Prior-Guided Attribution of Deep Neural Networks for Obstetrics and Gynecology

Abstract: Obstetrics and gynecology (OB/GYN) are areas of medicine that specialize in the care of women during pregnancy and childbirth and in the diagnosis of diseases of the female reproductive system. Ultrasound scanning has become ubiquitous in these branches of medicine, as breast or fetal ultrasound images can lead the sonographer and guide him through his diagnosis. However, ultrasound scan images require a lot of resources to annotate and are often unavailable for training purposes because of confidentiality reasons, which explains why deep learning methods are still not as commonly used to solve OB/GYN tasks as in other computer vision tasks. In order to tackle this lack of data for training deep neural networks in this context, we propose Prior-Guided Attribution (PGA), a novel method that takes advantage of prior spatial information during training by guiding part of its attribution towards these salient areas. Furthermore, we introduce a novel prior allocation strategy method to take into account several spatial priors at the same time while providing the model enough degrees of liberty to learn relevant features by itself. The proposed method only uses the additional information during training, without needing it during inference. After validating the different elements of the method as well as its genericity on a facial analysis problem, we demonstrate that the proposed PGA method constantly outperforms existing baselines on two ultrasound imaging OB/GYN tasks: breast cancer detection and scan plane detection with segmentation prior maps.


Incomplete and possibly selective recording of signs, symptoms, and measurements in free text fields of primary care electronic health records of adults with lower respiratory tract infections

Abstract:
Abstract

Objectives
 To assess the completeness of recording of relevant signs, symptoms, and measurements in Dutch free text fields of primary care electronic health records (EHR) of adults with lower respiratory tract infections (LRTI).
Study Design and Setting
 Retrospective cohort study embedded in a prediction modeling project using routine health care data of the Julius General Practitioners' Network of adult patients with LRTI. Free text fields of 1,000 primary care consultations of LRTI episodes between 2016 and 2019 were manually annotated to retrieve data on the recording of sixteen relevant signs, symptoms, and measurements.
Results
 For 12/16 (75%) of the relevant signs, symptoms, and measurements, more than 50% of the values was not recorded. The patterns of recorded values indicated selective recording of positive or abnormal values. Recording rates varied across consultation type (physical consultation vs. home visit), diagnosis (acute bronchitis vs. pneumonia), antibiotic prescription issued (yes vs. no), and between practices.
Conclusion
 In EHR of primary care LRTI patients, recording of signs, symptoms, and measurements in free text fields is incomplete and possibly selective. When using free text data in EHR-based research, careful consideration of its recording patterns and appropriate missing data handling techniques is therefore required.


Dataset construction to detect human behavior with the help of emotions, sentiments and mood for Roman Urdu

Abstract: Roman Urdu and English are often used together as a hybrid language for communication on social media. Because writers don't worry about spelling when utilising the English alphabet to write Urdu during texting, it becomes challenging to interpret mixed codes for emotions. There are over 14,000 emotion lexicons in this dataset, each of which lists nine different emotions and their polarities. The NRC emotion lexicons [8] provided in Urdu have been transliterated into Roman Urdu. To verify that the provided translation is accurate, we used three online dictionaries of Urdu. A Python script that transliterates words from Urdu to Roman Urdu has been used to develop Roman Urdu transliteration. Sentiment and mood, depending on the emotion lexicon, are also provided. The textual data has been annotated using the unigram feature and distance estimation among strings and lexicons. Approximately 10,000 sentences from the baseline sample have been automatically annotated.


An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people

Abstract: Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.


Application of augmented reality using automatic markerless registration for facial plastic and reconstructive surgery

Abstract: This study aimed to present a novel markerless augmented reality (AR) system using automatic registration based on machine-learning algorithms that visualize the facial region and provide an intraoperative guide for facial plastic and reconstructive surgeries. This study prospectively enrolled 20 patients scheduled for facial plastic and reconstructive surgeries. The AR system visualizes computed tomographic data in three-dimensional (3D) space by aligning with the point clouds captured by a 3D camera. Point cloud registration consists of two stages: the preliminary registration gives an initial estimate of the transformation using landmark detection, followed by the precise registration using Iterative Closest Point algorithms. Computed Tomography (CT) data can be visualized as two-dimensional slice images or 3D images by the AR system. The AR registration error was defined as the cloud-to-cloud distance between the surface data obtained from the CT and 3D camera. The error was calculated in each facial territory, including the upper, middle, and lower face, while patients were awake and orally intubated, respectively. The mean registration errors were 1.490 ± 0.384 mm and 1.948 ± 0.638 mm while patients were awake and orally intubated, respectively. There was a significant difference in the errors in the lower face between patients while they were awake (1.502 ± 0.480 mm) and orally intubated (2.325 ± 0.971 mm) when stratified by facial territories (p = 0.006). The markerless AR can accurately visualize the facial region with a mean overall registration error of 1–2 mm, with a slight increase in the lower face due to errors arising from tube intubation.


A Bidirectional Feedforward Neural Network Architecture Using the Discretized Neural Memory Ordinary Differential Equation

Abstract: Deep Feedforward Neural Networks (FNNs) with skip connections have revolutionized various image recognition tasks. In this paper, we propose a novel architecture called bidirectional FNN (BiFNN), which utilizes skip connections to aggregate features between its forward and backward paths. The BiFNN accepts any FNN as a plugin that can incorporate any general FNN model into its forward path, introducing only a few additional parameters in the cross-path connections. The backward path is implemented as a nonparameter layer, utilizing a discretized form of the neural memory Ordinary Differential Equation (nmODE), which is named [Formula: see text]-net. We provide a proof of convergence for the [Formula: see text]-net and evaluate its initial value problem. Our proposed architecture is evaluated on diverse image recognition datasets, including Fashion-MNIST, SVHN, CIFAR-10, CIFAR-100, and Tiny-ImageNet. The results demonstrate that BiFNNs offer significant improvements compared to embedded models such as ConvMixer, ResNet, ResNeXt, and Vision Transformer. Furthermore, BiFNNs can be fine-tuned to achieve comparable performance with embedded models on Tiny-ImageNet and ImageNet-1K datasets by loading the same pretrained parameters.


Few-shot learning based oral cancer diagnosis using a dual feature extractor prototypical network

Abstract: A large global health issue is cancer, wherein early diagnosis and treatment have proven to be life-saving. This holds true for oral cancer, thus emphasizing the significance of timely intervention. Deep learning techniques have gained traction in early cancer detection, exhibiting promising outcomes in accurate diagnosis. However, collecting a substantial amount of training data poses a challenge for deep learning models in cancer diagnosis. To address this limitation, this study proposes an oral cancer diagnosis approach based on a few-shot learning framework that circumvents the need for extensive training data. Specifically, a prototypical network is employed to construct a diagnostic model, wherein two feature extractors are utilized to extract prototypical features and query features respectively, departing from the conventional use of a single feature extraction function in prototypical networks. Moreover, a customized loss function is designed for the proposed method. Rigorous experimentation using a histopathological image dataset demonstrates the superior performance of our proposed approach over comparison methods.


