Reviewerly/exHarmony huggingface dataset:
NAARPreC: A Novel Approach for Adaptive Resource Prediction in Cloud

Cloud computing is one of the most widely used web-based technologies for providing computer resources or network infrastructure services. End-users submit a variety of workloads to cloud providers in the form of web applications, digital transaction services, mobile computing infrastructure, and graphical quality services, all with various QoS requirements expressed in SLA. Cloud is highly dynamic and scalable in nature. Dynamic resource planning is a critical task to ensure the Quality of Services (QoS) in a cloud environment. To optimally utilize cloud resources, it is required to accurately predict future resource demands in a real-time environment. In the cloud computing paradigm, resources like servers, networks, and cloud storage can be allocated to end-users dynamically based on their demand. Since the cloud workload is massive as well as heterogeneous in terms of various attributes and cloud resource demands fluctuate, cloud service providers (CSP) are required to efficiently furnish the available resources. In this paper, we proposed an adaptive approach named NAARPreC to predict future resource demands in cloud environments, which influence resource administration. NAARPreC employs long short-term memory (LSTM), a deep learning model, and auto-regressive integrated moving average (ARIMA), a statistical analysis model. The proposed model significantly improves accuracy and execution time.
Data Centres in the University: From Tools to Symbols of Power and Transformation

The almost compulsory and compulsive use of digital platforms through which the university community connects to knowledge, research, teaching and management activities has intensified in the pandemic. Faced with this challenge, higher education institutions must address fundamental questions about learning in a postdigital landscape. This chapter explores how universities have created or adapted research centres to deal with data generation in their day-to-day activities. Although the centres analysed in this chapter have different profiles and expertise, they all seek to better prepare higher education institutions to cope with the datafication of society manifested in different ways (e.g. digital inclusion, artificial intelligence, privacy, ethical use of data, etc.). Based on a co-design and virtual ethnography, this work is structured in two phases: (1) identification and analysis of 31 websites affiliated to university datafication centres and (2) selection and deepening on four core dimensions of work of these centres. These comparative results highlight global trends, research agendas and priorities, but also illustrate the need to move towards a more multidisciplinary approach, understanding data not only as “tools” but also as “subjects” with an increasingly economical and symbolic power.
Gesture Recognition Glove for Speech and Hearing Impaired People

Communication is an essential need for every person in society. This socializing can be in audio, video and text forms. Gestures are the natural expressions of communication to facilitate a specific meaning. These gestures are combined with facial expressions to form a tool for the speech impaired and the hearing impaired which is known as Sign language. It varies according to the country’s native language as American Sign Language, British Sign Language, Japanese Sign Language, Indian Sign Language, etc. The researches in the field of SL recognition have been increased tremendously in the last 10 decades. This paper mainly aims at developing a Human Machine Interface based on gestures. Indian Sign Language is a visual-gestural language used to bridge the gap of differences within society and speech and hearing impaired, exclusion of translators and independent expressiveness. This system is designed with a wearable glove utilizing ten flex sensors and two accelerometers to recognize the words in the sign language vocabulary. The classified results are sent to voice module, where the voice corresponding to the gesture is played back through a speaker. The results of the first version of glove without accelerometers had an accuracy of 74.12%. The accuracy was improved to 97.2% in the second version by the placement of accelerometers over the back side of palm on both hands. Both the versions were verified with datasets varying with gender and signer. The proposed glove excels the existing gloves on constraints of sign misclassification, expenses and others related to image-based gesture recognition. Future extensions of this glove would be modification for other country’s sign language recognition or gesture-based controlled devices.
Word Sense Disambiguation from English to Indic Language: Approaches and Opportunities

Ambiguity is one of the major challenges in Natural Language Processing and the process to solve is known as Word Sense Disambiguation. It is useful to determine the appropriate meaning of polysemy words in a given context using computational methods. Generally, Knowledge, Supervised, and Unsupervised based approaches are the most common methods used to resolve ambiguity problems that occur in a sentence. The government of India has initiated many digital services for its citizen in the last decade. All these services require natural language processing to be easily accessed by web portals or any electronic gadget. Also, these services are provided by the government in Hindi or other Indian languages to better serve Indian citizens. Since English and other languages like Chinese, Japanese, and Korean have plenty of resources available to build applications based on natural language processing but due to low resources available for disambiguating polysemous words in Hindi and other Indian languages, it becomes a hindrance to building any application based on these languages. In this paper, the suggested method enables the assessment of the correct meaning in terms of sustaining data sequences. In order to automatically extract features, the proposed method uses an RNN neural network model. Additionally, it integrates glosses from IndoWordNet. The outcomes demonstrate that the suggested technique performs consistently and significantly better than the alternatives.
Corpus Building for Hate Speech Detection of Gujarati Language

