Reviewerly/exHarmony huggingface dataset:
Does Project Associated Situational Factors have Impact on Sustainability of Modern Code Review Workforce?

Code review is an essential practice to evaluate the quality of source code. Presently the Modern trend of code review known as Modern Code Review (MCR), an informal modified version of Fagan's inspection is widely used. In MCR process, MCR workforce that is author and reviewer work together to improve the code quality and software quality. It is a peer-reviewed process, the source code written by the author is evaluated by the reviewer. For the effective outcomes of the MCR process, it is required to focus on the sustainability of the MCR workforce so that they can sustain for a longer and produce the effective outcomes. However, the sustainability of MCR workforce is impacted by the unidentified project-related situational factors. The existing literature of MCR is lacking concerning the identification of project-related situational factors that impact the sustainability of the MCR workforce. Therefore, this study aims to perform the Systematic Literature Review (SLR) to identify the project-related situational factors that can impact the sustainability of the MCR workforce. The situational components are collected into two key classifications, for example, project release management and project attributes. The grounded hypothesis approach has been applied to get final unique and categorized list of project-related situational factors. The identified unique and categorized list of project-related situational components is additionally double checked by the expert panel for their categorizations, naming conventions and suggestions of additional project-related situational factors. The investigation results announced 18 project-related situational factors. The study will be advantageous for experts involved in situational software engineering research as well as the MCR workforce to sustain for longer by overcoming the challenge of unidentified situations.
Smart decimation method for fusion research data

New fusion research experiments will generate massive experimental data. In this context, fusion research appears in the scope of the big data where both search and access functions require new approaches and optimizations. One common data access functionality is decimation. It allows to retrieve a limited number of points of the total available. One classical mechanism of decimation is downsampling by an integer factor called step. It works by selecting a value every ‘step’ number of values. The main characteristic of classical decimation is that the selected values are uniformly distributed along the total. However, in case of time evolution experimental signals, the relevancy of data is not uniform. There are some intervals where the provided information is more complex and richer, and usually more interesting from the user point of view. This contribution presents a new data decimation technology for unidimensional time evolution signals where the limited number of accessed points are distributed following the criterion of data interest level. The new method implements, on one hand, a heuristic function which is able to determine the level of interest of an interval based on its data characteristics, and on the other hand, a selection algorithm where points are distributed based on weighted intervals.
Effect of Resonance in the Effective Control Model Based on the Spread of Influence on Directed Weighted Signed Graphs

This article proposes a method of selecting the damping factor (DF) based on finding the boundaries of the range of the domain of the DF for a model of effective controls, reflecting the spread of influence on the directed weighted signed graphs. The graph model is a fuzzy cognitive map (FCM). Two ranges of the subset values of the domain of the DF are proposed: (i) the admissible range, within which the co-directionality of the response and impact vectors is guaranteed to prevail, and (ii) the resonance range, within which there is a rapid rearrangement and reversal of the ranks of the vertices. An interpretation of the resonance surge for this model of the spread of influence is proposed.
Multifaceted sentiment analysis of public comments on the Dianping.com

Abstract Emotional analysis of text has always been a hot topic in natural language research. In view of the long-term dependence of recurrent neural networks and the fact that most models do not consider the correlation between input and output, this paper proposes a bidirectional LSTM model based on attention mechanism to judge the comment emotion. This method vectorizes the semantics of comments into the LSTM model, improves the relevance of input and output through the attention mechanism, fuses the aspect category and aspect Term, and outputs the results through the classifier. The experimental results of Dianping.com review data set provided by AI Challenger competition show that the improved method adopted in this paper is better than the common deep learning method.
Exploring Information Theory and Gaussian Markov Random Fields for Color Texture Classification

This paper proposes a novel approach to compute information theory measures from Gaussian Markov Random Field (GMRF) for color texture classification task. We firstly transform the three color channels of the input image into three set of sub-bands of the form LL, HH, HL and LH using three Discret Wavelet Transforms. We then visualize each sub-band as a GMRF from which we generate features by computing Fisher information matrix and Shannon’s entropy to encode the local spatial dependency. The concatenation of the computed features are then used as the texture descriptor, which in turn is used as input for the classifiers referred to in this work. Experiments were performed with color texture images from public databases widely used in the literature that demonstrate the efficiency of the proposed method.
Particle swarm optimization algorithm based on beetle antenna search optimization

