Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis for People’s Opinions about COVID-19 Using LSTM and CNN Models

The emergence of social media platforms, which contributed in activating the patterns of connection between individuals, leads to the availability of a huge amount of content such as text, images, and videos. Twitter is one of the most popular platforms of social media that encourage researchers to investigate people’s feelings and opinions among through sentiment analysis studies that elicited the interest of researchers in natural language processing field. Many techniques related to machine learning and deep learning models could be used to improve the efficiency and performance of sentiment analysis, especially in complex classification problems. In this paper, different models of long short-term memory recurrent neural network are used for the sentiment classification task. The input text was represented as vectors using Arabic pre-trained word embedding (Aravec). Experiments were conducted using different dimensions of Aravec on 15779 tweets about COVID-19 collected and labeled as positive and negative. The experimental results show an accuracy value of 98%.
Past, Present and Future Trends in Multi-Agent System Technology

Multi-Agent System (MAS) technology is one of the cores and promising areas in the field of Artificial Intelligence (AI) as well as in the stream of Computer Science. The technology is comprised of multiple decision-making agents that exist in an environment to achieve common or conflicting goals. Multi-Agent System technology has a rapid growth and evolution due to its marvelous features such as flexibility and intelligence that are very useful when solving complex distributed problems. This paper focuses on the history and evolution of MAS technology, present applications, and future trends by addressing more detailed explanations about the foundations or key principles of Multi-Agent System technology such as agents, agent taxonomy, agent communication approaches, MAS development frameworks as well as the history of agent technology. The goal of this paper is to provide broad and comprehensive knowledge about Multi-Agents System technology.
THE USE OF RIDDLE GAME TO IMPROVE STUDENTS’ VOCABULARY

Vocabulary mastery is essential part of English as a foreign language. In teaching vocabulary, the teachers had responsibility to make their teaching successful. There are four ways of teaching vocabulary. Definitional methods, Contextual methods, Organizational and Mnemonic instructional methods. In this research the researcher used the contextual method. Riddle game is the nouns all refer to something baffling or confusing which is to be solved. In this research the researcher used the qualitative research. After implementing riddle game, the criteria of success has been fulfilled. It means that the students’ vocabulary achievement was better than before. Based on these result, the students got many improvements, the improvements could be seen from the way they thought logically and creatively, they solved the puzzle the riddle provides and they did the test well.
BUILDING A QUALITY ASSESSMENT CRITERION BINARY BARKER-LIKE CODES

The objective of this article is to construct a criterion for evaluating the quality of binary Barker-like codes. The analysis of well-known studies on the formation of these codes with properties close to binary Barker codes is carried out. The analysis showed that in the works devoted to the use of well-known binary Barker-like codes in secure wireless communication systems and the formation of such codes, researchers primarily pay attention to such quality assessment indicators as the autocorrelation function, the length and number of such codes, and others. Based on the analysis of works [5, 9-25], the initial tuple of quality assessment of binary Barker-like codes is formulated, represented by the expression (1). However, the original tuple (1) lacks such an important quality assessment indicator as the linear complexity assessment, which is often taken as one of the primary indicators of crypto resistance. Based on this, the purpose of this article is to increase the accuracy of the evaluation of binary Barker-like codes by applying the identified criterion. The authors of the article, using the Berlekamp-Massey algorithm, evaluated the linear complexity of binary Barker-like codes presented in [9-23]. It has been established that a sufficiently large number of such codes (from 24% to 75%) have insufficient linear complexity, that is, they are non-cryptographic. As a result, they cannot be used in secure wireless communication systems. The result obtained is consistent with other studies in this field. Proceeding from this, when using well-known binary Barker-like codes in secure wireless communication systems, as well as when forming new binary Barker-like codes, it is necessary to pay attention not only to the known quality assessment indicators, but also to their linear complexity. The authors have modified the original tuple represented by expression (1). The modified tuple is represented by expression (2). According to the authors, the modified quality assessment criterion will improve the accuracy of the evaluation of binary Barker-like codes.
A Novel Gesture Recognition Technique based on sEMG Armband and Leap Motion Controller