An empirical study of counterfactual visualization to support visual causal inference

Abstract: Counterfactuals – expressing what might have been true under different circumstances – have been widely applied in statistics and machine learning to help understand causal relationships. More recently, counterfactuals have begun to emerge as a technique being applied within visualization research. However, it remains unclear to what extent counterfactuals can aid with visual data communication. In this paper, we primarily focus on assessing the quality of users’ understanding of data when provided with counterfactual visualizations. We propose a preliminary model of causality comprehension by connecting theories from causal inference and visual data communication. Leveraging this model, we conducted an empirical study to explore how counterfactuals can improve users’ understanding of data in static visualizations. Our results indicate that visualizing counterfactuals had a positive impact on participants’ interpretations of causal relations within datasets. These results motivate a discussion of how to more effectively incorporate counterfactuals into data visualizations.


A Parameter-independent Algorithm of Finding Maximum Clique with Seidel Continuous-Time Quantum Walks

Abstract: The maximum clique (MC) problem holds significance in network analysis. Quantum-based algorithms have recently emerged as promising approaches for this problem. However, these algorithms heavily depend on parameters of quantum system and vary significantly for different graphs. In order to tackle this issue, we initially demonstrate that continuous-time quantum walks (CTQW) driven by the Seidel matrix offer valuable insights into the clique structure of graphs, outperforming the CTQW driven by adjacency matrix. Specifically, we conduct an in-depth analysis for CTQW of 4 types of graphs, meticulously calculating the amplitudes associated with different vertices. Our findings consistently reveal that vertices belonging to MC exhibit the highest intensity at the largest frequency component of the probability amplitude for these types of graphs. Considering the varying intensities, we propose a parameter-independent algorithm for determining the MC. We compare our algorithm with a typical quantum-based algorithm, the results indicate that our algorithm exhibits greater stability.


Squeezing as a resource for time series processing in quantum reservoir computing

Abstract: Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.


Construction and optimization of vending machine decision support system based on improved C4.5 decision tree

Abstract:
Abstract
 The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.


Leveraging Sampling Schemes on Skewed Class Distribution to Enhance Male Fertility Detection with Ensemble AI Learners

Abstract: Designing effective AI models becomes a challenge when dealing with imbalanced/skewed class distributions in datasets. Addressing this, re-sampling techniques often come into play as potential solutions. In this investigation, we delve into the male fertility dataset, exploring 14 re-sampling approaches to understand their impact on enhancing predictive model performance. The research employs conventional AI learners to gauge male fertility potential. Notably, five ensemble AI learners are studied, their performances are compared, and their results are evaluated using four measurement indices. Through comprehensive comparative analysis, we identify substantial enhancement in model effectiveness. Our findings showcase that the LightGBM model with SMOTE-ENN re-sampling stands out, achieving an efficacy of 96.66% and an F1-Score of 95.60% through 5-fold cross-validation. Interestingly, the CatBoost model, without re-sampling, exhibits strong performance, achieving an efficacy of 86.99% and an F1-Score of 93.02%. Furthermore, we benchmark our approach against state-of-the-art methods in male fertility prediction, particularly highlighting the use of re-sampling techniques like SMOTE and ESLSMOTE. Consequently, our proposed model emerges as a robust and efficient computational framework, promising accurate male fertility prediction.


Center extraction method for reflected metallic surface fringes based on line structured light

Abstract: Using line structured light to measure metal surface topography, the extraction error of the stripe center is significant due to the influence of the optical characteristics of the metal surface and the scattering noise. This paper proposes a sub-pixel stripe center extraction method based on adaptive threshold segmentation and a gradient weighting strategy to address this issue. First, we analyze the characteristics of the stripe image of the measured metal’s surface morphology. Relying on the morphological features of the image, the image is segmented to remove the effect of background noise and to obtain the region of interest in the image. Then, we use the gray-gravity method to get the rough center coordinates of the stripes. We extend the stripes in the width direction using the rough center coordinates as a reference to determine the center of the stripes for extraction after segmentation. Next, we adaptively determine the boundary threshold utilizing the region’s grayscale. Finally, we use the gradient weighting strategy to extract the sub-pixel stripe center. The experimental results show that the proposed method effectively eliminates the interference of metal surface scattering on 3D reconstruction. The average height error of the measured standard block is 0.025 mm, and the repeatability of the measurement accuracy is 0.026 mm.


A collection of 157 individual neuromelanin-sensitive images accompanied by non-linear neuromelanin-sensitive atlas and a probabilistic locus coeruleus atlas

Abstract: The current dataset aims to support and enhance the research reliability of neuromelanin regions in the brainstem, such as locus coeruleus (LC), by offering raw neuromelanin-sensitive images. The dataset includes raw neuromelanin-sensitive images from 157 healthy individuals (8-64 years old). In addition, leveraging individual neuromelanin-sensitive images, a non-linear neuromelanin-sensitive atlas, generated through an iterative warping process, is included to tackle the common challenge of a limited field of view in neuromelanin-sensitive images. Finally, the dataset encompasses a probabilistic LC atlas generated through a majority voting approach with pre-existing multiple atlas-based segmentations. This process entails warping pre-existing atlases onto individual spaces and identifying voxels with a majority consensus of over 50 % across the atlases. This LC probabilistic atlas can minimize uncertainty variance associated with choosing a specific single atlas.


Lexicon dataset for the Hausa language

Abstract: This paper presents a comprehensive augmented lexicon sentiment analysis dataset for the Hausa language. The dataset was created by adopting words and phrases from a Hausa Language dictionary and then using the data augmentation method to expand the quantity of the dataset. The researchers manually annotated each phrase/sentence with positive, negative, or neutral polarity. The dataset consists of 14,663 rows, with 4,154 positives, 4,310 negatives, and 6,199 neutrals. The dataset is valuable because it contributes to the available resources for sentiment analysis, especially for Hausa, which is a low-resource language. The dataset will benefit researchers in sentiment analysis who want to develop a model to analyze Hausa posts on social media or product reviews in the Hausa language.


Abstract: Preview this article: Feature-exponence mapping in language contact, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/lab.23046.ale-1.gif


Model and data-centric machine learning algorithms to address data scarcity for failure identification

Abstract: The uneven occurrence of certain types of failures in optical networks results in a scarcity of data for less frequent failures, leading to imbalanced datasets for training machine learning (ML) models. This poses a significant bottleneck in terms of reliability and practical implementation of ML for failure management. Existing research works often overlook this aspect while demonstrating high accuracies by utilizing sufficiently balanced training datasets collected in controlled laboratory setups and simulations. However, this approach does not reflect a realistic network scenario. To address this issue, different model-centric and data-centric approaches have been investigated in this work to determine their potential for improving the learning of ML models, specifically neural networks (NNs), on less frequent failures with such imbalanced training datasets. For failure identification, the obtained results suggest that data-centric approaches tend to perform better in terms of classification accuracy, with an improvement of up to 5.5% in F1-score observed on less frequent failures compared to a baseline NN (i.e., without any model-centric or data-centric treatment). However, some data-centric approaches may also have significant additional computational complexity associated with them, and, therefore, a suitable approach should be chosen based on the desired classification performance and available computational resources.


CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

Abstract: Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.


