Reviewerly/exHarmony huggingface dataset:
A Time-varying Velocity Control Strategy for Balancing Exploration and Exploitation Behaviour of a Binary PSO

Combinatorial optimization problems (COPs) are NP-hard, and their solution space is discrete, which poses a significant challenge for optimization algorithms. Binary Particle Swarm Optimization (BPSO) is wel-known optimization technique that has been widely applied in various type of COPs. However, one of the challenges in BPSO is to balance exploration and exploitation to locate the optimal solution. This paper presents a Time-varying Velocity Control (TVC) strategy to balance exploration and exploitation in a BPSO. The TVC strategy utilizes a time-varying control parameter to adjust the velocity of the particles, allowing for more exploration in the early phases of the optimization process and more exploitation as the search progresses. The proposed strategy is evaluated on a set of benchmark combinatorial optimization problems, and the results demonstrate that it outperforms other commonly used BPSO strategies in terms of both efficiency and solution quality. Furthermore, the TVC strategy is shown to be robust to changes in the problem landscape, making it a promising approach that can be used for solving a wide range of combinatorial optimization problems.
Erudite Survivor: Usability Testing of a Gamification-based Mobile App for Disaster Awareness Among Children

Numerous peer-reviewed studies describe designs for a mobile application for children based on gamification and catastrophe awareness. However, few articles give extensive evaluations of their solutions’ usability for end users. The purpose of this study is to present the usability assessment findings of a mobile app aimed at raising children’s catastrophe awareness in Malaysia. The study includes 12 target users to test the usability of the Erudite Survivor mobile app prototype. The test evaluation contains a pre-test, post-task and post-test questionnaire (Post-Study System Usability Questionnaire). The primary sections of this Post-Study System Usability Questionnaire are Overall Satisfaction, System Usefulness, Information Quality, and Interface Quality. The Post-Study System Usability Questionnaire revealed the following mean scores: Overall satisfaction (2.09), System Usefulness (2.03), Information Quality (2.06), and Interface Quality (2.25). Users of the prototype were satisfied with the application because the score was near to 1. This application’s capacity to teach children about disaster awareness might be viewed as the initial step toward a future improvement of gamification-based disaster education among Malaysian children.
Social capital, social interaction, knowledge sharing behavior and online purchase intention of office workers in Ho Chi Minh City

The study proposes a model of the impact of social capital, social interaction on knowledge sharing and the impact of knowledge sharing on purchase intention of office workers. The study was conducted using Google Forms questionnaire to collect data by convenience sampling method from 300 office workers working in Ho Chi Minh City via email as well as applications such as Zalo, Viber, Facebook.... The factors of the model and scale are inherited from previous studies and adjusted through face-to-face interviews with experts in the field of study. The research hypotheses were tested by linear structural model (SEM) through SPSS 20.0 and AMOS 20.0 softwares. Research results show that personal interaction, social interaction relationship, trust, reciprocity have an impact on knowledge/information sharing and knowledge/information sharing has an impact on knowledge/information sharing intention to purchase online of office workers in Ho Chi Minh City. In addition, based on the research results, the author also proposes managerial implications to enhance the expansion of social capital as well as social interaction of customers and administrators for online shopping websites.
LB1708 Adoption and utility of asynchronous image capture skills learning module for facilitation of an institution wide teledermatology program

Background: We analyzed adoption and utility metrics for a learning model to improve image capture skills in primary care providers to facilitate a Teledermatology (TD) program. Methods: Duke TD data from September 2021 – July 2022 was analyzed for adoption metrics including the ratio of providers enrolled in the TD program who took the module and utility metrics including the rates of satisfactory versus unsatisfactory images for those who took the module compared to those providers who did not.
Advancing Hungarian Text Processing with HuSpaCy: Efficient and Accurate NLP Pipelines

This paper presents a set of industrial-grade text processing models for Hungarian that achieve near state-of-the-art performance while balancing resource efficiency and accuracy. Models have been implemented in the spaCy framework, extending the HuSpaCy toolkit with several improvements to its architecture. Compared to existing NLP tools for Hungarian, all of our pipelines feature all basic text processing steps including tokenization, sentence-boundary detection, part-of-speech tagging, morphological feature tagging, lemmatization, dependency parsing and named entity recognition with high accuracy and throughput. We thoroughly evaluated the proposed enhancements, compared the pipelines with state-of-the-art tools and demonstrated the competitive performance of the new models in all text preprocessing steps. All experiments are reproducible and the pipelines are freely available under a permissive license.
Internet Usage Among Children in Bangladesh

