Reviewerly/exHarmony huggingface dataset:
Impact of Visually Induced Motion Sickness from VR Depending on Viewing Patterns, View Movement, and Background Motion*

The impact of visually induced motion sickness from virtual reality (VR) because of viewing patterns, view movements, and background global motion was investigated experimentally through classification into four categories.Each of the ten subjects underwent watching four patterns with bio-signal measurements, such as electrocardiogram and respiration, answering a subjective questionnaire.The results of the subjective evaluation of biological effects indicate that the VR viewing pattern has a significantly lower influence on visually induced motion sickness than other patterns, such as viewing with global background motion or sight motion by operating the controller.Clinical Relevance— This study aimed to reduce the effects of visually induced motion sickness in VR viewing, particularly to reduce the burden on the eyes and body.
Identification of Seizure Onset Zone from Intracranial EEG Using Source Selection-Based Domain Adaptation

For focal epilepsy patients, correctly identifying the seizure onset zone (SOZ) is essential for surgical treatment. In automated realistic SOZ identification, it is necessary to identify the SOZ of an unknown patient using another patient's electroencephalogram (EEG). However, in such cases, the influence of individual differences in EEG becomes a bottleneck. In this paper, we propose the method with domain adaptation and source patient selection to address the issue of individual differences in EEG and improve performance. The proposed method was evaluated on intracranial EEG data from 11 patients with epilepsy caused by focal cortical dysplasia. The results showed that the proposed method significantly improved SOZ identification performance compared to existing methods without domain adaptation and source patient selection. In addition, it was suggested that data from residual-seizure patients may have adversely affected estimation performance. Visualization of the prediction on MRI images showed that the proposed method might detect SOZs missed by epileptologists.
Detecting Depression-related Movement Changes in Older Adults using Smart Home Motion Sensors - A Feasibility Study

Smart home sensor data is being increasingly used to identify health risks through passive tracking of specific behaviours and activity patterns. This study explored the feasibility of using motion sensor data to track changes in daytime movement patterns within the home, and their potential association with depression in older adults. This study analysed the motion sensor data collected during a one-year smart home trial, and explored their association with Geriatric Depression Scale (GDS) scores collected at three different time points during the trial (i.e., baseline, mid-trial, and end-trial). Our results showed that movement patterns are generally reduced when older adults are in a depressed state compared to when being in a not-depressed state. In particular, the reduced movement activity in depressed states was significant (p<.05) when the participant’s GDS state changed between depressed and not-depressed for the first time during the three time points of the trial when GDS was collected.Clinical relevance— Our results establish the feasibility and potential use of motion sensor data from ambient sensors in a smart home for passive and remote assessment of older adults’ depression status, that is comparable to their GDS scores, through changes in their in-home day-time movement patterns. Also since reduced movement activity may be a general indicator of potential health risks, this study provides preliminary evidence for using in-home movement activity monitoring as an general indicator of health risks.
A Large-Scale Clinical Benchmark of ResNet-based Deep Models for Newborn Face Recognition

Newborn face recognition is a meaningful application for obstetrics in the hospital, as it enhances security measures against infant swapping and abduction through authentication protocols. Due to limited newborn face datasets, this topic was not thoroughly studied. We conducted a clinical trial to create a dataset that collects face images from 200 newborns within an hour after birth, namely NEWBORN200. To our best knowledge, this is the largest newborn face dataset collected in the hospital for this application. The dataset was used to evaluate the four latest ResNet-based deep models for newborn face recognition, including ArcFace, CurricularFace, MagFace, and AdaFace. The experimental results show that AdaFace has the best performance, obtaining 55.24% verification accuracy at 0.1% false accept rate in the open set while achieving 78.76% rank-1 identification accuracy in a closed set. It demonstrates the feasibility of using deep learning for newborn face recognition, also indicating the direction of improvement could be the robustness to varying postures.
A Multimodal A* Algorithm to Solve the Two-Dimensional Optimization Problem of Accompanying a Person for an Intelligent Wheelchair