Perfect single-photon sources

Abstract: Abstract We introduce the gapped coherent state in the form of a single-photon source (SPS) that consists of uncorrelated photons as a background, except that we demand that no two photons can be closer in time than a time gap $$t_\text{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mtext>G</mml:mtext> </mml:msub> </mml:math> . While no obvious quantum mechanism is yet identified to produce exactly such a photon stream, a numerical simulation is easily achieved by first generating an uncorrelated (Poissonian) signal and then for each photon in the list, either adding such a time gap or removing all photons that are closer in time from each other than $$t_\text{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mtext>G</mml:mtext> </mml:msub> </mml:math> . We study the statistical properties of such a hypothetical signal, which exhibits counter-intuitive features. This provides a neat and natural connection between continuous-wave (stationary) and pulsed single-photon sources, with also a bearing on what it means for such sources to be perfect in terms of single-photon emission.


Who owns (or controls) health data?

Abstract: The ongoing debate on secondary use of health data for research has been renewed by the passage of comprehensive data privacy laws that shift control from institutions back to the individuals on whom the data was collected. Rights-based data privacy laws, while lauded by individuals, are viewed as problematic for the researcher due to the distributed nature of data control. Efforts such as the European Health Data Space initiative seek to build a new mechanism for secondary use that erodes individual control in favor of broader secondary use for beneficial health research. Health information sharing platforms do exist that embrace rights-based data privacy while simultaneously providing a rich research environment for secondary data use. The benefits of embracing rights-based data privacy to promote transparency of data use along with control of one's participation builds the trust necessary for more inclusive/diverse/representative clinical research.


An intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer

Abstract: Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI-RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (≥ 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.


Achieving widely distributed feature matches using flattened-affine-SIFT algorithm for fisheye images

Abstract: Performing correction first is the most common methods to address feature matching issues for fisheye images, but corrections often result in significant loss of scene details or stretching of images, leaving peripheral regions without matches. In this paper, we propose a novel approach, named flattened-affine-SIFT, to find widely distributed feature matches between stereo fisheye images. Firstly, we establish a new imaging model that integrates a scalable model and a hemisphere model. Utilizing the extensibility of the imaging model, we design a flattened array model to reduce the distortion of fisheye images. Additionally, the affine transformation is performed on the flattened simulation images, which are computed using the differential expansion and the optimal rigidity transformation. Then feature matches are extracted and matched from the simulated images. Experiments on indoor and outdoor fisheye images show that the proposed algorithm can find a large number of reliable feature matches. Moreover, these matches tend to be dispersed over the entire effective image, including peripheral regions with dramatic distortion.


Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving

Abstract: In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields.


On the compatibility of models with experiments

Abstract: Comment This is a commentary article in response to the following content: The importance of features and exponents


Supporting #TeamVet in navigating the XL bully ban

Abstract: Abstract BVA continues to actively support #TeamVet as the consequences of the introduction of the ban on XL bully dogs begin to be felt.


Multi-scale fusion for image enhancement in shield tunneling: a combined MSRCR and CLAHE approach

Abstract: Abstract Aiming at addressing the complex lighting conditions in shield construction environments and enhancing the contrast of low-light images while preserving image details, this paper presents an image enhancement technique based on multi-scale fusion. The proposed method first duplicates the input image into two copies. Subsequently, the first copy undergoes color and contrast adjustment via the adaptive weighted Retinex algorithm for color restoration (A-MSRCR) algorithm. Meanwhile, the second copy undergoes contrast enhancement and detail preservation by combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, a normalized weight map is introduced to facilitate multi-scale fusion of the two enhanced images, ensuring an overall enhancement in both contrast and detail. The experimental results show that the proposed method provides better visual quality and objective data than the compared methods. Moreover, it offers valuable support for subsequent recognition and localization tasks.


Block mapping and dual-matrix-based watermarking for image authentication with self-recovery capability

Abstract: Numerous image authentication techniques have been devised to address the potential security issue of malicious tampering with image content since digital images can be easily duplicated, modified, transformed and diffused via the Internet transmission. However, the existing works still remain many shortcomings in terms of the recovery incapability and detection accuracy with extensive tampering. To improve the performance of tamper detection and image recovery, we present a block mapping and dual-matrix-based watermarking scheme for image authentication with self-recovery capability in this paper. The to-be-embedded watermark information is composed of the authentication data and recovery data. The Authentication Feature Composition Calculation algorithm is proposed to generate the authentication data for image tamper detection and localization. Furthermore, the recovery data for tampered region recovery is comprised of self-recovery bits and mapped-recovery bits. The Set Partition in Hierarchical Trees encoding algorithm is applied to obtain the self-recovery bits, whereas the Rehashing Model-based Block Mapping algorithm is proposed to obtain the mapped-recovery bits for retrieving the damaged codes caused by tampering. Subsequently, the watermark information is embedded into the original image as digital watermarking with the guidance of a dual-matrix. The experimental results demonstrate that comparing with other state-of-the-art works, our proposed scheme not only improves the performance in recovery, but also extends the limitation of tampering rate up to 90%. Furthermore, it obtains a desirable image quality above 40 dB, large watermark payload up to 3.169 bpp, and the effective resistance to malicious attack, such as copy-move and collage attacks.


The Pine Cone Optimization Algorithm (PCOA)

Abstract: The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. It employs new and powerful operators to simulate the mentioned mechanisms. The performance of PCOA is analyzed using classic benchmark functions, CEC017 and CEC2019 as mathematical problems and CEC2006 and CEC2011 as engineering design problems. In terms of accuracy, the results show the superiority of PCOA to well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO). The results of PCOA are competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, the results of PCOA are reasonable. According to the Friedman test, PCOA’s rank is 1.68 and 9.42 percent better than EBOwithCMAR (second-best algorithm) and LSHADE (third-best algorithm), respectively. The authors recommend PCOA for science, engineering, and industrial societies for solving complex optimization problems.


What can TV viewing tell us about dogs' vision?

Abstract: Abstract Georgina Mills reports on new research that looks at how dogs react to video content around the home, and how this might help vets assess eyesight.


SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI CABANG MINIMARKET TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING BERBASIS WEB

Abstract: Minimarkets are shops that sell daily necessities. This mini market is located on Jalan Station Kauman, Krikilan Hamlet, Dawungan Village, Masaran District, Sragen Regency, Central Java Province. In developing inter-company leaders, it is difficult to make decisions about the location of new branches, because there are many criteria such as: strategic location, distance and population to facilitate decision making. The purpose of this study is that researchers assist company leaders in choosing the best new minimarket branch locations using the SAW algorithm method. This method was chosen because it is able to carry out the process of ranking and weighting the best alternatives by applying many criteria. The technique used in this research is observation (observation), interview (interview), and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. This application is made using the PHP programming language and the database uses MySQL. The final result is a report on the best location data. System testing is done by testing the functionality and testing the validity of the obtained results are 100% valid.


High accuracy, compact 3D face imaging method based on translational and online-switchable phase-shifting fringe projection

Abstract: In this paper, a compact, cost-effective, and fast translational online-switchable phase-shifting fringe (TOPF) projector is designed and fabricated for high accuracy three-dimensional (3D) face imaging. Compared with the conventional mechanical projectors, the main difference is that it utilizes a translational approach instead of a rotational one to achieve a better balance in terms of size, speed, accuracy, and cost. To mitigate the inconsistency of the motor's step size and ensure the stability of phase-shifting, an optical encoder-based feedback control mechanism is employed. Additionally, to address the random phase shift errors induced by mechanical motion, a fast, generalized phase-shifting algorithm with unknown phase shifts (uPSAs) that can calculate arbitrary phase shifts is proposed. Finally, a 3D imaging system consisting of the TOPF projector and two cameras is constructed for experimental validation. The feasibility, effectiveness, and precision of our proposed method are substantiated through the reconstruction of a static facial model and a dynamic real face.


