Reviewerly/exHarmony huggingface dataset:
The value of formative evaluation in an education program

Evaluation is a process that compares the results with the target, an initial situation with the final one. 'Evaluate' means making an assessment based on some measure or information and referring to some criteria. The Formative evaluation is a process which is used by trainers and trainees and provides feedback on updating current teaching and learning methods in order to improve trainer's achievement in relation of teaching goals. In this case study a research was carried out using the technique of formative evaluation in order to evaluate two thematic areas with the cooperation of the program's leaders. The aim of the study was to extract conclusions and opinions about the program's value, the achievement goals and the organisation implementation requirements and how all this results will been feed backed to program managers and policymakers with the appropriate information which are necessary to adopt appropriate corrective interventions for programs quality improvement.
Superior Machine Learning Method for breast cancer cell lines identification

We propose an artificial intelligence platform based on machine learning (ML) algorithm using Neighborhood Component analysis and K-Nearest Neighbors for breast cancer cell lines recognition. Our model presents up to 97% accuracy for identification of breast cancer cell lines.
Attention-Based Fusion of Directed Rotation Graphs for Skeleton-Based Dynamic Hand Gesture Recognition

Recent works on skeleton-based hand gesture recognition proposed graph-based representation of hand skeleton. In this paper, we propose a new attention-based directed rotation graph fusion method for skeleton-based hand gesture recognition. First, we present a novel double-stream directed rotation graph feature to jointly capture the spatiotemporal dynamics and hand structural information. We utilize the bone direction and rotation information to model the kinematic dependency and relative geometry in hand skeleton. The spatial stream employs a spatial directed rotation graph (SDRG) containing joint position and rotation information to model spatial dependencies between joints. The temporal stream employs a temporal directed rotation graph (TDRG), containing joint displacement and rotation between frames to model temporal dependencies. We design a new attention-based double-stream fusion framework ADF-DGNN, in which the two streams are fed into two directed graph neural networks (DGNNs), and the encoded graphs are concatenated and fused by a fusion module with multi-head attention to generate expressive and discriminative characteristics for identifying hand gesture. The experiments on DHG-14/28 dataset demonstrate the effectiveness of the components of the proposed method and its superiority compared with state-of-the-art methods.
Approximate Fast Fourier Transform-based Preprocessing for Edge AI

The emerging Edge Artificial Intelligence (AI) paradigm is a key driver of innovation in many areas such as audio or industrial sensor data processing. Edge AI relies on the integration of machine and deep learning-based signal processing into even the smallest embedded devices thus enabling local intelligence in real-world applications. Nevertheless, classical signal processing techniques such as the Fast Fourier Transform (FFT) still remain an essential component of Edge AI systems, especially in the context of data preprocessing. However, their optimization with respect to resource requirements is often neglected although it can increase the performance of the overall system. In this paper, we present a new approximate FFT (AFFT) approach that enables the end-to-end resource optimization of digital signal processing (DSP) pipelines in Edge AI systems. The approach combines an aggressive FFT parameter quantization with error mitigation techniques to trade off accuracy and performance. To evaluate the methodology, a flexible code generation framework is implemented. We analyze the accuracy of the AFFT, demonstrate its capabilities in an audio classification use-case involving a convolutional neural network (CNN) and benchmark the approach on an ARM Cortex-M4-based System-on-Chip (SoC). Our methodology maintains the classification accuracy of the CNN close to the full-precision level while outperforming the ARM CMSIS FFT both in execution time and energy consumption.
3D Communication System Integrating 3D Reconstruction and Rendering Display

In order to solve the problem that the existing 2D video communication has a high bandwidth occupancy rate, the inability to present three-dimensional portraits of characters and the high dependence of three-dimensional projection technology on hardware, we propose a 3D communication framework that integrates 3D reconstruction and rendering display. The framework transmits the video stream to the video encoding module. The video coding module mainly completes the work of face detection, key point feature extraction and depth map feature coding. It is transmitted to the terminal device through the transmission protocol for video decoding and reconstruction. Then combined with the 3D rendering algorithm, virtual viewpoint synthesis and stereo image encoding are performed to realize the 3D reconstruction of the real face. The frame structure opens up the technical path from ordinary 2D video acquisition to 3D communication system implementation. Experiments show that the use of this communication framework for real face 3D reconstruction has higher accuracy and better information fusion. The reconstruction rate can reach 15fps.
AI-based tank truck cleaning robot

