Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF THE CAUSALITY RELATIONSHIP BETWEEN BITCOIN ELECTRICITY CONSUMPTION, PRICE AND VOLUME

This study aims to analyze the causal relationship between electricity consumption, price and transaction volume of Bitcoin, which is the most important asset of the crypto money market in terms of both market capitalization and transaction volume. In this study, the Bitcoin electricity consumption variable is represented by Cambridge Bitcoin Electricity Consumption Index. As the data set, 1446 days of data between February 2017 and February 2021 were used. The causality relationship between the variables is analyzed using the Hatemi-J (2012) and Toda Yamamoto (1995) tests. In addition, this study is a rare study that examines the relationship between electricity and volume, together with the work done by Schinckus et al. (2020). According to the results of this study, the decrease in Bitcoin electricity consumption causes a decrease in the Bitcoin price. However, a negative relationship is detected Bitcoin electricity consumption and Bitcoin trade volume in this study, like the study by Schinckus et al. (2020), the relationship was found to be very weak.
Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

Recent years have seen a surge of interest in the field of explainable AI (XAI), with a plethora of algorithms proposed in the literature. However, a lack of consensus on how to evaluate XAI hinders the advancement of the field. We highlight that XAI is not a monolithic set of technologies -- researchers and practitioners have begun to leverage XAI algorithms to build XAI systems that serve different usage contexts, such as model debugging and decision-support. Algorithmic research of XAI, however, often does not account for these diverse downstream usage contexts, resulting in limited effectiveness or even unintended consequences for actual users, as well as difficulties for practitioners to make technical choices. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts. Towards this goal, we introduce a perspective of contextualized XAI evaluation by considering the relative importance of XAI evaluation criteria for prototypical usage contexts of XAI. To explore the context dependency of XAI evaluation criteria, we conduct two survey studies, one with XAI topical experts and another with crowd workers. Our results urge for responsible AI research with usage-informed evaluation practices, and provide a nuanced understanding of user requirements for XAI in different usage contexts.
IDEALIZED INFLUENCE, INSPIRATIONAL MOTIVATION, ORGANIZATIONAL CULTURE AND ORGANIZATIONAL PERFORMANCES

This study applied transformational leadership and perspective theory to ascertain the impacts of idealized influence and inspirational motivation on organizational culture. Data were gathered from information technology firm managers in Jordan, utilizing a quantitative approach – there were three levels of managers involved. Structural equation modeling with partial least squares (PLS-SEM) was used for data analysis. The results show positive impacts of idealized influence and inspirational motivation on organizational culture. The positive impact of idealized influence and inspirational motivation on organizational performance implies that organizational culture mediates the relation between idealized influence, inspirational motivation, and organizational performance. Several interesting findings were highlighted for the Information Technology sector in Jordan. Relevant companies worldwide could peruse the findings of this study to improve their organizational culture and performance. The current study adds to the epistemology new framework covering the transformational leadership theory perception to address the factors that influence organizational performance, including the transformational leadership styles to achieve superior performance through the activation of organizational culture.
Design of a Data Security Access Control Algorithm for the Electric Vehicle Internet of Vehicles Based on Blockchain Technology

The data transmission in the vehicle network is easily interfered with by the outside world, which makes the security of data access difficult to provide in order to meet the actual needs. Therefore, a data security access control algorithm for an electric vehicle network based on blockchain technology is proposed. Using the double-chain architecture of an alliance chain-private chain in a blockchain, the distributed database of data communication for vehicle networking is constructed. In the process of vehicle network communication, the long short-term memory neural network is introduced to analyze the risk of communication behavior. A generator and discriminator are used to avoid communication risk behavior and realize secure access to data. The experimental results show that the success rate of data tampering is lower than 0.09 when this design method is used to deal with external intrusion, and it has high security.
Modified Smoothing Algorithm for Tracking Multiple Maneuvering Targets in Clutter

This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
Colorectal Cancer Disease Classification and Segmentation Using A Novel Deep Learning Approach

In the world, with a death rate of about 35% of colorectal cancer aids as the most widespread type of tumor.The third most commonly diagnosed cancer compared to breast and lung cancer is colorectal cancer.Specifically, in the minimization of health inequalities, it can be supported by the clinical care of AI guidance.Therefore in this paper an effective classification is performed by the deep learning technique called MobileNetV2.Before the classification task, cycle GAN based data augmentation technique is applied to solve the data imbalance problem.Moreover, Mask R-CNN based segmentation is applied to improve the classifier's performance.For the classification of colorectal cancer, the proposed method is examined on a colorectal histopathological dataset (MNIST).The performance of the work will be estimated using precision, accuracy, F-score and recall metrics and the simulation results shows that the proposed classifier achieves 99.91% accuracy, 100% precision, 99.87% recall and 0.999% f1-score over the other state-of-art techniques.
Fast Extreme Learning Machine for Berber Handwritten Latin Script

