: Children spend a substantial amount of time consuming screen media—particularly television and mobile apps. Research has shown that non-native and non-standard accents tend to be underrepresented in adult-oriented and children’s media, and that representations of accents tend to be stereotypical in nature. Relatedly, research has shown that children demonstrate social preferences and ascribe traits on the basis of spoken accents. In a series of four experiments, the present research investigated children’s accent-based preferences and stereotypes in two media contexts. Children aged 5-6 and 9-10 years selected characters from a variety of exemplars that spoke in different accents to play different archetypical characters in a television program (Study 1) or to serve as teachers in an educational mobile app (Study 2). Study 1 (Experiments 1 and 2) showed that children generally preferred for television characters to speak with a native, Canadian accent (compared to high-status British and non-native accents). However, 9-10-year-old children held stereotypes about the Russian accent specifically, and preferred for negative television characters (e.g., mad scientist, villain) to speak with a Russian accent. The same pattern was not observed for Chinese or Indian accents. Study 2 (Experiments 3 and 4) revealed that children generally preferred for teaching characters in educational apps to speak with a native, Canadian accent (compared to high-status British and non-native accents), particularly when the subject matter being taught was culturally neutral (e.g., oceans, space). Children aged 9-10 preferred for teaching characters to speak with non-native accents (Russian, Chinese, and Indian) when the subject matter was culturally relevant (e.g., Russian dancing, Chinese pottery, Indian cuisine). Findings of this research contribute to our knowledge about children’s accent-based biases in social perception and accent-based assumptions about cultural expertise, and may help guide the development of more inclusive and less stereotypical media offerings for children.


Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Abstract: Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aimed of this study to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.


PENGARUH MEDIA PEMBELAJARAN WORDWALL TERHADAP MOTIVASI BELAJAR MATEMATIKA PERKALIAN BILANGAN BULAT

Abstract: This research aims to determine the effect of using word wall based educational games in mathematics learning on student learning motivation in grade VI at SDN Bojonggenteng Elementary School. This research is quantitative research using survey methods. The subjects of this research were 87 Class VI students, where the sample was 37 Class VI students. The data collection technique in this research is by using a questionnaire. Information obtained from respondents using this questionnaire was used to determine the effect of using wordwall-based educational games on learning motivation. This research uses partial testing (t test) to determine the hypothesis. The hypothesis results obtained based on the partial test (t test) are 3.552 (tcount) > 2.045 (ttable) with a significance of 0.000 < 0.05. These results show that H₁ is accepted and H₀ is rejected, which means that there is a positive and significant influence between wordwall-based learning media on learning motivation in mathematics learning for class V1 students at SDN Bojonggenteng.


A 5-D memristive hyperchaotic system with extreme multistability and its application in image encryption

Abstract: Abstract By coupling memristors to nonlinear circuits, more complex dynamical behaviors can be induced. However, to date, there has been insufficient attention given to high-dimensional chaotic systems based on memristors. In this paper, a magnetic-controlled memristor is combined with a three-dimensional chaotic system, resulting in a five-dimensional memristive chaotic system. Through dynamic analysis and numerical simulations, the chaotic nature of the system is elucidated based on fundamental system behaviors, including Lyapunov dimension, dissipativity, stability of equilibrium points, 0–1 test, and Poincaré mapping. During the complex dynamical analysis of this system, unique dynamical behaviors are discovered, including intermittent chaos, transient chaos, extreme multistability, and offset-boosting. Moreover, the consistency between numerical calculations and the physical implementation of the actual system is verified through equivalent circuit design. Finally, this system is applied to image encryption, leading to the design of an efficient and secure hyper-chaotic image encryption algorithm, whose effectiveness is confirmed through several security tests.


Benefits of adding radial phase dimples on scalar coronagraph phase masks

Abstract: Current scalar coronagraph focal plane mask designs are performance-limited by chromaticity. We investigate the effects of adding central Roddier and dual zone phase dimples to scalar masks to improve broadband performance by suppressing the chromatic stellar leakage. We present hybrid designs with radial phase dimples integrated with the sawtooth vortex, wrapped vortex, and cosine phase mask. We show that, using these dimples, it is possible to substantially improve the broadband contrast performance of scalar phase masks. We also show that, although adding a phase dimple does not increase the sensitivity to low-order aberrations, suppressing the central leakage of scalar vortex coronagraphs does not restore the aberration sensitivities to their notional state.


