An Iterative Method for the Distance Constraints in a Multi-Sensor Positioning System

Abstract: The distance constraint can enhance the state estimation performance of a multi-sensor positioning system. However, the existing methods encounter problems such as low state estimation accuracy and high computational complexity. This study proposes an iterative constraint algorithm that effectively solves the distance constraint problem in a multi-sensor positioning system. The proposed algorithm linearizes the distance constraint in each iteration to obtain an approximate linear constraint model. Then, it re-estimates the approximate system state by using the estimation projection algorithm. In the last iteration, the proposed algorithm uses the approximate state estimate as input to estimate a more accurate system state until the iteration is terminated. Three simulations are provided to demonstrate the effectiveness and superiority of the proposed algorithm.


A Theoretical Analysis of Density Peaks Clustering and the Component-Wise Peak-Finding Algorithm

Abstract: Density peaks clustering detects modes as points with high density and large distance to points of higher density. Each non-mode point is assigned to the same cluster as its nearest neighbor of higher density. Density peaks clustering has proved capable in applications, yet little work has been done to understand its theoretical properties or the characteristics of the clusterings it produces. Here, we prove that it consistently estimates the modes of the underlying density and correctly clusters the data with high probability. However, noise in the density estimates can lead to erroneous modes and incoherent cluster assignments. A novel clustering algorithm, Component-wise Peak-Finding (CPF), is proposed to remedy these issues. The improvements are twofold: 1) the assignment methodology is improved by applying the density peaks methodology within level sets of the estimated density; 2) the algorithm is not affected by spurious maxima of the density and hence is competent at automatically deciding the correct number of clusters. We present novel theoretical results, proving the consistency of CPF, as well as extensive experimental results demonstrating its exceptional performance. Finally, a semi-supervised version of CPF is presented, integrating clustering constraints to achieve excellent performance for an important problem in computer vision.


An Adaptive Fuzzy C-Means segmentation and deep learning model for efficient mammogram classification using VGG-Net

Abstract: Breast cancer mortality can be prevented by only early, accurate mammography screening and diagnosis. Although CNN-based computer-aided diagnosis (CAD) systems for breast cancer have made tremendous progress recently, accurate identification of mammography lesions is still difficult because of poor signal-to-noise ratio (SNR) and physiological features. In this manuscript, an Adaptive Fuzzy C-Means Segmentation and Deep Learning Model for Efficient Mammogram Classification Using VGG-Net (AFCM-DCNN) is proposed. The input image is given to Grey Code Approximation Pre-processing (GCAP) algorithm to enhance the quality of image by adjusting the pixel contrast. The preprocessed image is given to Adaptive Fuzzy C-Means (AFCM) algorithm and is applied in segmenting dominant regions in an input image. But in conventional AFCM technique, the centroid values get generated randomly, which consumes more computational time. Hence to enhance the performance of traditional AFCM, centroid value is optimally chosen by means of optimization algorithm. A technique for classifying images called DCNN analyses the input image and categorizes it as either benign, malignant and normal. The method extracts the features of the image and train with VGG-16 Net classifier. The neurons at the output layer have been designed to compute Class Centric Disease Support (CCDS) towards various classes. Accordingly, the mammogram class is identified towards detecting the brain tumor. The performance of the proposed method AFCM-DCNN exhibits higher accuracy of 29.3%, 25.6% and 24.6%, higher sensitivity of 15.4%, 16.6% compared with the existing methods. Therefore, in future work, hope to enhance the performance depending on transfer learning with similar data.


CR-TransR: A Knowledge Graph Embedding Model for Cultural Domain

Abstract: As a combination of information computing technology and the cultural field, cultural computing is gaining more attention. The knowledge graph is also gradually applied as a particular data structure in the cultural area. Based on the domain knowledge graph data of the Beijing Municipal Social Science Project “Mining and Utilization of Cultural Resources in the Ancient Capital of Beijing,” this article proposes a graph representation learning model CR-TransR that integrates cultural attributes. Through the analysis of the data in the cultural field of the ancient capital of Beijing, a cultural feature dictionary is constructed, and a domain-specific feature matrix is constructed in the form of word vector splicing. The feature matrix is used to constrain the embedding graph model TransR, and then the feature matrix and the TransR model are jointly trained to complete the embedded expression of the knowledge graph. Finally, a comparative experiment is carried out on the Beijing ancient capital cultural knowledge graph dataset and the effects of the classic graph embedding algorithms TransE, TransH, and TransR. At the same time, we try to reproduce the embedding method with the core idea of neighbor node information aggregation as the core idea, and CRTransR are compared. The experimental tasks include link prediction and triplet classification, and the experimental results show that the CRTransR model performs better.


Monocular out-of-plane vibration measurement using parametric homography

Abstract: Vision-based measurement is an emerging method for vibration tests due to its non-contact nature and high spatial resolution, which make the full-field measurement possible. Conventional vision-based measurement requires two or more cameras to produce an out-of-plane response measurement. However, the additional cost of another camera and synchronization system makes applying this kind of measurement method to real-world engineering projects more challenging. This paper presents a single-camera method for measuring the out-of-plane vibration of plate structures. To accomplish that, the image variation induced by the motion of a plane is represented using a motion-parametric homography. Under the Lucas–Kanade framework, the motion-parametric homographies are estimated separately for each region of interest (ROI) between images, and the out-of-plane displacement response is obtained concurrently. The proposed method’s results are confirmed in good agreement with those of the laser Doppler vibrometer through an impact test on a steel plate structure.


Gated recurrent unit decision model for device argumentation in ambient assisted living

Abstract: The increasing elderly population worldwide is facing a variety of social, physical, and cognitive issues, such as walking problems, falls, and difficulties in performing daily activities. To support elderly people, continuous monitoring and supervision are needed. Due to the busy modern lifestyle of caretakers, taking care of elderly people is difficult. As a result, many elderly people prefer to live independently at home without any assistance. To help such people, an ambient assisted living (AAL) environment is provided that monitors and evaluates the daily activities of elderly individuals. An AAL environment has heterogeneous devices that interact, and exchange information of the activities performed by the users. The devices can be involve in an argumentation about the occurrence of an activity thus leading to generate conflicts. To address this issue, the paper proposes a gated recurrent unit (GRU) learning techniques to facilitate decision-making for device argumentation during activity occurrences. The proposed model is used to initially classify user activities and each sensor value status. Then a novel method is used to identify argumentation among devices for activity occurrences in the classified user activities. Later, the GRU decision making model is used to resolve the argumentation and to identify the target activity that occurred. The result of the proposed model is compared with other existing techniques. The proposed model outperformed the other existing methods with an accuracy of 85.45%, precision of 72.32%, recall of 65.83%, and F1-Score of 60.22%.


MMDataLoader: Reusing Preprocessed Data Among Concurrent Model Training Tasks

Abstract: Data preprocessing plays an important role in deep learning, which directly affects the training efficiency. Data preprocessing is performed on the CPU. The preprocessed data are then fed to the models that are trained on the GPU. We observed that data preprocessing on the CPU can potentially create a bottleneck in the entire process of a model training task. In order to tackle this issue, we have developed MMDataLoader, which enables reusing preprocessed data among multiple model training tasks. MMDataLoader automatically constructs a data preprocessing pipeline based on each task’s specific preprocessing workflow, allowing for maximum data reuse and reduced computing workload on the CPU. Unlike conventional data loaders that operate at the task level and provide data provision services to specific training tasks, MMDataLoader operates at the server level and provides data for all concurrently running tasks. We have conducted extensive experiments. The results show that MMDataLoader can significantly increase preprocessing throughput without affecting model convergence when compared to conventional methods where model training tasks are executed concurrently. For instance, with three tasks running, the preprocessing throughput can increase by 1.6x to 3.15x, depending on the tasks being executed and the proportion of preprocessing operations that are shared among them.


