Reviewerly/exHarmony huggingface dataset:
A GPU-Enabled Extension for Apache Ignite to Facilitate Running Genetic Algorithms

With the increasing rate of data generation in recent years, there is a need for modern tools to process these massive amounts of data. To that end, in-memory platforms are becoming increasingly popular, which can process high volumes of data at high speed and performance by utilizing Main Memory. Apache Ignite is one of the in-memory platforms that can process in parallel on multiple nodes. Although this platform provides many useful features, one of its limitations is the lack of utilizing the GPU's high processing power. Undoubtedly, using GPUs for operations that deal with heavy processing or high data volumes can be very beneficial, and significantly accelerate processing. One of the algorithms supported by Ignite is the Genetic Algorithm, which usually deals with large amounts of data, and might be very time-consuming. In this paper, we have provided an extension for Ignite in which users can utilize GPUs to run their Genetic Algorithm applications. Also, we have used various GPU-related optimization techniques to improve performance and finally evaluated our extension with three benchmarks. Our results proved the ease of use, and the high performance of the proposed work compared to Ignite.
Review of Bowker &amp; Ciro (2019): Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community

Abstract This article reviews Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community
A general framework for tactical exploitation of multi-source images

Satellite imaging reconnaissance and aerial video surveillance may be the most important means for monitoring objects on the ground. First, we discuss the requirements on reconnaissance information for different missions including strategic operations, tactical tasks and fire control. In terms of precision and timeliness, we analyze the characteristics of reconnaissance images or videos captured by different platforms such as satellites and aerial vehicles. Then, we propose a general framework for tactical exploitation of multi-source images, which can provide a Common Operational Picture (COP) which is both real-time and precise enough for tactical purposes or even for fire control. According to this framework, we detect, identify, and track objects, and obtain higher precision by integrating the aerial videos and the satellite images. To show how to integrate the aerial videos and the satellite images, we study image registration techniques, analyze the differences between these two kinds of images, and present a feature-based geo-registration procedure. By spatially aligning these images, we can obtain the geographical coordinates of high precision in real time. The framework can be adopted for striking time-sensitive ground targets.
IoT-Based Secure Communication to Enhance Blockchain Model

Internet of things (IoT) privacy and security remain a most important, challenging factor in this era, mostly due to distributed nature and immense scale of IoT networks. The blockchain technology provides decentralized security, public digital ledger and yet occupies significant delay, computational overhead, and high energy that is not fit for low-resource IoT devices. In this paper, our proposed work will analyze possibilities for enhancement of the blockchain in IoT without sacrificing security. However, the traditional blockchain technology cannot be applied to IoT devices with limited computing resource and network resources. Hence, we will solve the verification problem by generating random number to verify the correct transaction in the blockchain by using Keccak-256 hash function in IoT.
Predicting Student Intention to Use Cloud Services for Educational Purposes Based on Perceived Security and Privacy

Cloud services in its entirety has drastically changed the perception of how industry experts and academics alike foresee the information technology sector. The reach of cloud services stretches into many sectors of society. Such sectors include the use of cloud technologies in education, which is of growing interest to academics and higher education institutions. However, many institutions adopt these services into their workflow without properly identifying the potential impacts on their students, since it is the student who engages with the new technology to enhance their knowledge base. This chapter investigates how student's perceptions of security and privacy, influences their adoption of cloud services for use in an educational setting. This study proposes that student intention to use a cloud service (e.g., DropBox) for academic purposes can be predicted upon their perception of security and privacy when using a specific cloud service.
ПРОЦЕСС ЭВАЛЮАЦИИ В СОВРЕМЕННОМ ОБРАЗОВАНИИ (THE PROCESS OFEVALUATION IN MODERN EDUCATION)

The article shows the results of studies of modern problems that arise in the work. The process of evolution of communication models of many scientists and schools was studied, the tendency of their development was highlighted, and a communication model was developed that is adapted to modern conditions. In the context of globalization, changes in education were analyzed, including changes in school management combined with experience. On ways and methods of supporting the professional growth of school teachers. A draft school development plan was presented. The concept of evaluation was used in the learning process. The management of the school indicates ways to solve problems in the updated educational program, based on world practice.New training and evaluation of school development are provided. The issues of increasing the potential and improving the quality of education are revealed. The authors share their experience of teachers' professional development and involvement in leadership. "Nazarbayev Intellectual schools" are guided by the experience and models of making changes in Burke-Lithuania. The experiments" Lesson study" and" Mentoring"were studied. In practice, according to the results of the survey of talismans, the ways of development of the team were shown. Through Coaching,research and professional development of teachers were conducted.
Teamwork challenges and solution strategies of first-semester engineering students

