Reviewerly/exHarmony huggingface dataset:
Real-time continuous gesture recognition system based on PSO-PNN

Abstract In the field of natural human–machine interaction, wearable gesture interaction technologies have received considerable attention, particularly continuous gesture (CG) recognition. However, CG faces several challenges, including the impact of motion characteristics on gesture recognition and performance that is not sufficiently robust. Traditional algorithms are highly dependent on samples, thus meeting the requirements of low sample volume and high accuracy simultaneously is challenging. To address these challenges, we propose a real-time CG recognition system based on particle swarm optimization (PSO) and probabilistic neural network (PNN). This system employs principal component analysis for signal dimensionality reduction to alleviate computational burden and uses K-means clustering and Pearson correlation coefficient to extract optimal features for gesture classification. In offline gesture recognition experiments involving six CGs, the algorithm achieved an accuracy rate of 97% with a training set of 300 samples and a runtime of just 31.25 ms. Compared to other five algorithms, the proposed algorithm improved accuracy by at least 9% and reduced the runtime by 40.475 ms. Moreover, gesture recognition experiments were conducted using different datasets, with the PSO-PNN algorithm achieving an average recognition rate of 90.17%, at least 9.84% higher than other algorithms. Finally, in experiments on online CG control for robots in complex environments, the PSO-PNN demonstrated real-time performance of 28.56 ms and a task completion rate of 90.67%, validating the feasibility of PSO-PNN. This research provides a substantial theoretical and technical foundation for the ongoing enhancement and application of CG recognition technology.
A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking

Abstract Performance of modern trackers degrades substantially on transparent objects compared to opaque objects. This is largely due to two distinct reasons. Transparent objects are unique in that their appearance is directly affected by the background. Furthermore, transparent object scenes often contain many visually similar objects (distractors), which often lead to tracking failure. However, development of modern tracking architectures requires large training sets, which do not exist in transparent object tracking. We present two contributions addressing the aforementioned issues. We propose the first transparent object tracking training dataset Trans2k that consists of over 2k sequences with 104,343 images overall, annotated by bounding boxes and segmentation masks. Standard trackers trained on this dataset consistently improve by up to 16%. Our second contribution is a new distractor-aware transparent object tracker (DiTra) that treats localization accuracy and target identification as separate tasks and implements them by a novel architecture. DiTra sets a new state-of-the-art in transparent object tracking and generalizes well to opaque objects.
The development and evaluation of an educational board game on basic geotechnical soil characterization

This paper discusses the potential of gamification as a tool for teaching and learning in geotechnical engineering. Gamification involves incorporating elements of gameplay such as challenges, rewards, competition, and cooperation into teaching and learning environments to make the process more interactive and engaging. Although gamification is widely used in many fields, it is still relatively new in geotechnical engineering. This paper presents the ‘Soil Character’ board game developed by the GeoFUN group as an example of successful gamification in geotechnical engineering education. The game focuses on basic soil characterization, including soil classification systems, index properties, and geotechnical characterization tests such as sieving, sedimentation, and Atterberg limits. The paper provides background information on the development of the game, and a description of the game components. The online Portuguese version of the game was tested with eight civil engineering undergraduate students who had successfully undertaken the introductory soil mechanics module. Student’s satisfaction in terms of game design, rules, and gameplay was measured via a questionnaire. Results of the questionnaires showed that the game was well evaluated in all aspects. Student volunteers reported that they felt very motivated, and that they wished they had been able to play the game when they were learning the topic. Thus, results presented in this paper suggest that gamification has the potential to make geotechnical engineering education more interactive and engaging. Exploring the effectiveness of the game in various contexts and with diverse student populations constitutes a key direction for our future research.
DefenseFea: An Input Transformation Feature Searching Algorithm Based Latent Space for Adversarial Defense

Abstract Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool systems and further threaten their security. In this paper, we propose to use latent space protect image classification. Specifically, we train a feature searching network to make up the difference between adversarial examples and clean examples with label guided loss function. We name it DefenseFea(input transformation based defense with label guided loss function), experimental result shows that DefenseFea can improve the rate of adversarial examples that achieved a success rate of about 99% on a specific set of 5000 images from ILSVRC 2012. This study plays a positive role in the further investigation of the relationship between adversarial examples and clean examples.
Building Client Networks and Maximising Your Resources

