Reviewerly/exHarmony huggingface dataset:
Role of Popularity and Recency for the Discovery of Experts in Stack Exchange Software Engineering Q&amp;A Website

Question answering websites are prominent knowledge sharing websites on which people or software developers get the desired responses to their posted queries by experts. Recommending the proficient developer or an expert for answering a query in these sites is a challenging task. In recent literature, the various metrics like centrality, connectivity, and PageRank have been applied on developer social network for identification of experts. Page ranking technique analyzes the developer links to identify their popularity in a community. This technique has been immensely used in past studies. We believe that in addition to popularity, recency also plays a vital role in analyzing the expertise of developer as expertise decays with time. Thus, this paper proposes a time-based expertise model namely developer prediction using PageRank with time decay (PRTD). The proposed approach identifies the prominent developer based on popularity and recency for addressing queries. Performance of the PRTD has been analyzed on Stack Exchange Software Engineering Q&A website using metrics such as precision, mean reciprocal rank, and mean average precision. The results show that time decay factor improves the precision and proposed method, and PRTD outperforms existing PageRank-based developer prediction.
Merging Operation for Domain Ontologies in Semantic Web: Some Issues

In the Semantic Web era, ontologies are an essential component for the Semantic Web to represent, process, share knowledge in different domains. It is mainly defined as a vocabulary for Semantic Web to describe a particular domain with meanings of used terms. As ontologies are increasing day by day, so there is requirement or ontology management that involves ontology mapping, merging, creation, reuse, etc. Ontology provides interoperability among different Semantic Web applications across the heterogeneous system. For this ontology, merging and mapping tasks are used to join multiple ontologies to produce accurate, consistent merged ontology for interoperability among heterogeneous systems. This paper presents different techniques used for ontology merging, their characteristics, and the tools used for ontology merging.
A Study for Sign Language Detection Using Deep Learning Methods

In this paper, the image classification task for the objective of sign language has been done. We have created our own dataset and have used references of the ISL (Indian Sign Language) as well as looking into the ASL (American Sign Language), and our model will be trained on the Indian sign language. The pool of images while training and testing has been randomly sampled. We will be implementing and training from the scratch the state-of-the-art architectures such as VGGNet, ResNet, and other custom-made models. After fine-tuning and evaluating all the models, best performing model has been selected. For further process, various pre-processing techniques on the images such as flipping the images and segment capturing have been done. The resultant model classifies the input images with better performance.
Vehicle Object Detection Using Open CV

Detection and counting of automobiles are becoming more important inside the field of toll road control. The detection and counting of motors have significant role in a shrewd transportation device, especially for site visitors’ management. Traffic problems have become the largest problem in which city planners going through for years. The detection of moving automobiles extra accurately, numerous laptop vision strategies, vehicle counting is done by way of the usage of a digital detection area. Traffic evaluation will calculate for the variety of automobiles in an area consistent with a few arbitrary time periods and classify the vehicles. But the moving automobiles and their detection, monitoring, and counting are very crucial for drift of site visitors in monitoring, planning, and controlling. By studying the recorded video of site visitors waft sequence from a digital camera, a video-based solution era is applied in aggregate with digital detector and blob tracking technologies and YOLO are necessary. With this era, we carried out Open CV for real-time video applications. Such strategies assist us to come across, music, rely, and classify the shifting cars. However, because of the incredible size of cars, their discovery remains a task that once and for all contributes to the accuracy of vehicle statistics. To address this challenge, this paper proposes a calculator based on a car vision.
Movement Analysis of Mentally Challenged Individuals Using Cloud and Machine Learning Model

Movement analysis of mentally challenged becomes more open and prevalent with the advancement of sensors embedded in mobile phone devices and cloud infrastructure for data streaming. In this paper, ‘movement analysis of mentally challenged individuals using cloud and machine learning model’ a framework for monitoring specially-abled multiple individuals is proposed. In the proposed framework, different conventional classification techniques of machine learning (ML) algorithms such as logistic classifier, ensemble method, and deep learning (DL) networks such as recurrent neural network (RNN) and dense neural network are used to recognize their activities. The popularly known KAGGLE-UCI datasets which contain smartphone accelerometer data are used to develop the proposed ML model. Performance analysis of classification-based activities recognition schemes is evaluated in terms of precision, recall, $$F_1$$ -score, and accuracy. Results show that DL model with dense and dropout layers after hyperparameter tuning has performed better when compared with rest of the classifiers.
Blockchain Mechanism for Resolving Privacy Issues in a Smart City