Abstract For the standard particle swarm optimization algorithm, when optimizing multi-dimensional extreme value functions, it is easy to fall into the problem of local optimal solution and poor optimization effect. This paper proposes a particle swarm based on beetle antennae search optimization. This algorithm introduces beetle antenna search algorithm into particle swarm optimization algorithm. Each particle first updates its position according to the particle swarm optimization algorithm, and then takes the updated position as the initial value of the beetle antenna search algorithm. After iteration, a new location is obtained. By comparing the positions before and after the iteration, the optimal value is respectively compared with the individual history optimal value and the global optimal value, and the individual history optimal value and the global optimal value are updated. Joining beetles’ optimization strategy can better jump out of the local optimal value. Through simulation analysis of three different functions, it is concluded that the BASPSO algorithm has better optimization effect and better robustness.
Mobile Technology in Enhancing Students’ Higher Order Thinking Skill

Abstract 21st Century learning requires a lot of skills that students need to have. One of them is the technology literacy skills and High Order Thinking Skill (HOTS). Cognitive skills in technology literacy are based on the use of digital technology to manage information, communication tools or networks to ensure a person’s success and function in society. Many researchers pointed out that mobile technologies in general and mobile phones in particular play a fundamental role in both teaching and learning process. Today, the application of mobile technology is widely promoted in the national education system. Therefore, this paper is a concept paper aimed at discussing the capabilities of mobile technology in improving students’ HOTS based on findings from previous studies. The literature review is based on publications from 2015 to the present. It is hoped this study can be used to benefit the educators while integrating the mobile technology in their teaching and learning, and to improve the HOTS in line with current developments.
Multitasking Command and Control System of Equipment Test Based on Virtual Machine Platform

The Test Command and Control System or C2 system is an important part of equipment testing system, and its operation directly determines the success or failure of the task. With the increasing number of tasks, existing C2 system's resources and architectures have becoming difficult to meet the need to support multiple C2 operations simultaneously in a short period of time. With the help of cloud management platform, combined with virtualization, software definition network, security group and other technologies, this study proposed a method of building multi-tasking C2 system construction based on virtual machines, and successfully ran a number of parallel C2 systems on a physical machine according to this method. Through a Burn-In test, the system's performance is good. The study providing a solution for multitasking C2 of equipment testing.
Development of High Performance Computing Systems

The aim of the work is to present the development trends of high performance computers. The analysis focused on system architecture, processors and computing accelerators used. Particular attention was paid to interconnection networks, connecting system nodes. The problem of energy saving in the construction of large systems was also discussed. The data from the list of 500 largest computer systems was used in the analysis.
Eco-friendly Dynamic Task Scheduling for Regional Data Center

The resource requirement of service request of users are commonly uncertain. Thus, it is becoming increasingly inefficient to aggregate all data required for computation at a central data center. Recently, to achieve the better reliability and performance, the system has been largely depending on the regional data centers. Instead, a more recent trend is to distribute computation to meet data locality, thus reducing the resource requirement (e.g., CPU, RAM, bandwidth) while improving performance. Consequently, new challenges are emerging in task scheduling, where each task runs across regional data centers and, requiring coordination among regional data centers located in different edge, and stronger monitoring mechanism. Hereby, this paper proposes an eco-friendly dynamic task scheduling method (EcoRS) based on power prediction and threshold based queuing model for regional data centers to improve the quality of services (QoS), not merely should the traditional standards such as energy and cost be satisfied, but particular emphasis should be laid on some extended standards such as environmental footprint, while maintaining service level agreement (SLA).
Voice Command Intelligent System (VCIS) for Smart Home Application using Mel-frequency cepstral coefficients and linear prediction coefficients

Abstract Voice recognition, command and control systems have played a vital role to facilitate our modern lifestyle and routine operations through hands-free applications. Working people are busy with their routines, simple tasks such as to on/off electrical appliances at home are often overlooked leads to wastage of energy. Besides, the elderly and handicapped often face difficulties to control the electrical appliances due to their physical disability. To overcome, this project aims to develop a voice command intelligent system (VCIS) as an aid to these routine tasks such as to switch on/off a lamp, control a motor and control other modes of operation of home appliances using voice interface. In particular, VCIS involves several development processes such as database collection, pre-processing, feature extraction algorithms and classification modeling for application in smart home. This paper proposes three hierarchical stages in VCIS. The experimental results show that Mel-frequency cepstral coefficients (MFCC) surpassed linear prediction coefficients (LPC) to correlate with the voice commands in all classification stages using artificial neural network. The classification rates were successfully accomplished, 99.12% (MFCC) and 95.23% (LPC). In conclusion, the proposed VCIS can be employed as an efficient method for better quality of life.
Smart Meter Fault Diagnosis Via Data Cleaning and SAMME-CART Algorithm