A detailed study of speech signal cryptography using simple Put _operation and Get_operation

Abstract: Protecting digital speech files is an important issue. In this paper's research, a simplified method of speech file cryptography will be provided, and the encryption and decryption functions will require a reduced number of operations. The encryption function will perform a put_operation to reorder the speech sample to get the encrypted file, while the decryption function will perform a get_operation to reorder the encrypted samples to get the decrypted speech file; these operations will be implemented based on the generated secret indices key. The Put and get operations will affect the speech file by recording the samples keeping the histograms of the speech files (source, encrypted, and decrypted) the same and without any changes. The secret key generation phase will be analyzed for efficiency purposes, and two methods will be presented: The first one will use the chaotic logistic map model to generate the secret indices key, while the second method will use a selected secret image to generate the secret indices key, both methods will be tested and examined to give some recommendations for the users. Each of the introduced methods will provide a high level of security, the private keys will provide a huge key space and they will be very sensitive to resist any hacking attacks. The quality, sensitivity, security, and speed of the proposed methods will be examined, the method will be tested and implemented, and the obtained results will be analyzed to prove the achievements provided by the proposed method.


WAF and WMF to improve the performance of average and median filters

Abstract: Salt and spice noise is considered one of the most common types of noise that affect grayscale and color digital images, as it affects them negatively, and this negative effect increases with an increase in the noise ratio. Many digital filters are used to mitigate the negative effects of salt and pepper noise, and the most widely used of these filters are the average filter and the median filter. The average and median filters work on processing all pixels in the image, whether these pixels are intact from the noise or infected with it, and accordingly, mitigating this noise, especially if it has a high noise ratio, is ineffective. In this research paper, new window average and window median filters will be proposed to enhance the performance of standard average and median filters. The proposed filter will treat the infected pixels, leaving the clean pixels as they were. For a noisy pixel, a special window will be created and the pixel value will equal the mean (or average) of the cleaned pixels in the window (excluding the noisy pixels). To simplify the window processing an index window will be used, this window will point to the noisy and cleaned pixels in the selected pixel window. Several images with various values of noise ratios will be tested; several windows with different sizes will be examined to get the most suitable window size. The selected window size will be used to filter various noisy images, the obtained results will be compared with average and median filter results to show the improvements provided by the proposed method.


Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

Abstract: In the context of real-time video defogging, accurately estimating the transmittance of sky regions and deep fault regions remains a challenging task due to severe color distortion and halo effects. To address this issue, we propose an innovative adaptive wavelet transform defogging (AWTD) scheme aimed at precisely modifying the transmittance of the sky and deep fault regions. Additionally, we present a dedicated field programmable gate array defogging architecture capable of direct application for real-time synchronous defogging of Full HD (1920×1080) videos. Through extensive experiments and comparisons, we demonstrate the effectiveness and practicality of the proposed AWTD approach in mitigating color distortion, halo effects, and other fog-induced artifacts. The results show that the dehazing algorithm attains 60 frames per second for the image resolution of 1920×1080, which is suitable of real time applications.


Monocular vision-based dynamic calibration method for determining the sensitivities of low-frequency tri-axial vibration sensors

Abstract: Low-frequency vibrations exist widely in the natural environment and in human activities. Low-frequency tri-axial vibration sensors are enormously applied in the fields of seismic monitoring, building structure health monitoring, aerospace navigating, etc. Their sensitivity calibration accuracy directly determines whether their applications can work reliably. Although the laser interferometry recommended by the International Standardization Organization (ISO) is commonly used to achieve the vibration calibration, it suffers from the shortages of low-frequency range, high cost, low efficiency, and limited applicable environment. In this study, a novel monocular vision-based dynamic calibration method is proposed, which determines the whole sensitivities of tri-axial sensors by the monocular vision method to accurately measure the spatial input excitation. This method improves the calibration performance by eliminating the installation error and enhancing calibration efficiency via decreasing reinstallations. The experimental results compared with the laser interferometry demonstrate that the investigated method can obtain similar calibration accuracy in the range of 0.16-2 Hz with more efficiency. The corresponding maximum relative deviations of X-, Y-, and Z-axial sensitivities were approximately 2.5%, 1.8%, and 0.4%. In addition, the maximum relative standard deviation of the investigated method was only about 0.3% in this range.