Analysis of Word Choice Errors in Saudi EFL Students’ Spoken Language

Abstract: The purpose of this research paper is to identify and analyze word choice errors committed in spoken English by Saudi EFL students at King Khalid University. It also seeks to identify the origins of the errors and to suggest relevant solutions to assist learners in developing lexical competence and reducing the occurrence of these errors. The research focuses on five different forms of word choice errors. These errors consist of errors of redundancy, literal translation, subject /verb agreement, collocation, and inappropriate usage of formal and informal words. To gather data, the study is based on conducting structured interviews with three EFL-speaking teachers. It also employs a speaking test as a data collection technique from fifteen EFL learners chosen at random, who are required to speak one by one in front of the class for 3-6 minutes. Their oral performance is recorded by mobile phones. According to data analysis, the most common error category is literal translation (33.97%), which is followed by collocation errors (24.88%) as well as subject/verb agreement (18.66%). Furthermore, there are redundancy errors with a percentage of (12.44%). The last type of error committed is inappropriate use of formal and informal language, which has a percentage of (10.04%). The study attributes these errors to a variety of causes, including the Arabic language's impact as well as ineffective teaching techniques and learning strategies. The study offers several recommendations and pedagogical implications to help EFL learners overcome and minimize word choice errors.


Knowledge distillation under ideal joint classifier assumption

Abstract: Knowledge distillation constitutes a potent methodology for condensing substantial neural networks into more compact and efficient counterparts. Within this context, softmax regression representation learning serves as a widely embraced approach, leveraging a pre-established teacher network to guide the learning process of a diminutive student network. Notably, despite the extensive inquiry into the efficacy of softmax regression representation learning, the intricate underpinnings governing the knowledge transfer mechanism remain inadequately elucidated. This study introduces the ‘Ideal Joint Classifier Knowledge Distillation’ (IJCKD) framework, an overarching paradigm that not only furnishes a lucid and exhaustive comprehension of prevailing knowledge distillation techniques but also establishes a theoretical underpinning for prospective investigations. Employing mathematical methodologies derived from domain adaptation theory, this investigation conducts a comprehensive examination of the error boundary of the student network contingent upon the teacher network. Consequently, our framework facilitates efficient knowledge transference between teacher and student networks, thereby accommodating a diverse spectrum of applications.


Children and extensive reading motivation

Abstract: Abstract Extensive reading (ER), which refers to reading a lot of often self-selected material for information, overall meaning, pleasure, and enjoyment, has been growing in popularity in Asia as an effective way for English as a foreign language (EFL) students to improve reading skills and has provided opportunities for comprehensible input ( Nation & Waring, 2013 ). The focus of this action research (AR) project is on young EFL learners in a private language school setting without a grading system. Therefore, it is important that students be intrinsically motivated to read for the school’s ER program to be successful, specifically, the motivation to check out and read books from a school library. Furthermore, this AR project sought to find whether certain methods can increase students’ intrinsic motivation to read. The research methods include questionnaires, student voice recordings, and empirical data on book-borrowing behaviors. The findings show that indirect, rather than direct, teacher recommendation of books seems to be the most effective method to increase intrinsic motivation to do ER.


An optical authentication scheme based on all-optical neural network

Abstract: Diffractive deep neural network is architectural designs based on the principles of neural networks, which consists of multiple diffraction layers and has the remarkable ability to perform machine learning tasks at the speed of light. In this paper, a novel optical authentication system was presented that utilizes the diffractive deep neural network principle. By carefully manipulating a light beam with both a public key and a private key, we are able to generate a unique and secure image representation at a precise distance. The generated image can undergo authentication by being processed through the proposed authentication system. Leveraging the utilization of invisible terahertz light, the certification system possesses inherent characteristics of concealment and enhanced security. Additionally, the entire certification process operates solely through the manipulation of the light beam, eliminating the need for electronic calculations. As a result, the system offers rapid certification speed. The proposed optical authentication scheme is further validated through computer simulations, which showcase its robust security and high precision. This method holds immense potential for diverse applications in optical neural network authentication, warranting a broad scope of future prospects.


Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

Abstract: To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.


Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Abstract: Abstract Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.


The use of deep learning integrating image recognition in language analysis technology in secondary school education

Abstract: This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse's speaking rate, speech intelligibility, average sentence length, and content similarity are -0.56, -0.71, -0.71, and -0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.


Respecting Partial Privacy of Unstructured Data via Spectrum-Based Encoder

Abstract: Since the popularity of Machine Learning as a Service (MLaaS) has been increasing significantly, users are facing the risk of exposing sensitive information that is not task-related. The reason is that the data uploaded by users may include some information that is not useful for inference but can lead to privacy leakage. One straightforward approach to mitigate this issue is to filter out task-independent information to protect user privacy. However, this method is feasible for structured data with naturally independent entries, but it is challenging for unstructured data. Therefore, we propose a novel framework, which employs a spectrum-based encoder to transform unstructured data into the latent space and a task-specific model to identify the essential information for the target task. Our system has been comprehensively evaluated on three benchmark visual datasets and compared to previous works. The results demonstrate that our framework offers superior protection for task-independent information and maintains the usefulness of task-related information.


Model-Agnostic Binary Patch Grouping for Bone Marrow WSI Representation

Abstract: Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides, i.e. whole slide images (WSIs). Trained histopathologists can help to diagnose disease by examining WSIs visually, but this process is time-consuming and prone to variability. To address these issues, AI models are being developed to create slide-level representations of WSIs, summarizing the entire slide as a single vector. This enables various computational pathology applications, including inter-slide search, multi-modal training, and slide-level classification. Achieving expressive and robust slide-level representations hinges on patch feature extraction and aggregation steps. We propose integrating an additional Binary Patch Grouping (BPG) step, a plugin that can be integrated into various slide-level representation pipelines to enhance the quality of slide-level representation in bone marrow histopathology. BPG excludes patches with less clinical relevance through minimal interaction with the pathologist: a one-time human intervention for the entire process. We further investigated domain-general versus domain-specific feature extraction models based on convolution and attention and examined two different feature aggregation methods, with and without BPG, showing BPG’s generalizability. We show that BPG boosts the performance of WSI retrieval (mAP@10) by 4% and improves WSI classification (weighted-F1) by 5% relative to not using BPG. Additionally, we found that the pipeline with BPG, domain-general large models and parameterized pooling produced the best-quality slide-level representations.