Teamwork is an important skill for today's engineers, who often work in international teams where decision making, problem solving, and all communication aspects need to be dealt with efficiently. It is important therefore to familiarize engineering students with challenges they will face in such situations and provide them with related practical experiences before they enter the labour market. And while engineering students typically have opportunities to work in project teams, those teams are often assembled by students themselves, unlike real project teams in which engineers cannot always choose their teammates. This paper explores dynamics of student teams formed in a way that maximizes group heterogeneity in terms of academic success, geographical origin, and gender, and thereby minimizes probability of students already knowing each other. In such conditions, students are assigned a task to solve in teams of five. Teams' work results and reflections collected through a questionnaire were used to identify typical challenges students face, and strategies they employ to solve them. Educational implications and improvements of this intervention are discussed in the paper.
Machine Vision based Automation to Improve Process and Quality Control in Rolling Mills<sup>§</sup>

Steel manufacturing industries take advantage of growing infrastructure market by producing high volumes of rolling products like rebars. However, in the face of competition, better cost effective automation solutions are needed to run rolling mills at a high speed which would guarantee a high return of investment. In this paper, automatic, visual inspection methods are proposed to investigate rebar falling patterns and rebar surface properties at very early stage during the production of rolling stock. We propose a multi-modal adaptive control approach for rolling processes with camera, process, and material data to improve process and quality control in rolling mills.Image processing methods are proposed to detect and identify abnormalities in rolling stock and communicate visual information with a feedback mechanism to a control system and/or a manual operator. The visual information is transformed into sequential data with exponential weighted mean statistics and symbolic aggregate approximation techniques. We demonstrate, on real video streams, deviation statistics and identify set point corrections to improve time to deliver the stock; generate interpretable labels to quantify material loss and identify extent of deficiencies in rolling stock for assistance to manual operators.
PCHPS: The Estimation of 3D Hand Pose and Shape using Point Cloud from a Single Depth Image

The estimation of 3D hand pose and shape is important for vision-based applications such as augmented reality and virtual reality. Most current methods in 3D hand analysis from a single depth image only focus on joints positions, which cannot fully express the 3D shape of hand. In this paper, Point Cloud for Hand Pose and Shape estimation (PCHPS) which combines the structure of the encoder of So-Net and the structure of Graph Convolutional Neural Network (GCNN) is proposed to recover a complete hand shape and locate hand joint positions simultaneously. A million-scale synthetic dataset with ground truth of 3D meshes and poses called SynHand5M is used to train PCHPS. The normalized point clouds and SOM nodes are inputted to PCHPS to capture the complex structure of 3D hand. A joint training strategy with real and synthetic datasets is introduced to fine-tune PCHPS on real-world datasets without 3D ground truth. Experiments are conducted on the NYU, ICVL and MSRA dataset to compete with the state-of-the-art methods, which shows that PCHPS can achieve superior results in the estimation of 3D hand pose and is real time.
Information extraction using 2 satisfiability based reverse analysis method in MOBA game

League of Legends (LoL) has been on a rise in becoming an extremely favored multiplayer online battle arena (MOBA) game ever since its release in 2009. This paper presents a logic mining technique to model the results (Win / Lose) of the LoL games played in the professional league of South Korea, most commonly known as LCK. In this research, a method, namely k satisfiability based reverse analysis method (kSATRA) is brought forward to obtain the logical relationship among the gameplays and objectives in the game. The logical rule obtained from the LoL games is used to categorize the results of future games. kSATRA made use of the advantages of Hopfield Neural Network and k Satisfiability representation. The data set used in this study included the data of all 10 teams in LCK, which composed of all games from Spring Season 2018. The effectiveness of kSATRA in obtaining logical rule in LoL games is tested based on root mean square error (RMSE), mean absolute error (MAE) and CPU time. The main issue was to determine the relationship among gameplays or objectives and how they affect the outcome of the game. Results acquired from the computer simulation shows the effectiveness of kSATRA in exhibiting the performance of the LoL teams.
Further investigation of the survivability of code technical debt items