Impaired mobility have far-reaching consequences for handicapped persons and their relatives. Mobile robotic technologies enable intelligent wheelchairs to regain mobility for those affected. Multiple research projects address human aware navigation and the task of following a person for assistive robots. But just a few projects focus on accompanying a person to enable social interaction. Therefore, we present a navigation system for indoor navigation in dynamic and cluttered environments as well as a novel algorithm for accompanying a person.First, we developed an autonomous driving wheelchair for indoor navigation based on the robot operating system (ROS). Thereby, a multi sensor setup using cameras and laser scanner enables localization within a map. People are detected by the same sensors and tracked by a Kalman filter. Afterwards we propose a novel algorithm to achieve a dynamic accompanying behaviour. An attractiveness distribution is introduced to evaluate the possible accompanying positions next to the manually selected target person regarding social interaction. The resulting two-dimensional optimization problem is solved by a novel multimodal extension of the A* algorithm.The proposed intelligent wheelchair is able to navigate in indoor environments and to accompany any person. In addition it allows social interaction while walking to relieve relatives or nursing staff, which otherwise need to push the wheelchair. The aim is to increase participation in everyday life for those affected.
Trace Augmentation with Missing EHRs for Sepsis Treatments

Real-world Electronic Health Records (EHRs) are often plagued by limited data availability with missing values and small amount of traces. The limited data availability can extremely limit many downstream machine learning techniques that generally require complete data as input and prefer a great amount of data for training neural networks. Our study aims to provide robust and safe trace augmentation that can automatically induce high-quality and shareable data across research groups and institutions.
An LSTM-based Gesture-to-Speech Recognition System

Fast and flexible communication options are limited for speech-impaired people. Hand gestures coupled with fast, generated speech can enable a more natural social dynamic for those individuals - particularly individuals without the fine motor skills to type on a keyboard or tablet reliably. We created a mobile phone application prototype that generates audible responses associated with trained hand movements and collects and organizes the accelerometer data for rapid training to allow tailored models for individuals who may not be able to perform standard movements such as sign language. Six participants performed 11 distinct gestures to produce the dataset. A mobile application was developed that integrated a bidirectional LSTM network architecture which was trained from this data. After evaluation using nested subject-wise cross-validation, our integrated bidirectional LSTM model demonstrates an overall recall of 91.8% in recognition of these pre-selected 11 hand gestures, with recall at 95.8% when two commonly confused gestures were not assessed. This prototype is a step in creating a mobile phone system capable of capturing new gestures and developing tailored gesture recognition models for individuals in speech-impaired populations. Further refinement of this prototype can enable fast and efficient communication with the goal of further improving social interaction for individuals unable to speak.
The Intersectionality of Offensive Cybersecurity and Human Factors: A Position Paper

Abstract This position paper delves into the complex interplay between proactive cyber defense mechanisms and inherent human susceptibilities. The researchers posit that as cyber systems advance in technical robustness, malicious entities progressively exploit human psychological tendencies and behavioral inconsistencies to circumvent security measures. These exploitations are not merely opportunistic but are strategic, leveraging a profound comprehension of human cognitive biases to undermine security infrastructures. The researchers argue for broadening the intellectual horizon in this domain, underscoring the imperative for a multidisciplinary methodology that amalgamates the empirical acumen of psychology, sociology, and computer science. The article asserts that the fortification of cyber defenses is contingent upon the comprehensive integration of human vulnerability assessments within cybersecurity frameworks. This position paper calls for an augmentation of scholarly research at the nexus of offensive cybersecurity and human factors, formulating defense mechanisms centered around the human element and promulgating inclusive cybersecurity policies that harmonize human and technological considerations.
Luby Transform Coded Computation with Error Detection in Wireless Networks

In wireless distributed computing, coded computation is adopted through computational redundancy to mitigate the stragglers’ effect. Moreover, transmission errors may occur due to noise, channel fading and so on. Existing works design coded computation and error detection separately. However, this leads to frequent encoding and inefficient allocation. In this paper, we propose a joint computation and transmission coding (JCTC) scheme to design coded computation and error detection jointly. The coded computation is based on Luby transform (LT) code and linear error-detecting codes are applied for the retransmission mechanism. To achieve the low dynamic encoding, two-layer encoding is adopted. Then, the performances of JCTC scheme are analyzed. Finally, in order to achieve efficient task and redundancy allocation, the wireless LT coded computation with error detection (WLTCC-ED) algorithm is given. Through the simulation, it shows that our proposed JCTC scheme has significant advantages over separate designs.
Application of Short Video Semantic Understanding Technology Based on Big Data Analysis in Education Management

With the emergence of short video and the development of mobile internet, short video software, such as TikTok and Kwai, has emerged. Based on the semantic understanding technology of teaching short videos, a teaching management platform was built to push healthy and positive short video for students' content in a targeted way. Taking the 21st grade students majoring in Chinese in Guizhou Normal University as an example, the authors discusses the effect of teaching management platform on college students. In this process, the following conclusions are drawn: (1) Among college students, the viewing rate of short videos has exceeded 95%, and short videos have become an indispensable entertainment for most college students. (2) Through short video semantic understanding technology and short video screening program, excellent short video can be effectively pushed to students. (3) The actual effect shows that the short video teaching management platform can effectively improve the values of the cultural level of students.
Research of Two-Modulus Weighted Codes with Summation Involving Arbitrary Partitions of Several Data Symbols

