Reviewerly/exHarmony huggingface dataset:
Blockchain Technology and Environmental Sustainability

Blockchain technology has become known as a groundbreaking achievement with the ability to have an impact on a wide range of sectors, including environmental sustainability. The purpose of this abstraction is to investigate the influence of blockchain technology on environmental sustainability in Bahrain. Bahrain, being a fast-expanding country, must cope with the environmental issues related to energy consumption, waste management, and carbon emissions. Blockchain technology provides a decentralized and transparency framework that can successfully solve these challenges. For instance, the immutability and transparency of blockchain enable the development of dependable structures for tracking and overseeing the environmental resources and processes. Environmental data concerning energy consumption, waste management, and water usage may be collected, validated, and shared across stakeholders in a safe and impermeable way using smart contracts and IoT sensors. Furthermore, blockchain technology makes it easier to integrate renewable energy sources and create distributed energy trading systems. Both individuals and organizations may use this to create, distribute, and exchange renewable energy, establishing a decentralized and sustainable energy environment. This literature review aims to contribute to a better understanding of the impact of blockchain technology on environmental sustainability and provide insights into the potential benefits and drawbacks of using blockchain technology for environmental sustainability in Bahrain.
Does Institutional Quality matter to the Inflation Targeting-Financial Stability Nexus?

Abstract This study examines the quality institutions role played in the inflation targeting- financial stability nexus. A sample of 65 developed and developing countries, including 33 inflation-targeting countries (10 developed and 23 developing), and 32 non-inflation-targeting countries (12 developed and 20 developing), during the 1996 - 2020 period. Using Two Step GMM estimation, results show that inflation targeting stimulates financial stability. This positive relationship between inflation targeting and financial stability is proved, regardless of the inflation targeting regime in place; Soft or Full-Fledged. Results from institutional quality variables prove that inflation-targeting countries with poor institutional quality are financially vulnerable, and that for good institutional quality are able to promote financial stability. JEL classification number: E4, E52, E58. Keywords: Inflation targeting, Financial stability, Developed and developing countries, Quality of institutions, Tow Step GMM System.
Change in Attitude toward Artificial Intelligence through Experiential Learning in Artificial Intelligence Education

Given the rapid advancements in artificial intelligence (AI), the education sector has been actively striving to instill AI-related competencies in students. In a notable development in 2022, South Korea took a pioneering step by overhauling its curriculum with a primary focus on enhancing students' AI skills. However, despite these efforts, a persistent challenge remains: many students continue to harbor unfavorable perceptions and attitudes toward AI. The existing educational methods have proven insufficient in addressing this issue. Consequently, this study embarked on a quest to identify effective strategies for cultivating a more positive outlook on AI among middle school students. To tackle this challenge head-on, an experiential learning-based AI education program was meticulously designed and implemented for middle school students in Korea. The study rigorously evaluated the program's impact on students' attitudes toward AI. The results unveiled a significant improvement in students' perceptions of AI following the intervention, providing solid empirical evidence of the efficacy of the experiential learning-based AI education program in reshaping middle school students' attitudes toward AI. This research underscores the paramount importance of practical, hands-on experiences in education as a potent means to bridge the gap between knowledge and perception. It offers invaluable insights that can guide the development of AI education curricula worldwide, emphasizing the indispensable role of experiential learning approaches in nurturing positive attitudes and beliefs about AI among students.
High-precision measurement of gear tooth profile using line spectral confocal method

The optical measurement of gear tooth profile has the advantages of high efficiency and non-contact, but its measurement accuracy has not reached the accuracy of the existing contact method. Therefore, a line spectral confocal high-precision gear tooth profile measurement method is proposed. First, the gear optical measurement coordinate system is established using a standard mandrel and the optimal measurement position model for the measured tooth profile not blocked by the adjacent tooth is established. Then, to solve the problem that the tooth profile information contained in the optical measurement results and contact measurement results is inconsistent and cannot be directly compared, a virtual probe envelope filtering method for the tooth profile optical measurement data is proposed. Next, to verify the accuracy of the line spectral confocal gear measurement system, the tooth profile deviation measurement experiments using a two-accuracy-level gear involute master are conducted. The results show that the proposed line spectral confocal method has high measurement repeatability and accuracy. The maximum absolute difference between the tooth profile deviation measurement results and the verification results is 0.16 µm, and that the tooth profile deviation curve maintains highly consistent with the verification curve. Finally, the tooth profile deviation measurement experiments using a two-accuracy-level spur gear are performed. The results indicate that the maximum absolute difference between the tooth profile deviation evaluation results measured with the proposed method and the gear measuring center is 0.28 µm. The maximum absolute difference between the two profile deviation curves by point-to-point is 0.52 μm, and the average absolute difference is 0.16 μm. The proposed line spectral confocal method can realize the high-precision measurement of gear tooth profile.
Player Exploration Patterns in Interactive Molecular Docking with Electrostatic Visual Cues

