Reviewerly/exHarmony huggingface dataset:
T-SignSys: An Efficient CNN-Based Turkish Sign Language Recognition System

Sign language (SL) is a communication tool playing a crucial role in facilitating the daily life of deaf or hearing-impaired people. Large varieties in the existing SLs and lack of interpretation knowledge in the general public lead to a communication barrier between the deaf and hearing communities. This issue has been addressed by automated sign language recognition (SLR) systems, mostly proposed for American Sign Language (ASL) with limited number of research studies on the other SLs. Consequently, this paper focuses on static Turkish Sign Language (TSL) recognition for its alphabets and digits by proposing an efficient novel Convolutional Neural Network (CNN) model. Our proposed CNN model comprises 9 layers, of which 6 layers are employed for feature extraction, and the remaining 3 layers are adopted for classification. The model is prevented from overfitting while dealing with small-scale datasets by benefiting from two regularization techniques: 1) ignoring a specified portion of neurons during training by applying a dropout layer, and 2) applying penalties during loss function optimization by employing $$L_2$$ kernel regularizer in the convolution layers. The arrangement of the layers, learning rate, optimization technique, model hyper-parameters, and dropout layers are carefully adjusted so that the proposed CNN model can recognize both TSL alphabets and digits fast and accurately. The feasibility of our proposed T-SignSys is investigated through a comprehensive ablation study. Our model is evaluated on two datasets of TSL alphabets and digits with an accuracy of 97.85% and 99.52%, respectively, demonstrating its competitive performance despite straightforward implementation.
Implementation features of the physical education program for secondary school students in the distance education condition

Distance learning, also known as online learning or e- learning, is an educational method that utilizes technology to deliver instruction to students who are not physically present in a traditional classroom setting. This approach enables students to access educational content and interact with instructors from anywhere with an internet connection. Various tools, such as video lectures, discussion forums, and online assessments, facilitate the learning process. The growing popularity of distance learning is a global trend, especially in the context of modern technology and global changes such as the COVID-19 pandemic or war. Features of distance learning may be distinctive from different countries depending on the infrastructure, educational systems, cultural characteristics and other factors. The main distance learning features: flexibility and convenience; using modern technologies and societal trends; lack of physical presence; independence; assessment is usually based on tests, projects and other tasks that the student completes in an online environment; development of e-communication skills; the need for regular materials updating. Twenty-one boys and sixteen girls of the eighth class took part in the research. All secondary school students were classified in the main medical group. The developed methodology contributes to the realization of the strengths and knowledge acquired by students in the basic component, the formation values life, mastering the experience of self-organization, self-realization, self-control and helps in professional self-determination. Analyzed the implemented methodology supported by positive digital dynamics, come to an agreement that the methodology aimed at the implementation of the physical culture program has the right to exist at present.
Playing Checkers with an Intelligent and Collaborative Robotic System

Collaborative robotics represents a modern and efficient framework in which machines can safely interact with humans. Coupled with artificial intelligence (AI) systems, collaborative robots can solve problems that require a certain degree of intelligence not only in industry but also in the entertainment and educational fields. Board games like chess or checkers are a good example. When playing these games, a robotic system has to recognize the board and pieces and estimate their position in the robot reference frame, decide autonomously which is the best move to make (respecting the game rules), and physically execute it. In this paper, an intelligent and collaborative robotic system is presented to play Italian checkers. The system is able to acquire the game state using a camera, select the best move among all the possible ones through a decision-making algorithm, and physically manipulate the game pieces on the board, performing pick-and-place operations. Minimum-time trajectories are optimized online for each pick-and-place operation of the robot so as to make the game more fluent and interactive while meeting the kinematic constraints of the manipulator. The developed system is tested in a real-world setup using a Franka Emika arm with seven degrees of freedom. The experimental results demonstrate the feasibility and performance of the proposed approach.
Legal regulation of the participation of artificial intelligence and other generators in the process of creating results comparable to works