Implementing distributed collaboration and applying the YOLO algorithm to robots

Abstract: Recently, the world has witnessed the development of artificial intelligence and robot programming, which have enabled robots to work together to perform specific assigned tasks while overcoming obstacles in the environment. Robots can now operate independently of each other. In this research, four-wheeled robots were created in webots and placed in different environments in Gazebo. These robots are associated with LIDAR and Kinect cameras. Due to the distributed collaboration between robots, a robot cannot traverse a path that it has previously traversed. The three robots are given the same goal, and the first robot to reach the goal signals the end of the mission to the remaining robots and learns about the surrounding objects on the way to the goal. Therefore, the YOLO algorithm was used (You Only Look Once). This is one of the best algorithms for detecting objects in their environment. Regarding the results obtained in the simulation, the robot performed all the assigned tasks.


Predicting visually induced motion sickness with lifestyle factors: An online survey study

Abstract: Visually induced motion sickness (VIMS), a type of motion sickness (MS), is a common side-effect of visual devices and Virtual Reality (VR) usage. Due to its complexity, individual lifestyle factors may be possible predictors of experiencing VIMS and MS. This study aimed to (1) explore the association between lifestyle factors of video game use, fitness, diet, and substance use and self-reported VIMS and MS, as well as, to (2) determine the overall prevalence of VIMS. Responses to an online survey from 325 adults, between the ages of 18-49, were collected and analysed. Significant sex differences were observed for both VIMS and MS susceptibility. With regards to VIMS susceptibility, lifestyle factors including video game usage, physical activity as well as the consumption of fruits and vegetables, legumes, and carbohydrates were identified as relevant predictors. Lifestyle factors were noted to be not relevant for MS susceptibility.


A Survey on Computer Vision and Its Applications

Abstract: Computer vision is an artificial intelligence field that teaches computers to interpret and comprehend images. Using digital images from cameras, movies, and deep learning models, machines can precisely identify and locate objects, then react to what they "see." As computer vision progressed, programming methods were developed to address specific problems. With practice, machines improved their vision recognition abilities. Deep learning techniques and technology have advanced dramatically over the years. We can now programme supercomputers to self-train, self-improve over time, and deliver capabilities to enterprises as web applications. Keywords— Solid-state physics, Neurobiology, Signal processing, Robotic Navigation, Distinctions


Research on logistics supply chain optimization strategy based on machine learning

Abstract: With the process of globalization and the continuous development of the economy, the role of the logistics supply chain in corporate operations has become more and more prominent, and its optimization has become a core topic of modern management. This research aims to explore the optimization strategy of logistics supply chain through machine learning technology. First, based on an in-depth analysis of machine learning theory and supply chain processes, a specific machine learning model adapted to this scenario was selected, and empirical research was conducted to explore the problems and challenges that may be encountered in practical applications, and for this purpose, specific suggestions were put forward. sexual optimization plan. It is expected that through the application of machine learning, efficient, accurate and flexible management of the logistics supply chain can be achieved, bringing substantial benefit improvements to modern enterprises.


Analysis of the destruction resistance of multi-mode freight network in Beijing-Tianjin-Hebei urban agglomeration

Abstract: Based on the county-level unit cities in the Beijing-Tianjin-Hebei urban agglomeration as nodes, the multi-mode freight network topology model between urban agglomerations is constructed, and Python is used to realize random attack simulation and Intentional attack simulation of multi-mode freight network and single-mode freight network of urban agglomeration, and the multi-mode freight network and single-mode freight network of urban agglomeration are evaluated by maximum connectivity index and network efficiency index. This paper selects the Beijing-Tianjin-Hebei urban agglomeration as an example object, mainly measures the destruction resistance of the road-railway multi-mode freight network of the urban agglomeration, hoping to provide decision-making support for the freight safety and improve the destruction resistance of the freight network to natural and man-made disasters in the Beijing-Tianjin-Hebei region.