A method has been proposed for constructing two-modulus sum codes that are suitable for solving the synthesis problems of self-checking digital computing devices and systems, as well as the technical means of their diagnostics. In the process of constructing a code, a set of data symbols is divided into two subsets, forming separate vectors for which the total weight is calculated according to a predetermined modulus. Beforehand, in each of the selected vectors, one of the data symbols is weighted with a coefficient from a natural series. Codes obtained with different values of weight coefficients and modulus for determining the total weight form a separate family of uniform binary codes with a fixed number of check symbols. The article provides an example of constructing a sum codes in the ring of residues modulus M = 4. Using this modulus enables the construction of a two-modulus code with k = 4 check symbols for any value of the m number of data symbols. As the value of m increases, the relationship between the number of data symbols and check symbols will change significantly, and the resulting code will have low redundancy. This is important when using the considered class of codes in the construction of digital devices. Previously unknown properties of the discussed class of sum codes have been identified, and taking these into account is beneficial when addressing problems related to the synthesis and technical diagnostics of digital devices.
Yoga Pose Estimation Using Angle-Based Feature Extraction

Objective: This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by the COVID-19 pandemic and the subsequent shift to virtual platforms for yoga instruction. This research aims to develop a mechanism for detecting correct yoga poses and providing real-time feedback through the application of computer vision and machine learning (ML) techniques. Methods and Procedures: This study utilized computer vision-based pose estimation methods to extract features and calculate yoga pose angles. A variety of models, including extremely randomized trees, logistic regression, random forest, gradient boosting, extreme gradient boosting, and deep neural networks, were trained and tested to classify yoga poses. Our study employed the Yoga-82 dataset, consisting of many yoga pose images downloaded from the web. Results: The results of this study show that the extremely randomized trees model outperformed the other models, achieving the highest prediction accuracy of 91% on the test dataset and 92% in a fivefold cross-validation experiment. Other models like random forest, gradient boosting, extreme gradient boosting, and deep neural networks achieved accuracies of 90%, 89%, 90%, and 85%, respectively, while logistic regression underperformed, having the lowest accuracy. Conclusion: This research concludes that the extremely randomized trees model presents superior predictive power for yoga pose recognition. This suggests a valuable avenue for future exploration in this domain. Moreover, the approach has significant potential for implementation on low-powered smartphones with minimal latency, thereby enabling real-time feedback for users practicing yoga at home.
Robust quantum state transfer with topologically protected nodes

Robust quantum state transfer (QST) is the foundation for information exchange among nodes in quantum networks. In this paper, we propose a robust QST protocol that utilizes topological edge modes in the qubit chains to encode (decode) quantum states (flying qubits). By employing qubits with tunable couplings, we construct Su-Schrieffer-Heeger (SSH) chains as the nodes of a quantum network. The end qubit of each SSH chain is dissipatively coupled to a chiral waveguide, and the dissipative strength is a constant. We refer to the SSH chain with a dissipation channel at the end qubit as the non-Hermitian SSH chain. Comparing the symmetry and energy spectra of the non-Hermitian SSH chain with those of the SSH chain, our analysis reveals that the dissipative dynamics of the topological edge state in the non-Hermitian SSH chain are governed by its imaginary spectra. The edge mode with the imaginary spectrum can be used to encode (decode) quantum states (flying qubits), thereby enabling robust QST between two remote mirrored non-Hermitian SSH chains. Our numerical simulations demonstrate that high-fidelity QST can be achieved even in the presence of coupling errors. Furthermore, we extend our analysis to consider QST in imperfect chiral waveguides, providing insights into the robustness of our protocol under realistic conditions. Our discussion is applicable to various quantum platforms and holds significant implications for constructing large-scale quantum networks.
EFL Students' Strategies for Learning Academic Vocabulary with Mobile Phones at Van Lang University in Vietnam

This research has investigated how EFL (English as a Foreign Language) students use mobile applications to learn academic vocabulary in a self-directed and autonomous manner. Forty first-year students at Van Lang University taking part in the research process were divided into experimental (N=18) and control (N=22) groups. The experimental group utilized a mobile app throughout the semester, whereas those in the control group used traditional methods. Three final tests every semester were used to check the participants' newly gained vocabulary knowledge. The findings revealed that the students in both groups could enhance their vocabulary proficiency. Nevertheless, the experimental group achieved better results in all three final tests. Moreover, this group also saved significant time when using the phone to support vocabulary learning, while the control group gave unstable test results. The findings demonstrate the potential of mobile apps for independent academic vocabulary acquisition and provide impacts on EFL vocabulary learning.
BA-YOLO for Object Detection in Satellite Remote Sensing Images