this project designs a robot that uses magnetic adsorption materials to fix the device on the inner wall of the oil tank to clean and recover oil pollution. Compare and analyze the disadvantages of current manual cleaning methods and the cost and convenience of large cleaning machines, showing the adaptability and flexibility of small machines. Solve the safety and adaptability of the screw oil scraping device and the source and control of the high-pressure water flow application, and study how to further improve the cleaning efficiency of the device. At the same time, the function integration of the cleaning and recycling device is realized, and the waste of cleaning resources in the work process is avoided by optimizing the steps.
Introducing Multispectral-Depth (MS-D): Sensor Fusion for Close Range Multispectral Imaging

In this work, we present a Multispectral-Depth (MS-D) system for close range multispectral imaging comprised of a multispectral camera and an RGB-D camera. The proposed system outputs a multispectral point cloud, enabling robust and accurate calculation of numerous vegetation indices. The accuracy of the MS-D system is quantitatively compared with the state-of-the-art methods for close range multispectral imaging. The analysis shows that MS-D outperforms the state-of-the-art approaches with additional advantage of high invariance to the environmental conditions. Contrast to the state-of-the-art feature matching approaches, this system, once calibrated, is applicable as is, without the need for recalibration. The accompanying method for autonomous extrinsic calibration of the system, which extends the capabilities of previous approaches, is also presented.
Performance Modeling and Assurance for Cross Chain

Since the invention of Bitcoin in 2008, there has been a surge in the number of blockchain networks with a variety of heterogeneous designs and functionalities. With the increasing number of blockchain applications and users, it becomes necessary to allow interoperability across isomorphic/heterogeneous blockchains, and further the interoperability is highly demanded in NFT (Non-Fungible Token)-to-NFT trading. This paper proposes a communication model across the blockchains, and an M/Cox/1 queueing model-based model. The cross chain model considers two distinct types of the communication such as atomic swaps and inter-ledger asset transfer. There are two types of communication controls in consideration, namely, Hashed TimeLock Contract (HTLC) for crossing isomorphic chains and Interledger Asset Transfer Protocol (IATP) for crossing heterogeneous chains. In the proposed performance model, a Poisson arrival process is assumed at a rate of λ, and two service rates are assumed to be exponentially distributed at a rate of µ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> (asset swap / transfer Pre - Commit and verify) and µ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> (transfer commit/rollback), respectively. Lastly, the selection ratio of a communication protocols between IATP and HTLC is assumed to be at a rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> and ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1 -p$</tex> ), respectively. Extensive numerical simulations are performed to study the impact of communication request arrival rates, communication service rate, communication traffic rates and proportion of the communication protocols on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> (number of transactions in the system) and W(average waiting time in the system of transaction). This paper will establish a sound theoretical foundation to identify the interrelation and impacts between various design variables on the performance, and ultimately will reveal an optimal solution to a high performance design of cross chain across isomorphic /heterogeneous chains.
Development Of Digital Based Flash Card Media On Thematic Learning In Ibtidaiyah Madrasah

This research is research and development. This study uses the Four D development model. The stages in the Four D development model are definition, design, development, and dissemination. However, in this study, the stages used were only up to the development stage. The subjects of this study were students of class II MIN 15 Langkat, totaling 24 people with details of 13 males and 11 females. The object of research is digital-based flashcard media. This study aims to develop digital-based flashcard media. The data collection instruments were interviews, observations, questionnaire validation, and student responses. From the results of the validation carried out by media experts, material experts, and linguists it was declared feasible.
LAGOON

This paper presents LAGOON - an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities. The platform currently utilizes spatiotemporal graphs to store and investigate the artifacts produced by these communities, and help analysts identify bad actors who might compromise an OSS project's security. LAGOON provides ingest of artifacts from several common sources, including source code repositories, issue trackers, mailing lists and scraping content from project websites. Ingestion utilizes a modular architecture, which supports incremental updates from data sources and provides a generic identity fusion process that can recognize the same community members across disparate accounts. A user interface is provided for visualization and exploration of an OSS project's complete sociotechnical graph. Scripts are provided for applying machine learning to identify patterns within the data. While current focus is on the identification of bad actors in the Python community, the platform's reusability makes it easily extensible with new data and analyses, paving the way for LAGOON to become a comprehensive means of assessing various OSS-based projects and their communities.
Expert Initialized Reinforcement Learning with Application to Robotic Assembly