Unmanned surface vehicles path planning based on improved PRM algorithm

Abstract: Unmanned Surface Vehicles(USV) path planning has important application value in the field of autonomous navigation. In order to plan a path with simple calculation, less time consumption and low complexity, an improved Probabilistic Roadmap(PRM) algorithm based on USV path planning is proposed. The randomly generated sampling points in the traditional PRM algorithm results in high computational complexity and insufficient flexibility and smoothness in the planned path. In order to optimize the generation strategy of sampling points and improve the smoothness of the path, the boundary box analysis technology and Catmull-Rom Spline interpolation of the path are introduced into the PRM algorithm to obtain I-NPRM algorithm. Using three different path planning schemes for simple, complex and special water environments and comparing them with the planning effectiveness of I-NPRM algorithm, NPRM algorithm, PRM algorithm and A* algorithm under three indicators of runtime, path length and number of inflection points. The experimental results indicate that the I-NPRM algorithm can optimize the sampling point generation strategy while reducing runtime and path length, as well as reducing the number of inflection points. The planned path has high smoothness, which is more in line with the motion constraints and maneuverability of the USV and improves the safety and stability of the autonomous navigation system


Development of a suspended intelligent detection system for pipe culverts

Abstract: To solve the problems of low detection efficiency, high safety risk and production cost of urban pipe culverts and drainage outlets, a suspended intelligent detection system has been developed. The system adopts self-developed RGBD camera and amphibious suspended carrier, and develops 3D reconstruction technology of underground space, which can realize the output of panoramic 3D models. Test results show that the robot has strong adaptability to the underground environment, and the modeling error and positioning accuracy can meet the engineering requirements. The system overcomes the limitation that detectors installed on traditional carriers cannot fully adapt to the complex environment inside the pipe culvert, and has been applied in several water environment and utility tunnel projects.


Object detection for traffic management based on YOLO

Abstract: This paper presents an object detection method for traffic management based on the YOLOV7 model, using the bdd100k dataset for experimentation. The results show that the proposed method has good detection performance in traffic scenes. The main contribution of this paper is to improve the accuracy and efficiency of object detection in traffic scenes by applying the YOLOV7 model to the field of traffic management. The research results of this paper are of great significance for the optimization and improvement of traffic management systems. Future research can explore YOLOV7's performance on other target categories and consider algorithm optimizations to improve accuracy on new datasets.


Multi-scale object detection based on attention mechanism

Abstract: In order to solve the problem of missing target detection in traffic road target detection task due to excessive change of target scale in complex environment. An improved object detection method based on YOLOv5 is proposed, which introduces Coordinate Attention (CA), it fuses the coordinate attention mechanism into the last layer of the backbone network C3 module and the Bottleneck module in the C3 module to enhance the feature extraction ability of the target. The experimental results show that the vehicle detection accuracy of the fusion model reaches 96.5% on KITTI data set. Compared with other models, it can effectively improve the problem that the target scale of traffic road changes greatly.


Deep Fake Face Detection Using Deep Learning Tech with LSTM

Abstract: The fabrication of extremely life like spoof films and pictures that are getting harder to tell apart from actual content is now possible because to the quick advancement of deep fake technology. A number of industries, including cybersecurity, politics, and journalism, are greatly impacted by the widespread use of deepfakes, which seriously jeopardizes the accuracy of digital media. In computer vision, machine learning, and digital forensics, detecting deepfakes has emerged as a crucial topic for study and development. An outline of the most recent cutting-edge methods and difficulties in deep fake detection is given in this abstract. In this article, we go over the fundamental ideas behind deepfake creation and investigate the many approaches used to spot and stop the spread of fake news. Methods include sophisticated machine learning algorithms trained on enormous datasets of real and fake media, as well as conventional forensic investigation. We explore the principal characteristics and artifacts that differentiate authentic video from deepfakes, such as disparities in audio-visual synchronization, aberrant eye movements, and inconsistent facial emotions. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), two deep learning frameworks, have been used by researchers to create sophisticated detection models that can recognize minute modifications in multimedia information. The fast developments in deep fake generating techniques, however, continue to exceed efforts in detection and mitigation, making deep fake detection a daunting problem. The issue is made worse by the democratization of deepfake technology and its accessibility to non-experts, which calls for creative solutions and multidisciplinary cooperation to counter this expanding danger. Keywords: convolutional neural network,, generative adversarial network, deep fake ,long short term memory,