The Internet has become an integral part of human life.The Internet has opened a new land of opportunities for people of all ages and races.Due to the enormous contribution of science, humanity has stepped into the era of globalization and technological advancement.Multiple innovations to make human life easier are the core concern of experts and researchers across the world.The amazing development of the computer and the consequent birth of the Internet are considered the most significant advancements in human history in the twentieth century.The Internet has dramatically revolutionised different fields across the globe.Nowadays, the Internet has become a global means of communication in every day's life and the lives of humans.Today, human beings are living in the era of the information age, where the Internet plays the most significant role and thus has become an inevitable part and parcel of human life.Till January 2023, more than sixty-four (64.4%) percent of the entire human race were Internet users.This clearly defines today's Internet, which has captured almost every aspect of human life and living.In the era of technological advancement, no one can escape the impact of the Internet, especially children, who are the largest users among different age groups.This article explores the usage of the Internet and its impact on the children of Bangladesh while considering the worldwide scenario of Internet usage.
Driving Safety Education at Sewon Muhammadiyah Middle School, Bantul, Daerah Istimewa Yogyakarta

Education about the importance of road safety is a way to shape children mindset and character, especially adolescence who have the potential to actively traffic on the road. Equipping them with knowledge and raising awareness of traffic rules in teenagers is expected to be able to help give them the best chance of keeping safe while they’re young and as they get older -become obeying people and pioneers of road safety. This service activity is a concrete step to create young people awareness about the importance of road and driving safety. Driving safety education at Sewon Muhammadiyah Middle School, Bantul becomes an effort to provide knowledge readiness for pre-teenagers (under 16 years) so that it is hoped that they will be scientifically and mentally ready to drive at the age of 17 and over (after they get a driver's license/SIM). From this community service activity, the results showed an increase in understanding related to driving safety for adolescence, especially children in grade 2 junior high school at Sewon Muhammadiyah Middle School, as indicated by the average pre-test results that answered correctly at 48.18% and post-test results that answered correctly at 81. 90%. There was an increase in understanding of safety education participants by 33.72%.
EOG acquisition system based on ATmega AVR microcontroller

Abstract This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes.
Exploring the effects of 3D-360°VR and 2D viewing modes on gaze behavior, head excursion, and workload during a boxing specific anticipation task

Recent evidence has started to demonstrate that 360°VR, a type of VR that immerses a user within a 360° video, has advantages over two-dimensional (2D) video displays in the context of perceptual-cognitive evaluation and training. However, there is currently a lack of empirical evidence to explain how perceptual-cognitive strategies differ between these two paradigms when performing sports-related tasks. Thus, the objective of this study was to examine and compare the impact of different viewing conditions (e.g., 3D-360°VR and 2D video displays), on gaze behavior and head excursions in a boxing-specific anticipatory task. A secondary objective was to assess the workload associated with each viewing mode, including the level of presence experienced. Thirdly, an exploratory analysis was conducted to evaluate any potential sex differences.Thirty-two novice participants (16 females) were recruited for this study. A total of 24 single-punch sequences were randomly presented using a standalone VR headset (Pico Neo 3 Pro Eye), with two different viewing modes: 3D-360°VR and 2D. Participants were instructed to respond to the punches with appropriate motor actions, aiming to avoid punches. Gaze behavior was recorded using a Tobii eyetracker embedded in the VR headset. Workload and presence were measured with the SIM-TLX questionnaire. Fixation duration, number of fixations, saccades, search rate and head excursions (roll, pitch, yaw) were analyzed using linear mixed models.The results revealed significant shorter fixation durations and more head excursions (roll, pitch) in 3D-360°VR, compared to the 2D viewing mode (ps < 0.05). The sense of presence was found to be much higher in the 3D-360°VR viewing mode (p < 0.05). No sex differences were observed. These results demonstrate that 360°VR elicited shorter fixation durations but mostly greater head excursions and immersion compared to a 2D projection in the context of a boxing-specific task.These findings contribute to the understanding of previous evidence supporting the possible advantages of using 360°VR over 2D for perceptual-cognitive evaluation and training purposes. Further validation studies that compare behaviors and performance in 360°VR with those in the real-world will be needed.
Suitcase Pro