Touch or click friendly: Towards adaptive user interfaces for complex applications

Abstract: This study evaluated the usability of a direct manipulation device (touchscreen) vs. indirect manipulation devices (mouse and touchpad) on the selected Microsoft (MS) Word tasks as per ISO-9241-11 standard. MS Word was taken as an example of a complex application. The tasks were evaluated in terms of touch-friendly or click-friendly using efficiency, effectiveness, and satisfaction parameters to propose a customized task menu. The experiment was conducted with fifty-four participants, divided into three MS Word usage-based expertise groups. Each participant performed fifty-six tasks using a mouse, a touchpad, and a touchscreen. To assess task-level usability, individual one-way ANOVAs were performed for each task to gauge both efficiency and effectiveness. It’s worth noting that the touchscreen significantly outperformed other input methods in just one specific task regarding effectiveness. Consequently, an ANCOVA was employed, with task completion time as the independent variable and the number of errors as a covariate, to further investigate effectiveness. A total of 19 (34%) of the total tasks were found to be significantly efficient with a mouse, while 21 (37.5%) were significantly efficient with a touchscreen. Based on the results, a customized menu is recommended for MS Word-like applications that combine actions in touch-friendly tasks and mouse-friendly tasks separately.


Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem

Abstract: Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 44% of package versions are affected by RUF, causing compiling failures for at most 12% of package versions. To mitigate wide RUF impacts, we further design and implement a RUF-compilation-failure recovery tool that can recover up to 90% of the failure. We believe our techniques, findings, and tools can help stabilize the Rust compiler, ultimately enhancing the security and reliability of the Rust ecosystem.


On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization

Abstract: One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the graphical user interface (GUI).


EDEFuzz: A Web API Fuzzer for Excessive Data Exposures

Abstract: APIs often transmit far more data to client applications than they need, and in the context of web applications, often do so over public channels. This issue, termed Excessive Data Exposure (EDE), was OWASP's third most significant API vulnerability of 2019. However, there are few automated tools---either in research or industry---to effectively find and remediate such issues. This is unsurprising as the problem lacks an explicit test oracle: the vulnerability does not manifest through explicit abnormal behaviours (e.g., program crashes or memory access violations).


Reflecting on Culture in an Immersion Experience: How to Prepare Students for the Unexpected

Abstract: Her research focus includes self-efficacy, mental toughness, and microaggressions.Darnishia is also


Prescriptive method for fiber polarization compensation in two bases

Abstract: Single-mode optical fibers exhibit a small but non-negligible birefringence that induces random polarization rotations during light propagation. In classical interferometry these rotations give rise to polarization-induced fading of the interferometric visibility, and in fiber-based polarimetric sensors as well as quantum optics experiments, they scramble the information encoded in the polarization state. Correcting these undesired rotations is consequently an important part of many experiments and applications employing optical fibers. In this Lab Note we review an efficient method for fully compensating fiber polarization rotations for general input states. This method was not originally devised by us, but does, to the best of our knowledge, not appear in the literature, and our interactions with the community have indicated that it is not well known.


An Improved Genetic Algorithm Using Intelligent Symbolic Regression

Abstract: In this paper, an optimization technique based on intelligent symbolic regression is presented.Intelligent symbolic regression methodology seeks to replace implicit functions of the original design optimization problem with an approximation model less expensive to evaluate.In order to address the issue of the high-computational cost of the implicit function evaluation, genetic programming methodology has been investigated to build approximation models of the best possible quality.The goal of genetic programming is to evolve mathematical expressions with no assumption about the structure, which is given as part of the solution.Once the symbolic regression model is available, the Genetic Algorithm, on subsequent runs, searches the inexpensive regression model for the global optimum, only rarely sampling the high-fidelity model.The efficiency of the proposed method has been tested on a set of standard optimization test problems and it was found to reduce the computational overhead by a significant amount without much loss of accuracy.Test results of optimization problems revealed that real-valued representation technique outperforms the binary representation technique.Such a representation is of particular concern for engineering applications, which motivated the present work.


Creating a Framework for Virtual Reality Learning Environment (VRLE) for Studio-Based Learning

Abstract: Higher education academics are lacking the knowledge on how to implement Design Thinking (DT) for transdisciplinary design studies that would produce Fourth Industrial Revolution Workforce Skills (4IRSkills), due to the sparsity in literature and implementation guidelines. This article discusses on the creation process of a Teaching and Learning Framework (TLF) for transdisciplinary design studies with a desktop-based Virtual Reality Learning Environment (VRLE) simulation of studio-based learning. The framework was created using a Selander’s Learning Design Sequence (LDS) with DT approach. The LDS helped the researcher to understand the processes of teaching and learning, especially on the affordances of various modes. The DT approach was used to facilitate the process of solving problems through the practice of empathy, reframing, prototyping, experimentation, testing, and redesign. A sequential explanatory mixed method was used, and the data were analyzed through inferential statistical analysis, thematic analysis and convergent coding matrix triangulation protocol. The findings indicated that the TLF with VRLE showed a positive and significant relationship with students' achievement of 4IRSkills. The TLF Design Process and Collaborative Design variables were positively correlated with students' 4IRSkills, while there was no correlation for the VRLE Representational Fidelity and Learner Interaction variables. In conclusion, the TLF with VRLE proves to be an effective and suitable method for guiding the implementation of pedagogies that teaches 4IRSkills at the tertiary level in fully online learning environments.


The Impact of Transfer Learning on AI Performance Across Domains

Abstract: This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems


Towards a more practical analysis of Newton’s rings using deep learning

Abstract: As a typical form of optical fringes with a quadratic phase, Newton's ring patterns play an important role in spherical measurements and optical interferometry. A variety of methods have been used to analyze Newton's ring patterns. However, it is still rather challenging to fulfill the analysis. We present a deep-learning-based method to estimate the parameters of Newton's ring patterns and fulfill the analysis accordingly. The experimental results indicate the excellent accuracy, noise robustness, and demodulation efficiency of our method. It provides another applicable approach to analyzing Newton's ring patterns and brings insights into fringe analysis and interferometry-based measurements.


Analysis of Fitness Based on Smart Watch Data

Abstract: Smart watch, a new trend to many young as well as aged persons now a days, can serve as a fitness tracker and be far more accurate than a phone. Besides many technological advantages, a smartwatch can easily collect the data related to fitness and other movements also. Smart watch provides such an organized human health and activities dataset so that by this dataset a human activities analysis can be performed and by this analysis a conclusion can be drawn about their health. Accordingly, our Project aim is to analyze the data and examine whether the data collected through smart watches can describe the physical fitness of individuals on the basis of some of the factors related to daily activities. Keywords— Data Visualization, Correlation plot, Scatter plot, MANOVA, Fisher Exact Test, P-value


Self-Confidence of Undergraduate Students in Designing Software Architecture

Abstract: Software architecture students, often, lack self-confidence in their ability to use their knowledge to design software architectures.This paper investigates the relations between undergraduate software architecture students' self-confidence and their course expectations, cognitive levels, preferred learning methods, and critical thinking.We developed a questionnaire with open-ended questions to assess the self-confidence levels and related factors, which was taken by one-hundred ten students in two semesters.The students answers were coded and analyzed afterward.We found that self-confidence is weakly associated with the students' critical thinking and independent from their cognitive levels, preferred learning methods, and expectations from the course.The results suggest that to improve the self-confidence of the students, the instructors should work on improving the students' critical thinking capabilities.


