Reviewerly/exHarmony huggingface dataset:
Learnchain: Transparent and Cooperative Reinforcement Learning on Blockchain

We consider multi-agent reinforcement learning (MARL) with the popular paradigm of centralized training and decentralized execution (CTDE). CTDE empowers sharing knowledge from agents in different environments for updating a shared model. A wide range of applications is supported through CTDE in MARL, such as self-driving vehicle coordination, traffic lights synchronization, or cooperation in various aspects of the Internet of Things (IoT), including resource management. Despite the drawbacks of relying on a central authority for handling model updates, incorporating multiple sources of data raises concerns about the trustworthiness of the process. For instance, participating agents could provide data in the favor of their experiences to shift the model toward certain behaviors. Similarly, sending falsified data for updates could lead to adversarial attacks. To overcome these challenges, it is essential to integrate the Ethereum Blockchain technology to handle model updates in the CTDE paradigm by achieving decentralized storage and consensus mechanism for model updates. In the literature, there exist multiple efforts that propose using reinforcement learning (RL) on Blockchain; however, none of them have considered updating MARL of CTDE on-chain, allowing transparent and auditable record of the training process. Therefore, we propose LearnChain, a framework that offers an integration between the CTDE mechanism and a Consortium Blockchain built between authorized participants, thus avoiding gas costs. At the core of LearnChain, RL is integrated with Quorum, offering separate smart contracts for deployment, data handling with incentive mechanisms, training, target update, and inference. Based on a real use-case entailing management of Vehicular Edge Computing tasks through multi-agent synchronization, we implement LearnChain and evaluate its performance and cost in different settings. Our results show the ability to improve learning from shared experiences and to adapt to environment changes on the Quorum BlockChain.
An Assume-Guarantee Framework for Multiple-Obstacle Collision Avoidanc

In this paper, an assume-guarantee reasoning approach is developed for obstacle avoidance in unmanned aerial vehicles (UAVs) in the presence of multiple obstacles in an obstacle field. This construct assumes certain properties of the environment and the vehicle to guarantee the safety and performance of the UAV (in this case, executing safe collision avoidance trajectories). In the presence of a single obstacle, the assumptions on the environment and the vehicle parameters are constructed such that the UAV can plan a safe trajectory once the obstacle is detected. The approach to guaranteeing safety in the presence of multiple obstacles requires enforcing additional assumptions which is done by constructing a region of influence (RoI) around each obstacle, whose size depends on the environment and the vehicle parameters. The safe combinations of these parameters (codified as contracts) are developed such that the RoIs in the obstacle field do not intersect. The aforementioned approach is then used to decompose the general multiple obstacle avoidance problem into a sequential single-obstacle avoidance problem by constructing an induction-based algorithm framework. The proposed methodology is validated by an illustrative example with minimum obstacle detection range, maximum allowed cruise velocity, maximum allowable agility as vehicle properties; and maximum obstacle size and minimum obstacle separation as environmental properties. Contract generation for specific scenarios and implementation of sequential avoidance on a 6-DoF quadcopter simulation are demonstrated. Finally, the effect of tracking error on the contract-based framework is discussed, along with a mechanism to incorporate this source of uncertainty into the contract.
Analyzing the Discriminative Power of EEG Microstates Over Mental Tasks

Microstate analysis of EEG recordings has long been an instrumental tool for studying the temporal dynamics of whole-brain neuronal networks.The characteristics of EEG microstate sequences have been used mostly for diagnostic purposes, including the detection of schizophrenia, epilepsy, Alzheimer's disease, and early dementia.Aside from diagnostics, the use of this methodology has been limited.In this study, we examine the discriminative power of EEG microstates to differentiate between mental tasks, and we assess the generalizing power of microstate representations over different subjects and recording sessions.For this purpose, we inspect both the characteristics of discrete microstate sequences, as well as various features generated from the association of detected microstates with the continuous EEG data.For demonstration purposes, we use two distinct datasets, which contain recordings from multiple subjects, while performing different mental tasks.
A Mobile Serious Game to Foster Music Sight Reading with Different Clefs

This work introduces a mobile app that aims to promote the sight-reading of music with different positions of the clef on the stave.Relying on the principles of game-based learning, the app offers applications primarily in the didactic field: by facilitating and encouraging the learning of this challenging aspect of music, the app on one side contributes to the preservation of intangible musical heritage and, on the other, serves practical educational purposes such as the preparation for Conservatory exams.The results of early experimentation show a general appreciation by test users as it concerns engagement, but also highlight a number of interaction aspects to be improved.
Level of Acceptability to Sinugbuanong Binisaya as the Medium of Instruction in General Santos North District, General Santos City Division