Recent Advances on Semantic <scp>IoT</scp> Data Integration

Abstract: Internet Technology LettersVolume 7, Issue 2 e509 EDITORIAL Recent Advances on Semantic IoT Data Integration Xingsi Xue, Corresponding Author Xingsi Xue [email protected] orcid.org/0000-0002-3008-8782 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China Correspondence Xingsi Xue, Email: [email protected]Search for more papers by this authorJeng-Shyang Pan, Jeng-Shyang Pan College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, ChinaSearch for more papers by this authorPei-Wei Tsai, Pei-Wei Tsai Department of Computer Science and Software Engineering, Swinburne University of Technology, Victoria, AustraliaSearch for more papers by this authorDhanalakshmi Samiappan, Dhanalakshmi Samiappan Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, IndiaSearch for more papers by this author Xingsi Xue, Corresponding Author Xingsi Xue [email protected] orcid.org/0000-0002-3008-8782 Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China Correspondence Xingsi Xue, Email: [email protected]Search for more papers by this authorJeng-Shyang Pan, Jeng-Shyang Pan College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong, ChinaSearch for more papers by this authorPei-Wei Tsai, Pei-Wei Tsai Department of Computer Science and Software Engineering, Swinburne University of Technology, Victoria, AustraliaSearch for more papers by this authorDhanalakshmi Samiappan, Dhanalakshmi Samiappan Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, IndiaSearch for more papers by this author First published: 14 February 2024 https://doi.org/10.1002/itl2.509Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES 1Kou X, Feng J, Wang Y, Cui W. A multi-objective particle swarm optimization for matching domain ontologies. Internet Technology Letters. 2022;e405. https://doi.org/10.1002/itl2.405 10.1002/itl2.405 Web of Science®Google Scholar 2Zhao Z. An evolutionary intelligent data analysis in promoting smart community. Internet Technology Letters. 2023;e407. https://doi.org/10.1002/itl2.407 10.1002/itl2.407 Web of Science®Google Scholar 3Lei X. Data fusion-driven difference analysis of farming culture between China and Japan. Internet Technology Letters. 2023;e412. https://doi.org/10.1002/itl2.412 10.1002/itl2.412 Web of Science®Google Scholar 4Wang M, Pan D. Efficient music analysis mechanism based on AI and IoT data mining. Internet Technology Letters. 2023;e436. https://doi.org/10.1002/itl2.436 10.1002/itl2.436 Web of Science®Google Scholar 5Han T. Intelligent language analysis method for multi-sensor data fusion. Internet Technology Letters. 2023;e441. https://doi.org/10.1002/itl2.441 10.1002/itl2.441 Web of Science®Google Scholar 6Liu X, Lv Q, Rong Q. Network public opinion monitoring and semantic event discovery strategy in mobile edge computing scenario. Internet Technology Letters. 2023;e454. https://doi.org/10.1002/itl2.454 10.1002/itl2.454 Web of Science®Google Scholar 7Jin Y, Zhang Y, Xue W, Shen P, Jin Z, He T. Semantic automatic annotation method based on artificial intelligence for electric power internet of things. Internet Technology Letters. 2023;e455. https://doi.org/10.1002/itl2.455 10.1002/itl2.455 Web of Science®Google Scholar 8Wu Q, Shen Y. Human resource attendance mechanism based on the internet of things: A method based on data fusion. Internet Technology Letters. 2023;e464. https://doi.org/10.1002/itl2.464 10.1002/itl2.464 Web of Science®Google Scholar 9Ma X, Zhang J. Integrated information management of supply chain in logistics parks based on the IoT. Internet Technology Letters. 2023;e469. https://doi.org/10.1002/itl2.469 10.1002/itl2.469 Web of Science®Google Scholar 10Zhou B. Sanda training program based on multi-sensor data fusion in the internet of things environment. Internet Technology Letters. 2023;e470. https://doi.org/10.1002/itl2.470 10.1002/itl2.470 Web of Science®Google Scholar 11Jiang H. Exploration on load balancing data aggregation algorithm in wireless sensor network based on big data artificial intelligence. Internet Technology Letters. 2023;e488. https://doi.org/10.1002/itl2.488 10.1002/itl2.488 Web of Science®Google Scholar 12Zhao Z, Wang L. Operation and maintenance strategy of radio transmission equipment based on big data. Internet Technology Letters. 2023;e486. https://doi.org/10.1002/itl2.486 10.1002/itl2.486 Web of Science®Google Scholar 13Liu X, Zuo L, Wang L. Secure and controllable data management mechanism for multi-sensor fusion in internet of things. Internet Technology Letters. 2023;e485. https://doi.org/10.1002/itl2.485 10.1002/itl2.485 Web of Science®Google Scholar 14Zeng X, Yi J. The supply chain information sharing path based on the internet of things. Internet Technology Letters. 2023;e491. https://doi.org/10.1002/itl2.491 10.1002/itl2.491 Web of Science®Google Scholar 15Lateef Haroon PSA, Patil SN, Bidare Divakarachari P, Falkowski-Gilski P, Rafeeq M. An optimized system for sensor ontology meta-matching using swarm intelligent algorithm. Internet Technology Letters. 2024;e498. https://doi.org/10.1002/itl2.498 10.1002/itl2.498 Web of Science®Google Scholar 16Geng J. Improving network data security interaction methods under wireless communication. Internet Technology Letters. 2024;e497. https://doi.org/10.1002/itl2.497 10.1002/itl2.497 Web of Science®Google Scholar Volume7, Issue2March/April 2024e509 ReferencesRelatedInformation


