Reviewerly/exHarmony huggingface dataset:
Serverless Computing Revisited: Evolution, State-of-the-Art, and Performance Challenges

Market analysts are agreed that serverless computing has strong market potential, with projected compound annual growth rates varying between 21% and 28% through 2028 and a projected market value of 36.8 billion by that time. Although serverless computing has gained significant attention in industry and academia over the past years, there is still no consensus about its unique distinguishing characteristics and precise understanding of how these characteristics differ from classical cloud computing. For example, there is no wide agreement on whether serverless is solely a set of requirements from the cloud user's perspective or it should also mandate specific implementation choices on the provider side, such as implementing an autoscaling mechanism to achieve elasticity. Similarly, there is no agreement on whether serverless is just the operational part, or it should also include specific programming models, interfaces, or calling protocols. In this talk, we seek to dispel this confusion by evaluating the essential conceptual characteristics of serverless computing as a paradigm, while putting the various terms around it into perspective. We examine how the term serverless computing, and related terms, are used today. We explain the historical evolution leading to serverless computing, starting with mainframe virtualization in the 1960 through to Grid and cloud computing all the way up to today. We review existing cloud computing service models, including IaaS, PaaS, SaaS, CaaS, FaaS, and BaaS, discussing how they relate to the serverless paradigm. In the second part of talk, we focus on performance challenges in serverless computing both from the user's perspective (finding the optimal size of serverless functions) as well as from the provider's perspective (ensuring predictable and fast container start times coupled with fine-granular and accurate elastic scaling mechanisms).
Analysing Static Source Code Features to Determine a Correlation to Steady State Performance in Java Microbenchmarks

Source code analysis is an important aspect of software development that provides insight into a program's quality, security and performance. There are few methods for consistently predicting or determining when a written piece of code will end its warm-up state and proceed to a steady state. In this study, we use the data gathered by the SEALABQualityGroup at the University of L'Aquila and Charles University and extend their research of steady state analysis to determine whether certain source code features could provide a basis for developers to make more informed predictions on when a steady state would occur. We explore if there is a direct correlation between source code features on the time and ability of a Java microbenchmark to reach a steady state to build a machine learning-based approach for steady-state prediction. We found that the correlation between source code features and the probability of reaching a steady state go as high as 10.9% for Pearson's correlation coefficient, whereas the correlation between source code features and the time it takes to reach a steady state go as high as 21.6% for Spearman's correlation coefficient. Our results also show that a K Nearest Neighbour Classifier with features selected with either Spearman's or Kendall's correlation coefficient boasts an accuracy of 78.6%.
Indoor person behavior recognition based on deep learning and knowledge graph

At present, indoor human behavior recognition technology is widely used in the fields of video intelligent monitoring, intelligence elderly care and intelligent medical care, etc. The existing research methods are more focused on classifying human behavior and ignore the connection between objects in the scene and human behavior. In order to make full use of the association between the objects in the scene and indoor human behavior, this paper proposes an indoor human behavior recognition model based on deep learning and knowledge graph. Firstly, we use YOLOv5 target detection network to identify the objects in the scene, and also use human keypoint detection algorithm to locate the skeletal keypoints of human body, extract and analyze the spatial features of objects and keypoints to obtain the human behavior feature triad, construct the behavior detection knowledge graph and perform search and inference to realize human behavior recognition. The experimental results show that the recognition accuracy of the model is 94.9% on the homemade test set.
Quantization Bits Allocation for Wireless Federated Learning

Federated learning (FL) enables multiple clients to collaborate on a common learning task via only exchanging model updates. With the progressive improvements in deep learning models, communication is becoming a primary bottleneck of FL. Quantization of model updates before transmitting is an effective technique to reduce communication overhead. Most prior literature assumes lossless transmission, but in practice, quantized model updates are distorted by wireless channels due to the variation of client locations. Therefore, this paper focuses on analysis and design of personalized model update quantization with explicitly incorporating channel diversity in wireless FL. We present a novel convergence analysis of quantized FL, which encompasses full and partial client participation, single and multiple local training iterations, and convex and non-convex loss functions. This analysis explicitly embodies the impact of personalized quantization error, channel diversity and model aggregation in FL, and also elucidates their tradeoff on tightening a convergence rate upper bound. An optimization framework, which seeks an optimal allocation scheme given a total budget of quantization bits, is proposed by minimizing an upper bound with respect to channel quality. A nearly optimal solution is derived for this non-convex integer programming problem via analytically solving Karush–Kuhn–Tucker (KKT) optimality conditions and linear search. From a perspective of outlier detection, this channel-aware allocation scheme is also extended to robust model aggregation against client dropouts. Comprehensive numerical evaluation demonstrates the performance enhancement of the proposed scheme over the vanilla allocation scheme with equal quantization bits, particularly in terms of training stability, test accuracy, and robustness.
Blend CAC: Integration for the Blockchain for Distributed Potential Network Access for the Internet of Things