This study aimed to determine the level of acceptability to Sinugbuanong Binisaya as the medium of instruction in selected elementary schools in General Santos North District, General Santos City Division School Year 2017-2018. The researcher employed descriptive phenomenology research to generate data on the level of acceptability relative to teachers’ practices, beliefs, and management using Sinugbuanong Binisaya as the medium of instruction. The respondents were 66 Grades 1 to 3 General Santos North District teachers, General Santos City Division. A Focus Group Discussion was conducted to determine issues and concerns relative to acceptability of Sinugbuanong Binisaya as the medium of instruction. Findings of the study revealed that the demographic percentage of respondents according to the number of years in teaching rarely indicate that most of the teachers teaching the MTB-MLE were used with the K to 12 curriculums in which Sinugbuanong Binisaya was used as the medium of instruction since the said curriculum was implemented for 6 years now. The acceptance of the teachers in the implementation of the language policy revealed that 100 percent of the teachers as the primary implementers of the MTB-MLE are not attained because of the transition period from the bilingual education to the mother-tongue based education as part of the k to 12 curriculum reform of the Department of Education. Some problems encountered by the teachers in implementing mother tongue - based instruction include absence of books written in mother tongue, lack of vocabulary, and multicultural/ multilingual differences affect teaching in MTB. Moreover, learners’ performance in English was affected by the implementation of the new K to 12 curriculums.
Chinese Character Font Classification in Calligraphy and Painting Works Based on Decision Fusion

Font recognition is an important part in the field of painting and calligraphy style recognition. Traditional font classification methods are mainly based on texture feature extraction and other methods, which need to be improved in classification accuracy. The mainstream classification methods mainly use convolutional neural networks, but such methods have poor interpretability and may face the problem that some detailed features cannot be accurately extracted. Based on convolutional neural network, the gray-level images, Local Binary Pattern (LBP) feature and Histogram of Oriented Gradient (HOG) of the images in the font dataset are respectively trained. Finally, the results of the three networks are fused by means of average decision fusion. The experimental results of font recognition show that the proposed method can extract the detailed features of fonts more accurately and obtain higher classification accuracy.
Effectiveness of Digital Flashcards in Enhancing Engagement and Vocabulary Acquisition in Mathematics: The Case of Students with Learning Disabilities

This study investigated the effectiveness of digital flashcards in enhancing engagement and vocabulary acquisition in mathematics among students with learning disabilities. The researcher used a quasi-experimental specifically the Pretest-Posttest Design. Descriptive statistics (mean) was utilized to present the students’ engagement level and vocabulary before and after exposure to the traditional and digital flashcards. The results of the study revealed that before exposure to the print flashcards, the students had a very low level of engagement while they had a moderate level of engagement in the digital flashcards. Meanwhile, the student’s vocabulary level before exposure to print and digital flashcards did not meet the target. On the other hand, the students have a moderate level of engagement after exposure to the print flashcards, while they have a high level of engagement in the digital flashcards. Meanwhile, the students have a vocabulary level that is Approaching the target after exposure to print flashcards and at target after being taught the digital flashcards. Test of difference showed that the student’s level of engagement and vocabulary before exposure to the treatments were significantly different between the traditional and computer-based learning groups. Those in the print flashcards group had a lower engagement and vocabulary level than the digital flashcard group. The magnitude of the difference between digital flashcard learning and print flashcard treatment with respect to the level of engagement is small. However, the effect size for their difference in increasing the student’s vocabulary is much larger than typical. In conclusion, the use of digital flashcards is an effective intervention than print flashcards in enhancing the vocabulary of the students, but not much in the level of engagement. Thus, the use of digital flashcards in teaching mathematics improves the vocabulary acquisition of children with disabilities and is hereby recommended by the researcher.&#x0D;
An adaptive image steganography algorithm based on the use of non-cryptographic hash functions for data extraction

An adaptive steganography algorithm for data hiding in full-color images based on iterative introduction of minor distortions into blocks of container images and the use of high-speed non-cryptographic hash functions for data extraction is considered. Modification of the minimum number of container elements compared to the length of the hidden message is a distinctive feature of the algorithm. This feature allows the hidden throughput to be increased and the visual and statistical visibility of hidden data to be reduced. The algorithm is compared with modern algorithms of adaptive spatial steganography in terms of assessing the level of distorting changes in stego-containers. In addition, a modified version of the algorithm that implements covert channel multiplexing using a common subset of container elements when embedding various messages into them is considered.
How to Improve Speaking Skills for Undergraduates at Dong Nai Technology University