The reduction of production costs and the convenience of communication have made the number of smart meters in operation already very large, resulting in later operation and maintenance work being more difficult. The fault diagnosis method for smart meters is proposed based on stage-wise additive modeling using a multi-class exponential loss function - classification and regression trees (SAMMECART). Firstly, the raw fault data of smart meter was cleaned by the algorithm of isolated forest, and the strong correlation index was selected as the input feature data using the Pearson correlation coefficient method. Then, a multi-classification model of SAMME-CART was established to classify five common fault types. The experimental results show that compared with other conventional classification algorithms, the proposed method has greatly improved the accuracy of smart meter fault classification, and the overall accuracy rate reaches 75.72%.
Using the IaC Approach for Building a Distributed Laboratory Complex for Modern Communication Infrastructures Investigation

Intensive implementation of virtualization technologies forces transition from traditional command-line (CLI-based) to software-oriented approaches in the communications infrastructures management. The modern “Infrastructure as Code” (IaC) approach is based on unified program-based control processes for components of the communication and computing infrastructures which makes comprehensive automation of these processes possible. Crucial changes, that have occurred in the field of telecommunications over the past decade, present lots of brand new requirements for the educational process organization, which, at first glance, forces doubts on the fruitfulness of traditional laboratory facilities using, that are available today in educational institutions. These changes, however, make it possible to change existed approaches for network disciplines studying and to expand laboratory complexes' abilities in educational institutions to provide developing and consolidating corresponding skills and knowledges for students. Our work presents results of using the IaC approach to combine disparate laboratory resources of an educational institution into a single software-controlled automated system to provide modern methodologies introduction in telecommunication system's studying area.
Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data

The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management. Most of the mapping and tracking efforts are manual which is resource-intensive and often lead to delivery delays. Deep Learning methods have boosted the capacity of relief efforts via recognition, detection, and are now being used for non-trivial applications. However the data commonly available is highly imbalanced (similar to other real-life applications) which severely hampers the neural network's capabilities, this reduces robustness and trust. We give an overview on different kinds of techniques being used for handling such extreme settings and present solutions aimed at maximizing performance on minority classes using a diverse set of methods (ranging from architectural tuning to augmentation) which as a combination generalizes for all minority classes. We hope to amplify cross-disciplinary efforts by enhancing model reliability.
Collaborative Generations Of Sm9 Private Key And Digital Signature Using Homomorphic Encryption

SM9 is a class of identity based cryptography (IBC) schemes released by the State Cryptography Administration of China. With SM9, a user's private key is generated by a central system called private key generator (PKG) using a master key. To prevent the disastrous damage caused by the master key leakage and to meet the corporate customers' requirement of involving in the key generation process, two collaborative SM9 private key generation schemes are proposed, in which a user's SM9 private key is collaboratively generated by two separate PKGs sharing the master key. The two PKGs are deployed in an SM9 private key service provider's site or one of the PKGs is deployed in the private key service provider's site while the other is deployed in a corporate customer's site. To alleviate the possible loss caused by the leakage of a user's private key in the end device that has no dedicated cryptographic hardware, an SM9 signature collaborative generation scheme is proposed, in which the secret of a user's SM9 private key is shared between the user's end device and a dedicated cryptographic device, and the devices collaborate to generate the signature to a message with their respective secret shares when signing a message with the SM9 private key is required. The approach for the proposed schemes is secret sharing and homomorphic encryption. The proposed schemes effectively address the private key leakage issues mentioned above.
A New Algorithm Watermark for Improving Objects Based on Clusters and Perceptions