A report on how an Irish professional dog behavior consultant created a social media based network of past clients who were able to provide opportunities to learn and practice behavior modification skills without the direct supervision of the consultant. The structure of “The Alumni”’ is discussed alongside observations of the beneficial role such a network plays in a particularly challenging semi-rural Irish environment.
Improving Mass Gathering Management Using Process Mining Techniques

Crowd management is a significant topic especially for countries that support gathering events frequently. The Kingdom of Saudi Arabia hosts and manages one of the world class annual religious gatherings known as “pilgrimage”. Several challenges are raised for managing and controlling such mass gathering event. In this paper we propose a comprehensive framework for event processes modelling and management. The framework consists of four main stages starts with acquiring temporal data and ends by modelling different processes of the event. The main contribution of this work is to demonstrate how process mining techniques can be used innovatively to model the movement flow of crowd. Synthetic data is used to show a proof-of-concept of the proposed framework and the applicability of using it in modelling and monitoring real crowd movement scenarios.
Android Application for Online Fertilizer Selling and Accounting

The "Android Application for Online Fertilizer Selling and Accounting" aims to streamline the process of buying and selling fertilizers through a user-friendly mobile application. This comprehensive platform facilitates a secure and convenient marketplace for users, integrating essential features for both buyers and sellers. The application encompasses user authentication, a detailed product catalog, efficient shopping cart functionality, secure checkout and payment options, order tracking, and dedicated interfaces for sellers to manage their products and orders. For buyers, the application provides an intuitive interface to explore a diverse catalog of fertilizers, categorized for easy navigation. Users can securely authenticate, add products to their cart, and complete transactions using various payment methods. The order tracking feature ensures transparency and keeps users informed about the status of their purchases. The application also maintains an order history for reference. Sellers benefit from a dedicated management interface, allowing them to efficiently update and manage their fertilizer inventory. The platform ensures accurate accounting through the integration of invoicing features, generating detailed invoices for each transaction. This streamlines the selling process and helps sellers keep track of their sales and revenue. By combining the convenience of online shopping with robust accounting functionalities, the Android application for online fertilizer selling and accounting seeks to enhance the overall experience for users in the agricultural sector. The goal is to foster a reliable and efficient marketplace that meets the needs of both buyers and sellers in the fertilizer industry.
Wearable IoT System for Hand Function Assessment Based on EMG Signals

Evaluating hand function presents a significant challenge in the realm of remote rehabilitation, particularly when highlighting the need for comfort and practicality in wearable devices. This research introduces an innovative wearable device-based Internet of Things (IoT) system, specifically designed for the assessment of hand function, with a focus on a wearable wristband. The system, enhanced by cloud technology, offers comprehensive solutions for remote health management and therapeutic services. Firstly, it uses electromyography (EMG) signals from the arm to assess hand function. By employing sophisticated classification and regression models, this system can automatically identify user gestures and accurately measure grip strength. Additionally, the integration of additional sensor data ensures that the system fulfills essential criteria for hand function assessment. Leaving conventional grip strength classification methods, this study explored four distinct regression models to accurately represent the grip strength curve. The findings reveal that the Random Forest Regression (RFR) model is the most effective, achieving an R2 score of 0.9563 on the test data. This significant outcome not only confirms the practicality of the wearable wristband, which relies on EMG signals, but also underscores the potential of the IoT system in assessing hand function.
Reimagining STEM Education in South Africa: Leveraging Indigenous Knowledge Systems Through the M-Know Model for Curriculum Enhancement

This conceptual paper investigates the integration of indigenous knowledge systems (IKS) into South African Science, Technology, Engineering, and Mathematics (STEM) education, exploring its conceptions, historical roots, significance, and the challenges and benefits associated with such integration. The objective was to understand the role of IKS in enriching STEM education and its potential contributions to global scientific knowledge. A culturally sensitive research approach was employed, with criteria set for selecting relevant literature on IKS and STEM. The results highlighted the profound historical significance of IKS in South Africa and its potential in bridging cultural gaps in education. Pedagogical innovations rooted in indigenous practices were identified as key to revitalising the STEM learning experience. However, challenges such as educational barriers, policy constraints, and institutional norms were also noted. Thus, the Multiple-ways of knowing (M-Know) model is proposed to demonstrate the possibility of using an integrated approach for the inclusion of IKS and enhancement of the STEM higher education school curriculum in South Africa. The model negotiates for a synergic or integrated arrangement between formal STEM curriculum and IKS in terms of using IKS to interrogate, complement, and consolidate the contents and pedagogy of Westernised STEM education in higher education.
Hate speech and environmental activist discourse

