Reviewerly/exHarmony huggingface dataset:
Patch Density Estimation for Anomaly Detection with Deep Pyramid Features

Anomaly detection and localization are critical in modern manufacturing for the quality control of products. A particular challenge is that the collecting and labeling of anomaly examples are usually infeasible before implementation. To tackle the problem, a novel two-stage framework is proposed in this paper to build anomaly estimators with normal data only. Specifically, unsupervised deep representations are learned first by a modified SimSiam where an adaptation for one-class learning is implemented. Then the non-parametric method is adopted to model the distribution of training data on the learned representations as the one-class classifier to detect anomaly. Moreover, we model the distribution with different hierarchy level’s features of the convolutional neural network to achieve both image-level and pixel-level detections. Experiments are conducted on MVTec anomaly detection dataset. Competitive results of 92.6% AUROC score for image-level detection and 95.4% for pixel-level detection are obtained to demonstrate the effectiveness of the proposed method.
Three-Dimensional Reconstruction Algorithm for Facial Plastic Surgery using High-Precision Monocular Structured Light

Current facial plastic surgery lacks objective quantitative data support, resulting in unstable results. The three-dimensional reconstruction technology using structured light can scan to obtain accurate face point cloud information, and provide data support for facial plastic surgery. In this paper, the traditional three-dimensional reconstruction using the monocular structured light method is improved. The maximum values in the phase error maps of the spatial and Hilbert domains are taken as the phase error coefficients respectively, and the phase errors of the two domains are averaged by combining the optimization coefficient iteration method to compensate for the nonlinear errors of the wrapped phase and reduce the gamma distortion. Simulations and experiments are conducted to verify the feasibility of this method. The experimental results show that the proposed method reduces the maximum phase error by at least 11.0% and the RMS error by at least 10.4% compared to the average phase method.
Importance of Artificial Intelligence (AI) and Robotic Process Automation (RPA) in the Banking Industry: A Study from an Indian Perspective

The paper’s primary focus is intelligence creation by AI and fast implementation by RPARobotic Process Automation (RPA). It starts with the applicability of the Turing testTuring test propounded by Alan Turing, the father of Artificial Intelligence (AI). At the backdrop lies various events, namely the recent motivation addition of various bank account holders. These factors fuelled the demand for AI and RPAArtificial Intelligence and Robotic Process Automation (AI and RPA) implementation in the banking industry. It pitched how AI and RPA work in real-time scenarios such as financial fraud and money laundering. It discusses how AI builds the knowledge graph and recommends products and services for each customer. This knowledge is implemented and delivered using RPA. The AI application gained prominence in every banking business segment, such as equity, personal, investment and loan. The application of RPA is present in all business segments, although the percentage is increasing yearly. The AI and RPA can help banks to convert the challenges to opportunities. There have been various challenges, and the application of AI and RPA combinations is the key to solving the inefficiencies. Advanced analytical techniques on open-source data have been used in this paper.
Siamese Graph Learning for Semi-supervised Age Estimation

In this paper, we propose a Siamese graph learning (SGL) approach to alleviate aging dataset bias. While numerous semi-supervised algorithms have been successfully applied to classification tasks, most of them assume that both the labeled and unlabeled samples are drawn from identical distributions. However, this assumption may not hold due to the heterogeneity of face aging data, which gives rise to a bias and unpromising prediction. Motivated by this, our SGL learns to align the sparse distribution with the dense one for dataset debias with preserving the real aging smoothness. To achieve this, we adopt a mixup strategy to plausibly generate hallucinatory samples, which leverages amounts of unlabeled data to enhance the diversity of unbalanced classes. Moreover, we develop a graph contrastive regularization to suppress the noise introduced by auxiliary unlabeled samples. Extensive experimental results show compelling performance by only utilizing the limited scalability of training annotations.
Neural Attention Model for Abstractive Text Summarization Using Linguistic Feature Space

Summarization generates a brief and concise summary which portrays the main idea of the source text. There are two forms of summarization: abstractive and extractive. Extractive summarization chooses important sentences from the text to form a summary whereas abstractive summarization paraphrase using advanced and nearer-to human explanation by adding novel words or phrases. For a human annotator, producing summary of a document is time consuming and expensive because it requires going through the long document and composing a short summary. An automatic feature-rich model for text summarization is proposed that can reduce the amount of labor and produce a quick summary by using both extractive and abstractive approach. A feature-rich extractor highlights the important sentences in the text and linguistic characteristics are used to enhance results. The extracted summary is then fed to an abstracter to further provide information using features such as named entity tags, part of speech tags and term weights. Furthermore, a loss function is introduced to normalize the inconsistency between word-level and sentence-level attentions. The proposed two-staged network achieved a ROUGE score of 37.76% on the benchmark CNN/DailyMail dataset, outperforming the earlier work. Human evaluation is also conducted to measure the comprehensiveness, conciseness and informativeness of the generated summary.
A Systematic Literature Mapping on Using Blockchain Technology in Identity Management