Building S-STEM scholars' knowledge and skills through technical and career-development seminars

Abstract: Abstract This paper first presents an overview of the objectives and the accomplishments of the S-STEM ACCESS project and then focuses on a specific ACCESS program activity - the technical and career-development seminars and panels - which support ACCESS scholars' knowledge and skills development and contribute toward achieving several project objectives. The technical seminars exposed students to state-of-the-art topics and industry practices related to the rapidly evolving field of cybersecurity. The career-development seminars included topics on obtaining security clearances, understanding different cybersecurity workforce career paths, as well as on pursuing graduate degrees and research-centered careers. Since seminars and panels were open to all West Virginia University (WVU) students, they helped publicize the ACCESS program and the B.S. and Area of Emphasis (AoE) in Cybersecurity and therefore also served as a recruitment mechanism. The results of the external evaluation showed that the seminars and panels were of exceptional value to students who unanimously gave them high positive scores. Qualitative responses to the survey and focus group input revealed that students appreciated the seminars for providing opportunities to connect with professionals and peers, to learn about potential career paths, to gain the knowledge and confidence needed to successfully secure an internship position, and other benefits.


Generating arbitrary analytically solvable two-level systems

Abstract: Abstract We present a new approach for generating arbitrary analytically solvable two-level systems. This method offers the ability to completely derive all analytically solvable Hamiltonians for any analytical evolutions of two-level systems. To demonstrate the effectiveness of this approach, we reconstruct the Rosen–Zener model and generate several new exact solutions. Using this approach, we present the exact evolution of the semi-classical Rabi model with new analytical properties. The parameters used to generate Hamiltonians have direct physical interpretations within the Bloch sphere, the quantum speed limit, and the geometric phase. As a result, the physical properties of the generated Hamiltonian are highly controllable, which plays a significant role in the fields of quantum control, quantum computing, and quantum information.


“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field

Abstract:
In 2019, Competition Bureau Canada (“the Bureau”) sent letters to approximately 100 brands and marketing agencies that engage in influencer marketing—advising them to ensure that their marketing practices are in compliance with the law. Against this background, this research uses semi-structured interviews to examine the regulatory compliance efforts of 21 influencer intermediaries who liaise between brands and social media influencers. The research assesses these intermediaries’ disclosure practices together with their thoughts on the Bureau’s targeted outreach and law enforcement in the field. Additionally, the research explores how the intermediaries adjust their regulatory compliance efforts to technological innovation on social media platforms.

This research contributes four main findings. First, intermediaries consign regulatory expectations for disclosure to a secondary level of importance when working to meet the needs of brands and influencers. Second, there is limited intermediary knowledge of the Bureau’s legal standing and activities in the Canadian influencer field. Third, there are various algorithmic (e.g., algorithmic deprioritization) and non-algorithmic (e.g., lengthy disclosure processes) challenges to maintaining high standards of compliance in evolving digital environments. Finally, intermediaries have access to forms of social, cultural, and technical power that can be maximized to influence high standards of compliance. This research contributes to a growing body of scholarship focused on the perspectives of professionals who manage influencer marketing collaborations.


Sand Painting Generation Based on Convolutional Neural Networks

Abstract: Neural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content. In this paper, we propose a style transfer approach for sand painting generation based on convolutional neural networks. The proposed approach aims to improve sand painting generation via neural style transfer, which can address the problem of blurred objects. Furthermore, it can reduce background noise caused by neural style transfers. First, we segment the main objects from the content image. Subsequently, we perform close–open filtering operations on the content image to obtain smooth images. Subsequently, we perform Sobel edge detection to process the images and obtain edge maps. Based on these edge maps and the input style image, we perform neural style transfer to generate sand painting images. Finally, we integrate the generated images to obtain the final stylized sand painting image. The results show that the proposed approach yields good visual effects from sand paintings. Moreover, the proposed approach achieves better visual effects for sand painting than the previous method.


In-Situ Bending Moment Visualization of a Structure Using Augmented Reality and Real-Time Object Detection

Abstract: Agile technology


Generative AI-Powered Chatbots

Abstract: Imagine having a concept for a literary work but being unable to see it take shape because of a plethora of reasons: paucity of time, writer's block, linguistic barriers, among others. Gen AI promises to be a supportive partner in these creative endeavours. With the synergy of mind and machine, literary texts are born. This chapter attempts to provide insights into the comparison between the nuances of human-AI co-creative partnerships through the crafting of short stories. Using the lens of Collaborative Autoethnography, two seasoned educators collaboratively reflect on cognitive, behavioural, and affective dimensions of co-creating with ChatGPT, a chatbot developed by Open AI. The chapter analyses the co-creation process followed by each of the authors and brings out the commonalities and specificities of the individual lived experience to achieve a narrative piece of work. The insights of the co-creation process would be beneficial to educators, curriculum designers, technology specialists, students and those who intend to use AI-powered chatbots as co-creators.


Central Bank Digital Currency (CBDC) in Pakistan

Abstract: This chapter explores the implications and prospects of Central Bank digital currencies (CBDC) in Pakistan, focusing on its potential to transform the financial ecosystem. The emergence of blockchain and distributed ledger technologies has incited significant interest in digital currencies, leading central banks worldwide to research and develop CBDCs. In Pakistan, the implementation of CBDC presents opportunities for modernizing the financial infrastructure, enhancing financial inclusion, and fostering economic growth. The study employs a mixed-method approach, integrating qualitative and quantitative analyses. It examines the types of CBDCs, namely wholesale and retail, and their potential impacts on payment safety and efficiency. A thorough literature review provides insights into the multifaceted consequences of CBDCs on payment systems, monetary policy, and financial stability. Additionally, the research explores the challenges of CBDC implementation in Pakistan, including cybersecurity risks, technological limitations, and potential threats to financial stability. The findings highlight the need for comprehensive research, international collaboration, and a robust regulatory framework to mitigate risks and maximize the benefits of CBDCs. This study contributes to the ongoing discourse on digital currencies and their role in shaping the future of money and banking in emerging economies like Pakistan.


Between Partial Translation and Summary Translation in Translating the Sundanese Folklore into English

Abstract: The strategies of partial translation and summary translation are related but not exactly the same. There are some characteristics indicating each of them. This research article is about the translation of the most popular Sundanese folklore, which is written in the Sundanese language and translated into English. It deals with literary translation as the data source is folklore, and folklore is included as literary work. Using content analysis of the descriptive qualitative research method, this research aims to identify what information in the source text is partially translated and which information is summarized. The theory used is one of the pragmatic strategies given by Chesterman: the strategy of partial translation. The results show that the strategies used in identifying the missing information are partial translation, summary translation, and the combination of partial translation and summary translation. The partial translation is used to avoid repetition, eliminate very detailed information or complex information, and change explicit information into implicit information. The summary translation is used to create a short summary of information involving the key points or messages given in the source text by adopting the syntactic form of the target text based on the grammatical rules.


Implementation of Gender-Based Interactive E-Module Learning Media

Abstract: This research aims to determine the validity and practicality of gender-based interactive e-modules, which are innovative media developed to increase students' interest in learning, as well as introducing learning media that is not gender biased so that it can be well received by male and female students, without distinguish between the two in the learning process, specifically in the availability of teaching materials or learning media in schools. This research method uses a development model that was developed using the Research and Development (R&D) method, namely research that creates, produces or develops a product in the form of planning through to evaluating the validity of the product that has been produced. This research uses the ADDIE model, where the ADDIE model consists of 5 stages which include analysis, design, development, implementation and evaluation, but this research only reaches the development stage. The data collection techniques used were observation, interviews and distributing questionnaires. The data analysis technique used is analysis of the validity and practicality of interactive e-modules with a Likert scale measurement scale. The results of the development of this interactive e-module were validated by experts including media experts with results (98.75%), material experts (98.88%), language experts (95%), and gender experts (98.46%) with The average validation result (97.77%) is "very valid" and suitable for use. The results of the interactive e-module practicality trial obtained scores from educators (94.44%), one to one (88.71%) and small group (92.71%) with average results (91.95%) which were categorized as very practical to apply during the learning process.