With the continuous emergence of smart cities around the world, not only the economic development has been improved, but also the convenience of people's life has been improved. However, in the construction of smart cities, a large number of data references have been leaked, causing people to panic about privacy information. Firstly, this paper summarizes the security and privacy issues in smart city based on the literature about smart city and then summarizes the classification, characteristics and applications of blockchain applied in smart city. Finally, according to the wisdom of the city construction, according to the data acquisition, recording, and analysis of the privacy and security issues of wisdom city in the future were brought forward the research question, research methods in data security and data privacy, and data security and privacy protection for the study of wisdom city provided an important research direction, to fill the gaps in this field.
Twitter Sentiment Analysis Using Machine Learning Techniques: A Case Study Based on Farmers Protest

Twitter is an excellent initial point for social media analysis. People directly share their opinions through Twitter with the public. One of the very common analyses which can perform on many tweets is sentiment analysis. The 2020–2021 Indian farmer’s protest is a protest against three farmer acts which were passed in parliament of India in 2020. In this paper, we have performed sentiment analysis of the protest of farmers in India (2020–2021) by considering the opinion of the people. The data is taken from hashtags that are related to farmers protest and some minor hashtags related to farmers. Based on the result of analysis, we conclude the impact of the protest done to repeal the farmer’s act, on India. On analysis, we obtained the result as percentages of positive, negative and neutral.
Detecting Hate Speech in Arabic Tweets During COVID-19 Using Machine Learning Approaches

Content on the Web is increasing day by day, especially on social media, as all users can express their opinions freely and without restrictions. Accordingly, many negative activities have appeared, such as abusive language, racism, and hate speech. Hate speech is one of the negative social media manifestations that require tools to be detected. In this paper, we try to detect hate speech in Arabic tweets published during the COVID-19 pandemic. We compiled a dataset during the pandemic period from January 31 to March 6, 2021. We used a set of machine learning models, namely support vector machine (SVM), random forest (RF), logistic regression (DT), decision tree, AdaBoost, k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB). For feature extraction, we used TF-IDF, where we trained the dataset in three types: unigram, bigram, and trigram. The best results were achieved by LR, RF, and SVM, with an accuracy of 90.8% for LR.
Machine Learning-Based Movies and Shows Recommendation System

Netflix is a subscription-oriented streaming service platform which is available on various application stores that allows customers or viewers to view television series and movies through any Internet-connected device (irrespective of small screen or large screen) without any advertisement interruptions. Since Netflix has such a large collection of content, it can be difficult to decide which movie or show to watch. Users on the Internet are always looking for something new and something that they like, and to do so, they search endlessly on the Internet. If the time spent searching does not yield a fruitful result, all the time spent searching will be wasted, and the entire purpose of being on an “over the top” platform that promotes instant playback so that people can watch anything at any time will be futile. And, if the user is new to the platform, he or she will take some time to become acquainted with the environment, such as the variety of content, his or her area of interest, and following up with a specific content. A recommendation system can solve the problem of searching by intelligently filtering a large volume of dynamically generated data to provide customised services. It can assist people in quickly finding the movie or show of their choice. In this work, a movie and show recommendation system have been developed for Netflix services using the support vector machine learning approach. The emphasis has been given on developing a simple recommendation system which provides a good user experience. It is found that the system can recommend a list of movies or shows based on the user’s searching interest, thereby saves time.
Blood Cancer Detection with Microscopic Images Using Machine Learning

K-means transformation, histogram equalization, linear contrast stretching, and share-based features are all used to detect leukemia. A method for automatically classifying leukocytes using microscopic images is proposed. This proposed model used MATLAB to find leukemia cells in healthy blood cells, and it requires no medical equipment or expert and heavily relies on automation. This technology can detect anemia, malaria, vitamin B12 deficiency, and brain tumors. The proposed method correctly identifies WBCs and leukoblasts in images and refines the identification, thresholding, and segmentation phases. This improves WBC counting and overall segmentation accuracy, which leads to better shape feature extraction, which is critical for this problem. New features for this type of analysis must also be studied and analyzed. Finding the most discriminatory features will provide the best accuracy. Determining whether adjacent leukocytes can be separated is critical for counting all leukocytes in an image.
Analysis of Variants of BERT and Big Bird on Question Answering Datasets in the Context of Scientific Research Article Reviews