Due to the rise of the IoT, disparate embedded smart devices may now work together to provide intelligent services, with or without human intervention. When used in massive IoT-based requests like Smart Grid or Smart Cities, IoTs also generate significant privacy and security problems. One of the main security challenges for IoTs is access authorization. For the sake of cooperation and information safety, it is crucial. One problem with current access control systems is the reliance on a centralized authorization server, which may act as either a performance bottleneck or a single point of failure (AC). In order to ensure the safety of IoT devices, this research offers Blend CAC, a blockchain-supported decentralized capability-based AC. With the Blend CAC, we want to provide effective mechanisms for controlling access to devices, facilities, and data in large-scale IoT networks. dependent upon A capacity allocation method is suggested to disseminate authorization credentials throughout a blockchain network. In order to have a secure identity-based capability token management approach, it is recommended to employ smart contracts for the permission registration, dissemination, and cancellation processes. Blend CAC is a proposal that might allow IoT systems to independently manage their controller and assets with having to communicate with any higher-ups. tried out on a Pic Microcontroller with a local private blockchain.
Nonparametric Keyed Hypothesis Tests Machine Learning Defense Against Poisoning Attacks

As the use of machine learning continues to grow in prominence, so does the need for increased knowledge of the threats posed by artificial intelligence. Now more than ever, people are worried about poison attacks, one of the many AI-generated dangers that have already been made public. To fool a classifier during testing, an attacker may "poison" it by altering a portion of the dataset it utilised for training. The poison-resistance strategy presented in this article is novel. The approach uses a recently developed basic called the keyed nonlinear probability test to determine whether or not the training input is consistent with a previously learnt Ddistribution when the odds are stacked against the model. We use an adversary-unknown secret key in our operation. Since the caveats are kept hidden, an adversary cannot use them to fool a keyed nonparametric normality test into concluding that a (substantially) modified dataset really originates from the designated dataset (D).
IoT Wireless Technology using lattice-based open source public-key NTRU cryptosystem

This article covers a grid-centred, open accessible, public-key NTRU crypto device that can be utilised with in Internet of Things (IoT). Because the IoT includes consumers' consumer sensitive data as well as numerous key aspects of customer’s personal sphere like necessitates specific safeguards. IoT security involves information security as well as the safety of the gadgets to which they are linked. To prevent information threats, encryption is the best answer. NTRU can implement the NTRU Encrypt public key encryption algorithm and can resist quantum computer attacks. NTRU is therefore significantly faster and capable of long-term cryptanalysis than other public-key cryptosystems such as RSA and ECC. NTRU can use one-time keys that can be rekeyed in seconds, making the encryption difficult to break even if an attacker attempts to obtain more than a few thousand keys.
Multiclass Classification of Prostate Cancer Gleason Grades Groups Using Features of multi parametric-MRI (mp-MRI) Images by Applying Machine Learning Techniques

Prostate cancer (PCa) accounted for 7.8% of all new cases and was the fourth most common cancer in 2020 with 1.4 million new cases. With 15.4% of all newly diagnosed cases in 2020 being prostate cancer, it was the second most prevalent type of cancer in men globally. Due to complex nature of PCa, it is matter of concern that development of Computer Aided Diagnosis (CAD) systems for detection PCa is not keeping up with other cancer disciplines. Feature extraction using region of interest (ROI) is an important step for developing CAD systems. Around the centre of 112 PCa lesions from 99 patients, region of interest was extracted from BVAL, ADC, and T2W MRI images. Features based on two and three dimensions are extracted from the ROI. Total 444 features are extracted and used for machine learning based classification. Comparison of the proposed approach for feature extraction is tested on three classifiers viz. Support Vector Machine (SVM), Naïve Bayes (NB) and k-Nearest Neighbour (KNN). The assessment measures used to compare the aforementioned classifiers include accuracy, recall, precision, and accuracy as well as the F1-score, Receiver Operating Characteristics Curve (ROC), AUC, and U. Kappa. SVM classification outperform as best model with features extracted from ADC and T2W modality with an accuracy of 44.64 %, FPR 0.1604, and PPVGG>1 = 0.7500.
Variants of Naïve Bayes Algorithm for Hate Speech Detection in Text Documents

