Reviewerly/exHarmony huggingface dataset:
Attention–Survival Score: A Metric to Choose Better Keywords and Improve Visibility of Information

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author’s paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention–survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention–survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors’ keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests.
Design-thinking skill enhancement in virtual reality: A literature study

As a methodology, design thinking involves practicing “a way of thinking” that non-designers can use as a source of inspiration instead being limited to a group of professional designers. This methodology has gained research attention because of the growing demands for social innovation and sustainability. The general public is expected to gain design-thinking skills through training or by applying design-thinking tools. Virtual reality (VR) is considered a potential tool to help accelerate augmenting design-thinking skills because it allows users to have embodied and immersive experiences. This study reviews existing literature on how VR has been used to enhance design-thinking skills. The general features of the publications such as the year of publication, design-thinking stages, VR types, targeted participants, and publication fields are analyzed for determining the latest trends and scenarios under this research topic. Further, a thematic analysis that follows creative enhancement structures is conducted to understand the role of VR in enhancing design-thinking skills, and future research directions are discussed based on the results. The review concludes that VR has the potential to enhance creativity in many aspects. Moreover, it highlights the need of gaining deeper understanding about 1) art, humanities, and societal perspectives; 2) cognition processes in VR; 3) emphasizing and defining stages in the design-thinking process; 4) technological improvements combined with the Metaverse; and 5) hybrid of the virtual and real worlds.
Epistemic and Existential, E2-sustainability. On the need to un-learn for re-learning in contemporary spaces

This paper contributes to a re-thinking, un-learning, and re-learning agenda by interrogating some core ideas and assumptions related to contemporary societal and planetary concerns, including concerns within the research enterprise. Transcending the first step that calls for pausing and re-thinking, this paper troubles universalizing vocabularies that naturalize conceptual framings and ways/waves-of-being in research and educational practices. Furthermore, it illuminates the ways in which scholarship has become complicit in re-cycling and re-creating reductionistic ideas that loop back into educational practices. Its overarching argument aligns with an emergent call within research and higher education for going beyond its universalizing monolithic ethos that has become naturalized in contemporary digital-analog entangled existence. Framed as alternative theorizing that is variously termed post/decolonial/southern thinking, these emergent perspectives are part of the introspection that is critically needed in mainstream academia, in particular in the Learning Sciences. This paper argues that this is needed to contribute to both Epistemic and Existential sustainability, i.e., E2-sustainability . E2-sustainability enables transcending issues of environmental-, economic-, social-, and cultural-sustainability: E2-sustainability assumes and includes these. Marked by alternative conceptual framings and pushed by a mobile gaze , this theoretical paper argues that major and minor shifts in thinking are needed for attending to contemporary societal and planetary challenges. E2-sustainability in the scholarly realm has relevance for transcending ethnocentrically framed biases and siloed framings of contemporary education and higher education, including teacher education. Troubling key, taken-for-granted universalizing truths and using the areas of language and educational scholarship as illustrative points of departure, this paper raises concerns regarding the outsourcing of important educational agendas to technologies, including digitalization on the one hand and concepts that build on contentious assumptions on the other hand. It is such default outsourcing that is troubled through a curiosity-driven multiversal and global-centric mobile gaze wherein both northern and southern knowledge-regimes need to be privileged. The theorizing presented in this paper builds on a Second Wave of Southern Perspectives (SWaSP) framing that has relevance for both north-centric and south-centric scholarship, including writing research. Explicitly multi/inter/cross/trans-disciplinary, this work is relevant to Epistemic and Existential sustainability given its non-allegiance to the imaginaries of mono-disciplinarity, nation-state essences, or universalisms.
A Survey of Renewable Energy Approaches in Cloud Data Centers