News Personalization: Do Journalism Audiences Prefer Algorithms Over Editors?

Abstract:
This project-paper explores what motivates news organizations to employ algorithmically driven news personalization techniques and develops a methodology to determine whether journalism audiences have a preference between story lineups determined by an editor or by an algorithm. This work also examines whether news personalization systems meet the information needs of news audiences and works to determine what algorithmic approaches could better meet these needs.

Through the creation of a simple online news personalization system, this project has developed a method driven by analytic measurement coupled with a survey approach to determine audience opinions on news recommendation systems. A small user study was conducted that supported the feasibility of the system as a research tool and identified possible improvements to my methodological choices.

The research presented as part of this project-paper found that news organizations use news personalization systems for a variety of economic and editorial reasons. This paper also explores the social impacts of news personalization techniques and posits that there is nothing inherent to the design of personalization systems that precludes supporting the democratic and social ideals of journalism.


Cryptanalysis and improvement of quantum secure multi-party summation using single photons

Abstract: Abstract A previous study [Entropy 25.4 (2023): 590] proposed a quantum secure multi-party summation protocol wherein n participants could obtain the modulo-2 summation result using single photons and single-particle operations. Owing to the absence of an entanglement state, this protocol was more practical than other quantum summation protocols that used entanglement properties. Thus, this study investigated the claim that this proposed protocol prevented the participants’ secret inputs from being disclosed. It was found that the previously proposed protocol was vulnerable to a quantum Trojan horse attack owing to the circular qubit transmission mode. Using a security loophole, this study proposed an attack strategy that allowed an outside attacker to steal any participant’s secret input without being detected by a third party and participants. In addition, a simulation experiment was conducted to demonstrate the feasibility of the proposed attack and evaluate the number of quantum Trojan horse probes that the attacker must inject into regular qubit sequences. Finally, a solution was proposed by modifying the protocol to securely prevent quantum Trojan horse attacks.


The debiased morphological transformations of galaxies since z=3 in CANDELS

Abstract: Morphological quantitative measurements and visual-like classifications are susceptible to biases arising from the expansion of the Universe. One of these biases is the effect of cosmological surface brightness dimming (CSBD): the measured surface brightness of a galaxy decays with redshift as $(1+z)^ $. This effect might lead an observer to perceive an altered morphology compared to the real one. Our goal is to investigate the impact of CSBD on morphological classifications to determine the true evolution of morphological classes over redshift for field galaxies, and to interpret these results in the context of morphological transformations and star formation quenching. We employed artificial redshifting techniques on a sample of 268 galaxies in the five CANDELS fields, spanning redshifts from $z=0.2$ to $z=3.0$. We compared the visual classifications and morphological coefficients ($G$, $M_ $, and $A_s$) obtained from the original and simulated images. Subsequently, we developed two correction methods to mitigate the effects of CSBD. Our findings reveal that CSBD, low resolution, and signal-to-noise significantly bias the visual morphological classifications beyond $z>1$. Specifically, we observed an overestimation of the fractions of spheroids and irregular galaxies by up to $50<!PCT!>$, while the fractions of early- and late-type disks were underestimated by $10<!PCT!>$ and $50<!PCT!>$, respectively. However, we found that morphological coefficients are not significantly affected by CSBD at $z<2.25$. We validated the consistency of our correction methods by applying them to the observed morphological fractions in the IllustrisTNG-50 sample and comparing them to previous studies. We propose two potential sources of confusion regarding the visual classifications due to CSBD. Firstly, galaxies may be misclassified as spheroids, as the dimming effect primarily renders the bulge component visible. Secondly, galaxies may be misidentified as irregulars due to their more diffuse and asymmetric appearance at high redshifts. By analyzing the morphological fractions of star-forming and quiescent subsamples as a function of redshift and stellar mass, we propose a scenario where late-type disks transform into quiescent spheroids through mergers or to early-type disks through secular evolution or active galactic nucleus feedback.


COMPARATIVE ANALYSIS OF MERN, MEAN AND MEVN STACKS IN WEB DEVELOPMENT

Abstract: Choosing a technology stack is a critical decision that affects how software projects progress in the ever- changing field of web development. Regarding web development, a technology stack is a set of tools, frameworks, and programming languages that are utilized in the construction of an application's front end and back end. In addition to making the development process easier, these stacks provide the groundwork for the finished product's performance, scalability, and maintainability. JavaScript-based stacks are becoming serious contenders as businesses and developers search for frameworks that provide efficiency, scalability, and flexibility. Starting with a thorough examination and comparison of three popular JavaScript stacks, this research paper examines MEAN (MongoDB, Express, Angular and Node.js), MERN (MongoDB, Express, React and Node.js) and MEVN (MongoDB, Express, Vue.js and Node.js). Keywords: Web Development, MERN Stack, MEAN Stack, MEVN Stack, ReactJS, AngularJS, VueJS


VERIFYING AND ACCESSING STUDENT PROJECT APPLICATION

Abstract: This abstract presents a conceptual overview of an application design to verify and accessing the project the student project using which maintain the information about the project present in the system that contains project documentation, report, code and so on. It is difficult to maintain and search all this information manually in an Organization as it takes lot of time to do. The proposed app aims to overcome the manual search to avoid difficulty in searching. Owing to the advancement of technology, this project offers a holistic solution to enhance the validation and approval process for project development by students. By fostering collaborations, improving communication, and facilitating efficient workflows, the app aims to elevate the quality of software development outcomes while reducing project timeline and costs. It is potential to transform how project validation is conducted makes it a valuable tool for modern software development teams and organizations.


Advanced Drone Surveillance System for Traffic Management

Abstract: The rapid urbanization and increasing vehicle traffic in metropolitan areas have given rise to a significant challenges in traffic management. Traditional methods of a traffic monitoring and control often struggle to cope with the dynamic and complex nature of urban traffic patterns. In this context, we propose an innovative Drone Surveillance System for Traffic Management to revolutionize the way traffic is monitored, analyzed, and optimized in urban environments. Our system leverages state-of-the-art drone technology equipped with advanced sensors, high-resolution cameras, and intelligent algorithms to provide real-time traffic insights. Drones are deployed strategically across key traffic arteries, allowing for comprehensive aerial coverage and data collection. The system focuses on several key objectives: real-time traffic monitoring, incident detection, traffic flow analysis, optimized traffic signal control, emergency response support, data-driven decision making, and ensuring privacy and security compliance. By the employing cutting-edge computer vision algorithms, machine learning techniques, and data analytics, the Drone Surveillance System processes vast amounts of traffic data with speed and precision. The system detects the traffic incidents and the such as accidents and road hazards promptly, enabling swift a responses from emergency services. It analyzes traffic patterns and predicts congestion, allowing for adaptive traffic signal control and dynamic route optimization, thereby minimizing congestion and reducing travel time for a commuters. One of the system’s notable features is its a ability to the support emergency response teams effectively. Drones identify incidents and relay on the critical information to emergency services, International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 02 | February - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM28534 | Page 2 enabling rapid deployment and efficient management of emergencies. Moreover, the system ensures the privacy of individuals through strict adherence to regulations and deploys robust security measures to safeguard data integrity. An important advancement in the urban traffic control system is the Drone Surveillance System for Traffic control. The technology turns traditional traffic management into a proactive, flexible, and data-driven procedure by utilizing the power of drones, real-time data processing, and sophisticated algorithms. This innovation not only makes urban transportation more efficient, but it also helps to improve road safety by lowering accident rates, easing traffic, and creating a more sustainable urban environment. Keywords: Traffic Management, Real-time Monitoring, Incident Detection, Traffic Flow Analysis, Optimized Traffic Signal Control, Machine Learning Techniques. Drone Surveillance System