BERT and Big Bird are some of the greatest successful representation models in the field of natural language processing (NLP) in the recent past. Question answering comes as a significant part when comes to automating the scientific research article reviewing process. No major research study is available how BERT, Big Bird, and its variants could be efficiently utilized for question answering (QA)-based problems in the context of automating scientific research article reviews. This article focuses on a detailed study of NLP-based state-of-the-art methods, on question answering datasets and its usage in the field. The major insights and observations identified are mentioned for helping the researchers in the similar field of study. This work is conducted as a base work to identify how well these transformer-based methods and QA datasets can be applied for building an efficient and automated research article review system in the near future.
Analysis of Single Missing Gate Faults in Quantum Circuit

Paul, Shubhrojit Handique, Mousum Sarma, Hiren Kumar DevaThe basic feature of a computational machine which makes it easy to perform the tasks depends largely on the computational power. The recent advancement of quantum computation has gained importance because of its potential use of increasing computational power and high efficiency of information processing. In general, finding two prime numbers of a very large number in a classical computation is a laborious task, but the quantum computation shows the evidence of fast computation to solve this problem. Due to the intrinsic properties of quantum computation, it is different from classical computers. In this paper, we specifically deal with quantum circuits and its faults. Here, quantum circuits involve the computational routine of quantum operations on quantum data such as qubits and real-time classical computation. In similar to the conventional circuit, the testing procedure is needed for observing the correct behaviour on qubits in quantum circuits. In this paper, we basically deals with single missing gate fault (SMGF) that might occur in quantum circuits. For this purpose, we proposed a fault detection method for SMGFs and experimental analysis is performed.
Differential fault analysis on the lightweight block cipher plug‐in plug‐out

Abstract In recent years, many lightweight block ciphers were proposed to provide security for resource‐constrained environments such as Internet of Things (IoT). PIPO, which stands for “plug‐in plug‐out”, is just a lightweight bit‐sliced block cipher offering excellent performance in 8‐bit AVR software implementations. In fact, PIPO owns 64‐bit input and output, 128‐bit secret key. In this article, we consider the differential fault analysis (DFA), a typical side‐channel attack, on the PIPO cipher. More concretely, for the first time, we apply the mixed attack model, which considers the DFA on the encryption state and key schedule simultaneously, to recover PIPO's 128‐bit master key. The theoretical analysis shows that, in average, after injecting 4‐byte faults, the complexity of obtaining the master key reduces from 2 128 reduces to 2 14 . In fact, this attack model alleviates the assumption on attacker than the bit‐injection case. It should be noted that our analysis also holds for other bit‐sliced block ciphers. Finally, the simulations show that our proposed DFA on PIPO cipher is rather practical.
Gesture-Based Drawing Application: A Survey

In the software sector, there is a wide range of painting software. Each of these painting applications has its own set of benefits and drawbacks. The common disadvantage in all these programs is that each of them requires a pointing device to draw. Our project is centered on the creation of a system that draws on a screen using human hand gestures. The user doesn’t have to make use of any external hardware device to draw. This system makes use of different hand gestures to draw on the screen. This survey paper presents a comparison of various methodologies proposed by different authors on “hand gesture-based drawing” system. These systems make use of Human Hand Gestures for writing characters, words, or free drawing in free space. There are systems that make use of a stylus, digital pen, electronic hand gloves, and other external equipment to interact with computers and there are certain systems that do not make use of such external equipment but, make use of only hand gestures to interact with computers. Each of these methods has its own advantages and disadvantages. The aim of this survey paper is to present the most efficient and accurate method out of the below-elaborated methodologies.
Blockchain-Based Framework to Handle Security and Privacy for IoT System