Social media is a rapidly expanding platform where users share their thoughts and feelings about various issues as well as their opinions. However, this has also led to a number of issues, such as the dissemination and sharing of hate speech messages. Hence, there is a need to automatically identify speech that uses hateful language. Hate speech refers to the aggressive, offensive language that focuses on a specific people or group as far as their ethnic group or race (i.e., racism), gender (i.e., sexism), beliefs, and religion. The aim of this paper is to examine how hate speech contrasts with non-hate speech. A corpus of Gujarati tweets has been collected from Twitter. The dataset was cleaned and pre-processed by removing unnecessary symbols, URLs, characters, and stop words, and the cleaned text was analyzed. Pre-processed data was annotated by twenty-five people and has achieved Fleiss’s Kappa coefficient with 0.87 accuracies for agreement between the annotators.
Effort Informed Roadmaps (EIRM*): Efficient Asymptotically Optimal Multiquery Planning by Actively Reusing Validation Effort

Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across planning queries. This information may be computed before or during the search and often includes knowledge of valid paths. Using known valid paths to solve an individual planning query takes less computational effort than finding a completely new solution. This allows multiquery algorithms, such as PRM*, to outperform single-query algorithms, such as RRT*, on many problems but their relative performance depends on how much information is reused. Despite this, few multiquery planners explicitly seek to maximize path reuse and, as a result, many do not consistently outperform single-query alternatives. This paper presents Effort Informed Roadmaps (EIRM*), an almost-surely asymptotically optimal multiquery planning algorithm that explicitly prioritizes reusing computational effort. EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort. This allows it to find initial solutions up to an order-of-magnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.
Clutter-Resilient Autonomous Mobile Robot Navigation with Computationally Efficient Free-Space Features

This paper proposes free-space motion tubes, a motion primitive for the local navigation of mobile robots equipped with range sensors. The geometry of a candidate motion tube captures the free-space required such that the robot may execute a maneuver without colliding with obstacles. Computational efficiency is achieved by selecting meaningful samples of the tube and evaluating them at run-time in the sensor space. Increasing the sensor resolution or the number of obstacles do not have any impact in the computational cost. Experimental results with a mobile platform show that free-space motion tubes are well-suited for navigating in cluttered environments and narrow passages.
Conceptual Study of Latest Trends Used in AI

Villagers need to access information for central government and state government schemes which are launched for benefit of villagers. Today is the world of computerization and every information is available in digital mode. Citizen can access all information at their location due to artificial intelligence. But still no digital tool available for villagers to get information to access benefit of central and government scheme. A lot of budget lapsed or used by on eligible citizen. In this review, paper in brief describes the artificial intelligent used for information. Brief discussion of chatbot working, AIML format, software used for creating a chatbot. In last, it describes the working of chatbot and reviews the research paper of chatbot.
Reliable Network-Packet Binary Classification

A network packet identification and classification is a fundamental requirement of network management to maintain the quality of service, quality of experience, efficient bandwidth utilization, etc. This becomes increasingly significant in light of the Internet’s and online applications’ rapid expansion. With the advent of secure applications, more and more encrypted traffic is proliferated on the internet. Specifically, peer-to-peer applications with user-defined protocols severely affect network management. So there is necessary to identify and classify encrypted traffic in a network. To overcome this, our proposed approach network-packet binary classification is implemented to classify the network traffic as encrypted or compressed packets, with better classification accuracy and with the usage of a limited amount of classification time. To achieve this, our model uses a Decision tree classifier with one of the efficient feature selection methods, Autoencoder. Our experimental results show that our model outperforms the most state-of-the-art methods in terms of classification accuracy. Our model achieved 100% classification accuracy within 0.009 s of processing time.
Profiles Behavior Analysis in Blockchain Social Network