A Closed-form Algorithm to Shadow Segmentation using a Single Image

Abstract: Abstract Abstract—The presence of shadow in natural scenes continue to pose a challenge to computer vision algorithms. While there have been many proposed approaches to detect and segment shadow in natural scenes, these methods fail to do so in real-time without requiring a priori information. This paper presents a model that can decompose an image into the product of an illumination component L and a reflectance component R and use the reflectance component to segment the shadow region, all in real-time. This is done by exploiting the fact that light intensity has less effect on the reflectance of an image and by assuming there is a single point light source. Our method is highly effective in detecting shadows in an image with a surface material that exhibits both specular and diffuse reflectance properties.


LifeWatch ERIC: papers collection on original datasets and new e-services for the biodiversity and ecosystems’ scientific community

Abstract: Papers including articles that are produced because of the activities of LifeWatch ERIC, in the context of its second implementation period (2022 - 2026) and through the implementation of its new Strategic Working Plan, are published in this special collection. The articles include data papers, papers describing the development and functioning of analytical services and papers describing any other research outcome, produced either by LifeWatch ERIC or by any collaboration with any other ERIC, Research Infrastructure, global aggregator or other legal entity.


Artificial intelligence and quantum cryptography

Abstract: Abstract The technological advancements made in recent times, particularly in artificial intelligence (AI) and quantum computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the ‘quantum threat’. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.


Quantum multiparameter estimation enhanced by a topological phase transition

Abstract: In quantum multiparameter estimation, multiple to-be-estimated parameters are encoded in a quantum dynamics system by a unitary evolution. As the parameters vary, the system may undergo a topological phase transition (TPT). In this paper, we investigate two SU(2) TPT models and propose the singular behavior of the quantum metric tensor around the TPT point as a tool for the simultaneous optimal estimation of multiple parameters. We find that the proposed TPT sensing protocol can achieve the same metrology performance as the quantum-control-enhanced one. Moreover, the probe state of the TPT sensing protocol is only the ground state of the Hamiltonian rather than the entangled state required in the control-enhanced one. In addition, an adaptive multiparameter estimation strategy is developed for updating the estimated values until the desired quantum Cram\'er-Rao bound is approached. Our work reinforces the connection between quantum multiparameter estimation and topology physics, with potential inspiration for quantum critical metrology.


Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Abstract: Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.


Motion magnification-inspired feature manipulation for deepfake detection

Abstract: Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation unit. Although there is existing literature on the use of motion magnification as a preprocessing step for deepfake detection, in our work, we aim to utilize learning-based magnification elements to develop an end-to-end deepfake detection model. In this research, we investigate different variations of feature manipulation networks, both with spatially constant and spatially varying amplification. To clarify, although the proposed model draws from existing literature on motion magnification, we do not perform motion magnification in our experiments but instead use the underlying architecture of such networks for feature enhancement. Our objective with this work is to take a step towards applying learnable motion manipulation in improving the target accuracy of a task at hand.


Antecedents of Employees Retention in Labor Market in Saudi Arabia

Abstract: Employee retention is a crucial factor that drives the attention of organizations. The objective of this study is to explore the factors that affect employee retention in the labor market in Saudi Arabia. It focuses specifically on person-organization fit, work-life balance, and job satisfaction as antecedents that affect employee retention. The study used an online survey to collect responses to examine the antecedents. The questionnaire was statically analyzed using SPSS and Amos. The total number of respondents that participated in the study is 250 from both genders and variations in experience and age. The key findings of the study show that person-organization fit and Work-life balance have emerged as major antecedents which impact job satisfaction and retention. It discusses the effect of job satisfaction as a mediator between Person-Organization fit and retention. In addition, it discusses the effect of job satisfaction as a mediator between work-life balance and retention.


Abstract: This study aligns with Sustainable Development Goal 8, which borders on “economic growth by ensuring external debt is invested well in development projects that can spur economic growth”. It contributes to the economic growth literature by evaluating the role of external debt and corruption on economic growth. The Sub-Saharan African region has witnessed an increase in external debt. However, the economic growth has been reducing. The region witnessed a contraction in economic growth to 2.7 percent in 2022 down from 4.4 percent of 2021. The literature indicates that corruption can impede external debt from achieving the intended purposes. Many studies have revealed the role of external debt in economic growth but have not studied the moderating effect of corruption thus creating a gap for this study. To achieve the objective, the study uses the Fixed Effects Panel data approach on unbalanced panel data on 46 sub-Saharan African countries from 1998 to 2021 to interrogate the nexus. The results revealed a positive relationship between external debt and economic growth. However, corruption was found to reduce the effectiveness of external debt in realizing economic growth. The study therefore recommends that governments of the countries in the region need to address corruption so to achieve the desired economic growth. They should implement measures focusing on improving institutions quality to control financial corruption. In addition, the policy makers should relook at increasing anti-corruption initiatives aimed at reducing poor governance for instance financing anti-corruption agencies to ensure corruption is reduced. Key Words: Economic growth, External Debt, Corruption&nbsp


SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification

Abstract: Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification.This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques.This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets.SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets.These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.


Abstract: This experimental research entitled “Reading Proficiency of Grade Two Learners Using Big Books with Historical Content” is a scientific study on the reading proficiency of grade two learners in Banago Elementary School I when using Big Books with Historical Content during the teaching-learning process especially in reading. It mainly utilized mother tongue language, Hiligaynon, as medium of instruction. Forty (40) grade two learners who were equally distributed into two groups, the control group and experimental group with twenty (20) learners, made up the subject-respondents of the study. Data were collected through experimental procedure where the experimental group was exposed to the use of big books with historical content while the control group used the regular big books. Mean, Frequency and Percentage, T-test and Spearman rho were used as statistical tools. Based on the scientific results of this study, a proposed reading enhancement program was crafted to further enhance reading performance of learners benefiting the implementers, teachers, and learners of the program. Findings of the study recommends on the use of big books with historical content to improve reading proficiency levels of primary learners when using Hiligaynon as medium of instruction in assessments and reading remediation. Results further suggested the development of additional localized and indigenized big books with historical content as well as big books across disciplines as supplementary learning materials in the delivery of instruction in Elementary Social Studies and reading remediation in Hiligaynon for Key Stage 1. Implementation of the proposed program in school and division level will be beneficial to the stakeholders, administrators, teachers, and learners in the primary level.