On 14 October 2022, two activists from the environmental group Just Stop Oil threw a can of tomato soup at Van Gogh’s famous Sunflowers at the National Gallery in London, gluing themselves to the wall beneath the painting. The action, covered in a tweet by the organisation, received a backlash of negative comments by social media users. The present paper focuses on hate speech in response to the controversial tweet by Just Stop Oil in a dataset of about 2700 user comments. Moving from critical discourse studies of hate speech in digital contexts, the manual and software-assisted qualitative analysis employs the appraisal framework and discourse-historical strategies to observe the discursive construction of hate themes against environmental activism. In line with ecolinguistics, findings suggest that, when something valued as extremely positive and important such as art is under attack, people may fail to recognise the motivations behind activist action, appraising it negatively through hate speech, and even distancing themselves from environmental values.
Evaluating The Effectiveness of Information and Communication Technologies (Icts) in Fire Response in Disaster Management in Batam, Indonesia

This research aims to evaluate the utilization of Information and Communication Technologies (ICTs) in the context of fire response for disaster management in Batam, Indonesia, spanning the years 2018 to 2023. Employing a mixed-methods approach involving surveys and case studies, the study investigates strategies and provides recommendations for health adaptation actions during climate-related disasters. The research assesses the effectiveness of ICTs in disaster risk management, with a specific focus on evaluating the role of the Disaster Management Act in Indonesia. The findings reveal that ICT-based early warning systems, enhanced communication networks, and efficient data management significantly contribute to improving Batam's preparedness and response to disasters. Despite these positive outcomes, the study identifies challenges, including issues related to equitable technology access, infrastructure limitations in remote areas, and privacy concerns that warrant attention. This study presents valuable quantitative insights into the effectiveness of ICTs in disaster management in Batam, Indonesia. It emphasizes the importance of addressing the identified challenges for a more robust disaster mitigation framework. The results have significant implications for policymakers and practitioners, underscoring the need for targeted interventions to overcome barriers and ensure the effective integration of ICTs in disaster response and management practices in Indonesia.
I ASKED CHATGPT WHAT THE NEXT TREND WOULD BE

This critical review explores AI’s impact on trend research within fashion, scrutinising its capacity for ethical and nuanced analysis amidst industry reliance on its speed and perceived neutrality. Highlighting the risks of oversimplification and ethical oversight, it questions AI’s role in shaping a future that should reflect diverse cultural and professional integrity. It argues for a responsible AI utilisation that respects cultural nuances and social equities, urging for co-designing with diverse voices to ensure equitable and inclusive futures. Through a conversation with ChatGPT, this study underscores the need for a deeper understanding of AI’s application in creative processes, advocating for a thoughtful approach beyond mere replicating trends.
Classification Of Electronic Crimes According To The Nature Of The Right Assaulted (A Comparative Study Between The Palestinian And Emirati Legislations)

This research investigates the issue of the different classifications of electronic crimes, and the importance of classifying them in the criminal law, with a focus on the specificity and the importance of the right assaulted in classifying them, as well as the importance of classifying them according to this criterion at all stages of the legal process to Combatting phenomenon of electronic crime. In this study we also investigates, the approach of Palestinian and Emirati legislators in classifying and regulating the provisions electronic crimes and Communications Crimes &amp; information technology, pursuant to Decree-Law No. 10 of 2018, and Federal Law Decree-Law No. 34 of 2021 on combating phenomenon of electronic crime. At the end of the study, the researcher reached a set of conclusions and recommendations, One of the most important of conclusions is that The classification of electronic crimes based on the criterion of the right assaulted constitutes a vital issue in confronting these crimes, just as the Palestinian legislator did not follow a specific approach in his classification of electronic crimes and their substantive and procedural provisions, as the UAE legislator did in organizing and classifying crimes according to a clear approach. One of the most important recommendations is the necessity of the legislator the need for the Palestinian legislator to make amendments to the electronic crimes Law, classify these crimes, and separate the substantive and procedural provisions within this law, as well as amend the texts of Articles (21, 28, 48) of this law.
Arsenic geochemical species modeling, prediction, and bioavailability in groundwaters of the Oban Massif, southeastern Nigeria