The ability to communicate in both written and spoken form is the main goal of learning English. Teachers need to provide students with opportunities to communicate in English so that they can become fluent speakers. Nonetheless, the majority of non-major students at Dong Nai Technology University are struggling with conversations due to brief and unnecessary pauses. While speaking ability among students must be developed, it has been seen that listening and speaking skills are not given enough attention in the practice of English teaching and learning. The current study seeks to increase non-major sophomores’ oral English fluency. The teaching methodology employed in the study was task-based language teaching (TBLT) which encourages students to participate more in communicative activities and ensures that they will be able to grasp the target language. The researcher used different types of study devices to get the data, including tests, observations as well as questionnaires, to determine how task-based language instruction enhanced students’ speaking abilities and what opinions students had towards the teaching method. By analysing data from test scores, observation notes, and questionnaires, it was concluded that there is a necessity for TBLT for the development of speaking fluency.
Students' Perception on the Use of Youtube Song Lyrics for Learning English

The objective of this research was to identify the Student’s perception to use youtube song for learning English at SMA 7 Kota Bengkulu. This study use quantitative method. The subject of this research was consisted of 28 students ten grade X IPA 8. In this research, the researcher used questioner to know the students perception to use youtube song for learning English. The result of this research, The result, 83% students gave positive respond in every activity conducted in the classroom because the song make the class become more conducive, fun, and meaningful, so it influenced their ability in listening improved well. Not only that, they also mastered the component of language in English, such as grammar, vocabulary, and pronunciation. The findings of the, learning English using YouTube while listening to English songs videos is more effective. The study demonstrated the value of YouTube songs as a learning tool for English.
Boosting the Learning for Ranking Patterns

Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches.
An intelligent shell game optimization based energy consumption analytics model for smart metering data

Smart metering is a hot research topic and has gained significant attention since the electromechanical metering is not reliable and requires more energy and time.All the existing methods are focused only on how to deal with data rather than how to do efficiently.Prediction of electricity consumption is essential to gain intelligence to the smart gird.Precise electricity prediction allows a service provided in resource planning and also controlling actions for the demand and supply balancing.The users are beneficial from the smart metering solution by effective interpretation of their energy utilization, and labelling them to efficiently handle the utilization cost.With this motivation, the paper presents intelligent energy consumption analytics using smart metering data (ECA-SMD) model to determine the utilization of energy.The presented ECA-SMD model involves three major processes namely data pre-processing, feature extraction, classification, and parameter optimization.The presented ECA-SMD model uses Extreme Learning Machine (ELM) based classification to determine the optimum class labels.Besides, shell game optimization (SGO) algorithm is applied for tuning the parameters involved in the ELM and boosts the classification efficiency.The efficacy of the ECA-SMD model is validated using an extensive set of smart metering data and the results are investigated based on accuracy and mean square error (MSE).The proposed model exhibited supremacy with the maximum accuracy of 65.917 % and minimum MSE of 0.096.
Neuro-inspired continual anthropomorphic grasping

Humans can learn continuously grasping various objects dexterously. This ability is enabled partly by underlying neural mechanisms. Most current works of anthropomorphic robotic grasping learning lack the capability of continual learning (CL). They utilize large datasets to train grasp models and the trained models are difficult to improve incrementally. By incorporating several discovered neural mechanisms supporting CL, we propose a neuro-inspired continual anthropomorphic grasping (NICAG) approach. It consists of a CL framework of anthropomorphic grasping and a neuro-inspired CL algorithm. Compared with other methods, our NICAG approach achieves better CL capability with lower loss and forgetting, and gets higher grasping success rate. It indicates that our approach performs better on alleviating forgetting and preserving grasp knowledge. The proposed system offers an approach for endowing anthropomorphic robotic hands with the ability to learn grasping objects continually and has great potential to make a profound impact on robots in households and factories.
Agency Theory: Review and Evidence

Abstract: Every month, there are headlines concerning scams on famous online sites. Due to the ease with which scams may be spread, they have become one of the most popular crimes worldwide, inflicting significant emotional, financial, and psychological consequences on millions of people. Despite their significant and pervasive influence, we seem to know very little about why some people fall prey to scams while others remain immune to the strategies used by scammers to entice potential victims. For example, research on the impact of demographic traits (e.g., age) as well as psychological variables (e.g., risktaking) on individuals' vulnerability to scams has yielded inconsistent results. Even little is known about how the type or character of a scam influences a person's susceptibility. Gaining a better knowledge of these issues is essential for developing preventive programs and lowering the occurrence of victimization. We highlight potential interesting directions, present knowledge gaps, and the necessity for decision scientists to solve this crucial challenge.
DATA VISUALIZATION IN EMOTIONAL ANALYSIS OF INTERNET-TEXTS IN RUSSIAN, BACKED BY THE MODEL OF EMOTIONS “CUBE OF LÖVHEIM”