Context: Technical Debt (TD) discusses the negative impact of sub-optimal decisions to cope with the need-for-speed in software development. Code Technical Debt Items (TDI) are atomic elements of TD that can be observed in code artefacts. Empirical results on open-source systems demonstrated how code-smells, which are just one type of TDIs, are introduced and "survive" during release cycles. However, little is known about whether the results on the survivability of code-smells hold for other types of code TDIs (i.e., bugs and vulnerabilities) and in industrial settings. Goal: Understanding the survivability of code TDIs by conducting an empirical study analysing two industrial cases and 31 open-source systems from Apache Foundation. Method: We analysed 133,670 code TDIs (35,703 from the industrial systems) detected by SonarQube (in 193,196 commits) to assess their survivability using survivability models. Results: In general, code TDIs tend to remain and linger for long periods in open-source systems, whereas they are removed faster in industrial systems. Code TDIs that survive over a certain threshold tend to remain much longer, which confirms previous results. Our results also suggest that bugs tend to be removed faster, while code smells and vulnerabilities tend to survive longer.
Runtime Hook on Blockchain and Smart Contract Systems

Using hard-fork mechanism on the blockchain to recover the losses caused by attacks contradicts the immutable characteristic of a blockchain system. To prevent malicious transactions from getting into blockchains in advance, we propose a runtime hook technique to synchronize and analyze the ongoing transactions exposed to the Ethereum transaction pool. Having a complete view of the past and the ongoing transactions, we can identify and enforce abortion of malicious transactions and prevent losses due to attacks being executed and recorded in the blockchain. Specifically, we modify the Ethereum source code to instrument the entry point of a node to synchronize data received from the Ethereum P2P network and systematically scan suspicious patterns in transactions to identify potential attacks. As a proof-of-the-concept, we show how to deploy the proposed runtime hook system on a private blockchain system, such that we can detect and prevent transactions of double spending on the 51% attack and reentrancy attack of smart contracts.
Using Multi-Criteria Optimization in Decision Support under Risk

The chapter presents an extension of a previous method for decision support under risk. The decision-making process is modeled by a multi-criteria optimization problem, in which the individual evaluation functions represent the results of decisions in several possible scenarios with associated risks. The decision support method is an interactive decision-making process. The choice is made by solving the problem depending on the control parameters that define the aspirations of the decision maker as well as on an evaluation of the obtained solutions. The decision maker selects a set of parameters representing various risks’ impacts that influences a solution, and then he/she evaluates the obtained solution by accepting or rejecting it. In another case, the decision maker selects a new value and the problem is solved again for the new parameter. In this chapter, an example of supporting decision-making under risk is presented.
Speed Estimator Design of an Induction Motor Using an Adaptive Neuro-Fuzzy Inference System (ANFIS)

This paper deals with the speed estimation of an induction motor (IM) based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the speed control of the induction machine using the sensorless control technique. The estimation technique ANFIS was reviewed, as well as its implementation detailed. Firstly, the induction motor was simulated with the transformations that allow obtaining the components of the voltages and currents in the direct and quadrature axes. Then the sensorless control method was simulated. Finally, to verify the experimental results, the speed at the output of the ANFIS estimator and the induction motor were compared.
Testing the Intermediate Disturbance Hypothesis in Concurrent Evolutionary Algorithms

Concurrency is a powerful abstraction that can be used to model and implement multi-deme evolutionary algorithms, opening up additional design questions such as what the different populations in various threads can do and how they interact with each other (via a combination of populations). One approach is synchrony: although threads can run asynchronously, they often perform the same amount of work, which brings a (rough) synchrony to appear within them. Our intention in this paper is to test if the intermediate disturbance hypothesis holds: this kind of synchrony is a small disturbance, which likewise big disturbances will not boost diversity; however, moderate disturbances will. We tested several ways of creating this intermediate disturbance by changing how different threads operate or modifying its working in alternative ways.
An Augmented Reality Approach to 3D Solid Modeling and Demonstration

This paper presents an intuitive and natural gesture-based methodology for solid modelling in the Augmented Reality (AR) environment. The framework of Client/Server (C/S) is adopted to design the AR-based computer aided design (CAD) system. The method of creating random or constraints- based points using gesture recognition is developed to support modelling. In addition, a prototype system of product 3D solid modelling has been successfully developed, we have compared it with traditional CAD systems through several basic design modeling tasks. Finally, analysis of questionnaire feedback survey shows the intuitiveness and effectiveness of the system, and user studies demonstrate the advantage of helping accomplish the product early design and creating and manipulating the 3D model in the AR environment.
KidNet