To ascertain the distribution patterns, source identification, chemical speciation, and bioavailability of arsenic in basement aquifers, 64 groundwater samples were analyzed using hydrochemical and geochemical modeling. It was determined that important ion geochemistry, hydrogeochemical facies, and basic hydrogeochemical parameters are interdependent. Arsenic (As) concentrations varied from 0.001 mg/l to 0.03 mg/l, with a mean of 0.007 mg/l. The results of 4.68% of samples were higher than the allowable level of 0.01 mg/l, i.e., groundwater arsenic levels are not dangerous. Significant ion concentrations decreased from dry to rainy seasons suggesting that ionic concentrations generated by silicate weathering in aquifers became diluted. Abundance trends in metal concentration during the dry and rainy seasons were Fe &gt; Mn &gt; Zn &gt; Ni &gt; Cu &gt; As &gt; Pb &gt; Cd and Fe &gt; Zn &gt; Mn &gt; Ni &gt; Pb &gt; Cu &gt; As &gt; Cd, respectively. Of the two hydrochemical facies discovered, the predominant one was the main alkaline-earth-bicarbonate facies [Ca-(Mg)-HCO3] typical of basement terrains, suggesting the fundamental stage of groundwater evolution. Plots of ionic ratios, metal loads, and principal component analyses showed that ionic concentrations are controlled by geology. Geochemical modeling revealed the presence of aqueous arsenic species, which are the less dangerous arsenates at unsaturated levels and do not currently pose a concern.
Design of an Efficient &amp; Secure Steganographic Model using Modified LSB &amp; Encryption Process

This paper introduces a novel steganographic model for robust multimodal data security, seamlessly integrating a modified Least Significant Bit (LSB) technique with encryption, making it applicable to diverse data types such as images, audio, video, and text. Overcoming challenges posed by existing complex models and communication delays, our approach employs a modified LSB technique to encode similar sized data samples, followed by dynamic bioinspired elliptic curve cryptography (BECC) utilizing a Mayfly Optimization (MO) Model. This adaptive strategy optimizes curve types and prime key sets, significantly enhancing data security while minimizing delays and complexities across diverse data sizes. The proposed model achieves an 8.3% reduction in encryption and steganographic process delays, while simultaneously maintaining superior Peak Signal to Noise Ratio (PSNR) and lower Mean Squared Error (MSE) levels compared to existing methods when applied to the same data samples. This highlights its effectiveness in securing dynamic datasets without compromising efficiency.
Analysis and Comparison of 3D Printed Soft Pneumatic Actuator Configurations for Swaying Lateral Motion of Planar Objects

Soft pneumatic actuators (SPAs) offer unique possibilities in robotics and automation due to their compliance and versatility. Unlike conventional actuators that can move either revolute or prismatic, SPAs can produce complex motions, especially when configured in combinations. This paper systematically investigated the performance of four distinct SPA configurations (Types 1-4) concerning the task of moving a planar square object and swaying it laterally to the right and left. These configurations are analyzed for their ability to achieve desired movements and orientations. Type 4, involving two SPAs, emerges as the most effective configuration, successfully navigating the challenges posed by the desired task. In contrast, Type 2, consisting of four SPAs, encounters difficulties in executing the task attributed to increased inertia, which was similar as type 3. Types 1, while not precisely meeting the primary task, reveal distinctive movements - left-right motion without orientation change. This study contributes valuable insights into the complex dynamics of SPAs, offering considerations for optimal design and performance. The outcomes could be developed in the future in soft actuator applications, especially those involving configurations based on specific needs.
Analisis kemampuan literasi numerasi mahasiswa pada implementasi program kampus mengajar

The aim of this research is obtain a profile of the numeracy literacy abilities of students who have participated in the Teaching Campus program through analysis of the problem solving. This type of research is a case study. The subjects of this research were alumni of the Class 3 Teaching Campus at Widya Dharma University. Research data collection uses test methods and interview methods. The research instruments are the main instrument (researcher) and auxiliary instrument (numeracy literacy test). This research uses triangulation techniques to obtain valid data. The results of this research are (1) in text type questions students mention important information, do not formulate the problem, do not write down strategies, solve the problem correctly, check again without alternative solutions; (2) on text and image type questions, students mention important information and formulate problems, convey strategies, implement strategies with wrong image patterns, recheck answers; and (3) on text and table type questions, students mention important information and formulate the problem briefly, do not convey strategies, solve problems by paying attention to tables, and are less careful in analyzing text and table.
Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets

Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%.