This paper investigates the advantages and boundaries of actor-critic reinforcement learning algorithms in an industrial setting. We compare and discuss Cycle of Learning, Deep Deterministic Policy Gradient and Twin Delayed Deep Deterministic Policy Gradient with respect to performance in simulation as well as on a real robot setup. Furthermore, it emphasizes the importance and potential of combining demonstrated expert behavior with the actor-critic reinforcement learning setting while using it with an admittance controller to solve an industrial assembly task. Cycle of Learning and Twin Delayed Deep Deterministic Policy Gradient showed to be equally usable in simulation, while Cycle of Learning proved to be best on a real world application due to the behavior cloning loss that enables the agent to learn rapidly. The results also demonstrated that it is a necessity to incorporate an admittance controller in order to transfer the learned behavior to a real robot.
A Novel Curve-Energy Framework to Find the Shortest Possible Lines Through Computer Vision

The dispersion curves are used in the research to propose a novel approach for dealing with frame division computer vision problems. This framework combines boundary- and region-based framework division components with a curve-based optimization problem to discover collection of curves with the smallest size. A training algorithm approach is used to minimize the stated optimization problem. The collection of starting curves is transmitted towards the greatest division underneath the impact of boundary- and region-based pressures and is also constrained by a regular pressure, as per the computed equation of motion. Variations in topologies are addressed organically because of the stage setting, architecture. In addition, a connected multi-stage propagating is developed, which enforces the principle of independent excluded propagation curves while increasing resilience and convergence speed. The three main examples in machine learning, image and guided pattern recognition in poor eyesight, motion detection and control are used to evaluate the proposed framework.
Gender Differentiation in the Management of General Secondary Education Institutions

The article provides a theoretical analysis of the term “gender” and identifies the main gender stereotypes that exist among school teaching staff. There were revealed the peculiarities of gender differentiation in the system of general secondary education. As a result of the study, the list of “male” and “female” positions and specializations in the field of general secondary education was determined. There have been developed the recommendations for heads of general secondary education institutions to ensure gender equality in the teaching staff.
Performance Analysis of Hooks Functionality in React and Vue Frameworks

Front end plays a key role in web applications since it ensures the users have a great experience when interacting with the interface. Hence, optimizing front-end performance is important in building web applications. ReactJS and Vue JS are popular front-end frameworks that commonly be used in developing web-based applications and also introduced hooks functionality. There have been many debates regarding which library or framework is better between these two. Therefore, this study discusses a comparative study relating to the performance of ReactJS and Vue JS frameworks, specifically focusing on hooks functionality. For the study, two sample web app prototypes was developed using both frameworks. The performance test of these two frameworks is utilizing an extension for Google Chrome for, using the frameworks' respective hooks and standard CRD (Create, Read, Delete) - API (Application Programming Interface). The result of this research shows that React's hooks functionality performed slightly better for API requests and managing states on the web application.
Text Classification Using Document-Relational Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have received considerable attention in the field of artificial machine intelligence (AMI) and natural language processing research because they can build more sophisticated accompanying graph structures than traditional neural networks for feature engineering. Graph is used as feature in neural network because it is easy to find relations among nodes. In text classification applications, a GCN can create complex and rich relation-based adjacent matrix graphs as features to be trained. The existing methods, on the other hand, only generated adjacent matrix graphs in GCN at the word-document and word-word levels as features. In this paper, we propose a document-relational GCN to achieve superior accuracy in text classification by adding cumulative term frequency-inverse document frequency (TF-IDF) document-document relations as features. The performance of the proposed method is evaluated using five popular benchmark databases. In addition, different hidden nodes and proportions of document-document features are tested to achieve an advantageous outcome.
Two-stage Object Tracking Based on Similarity Measurement for Fused Features of Positive and Negative Samples