An off-policy multi-agent stochastic policy gradient algorithm for cooperative continuous control

Abstract: Multi-agent reinforcement learning (MARL) algorithms based on trust regions (TR) have achieved significant success in numerous cooperative multi-agent tasks. These algorithms restrain the Kullback–Leibler (KL) divergence (i.e., TR constraint) between the current and new policies to avoid aggressive update steps and improve learning performance. However, the majority of existing TR-based MARL algorithms are on-policy, meaning that they require new data sampled by current policies for training and cannot utilize off-policy (or historical) data, leading to low sample efficiency. This study aims to enhance the data efficiency of TR-based learning methods. To achieve this, an approximation of the original objective function is designed. In addition, it is proven that as long as the update size of the policy (measured by the KL divergence) is restricted, optimizing the designed objective function using historical data can guarantee the monotonic improvement of the original target. Building on the designed objective, a practical off-policy multi-agent stochastic policy gradient algorithm is proposed within the framework of centralized training with decentralized execution (CTDE). Additionally, policy entropy is integrated into the reward to promote exploration, and consequently, improve stability. Comprehensive experiments are conducted on a representative benchmark for multi-agent MuJoCo (MAMuJoCo), which offers a range of challenging tasks in cooperative continuous multi-agent control. The results demonstrate that the proposed algorithm outperforms all other existing algorithms by a significant margin.


Real and Synthetic Punjabi Speech Datasets for Automatic Speech Recognition

Abstract: Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.


Fractal modeling of oil and gas geochemical data in the Taiwan Strait Basin

Abstract: This study investigated the fractal characteristics of oil and gas indicators in the Taiwan Strait Basin using multifractal methods. The results revealed that these indicators can be categorized into five groups through cluster analysis, principal component analysis, and multifractal analysis. Acidolysis hydrocarbon indicators exhibited strong multifractal characteristics, while other indicators showed weak multifractal characteristics. The high values of Polycyclic aromatic hydrocarbons (F) indicators were mainly found in the southwest direction of the study area, whereas the high values of total aromatic hydrocarbons and their derivatives (UV) indicators were distributed in the eastern part. Erosion carbonate (ΔC) indicators were predominantly located in the western part, while the distribution of acid extractable hydrocarbons (A) and pyrolytic hydrocarbons (P) indicators was sporadic. The distribution of Erosion carbonate (ΔC) indicators in the Jinjiang Depression resembled that in the Jiulongjiang Depression, but the distribution of other indicators varied. The S-A method was employed to identify oil and gas anomaly target areas and predict and evaluate favorable prospect zones in the basin. This study provides a theoretical foundation for future oil and gas exploration and evaluation in the Taiwan Strait.


The nexus of supply chain performance and blockchain technology in the digitalization era: Insights from a fast-growing economy

Abstract: This article extends the coverage of previous studies and addresses persistent gaps in the literature by assessing the simultaneous effects of adopting blockchain technology (BCT) on supply chain performance. It empirically examines the indirect effect of BCT adoption on supply chain performance through the mediating variables of supply chain agility, adaptability, transparency and coordination, based on the expanded triple A supply chain concept. The study analyzes data from manufacturing firms listed on the Indonesian Stock Exchange. The findings align with propositions derived from the Resource Based View (RBV), in which capabilities viewed as mediators—rather than testing for direct effects of BCT on organizational performance—are the most likely scenario for multifaceted supply chain operations. This study contributes to the ongoing debate on the relationship between BCT and organizational performance. The research offers insights for policymakers and blockchain vendors into this relationship.


Performance evaluation of generative adversarial networks for generating mugshot images from text description

Abstract: The process of identifying photos from a sketch has been explored by many researchers, and the performance of the identification process is almost perfect, particularly for viewed sketches. Suspect identification based on sketches is one of the applications in forensic science. To identify the suspect using these kinds of methods, a face sketch is required. Hence, the methods require skilled artists to sketch the suspect based on descriptions provided by eyewitnesses. However, the skills of these artists are different from one another, which results in different rendered sketches. Therefore, this work attempts to propose a new identification method based only on forensic face-written descriptions. To investigate the feasibility of the proposed method, this study has evaluated the performance of some text-to-photo generators on both viewed and forensic datasets using three different models of GAN which are SAGAN, DFGAN, and DCGAN. Then, the generated images are compared to the real photo contained within those datasets to evaluate how well the proposed method recognizes the faces. The results demonstrated that the recognition rate for the generated photos by the DCGAN models is better than the other two models which achieve a 38.3% recognition rate at rank-10 for mugshot identification.


An answer set programming-based implementation of epistemic probabilistic event calculus

Abstract: We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics.


Fractal modeling for geochemical data of deep-sea surface sediments: A case study from Zhongsha Island, Southern China Sea

Abstract: This study applied principal component analysis (PCA), concentration-area (C-A) and spectrum-area (S-A) fractal models to identify geochemical element combination anomalies related to heavy metals from the data for 43 geochemical elements in surface sediments of the Zhongsha Islands. The relationships between these 43 elements were first examined by applying correlation and cluster analysis to the surface sediment sample dataset. The multifractal spectral functions of the 43 elements were then calculated and all elements were classified using the curve shape of the spectral function. The results of classification were consistent with that of cluster analysis, thereby verifying the reliability of fractal analysis of the study area. PCA was then used to identify element combination anomalies and to assess high-value areas of element contents, and the S-A model was used to distinguish between the anomaly and background signal. The results of this multiple-analysis approach show that the element combination anomaly is mainly located in the deep-sea plain area, with a few elevated values in the Zhongsha South Basin. This study showed that quantitative evaluation of anomalies of multi-element associations can effectively delineate a target area and that anomalies of element associations extracted by the S-A fractal method can provide a more accurate outline areas containing high contents of combined elements. This study can act as a theoretical reference for the environmental assessment of the Zhongsha sea area.


Performance counter dataset for behavioural biometric purpose

Abstract: In the pursuit of advancing research in continuous user authentication, we introduce COUNT-OS-I and COUNT-OS-II, two distinct performance counter datasets from Windows operating systems, crafted to bolster research in continuous user authentication. Encompassing data from 63 computers and users, the datasets offer rich, real-world insights for developing and evaluating authentication models. COUNT-OS-I spans 26 users in an IT department, capturing 159 attributes across diverse hardware and software environments over 26 h on average per user. COUNT-OS-II, on the other hand, encompasses 37 users with identical system configurations, recording 218 attributes per sample over a 48-hour period. Both datasets utilize pseudonymization to safeguard user identities while maintaining data integrity and statistical accuracy. The well-balanced nature of the data, confirmed by comprehensive statistical analysis, positions these datasets as reliable benchmarks for the continuous user authentication domain. Through their release, we aim to empower the development of robust, real-world applicable authentication models, contributing to enhanced system security and user trust.


Dynamic adaptive control of boiler combustion based on improved GNG algorithm

Abstract: The boiler combustion process contains complex physicochemical changes, which is a nonlinear time-varying industrial process with strong interference and multivariate strong coupling. For this kind of boiler combustion process with typical nonlinear characteristics and complex mechanism, it is difficult to establish an accurate mechanism model by conventional modeling methods, so it is difficult to meet the new requirements for the optimal control of the boiler. The large amount of data accumulated in the operation process contains rich system information, and the data-driven modeling and control method provides an effective way for the operation optimization of the unit. Data dynamic characterization and control technology is an important means of data mining, coal-fired boiler data has obvious temporal and drift characteristics, for the current data tracking and supervision algorithms mostly lack of dynamics, real-time and stability issues, design an adaptive clustering model based on the improved growth of neural gas model (GNG), the establishment of nodes based on the probability, the range of the search, the average distance of node A node generation and deletion mechanism based on probability, range search and average distance of nodes is established to realize real-time monitoring of drift data. Finally, the experiments are carried out by analyzing the dynamic data of coal-fired boiler, and the experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can accurately and effectively monitor and control the dynamic data of coal-fired boiler.