Suitcase Pro introduces a QR-based flight inventory management solution, aiming to optimize the efficiency and precision of flight inventory control procedures. This innovative system revolves around a digital approach to travel bookings, eliminating the need for paper and embracing mobile connectivity, all facilitated through QR codes. Through a browser-accessible web page, users gain access to comprehensive flight schedules and an online reservation platform. This platform empowers customers to securely purchase flight tickets with their debit/credit cards, accompanied by web connectivity. Notably, the implementation employs QR codes to oversee and keep luggage records up to date. These QR codes enable the airport department to effortlessly access passenger details. Concurrently, distinct QR codes are assigned to merchandise within the airport, underscoring the versatility of QR codes in effectively tracing product location and ownership information
Open OnDemand Connector for Amazon Elastic Kubernetes Service (EKS)

Demand for computational resources is increasing in the classroom, and instructors want to train users on real-world problems by providing access to the latest resources. The Open OnDemand Connector for Amazon Elastic Kubernetes Service (EKS) is built on top of Open OnDemand (OOD), a web-based platform for accessing and managing Cluster and Cloud resources. In this paper, we have discussed how to leverage the power of Kubernetes and the flexibility of Open OnDemand to deploy a complex cloud-native solution using containerized applications, such as Jupyter, in a secure, scalable, and efficient manner.
Continuous Gesture Segmentation Method Based on Micro-Doppler Feature Via Millimeter Wave Radar

Gesture recognition technology via millimeter wave radar is an efficient human-computer interaction (HCI) solution with a more significant environmental adaptive capability compared with optical sensors. Gesture segmentation is an essential step in gesture recognition process, and most of the previous work has focused on segmenting fixed-length discrete gestures, while less research has been done on segmentation algorithm for a continuous sequence of variable-length gestures. A gesture segmentation method for variable-length continuous gesture sequences based on micro-Doppler feature is proposed. Particularly, the Bidirectional One-Sided CFAR Algorithm (BOS-CFAR) which is derived from Constant False Alarm Rate Algorithm (CFAR) is skillfully designed to detect valid gesture frames. Then the valid frames is set as growth points and clustering operation is implemented to obtain the approximate segmentation parts of each gesture. Finally, the gesture domain is fine-tuned according to the maximum and minimum micro-Doppler values of neighborhood to achieve the segmentation task of variable-length continuous gestures with over 96.6% recall and nearly 100% precision of six gestures.
Assessing Drivers' Situation Awareness in Semi-Autonomous Vehicles: ASP based Characterisations of Driving Dynamics for Modelling Scene Interpretation and Projection

Semi-autonomous driving, as it is already available today and will eventually become even more accessible, implies the need for driver and automation system to reliably work together in order to ensure safe driving.A particular challenge in this endeavour are situations in which the vehicle's automation is no longer able to drive and is thus requesting the human to take over.In these situations the driver has to quickly build awareness for the traffic situation to be able to take over control and safely drive the car.Within this context we present a software and hardware framework to asses how aware the driver is about the situation and to provide human-centred assistance to help in building situation awareness.The framework is developed as a modular system within the Robot Operating System (ROS) with modules for sensing the environment and the driver state, modelling the driver's situation awareness, and for guiding the driver's attention using specialized Human Machine Interfaces (HMIs).A particular focus of this paper is on an Answer Set Programming (ASP) based approach for modelling and reasoning about the driver's interpretation and projection of the scene.This is based on scene data, as well as eye-tracking data reflecting the scene elements observed by the driver.We present the overall application and discuss the role of semantic reasoning and modelling cognitive functions based on logic programming in such applications.Furthermore we present the ASP approach for interpretation and projection of the driver's situation awareness and its integration within the overall system in the context of a real-world use-case in simulated as well as in real driving.
Explainable Answer-set Programming

The interest in explainability in artificial intelligence (AI) is growing vastly due to the near ubiquitous state of AI in our lives and the increasing complexity of AI systems.Answer-set Programming (ASP) is used in many areas, among them are industrial optimisation, knowledge management or life sciences, and thus of great interest in the context of explainability.To ensure the successful application of ASP as a problem-solving paradigm in the future, it is thus crucial to investigate explanations for ASP solutions.Such an explanation generally tries to give an answer to the question of why something is, respectively is not, part of the decision produced or solution to the formulated problem.Although several explanation approaches for ASP exist, almost all of them lack support for certain language features that are used in practice.Most notably, this encompasses the various ASP extensions that have been developed in the recent years to enable reasoning over theories, external computations, or neural networks.This project aims to fill some of these gaps and contribute to the state of the art in explainable ASP.We tackle this by extending the language support of existing approaches but also by the development of novel explanation formalisms, like contrastive explanations.
On the Independencies Hidden in the Structure of a Probabilistic Logic Program

