Reviewerly/exHarmony huggingface dataset:
Stacked Extreme Learning Machine with Horse Herd Optimization: A Methodology for Traffic Sign Recognition in Advanced Driver Assistance Systems

In the quest for autonomous vehicle safety and road infrastructure management, traffic sign recognition (TSR) remains paramount.Recent advancements in accuracy across various benchmarks have been identified in the literature concerning this essential task.Such technology might remain absent in older vehicles, while integration into Advanced Driver Assistance Systems (ADAS) is common in more recent models.Yet, the capability of these systems to function proficiently under diverse driving conditions has not been widely investigated.A framework has been devised to allow a moving vehicle to detect traffic signs, targeting the enhancement of driver safety and the diminishment of accidents.The present research introduces an innovative methodology, amalgamating the extreme learning machine (ELM) method with deep-learning paradigms, in response to experimental discoveries.As a pioneering computational approach in neural network-based learning, ELM facilitates rapid training and commendable generalization.An accuracy of 95.00% was achieved by the proposed model.By utilizing the Horse Herd Optimization method (HHOA), the memory consumption is minimized in the more sophisticated approach of stacked ELM (SELM) within the deep-learning framework.This study contributes to the understanding of potential challenges that may be encountered during TSR tasks, and lays the groundwork for future investigation by proffering a diverse set of evaluations for various road scenarios.Consistency in the utilization of professional terms is maintained throughout.
Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.
Exploring a Novel Mexican Sign Language Lexicon Video Dataset

This research explores a novel Mexican Sign Language (MSL) lexicon video dataset containing the dynamic gestures most frequently used in MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. The MX-ITESO-100 dataset is composed of a lexicon of 100 gestures and 5000 videos from three participants with different grammatical elements. Additionally, the dataset is evaluated in a two-step neural network model as having an accuracy greater than 99% and thus serves as a benchmark for future training of machine learning models in computer vision systems. Finally, this research provides an inclusive environment within society and organizations, in particular for people with hearing impairments.
Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning

In recent years, integrating structured light with deep learning has gained considerable attention in three-dimensional (3D) shape reconstruction due to its high precision and suitability for dynamic applications. While previous techniques primarily focus on processing in the spatial domain, this paper proposes a novel time-distributed approach for temporal structured-light 3D shape reconstruction using deep learning. The proposed approach utilizes an autoencoder network and time-distributed wrapper to convert multiple temporal fringe patterns into their corresponding numerators and denominators of the arctangent functions. Fringe projection profilometry (FPP), a well-known temporal structured-light technique, is employed to prepare high-quality ground truth and depict the 3D reconstruction process. Our experimental findings show that the time-distributed 3D reconstruction technique achieves comparable outcomes with the dual-frequency dataset (p = 0.014) and higher accuracy than the triple-frequency dataset (p = 1.029 × 10−9), according to non-parametric statistical tests. Moreover, the proposed approach’s straightforward implementation of a single training network for multiple converters makes it more practical for scientific research and industrial applications.
Improving Students’ Literacy and Numeracy Using Mobile Game-Based Learning with Augmented Reality in Chemistry and Biology

The purpose of the current study is to enhance students’ literacy and numeracy using mobile game-based learning with augmented reality (ARGBL) in chemistry and biology. In this quasiexperimental design, 714 10th and 11th grade students from five high schools in Indonesia were recruited as participants. To gather the data, the Chemical Literacy and Numeracy Test (C-LNT) and the Biological Literacy and Numeracy Test (B-LNT) were administered to the treatment and control group (CG) students. Quantitative data were then analyzed using descriptive statistics and t-tests. The results highlighted that the treatment group students who were instructed using ARGBL had higher literacy and numeracy scores than the comparison group students who were taught using PowerPoint (PPT) slides. This indicates that ARGBL is effective in promoting literacy and numeracy among high school students in chemistry and biology. Thus, we suggest teachers apply ARGBL to elevate students’ literacy and numeracy to a satisfactory level.
Pancasila Educational Games with Zone Proximal Development: An Analysis of Human-Computer Interaction Perception Based on Technology Acceptance Model