Serious games rely on sensory cues from the system of interest to guide players in performing a specific task. For instance, a serious game used in research and education is interactive molecular docking, where players try to bind a small molecule (ligand) to a protein (receptor) exploring the high-dimensional space of possible molecular conformations. Players are guided by a score that depends on how strongly the two molecules attract or repel each other, and by visual inspection of the three-dimensional protein surface in search of cavities where the ligand may fit. In addition, some interactive molecular docking games can also display molecules according to the charge distribution on their surface, where positively and negatively charged regions are shown in different colors. It is assumed that this color scheme helps players, since attraction and repulsion of charges contribute to the electrostatic energy between the molecules, and thus the score. In this paper we test whether adding charge information as a visual cue contributes to higher scores and exploration of more favorable energy states. For two distinct sets of ligand-receptor pairs, to test our hypothesis we compare two models: One in which players have no electrostatic information, and the other where electrostatic colors are displayed, and players are told to match parts of the molecules that may attract each other based on their colors. We collect player data and cluster energy values into states, which are treated as Markov states. Transitions between states are interpreted as players making significant changes to the score and position of the molecules, with observation of different transition probabilities corresponding to distinct exploration patterns between the two models.
Designing Hand-held Controller-based Handshake Interaction in Social VR and Metaverse

This work presents four possible designs for the handshake interaction in a Social VR-like virtual environment in which the user operates using hand-held controllers: a first design based on a graphics user interface (GUI), a second design leveraging a physical button on hand-held controllers, and two designs based on recreating the handshaking gesture by grabbing the other party’s hand and shaking it. The four designs were evaluated and compared through a user study which involved 24 participants, analyzing factors pertaining to embodiment, presence and social presence, usability, and handshake quality of experience. Results indicated that the gesture-based design was preferred, overall.
A Method of Image Dehazing Based on Atmospheric Veil Prediction by ResNet

Image defogging is an important prerequisite for video summary. In existing defogging methods, there are some weaknesses such as too long parameters calculation time such as transmission map and atmospheric veil estimation. We propose a new method independent of the transmission map, a deep ResNet are trained to learn and predict the veil. There is no need to estimate more parameters for the defogging algorithm, and thus the quality of scene radiation recovery is improved, meanwhile parameter calculations training is speed up. From the experimental results on the dataset, we can see that the training time required for parameter calculation is reduced greatly, while the performance of image defogging is improved by proposed method, meanwhile, the distortion of depth integrity is minimal.
Objective Evaluation Metric for Motion Generative Models: Validating Fréchet Motion Distance on Foot Skating and Over-smoothing Artifacts.

Nowadays, Deep Learning-powered generative models are able to generate new synthetic samples nearly indistinguishable from natural data. The development of such systems necessarily involves the design of evaluation protocols to assess their performance. Quantitative objective metrics, such as Fréchet distance, in addition to human-centered subjective surveys, have become a standard for evaluating generative algorithms. Although motion generation is a popular research field, only a few works addressed the problem of the design and validation of a robust objective evaluation metric for motion-generative models. These previous works proposed to degrade ground truth motion samples with synthetic noises (e.g., Gaussian, Salt& Pepper) and studied the behavior of the proposed metric. However, this degradation does not mimic common motion artifacts produced by generative models. In this work, we propose (1) to validate Fréchet distance-based objective metrics on motion datasets degraded by two realistic motion artifacts, foot skating and over-smoothing, often found in motion synthesis results, and (2) a Fréchet Motion Distance (FMD), using Transformer-based feature extractor, able to capture the motion artifacts and also robust towards the variation of motion length.
Virtual Joystick Control Sensitivity and Usage Patterns in a Large-Scale Touchscreen-Based Mobile Game Study