The tracking algorithm based on Siamese networks cannot change the corresponding templates according to appearance changes of targets. Therefore, taking convolution as a similarity measure finds it difficult to collect background information and discriminate background interferents similar to templates, showing poor tracking robustness. In view of this problem, a two-stage tracking algorithm based on the similarity measure for fused features of positive and negative samples is proposed. In accordance with positive and negative sample libraries established online, a discriminator based on measurement for fused features of positive and negative samples is learned to quadratically discriminate a candidate box of hard sample frames. The tracking accuracy and success rate of the algorithm in the OTB2015 benchmark dataset separately reach 92.4% and 70.7%. In the VOT2018 dataset, the algorithm improves the accuracy by nearly 0.2%, robustness by 4.0% and expected average overlap (EAO) by 2.0% compared with the benchmark network SiamRPN++. In terms of the LaSOT dataset, the algorithm is superior to all algorithms compared. Compared with the basic network, its success rate increases by nearly 3.0%, and the accuracy rises by more than 1.0%. Conclusions: The experimental results in the OTB2015, VOT2018 and LaSOT datasets show that the proposed method has a great improvement in the tracking success rate and robustness compared with algorithms based on Siamese networks and particularly, it performs excellently in the LaSOT dataset with a long sequence, occlusion and large appearance changes.
DETERMINING THE CONDITIONS OF DISTANCE LEARNING AND THEIR CHARACTERISTICS

Introduction. This publication presents an analysis of the conditions of “distance learning” in the implementation of the educational process, the development of which is facilitated by the historical, socio-economic aspects of the emergence of distance learning and the pandemic caused by Covid-19. The scientific novelty of this article lies in determining the conditions of “distance learning” and their characteristics that contribute to the correct design and implementation of the modern educational process. Materials and Methods. To achieve the research goal, general theoretical methods are used: analysis of theoretical sources, generalization and systematization of the data obtained; empirical methods: observation. The research materials are historical facts, scientific articles, dissertation research, data from practitioners and experts, opinions of leaders in the development of education in Russia and other authors. Results. The list of conditions in which the current educational process is carried out as well as their characteristics are defined. For this purpose, the analysis of the regulatory framework for the organization of the educational process with the possibility of introducing an electronic component into it as one of the conditions for combining real and virtual academic activities is carried out; the historical socio-economic aspects of the emergence of “distance learning” are presented; the characteristics of the implementation of the modern educational process are considered; the need for the formation of IT competence among teachers as a condition for the development of “distance learning” is revealed.
Smarter Contracts to Predict using Deep-Learning Algorithms

Deep learning techniques can predict cognitive intelligence from large datasets involving complex computations with activation functions. However, the prediction output needs verification for trust and reliability. Moreover, these algorithms suffer from the model's provenance to keep track of model updates and developments. Blockchain smart contracts provide a trustable ledger with consensus-based decisions that assure integrity and verifiability. In addition, the immutability feature of blockchain also supports the provenance of data that can help deep learning algorithms. Nevertheless, smart contract languages cannot predict due to the absence of floating-point operations required by activation functions of neural networks. In this paper, we derive a novel method using the Taylor series expansion to compute the floating-point equivalent output for activation functions. We train the deep learning model off-chain using a standard Python programming language. Moreover, we store models and predict on-chain with blockchain smart contracts to produce a trusted forecast. Our experiment and analysis achieved an accuracy (99%) similar to popular Keras Python library models for the MNIST dataset. Furthermore, any blockchain platform can reproduce the activation function using our derived method. Last but not least, other deep learning algorithms can reuse the mathematical model to predict on-chain.
Terrorist Tweets Detection using Sentiment Analysis: Techniques and Approaches

Many people’s company and social connections have changed as a result of online social networking, the proliferation of these social media sites has ushered in a new age of Unfortunately extremism, where they are being used to inspire terrorist attacks, recruitment, and other forms of extremism. Terrorist groups such as the Islamic State of Iraq and Syria (ISIS) use social media platforms like Twitter to spread their propaganda online, and the vast amount of traffic generated in social media makes it nearly impossible to manually identify terrorist content and extremism online, Although law enforcement officials, experts, and volunteers are actively is working to resolve this issue, because of the urgent need to find modern technologies to protect humanity from terrorism. In this paper reviews most of the works it is based on 15 papers and in most of the research the sentiment analysis (SA) techniques have been used to elicit opinions or to detect and predict terrorist attacks. Using machine learning and recent studies have reached promising results using deep learning.