Educational games are becoming a learning trend for elementary school students today because of their fun nature and attractive appearance which makes students interested in trying them.Pancasila educational games are also designed to make learning more interesting.Testing of educational games and their compatibility with human-computer interaction needs to be done so that games are more optimal when implemented and distributed more widely.The purpose of this study was to determine the results of testing the acceptance of Pancasila educational games using the characteristics of the technology acceptance model.This study involved 300 respondents of early childhood, aged 5-6 years in 3 schools in East Java, Indonesia.Modelling and data analysis using SEM AMOS.The results showed that as many as 97.4% of students liked and were helped by this educational game.Another important factor is in terms of perceived ease of use game 99.8% of students like this aspect and from the aspect of perceived usefulness, 95% of students choose this aspect.Where both give good value to the Pancasila educational game.As for the development of educational games, they will be readjusted to the conditions of learning in each school.
RHS-TRNG: A Resilient High-Speed True Random Number Generator Based on STT-MTJ Device

High-quality random numbers are very critical to many fields such as cryptography, finance, and scientific simulation, which calls for the design of reliable true random number generators (TRNGs). Limited by entropy source, throughput, reliability, and system integration, existing TRNG designs are difficult to be deployed in real computing systems to greatly accelerate target applications. This study proposes a TRNG circuit named RHS-TRNG based on spin-transfer torque magnetic tunnel junction (STT-MTJ). RHS-TRNG generates resilient and high-speed random bit sequences exploiting the stochastic switching characteristics of STT-MTJ. By circuit/system co-design, we integrate RHS-TRNG into a RISC-V processor as an acceleration component, which is driven by customized random number generation instructions. Our experimental results show that a single cell of RHS-TRNG has a random bit generation speed of up to 303 Mb/s, which is the highest among existing MTJ-based TRNGs. Higher throughput can be achieved by exploiting cell-level parallelism. RHS-TRNG also shows strong resilience against PVT variations thanks to our designs using bidirectional switching currents and dual generator units. In addition, our system evaluation results using gem5 simulator suggest that the system equipped with RHS-TRNG can achieve 3.4-12x higher performance in speeding up option pricing programs than software implementations of random number generation.
Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition

Abstract Research in the field of explainable artificial intelligence has produced a vast amount of visual explanation methods for deep learning-based image classification in various domains of application. However, there is still a lack of domain-specific evaluation methods to assess an explanation’s quality and a classifier’s performance with respect to domain-specific requirements. In particular, evaluation methods could benefit from integrating human expertise into quality criteria and metrics. Such domain-specific evaluation methods can help to assess the robustness of deep learning models more precisely. In this paper, we present an approach for domain-specific evaluation of visual explanation methods in order to enhance the transparency of deep learning models and estimate their robustness accordingly. As an example use case, we apply our framework to facial expression recognition. We can show that the domain-specific evaluation is especially beneficial for challenging use cases such as facial expression recognition and provides application-grounded quality criteria that are not covered by standard evaluation methods. Our comparison of the domain-specific evaluation method with standard approaches thus shows that the quality of the expert knowledge is of great importance for assessing a model’s performance precisely.
LB1652 Images of the hand are comparable to those of the face for predicting chronological age via AI

The prediction of a person's chronological age (CA) from visible skin features using artificial intelligence (AI) is now commonplace in computer vision. Most often, this is accomplished by convolutional neural network (CNN) models based on facial images as the biometric data. However, hands also hold telltale signs of a person's age, maybe even more telling than the face. To determine the utility of hand images in predicting CA, we developed two deep CNNs, one based on standardized dorsal hand images (H) and another on frontal face images (F).
Quantitative Reachability Stackelberg-Pareto Synthesis is NEXPTIME-Complete

In this paper, we deepen the study of two-player Stackelberg games played on graphs in which Player $0$ announces a strategy and Player $1$, having several objectives, responds rationally by following plays providing him Pareto-optimal payoffs given the strategy of Player $0$. The Stackelberg-Pareto synthesis problem, asking whether Player $0$ can announce a strategy which satisfies his objective, whatever the rational response of Player $1$, has been recently investigated for $ω$-regular objectives. We solve this problem for weighted graph games and quantitative reachability objectives such that Player $0$ wants to reach his target set with a total cost less than some given upper bound. We show that it is NEXPTIME-complete, as for Boolean reachability objectives.
StableVideo: Text-driven Consistency-aware Diffusion Video Editing