ATBBC: Named entity recognition in emergency domains based on joint BERT-BILSTM-CRF adversarial training

Abstract: With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model.


Critique of the paper, ‘Public's perception on nursing education during the COVID-19 Pandemic: Sentiment Analysis of Twitter Data’

Abstract: Textual data derived from social media platforms constitutes a valuable resource for scholars, policymakers, and governmental entities seeking real-time insights into the prevailing sentiments of the general populace across diverse subject matters. In this article, we have presented our critique of the paper titled 'Public's Perception in Nursing Education during the COVID-19 Pandemic: Sentiment Analysis of Twitter Data' by highlighting the challenges unique to analyzing textual data and the issues that arise from relying on lexicon-based pre-defined libraries in textual data analysis. Further in the article, we have discussed the importance of providing reliable metrics like F1 score, accuracy, precision, and recall to ensure that the model used in the study to analyze the data is suitable. Due to the absence of an attention mechanism, a vital aspect in advanced deep learning techniques, pre-defined lexicon-based sentiment analysis dictionaries often fail in unstructured, complicated textual data that is filled with variations, nuanced references, and subtle emotional nuances. We have further analyzed the significance of addressing potential data bias in the article.


KEPA-CRF: Knowledge expansion prototypical amortized conditional random field for few-shot event detection

Abstract: Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time.


When Evolutionary Computation Meets Privacy

Abstract: Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation.


How to Build Self-Explaining Fuzzy Systems: From Interpretability to Explainability [AI-eXplained]

Abstract: Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual automated explanations can justify the output of fuzzy rule-based classifiers, and thus contribute to making them more trustworthy. Moreover, we demonstrate how end user preferences can be handled by customizing automated explanations, making them interactive, personalized, and therefore, human-centric. The full immersive article at IEEE Xplore provides detailed interactive examples for better understanding.


MFCANet: A road scene segmentation network based on Multi-Scale feature fusion and context information aggregation

Abstract: Road scene segmentation is the basic task of autonomous driving. Recent representative scene segmentation methods adopt the full convolutional network based on the encoder-decoder. However, the framework can cause the loss of image fine-grained information in the process of down-sampling, feature extraction and feature fusion, resulting in blurred boundary details and chaotic segmentation effect. In this work, a road scene segmentation network based on multi-scale feature fusion and context information aggregation is proposed, in which context information is used to guide feature fusion and enhance semantic feature extraction. Three plug-and-play modules are designed to extract multi-scale features with strong semantic information from high-level features, which compensate for the loss of spatial information in the upper sampling stage, and capture the information dependence among pixels to improve pixel-by-pixel segmentation. Experimental results on Camvid and Cityscapes show that the proposed multi-scale feature fusion and context information aggregation network (MFCANet) can achieve satisfactory performance compared with the state-of-the-art segmentation methods.


Surveillance video synopsis framework base on tube set

Abstract: Video synopsis technology can shorten the length of the video, which has attracted wide attention. However, due to the limitation of object extraction technology and the difficulty of preserving interactivity, synopsis videos will lose the semantic information of the original video. To address the above problems, we propose a video synopsis framework based on tube sets. Firstly, we propose a video tracking algorithm based on Yolov4 and Kalman Filter, which can effectively alleviate the problem that it is difficult for object tracking technology to extract occluded objects. Secondly, we propose a method that combines moving direction and dynamic threshold to judge interactivity. Accurately judging interactivity can ensure that the interactive objects are not separated in the synopsis video. Finally, we propose a tube set mapping model (TSMM) that can rearrange with the tube set as the basic unit. Experimental results demonstrate that our method can achieve superior performance than other state-of-the-art methods.


FedAGA: A federated learning framework for enhanced inter-client relationship learning

Abstract: Exploring relationships is important for effective model aggregation in Personalized Federated Learning (PFL). However, state-of-the-art (SOTA) PFL methods primarily focus on simple model aggregation through inter-client relationships within Euclidean space, overlooking the intricate nature of unstructured relationships. To remedy this, we propose a novel framework named Federated Learning with Adaptive Graph-based Aggregation (FedAGA) tailored for non-Euclidean space. FedAGA captures inter-client relationships through similarities based on accumulated gradients of various client models, constructing a dynamic graph topology. To enhance the reliability and accuracy of graph topology, we introduce two criteria from the perspectives of model convergence and model divergence. Our proposed approach is extensively evaluated on two PFL benchmark datasets and four real medical datasets. The results demonstrate that FedAGA accurately captures inter-client relationships across diverse models, leading to superior performance compared to the SOTA baselines. In particular, we observe notable accuracy improvements of 6.8% on CIFAR10 and 4.0% on PathMNIST compared to the existing methods.


GeneConnector: Unlocking the full potential of Genbank metadata

Abstract: Genbank currently stands as one of the most significant global repositories of genetic information. However, despite its vast quantity and diversity of data, a considerable portion of the existing records suffer from disjointed and often lacking metadata, failing to provide the necessary context of their acquisition. In light of this, we propose GeneConnector, a tool that harnesses shared information among multiple records of the same specimen in Genbank, aiming to enhance the completeness of poorly annotated nodes across various information domains. To demonstrate the tools capabilities, we conducted a comprehensive review and aggregation of available data using the Genbank database of Genera of Phytopathogenic Fungi (GOPHY). Through our evaluation, we observed substantial gains in information by analyzing shared data among nodes connecting Genbank specimen records, resulting in impressive increments ranging from 2% to a remarkable 60%. Our approach empowers users to make precise, straightforward, and accurate assessments of the context associated to results, facilitated by two metrics that gauge the current level of data annotation and the potential information gain achievable following our evaluation.


Generation of probabilistic synthetic data for serious games: A case study on cyberbullying

Abstract: Synthetic data generation has been a growing area of research in recent years. However, its potential applications in serious games have yet to be thoroughly explored. Advances in this field could anticipate data modeling and analysis, as well as speed up the development process. To fill this gap in the literature, we propose a simulator architecture for generating probabilistic synthetic data for decision-based serious games. This architecture is designed to be versatile and modular so that it can be used by other researchers on similar problems (e.g., multiple choice exam, political surveys, any type of questionnaires). To simulate the interaction of synthetic players with the game, we use a cognitive testing model based on the Item Response Theory framework. We also show how probabilistic graphical models (in particular, Bayesian networks) can introduce expert knowledge and external data into the simulation. Finally, we apply the proposed architecture and methods in the case of a serious game focused on cyberbullying. We perform Bayesian inference experiments using a hierarchical model to demonstrate the identifiability and robustness of the generated data.


Daily Video: A tool for quality of experience (QoE) in long-term context research

Abstract: This paper presents ‘Daily Video’, an innovative application designed to facilitate longitudinal studies in the realm of quality of experience (QoE). The application simulates real-life video service consumption and collects daily subjective quality assessments from users, promoting an in-depth understanding of user engagement and habits over time. Key features include scheduling, customised queries, user feedback, device orientation tracking and instant access to results, all of which enhance the application’s adaptability and usability for a variety of experimental conditions. The application, available as open-source code, supports state-of-the-art QoE research.