Pearl and Verma developed d-separation as a widely used graphical criterion to reason about the conditional independencies that are implied by the causal structure of a Bayesian network. As acyclic ground probabilistic logic programs correspond to Bayesian networks on their dependency graph, we can compute conditional independencies from d-separation in the latter. In the present paper, we generalize the reasoning above to the non-ground case. First, we abstract the notion of a probabilistic logic program away from external databases and probabilities to obtain so-called program structures. We then present a correct meta-interpreter that decides whether a certain conditional independence statement is implied by a program structure on a given external database. Finally, we give a fragment of program structures for which we obtain a completeness statement of our conditional independence oracle. We close with an experimental evaluation of our approach revealing that our meta-interpreter performs significantly faster than checking the definition of independence using exact inference in ProbLog 2.
On the Potential of CLIP for Compositional Logical Reasoning

In this paper we explore the possibility of using OpenAI's CLIP to perform logically coherent grounded visual reasoning. To that end, we formalize our terms and give a geometric analysis of how embeddings in CLIP's latent space would need to be configured in order for the system to be logically coherent. Our main conclusion is that, as usually configured, CLIP cannot perform such reasoning.
Enhancing Object Detection Algorithms by Synthetic Aerial Images

In order to accurately perform object detection by deep convolutional neural networks (DCNN) in videos, ob­tained from unmanned aerial vehicles (UAVs), many example images of objects containing annotations such as ground truth class information, bounding box, optical flow, occlusion and segmentation are required. Due to the difficulties faced during annotation of scenarios, and due to the inadequacy of the scenario diversity resulting from environmental conditions, a dataset containing above mentioned ground truths has not been found in the literature. In this study, synthetic aerial images with various annotation information were created in different scenarios while composing virtual worlds, and enhancing object detection algorithms is aimed. Enhancement of detection results of DCNN based object detection algorithms, trained with the support of synthetic aerial images, on real-world aerial images significantly, was observed during the experiments, conducted.
A Hybrid Text Classification Approach for Chatbots

Chatbots are preferred in many fields due to their ability to provide fast and uninterrupted customer service at all hours. Most chatbots work by classifying input text and responding accordingly. In this work, a hybrid chatbot app-roach is presented by combining a commercial system and a deep learning-based text classification model. Additionally, active learning-based label correction and data expansion approaches are used to increase chatbot performance and keep it up-to-date. In offline tests, the hybrid method made three times fewer errors than the methods it was composed of. Online evaluations performed after deployment show that the hybrid method was able to preserve its superiority and that keeping the dataset up-to-date had positive contributions.
AN ANALYSIS OF STUDENT’S MOTIVATION IN LEARNING ENGLISH

Motivation is one of the keys to get achievement in learning. This study is designed to analyze students’ motivation in learning English. Specifically, this study focuses on finding out whether the students’ motivation is either integrative or instrumental. This study is conducted in the framework of mixed-method research in which the researcher used both qualitative and quantitative methods. SMP Berea Tondano is the location where the research is carried out. The data sources of this research are all the students at SMP Berea Tondano who lived at Etty Orphanage. To collect the data, the researchers uses a questionnaire consisting of 10 questions where 5 statements are to measure students’ integrative motivation and 5 questions for measuring their instrumental motivation. The data obtained are analyzed quantitatively and qualitatively. The findings reveal that the students have both integrative and instrumental motivation in learning English. This study further shows that students’ integrative motivation (with a mean score of 3.96) is higher than their instrumental motivation (with a mean score of 3.87). Motivation is an important part of the learning process. Because it affects the students’ attitudes, morals, and achievement in learning not just English but all the other subject.
Structure Learning in Deep Multi-Task Models

Multi-Task Learning (MTL) aims at improving the learning process by solving different tasks simultaneously. Two general approaches for neural MTL are hard and soft information sharing during training. Here we propose two new approaches to neural MTL. The first one uses a common model to enforce a soft sharing learning of the tasks considered. The second one adds a graph Laplacian term to a hard sharing neural model with the goal of detecting existing but a priori unknown task relations. We will test both tasks on real and synthetic datasets and show that either one can improve on other MTL neural models.