The virtual sliding joystick input method for touchscreen devices is widely used for games on mobile devices. Existing studies compare virtual joysticks to other input methods, but do not consider potential impacts of large differences in player control precision. For example, previous work has not investigated the ways in which the style of use by the player and the variations in available devices could affect the sensitivity (degree of heading change per touch displacement) in choosing a direction for the game character. In this paper, we present gameplay data from an ongoing study to determine what effects, if any, these factors have on player game performance. While different common touch patterns were observed on the joystick itself that would lead to differing input sensitivity levels, the players’ actual performance in a fast-paced, complex, dynamic obstacle avoidance game was not significantly changed. This suggests virtual joysticks are robust to differences in players and the physical devices themselves, at least for the motion-based obstacle avoidance game featured in this work.
Mechanisms for Activating the Personal Potential of Students in the Context of Digitalization of University Education

The purpose of this paper is to design a psychological-pedagogical construct of the implementation of information technologies in the process of teaching students for developing their personal potential. The rapid digitalization of higher education has caused the need to ensure the didactically correct information-communicative technology (ICT) introduction in education, which must organically comply with the structure and logic of the learning process, as well as the task of personal development. Solving these tasks, the authors propose not direct, but mediated use of different ICTs through the development of a system of various educational communications that are built and supported by the teacher. This is the novelty of the study and its dissimilarity to most of the current projects of forced, technocratic digitalization of students study, which do not sufficiently take into account the laws and principles of a holistic educational process and the becoming of personality in it. Based on psychological and didactic approaches and methods, the authors reconstruct the pedagogically controlled process of ICT introducing into education through the implementation by the teacher of interrelated mechanisms – environment-forming and trigger mechanisms. The environment-forming mechanism unfolds the logic of the phased introduction of ICT – from the principles of their use through the construction of educational communications to the methods for activating of students personal potential in the form of trigger mechanisms. These activators are modes for the practical inclusion of ICT in education and are set by the mechanism for personalizing educational communications. Assuming the attunement of ICT to the personality of the student, this mechanism brings the teacher to the fore in the process of digitalization of learning. Being the initiator and organizer of educational communications, the teacher can adequately and fully ensure this attunement and targeting of ICT use by implementing a set of conjugated mechanisms: responsive, polycontact, contemporal, meaning-centric, project-constructing, cultural-mediating, consolidating, inclusive, comparative and the mechanism of problematization. Approbation of the proposed mechanisms in the practice of online-learning has shown their sufficient effectiveness, students have an increase in indicators of self-realization and self-regulation, as well as the significance of values of self-development in education.
SSTA-Net: Self-supervised Spatio-Temporal Attention Network for Action Recognition

Action recognition aims to identify the action categories and features in the video by analyzing the actions and behavior patterns that are significant to the development of intelligent security, automatic driving, smart home, and other fields. However, current methods fail to adequately model the spatio-temporal relationships of actions in videos, and video annotation is a time-consuming and expensive process. This paper proposes a Self-Supervised Spatio-Temporal Attention Network (SSTA-Net) for action recognition to solve the above problems. Firstly, we use a self-supervised method for training, which does not require a large amount of labeled data and can explore unknown or hidden information in the data. Secondly, in the feature extraction part, Multi-Scale Convolution Attention Module (MC-AM) is proposed. By performing convolution operations on the input image at different scales, the details and edge information in the image are enhanced, and the image quality of the original sampling frame is improved. Finally, a Spatio-Temporal Attention Module (ST-A) is proposed. The module is used to capture the spatio-temporal signal sensitivity in the video, which effectively improves the accuracy of action recognition.
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks

Algorithmic decision-making driven by neural networks has become very prominent in applications that directly affect people’s quality of life. This paper focuses on the problem of ensuring individual fairness in neural network models during verification, training, and prediction. A popular approach for enforcing fairness is to translate a fairness notion into constraints over the parameters of the model. However, such a translation does not always guarantee fair predictions of the trained neural network model. To address this challenge, we develop a counterexample-guided post-processing technique to provably enforce fairness constraints at prediction time. Contrary to prior work that enforces fairness only on points around test or train data, we are able to enforce and guarantee fairness on all points in the domain. Additionally, we propose a counterexample-guided loss as an in-processing technique to use fairness as an inductive bias by iteratively incorporating fairness counterexamples in the learning process. We have implemented these techniques in a tool called FETA. Empirical evaluation on real-world datasets indicates that FETA is not only able to guarantee fairness on-the-fly at prediction time but also is able to train accurate models exhibiting a much higher degree of individual fairness.
Algorithmic Censoring in Dynamic Learning Systems