Integrating Machine Learning in Legal Management for Intelligent Supply Chain Governance

Abstract: The integration of machine learning in legal management and the development of intelligent supply chain governance represents transformative approaches to address the evolving complexities of the legal and supply chain domains. This chapter summarizes the key findings and insights drawn from the outlined sections. The problem statement underscores the growing need for data-driven strategies to navigate the intricate legal landscape and meet the challenges of modern supply chains. With a focus on transparency, accountability, and risk management, the significance of supply chain governance is highlighted, emphasizing its critical role in resilient and efficient supply chain operations. Legal management emerges as a pivotal player in this transformation, tasked with utilizing machine learning to navigate a data-rich and complex legal environment.


LiDeNeRF: Neural radiance field reconstruction with depth prior provided by LiDAR point cloud

Abstract: Neural Radiance Fields (NeRF) is a technique for reconstructing real-world scenes from multiple views. However, existing methods mostly focus on the visual effects of scene reconstruction while neglecting geometric accuracy, which is crucial in photogrammetry and remote sensing. In this paper, we propose a method called LiDeNeRF which uses LiDAR point cloud to provide depth priors for NeRF reconstruction. The goal of LiDeNeRF is to achieve real-time rendering of NeRF scenes and 3D reconstruction results with high geometric accuracy. In this method, first, the LiDAR point cloud is projected onto images to generate a sparse depth map. Then, by triangulating the sparse depth and using a multi-view image depth propagation method, a dense depth map with high accuracy is quickly generated as the depth prior for NeRF. Finally, a new depth correction module is designed and embedded into the NeRF rendering pipeline to improve the accuracy of scene depth estimation. The experimental results evince that our methodology has attained paramount performance in both novel view synthesis and 3D reconstruction tasks. Our source code is made available at https://github.com/WPC-WHU/LiDeNeRF.


Exploration of glitch phenomena in Holographic art

Abstract: The concept of imperfection, where perfection itself is an imperfection, aligns with the aesthetic of glitch art in holographic art. Originating in the 1960s as an integration of science, technology, and art, the creation of holograms requires meticulous craftsmanship in optical laboratories, blending specific technical skills with extensive practical experience. This paper introduces glitch art within the realm of holographic art and explores its historical significance in holograms. It identifies 4 key factors, stability during exposure, control of exposure parameters, path length difference control and post-processing techniques, that commonly induce glitches. Additionally, the paper examines recent glitch-infused holograms encountered by the author, thereby enhancing the understanding of glitches as a unique artistic expression within holographic art. This exploration sheds light on the technical aspects of hologram creation and contributes to the evolving narrative of digital art in contemporary practice.


DOMAS: a data management software framework for advanced light sources

Abstract: In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF).


Abstract TP31: Identifying Multivariate Nonlinear Bias in Sammpris Using Graph-Based AI Analysis

Abstract: Background: Clinical trials are the most rigorous way of testing how novel treatments compare with existing ones regarding outcomes. The randomized controlled clinical trial has become the standard means of providing proof of safety and effectiveness across the board in medicine, including stroke. Yet, the risk factors among patients that are randomized might not always be equally distributed such that a bias may occur. In this study, we focus on the SAMMPRIS clinical trial aimed to assess the safety and efficacy of aggressive medical management versus stenting for preventing recurrent stroke in patients with symptomatic intracranial arterial stenosis. Methods: We developed and evaluated a machine learning model to identify potential multivariate bias present in the dataset that could not have been identified using standard statistical tests. Our approach utilizes a machine learning model based on the spectral regression model (SR-KDA) to identify biases created by combinations of variables. SR-KDA is based on a graph representation of the data that is processed via discriminative analysis to identify groups of patients that share common characteristics. By using both the treatment and outcome variables as constraints, the model is able to identify nonlinear relationships between clinical variables and the outcome. It can also be used to identify bias as part of the randomization that could have inadvertently skewed the statistical results with standard methodologies. Results: Our approach was successfully applied to the SAMMPRIS clinical dataset, which includes 121 clinical variables. The algorithm was able to automatically identify 6 clusters of patients that had a different response to treatment when considering the clinical variables. Each group was then assigned a set of the variables' cut-off values that best describe them. Conclusion: We have developed a sophisticated system that is capable of detecting non-linear correlations in clinical data collected during trials. This solution has the potential to mitigate any biases that may arise during the randomization process, which could ultimately improve recruitment efforts. Moving forward, this innovative tool could be applied proactively to future clinical trials.


Two-photon quantum state tomography of photonic qubits

Abstract: We provide a tool for measuring the Stokes parameters and the degree of polarization of single photons by employing second-order interference, namely, the Hong-Ou-Mandel (HOM) interferometer. It is shown that the technique is able to distinguish a partially polarized photon where the polarization state is coupled to an internal degree of freedom, such as time of arrival, from partial polarization due to external entanglement with the environment. The method does not directly resort to any kind of polarization-selective components and therefore is not limited by the extinction ratio of polarizers. Moreover, the technique can be generalized to any two-level encoding of quantum information in single photons, such as time-bin or orbital angular momentum qubits.


Shadow detection using a cross-attentional dual-decoder network with self-supervised image reconstruction features

Abstract: Shadow detection is a challenging problem in computer vision due to the high variability in lighting conditions, object shapes, and scene layouts. Despite the positive results achieved by some existing technologies, the problem becomes particularly challenging with complex and heterogeneous images where shadow-casting objects coexist and shadows can have different depths, scales, and morphologies. As a result, more advanced and accurate solutions are still needed to deal with this type of complexities. To address these challenges, this paper proposes a novel deep learning model, called the Cross-Attentional Dual Decoder Network (CADDN), to improve shadow detection by using fine-grained image reconstruction features. Unlike other existing methods, the CADDN uses an innovative encoder-decoder architecture with two decoder segments that work together to reconstruct the input images and their corresponding shadow masks. In this way, the features used to reconstruct the original input image can be used to support the shadow detection process itself. The proposed model also incorporates a cross-attention mechanism to weight the most relevant features for detecting shadows and skip connections with noise to improve the quality of the transferred features. The experimental results, including several benchmark image datasets and state-of-the-art detection methods, demonstrate the suitability of the presented approach for detecting shadows in computer vision applications.


Enhancing Quantum Temporal Steering via Frequency Modulation

Abstract: Abstract Various strategies have been proposed to harness and protect space-like quantum correlations in different models under decoherence. However, little attention has been given to temporal-like correlations, such as quantum temporal steering (TS), in this context. In this work, we investigate TS in a frequency-modulated two-level system coupled to a zero-temperature reservoir in both the weak and strong coupling regimes. We analyze the impact of various frequency-modulated parameters on the behavior of TS and non-Markovian. The results demonstrate that appropriate frequency-modulated parameters can enhance the TS of the two-level system, regardless of whether the system is experiencing Markovian or non-Markovian dynamics. Furthermore, a suitable ratio between modulation strength and frequency (i.e., all zeroes of the 0th Bessel function J 0 (δ/Ω)) can significantly enhance TS in the strong coupling regime. These findings indicate that efficient and effective manipulation of quantum TS can be achieved through a frequency-modulated approach.


A Novel Model for Recommending Information Systems with Suitable Cloud Computing Provider

Abstract: Cloud computing has been adopted widely by information systems due to its scalability and availability of resources and services. Cloud computing can provide different types of services through internet for Information Systems (IS). There are many cloud computing providers such as Google, Microsoft, and IBM are seeking for adopting of cloud services. Selecting the suitable cloud provider can maximize the benefits of cloud services. There is a need to determine the suitable cloud computing provider to achieve organizations' goals and maximize the benefits of cloud services. This work aims to introduce a decision support model based on trapezoidal neutrosophic numbers for determining the suitable cloud computing provider. The proposed model is applied by the Zagazig University (ZU) and the results indicated that the model can provide flexible method that can handle uncertainty in decision making for detecting the suitable provider. Also, the results show that the proposed model can support information systems in the choosing the suitable cloud computing provider.