With the rapid development of human-computer interaction technology, it has become very common to use various sensors for gesture recognition to control machines or to complete specified tasks. This study proposes a fusion method that uses Leap Motion for gesture recognition and gForcePro+ to evaluate actual force of hands. A three-dimensional (3-D) virtual gesture recognition platform was designed in Unity. Leap Motion was used to control gestures in the virtual environment, and surface electromyography (sEMG) data was used to distinguish different forces of the same gesture. The success of grasping objects was judged by detecting and analyzing the sEMG signals and Leap Motion signals, combined with the size of the grasping objects. Experimental results showed that Leap Motion and gForcePro+ can be used together to control hand motion. In addition, the proposed system can be used to distinguish the same gesture with different force sizes, which can reflect different forces when the hand grasp objects with the same action. It was proved that the fusion technique of gesture recognition system based on Leap Motion and sEMG is effective for online gesture recognition.
DATA MINING CLUSTERING FOOD EXPENDITURE IN INDONESIA

The availability of food in a country is determined by a conducive climate. Prolonged droughts, floods, and natural disasters, especially for food crop production areas, will have an impact on the availability of natural disaster conditions faced by all countries including Indonesia is the Covid-19 pandemic, where this will affect food security in Indonesia. Data mining is the process of discovering the hidden meaning of a very large data set. The technique used in this study is Data Mining Clustering and the validity index used is Davies-Bouldin. This study aims to determine the Food Security Strategy in Indonesia through the Data Mining Clustering process based on food expenditure data and the Indonesian people's food expenditure per capita. The methodology used is Cross Industry Standard for Data Mining using the K-Means and K-Medoids Algorithm. The best cluster for the K-Means Algorithm is K=7 with a value of 0.341 and for the K-Medoids Algorithm, it is K=7 with a value of 0.362. This research produces the best algorithm, namely K-Means with a value of 0.341, which has a smaller value than K-Medoids with a value of 0.362. The results showed that the regional. cluster with the highest average expenditure on food was cluster 5 covering the DKI Jakarta area, while the cluster with the lowest expenditure was cluster 6 covering Central Java, East Nusa Tenggara, Southeast Sulawesi, Gorontalo, and West Sulawesi. In cluster 6, it is necessary to implement a strategy to increase food security by increasing production capacity and food reserves in each region.
E-Spin: A Stochastic Ising Spin Based on Electrically-Controlled MTJ for Constructing Large-Scale Ising Annealing Systems

With its unique computer paradigm, the Ising annealing machine has become an emerging research direction. The Ising annealing system is highly effective at addressing combinatorial optimization (CO) problems that are difficult for conventional computers to tackle. However, Ising spins, which comprise the Ising system, are difficult to implement in high-performance physical circuits. We propose a novel type of Ising spin based on an electrically-controlled magnetic tunnel junction (MTJ). Electrical operation imparts true randomness, great stability, precise control, compact size, and easy integration to the MTJ-based spin. In addition, simulations demonstrate that the frequency of electrically-controlled stochastic Ising spin (E-spin) is 50 times that of the thermal disturbance MTJ-based spin (p-bit). To develop a large-scale Ising annealing system, up to 64 E-spins are implemented. Our Ising annealing system demonstrates factorization of integers up to 264 with a temporal complexity of around O(n). The proposed E-spin shows superiority in constructing large-scale Ising annealing systems and solving CO problems.
A Human-like Interactive Chatbot Framework for Vietnamese Banking Domain

In recent years, the application of chatbots evolved rapidly in numerous fields and received increasing attention in the academic and industrial communities. In this paper, we present a novel chatbot framework based on machine learning and deep learning approaches. Our framework not only answers the domain questions but also consists of three primary features of a human-like interactive chatbot, including (1) Conversation tracking, (2) Recommendation, and (3) Asking again. Further-more, we integrate one feature for adding accents to the non-accent sentence using Transformer-based architecture. Based on the experimental results and deployment on production for the banking domain, we demonstrated that our framework is stable and ensures specific requirements (e.g., computational resources, response time, performance, user experience). With flexibility and adaptation, our proposed framework can be developed and deployed to other domains or business contexts.
Modelling of critical success factors for blockchain technology adoption readiness in the context of agri-food supply chain

The agri-food supply chain is continuously facing several challenges; the most severe are food quality and safety issues. These issues debilitate the performance of the supply chain and often harm the consumer's health. Therefore, there is an urgent need to address food quality and safety assurance in the supply chain. Blockchain technology (BCT) holds the potential to resolve these issues by enhancing security and transparency. The present study explores the critical success factors (CSFs) of BCT adoption readiness in the AFSC. Initially, CSFs are identified through a literature survey and finalised by experts' opinion. The finalised factors are prioritised using the fuzzy best-worst method, followed by sensitivity analysis. The results reflect that 'food quality control', 'provenance tracking and traceability', and 'partnership and trust' as the top three success factors. The study's findings will assist policymakers, managers, and practitioners in strategising the decision-making process while BCT dissemination.
Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. We present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.
Parallel border tracking in binary images for multicore computers