In the paper, we discuss the problem of tools supposed to be effective for visualization of data achieved as result of running algorithms for emotional text analysis. We start by overviewing some technics used to visualize data in projects devoted to exploratory data analysis, sentiment-analysis and emotional text analysis. To continue, we suggest two variants of visualization for emotional representation in texts analyzed within the framework of the theoretical approach known as “Levheim's Cube”, in which emotion is represented as a combination of three neurotransmitters. Such a model makes it possible to operate with spatial coordinates for visualization, but, at the same time, it inevitably imposes a number of restrictions and difficulties.
Inflammatory and cytotoxic mediators in COVID-19 patients and in ChAdOx1 nCoV-19 (AZD1222) vaccine recipients

Introduction: Immunological and cytotoxic mediators are induced in natural infection and are essential for the effectiveness of vaccination.Vaccination is useful to prevent the spread of SARS-CoV-2 and limit the morbidity/mortality of COVID-19.ChAdOx1 nCoV-19 is one of the most widespread vaccines in the world.Objectives: We compared the detection of anti-S1 SARS-CoV2 IgG, and the profile of inflammatory and cytotoxic responses of patients who developed different clinical outcomes of COVID-19 with individuals previously exposed or not to the virus received the first and booster doses of ChAdOx1 nCoV-19.Methodology: Plasma from 35 patients with COVID-19 and 11 vaccinated were evaluated by multiplex and ELISA assays.Results: Here, no vaccinated subjects had serious adverse effects.Those vaccinated with a booster dose had lower anti-S1 IgG than mild/moderate and recovered patients.Critically ill and deceased patients had IgG levels like those immunized.IL-2, IL-17, and perforin do not differentiate between patients and vaccinated individuals.Granzyme A increased at dose 1, while patients had their levels reduced.High levels of granulysin, Fas, and IL-6 were detected in the deaths, but after vaccination, all were declined. Conclusion:Our data confirm the ability of the ChAdOx1 vaccine to produce specific antibody levels up to booster time.Furthermore, our data suggest that the vaccine can regulate both the hyper-production and the kinetics of the production of inflammatory and cytotoxic mediators involved in the cytokine storm, such as granulysin, Fas, and IL-6.
Access and Secure Storage Based Block Chain Scheme with IPFS Implemented in Electronic Medical Record in Lahore, Pakistan

This work is now contributing to creating a system built on the effective sharing of secure IPFS (Interplanetary file system) for electronic medical records and, in addition, implementing a cipher text policy system in conjunction with the interplanetary file system (IPFS) and the most recent developments in blockchain technology.This strategy helps to govern the effectively while preventing retrieval access to electronic medical data.In addition, with the use of the technology known as the blockchain, which has traceable features along with being safe and allowing for the search of medically relevant data, it also contains a security-proof method related to assaults on the term that specify.The fact that the performance-based analysis system and the primary data set are being simulated demonstrates that it is both possible and efficient.
A Novel Biased Probability Neural Network (BPNN) and Regularized Extreme Learning Machine (RELM) based Hearing Loss Prediction System

: Hearing loss or hearing impairment is one of the most leading cause highly affecting the people around the world in present days. Also, the youngsters and adults are mostly affecting by this disease, which affects their normal/social life, carrier, education, and etc. Hence, it should be properly identified and diagnosed for providing an earlier treatments to save the people live. For this purpose, the different types of automated hearing loss prediction/detection systems are developed in the conventional works. The existing works are mainly focused on deploying the machine learning/deep learning based prediction approaches for disease identification. However, it lacks with the flaws of difficult computational steps, more training &amp; testing time, increased mis-prediction results, and error outputs. Therefore, the proposed work objects to develop a Human Age - Hearing Impairment &amp; Level (HAHIL) prediction system by using the machine learning methodologies. Here, three distinct prediction models are deployed for age prediction, hearing loss detection, and its severity level prediction. The Biased Probability Neural Network (BPNN) technique is utilized to predict the age based on simulated human acoustical signals. Then, the Regularized Extreme Learning Machine (RELM) mechanism is deployed for predicting the hearing impairment by constructing the weight and target matrices. During evaluation, the performance of the proposed HAHIL prediction system is validated and tested by using various evaluation indicators.