Path planning method for unmanned aerial vehicles

Abstract: Unmanned aerial vehicles, commonly known as drones, have seen tremendous growth and wide-spread use in the last ten years, largely because of unheard-of technological improvements. It is currently one of the most significant study subjects since it involves many different areas of robotics and control systems. This study aims to thoroughly assess the most popular approaches for unmanned aerial vehicle path planning, while also outlining their benefits, drawbacks, potential uses, and overall effectiveness. The objective is to present the academic community with a summary of the existing environment and the development direction of future unmanned aerial vehicle path planning algorithms to encourage more research for quickly evolving planning of intelligent systems.


ProPane: Image Warping with Fire

Abstract: Abstract In this paper we introduce the software package ProPane, written for the r data analysis language. ProPane combines the full range of wcslib projections with the C++ image manipulation routines provided by the CImg library. ProPane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. ProPane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053).


Post-Earthquake Serviceability Assessment Using the Convolutional Neural Networks and Deep Neural Networks

Abstract: There have been studies using image processing techniques to assess damage of structure especially columns without sending individual to onsite. Convolutional neural networks (CNNs) and the entire Deep neural network (DNN) have shown state-of-art results in object detection and image classification tasks. This study proposed cascaded deep learning network for post-earthquake structural serviceability assessment. Major target deficiency components (crack, spalled area, transverse bar, and longitudinal bar) were used to determine the proposed damage states to assess serviceability of structure. Concrete surface cracks are major defect in civil structures. Structural Inspection which is done for the evaluation of rigidity and tensile strength of the building. Crack detection plays a major role in the building inspection, finding the cracks and determining the Structural health in the case of post-earthquake. The research paper is all about finding the serviceability results of the structure.


Optical voice encryption based on speckle-illuminated fourier ptychography and plaintext-related chaotic random phase mask

Abstract: Abstract In this paper, we propose an optical voice encryption scheme based on speckle-illuminated Fourier ptychography (FP) and plaintext-related chaotic random phase mask (CRPM). In this proposed encryption scheme, the plaintext-related CRPMs are generated by chaotic Lozi map and secure hash algorithm (SHA-256). During the encryption process, the voice signal to be encrypted is first converted into a two-dimensional (2D) voice map. Then, with the help of CRPMs and speckle-illuminated FP, the voice map is encrypted into a series of noise-like low-resolution images. During the decryption process, the original voice signal can be recovered from the series of noise-like low-resolution images via Fourier ptychographic phase retrieval algorithm and the CRPMs. To the best of our knowledge, it is the first time to use the Fourier ptychography and chaotic random phase mask to implement the optical encryption of the voice signal. In addition, in this proposed encryption scheme, the chaotic parameters can replace the whole random phase masks as the secret keys, which makes the management and transmission of the secret keys become more convenient. Moreover, since the plaintext-related chaotic parameter keys can be updated dynamically, the security of the proposed encryption scheme can be further improved. The feasibility, security and robustness of the proposed encryption scheme are further analyzed by numerical simulations.


Interaction graph engineering in trapped-ion quantum simulators with global drives

Abstract: Abstract Trapped-ion quantum simulators have demonstrated a long history of studying the physics of interacting spin-lattice systems using globally addressed entangling operations. Yet despite the multitude of studies so far, most have been limited to studying variants of the same spin interaction model, namely an Ising model with power-law decay in the couplings. Here, we demonstrate that much broader classes of effective spin-spin interactions are achievable using exclusively global driving fields. Specifically, we find that these new categories of interaction graphs become achievable with perfect or near-perfect theoretical fidelity by tailoring the coupling of the driving fields to each vibrational mode of the ion crystal. Given the relation between the ion crystal vibrational modes and the accessible interaction graphs, we show how the accessible interaction graph set can be further expanded by shaping the trapping potential to include specific anharmonic terms. Finally, we derive a rigorous test to determine whether a desired interaction graph is accessible using only globally driven fields. These tools broaden the reach of trapped-ion quantum simulators so that they may more easily address open questions in materials science and quantum chemistry.


Eye-tracking on virtual reality: a survey

Abstract: Abstract Virtual reality (VR) has evolved substantially beyond its initial remit of gaming and entertainment, catalyzed by advancements such as improved screen resolutions and more accessible devices. Among various interaction techniques introduced to VR, eye-tracking stands out as a pivotal development. It not only augments immersion but offers a nuanced insight into user behavior and attention. This precision in capturing gaze direction has made eye-tracking instrumental for applications far beyond mere interaction, influencing areas like medical diagnostics, neuroscientific research, educational interventions, and architectural design, to name a few. Though eye-tracking’s integration into VR has been acknowledged in prior reviews, its true depth, spanning the intricacies of its deployment to its broader ramifications across diverse sectors, has been sparsely explored. This survey undertakes that endeavor, offering a comprehensive overview of eye-tracking’s state of the art within the VR landscape. We delve into its technological nuances, its pivotal role in modern VR applications, and its transformative impact on domains ranging from medicine and neuroscience to marketing and education. Through this exploration, we aim to present a cohesive understanding of the current capabilities, challenges, and future potential of eye-tracking in VR, underscoring its significance and the novelty of our contribution.


A preliminary geochemical study of the archaeological site of the Battle of Jenipapo (Piauí state, Brazil)

Abstract: Abstract This paper presents, for the first time, geochemical data related to the Battle of Jenipapo, a significant event that occurred on March 13, 1823, marking a political activity favorable to Brazil's independence. The present work aimed to analyze the contents of lead and copper as markers of historical armaments in sediments for geochemical prospecting of the archaeological area in which the Battle of Jenipapo probably occurred. Nine sediment samples were collected: seven from the prospect area and two control samples. Sedimentological characteristics were evaluated, such as pH (6.1–6.9), acidity (0.19–3.00 mmol L −1 ), organic matter (0.86–3.37% w/w), and granulometric classification (sandy predominance). The metals (lead and copper) were determined by inductively coupled plasma mass spectrometry (ICP‐MS) after acid digestion of the sediments. Lead content ranged from 7.9 to 14.9 mg kg −1 for samples and 5.4 mg kg −1 for control samples. Copper content ranged from 14.2 to 29.3 mg kg −1 . The results showed that only lead could be used for the geochemical prospecting of the area that presented strong evidence of anthropic intervention allowing the carrying out of excavation steps.


China eyes economic gains with AI standards

Abstract: Significance The document calls for the publication of standards in over 50 areas by 2026, in an effort to steer the development of this emerging technology and its industrial applications. Beijing’s focus on standardisation is an attempt to jumpstart the broad, real-world applications of AI technology at low cost. Impacts The AI standardisation effort further embeds Beijing’s goal of ensuring that these technologies serve its political and economic aims. Beijing will try to align with and influence ongoing moves to standardise AI technologies at international bodies. Standardisation could help China foster an international exports market for its AI, particularly across developing countries.


Decentralized IoT Data Authentication with Signature Aggregation

Abstract: The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge aggregating servers and Ethereum Layer 2 rollups, offering a scalable and secure IoT data authentication solution that reduces the need for continuous, direct interaction between IoT devices and the blockchain. We utilize and compare the Nova and Risc0 proving systems for authenticating batches of IoT data by verifying signatures, ensuring data integrity and privacy. Notably, the Nova prover significantly outperforms Risc0 in proving and verification times; for instance, with 10 signatures, Nova takes 3.62 s compared to Risc0’s 369 s, with this performance gap widening as the number of signatures in a batch increases. Our framework further enhances data verifiability and trust by recording essential information on L2 rollups, creating an immutable and transparent record of authentication. The use of Layer 2 rollups atop a permissionless blockchain like Ethereum effectively reduces on-chain storage costs by approximately 48 to 57 times compared to direct Ethereum use, addressing cost bottlenecks efficiently.