In the modern world, some components of legal relations that do not have legal personality — animals, technical devices, artificial intelligence systems — are able to generate results comparable to works. However, neither regulation of this process, nor legal protection regime of its results are provided in international treaties, and are established less than fragmentary in national law. In this regard, the following questions arise: are the results of the activities of the listed components protected under copyright law and who exactly is the author of such results — the owner of the animal/device/technology, or the direct creator of the result, who is not a human being. A general term “generators of results comparable to the results of intellectual activity” is introduced into the doctrinal turnover, by which it is proposed to understand a component of a legal relation that does not have legal personality in accordance with the law of modern states — animals, technical devices, artificial intelligence, etc., — which, participating in process, partly comparable to human creative activity, are capable of producing (generating) results comparable to objects protected by intellectual property law, in particular, works. Up to date, the idea of generators as objects, not subjects of law, has not been shaken, and copyrights are not granted to them. In view of the possible commercial and artistic value of the results of their “creativity,” it may be advisable to establish a special protection regime without granting anyone non-property (moral) rights, however, with the recognition of all or some of the property rights in the created object for the generator’s proprietor for a reduced period of time.
EDGE-Net: an edge-guided enhanced network for RGB-T salient object detection

With similar textures, dark backgrounds, and complex scenes, RGB images are usually unable to provide discriminative information for model training, which often leads to inaccurate prediction results. Compared with RGB salient object detection (SOD) methods, RGB-T SOD have thermal infrared (TIR) information as an informational supplement. As a result, RGB-T SOD can adapt to more complex environments and achieve better results. However, existing methods do not efficiently integrate features between different modalities and do not fully exploit spatial information from the shallow-level features. Accordingly, we propose an EDGE-Net. First, we propose an edge extraction module to capture the edge information in the shallow-level features and use it to guide the subsequent decoding. The original edge features are then weighted after channel attention processing. Second, to additionally suppress the noise of the shallow-level features, we design a global information extraction module. In this module, multiple convolutions are used instead of single convolutions to reduce the computational effort, and convolutions with different dilation rates are used to obtain different receptive fields. We conduct extensive experiments on the RGB-T dataset and show that the proposed method achieves superior performance compared to several state-of-the-art algorithms. The code and results of our method are available in a Github repository available at: https://github.com/BorreloadD/EDGE-Net.git.
Students' Decisions to Study English at Foreign Language Centers Using Bayesian Model Averaging Algorithm

Along with other languages, English is a powerful tool for communication as well as for the linguistic integration and growth of people. English is considered the universal language everywhere in the world. It is widely used around the world; it might be considered a foreign language of major global significance. On the other side, previous academics stated that factors affect students' decision to learn English at foreign language centers such as study program, tuition fee, location, promotion, social factors, psychological factors, etc. However, these studies just only choose a single model among alternative feasible models. This study utilizes the Bayesian model averaging approach to determine the optimal models for determinants influencing students' decision to study English at foreign language centers. The findings through the Bayesian model averaging algorithm revealed the best two models for determinants impacting students' decisions to learn English at foreign language centers.
A comparison between marginal likelihood and data augmented MCMC algorithms for Gaussian hidden Markov models

We provide a comparison between marginal likelihood and data augmented Markov chain Monte Carlo (MCMC) algorithms for Bayesian estimation of hidden Markov models. In particular, we focus on a specification of these models based on a multivariate Gaussian distribution for the response variables. We evaluate the performance of both methods on the basis of simulated samples and an empirical application, where we compare these approaches in terms of standard measures of estimation and time efficiency. The results show that the augmented algorithm is preferable, obtaining optimal convergence properties and producing larger values of the effective sample size. Instead of using variable dimensional techniques for model selection, we suggest a method based on the so-called parallel sampling for this aim.
Decolonizing mHealth Technology for User Empowerment and Persuasion in the Global South Healthcare Context: A Case Study

This article explores the extent to which Global North mHealth apps are designed for user empowerment and persuasion in the Global South healthcare context. Findings from a case study underscore the need for decolonizing digital technology to promote more inclusive and equitable access to the digital ecosystem. The article suggests that deploying rhetorically nuanced, justice-driven decolonial design approaches can help stamp out digital colonialism and build a just future by bridging the North-South divide.
Study Delivery Mechanism and Preparation

In higher educational institutions, the development of study delivery mechanism and preparation is regarded as one of the major aspects that would promote student learning and help in the achievement of academic goals and objectives. The educators need to focus upon bringing about development and preparation in the teaching-learning materials. They need to promote modern and innovative methods to enrich the system of education. The advancements in teaching-learning materials are brought about on the basis of number of aspects. When these are introduced, the educators need to ensure that they prove to be beneficial to the students in achieving academic goals. Research has indicated that the introduction of modern and innovative methods and teaching-learning materials preparation have led to developments of the overall system of education and helps to achieve the learning outcomes in higher education in an effective way.
Digital Natives' Attitudes toward Blockchain Technology Usage