Diffusion-based methods can generate realistic images and videos, but they struggle to edit existing objects in a video while preserving their appearance over time. This prevents diffusion models from being applied to natural video editing in practical scenarios. In this paper, we tackle this problem by introducing temporal dependency to existing text-driven diffusion models, which allows them to generate consistent appearance for the edited objects. Specifically, we develop a novel inter-frame propagation mechanism for diffusion video editing, which leverages the concept of layered representations to propagate the appearance information from one frame to the next. We then build up a text-driven video editing framework based on this mechanism, namely StableVideo, which can achieve consistency-aware video editing. Extensive experiments demonstrate the strong editing capability of our approach. Compared with state-of-the-art video editing methods, our approach shows superior qualitative and quantitative results. Our code is available at \href{https://github.com/rese1f/StableVideo}{this https URL}.
Artificial Intelligence, Robotics and Information, and Communication Technology (ICT) as Tools for Business and Education Management

Intuitively, artificial intelligence, robotics, and information and communication technologies are important because they give enterprises and institutions insights into their operations that they may not have been aware of previously and because, in some cases, they perform tasks better than humans. As tools for business and education management, artificial intelligence, robotics, and information and communication technologies in the 21st century are Business and education systems have been described as vital to keeping abreast with rapidly changing technologies. Its importance has been translated into huge potential in terms of positive outcomes, although investments in ICT in Nigeria’s business and education systems have not yielded much when compared to similar investments made in communication. This paper briefly discussed the roles of artificial intelligence, robotics, and information and communication technologies as tools for business and educational management. At the end of the paper, suggestions were made for how to use artificial intelligence, robotics, and information and communication technologies in business and education management in the best way possible.
Factors Influencing Dividend Payment Decisions: A Case of Mutual Funds in Tanzania

The study aimed at investigating the factors influencing dividend payout decisions of mutual funds in Tanzania for panel data in the time interval of 2019 to 2022. Using the modified Lintner model as a theoretical framework, explanatory research design was used. Employment of explanatory variables including past dividend, free cash flow, liquidity, sales growth, return on assets, financial leverage, firm size and market risk and using descriptive and inferential statistical tools i.e. correlation, fixed effect and random effect regression models the facts were realized. The results of analysis found that dividend payout decision is positively and significantly affected by past dividend and return on assets. Also it was revealed that free cash flows, liquidity, sales growth, financial leverage , firm size had a positive and insignificant effects on dividend payout decision. Either market risks revealed to have negative and insignificant influence on dividend payout. These results therefore recommend that mutual funds management should make much concentration on paying for floating dividend. Furthermore, the findings remind mutual funds management to ensure firm makes profit for firm realize performance, cash inflows, liquidity and growth. Satisfactory payment of dividend ensures for a firm value increase, firm growth and investors prosperous measured in terms of increase in financial leverage. Increase in financial leverage is the firms’ management performance which expects to help hedge the market risks for market efficiency hypothesis being realized important for perpetual future dividend payment.
The concept of creating multimedia tools for educational space of dual education

established. A set of criteria for evaluating the quality of MEC as a multimedia product is proposed. Examples of the implementation of the concept for the educational program "Technologies of electronic multimedia publications" at the Simon Kuznets KharkivNational University of Economics are presented.
Efficient coding of wideband ISF parameters : Application of variable rate SSVQ scheme

In this paper, we propose an inter-frame variable rate version of the switched split vector quantizer (SSVQ) developed for efficient coding of AMR-WB ISF spectral parameters. Simulation results will show that our new ISF coding scheme, called ISF-VR-SSVQ coder, performs better than the conventional ISF SSVQ and can save 1–2 bits per frame.
Challenges and Opportunities in Password Management: A Review of Current Solutions