“Putting your money where your mouth is”: An empirical study on buyers’ preferences and willingness to pay for blockchain-enabled sustainable supply chain transparency

Abstract: This paper investigates how buyers assess the importance of various attributes of supply chain sustainability disclosed by suppliers. These include different types of disclosure (i.e., product, process, and sourcing network), self- and third-party verified disclosure, partial and full disclosure, as well as the attributes associated with information disclosure using blockchain technology: immutability and update frequency. Building on concepts in signalling theory and inter-organisational trust, our research uses a choice-based conjoint experimental design to elicit responses from 234 managers with decision-making roles in procurement. Using this design, we calculate the relative importance of attributes, part-worth utility, and marginal willingness to pay, and test hypotheses about buyer preferences and willingness to pay. Our research reveals that buyers prefer suppliers with sustainability signals that span across different types of disclosure and methods of disclosure. It emphasises the importance of how sustainability information is disclosed, highlighting buyer trust in self-disclosure and a preference for comprehensive, regularly updated information. However, we find mixed results for buyers' willingness to pay. For instance, buyers prefer third-party verified supply chain transparency, but we do not find a significantly higher willingness to pay for such information compared to self-disclosure. The implications suggest a competitive advantage for suppliers adopting voluntary disclosure, prioritising disclosure based on buyer preferences, and recognising the limited direct impact of blockchain technology. Our research contributes to advancing our understanding of information disclosure in supply chain transparency and presents new avenues of inquiry into the value of blockchain-enabled platforms in supply chain sustainability reporting.


A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge Computing

Abstract: Mobile Edge Computing (MEC) is a new distributed computing method based on the mobile communication network. It can provide cloud services and an IT service environment for application developers and service providers at the edge of the network. Computation offloading is a crucial technology of edge computing. However, computation offloading will consume the resources of the edge devices, and therefore the edge devices will not offload computation unconditionally. In addition, the service quality of edge computing applications is related to the cooperation rate of edge devices. Therefore, it is essential to design an appropriate incentive mechanism to motivate edge devices to execute computation offloading. However, the current existing incentive mechanisms have two problems: Firstly, existing mechanisms do not account for probability distortions under uncertainty in collaborator utility valuation models. Secondly, the platform ignores the risk preferences of collaborators in multiple rounds of decision-making. To address these issues, we propose an incentive mechanism based on risk preference, IMRP. The IMRP considers the collaborator’s probability distortion, introduces an uncertain utility bonus scheme, and builds a probability distortion model to influence the collaborator’s willingness to offload tasks. The IMRP also considers the collaborator’s risk preference and builds the collaborator’s risk preference model to influence the collaborator’s bidding decision. Simulation results show that our mechanism effectively improves the cooperation rate of edge devices and the utility of the requester.


Exploring the Integration of ICT in Promoting Inclusive Education: A Case Study of the College of Business and Economics at the University of Rwanda

Abstract: This research aimed to investigate the role of Information and Communication Technology (ICT) in fostering inclusive education within the College of Business and Economics at the University of Rwanda, focusing on the experiences, challenges, and outcomes associated with ICT integration. The study involved 210 undergraduate students in the faculty of business administration, with a targeted sample of 84 students selected through stratified random sampling. Employing a mixed methods design, qualitative data were gathered through in-depth interviews, employing thematic analysis to identify patterns and perspectives. Additionally, structured questionnaires were used for quantitative data collection, focusing on the extent of ICT integration, its influence on inclusive education, and perceived barriers. The findings revealed predominantly positive outcomes, including improved accessibility, enhanced student engagement, and the importance of teacher training. However, challenges such as addressing diverse learning styles, digital literacy, and ongoing adaptation were acknowledged. Recommendations included addressing digital literacy skills, infrastructure gaps, and monitoring to ensure equitable access and positive outcomes for all students, emphasizing the transformative influence of ICT on learning experiences while acknowledging the need for continuous efforts to overcome challenges and maximize inclusivity.


Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments

Abstract: The automated design of dispatching rules (DRs) with genetic programming (GP) has become an important research direction in recent years. One of the most important decisions in applying GP to generate DRs is determining the features of the scheduling problem to be used during the evolution process. Unfortunately, there are no clear rules or guidelines for the design or selection of such features, and often the features are simply defined without investigating their influence on the performance of the algorithm. However, the performance of GP can depend significantly on the features provided to it, and a poor or inadequate selection of features for a given problem can result in the algorithm performing poorly. In this study, we examine in detail the features that GP should use when developing DRs for unrelated machine scheduling problems. Different types of features are investigated, and the best combination of these features is determined using two selection methods. The obtained results show that the design and selection of appropriate features are crucial for GP, as they improve the results by about 7% when only the simplest terminal nodes are used without selection. In addition, the results show that it is not possible to outperform more sophisticated manually designed DRs when only the simplest problem features are used as terminal nodes. This shows how important it is to design appropriate composite terminal nodes to produce high-quality DRs.


Atom-based Optical Polarization Modulator

Abstract: In this work, we employ 87Rb atoms as rotation media to manipulate the polarization of optical fields in both magnetic and magnetic-free environments. Employing the nonlinear magneto-optical rotation mechanism, we achieve a state-of-the-art magneto-optical rotation coefficient of 1.74×108 rad⋅T-1⋅m-1 which is four orders of magnitude higher than commonly employed materials. Additionally, in a magnetic-free environment, we achieve all-optical cross-polarization modulation between the pump and probe light via Rb atoms. The nonlinear magneto-optical rotation configuration introduces inventive techniques for a new type of magneto-optical modulator while the all-optical configuration paves the way for exploring photonic integrated circuit (PIC) devices free from disruptions caused by electrical or magnetic crosstalk.


AR Fitting Room and Beyond

Abstract: Over the years, the AR technology has started to bloom, more and more customers are enjoying using their mobile device to experience this advanced technology. This research project mainly focuses on creating an AR integrated fitting room mobile application or prototype which offers a more detailed and refined virtual try-on experience. It has a five-stage process: Detection, detect user’s body features and setup referencing points. Selection, we will generate a specific body type for users to choose according to the user information we gathered. Interaction, fashion items can be freely try-on on the user's virtual body. Acquiring, users can simply go to the retailer website through our app. Social hub, where users can share their fashion try-on and share it within the community. This application can be utilized in physical retail fashion stores, fashion e-commerce, and basically anywhere with a mobile device. The design of the AR fitting room will be focusing on improving user flow, customer satisfaction, brand engagement and brand acceptance.


Board 372: Remote Engaged Student Learning through Hands-on Internet of Things

Abstract: Abstract Remote learning environments have become integral to higher educational institutions even when most courses, particularly in STEM, have returned to face-to-face or hybrid instruction. Although the impetus for this project was the COVID-19 pandemic which forced institutions of higher education everywhere to move to an online remote teaching and learning format, and as such negatively affected STEM fields which require hands-on labs and access to instrumentation, remote learning still remains part of most courses today. We describe continuing efforts to create learning environments and materials to support remote hands-on engaged student learning off-campus at two Hispanic Serving Institutions (HSIs) to enable and enhance student learning beyond the institutional walls. The approach utilizes Internet of Things (IoT) kits as remote learning tools that are provided to students to allow hands-on learning experiences at students' own chosen environment and time. Problem- and Project-based Learning approaches are utilized to achieve student learning outcomes, and equip the students with the problem-solving skills necessary in STEM fields. Team-based projects engage students from multiple disciplines and demographics to work on projects, make individual contributions, and integrate their work with that of their team members. General feedback from students has been positive. Assessment of student performance also demonstrates the effectiveness of IoT-based learning in engineering and computer science disciplines.