Abstract Border tracking in binary images is an important operation in many computer vision applications. The problem consists in finding borders in a 2D binary image (where all of the pixels are either 0 or 1). There are several algorithms available for this problem, but most of them are sequential. In a former paper, a parallel border tracking algorithm was proposed. This algorithm was designed to run in Graphics Processing units, and it was based on the sequential algorithm known as the Suzuki algorithm. In this paper, we adapt the previously proposed GPU algorithm so that it can be executed in multicore computers. The resulting algorithm is evaluated against its GPU counterpart. The results show that the performance of the GPU algorithm worsens (or even fails) for very large images or images with many borders. On the other hand, the proposed multicore algorithm can efficiently cope with large images.
Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

This work presents a novel method for motion sensor placement within smart homes. Using recordings from 3D depth cameras within six real homes, clusters are created with the resident’s tracked location. The resulting clusters identify the possible position of a sensor and its field of view. By using a sequence of clusters as input to a Recurrent Neural Network, we evaluate our method on the task of activity recognition and prediction. These results are compared to using sensor events as input sequence, from motion sensors that were installed empirically in the same homes. Different clustering methods are investigated and all outperform the installed motion sensors, achieving a significant increase of prediction accuracy and F1-score.
Blockchain Enabled Supply Chain Management

A distribution network is a mechanism that links a company and its suppliers to create and distribute a product to the end customer. This network is made up of numerous activities including people, entities, knowledge, and assets. The distribution network also represents the steps taken to get a good or service out of its inception to the customer. A supply chain links a company and its suppliers to create and distribute a product to the end customer. This network is made up of numerous actions, persons, entities, knowledge, and resources. The distribution network also represents the steps taken to get a service or product from its inception to the customer. Blockchain allows all parties in a supply chain to access the same data, potentially reducing communications or data transfer issues. Less time to be spent on data confirmation and more time can be spent on providing goods and services quality, cutting prices, or both. Blockchain allows all parties in a supply chain to access the same information, potentially reducing connection or data transfer issues. Less time that could be spent on data confirmation and more time could be spent on delivering products or services quality, cutting prices, or both. This article takes a broad look at how blockchain might assist manage supply chains. Also discussed is how a crypto supply network outperforms a supply chain.
An Unsupervised Rapid Network Alignment Framework via Network Coarsening

Network alignment aims to identify the correspondence of nodes between two or more networks. It is the cornerstone of many network mining tasks, such as cross-platform recommendation and cross-network data aggregation. Recently, with the development of network representation learning techniques, researchers have proposed many embedding-based network alignment methods. The effect is better than traditional methods. However, several issues and challenges remain for network alignment tasks, such as lack of labeled data, mapping across network embedding spaces, and computational efficiency. Based on the graph neural network (GNN), we propose the URNA (unsupervised rapid network alignment) framework to achieve an effective balance between accuracy and efficiency. There are two phases: model training and network alignment. We exploit coarse networks to accelerate the training of GNN after first compressing the original networks into small networks. We also use parameter sharing to guarantee the consistency of embedding spaces and an unsupervised loss function to update the parameters. In the network alignment phase, we first use a once-pass forward propagation to learn node embeddings of original networks, and then we use multi-order embeddings from the outputs of all convolutional layers to calculate the similarity of nodes between the two networks via vector inner product for alignment. Experimental results on real-world datasets show that the proposed method can significantly reduce running time and memory requirements while guaranteeing alignment performance.
Optimized Fully Convolutional Neural Network Encoder for Water Detection in SAR Images

Visual interpretation of Synthetic Aperture Radar (SAR) images plays an important role in remote sensing mainly because SAR images enable consistent monitoring in any lighting, weather, and cloud-cover conditions. An important application of SAR visualization is water detection. We introduce a Fully Convolutional Neural Network (FCN) Encoder to detect water in Sentinel-1 SAR images. Our FCN Encoder identifies water by the intensity of each pixel and also learns the spatial information of neighborhood pixels. We apply our method on standard benchmarks and real-world SAR images. The results are assessed both visually and from the point of view of classification accuracy. Compared with other classifiers, our FCN Encoder is more accurate. From visual inspection of the Seattle water detection result, the FCN Encoder produces a very clear (smooth) output. The results show that the FCN Encoder, trained with a harder dataset and hyperparameter optimization, improves significantly its generalization performance. In a real-world application, for the prediction phase, the FCN Encoder is about 40 times faster than a Convolutional Neural Network (CNN) with sliding window.
Clustering Ensemble-based Edge Bundling to Improve the Readability of Graph Drawings