For over six decades, passwords have served as the primary authentication mechanism for almost all modern computer systems. However, password management is a challenging task for most computer users, and that has led users to many malpractices that open the door for most information security breaches over time. Despite many efforts, no alternative solution has ever succeeded in replacing passwords as the primary authentication mechanism. As a result, users are now heavily relying on password managers to alleviate the burden of manual password management. This paper addresses the topic of password management about different types of password managers and their inherent limitations. By evaluating the existing password management approaches and identifying potential improvements, this paper aims to signify an important research gap that exists in the study area; the need for fully automating the process of manual password management.
EXCALIBUR: Encouraging and Evaluating Embodied Exploration

Experience precedes understanding. Humans constantly explore and learn about their environment out of curiosity, gather information, and update their models of the world. On the other hand, machines are either trained to learn passively from static and fixed datasets, or taught to complete specific goal-conditioned tasks. To encourage the development of exploratory interactive agents, we present the EXCALIBUR benchmark. EXCALIBUR allows agents to explore their environment for long durations and then query their understanding of the physical world via inquiries like: “is the small heavy red bowl made from glass?” or “is there a silver spoon heavier than the egg?”. This design encourages agents to perform free-form home exploration without myopia induced by goal conditioning. Once the agents have answered a series of questions, they can renter the scene to refine their knowledge, update their beliefs, and improve their performance on the questions. Our experiments demonstrate the challenges posed by this dataset for the present-day state-of-the-art embodied systems and the headroom afforded to develop new innovative methods. Finally, we present a virtual reality interface that enables humans to seamlessly interact within the simulated world and use it to gather human performance measures. EXCALIBUR affords unique challenges in comparison to presentday benchmarks and represents the next frontier for embodied AI research.
Perancangan Aplikasi Animasi Interaktif Japanese Vocabulary Berbasis Android

In Indonesia, Japanese is one of the languages that is less attractive. Because Japanese has a lot of vocabulary, it is difficult to learn the language. Therefore, in the research that we are currently doing, we will create and android based application program with an interface that is made as attractive as possible so that people are more interested in learning the language and make it easier to understand. The features provided by this application are the in the form of vocabulary and some games to test how well we understand the material given. In its development used data collection techniques in the form of observation, literature study, and questionnaires. Hopefully in the future this application can be useful and make people interested in the learning Japanese. The application was tested using the blackbox technique.
Software implementation of PSPISE-based models in the Python programming language

The manuscript presents the software implementation of Pspise-based models in the Python programming language. To demonstrate the model transformation process, examples of electrical circuits that present current and voltage resonance are considered. The implementation experiments were conducted based on models implemented in LTspice. Simulation results using Python are obtained that are similar to those from other software environments specialized for modelling electrical and electronic circuits.
Sampling is Matter: Point-Guided 3D Human Mesh Reconstruction

This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex. To resolve this problem, we propose to design a simple feature sampling scheme. The key idea is to sample features in the embedded space by following the guide of points, which are estimated as projection results of 3D mesh vertices (i.e., ground truth). This helps the model to concentrate more on vertex-relevant features in the 2D space, thus leading to the reconstruction of the natural human pose. Furthermore, we apply progressive attention masking to precisely estimate local interactions between vertices even under severe occlusions. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of 3D human mesh reconstruction. The code and model are publicly available at: https://github.com/DCVL-3D/PointHMR_release.
Assessing Model Requirements for Explainable AI: A Template and Exemplary Case Study

Abstract In sociotechnical settings, human operators are increasingly assisted by decision support systems. By employing such systems, important properties of sociotechnical systems, such as self-adaptation and self-optimization, are expected to improve further. To be accepted by and engage efficiently with operators, decision support systems need to be able to provide explanations regarding the reasoning behind specific decisions. In this article, we propose the use of learning classifier systems (LCSs), a family of rule-based machine learning methods, to facilitate and highlight techniques to improve transparent decision-making. Furthermore, we present a novel approach to assessing application-specific explainability needs for the design of LCS models. For this, we propose an application-independent template of seven questions. We demonstrate the approach’s use in an interview-based case study for a manufacturing scenario. We find that the answers received do yield useful insights for a well-designed LCS model and requirements for stakeholders to engage actively with an intelligent agent.