AR Fitting Room and Beyond


Deep Neural Network-Oriented Indicator Method for Inverse Scattering Problems Using Partial Data

Abstract: We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learned using a deep neural network (DNN). The method is easy to implement and effective as demonstrated by numerical examples. Rather than developing sophisticated network structures for the classical inverse operators, we reformulate the inverse problem as a suitable operator such that standard DNNs can learn it well. The idea of the DNN-oriented indicator method can be generalized to treat other partial data inverse problems.


A comparative study between zero-knowledge proof (ZKP) and ring signatures targeting the legal and regulatory implications with the general data protection regulation (GDPR) and general data protection regulation (GDPR)

Abstract: Privacy protection ensures that individuals have control over personal data, preventing abuse and preserving trust in the use of online services. In the “Digital Era”, where the collection, storage and processing of personal information have become ubiquitous, data privacy emerges as a relevant topic. In this sense, laws were created, such as the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in Europe, to control privacy and the processing of personal data. The article presents a comparative analysis of 2 (two) data privacy mechanisms, the Zero-Knowledge Proof (ZKP) and Ring Signatures, used in Blockchain, aiming at the legal and regulatory implications with the LGPD and GDPR. The comparative study between ZKP and Ring Signatures highlights the flexibility of ZKP in various contexts, including voting and secure authentication systems, while Ring Signatures offer significant advantages in terms of scalability and efficiency in systems where subscriber anonymity is considered fundamental. Furthermore, the legal and regulatory implications of the ZKP are discussed, mainly in relation to LGPD and GDPR. Finally, the article concludes that the comparative analysis offers insights into applications, challenges and legal and regulatory implications, particularly in relation to data privacy and compliance with regulations such as LGPD and GDPR.


Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients

Abstract: When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and treatment. Additionally, biomarker progression may be associated with clinically relevant time-toevent data. Therefore, statistical models are necessary to gain insight into complex disease mechanisms by properly accounting for unobservable heterogeneity in patients while jointly modelling longitudinal and time-to-event data. In this study, we leverage the key features of Latent Class modelling and Bayesian Network approaches and propose a unified framework to (a) uncover homogeneous subgroups of patients concerning their longitudinal and survival data and (b) describe patient subgroups within a multivariate framework.


Implementation of NOA@PAT

Abstract: Due to the publication of the NAMUR Open Architecture (NOA), there are new oppurtunities to digitalize the existing process industry. The acquisition and direct forwarding to the target address is especially for the operation of process analytical technologies (PAT) of interest to improve efficiency and avaliability. In this article, the deeper benefit of NOA for PAT is outlined by presenting the advantages of standardized information models for PAT vitality data. Furthermore, the successful implementation of a NOA use case for PAT is shown in terms of the field wiring, the generation of the information model and the IT/OT transfer. At last, open aspects to achieve a comprehensive NOA implementation in existing installations are mentioned.


An Interactive Application for Hand Muscle Fatigue Relief Based on Gesture Recognition

Abstract: Prolonged use of a keyboard or mouse can lead to continuous bending and twisting of the wrist, which can lead to muscle fatigue and pain. Finger exercises can reduce the hand muscle fatigue effectively. However, the repetitive exercises will bring boring and tedious experience. In this paper, we developed a hand rehabilitation exercise system based on gesture recognition technology and gamification design, which provides a more enjoyable, personalized and effective rehabilitation experience. The hand fatigue relief system was designed with the fusion of game elements and gesture interaction, which can enhance users’ engagement and their rehabilitation motivation.


Integrated Profitability Evaluation for a Newsboy-Type Product in Own Brand Manufacturers

Abstract: Effective inventory management depends on accurate estimates of product profitability to formulate ordering and manufacturing strategies. The achievable capacity index (ACI) is a simple yet efficient approach to measuring the profitability of newsboy-type products with normally distributed demand, wherein profitability is presented as the probability of achieving the target profit under the optimal ordering quantity. Unfortunately, the ACI is applicable only to retail stores with a single demand. In the current study, we addressed the issue of measuring the integrated profitability of newsboy-type products sold in multiple locations with independent demand levels, such as own-branding-and-manufacture (OBM) companies with multiple owned channels. We began by formulating profitability in accordance with multiple independent normal demands, and then developed an integrated ACI (IACI) to simplify expression. We also derived the statistical properties of the unbiased estimator to determine the true IACI in situations where demand patterns are unknown. Finally, we conducted hypothesis testing to determine whether the integrated profitability meets a stipulated minimum level. For convenience, we tabulated the critical values as a function of sample size, confidence level, the number of channels, and the stipulated minimum level. One can make decisions simply by estimating the IACI based on historical demand data from all channels and then looking up the critical value in the corresponding tables. Consequently, the proposed methods make it possible for OBM managers to address integrated profitability evaluation, which is effective in deciding the optimal timing to pull unprofitable items from the shelves by looking up generic tables. Furthermore, we also performed numerical and sensitivity analyses for a real-world case to illustrate the applicability and some managerial implications of the proposed scheme.


Errors in memory recall, ToT, SoT, FoK and BoP. Operational classification

Abstract: The evocation of memory is a critical cognitive process that involves the activation of engrams, which in turn evoke concepts. During memory recall, errors often occur, some of which are well-known, such as the tip-of-the-tongue (ToT) phenomenon or slip of the tongue (SoT). A related variable is verbal taxonomy closely associated with ToT and SoT, as well as the anxiety variable, which has been linked to the occurrence of these phenomena. The results of this research identify errors unrelated to verbs, leading to the operationalization of previously unidentified phenomena called Bias of Perception (BoP) and feeling of Knowing (FoK). This paper explores these errors and their systematic occurrence. In summary, this document seeks to increase understanding of errors that occur during memory recall, their causes, and their implications for cognitive processes.


Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description

Abstract: Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance.


Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Abstract: Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.


Covert communication in power Internet of Things

Abstract: Covert communication refers to achieving high-level secure transmission by protecting communication behaviour. This paper primarily centres on the examination of covert communication in the scene of the power Internet of Things (PIoT). Specifically, we explore the scenario where power users (PU) transmit information to the master station (MS) while being exposed to potential eavesdropping. In addition, we assume that Willie has noise uncertainty when detecting communication behaviour, which makes it difficult for Willie to accurately detect communication behaviour, and significantly affects the detection error probability (DEP) of Willie and the overall covert performance of the proposed model. It is worth noting that the simulation results indicate that the presence of noise uncertainty can enable MS and PU to successfully achieve covert transmission. By leveraging this uncertainty, MS and PU can effectively protect their communication behaviour and minimize the risk detected by potential Willie, which provides a solution for high-level secure communication in the field of PIoT and effectively protects the privacy of PU.


Automated identification of vehicles in very high-resolution UAV orthomosaics using YOLOv7 deep learning model

Abstract: The utilization of remote sensing products for vehicle detection through deep learning has gained immense popularity, especially due to the advancement of unmanned aerial vehicles (UAVs). UAVs offer millimeter-level spatial resolution at low flight altitudes, which surpasses traditional airborne platforms. Detecting vehicles from very high-resolution UAV data is crucial in numerous applications, including parking lot and highway management, traffic monitoring, search and rescue missions, and military operations. Obtaining UAV data at desired periods allows the detection and tracking of target objects even several times during a day. Despite challenges such as diverse vehicle characteristics, traffic congestion, and hardware limitations, the detection task must be executed swiftly and accurately. This study successfully achieved automated detection and instance segmentation of parked and moving vehicles across a large university campus by employing the robust learning capabilities of the You Only Look Once version 7 (YOLOv7) deep learning algorithm. The generation of an ultrahigh-resolution orthomosaic of the university campus was accomplished through photogrammetric processing, employing 20-megapixel aerial images obtained from RGB UAV flights with polygonal nadir-view and bundle-grid oblique-view imaging geometries. The vehicle dataset was created by cropping image patches containing vehicle objects from the orthomosaic and manually labeling the boundaries of the vehicle targets using the LabelMe annotation tool. After expanding the dataset by applying data augmentation, the YOLOv7 algorithm was trained and tested using the transfer learning approach. The accuracy metric of precision, recall, and mAP@0.50 scores for the bounding boxes and masks of vehicles were estimated as 99.79, 97.54, and 99.46%, respectively. In addition, the robustness of the trained algorithm was also tested on a short video and (>80%) prediction scores were achieved.