Impact of Project BANAS Materials to the Academic Performance of Learners: Basis for Enhancement Plan

Abstract: This descriptive quantitative research is a scientific inquiry which aimed to determine the level of Impact of Project BANAS Materials (a collective name of the competency-based division-initiated print and non-print resources in Social Studies) in relation to academic performance of learners. This study was conducted in the seven (7) elementary schools of District VII, Division of Bacolod City during the School Year 2022-2023. Researcher-made questionnaire used in this study was validated by three (3) experts. It has undergone reliability testing with thirty (30) respondents at Andres Bonifacio Elementary School I. Data were collected during the Second Quarter of the School Year from three hundred eighty-three (383) learners in Grades 1 to 5. Highlight of this study examined and analyzed the level of Impact of BANAS materials in terms of these four (4) areas namely: Alignment of Content, Quality of Material, Applicability, and Ease of Use. Findings showed that BANAS Materials in terms of these four (4) areas have a very high impact regardless of sex, grade level, school, and average family monthly income. Based on these findings, BANAS Materials is highly recommended to be utilized by Social Studies teachers and learners as supplementary reading and teaching materials respectively. Results further reveal that there is a need to conduct division training-workshops in the development and quality assurance of additional BANAS Materials focused on competencies not covered in the BANAS Outputs developed in the previous years. Conduct of SOK-SAY-LYMPICS Series: BANAS Quizzes, ATBP is highly recommended as sustainable initiatives aligned on Assessment and Monitoring Key Result Areas. Through Policy Recommendation, the long-term goal of this research is the Institutionalization of BANAS Learning Hub in 46 Elementary Schools in the Division of Bacolod City. Not only regular learners are the target direct beneficiaries of BANAS Print and Non-Print Materials but also Madrasah Education Program (MEP) and Special Needs Education (SNED) learners. Sustainability and successful implementation of this research-cum-innovation needs the collaboration between and among school leaders and DepEd Officials to truly realize the end goals on the need to address scarcity of localized resources, improve reading literacy across disciplines through in-depth love for one’s unique and distinct local heritage. Sequel researches are also highly recommended to done by future teacher-researchers.


Students' Interest in Learning English at Iain Syekh Nurjati Cirebon: Study of Instrumental and Integrative Motivation

Abstract: Motivation is vital for learning and mastering a language. Motivation related to second language learning is integrative and instrumental motivation. This research aims to describe students’ interest in learning English from the terms instrumental and integrative motivation. The research design is quantitative descriptive. The information obtained is interpreted as tables, diagrams and sentences. The research location is at the IAIN Syekh Nurjati Cirebon campus for the odd academic year 2023-2024. The sample size was 100 students studying English using simple random sampling techniques. Data collection tools are questionnaires related to instrumental and integrative motivation, with 20 statements. The results show that integrative motivation is higher than instrumental motivation for various reasons. The instrumental motivation of students' desire to study English leads to the conclusion that their motivation is tied to their employment, future profession, and education. Regarding integration motivation, they recognized that English is a crucial language for worldwide communication. They are also eager to visit English-speaking countries, engage with native speakers, and utilize English with English-speaking friends or acquaintances. The majority of students learn English because of integrative motivation.