Dynamic learning systems subject to selective labeling exhibit censoring, i.e. persistent negative predictions assigned to one or more subgroups of points. In applications like consumer finance, this results in groups of applicants that are persistently denied and thus never enter into the training data. In this work, we formalize censoring, demonstrate how it can arise, and highlight difficulties in detection. We consider safeguards against censoring – recourse and randomized-exploration – both of which ensure we collect labels for points that would otherwise go unobserved. The resulting techniques allow examples from censored groups to enter into the training data and correct the model. Our results highlight the otherwise unmeasured harms of censoring and demonstrate the effectiveness of mitigation strategies across a range of data generating processes.
Median-Krum: A Joint Distance-Statistical Based Byzantine-Robust Algorithm in Federated Learning

The wide spread of Artificial Intelligence-based services in recent years has encouraged research into new Machine Learning paradigms. Federated Learning (FL) represents a new distributed approach capable of achieving higher privacy and security guarantees than other methodologies since it allows multiple users to collaboratively train a global model without sharing their local training data. In this paper, an analysis of the characteristics of Federated Learning is therefore carried out, with a particular focus on security aspects. In detail, currently known vulnerabilities and their respective countermeasures are investigated, focusing on aggregation algorithms that provide robustness against Byzantine failures. Following this direction, Median-Krum is proposed as a new aggregation algorithm whose validity is observed on a set of simulations that recreate realistic scenarios, in the absence and presence of Byzantine adversaries. It combines the Distance-based Krum approach with the Statistical strategy of median based aggregation algorithm. Achieved results demonstrate the functionality of the proposed solutions in terms of accuracy and convergence rounds in comparison with FedAvg, Krum, Multi-Krum and FedMedian FL approaches under a correct and incorrect estimation of the attackers number.
A Korean emotion-factor dataset for extracting emotion and factors in Korean conversations

Abstract Humans express their emotions in various ways, such as through facial expressions and voices. In particular, emotions are directly expressed or indirectly implied in the text of utterance. Research on the technology to identify emotions included in human speech and generate utterances is being conducted in conversational artificial intelligence technology. Despite the importance of recognizing the factors of previously generated emotions to generate emotion-based utterances, most of the existing datasets only provide the classification of emotions in text and utterances. In addition, in the case of Korean datasets, the classification of emotions is not diverse, and it is mainly biased toward negative emotion classification. In this paper, we propose KEmoFact, a Korean emotion-factor dataset for extracting emotion and factors in Korean conversations. We also define two tasks for the KEmoFact dataset, EFE (Emotion Factor Extraction) and EFPE (Emotion-Factor Pair Extraction), and propose baseline models for the tasks. We contribute to the study of conversational artificial intelligence, especially in Korean, one of the low-resource languages, by proposing the KEmoFact dataset and suggesting baseline models for two tasks.
A Correlation Between Oral Reading Fluency And Reading Comprehension At Tenth Grade Students MAS TI Canduang

The researcher wanted to investigate the correlation between oral reading fluency and reading comprehension at the tenth grade of MAS TI Canduang academic years 2019/2020. The design of this research was correlation research. Then, the instruments that used in this research were oral test for measuring oral reading fluency and written test for measuring reading comprehension. The population of this research was all of the tenth grade students of MAS TI Canduang in the academic year of 2019/2020 which consists of 5 classes. The sample was 40 students taken by simple random sampling technique. The results of this research show that there is a correlation between oral reading fluency and reading comprehension at the tenth grade of MAS TI Canduang in academic years 2019/2020. The researcher found that the coefficient of correlation between oral reading fluency and reading comprehension at the tenth grade of MAS TI Canduang was 0,47. The correlation was positive and significant. It means that Ho is rejected. In short, there is a positive correlation between oral reading fluency and reading comprehension at the tenth grade of MAS TI Canduang. Related to the result of the research, it can be concluded that oral reading fluency has positive contribution in reading comprehension. The notion is the more fluent oral reader, the more proficient she/he is in reading comprehension. In summary, this research is conducted for finding the correlation between oral reading fluency and reading comprehension at the tenth grade of MAS TI Canduang.