Although blockchain is an emerging technology, it has been applied in a lot of domains by leveraging its features. Traditional identity management systems have many issues regarding security and privacy of personal data. Blockchain has the potential to mitigate and avoid such issues by creating trust among the parties involved in the system while reducing reliance on third-party authorities. The first blockchain-based identity management solutions were launched in 2016. Since then, due to high demand, numerous primary and experimental studies and intatives have been carried out to provide solutions to this research topic. Along with that, there are also a lot of secondary studies to overview the current state of research on this topic. However, the number of systematic research articles is still limited and each research has it limitation. Through this study, we provide a novel systematic literature including categorization of studies into predefined categories (domain, research type, place of publication), analysis of publication frequency, co-authorship, number of papers citing each paper of all studied papers. Comparing to other systematic literature mapping studies, our paper provides a more comprehensive view of the studied articles. In particular, we analyze the number of citations, which no study has ever done. In this research, we studied 361 papers published from January 2009 to April 2022 in four big databases (IEEE Explore, ACM Digital Library, ScienceDirect, Springer Link), the largest number of articles studied compared to previous researches. The obtained results show that most of the articles under validation research type (providing solution and implementing that solution but not in real-world scenarios) propose solutions/systems, models/schemes and architectures to address general problems. We also find that the majority of authors works alone or collaborate in a separate group and co-work in only one paper. This shows that there is no long term collaboration in blockchain-based IdM identity management, and thus subsequent publications presenting real-world blockchain-based identity management products do not exist.
Accurate and Efficient Federated-Learning-Based Edge Intelligence for Effective Video Analysis

Video data is the biggest IoT data which is challenging for effective analysis with good performance. Object misdetection is usually inevitable in edge-based distributed cross-scene video analysis. Traditional centralized model training can potentially result in edge data leakage. Even though joint model can be trained with federated learning while maintaining data privacy, the size of gradient data transmitted is large for computer vision models used. To address these problems, this article proposed an accurate and efficient federated learning-based edge intelligence for effective video analysis method called EIEVA-AEFL. In EIEVA-AEFL, a federation misdetection reinforcement network (FMRN) is designed to alleviate the misdetection problem. FMRN contains a vanilla object detection network and a misdetection reinforcement branch, which finetunes object detection via feature re-extraction to reduce object misdetection. To reduce the communication cost in training, an efficient federated learning strategy is designed. In this strategy, an oscillation suppression loss function is proposed to suppress the loss fluctuation resulting from data on edge clients. Average accuracy and recall increase 0.5 and 0.7 with FMRN on the Microsoft common objects in context (MS COCO) data set, respectively, and with improvements of 4.5 and 5.5 with FMRN on our self-made mis-detection data set, respectively. EIEVA-AEFL can reduce the training speed on the premise of ensuring the accuracy of the model. The model parameters, data amount, transmission delay, and convergence epochs on EIEVA-AEFL model training are reduced by 78%, 89%, 84%, and 36%, respectively.
$$\alpha$$ILP: thinking visual scenes as differentiable logic programs

Abstract Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs do not explicitly encode objects and relations among them. This limits their success on tasks that require a deep logical understanding of visual scenes, such as Kandinsky patterns and Bongard problems. To overcome these limitations, we introduce $$\alpha {\textit{ILP}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mi>ILP</mml:mi> </mml:mrow> </mml:math> , a novel differentiable inductive logic programming framework that learns to represent scenes as logic programs—intuitively, logical atoms correspond to objects, attributes, and relations, and clauses encode high-level scene information. $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> ILP has an end-to-end reasoning architecture from visual inputs. Using it, $$\alpha$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>α</mml:mi> </mml:math> ILP performs differentiable inductive logic programming on complex visual scenes, i.e., the logical rules are learned by gradient descent. Our extensive experiments on Kandinsky patterns and CLEVR-Hans benchmarks demonstrate the accuracy and efficiency of $$\alpha {\textit{ILP}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>α</mml:mi> <mml:mi>ILP</mml:mi> </mml:mrow> </mml:math> in learning complex visual-logical concepts.
Privacy-Preserving User-Centric Authentication Protocol for IoT-Enabled Vehicular Charging System Using Decentralized Identity