This article deals with the problem of Berber handwritten character recognition using Extreme Learning Machine. This paradigm has gained significant attention in pattern recognition field thanks to its efficient learning speed and its high accuracy. In this paper, we have used a fast Extreme Learning Machine to recognize efficiently the Latin Berber characters. So, the proposed ELM has been trained over a Berber-MNIST dataset containing images of Amazigh alphabets. This algorithm learns much faster than traditional popular learning algorithms thanks to the use of JAX library which contains several functions to reduce the execution time of our solution. The simulation results show that the handwritten recognition system based on our developed extreme learning machine decreases computational cost and reduces the time required for the whole recognition process. Furthermore, the developed ELM achieves a high performance in terms of recognition accuracy.
An Assistance of Academic Writing and the Utilization of Online Libraries for Teachers at SMP Negeri 4 Metro City

The aim of this study gave an assistance to increase the teachers’ skill in academic writing and knowing the many source of online library. The researchers used ABCD (Asset Bassed Community Development). The sample was teachers The steeps in giving assistance involve inkulturasi, discovery, Design, and Define. The result showed that there were an impact on the understanding of teachers are well acquainted with article writing techniques, reputable international journal indexation, accredited national indexation (SINTA), and national journals (having ISSN). Besides that, the teachers could find out the various resources references by utilizing online libraries such as digilib or repository that available at every Perguruan PTKIN, PTKIS or PTU universities, the utilization of National Library membership, Google Scholar, etc. With a complex understanding, teachers can find out the steps in preparing a good manuscript.&#x0D; Keywords: Assistance Academic Writing, Online Library, Scientific Writing Journal
Graph convolutional networks fusing motif-structure information

Abstract With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data. At present, graph convolutional networks (GCN) have surpassed traditional methods such as network embedding in node classification. However, The existing graph convolutional networks only consider the edge structure information of first-order neighbors as the bridge of information aggregation in a convolution operation, which undoubtedly loses the higher-order structure information in complex networks. In order to capture more abundant information of the graph topology and mine the higher-order information in complex networks, we put forward our own graph convolutional networks model fusing motif-structure information. By identifying the motif-structure in the network, our model fuses the motif-structure information of nodes to study the aggregation feature weights, which enables nodes to aggregate higher-order network information, thus improving the capability of GCN model. Finally, we conduct node classification experiments in several real networks, and the experimental results show that the GCN model fusing motif-structure information can improve the accuracy of node classification.
PLANNING AND ORGANIZATION OF WORK OF THE EXTRACURRICULAR EDUCATIONAL INSTITUTION AS A SOCIO-PEDAGOGICAL ISSUE AND THE BASIS OF DEVELOPMENT OF A CREATIVE PERSONALITY

The article highlights the approaches to the study of planning and organization of out-of-school educational institution, which today is an important scientific issue and a necessary condition for the formation and development of a creative personality. Based on the analysis of the structure and content of planning the work of an out-of-school educational institution, the components and priority areas are presented, in which relation the educational policy of the institution is implemented. The theoretical bases of planning the work of an out-of-school institution are indicated. The requirements of the society to the development and upbringing of an individual in the work of an out-of-school educational institution are substantiated. The directions of work and functions which will promote successful realization of the programs directed on development of a creative personality of the child are defined.
Some Reflections on the Interface between Professional Machine Translation Literacy and Data Literacy

Due to the widespread use of data-driven neural machine translation, both by professional translators and layperson users, an adequate machine translation literacy on the part of the users of this technology is becoming more and more important. At the same time, the increasing datafication of both the private and the business sphere requires an adequate data literacy in modern society. The present article takes a closer look at machine translation literacy and data literacy and investigates the interface between the two concepts. This is done to lay the preliminary theoretical foundations for a didactic project aiming to develop didactic resources for teaching data literacy in its machine translation-specific form to students of BA programmes in translation/specialised communication.
Masked face with facial expression recognition based on deep learning

Wearing masks contributed to slowing the spread of the coronavirus disease (COVID-19) as the World Health Organization (WHO) recommended wearing face masks especially with the spreading of virus variants like omicron. Although people accept the idea of wearing these masks, it is still unknown the effect of covering parts of the face on social interaction among people in general and children in particular. Moreover, Social isolation affects emotional moods, which causes stress, sadness, and depression. In the current study, we have been exploring the emotional inferences on faces with and without a mask. The system can pick up the universal emotions: fear, disgust, anger, surprise, contempt, sadness, and happiness. The researchers in deep learning are concerned with global pandemic COVID-19 to enhance public health service. The proposed model is developed with a machine learning algorithm through the Haar feature-based cascade classifiers. The built model can detect people's emotions with mask and without a mask with high accuracy.
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog

We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information external to the current conversation (e.g., a database or document) to produce good responses. Experiments against an array of benchmarks that encompass task-oriented dialog, conversational QA, and grounded open-domain dialog show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot fine-tuning setups, in terms of both human and automatic evaluation. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses (extrinsic evaluation) in addition to their communicative features (intrinsic evaluation). We show that extrinsic evaluation offers improved inter-annotator agreement and correlation with automated metrics. Code and data processing scripts are publicly available.
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform

<h2>Summary</h2> Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning.
Artistic and Pedagogical Competences of the Fine Arts Teacher: an Adaptation to the Postmodern Society

The importance of the outlined problem lies in the fact that professional training of future fine arts teachers should take into account the trends of recent general scientific, socio-cultural and moral-aesthetic processes. Analysis of the professional training system in the context of the requirements of postmodern society gives grounds to claim that it requires significant updating. In particular, individualization and democratization of education, interdisciplinarity, departure from traditional patterns of professional training, rethinking of pedagogical ideas and postulates in the context of quality assurance, etc. are paramount. The article defines the structure of artistic and pedagogical competence of a fine arts teacher. Its structural elements (artistic component (artistic and aesthetic education, skill)) and pedagogical component (psychological and pedagogical culture, reflection) and ways of their formation are determined. Ways to improve professional training of future fine arts teachers taking into account the postmodern features of higher education development (ensuring continuity of creative education; updating the scope of professional training; the use of information technology in the process of professional training of future fine arts teachers; emphasis of the educational programme on personal and creative development, self-fulfilment of students of artistic specialties. Development of pedagogical conditions for formation of artistic and pedagogical competence in future fine arts teachers and organization of a pedagogical experiment in order to determine their effectiveness seems promising.
Fake Reviews Detection Using Supervised Machine Learning

Abstract: With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviours of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviours of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews, we compare the performance of machine learning classifiers; KNN, Naive Bayes (NB), Logistic Regression. The results reveal that Logistic Regression outperforms the rest of classifiers in terms of accuracy achieving best. The results show that the system has better ability to detect a review as fake or original.
Yoga Pose Monitoring System using Deep Learning

Abstract Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to learn and rehearse yoga postures accurately without help from anyone else. Using Deep learning algorithms, an approach has been taken to accurately detect and recognize various yoga poses. The chosen dataset consists of a total of 85 videos with 6 yoga postures performed by 15 participants. Initially, keypoints of the user are extracted using the Mediapipe library. A combination of convolutional neural network (CNN) and long short-term memory (LSTM) has been used for Yoga pose recognition through real-time monitored videos as a deep learning model. CNN layer is used for extraction of features from the keypoints and it is followed by LSTM that understands the occurrence of sequence of frames for predictions to be made. Then, the poses are classified as correct or incorrect. If a correct pose is identified, the system will give the user feedback through text/speech.
Aha, Ha! Moment: A Gestalt Perspective on Audiovisual Humour

In my previous work about film music, I had adopted Gestalt as a theoretical framework to explain the functions and effects of music in film, from a perspective that did not stem from musicology but from film studies. I developed what I call ‘micro/macro configurations’ analysis. In films, music contributes to the overall form with its specific gestalt (the configuration of the musical structures), and such musical gestalt meets the gestalt of some other cinematic device/s. Besides music, any device (light design, colour schemes, dialogue, acting, camerawork, cutting…) has a specific micro-configuration that can fuse with those of the other devices, and it can be analysed in terms of micro/macro-configuration. The product of the fusion of these micro-configurations is a macro-configuration in which the devices create an audiovisual whole that is ‘something else than the sum of its parts’. In this article I apply this Gestalt-inspired analytical approach to audiovisual humour, more specifically to ‘audiovisual puns’, ‘sight gags’, and ‘perceptual pranks’. The bulk of the examples come from the cinema of the Zucker-Abrahams-Zucker trio, whose comedy is largely based on a clash of incongruous micro-configurations, on perceptual accumulation that creates results similar to multistable figures, and even on comical optical illusions. Closing the article is a proposal that links Gestalt to the Release Theories of humour, explaining the laughter engendered by humour as a ‘Aha, Ha! moment’.
Reinforcement learning based adaptive metaheuristics

Parameter adaptation, that is the capability to automatically adjust an algorithm's hyperparameters depending on the problem being faced, is one of the main trends in evolutionary computation applied to numerical optimization. While several handcrafted adaptation policies have been proposed over the years to address this problem, only few attempts have been done so far at apply machine learning to learn such policies. Here, we introduce a general-purpose framework for performing parameter adaptation in continuous-domain metaheuristics based on state-of-the-art reinforcement learning algorithms. We demonstrate the applicability of this framework on two algorithms, namely Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Differential Evolution (DE), for which we learn, respectively, adaptation policies for the step-size (for CMA-ES), and the scale factor and crossover rate (for DE). We train these policies on a set of 46 benchmark functions at different dimensionalities, with various inputs to the policies, in two settings: one policy per function, and one global policy for all functions. Compared, respectively, to the Cumulative Step-size Adaptation (CSA) policy and to two well-known adaptive DE variants (iDE and jDE), our policies are able to produce competitive results in the majority of cases, especially in the case of DE.