The widespread use of blockchain technology (BCT) and the growing awareness of the numerous opportunities it offers its users are the focus of recent research. Digital natives are surrounded by technology from birth and are naturally inclined to embrace it, but previous research has not addressed their attitudes toward BCT. This paper aims to investigate digital natives' attitudes toward the use of BCT in Croatia. The survey method was applied, and data from 518 questionnaires were analyzed with PLS-SEM. Results show that Privacy threat, Social influence, Trust in BCT, and Trust in the Community of Blockchain users have a statistically significant positive effect on Intention to use BCT. The study identifies the importance of BCT use from the perspective of users who belong to the digital native category, developing a model to better understand behavioural intentions at the individual level and presenting a starting point for future research on this topic. <br />Implications for Central European audience: The research findings may be of interest to audiences in other Central European countries, where blockchain technology is an extremely important segment of emerging technologies, and Central Europe in particular is experiencing exponential growth in the use of this technology. Policymakers in Central European countries can benefit from the findings of this study to formulate policies that support the growth and regulation of the blockchain industry, including information that can contribute to informed decision-making and policy formulation, ultimately contributing to the growth and success of the blockchain technology sector in the region. From a theoretical point of view, the research results represent a contribution to the study of consumer behaviour, especially from the perspective of the use of innovative technologies. The practical contribution is reflected in the possibility of applying obtained results in the development of adequate marketing strategies that help BCT providers to improve the quality of their offer and thus meet the modern demands of the digital natives’ segment.
Special Issue “Algorithms in Data Classification”

Data classification is a well-known procedure, with many applications to real-world problems [...]
Per la storia del breviario notato nel secolo XI

The essay will discuss the importance of two bifolia in the Teresian Library in Mantua as one of the earliest examples of notated breviary. A possible origin will be established on the basis of the analysis of the musical notation and textual script; a full transcription will also be provided.
Beyond Session Counting: A Program Development Model for Information Literacy in a Liberal Arts College

Academic librarians have long struggled to evaluate, report, promote and grow library services in order to effect positive changes in curricula. Metrics reported to faculty and administration generally include how many information literacy sessions are taught, how many students were present, and what subject areas were taught. However, merely counting information literacy sessions does not convey goals, opportunity, or progress. The Information Literacy Program Development Model (ILPDM) described in this article demonstrates how academic librarians at a small, liberal arts college moved beyond counting the number of information literacy sessions as a measure of value by clearly defining what a successful information literacy program should look like, and by demonstrating the impact, strengths, and limitations of a developing information literacy program. The model further allows librarians to track progress and identify opportunities for information literacy in each department and program across campus, and provides focal points for goal setting, outreach, and assessment. The model is adapted from the software industry’s Capability Maturity Model (CMM), later known as the Capability Maturity Model Integration (CMMI), which tracks the evolution of complex processes along a continuum of five development levels.
EFAW: a new mining attack model combining FAW attacks with the Eclipse attack

Aim The Proof-of-Work consensus mechanism is the core mechanism of the blockchain, but the existing Fork After Withholding (FAW) attack can earn more rewards by launching attacks on the mining pools under the Proof-of-Work consensus mechanism. This paper proposes a new attack model based on the FAW attack, which further increases the attacker’s reward to explore the impact of the attack method on the blockchain network and the losses caused by the attack, which is helpful for maintaining the blockchain. Methods This paper proposes a new mining attack model called the "Eclipse Fork After Withholding (EFAW) attack" by combining FAW attacks with the eclipse attack. In the EFAW attack, the attacker infiltrates the victim pool by dispatching infiltrator miners. At the same time, they isolate a portion of miners in the victim pool through eclipse attacks, intercepting the valid information transmitted by these isolated miners. The attacker selectively discards or strategically releases the information to gain extra rewards. Results In this paper, we launch an EFAW attack against a single mining pool and two mining pools, respectively, and evaluate the relative extra rewards of the attacker with theoretical analysis and Monte Carlo simulation experiments. Our experimental data indicate that this attack can earn more rewards than FAW attacks. Conclusion The experimental results demonstrate that the lower bound of earnings in the EFAW attack is higher than in FAW attacks, and it is directly proportional to the number of miners isolated by the attacker’s eclipse attack in the victim pool. This indicates that the EFAW attack poses a greater threat compared to FAW attacks, representing one of the major conclusions drawn from the study.
Power Generation Prediction for Photovoltaic System of Hose-Drawn Traveler Based on Machine Learning Models