Currently, a significant portion of the population connects to social networks. Websites such as Facebook, Twitter, LinkedIn, and Instagram, among others, enable users to exchange ideas, information, and messages as well as build friendships. These online social networking sites are becoming a likely avenue for malevolent users who create fake accounts, hijack normal accounts, or propagate fake news. Conversely, social network companies exploit the value of user-generated content for for-profit purposes. Social networks have advantages and disadvantages, but privacy and security are among the most significant weaknesses. An alternate solution is proposed to address the existing problems with social networks. In this regard, Blockchain is one of the most promising technologies and can potentially improve social networking in various ways. In this paper, we present a study of existing research on analyzing profiles’ behavior in Blockchain-based social networks, focusing on Steemit, and explain the significance of Blockchain technology in social networks.
An Enhanced Artificial Hummingbird Algorithm for Workflow Scheduling in Cloud

The execution of complex, extensive scientific applications has been made much easier through workflows. Although it has many unresolved issues, the cloud serves as an appropriate platform for executing them. These issues must be resolved for an efficient workflow scheduling. Numerous algorithms have been put out for workflow scheduling; however, the majority of them fall short of taking into account the critical components of the cloud, such as heterogeneous resources, a pay-per-usage business model and elasticity. In this work, we present a hybrid approach based on the new Artificial Hummingbird optimization Algorithm (AHA) for modelling and optimizing a workflow-scheduling problem in cloud environment. The Predict Earliest Finish Time Algorithm (PEFT) is a heuristic model intervenes in the generation of the initial population. A good seed speeds up the process of discovering the best solution. The experimental outcomes for 100 tasks confirm that our method outperforms other cutting-edge algorithms.
Parallel Genetic Algorithms, Parameters and Design

Genetic Algorithms are widely used in the quest of optimization of real-world complex problems. Parallel Genetic Algorithms may be considered as an evolution of the traditional GA. However, they are designed differently. Designing a Parallel Genetic Algorithm depends on many parameters other than the selection, the crossover, and the mutation parameters. In the literature, we can run into multiple and confusing Parallel Genetic Algorithms model names. These models are a sort of combination of specific parameters. Some of these parameters are the number of populations, the granularity of each population, the migration operation, the overlapping operation. This article lists these parameters used to design Parallel Genetic Algorithms so that the practitioner can design his own model, knowing the meaning and the use of each parameter.
A Deep Convolutional Neural Networks for the Detection of Breast Cancer Using Mammography Images

Breast cancer (BC) is one of the malignant diseases that can affect women in the first degree. Its early diagnosis is necessary and effective in its early detection, which will contributes to its complete elimination, or slow down and delay its progression, and increases the success of treatment. Medical imaging plays an important role in the early diagnosis of diseases. In the case of BC, mammographic imaging allows clear visualization of the breast and discovery of BC in its early stages. Its classification is a technique that effectively helps radiologists in the analysis of medical images, it allows to detect pathologies and categorize them according to its appropriate stage. Deep convolutional neural network (DCNN) models have proven to be widely used in the medical field in recent years and achieve great feats in image classification, especially in terms of high performance and robustness. In this paper, we propose two approaches based on the Transfer Learning (TF) technique to classify BC using mammography images from the Mini-MIAS dataset. The first one consists in performing the classification process by fine-tuning the adopted pre-trained model with hyperparameters adjustment, while the second approach consists in using the pre-trained model as feature Extractor, then the classification phase is performed by Random Forest machine learning classifier. The DCNN model adopted in this work is VGG-19. Data augmentation technique is a necessary preprocessing step for the improvement of our small dataset. The experimental results show that the first approach classification give the highest accuracy of 97%.
Using MPI to Compare Two Protocols That Allow Paillier’s Cryptosystem to Perform Homomorphic Multiplication

Cloud computing delivers hitherto unknown storage and computation outsourcing possibilities. But the potential of exposing essential data to privacy and security issues prevents many users from transferring vital data to the cloud. Several encryption techniques have been created and studied in cloud security literature. In this paper, we explore Paillier’s encryption and its application to privacy-preserving computing outsourcing and securing the cloud, which may be helpful for various applications such as improving election, medicine, finance and banking, etc. We present a new implementation of Paillier’s cryptosystem that uses two protocols to perform homomorphic multiplications so that Paillier’s cryptosystem can be considered as a fully homomorphic encryption.
Security of Hadoop Framework in Big Data

A diversity of hardware and software solutions with distinct infrastructures make up big data. Huge volumes of data may now be stored and handled using the Hadoop architecture. It delivers rapid and economical Big Data solutions and is utilized across a range of industries, including social media, healthcare, and insurance. Built on a distributed computing architecture, Hadoop is an open source framework for processing and storing data on a cluster of shared equipment. Due to the framework’s adaptability, vulnerabilities develop. These weaknesses put the data at danger and attract attackers. Numerous vulnerability categories are studied in this research, along with various mitigation approaches. Then, we create essential security modules and a Hadoop security management system.
Emotion Detection in Real-Time Video Using Deep Learning