Fusion of standard and ordinal dropout techniques to regularise deep models

Abstract: Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor (β) is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.


Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism

Abstract: Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a set of statistics with sufficient item counts. We consider the Gaussian version of the sparse histogram mechanism and study the exact epsilon, delta differential privacy guarantees satisfied by this mechanism. We compare these exact epsilon, delta parameters to the simpler overestimates used in prior work to quantify the impact of their looser privacy bounds.


Cyberbullying of women on Twitter

Abstract: Over 40% of American adults experience online harassment. As the nation struggles with questions of internet safety vs. freedom of speech, one must consider the impact of online abuse on victims. This paper builds on prior research of cyber harassment by extending the investigation to relational and societal-level effects of online harassment of women and potential moderating effect of support. Results revealed individual, relational and social impacts of cyberbullying. We discuss implications for addressing online harassment.


Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

Abstract: Abstract This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals


Ensemble WGAN (EWG): Advancing Image Synthesis and Deepfake Detection with Heterogeneous Discriminator Approach

Abstract: In the present time, deepfakes pose a big threat to the security of our society. Concerns regarding these fake images being used for malevolent reasons on social networking sites have increased. As a solution it, this paper proposed a new model called EWG (Ensemble WGAN) which helps to detect deepfake using its unique ensemble architecture. The EWG model is an expansion of the WGAN architecture that improves deepfake detection and GAN training issues. It employs a voting ensemble of three unique discriminators and a single generator. The approach works with generator weights updated by the best discriminator on each epoch. The model dynamically selects the best discriminator based on a unique diverse loss function that combines adversarial loss and the SSIM metric, boosting diversified performance. Leveraging the “Indian Actor Images Dataset” and “5-Celebrity Faces,” the EWG model achieves remarkable deepfake detection accuracy of 98.480% and 96.417%, with computation times of 1813.251 and 2197.011 seconds. Furthermore, it mitigates GAN training challenges like mode collapses, gradient penalties, and convergence and provides superior image quality, surpassing basic WGAN and other state-of-the-art methods. The EWG model demonstrates its dependability and potential for countering deepfakes and improving GAN capabilities.


Governance of Permissionless Blockchain Networks

Abstract: A permissionless blockchain network is a system of physically distributed computers running a copy of a shared ledger and using the same software rules that enable all network participants to “read, submit, and validate transactions” (Beck, Müller-Bloch, and King, 2018, p. 1022). A permissionless system’s accessibility stands in contrast to that of permissioned systems, in which a central authority pre-selects validators and potentially restricts viewing and submission rights (Krause, Natarajan, and Gradstein, 2017; Beck, Müller-Bloch, and King, 2018).


The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Abstract: The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.


Classification of Typical Static Objects in Road Scenes Based on LO-Net

Abstract: Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.


Textile-Based Inkjet-Printed RFIDs: Exploring wearable antennas in the real world [Bioelectromagnetics]

Abstract: Growing demand in the wearable device industry for noninvasive, user-connected, and real-time activity trackers and body monitors is increasing the need for low-profile wearable antennas on fabric substrates, particularly for health and safety applications. Current data regarding environmental and human factors in this field that affect the real-world use of the devised antennas are incomplete. This article presents data regarding the impact of garment position, especially the distance between the skin and fabric substrate, on antenna performance. A distance of the substrate from the skin in the less than a 5-mm range was found to have a significant influence on antenna performance. The effects of humidity and dampness on the permittivity of five types of fabrics commonly used in commercial and health care-related garments are also investigated. The performance of a textile-based RFID printed directly on a nonconductive interface layer situated on single jersey knit cotton fabric was studied, and sheet resistance results of inkjet printing with two conductive nanoparticle inks from NovaCentrix are presented.


An Approach to Optimize Conversion Rate using Behavioral Economics

Abstract:
Purpose: This paper explores the integration of behavioral economics into the digital marketing landscape, emphasizing its potential to enhance conversion rates.
 Methodology: It highlights the significance of understanding human decision-making, incorporating behavioral elements into user profiles, and implementing a comprehensive conversion rate optimization framework.
 Findings: However, ethical considerations are crucial in this context, necessitating transparency, user autonomy, and responsible data handling to build enduring trust.
 Unique contributor to theory, policy and practice: As technology evolves, particularly with the rise of AI, establishing and reinforcing ethical guidelines becomes imperative for organizations to navigate the digital landscape with integrity and responsible behavioral economics practices.


Validation of widefield aberrometer for global peripheral aberration scanning

Abstract: Aberrometers based on Hartmann-Shack wavefront sensing methods have been widely used in the assessment of aberrations in the human eye, a crucial aspect in addressing or adjusting peripheral vision for myopia control, among other applications. Nevertheless, most available aberrometers exhibit limitations such as slow scanning speeds and restricted visual fields when measuring peripheral aberrations. Even the fastest scanning capabilities of commercialized aberrometer are confined to limited visual field and meridians. In this paper, a novel scanning aberrometer for efficient measurement of human eye peripheral aberrations is developed. This instrument allows the measurement of a large visual field angle up to ± 50° and takes all meridian scanning into account, which means it is a widefield aberrometer that can scan the peripheral aberrations globally. Due to the use of the precision motorized rotation stage, both the meridional and visual field angular resolutions of this instrument can be lower to 1°. During the entire measurement process, the subject only needs to focus on a single fixation target. No eye turn or head turn is required. By the instrument installation completed in April 2023, it stands as the only peripheral aberration scanning aberrometer capable of measuring up to a 50° visual field at all meridians using a single fixation target and achieving high angular resolution.


Two entanglement conditions and their connection to negativity

Abstract: We examine two conditions that can be used to detect bipartite entanglement, and show that they can be used to provide lower bounds on the negativity of states. We begin with two-qubit states, and then show how what was done there can be extended to more general states. The resulting bounds are then studied by means of a number of examples. We also show that if one has some knowledge of the Schmidt vectors of a state, better bounds can be found.


Using data clustering to reveal trainees’ behavior in cybersecurity education

Abstract: Abstract In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students’ cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the designers in performing such analytic and evaluation tasks is ad-hoc and insufficient. With unsupervised machine learning methods, we designed a tool for clustering the trainee actions that can exhibit their strategies or help pinpoint flaws in the training design. By using a k-means++ algorithm, we explore clusters of trainees that unveil their specific behavior within the training sessions. The final visualization tool consists of views with scatter plots and radar charts. The former provides a two-dimensional correlation of selected trainee actions and displays their clusters. In contrast, the radar chart displays distinct clusters of trainees based on their more specific strategies or approaches when solving tasks. Through iterative training redesign, the tool can help designers identify improper training parameters and improve the quality of the courses accordingly. To evaluate the tool, we performed a qualitative evaluation of its outcomes with cybersecurity experts. The results confirm the usability of the selected methods in discovering significant trainee behavior. Our insights and recommendations can be beneficial for the design of tools for educators, even beyond cyber security.