One of the commonly used techniques to improve the readability of large graph drawings is called edge bundling, which groups edges in such a way that reduces the visual complexity of the drawing. This paper proposes to treat this task as a clustering problem, using compatibility metrics to evaluate the generated solutions in an optimization pipeline, combined with a clustering ensemble approach. The goal was to solve the General-based Edge Bundling (GBEB) problem with relatively low computational costs using a method called Clustering Ensemble-based Edge Bundling (CEBEB) and evaluate the results. CEBEB proved to be a very promising alternative to solve GBEB, since it is capable of generating relatively good solutions with shorter run-times compared to an existing, well-established GBEB method.
Availability-Constrained Application Deployment in Hybrid Cloud-Edge Collaborative Environment

Cloud computing offers strong availability and lower cost, while edge computing has lower delay. Deployment of applications by placing microservices in containers in a cloud-edge collaborative environment is adopted by more and more enterprise application providers. For users, they care more about application response time and application availability. For application providers, they also need to save deployment costs to the maximum extent. Therefore, the application deployment in hybrid cloud-edge collaborative environment is a multi-objective optimization problem. In this paper, a genetic algorithm named DP-GA based on improved NSGA-II has been proposed to solve the multi-objective NP-hard problem. We balance the two objectives of minimizing deployment cost and average response time under availability constraints. Using the real dataset of Shanghai Telecom, the experimental results show that the proposed DP-GA is superior to the existing methods, reducing average response time by about 35% and saving deployment cost by about 15%.
What Constitutes a ‘Classroom Problem’ for Today's Teacher?: An Exploratory Study

&lt;p style="text-align:justify"&gt;Teachers face situations that are difficult to solve, which affect their practices in the classroom. Teachers´ability to face problematic situations been related to educators' adaptive and metacognitive capacity. This study aimed to contrast the recent literature on classroom problems with reports from Chilean teachers regarding what they mean today as critical situations in the classroom. The major problems with the highest prevalence were categorized based on a literature review. Subsequently, teachers from three educational levels (pre-school, primary and secondary) were asked to relate situations in which they had experienced a crisis in their profession. After a filtering based on the relevance and textual richness present, the stories (n=145) were subjected to a thematic analysis and contrasted with the categories obtained from the reviewed literature. The results show a low proportion of problems associated with learning management in the classroom and, on the other hand, a high proportion of behavioral problems in students focused on situations of aggression towards the teacher. These conclusions are discussed to reconsider the concept of "classroom problem situations" and, based on this, questioning the role of teacher training in the development of skills for the management of authentic problem situations.&lt;/p&gt;
ESPRESO: An algorithm to estimate the slice profile of a single magnetic resonance image

To reduce scan time, magnetic resonance (MR) images are often acquired using 2D multi-slice protocols with thick slices that may also have gaps between them. The resulting image volumes have lower resolution in the through-plane direction than in the in-plane direction, and the through-plane resolution is in part characterized by the protocol's slice profile which acts as a through-plane point spread function (PSF). Although super-resolution (SR) has been shown to improve the visualization and down-stream processing of 2D multi-slice MR acquisitions, previous algorithms are usually unaware of the true slice profile, which may lead to sub-optimal SR performance. In this work, we present an algorithm to estimate the slice profile of a 2D multi-slice acquisition given only its own image volume without any external training data. We assume that an anatomical image is isotropic in the sense that, after accounting for a correctly estimated slice profile, the image patches along different orientations have the same probability distribution. Our proposed algorithm uses a modified generative adversarial network (GAN) where the generator network estimates the slice profile to reduce the resolution of the in-plane direction, and the discriminator network determines whether a direction is generated or real low resolution. The proposed algorithm, ESPRESO, which stands for "estimating the slice profile for resolution enhancement of a single image only", was tested with a state-of-the-art internally supervised SR algorithm. Specifically, ESPRESO is used to create training data for this SR algorithm, and results show improvements when ESPRESO is used over commonly-used PSFs.