Challenges such as emotion recognition can not typically be solved through classical machine learning techniques and these results are limited in the context of processing very large amounts of data. A new line of research is being developed with the emergence of the concept of deep learning and large databases. The aim of this work is to describe the survey of Face Expression Recognition (FER) techniques, which includes the three major stages such as preprocessing, feature extraction, and classification, and develop the basis that leads to Convolutional Neural Networks (CNN) to create a classifier that recognizes the facial expression of a human in real-time. The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with an in-the-wild setting of 7 distinct emotions. The proposed method is competitive with existing methods in terms of training time, testing time, and recognition accuracy.
Explainable Machine Learning for Identifying Malicious Profiles in Online Social Networks

According to Our World In Data, more than two-thirds of all Internet users regularly utilize numerous social networking networks. A fake virtual identity might result in issues including the transmission of spam, malware, and misinformation, so verifying a genuine profile is a subject of significant concern. In this study, we focus specifically on an Instagram dataset using machine learning (ML). How do we understand the decisions suggested by a fake account detection system in order that we can trust them? Here, we have included approaches for understanding complicated Machine Learning Models to understand the reasoning behind a model choice, such as SHAP values and Anchors. At the very least, explainability can facilitate understanding of various opaque models. Consequently, there has been a significant resurgence in recent years of scholarly interest in the topic of Explainable Artificial Intelligence (XAI), which is focused on creating novel techniques for interpreting and explaining machine learning models. In particular, the interpretation of the decisions made by the fake account detection system used in the Instagram dataset is the subject of this study’s analysis of machine learning interpretability techniques.
A Coupled Graph Theoric and Deep Learning Approaches for Nonrigid Image Registration

Image registration is one of the essential elements in computer vision and several practical domains. Its objective is to recover a spatial transformation that aligns images. This is frequently formulated as an optimization problem based on an objective function that measures the quality of transformation with respect to the picture data and some prior information. This article revisits the concept of graph cuts as an effective optimization technique for image registration. We present a combination of graph cuts with superpixels segmentation via convolutional neural network (CNN), which produces a meaningful graph representation that can overcame the high computation cost.
Smart Learning Using Autonomous Chatbot Based on NLP Techniques

Artificial Intelligence (AI) technologies have shown exponential growth in different areas. Many companies and organizations adopt AI in their workflow to perform tasks efficacy and efficiency. Schools and universities were not an exception. They integrate several AI solutions to help students in their learning processes. AI chatbots are among the AI technologies widely used in the education domain. They understand human language and interact correctly with it through two main components: (1) Natural Language Processing techniques to understand users’ requests and generate the appropriate answers. (2) Knowledge Base (KB) to store and centralize all chatbot knowledge. Chatbot’s KB is the brain behind the chatbot. It is the central component behind any AI Chatbot. However, several researchers still construct the chatbot’s KB manually which consumes time, energy, and cost. In our paper, we propose an autonomous solution to collect a chatbot KB, preprocess it, and store it through the Python programming language. Specifically, to demonstrate the feasibility of our solution, we focus on creating a solid KB for an educational chatbot that answers students’ questions to offload teachers and automate repetitive tasks. In our paper we make four main contributions: (1) Collect the chatbot’s KB through our autonomous solution. (2) Preprocess the chatbot’s KB and makes it clean to use. (3) Understand students’ requests by applying four main techniques: Latent Dirichlet Allocation (LDA), Term Frequency-Inverse Document Frequency (TF-IDF), BERTOPIC and KEYBERT. (4) Develop an AI chatbot to explore the KB and satisfy students’ needs.
Lexical Simplification of Arabic Educational Texts Through a Classification Approach

Readers wishing to learn a new language need texts adapted to their reading level. Indeed, readers with language or learning difficulties encounter challenges in understanding texts containing complex words. Therefore, identifying these words and generating easier synonyms and definitions can enhance text comprehension. In order to confront these problems, we introduce in this study a lexical simplification approach to educational Arabic texts based on readability measurements. This approach involves the task of identifying complex words and proposing substitution candidates followed by a process of selecting the best substitution and replacing it in the original text. The performance of the approach is evaluated on two different corpora dedicated to readers of Arabic as a first foreign language. The obtained results are very encouraging and constitute a first attempt to automate the simplification of Arabic texts in order to support new learners.