The increasing demand for cloud computing and cloud storage services in recent years have caused the unprecedented growth of data centers in the world. As the main consumers of energy, these growing data centers spend a lot of money on their power consumption every year. One of the most important consequences of the power production process is the increase in environmental pollution and greenhouse gas emissions. The power sector which is dependent on fossil fuels, is one of the main sources of carbon dioxide emissions in the world and plays a significant role in increasing greenhouse gas emissions which contribute to global warming and accelerated climate change. Therefore, the problem of enhancing and transforming the process of generation and consumption of energy in data centers and utilizing energy-efficient technologies has become more important. To overcome this problem, it is essential to integrate renewable energy as a clean source of energy, inexhaustible and always available, into data centers. In this article, we provide a taxonomy of the literature on methods of production, utilization, forecasting, and management of renewable energy integration into data centers. We discuss research challenges and open issues for future work to facilitate current and future researchers to work on the research problems in the field of renewable energy utilization in cloud data centers.
On the Performance Intricacies of Persistent Memory Aware Storage Engines

As key components of DBMSs, various storage engines and index structures have been proposed based on incorrect assumptions before PMem hardware is publicly available. Recent studies reveal that there is a significant performance gap in evaluating index structures on real PMem platforms as compared to DRAM-based emulators. However, a comprehensive evaluation for those PMem-aware database storage engines on real PMem hardware is still missing. Meanwhile, dynamic memory management is more important on PMem systems because PMem is slower than DRAM and unfriendly to random small-writes, and ensuring crash-consistency for the metadata of PMem allocators introduces extra overhead. Therefore, it is essential to understand the performance intricacies of PMem-aware database storage engines from the perspective of PMem allocators. This paper presents a systematic evaluation of three PMem-aware database storage engines using representative workloads and a unified benchmarking framework that is integrated with four PMem allocators. Besides the commonly used metrics, the impact of different hardware configurations (such as NUMA and eADR) on performance is also considered. Through in-depth analysis, we reveal caveats and pitfalls on using or designing PMem-aware storage engines and important insights that can serve as guidelines for future development of PMem allocators and other related components.
Smart Suspenders With Sensors and Machine Learning for Human Activity Monitoring

Most of the human activity recognition (HAR) research focuses on the use of smartphone in-built accelerometer sensors. As accelerometers are location-centric, they measure acceleration signals only at the installation points, increasing the number of sensors required to identify the whole human body activity. They have an inherent property of being noisy, thereby increasing the processing complexity and duration. This article, for the first time, proposes a body-worn suspender integrated with a strain sensor system that captures the body movement’s periodicity, resulting in less noisy readings with nonlocalized measurements. The proposed smart suspender system reduces the need for localized sensors to measure complex activities at various points and lessen the preprocessing time for smoothening the noise signals. The system recognizes three simple and eleven complex human activities using machine and deep learning algorithms with the best accuracy value of 97.85%. A comparison of the performance between kernel discriminant analysis (KDA) and linear discriminant analysis (LDA) for this system is made and KDA outperformed LDA across most classifiers.
Variant-Depth Neural Networks for Deblurring Traffic Images in Intelligent Transportation Systems

Intelligent transportation systems (ITS) with surveillance cameras capture traffic images or videos. However, images or videos in ITS often encounter blurs due to various reasons. Considering resource limitations, although recent technologies make progress in image-deblurring, there are still challenges in applying image-deblurring models in practical transportation systems: the model size and the running time. This work proposes an artful variant-depth network (VDN) to address the challenges. We design variant-depth sub-networks in a coarse-to-fine manner to improve the deblurring effect. We also adopt a new connection namely stack connection to connect all sub-networks to reduce the running time and model size while maintaining high deblurring quality. We evaluate the proposed VDN with the state-of-the-art (SOTA) methods on several typical datasets. Results on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) show that the VDN outperforms SOTA image-deblurring methods. Furthermore, the VDN also has the shortest running time and the smallest model size.
A Deep Learning Based Person Detection and Heatmap Generation Technique with a Multi-Camera System