Kidney cancer is one of the 10 most common cancers in both men and women. The lifetime risk for one developing kidney cancer is about 1.6%. The rate of kidney cancer diagnosis has been rising since the 1990s due to the use of newer imaging tests such as CT scans. The kidneys are deep inside the body and hence small kidney tumours cannot be seen or felt during a physical examination. Existing work on kidney tumour diagnosis uses traditional machine learning and image processing techniques to find and classify the images. Deep learning systems do not require this domain-specific knowledge. The kidney tumour diagnosis system uses deep learning and convolutional neural networks to classify CT images. A deep learning neural network model named KidNet has been implemented. It has been trained using labelled kidney CT images. To achieve acceleration during the training phase, GPUs have been used. The network when trained with abdominal CT images achieved 86.1% accuracy, and the one trained with cropped portion of kidney images achieved 89.6% accuracy.
Underwater Image Processing by an Adversarial Network with Feedback Control

Owing to absorption and scattering of light in water, raw underwater images suffer from low contrast, blurred details, and color casts. These characteristics significantly interfere the visibility of underwater images. In this paper, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptive mechanism for underwater image enhancement. A new method for simulating underwater-like training dataset by underwater image formation model is proposed. Upon this, a novel framework, which introduces a domain adaptive mechanism and a physics model constraint feedback control, is trained to enhance the underwater scenes. Extensive experiments demonstrate the significant superiority of the proposed framework compared with the state-of-the-art methods.
Tracing Method for Electronic Snapshot System of Black Smoke Vehicle

The calibration device and the method of source tracing for the electronic snapshot system of black smoke vehicle are researched in this paper. Through a large number of tests on the environmental factors, the appropriate calibration conditions are determined. The accurate and reliable tracing of the electronic snapshot system of black smoke vehicle can be realized through this research, which makes up for the technical blank in this field. It also provides technical support for the environmental protection department to monitoring vehicle exhaust black smoke situation, which plays important role for monitoring and screening black smoke vehicle.
A Study on Ontology Based Semantic Information Classification for Epileptic Seizure Using Optimal Fuzzy System

An Epileptic seizure is an unpredicted medical situation defined with an inter-connected motion of sensory organs, which occurred with an interval of time of existence. The recordings are mapped using Electroencephalogram (EEG) on a larger research. Various approaches have been proposed to diagnose and retrieve the causes and pre-occurrence scenarios for the last decade. In this paper, various approaches and defined terminologies to detect, predict, and analyze ES is discussed with an ideal solution of discussion using semantic information classification based ES detection using machine learning terminology.
Multi-view foreground segmentation based on spatial consistency constraints

Recent researches on object segmentation mostly concentrate on single-view images or objects in 3D settings. In this paper, a novel method for efficient multi-view foreground object segmentation is presented, using spatial consistency across adjacent views as constraints to generate identical masks. Even though the conventional segmentation results at different views are relatively accurate, there always are inconsistent regions where the boundaries of the mask are different over the same area across multiple views. The central idea of our method is to utilize the camera parameters to guide the refocusing procedure, during which each instance across different views is refocused using multi-view projections. The refocused images are then used as the input of instance segmentation network to predict the bounding box and object mask. The final step takes the network output as the prior information for the GMMs to achieve more accurate segmentation results. While many concrete implementations of the general idea are feasible, satisfactory results can be achieved with this simple and efficient approach. Experimental results demonstrate both qualitatively and quantitatively that the proposed method outputs excellent results with less background pixels, thus allowing us to improve the 3D display quality eventually. We hope this simple and effective method can be of help to future researches in relevant tasks.
Recognition of Cursive Caption Text Using Deep Learning - A Comparative Study on Recognition Units

Text appearing in videos carries rich information that can be exploited for content based retrieval applications. An important component in such systems is the development of a video optical character recognition (V-OCR) system that converts images into text. Though mature recognition system have been developed for recognition of text in non-cursive scripts, recognition of cursive text remains a challenging problem. An important factor in developing recognition systems for cursive text is the choice of recognition unit. Unlike words which can be extracted in non-cursive scripts, recognition of cursive text relies either on partial words/ligatures (holistic techniques) or characters (analytical techniques). This paper presents a comparative study on the effectiveness of holistic and analytical recognition using the latest deep learning techniques. More specifically, we employ convolutional neural networks for recognition of ligatures segmented from caption text and a combination of convolutional and recurrent neural networks for recognition of characters from text line images. Experiments are carried out on 16,000 text lines (extracted from 5000 video frames) containing cursive Urdu text extracted from videos of various News channels. The experimental results demonstrate that analytical techniques are more robust as compared to the holistic techniques and the findings can be generalized to other cursive scripts as well.