Social media provides an inexpensive way to communicate with millions of users. These websites provide a platform to propagate your ideas to millions of people. The power of social media is often misused. Freedom of expressing opinions and beliefs on social media websites has been resulted in spread of hate speech. It has become a major challenge to check the dissemination of hate speech on websites of social media. We used Naive Bayes algorithm to classify text tweets into three classes i.e., tweets containing hate speech, tweets containing offensive language and tweets containing neither hate speech nor offensive language. It is a supervised machine learning based algorithm. We performed experiments with three variations of Naive Bayes i.e., Bernoulli's, multinomial and Gaussian Naive Bayes classifier. Bernoulli’s and Multinomial Naïve Bayes delivered the best accuracy values.
Emoji Prediction using Sentiment Analysis

Emoji prediction is a part of sentiment analysis, and sentiment analysis is a good tool used to classify bodies of text. Reaction to recent events on social media leads to understanding public opinions; hence sentiment analysis can be used to analyze these reactions, thus predicting the public's opinion in advance. The objective of this paper is to examine the dataset from a well-known social media platform and then, based on our analysis, foresee an emoji best suited to the mood and content of the text. To achieve this, we conducted experiments and trained and tested a few AI models on the dataset using supervised learning algorithms. The approach is very straightforward; we took a lot of texts with emojis and trained and tested our models on this data. We find that profound learning calculations (e.g., LSTM and Bi-LSTM) consistently accomplish the best exhibition.
Personality Analysis using Edge Detection

How long will it take to figure out anyone's character? How about a glance? This paper deals with the study, placement, and design of facial features with their co-relation with one's inherent personality. The face tells the story of life, which we are recorded in our body as our Facial Features. Hence one can judge the personality of someone by analyzing the facial features extractions. The shape of a face, distance between eyes, length of ear, and width of the forehead are considered for face reading. We can judge an individual's personality based on an analysis of these facial features. It can be used to identify someone's personality for different purposes. An application of the same is used in interview selection. It is predicated on the notion that persons who share physical qualities also share psychological traits. This experiment was carried out on a number of individuals, and the outcomes are consistent with the personalities of the participants. Given that it is based on measuring face traits, this method is both economical and effective. In this research paper, we are introducing a new and effective Personality Analysis System named PA, its internal mechanism, its applications, its importance/benefits, and its future scope and advancement.
An Efficient Medical Records Access Control with Auditable Outsourced Encryption and Decryption

Existing access control schemes for IoT-Cloud-based settings generally focus on investigating the fine-grained access featured with the lightweight decryption. However, these requirements are not adequate for sensitive and high volumes of data such as IoT healthcare data that is outsourced in the cloud. In this paper, we proposed a secure, fine-grained, and batch-auditable access control scheme, that supports both lightweight encryption and decryption for outsourced IoT-based electronic medical records (EMRs). Technically, our proposed scheme fully offloads a ciphertext-policy attribute-based (CP-ABE) encryption and decryption to the fog nodes to minimize the communication and computation cost for both data owners and data users. We employed blockchain to store the record’s indices and access transactions and developed smart contracts to automate user authentication and verification. In addition, we developed a ciphertext auditing algorithm to efficiently handle batch auditing. For the evaluation, we conducted comparative experiments to show that our scheme is more efficient than related works.
Information Security Awareness in Higher Education Institutes: A Work in Progress

The demands for information security in Higher Education Institutions (HEIs) are expanding as HEIs are vulnerable because of the involvement of human factors. Hence, maintaining data privacy is paramount, where most individuals interacting with systems and applications are the main stakeholders (lecturers, students, and non-academic staff). In this regard, existing literature and security experts claim that enhancing users’ Information Security Awareness (ISA) is one of the most effective protective techniques. Therefore, this study aims to propose a conceptual security awareness framework consisting of devices, application areas, and security practices and their related activities for HEIs. Moreover, five conceptual dimensions are suggested that affect users’ ISA and are necessary for HEIs while measuring the ISA of their stakeholders. For investigating and understanding these issues, interviews were conducted with IT security experts working in HEIs.
Question Classification for Thai Conversational Chatbots Using Artificial Neural Networks and Multilingual BERT Models

Question-Answering (QA) models are part of Natural Language Processing (NLP) field used for ensuring questions match the answers appropriately. QA consists of several steps, one of which is called Question Classification, which is to classify the context of communication. In this step, it categorizes group of questions based on what users need to know in order to combine answers within the same category and respond accurately. It helps saving us time to search for answers as well. In this paper, we present a question classification model for Thai Conversational Chatbot using Artificial Neural Network and Multilingual Bidirectional Encoder Representations from Transformer (BERT) models using BERT-base multilingual cased combined with Multilayer Perceptron (MLP). The method yields the highest accuracy of 92.57%, compared to the BERT-base multilingual cased combined with other classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN) and Decision Trees (DTs) with the accuracy scores of 88.57%, 80.00%, 78.57% and 60.29%, respectively. In addition, we also compare the performance of our proposed BERT model with another well-known Thai word embedding model, called Thai2Vec, which also combines with other classification models including MLP, SVM, NB, KNN and DTs, and their results of accuracies are: 85.71%, 85.71%, 75.71%, 75.71% and 58.86%, respectively. From the experiments, the BERT model combined with MLP can achieve the highest performance in term of accuracy among other methods.
Context-Aware and Attention-Driven Weighted Fusion Traffic Sign Detection Network