There has been a shift towards the use of Electric Vehicles (EV) in recent years. Though EVs offer many advantages, there are concerns on the cyber security of its components and the privacy of its users. When users charge their EVs at a charging station, they need to reveal their personal details. An attacker can compromise the users' privacy by identifying and tracking where users charge their EVs. Hence, there is a need to protect EVs from cyber-attacks and preserve its users' privacy. In this paper, we address the problem of privacy preservation of users while charging their EVs in a 5G-enabled vehicular charging system. We propose a user-centric authentication protocol for EV charging based on Decentralized Identifier (DID) and blockchain. We use Verifiable Credential (VC) together with DID which provides Zero-Knowledge Proof (ZKP) about the user. Users have complete control over their identities resulting in user-centric authentication. At the same time, a third party can verify the user's legitimacy before providing services. Hence, our protocol makes the charging service available in a secure way, preserving the privacy of the user.
Cracking the Anonymous IoT Routing Networks: A Deep Learning Approach

In recent years, IoT technology has been one of the most rapidly expanding fields, connecting over 27 billion connected devices worldwide. Increasing security concerns, such as software flaws and cyberattacks, limit the use of IoT devices. Tor, also known as “The Onion Router,” is one of the most popular, secure, and widely deployed anonymous routing systems in IoT networks. Tor is based on a worldwide network of relays operated by volunteers worldwide. Tor continues to be one of the most popular and secure tools against network surveillance, traffic analysis, and information censorship due to its robust use of encryption, authentication, and routing protocols. However, ToR is not anticipated to be entirely safe. The increasing computational capabilities of adversaries threaten Tor's ability to withstand adversarial attacks and maintain anonymity. This article describes the foundation of the Tor network, how it operates, potential attacks against Tor, and the network's defense strategies. In addition, the authors present a framework for deep learning that uses bandwidth performance to identify the server's location in Tor, thereby compromising anonymity. This article examines Tor's network's current and projected future in the Internet of Things.
A lightweight encrypted deduplication scheme supporting backup

Data deduplication technology is frequently used by many cloud service providers to handle the exponential growth in data. To protect their data privacy, users frequently encrypt their data before uploading it to a cloud storage provider. Therefore, efficient encrypted deduplication technology has been widely studied. Although cloud storage provides convenience for users, it also has the problem of high data concentration. Once an accident occurs in the data center, the impact is enormous. Therefore, it is necessary to back up the ciphertext data after deduplication. In this paper, we propose an efficient encrypted data deduplication technology that supports backup. Our scheme uses a one-way hash chain to verify the legitimacy of a user’s identity. It updates the user’s ownership of the data using the user’s index table. In this paper, we use the XOR operation to re-encrypt data to obtain backup ciphertext, reducing computational complexity. In LEDB, the user can extract the key from the data label. Therefore, the user does not need to manage the key separately. In addition, LEDB supports a two-level deduplication mechanism, which can not only ensure transmission security but also reduce network transmission bandwidth. Finally, LEDB allows the user to determine the integrity of the data before decrypting the ciphertext. Security analysis and experimental results show that our scheme is secure and efficient for data encryption and decryption.
PHYSICOCHEMICAL AND MICROBIAL QUALITY OF SURFACE WATER RESOURCES IN SOME GOLD MINING COMMUNITIES OF OSUN STATE, SOUTHWESTERN NIGERIA

Seasonal and spatial variations in the physicochemical and microbial properties of surface water bodies in some gold-mining communities of Southwestern Nigeria were determined.The physicochemical parameters were analysed using APHA standard methods.Microbial parameters analysed were total heterotrophic bacteria count (THBC), total heterotrophic fungal count (THFC), total coliform count (TCC) and the antibiotic sensitivity test.The mean concentrations of the parameters notably acidity, DO, organic matter, total hardness and nitrate were significantly higher in the rainy than dry seasons (p < 0.05).Parameters such as TS, conductivity, acidity, total hardness, TDS, DO and BOD5 were not impacted in the non-mining stations.However, pH, conductivity and TDS showed significant (p < 0.05) difference in the mining and non-mining stations while only turbidity showed very highly significant (p < 0.001) difference in the mining stations.THBC ranged from 0 to 8.3×10 6 cfu/ml, THFC ranged from 0 to 7.8×10 5 cfu/ml and TCC ranged from 0 to 2.5×10 6 cfu/ml.The mean microbial counts of THB (9.1×10 5 ± 4.72 cfu/ml), TC (1.3×10 5 ± 1.25×10 5 cfu/ml) and THF (6.1×10 4 ± 3.9×10 4 cfu/ml) were higher in the rainy than dry seasons.Also, THB (1.8×10 8 cfu/ml), TC (1.2×10 5 cfu/ml) and THF (2.8×10 5 cfu/ml) mean microbial counts were generally high in surface water sources.Bacterial isolates exhibited resistance to antibiotics in the increasing order fluoroquinolones (44.8%), macrolide (51.7%), aminoglycosides (69.0%), phenicol (71.7%) sulphonamides/trimethoprim (84.5%) and βlactams 96.6%.This study established that water sources close to gold mining sites was negatively impacted making them unsuitable for domestic uses.
Identification of Fake Products using Blockchain Technology