Crossed-IoT Device Portability of Electromagnetic Side Channel Analysis: Challenges and Dataset

Abstract: Internet of Things (IoT) refers to the network of interconnected physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. IoT forensics aim to investigate cybercrimes, security breaches, and other malicious activities that may have taken place on these connected devices. In particular, Electromagnetic Side-Channel Analysis (EM-SCA) has become an essential tool for IoT forensics due to its ability to reveal confidential information about the internal functions of IoT devices without interfering with these devices or wiretapping their networks. However, the accuracy and reliability of EM-SCA results can be limited by device variability, environmental factors, and data collection and processing methods. In fact, very few studies have explored potential solutions to address the significant impact of these limitations on the accuracy of EM-SCA approaches applied to crossed IoT devices. Therefore, this paper examines the impact of device variability on the accuracy and reliability of machine learning (ML)-based approaches for EM-SCA. We first present the background, basic concepts and techniques used to evaluate the limitations of current EM-SCA approaches and datasets. Our study then addresses one important limitation, which is caused by the multi-core architecture of the processors (aka. System-On-Chip). We present an approach to collect the EM-SCA datasets and demonstrate the feasibility of using transfer learning to obtain more meaningful and reliable results from EM-SCA in IoT forensics of crossed-IoT devices. Moreover, this study contributes a new dataset for using deep learning (DL) models in analyzing Electromagnetic Side-Channel data with regard to the cross-device portability matter.


Research on the construction of urban wisdom logistics platform based on blockchain

Abstract: As a modern industry, smart logistics is of great significance for promoting the transformation and development of the green economy, optimising the government's macro-control, and creating an upgraded version of China's economy. In order to improve the efficiency of logistics, improve the low information transparency and poor security of urban logistics industry, according to the current situation of the logistics industry, based on the technical characteristics of the blockchain, the model of the smart logistics platform is constructed, and then the smart logistics platform is designed from the technical architecture of the smart logistics platform, the security layer, the business application layer, the data layer, and the data layer, and finally, this paper clarifies the aspects of reducing the cost of logistics operation, improving the transparency and traceability of data information, strengthening the information security, and optimising the supply chain management. Finally, this paper clarifies that blockchain has a significant impact on the development of smart logistics from the aspects of reducing logistics operation cost, improving data information transparency and traceability, strengthening information security, and optimising supply chain management.


Roadside LiDAR background filtering method based on differences in the distance of points in the spherical coordinates

Abstract: To obtain object micro traffic information, LiDAR plays an important role in intelligent perception. Many background filtering methods based on LiDAR can detect the target, but the data process is not simple enough and data information will be lost easily. In this work, from the principle of original point cloud data capture, an advanced background filtering algorithm based on PCAP (Packet Capture) file is proposed. Firstly, a data extraction algorithm for PCAP files is designed. Then, one non-target frame is chosen as the background frame. The distance difference method is used to filter the background under the spherical coordinate system with the relative horizontal angle and vertical angle as discrete coordinates. The algorithm is improved twice by reconstructing the background frame based on the hierarchical clustering method and changing the distance difference threshold. Finally, the effectiveness of the three algorithms is verified.


Modeling analysis of flight support vehicle scheduling based on fuzzy colored petri nets

Abstract: In order to better describe the flight support vehicle scheduling system, a fuzzy colored Petri net (FCPN) model is proposed, and its formal definition and specific inference algorithm are listed. A dispatching model with active strategies is developed using FCPN. An application example yields that the FCPN model significantly reduces the model complexity and is able to more accurately describe the vehicle scheduling system.