The Internet of Things is the concept of connecting a large number of devices connected to a common network. This data communicates with each other via the Internet. Most connected devices in a single network create a complete picture of what is happening around, increase comfort and improve the quality of life of people. This article proposes a layout based on marking and improved clustering that resolves the conflict between security and perceptual resource levels. The Internet of Things, or IOT, refers to the billions of physical devices around the world that are now connected to the Internet that collect and exchange data. Thanks to low-cost processors and wireless networks, IOT can be partly turned into anything - from simple devices to an airplane. This adds a level of digital intelligence to devices, allowing them to communicate without human intervention and the fusion of the digital and physical worlds. To improve security, we are developing a stochastic positioning strategy based on a clustering algorithm to calculate the position embedded in the temporal dynamics of data measurement. This security vulnerability created by a stationary built-in situational situation can not only be effectively eliminated, but also lead to zero data breach. The results of our studies show that the proposed algorithm can effectively provide the integration of low-cost data, as well as reduce energy consumption and increase the lifetime of the network.
Cybersecurity in the Software Development Life Cycle

The range of software development life cycle models includes the waterfall model, agile software development, scrum, rapid application development, joint application development, and the spiral model. Many organizations use software development processes to create software products, and these include developers of major enterprise software packages, firms implementing commercial off-the-shelf software, and custom in-house development. In order to understand how security can be built into the software product, it is necessary to have an understanding of software development methodologies. The core pillars for robust and resilient cyber defenses include secure software, user/customer training and education, and risk-driven management practices. The waterfall model, also called the software development life cycle model, is the oldest and a popular software development methodology. The essential idea behind the agile software development movement is that the focus of software development should be on creating value for users by providing them with working software in the shortest possible timeframe.
The Technique of Data Analysis Tasks Distribution in the Fog-Computing Environment

Cognitive assistants are the promising and intensively growing field nowadays. Some of them provide powerful mechanisms of monitoring and control of individual’s health, and mental health, in particular. Yet, some aspects of mental health and individual’s safety are still uncovered: the problem has not been considered in terms of Social Media effect on the individual’s life and self-estimation level. In the current paper the problem of individual’s safety in the Internet is proposed to be solved with the cognitive assistant, based on the authorship identification techniques. The search of fake accounts is a time-consuming procedure, so it is highly desirable to decrease the time of data processing. Taking into account the peculiarities of the fog-computing concept, the technique of data analysis tasks distribution is proposed. It is based on the appropriate location of the search agents nearby the data source, where the information stores.
The Impact of Software on Eyecare in India

India has a population of 1.3 billion. Over the last decade, it has had record economic growth and reductions in poverty and infant mortality as well as an increase in life expectancy. India has managed to build industries in the areas of pharmaceutical and biotechnology manufacturing and software products with services. India is mostly rural, with more than 65-70% of its population living in vast hinterlands. The need to deliver health-care services in the last mile of every village and to every citizen is a challenge. Of the 1.2 million healthcare workers, 59.2% <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> serve 27.8% of the urban population, whereas 40.8% of health-care workers serve 72.2% of the rural population.
Blockchain-Based Distributed Energy Trading Scheme

With the vigorous development of distributed energy, the national energy industry has undergone major changes, but the imperfection of distributed energy trading systems has limited the development of distributed energy in China. Blockchain technology, as a disruptive information and communication technology, has brought about major changes in the financial and energy industries due to its decentralization, security, openness and independence. Distributed energy trading combined with blockchain can create a decentralized electricity market and promote the development of distributed energy. In this paper the market situation of distributed energy trading is analyzed and a distributed energy trading scheme is designed based on distributed energy trading and blockchain technology. The pain points of opaque information and cumbersome transaction are solved by combining the advantages of the blockchain, which gives users more initiative.
Image Semantic Segmentation based on E-Net with Different Patch-Size Convolution

Computer Vision maybe those fields that aides machine over the understanding of features of images and videos. It is making massive advances in the field of self-driving, robotics, and automation. In the self-driving car, a navigation system must be there to make a decision. The navigation system has to scan the view to decide the next move. By segmenting the image using Semantic Segmentation the navigation system can make a clear decision. Semantic segmentation is the primary step in object detection. In this research paper, various networks do the semantic segmentation are explained. While using different patch size objects can be more utilized. Here in this research demonstrated different patch sizes 3x3, 4x4, 5x5, and 6x6 for training ENET. Deep Neural Network gives the best result in semantic segmentation in terms of Intersection over Union parameter. In the results and analysis section, the results are generated by changing the patch size from the CityScape dataset images. From the analysis, it has been said that 5×5 patch size gives the highest Intersection over Union compared to other deep networks.