Blockchain, which has recently received a lot of attention, has a wide range of uses. Bitcoin, a widely used cryptocurrency, employs blockchain technology to authenticate transactions without relying on a central structure, thereby solving the problem of double spending. Consequently, the application that uses blockchain technology as its core ensures the authenticity of the data. The research uses a concept that is based on the decentralised blockchain to demonstrate that customers do not primarily rely on stores to evaluate the legitimacy of products. This study describes a decentralised blockchain architecture that is free of counterfeit items so that makers can use it to make real products without having to contact merchants directly, which will result in a significant reduction in the cost of product quality monitoring.
Game Theory based Artificial Player for Morabaraba Game

Making computers that can play games against human opponents has been a challenge since the beginning of research into Artificial Intelligence. However, it has become well known for computer programs beating grandmasters in games such as Chess and Othello. Nevertheless, there are still many board games for which computers have not been able to achieve the level of play equivalent to human players. One such game is Morabaraba, an ancient African board game. No prior research has been conducted in creating an artificial player for this indigenous game. This research aims at introducing a design of an artificial player for the Morabaraba game. An attempt is made to systematically determine an optimal strategy for the game using game theory. This study posit that the artificial player will use the most optimal strategy to play and win the game. The paper endorses the view that the equilibrium strategy describes a Morabaraba player’s moves and could be used in the implementation of an artificial player. This research study prove that an equilibrium point exists between two Morabaraba strategies and show that for fixed utility values using a game matrix, there exists an equilibrium point for which the strategy of both players remains unchanged. With the obtained results, the research recommends the implementation of an artificial player that does not counterattack. The author presents an investigation on how to find an optimal strategy for the Morabaraba game which will benefit the implementation of an artificial player.
An Empricial Study of Brute Force Attack on Wordpress Website

This paper demonstrates the brute force attack on WordPress web site and proposes the counter solution for the same. It gives step by step procedure for launching of brute force attack to enumerate the usernames and passwords of the users on the control panel of WordPress web site. This paper is motivated by the recent attack on Optus Australia by hackers and throws light on the modus of operandi of hackers. The paper also proposes few counter measures to avoid this attack.
Analysis and Detection of Fake Profile Over Social Media using Machine Learning Techniques

Social media provides a digital meeting place for people of all generations. People are making use of this medium to disseminate data and information. However, some of them are abusive, in that they exploit the systems of online media. Users and bots alike are creating phony personas to further their malicious goals, and then using those profiles to disseminate offensive content. The proposed study will benefit greatly from this insight into the online media landscape and the security threats and mitigation strategies that exist there. In this one-of-a-kind scenario, the growth of online media over the past year is studied, and the content is collected and organized according to its field. Ideas like profile characteristic assessment and substance investigation-based processes are generally promising among them. Thus, a different model is planned with the help of the concept of both types of approach, and their practical implications are discussed. The proposed working model will be implemented in the near future, and their presentation will be illustrated.
Usage of Machine Learning and Artificial Intelligence in Industry 4.0 and Banking Sector

In the banking sector, increased digitization of the economy has forced the businesses to re-evaluate their conventional business models, which is an indication that there is a need for quick and effective change in the banking industry to meet the demands of their clients while offering safe, simple, and connected services to the generation z customers. Industrial revolution 4.0 has affected and changed the banking industry more than 1.0 and 2.0 combined. This paper discusses how the industrial revolution 4.0 has affected and changed the financial and banking sector and primarily revolves around commercial and investment banks rather than reserve banks. It provides a vivid view starting from the digital economy to digitization of banks to the latest trends in the technology incorporated by the banks, to how it has changed the financial markets, to security advancements and finally to how it has changed the banking industry and how it has affected lives.
Analysis of Emotion Detection of Images using Sentiment Analysis and Machine Learning Algorithm

During communication, people are capable of producing hundreds of expressions that differ in complexity, intensity, and meaning. The main factor in a person’s life is their emotions. Even though these pictures are so diverse from one another, they all express the same emotion–-"love." This study investigates the possibility to anticipate an image’s underlying emotion using deep learning. However, there are times when it is difficult to comprehend the emotions that a person must express. In addition to being developed and managed for security concerns,observation cameras are widely used in the media industry for player articulation. The method used to capture a person’s exact emotion on camera. For the purpose of recognizing emotions, machine learning techniques, CNN algorithms, and deep learning models are also used.