The detection and recognition of traffic signs in complex environments has received extensive attention, and the correct detection of small targets and occluded targets are two key issues. This paper proposes a context-aware and attention-driven weighted fusion network for traffic sign detection. Specifically, the design of context module not only enhances the diversity of global features, but also reduces the sensitivity of convolution to occluded objects. In addition, the attention-driven weighted fusion feature pyramid is designed to efficiently fuse deep semantic information and shallow representation information, enhance the transfer of foreground positioning information, eliminate the problems of semantic spacing and noise interference in the process of feature fusion, and enhance the network’s representation of small and occluded traffic signs. The experimental results on the traffic sign dataset TT100K show that the method is effective and superior.
Review on Artificial Intelligence Role in Implementation of Goods and Services Tax(GST) and Future Scope

Tax professionals now have access to new statistical and analytic tools that offer efficiency and simplicity. With the development of AI technology, a new forecasting and statistical model for tax audits has been created. AI can aid in the detection of fraud, which will aid in governmental supervision and control. Expert systems have been found to offer users a significant amount of control over how they search for solutions, freedom to choose whether to follow system recommendations, and a decrease in the need for supervision. Tax systems seem to allow for more independent work to be done, more quick decisions to be made, and a wider range of issues to be addressed. An important source of public funding in a developing country like India is taxes. The recent deployment of a nameless, AI/ML-driven tax assessment system by the Indian government. How do taxpayers feel about employing an AI-powered system for tax assessment? This Study focuses on several GST features made possible by AI and makes suggestions for improving the tools now in use.
Image Processing with Intelligence System Using Sensing in Cyber Security

The main goal of transmission map is to reproduce the provided picture or image as closely as possible to the 3-billion world brightness while synchronising the human reader within the display devices. Medicinal imaging uses these techniques to reduce noise and sharpen details to improve the visual definition of the image. When presenting medical photos, it's essential to focus on the most crucial possibilities since details are so crucial in assessing evidence and treating sickness. It might be a technique for lowering tall data's unpredictable nature. With less data, you may rough out comprising using essential component analysis. Each assessment, which is considered as the most crucial element, is a direct combination of the underlying elements and the volume of data. This information may be utilised to address a variety of recurring issues. It also demonstrated how successfully Web and picture data sources can be analysed using big data. privacy violations, techniques for safeguarding the systems' and surroundings' pattern parts, Conversely, edges, which almost always constitute intriguing aspects of a picture, are also characterised by strong shifts in gray scale image, therefore averaging lenses have the unfavourable side effect of blurring edges. The smoothing of erroneous contours caused by the use of a small range o gray value is yet another use for this sort of technique.
Innovative Cyber Security Solutions Built on Block chain Technology for Industrial 5.0 Applications

A paradigm change toward a more consumer approach has been observed in Industry 4.0, moving away from cyber-physical platforms (CPS) that seek for huge automation. The development of hyper cognitive systems, the incorporation of extended and virtual reality, the prototyping of digital machinery, and twin designs have all been cited as contributing factors to the shift. The collaborative robots, trusted machine boundaries, and supply chains powered by artificial intelligence (AI). It is anticipated that this new trend, known as Industry 5.0, would go beyond the purview of Industry 4.0 ecosystems and use large production with user-centric customization. Industry 5.0 is anticipated to support a variety of industrial verticals, including healthcare, smart agriculture, aircraft, microgrids, and supply chain manufacturing ecosystems. In contrast, information is shared across numerous heterogeneous networks that span various authority domains. So, for the industrial perimeters to synergize and be secure, trusted and secure data communication is essential. Due to its intrinsic property of absoluteness, chronological, and integrity in industrial systems, one of the most promising technologies for enhancing the security of Industrial 5.0 environments is blockchaintechnology.. Only a few books are suggested that outline the applications for Industry 5.0 that are supported by Blockchain. The piece delivers a groundbreaking study on Block chain’s role in Industry 5.0 as a security enabler. With the use of a subjective survey method and study objectives, we were able to identify the most pressing drivers, potential uses, and present an institutional vision for Blockchain-based Industry 5.0 across many different sectors. The poll seeks to offer suggestions for new Even though solutions in Industry 5.0 verticals for industry practitioners, academics, and researchers.