A photovoltaic (PV)-powered electric motor is used for hose-drawn traveler driving instead of a water turbine to achieve high transmission efficiency. PV power generation (PVPG) is affected by different meteorological conditions, resulting in different power generation of PV panels for a hose-drawn traveler. In the above situation, the hose-drawn traveler may experience deficit power generation. The reasonable determination of the PV panel capacity is crucial. Predicting the PVPG is a prerequisite for the reasonable determination of the PV panel capacity. Therefore, it is essential to develop a method for accurately predicting PVPG. Extreme gradient boosting (XGBoost) is currently an outstanding machine learning model for prediction performance, but its hyperparameters are difficult to set. Thus, the XGBoost model based on particle swarm optimization (PSO-XGBoost) is applied for PV power prediction in this study. The PSO algorithm is introduced to optimize hyperparameters in XGBoost model. The meteorological data are segmented into four seasons to develop tailored prediction models, ensuring accurate prediction of PVPG in four seasons for hose-drawn travelers. The input variables of the models include solar irradiance, time, and ambient temperature. The prediction accuracy and stability of the model is then assessed statistically. The predictive accuracy and stability of PV power prediction by the PSO-XGBoost model are higher compared to the XGBoost model. Finally, application of the PSO-XGBoost model is implemented based on meteorological data.
Feature-based search space characterisation for data-driven adaptive operator selection

Abstract Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide the search using domain-knowledge and act accordingly. Many problem solving algorithms use multiple complementary operators in patterns to handle such unpredictable cases. A well-characterised search space may help to evaluate the problem states better and select/apply a neighbourhood operator to generate more productive new problem states that allow for a smoother path to the final/optimum solutions. This applies to the algorithms that use multiple operators to solve problems. However, the remaining challenge is determining how to select an operator in an optimal way from the set of operators while taking the search space conditions into consideration. Recent research shows the success of adaptive operator selection to address this problem. However, efficiency and scalability issues persist in this regard. In addition, selecting the most representative features remains crucial in addressing problem complexity and inducing commonality for transferring experience across domains. This paper investigates if a problem can be represented by a number of features identified by landscape analysis, and whether an adaptive operator selection scheme can be constructed using Machine Learning (ML) techniques to address the efficiency and scalability problem. The proposed method determines the optimal categorisation by analysing the predictivity of a set of features using the most well-known supervised ML techniques. The identified set of features is then used to construct an adaptive operator selection scheme. The findings of the experiments demonstrate that supervised ML algorithms are highly effective when building adaptable operator selectors.
Analysis of Carbon Footprint in Faculty of Mathematics and Natural Sciences (MIPA), University of Hasanuddin

Abstract University of Hasanuddin is one of the largest campuses in Sulawesi which has 15 faculties. Faculty of Mathematics and Natural Sciences is one of the faculties that has complex activities in lecture activities and campus operations. All activities that take place produce a carbon footprint that can affect Greenhouse Gases (GHG). Therefore, this study aims to identify activities that generate a carbon footprint and analyze the burden of carbon footprint emissions and see the trend of the relationship between the number of activities and carbon footprint emissions. For data collection techniques by direct observation, interviews and questionnaires which will later be analyzed using the Tier 1 method based on the IPCC 2006. From the analysis results show that the total carbon footprint emission produced at this faculty is 213,778.33 KgCO2-e/year. The activity that produces the most carbon footprint emissions is electricity consumption, which is 206,085.79 KgCO2-e/year with a total consumption of 756,937.99 kWh/year. While the activity that produces the least carbon footprint emissions is the use of paper as much as 921.69 KgCO2-e/year with a total usage of 1,254 kg/year. For the trend of the relationship between the number of activities and carbon footprint emissions, the trend obtained is that the more activities that take place, the higher the carbon footprint produced will also be. However, there is a different phenomenon, namely the value of LPG consumption is less than the value of paper usage as much as 165 kg/year and 1.254 per year. However, the largest carbon footprint emission value is the use of LPG. This happens because the emission factor owned by the LPG source is greater than the paper source. It is hoped that this research can contribute to policy making at the University level. your abstract here…