At present, the number of devices connected to IoT (the Internet of Things) systems is growing exponentially, and the operation of these devices generates huge amounts of data. Easily accessible data, such as a home address, age, and medical history, can cause users’ privacy to be compromised. Managing and using such large amounts of data safely and effectively is a major challenge. The most important factor affecting the security of IoT operation is the security of IoT devices, which has not been properly addressed so far. Blockchain is a database composed of multiple nodes, with no central management equipment and institutions, security between nodes is verified through encryption technology, and there is no mutual trust. This article focused on the application cases of blockchain in the field of IoT system security and makes a literature review of the application of blockchain in the IoT systems. An important contribution of this paper is to provide a direction for subsequent researchers to conduct research on the security issues of the Internet of Things handled by blockchain. The application of blockchain technology in the IoT systems has become inevitable. Only by constantly strengthening the applicability and application research of blockchain technology in IoT can we meet the increasingly strong demand for IoT system technology and promote the further development of IoT systems.
A Semantic Approach to Data Integration from Clinical Polystore

Polystore refers to the data sources that span multiple data representations, structure and model. The data retrieval and integration systems experience challenges arise due to polystore. The Healthcare data retrieval applications combine the patient data from specialized clinical data stores. Resolving the heterogeneity of the data sources through transformation in single common data model is expensive and cannot be efficiently accomplished with Extract, Transform and Load (ETL)-based frameworks. The research work models the health data sources using Resource Description Framework (RDF) data standards to provides uniform access of underlying distributed data sources. The research work presents the ontology-based data integration solution with R2RML/RML mappings for relational and non-relational clinical polystores. The solution supports schema alignment which develops a common understanding of data. The work also presents the query translation of SPARQL to native database languages using R2RML/RML mappings to enable query answering across polystore data.
Research on English Literacy Among Chinese College Students

English is learned as a foreign language among Chinese college students. Traditionally, tests and examinations are two major approaches used to assess the achievements of English learning among students in China. Despite that the assessment based on these two approaches is effective on screening and selection, its role in monitoring education process and improving education quality is disregarded. The overall development of students’ English literacy, which includes their awareness to the differences between English and Chinese languages, their attitudes to English learning, their knowledge on English language and their competence to utilize English in authentic contexts, cannot be assessed via these two approaches. Therefore, it is of great necessity to construct a new framework of assessment covering these aspects. In this research, on the basis of the theory of educational assessment (the fourth generation) and the concept of teachers’ assessment literacy, such a framework is proposed and used to assess the level of English literacy among Chinese college students. This research intends to explore the level of English literacy among Chinese college students and the influential factors to their English literacy. This research will provide a theoretical significance as well as the practical significance to the development of assessment on English education in China.
Some results on relation algebra reducts: Residuated and semilattice-ordered semigroups

Abstract In this paper, we show that the class of representable residuated semigroups has the finite representation property. That is, every finite representable residuated semigroup is representable over a finite base. This result gives a positive solution to Hirsch and Hodkinson (2002, Relation Algebras by Games). The finite representation property for residuated semigroups also implies that the Lambek calculus has the finite model property with respect to relational models, the so-called $R$-models. We also show that the class of representable join semilattice-ordered semigroups is pseudo-universal and it has a recursively enumerable axiomatization. For this purpose, we introduce representability games for join semilattice-ordered semigroups.
Towards Tangible Algorithms: Exploring the Experiences of Tangible Interactions with Movie Recommender Algorithms

Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces (GUIs), there are no explorations of Tangible User Interfaces (TUIs) to improve the experience with such systems, despite the valid existing academic arguments in favor of this exploration. Therefore, centering on transparency and control, we analyzed how 18 users of movie recommender systems perceived four different TUIs using individual co-design sessions and post-interview questionnaires. Through thematic analysis, we identified seven design considerations while designing TUIs to interact with algorithmic movie recommender systems: (1) Distinctions between TUIs and GUIs; (2) TUIs replacing predominant interfaces; (3) Preference for single-device TUIs; (4) The relevance of granular control for TUIs; (5) Apparent transparency limitations of TUIs; (6) TUIs and algorithmic social computing; and (7) Overview of specific design choices, including advantages and disadvantages of soft, hard, rounded, cubic, and humanoid interfaces. These findings inspired Recffy: the first functional TUI designed to enhance awareness and control in personalized movie recommendations. Based on this study, we propose the concept of Tangible Algorithms: TUIs dedicated to enhancing the interaction of algorithmic systems and their profiling processes or decisions in a specific context. Furthermore, we describe the relevance of tangible algorithms and design guidelines to promote them in diverse AI contexts. Finally, we invite the HCI and CSCW community to continue exploring tangible algorithms to address the interaction with algorithmic systems, including the collaborative and social computing dynamics they can promote in diverse AI contexts.