This paper outlines a technical method for video analysis that may be used to identify persons in footage from several CCTV cameras and provide a heatmap of that information for a certain floor layout. The analysis of customer and employee behavior in retail and office settings, as well as motion tracking and advertising effectiveness research, can all be aided by the automatic creation of people density maps. With the use of video recordings made by common video surveillance cameras, density maps were created. We made advantage of CCTV cameras, which are dispersed across a retail establishment. Because the Yolov5 object detection algorithm may produce findings more quickly, we have chosen to employ it for human detection. Additionally, due to the short inference time, it is appropriate for real-time applications.
Computer Vision Based Object Detection and Recognition System for Image Searching

Computer Vision is a concept which works with the methods for automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. To fulfill the new challenges, the system which is proposed in this paper, is mainly being used for object detection and recognition of images so that the image search engine becomes more fruitful. A large dataset of 15 million high-resolution images have been taken from ImageNet with more than 21K different classes to the purpose of object detection and recognition system for image search engine which have been trained through machine learning. TensorFlow is an open source library for software which uses machine learning techniques as Convolution Neural Network to train datasets and to provide the percentage accuracy of objects in an image. The datasets have been trained so that the objects can be detected and figured out. The trained datasets create a protocol buffer (.pb) file which ensures the serialization of datasets according to their classes for matching the patterns. For giving input as well as viewing the result with best guesses, it has been designed a demo image search engine using Laravel5.4 framework of PHP language. By uploading several images on the demo image search engine, best guesses can be shown where the input image matches the pattern with protocol buffer file to provide the output results. There has been achieved highest accuracy rate of 99.983% best guesses and lowest accuracy rate of 89.282% best guesses for this proposed system which is better than the previous state-of-art.
Real-Time Hand-Gesture Recognition for the Control of Wheelchair

Currently, there are more than 1 billion disabled persons, who experience issues with the structure or function of their body, or challenges performing a task or action, worldwide. Many of them need to use wheelchairs to go around daily, yet they have been encountering many problems using traditional wheelchairs. This paper develops a prototype of an advanced algorithm for gesture recognition and decision-making for a control system for smart wheelchair (built with advanced and automated features) users, in line with contemporary technologies. Overall, hand and finger tracking using the MediaPipe framework and gesture recognition with decision-making by a mathematical model based on distances among the landmarks and thresholds to control a smart wheelchair are the main components of the proposed system. This method places a greater emphasis on users’ flexibility by requiring less hand movement. The experimental study shows that the proposed method outperforms existing methods.
Transformation from MVC Applications to Smart Contracts

A smart contract is a program running on a blockchain platform. Smart devices send data to smart contracts or change their own status based on smart contracts. Businesses want to use smart devices and smart contracts to streamline workflow because smart contracts reduce the need in trusted intermediators and cut down enforcement costs. However, developing smart contract applications is challenging due to different memory models, different interaction models, and a dearth of supporting tools and libraries. When developers are asked to reimplement conventional applications to smart contracts, it is thus ideal to automatically transform them, avoiding manual labor, as well as ensuring reliability and security. This paper contributes a set of rules to transform conventional applications in the Model-View-Controller (MVC) pattern into smart contracts running on Hyperledger Fabric, a blockchain platform preferred by businesses. Major transformations are performed in the model, while in the controller, model calls are replaced by smart contract calls. The source application and the target smart contract are all written in Java. Our rules add read-your-writes consistency that Hyperledger Fabric does not natively support. Runtime pre- and post-condition checking in the original application is supported in the transformed smart contract. We evaluated our rules on CoCoME and other MVC applications, and all code ran correctly and passed unit tests.
SignGest: Sign Language Recognition Using Acoustic Signals on Smartphones

Sign language is a bridge for communication be-tween people with normal hearing and impaired hearing. How-ever, only a few people with normal hearing have an understanding of sign language. Thus, Sign Language Recognition (SLR) has attracted significant interest from both industry and academia. Prior traditional methods have certain limitations such as light condition and extra hardware. To address these issues, this study aims to recognize sign languages using acoustic signals on a smartphone. We design an SLR system called SignGest, which captures user's sign language gestures with built-in microphones. Afterwards, we build a Convolutional Neural Network(CNN) model to extract features of different gestures to distinguish them. Furthermore, we use a Deep Convolutional Generative Adversarial Network(DCGAN) to generate abundant training data that look no different from the real samples. We handle various challenges including effective gesture segmentation and training data collection. Finally, SignGest is implemented on a server and an Android smartphone with built-in microphones and speakers without any extra hardware or infrastructure equipment. When users perform the sign language gestures before the smartphone, SignGest can recognize each of them and print the result. Through extensive experiments, our results show that SignGest can achieve robust and satisfactory performance.
Report on a Field Experiment of the Comprehension Strategies of Computer Science MSc Students