Two step vehicle trajectory reconstruction strategy based on an improved outlier detection and data smoothing algorithm

Abstract: Vehicle trajectory data contains abundant traffic information and plays a key role in traffic flow research. Due to unsuitable sensor position or perspective, there are some local errors in existing vehicle trajectory data sets. To solve this problem, a two-step vehicle trajectory data reconstruction strategy is proposed in this paper. In the first step, an outlier detection algorithm combines the cluster-based local outlier factor (CBLOF) and the k-means clustering algorithm (KCBLOF) is proposed to identify outlier in object dataset. In the second step, Savitzky-Golay (SG) filter is selected to smooth the data flow containing noise. In this paper, the prototype data of I-80 highway in the open source trajectory data set NGSIM is used for case study. Taking vehicle 1 as an example, the speed and acceleration data of vehicle 1 are reconstructed. Time Windows with length of 11 and 21 are selected respectively to implement SG filter. The results show that when the time window value is 21, the trajectory data shows a better smoothed result, and the data noise can be obviously eliminated, which verifies that the trajectory data smoothing strategy proposed in this paper has a certain reliability.


Fake Instagram Account Detection using ML Algorithms

Abstract: Abstract—Nowadays, the majority of people utilise social networking sites on a daily basis. Numerous people create profiles on social networking websites every day and connect with others there, regardless of their location or time. False identities are used in additional malicious operations in addition to playing a significant part in advanced persistent threats. Users of social networking sites can benefit from them, but they also have to worry about the security of their personal information. We must first determine the user's social network accounts before we can determine who is endorsing threats in these platforms. Social media usage has dramatically increased in recent years, according to statistics. Social networking sites have made things easier as they allow us to connect to people effortlessly and converse with them without the requirement for physical meetups. One of the major issues with Online Social Networks (OSNs) is fake interaction, which is used to artificially boost an account's popularity. Because phoney involvement causes businesses to lose money, inaccurate audience targeting in advertising, inaccurate product prediction systems, and an undesirable social network atmosphere, its detection is essential. Based on the classification, it is required to distinguish between real and phoney profiles on social media.Several categorization techniques have traditionally been used to identify phoney social media accounts. However, there are ways to improve social media's ability to detect phoney profiles. The suggested effort uses technology and machine learning to boost the percentage of predicted phoney profiles. Chi-square technique is used in feature selection models to find the best data. The several machine learning methods, including the Logistic Regression and Random Forest algorithms, are used in the classification approach. The classification outcome based on recall, sensitivity, specificity, f1-score, accuracy, and precision. Keywords—Machine learning, online social networks, Instagram, Social media, Natural language process.


Overview of the socio-economic profile of and challenges faced by African countries

Abstract: Africa consists of 54 countries, each of which is at a different level of development, and faces a number of challenges. The chapter gives an overview of the level of socio-economic development of African countries, some of the challenges they face, and the roles innovation and IP play in addressing development-related problems.


Human Curriculum Effects Emerge with In-Context Learning in Neural Networks.

Abstract: Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with "in-context learning" (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks "in context" - without weight changes - via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.


An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution

Abstract: Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews.


Calibration-Free Mobile Eye-Tracking Using Corneal Imaging

Abstract: In this paper, we present and evaluate a calibration-free mobile eye-traking system. The system’s mobile device consists of three cameras: an IR eye camera, an RGB eye camera, and a front-scene RGB camera. The three cameras build a reliable corneal imaging system that is used to estimate the user’s point of gaze continuously and reliably. The system auto-calibrates the device unobtrusively. Since the user is not required to follow any special instructions to calibrate the system, they can simply put on the eye tracker and start moving around using it. Deep learning algorithms together with 3D geometric computations were used to auto-calibrate the system per user. Once the model is built, a point-to-point transformation from the eye camera to the front camera is computed automatically by matching corneal and scene images, which allows the gaze point in the scene image to be estimated. The system was evaluated by users in real-life scenarios, indoors and outdoors. The average gaze error was 1.6∘ indoors and 1.69∘ outdoors, which is considered very good compared to state-of-the-art approaches.


Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data

Abstract: Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data may lead to over-optimistic evaluation due to possible overlap with test anomalies. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to a coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery despite minimal computational overhead.


Service robots and their implications for service delivery

Abstract: The digital service revolution will significantly change the way we do business. Service robots in various forms and shapes are a big part of this revolution. In this chapter, we illustrate the implication of service robots for the service sector. We compare service robots with traditional self-service technologies (SSTs) and human service personnel and identify opportunities and challenges. In the Service Robot Deployment Model (SRD Model), we highlight promising areas for human-robot collaboration and discuss the challenges of service robot deployment. Finally, we derive managerial implications for the service frontline in this new reality with service robots.