In recent years, there has been significant progress in object detection within the domain of natural images. However, the field of satellite remote sensing images has consistently presented challenges due to its significant scale variations and complex background interference. Achieving satisfactory results by directly applying conventional image object detection models has proven to be difficult. To address these challenges, this paper introduces BA-YOLO, an improved version of the YOLOv8 object detection model. It incorporates several notable enhancements. Firstly, to fuse an increased number of features more effectively, we introduce the design concept of a higher-performing Bi-directional Feature Pyramid Network (BiFPN). Secondly, to retain sufficient global contextual information, we integrated a module in BA-YOLO that combines multi-head self-attention and convolutional networks. Finally, we employed various data augmentation techniques such as Mixup, Cutout, Mosaic, and multi-scale training to enhance the model’s accuracy and robustness. Experimental results demonstrate that BA-YOLO outperforms state-of-the-art detectors and has been evaluated on the DOTA dataset. BA-YOLO achieves a mean average precision (mAP) of 0.722 on the DOTA dataset.
Human-Machine Task Allocation in Learning Reciprocally to Solve Problems

Solving problems by human-AI configurations will likely become a pervasive practice. Traditional models of task allocation between human and machine must be revisited in light of the differences in the learning of humans versus intelligent machines; performance can no longer be the sole criterion for task allocation. We offer a new procedure for allocating tasks dynamically that begins with the determination of the desired level of machine autonomy.
PE-Net: a parallel framework for 3D inferior mesenteric artery segmentation

The structural morphology of mesenteric artery vessels is of significant importance for the diagnosis and treatment of colorectal cancer. However, developing automated vessel segmentation methods for this purpose remains challenging. Existing convolution-based segmentation methods have limitations in capturing long-range dependencies, while transformer-based models require large datasets, making them less suitable for tasks with limited training samples. Moreover, over-segmentation, mis-segmentation, and vessel discontinuity are common challenges in vessel segmentation tasks. To address these issues, we propose a parallel encoding architecture that combines transformers and convolutions to retain the advantages of both approaches. The model effectively learns position deviations and enhances robustness for small-scale datasets. Additionally, we introduce a vessel edge capture module to improve vessel continuity and topology. Extensive experimental results demonstrate the improved performance of our model, with Dice Similarity Coefficient and Average Hausdorff Distance scores of 81.64% and 7.7428, respectively.
Precedent-Based Reasoning with Incomplete Cases

We extend the result model for precedent-based reasoning with incomplete case bases. In contrast to regular case bases, these consist of incomplete cases for which not all dimension values need to be specified, but rather each dimension is assigned a set of possible values. The outcome of cases then applies for each (combination of) the possible dimension values. Building on earlier proposed notions of justification and stability for incomplete focus cases, we introduce the notion of possible justification statuses, which are required to maintain consistency of the incomplete case base. We demonstrate how these theoretic notions can be applied in practice for human-in-the-loop decision support, discuss their computational complexity and provide efficient algorithms.
THE DARK SIDE OF USING SOCIAL MEDIA

One of the most popular forms of interactive computing is social media, which involves technology that allows peopleto communicate and exchange information. Being able to communicate easily and getting to know more about aparticular person has become easier nowadays. However, this can also be taken to another level where stalking becomes easier to be done by or for users. In Malaysia, the highest percentage of social media users are teenagers from 15 to 19 years old. This research will focus on how teenagers are more prone to develop cyberstalking tendencies and if they are aware of the line of privacy on social media. The method that the researcher used to conduct this research was in-depth interview to collect data. Twenty social media users were selected to be interviewed by the researcher and the respondents were interviewed with the questions that have been prepared. All the data and information was transcribed (verbatim) and thematic data analysis was used to sort out all the data and information. In particular, the findings highlight the truth behind the development of cyberstalking among teenagers due to the influence of social media.
Literacy as a Strategy for Improving the Quality of Education

This research aims to provide an understanding of the important role literacy plays in the world of education for the progress of students for the nation and state. Literacy exists and is provided from an early age as a means to increase students' knowledge so that education becomes much better. The type of research used is qualitative research with a descriptive approach. The data collection technique used by the author is literature study. Literacy provides many benefits, improving the quality of human resources for educational progress.