Program comprehension takes up a significant amount of the average developer's work time. Most development tasks require broad understanding of the code base. Modern integrated development environments (IDEs) possess a basic program comprehension toolset, but the primary goal of such tools is supporting program development, not comprehension. Standalone or built-in code comprehension tools provide a wider function set. Such tools could be especially important for junior programmers who lack experience with understanding large foreign codebase. In this paper, we describe an experiment we conducted with the participation of a group of Computer Science MSc students. The participants were given a simple programming task to solve which required understanding the software they modified. The students used CodeCompass, an open-source comprehension framework. During the experiment we collected various data on the students' comprehension strate-gies. We compare the results to previous comprehension models, considering the amount of programming experience and feedback from students.
Blockchain assisted Supply Chain Management System for Secure Data Management

Blockchain offers decentralized and immutable data storage. In recent years, logistics and supply chain management are slowly realizing Blockchain's impact. Leading-edge companies are trying to fight supply chain network complexity with block chain. Blockchain helps in enabling steady and cost-efficient delivery of products and improving traceability of products, coordination between the consumer's, partners, and financial aid. By considering this, the main objective of the proposed work is to merge decentralized behavior of blockchain with supply chain management to make it more protective, secure and transparent. For the implementation of the proposed framework, it uses Ganache, Metamask, MySQL, PHP, NodeJS, Solidity and JavaScript. Adding blockchain also helps in minimizing the interference of middle man attack in the processes. This technology helps in discarding forged products flowing in the marketplace. Hence, it overall maintains the integrity and authentication among all, the stages in between producer and consumer.
Game Controlling using Hand Gestures

The main goal of this system is to demonstrate how to play a computer game using human gestures. The system's secondary goal is to develop a system that allows a player to play a game without using a physical controller. This system seeks to create a gesture recognition application. The system's attached camera or webcam can be used to identify human hand gestures. The operations on the Game will be performed with the game's default gaming controls based on the system's study of detecting human Hand Gestures. A collection of instructions is included in this system for identifying human hand movements. The gestures should be performed using the palms of the hands. The system will be divided into three sections: user interface, gesture recognition, and analysis. The user interface module provides the user with all of the necessary graphical user interface to register the user's arm positions for performing gestures. To recognize gestures, the gestures recognition module can be utilized. Finally, depending on the estimated analysis, the analysis module will evaluate the human hand gesture and perform game controls. Hand gesture recognition systems that are totally reliable are currently being researched and developed. This system effort can serve as a first step in laying a foundation that can be expanded in the future.
Enhancing the Detection of Social bots on Twitter using Ensemble machine Learning Technique

Today our world experiences a large number of active social media users daily, twitter being one the most used platform for discussion on various topics like politics, sports, entertainment etc. It highly influences people's lives and therefore it is required to maintain a healthy environment in such places. Thus these places eventually become the epicenter of malicious activities, wherein someone tries to share hate or manipulate information as per their own interest. The common mass which comprises the most part of the user base having limited knowledge of such things, fall prey to these activities. At present millions of such automated accounts exist, also known as bots which are involved in malicious activities like spreading misinformation and manipulating public opinion. The work presented here is aimed at developing a framework by implementing ensemble machine learning approaches like Adaptive boosting, Gradient boost (GB) and Extreme Gradient boost (XGB) to detect these twitter bots. We have used a dataset that is publicly available from database community and evaluate our proposed approach to predict whether the user account is a bot or non-bot. Our experiment demonstrates that the estimator GB achieves highest accuracy in detecting the social bots.