Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Different Machine Learning Algorithms for Credit Card Fraud Detection

It is essential that credit card companies are able to detect fraudulent transactions so that customers are not charged for items they did not purchase. Data can be used to solve these issues. Science and its importance, as well as machine and soft learning, could not be more critical. When someone defrauds you of your money or otherwise harms your financial well-being through deception or other illegal means, this is referred to as financial fraud. Billions of dollars worth of financial fraud is committed every year. According to the Federal Trade Commission(FTC), the number of theft reports has more than doubled in the last two years. One of the major types of financial fraud is credit card fraud. As the number of online transactions is growing, so is the number of credit card frauds. An effective solution is necessary to reduce loss due to fraudulent transactions at the initial stage. An effective way to do so would be to use machine learning algorithms to detect credit card fraud. This paper examines latest advances and application in the field of machine learning-based credit card fraud detection. In this paper four machine learning algorithms have been analyzed and compared on the basis of their accuracies. It is found out that Catboost algorithm works best to detect credit card fraud with an accuracy of 99.87 percentage. The dataset for credit card fraud detection was taken from kaggle.
Novel Blockchain - Based Models for Healthcare and Life Science Solution

The health policy related to vaccination against infectious diseases of each country aims to limit epidemics and to preserve the health and workability of citizens, to prolong and to improve their lives. The coverage of the entire population with vaccination is one of the key factors in achieving these goals. This paper proposes several models, based on blockchain technologies, that ensure vaccination of the entire population, ignore the possibility of duplication and falsification of information, minimize paperwork between institutions involved in the vaccination process, improve the process of planning the necessary vaccines and more. The models are implemented on a private blockchain Hyperledger Fabric.
Technique for Improvement of Backup and Restore Strategy Based on Blockchain

In this paper a technique is presented for improving backup restoration strategy efficiency by using efficient backup strategies and blockchain technologies. An overview is provided of contemporary backup strategies, cloud providers and a mechanism is proposed for decentralized regulation of restore strategies. The restore algorithm is described and the calculation of parameters for quantitative measurement of the decentralized system reliability is provided. The paper provides efficiency improvement criteria for filesystem backup and restore strategies as well as proposes a new quantitative measurement for decentralized backup solutions.
Odyssey: A Social Network Mobile Application Using Hybrid Recommender System

Tourism is now one of the largest industries in the world, and there is a lot of curiosity arousing about exploring new locations. People who enjoy exploring different cultures and cities have a hard time finding information before they set out on an expedition. The paper describes Odyssey, an Android social network mobile application that makes use of a hybrid recommendation system that uses content-based and collaborative filtering where they will recommend items similar to those already consumed and to other users' characteristics. The methods based on Collaborative Filtering have been empirically proven to be more precise. The reason for recommender system is to provide users with lists of places that have been suggested by other users by observing their characteristics and preferences. Additionally, Odyssey provides various features such as sharing reviews about places, plan upcoming trips and interacting with the community.
Nowcasting of Corporate Research and Development trends through news article analysis by BERTopic: the case of Japanese electric company

Various means exist for obtaining information about a company. However, many of them are not timely and, from the investor’ s point of view, do not have enough information. In particular, it is even more difficult to find out trends of diversified companies with multiple segments. Therefore, this study aims to extract timely information on the trends of each segment of a company from news data using BERTopic. The analysis targets news headlines of diversified electronics firms listed on the Japanese stock market. The sample period was 24 years, from 1996 to 2019, and the number of news items for analysis was 26,058. As a result of the analysis, we found that (1) BERTopic can classify the target news into 46 topics, (2) it is possible to identify company segments and extract trends in company activities from the classified topics, and (3) it is also possible to visualize the time-series transition of topics related to each segment. (4) The results obtained from the analysis were used to determine the value of the company's investment in the market. In addition, the results obtained from the analysis were consistent with the descriptions in the annual reports. These results indicate the possibility of obtaining highly immediate information on corporate trends, such as R&D, through the analysis of news headlines via BERTopic.
Device Scheduling for Wireless Federated Learning with Latency and Representativity

Existing device scheduling methods in wireless fed-erated learning (FL) mainly focused on selecting the devices with maximum gradient norm or loss function and requires all devices to perform local training in each round. This may produce extra training costs and schedule devices with similar data statistics, thus degrading learning performance. To mitigate these prob-lems, we proposed to schedule a subset of representative devices and find the corresponding pre-device stepsizes to approximate the full participation aggregated gradient. Taking into account the limited wireless bandwidth, we formulate an optimization problem to capture the trade-off between representativity and latency by optimizing device scheduling and bandwidth allocation policies. Our analysis reveals optimal bandwidth allocation is achieved when all scheduled devices have the same latency consisting of computation and communication latencies. Then, by proving the non-monotone sub modularity of the problem, we develop a double greedy algorithm to solve the device scheduling policy. To avoid the local training of unscheduled devices, we utilize the historical gradient information of devices to estimate the current gradient for device scheduling design. Experimental results show that the proposed latency- and representativity-aware scheduling algorithm saves over 16% and 12% training time for MNIST and CIFAR-I0 datasets than the scheduling algorithms based on either latency and representativity individually.
Multi-step Attack Detection and Mitigation Enhancing In-Network Flow Classification

Recent in-network flow classification methods are able to run within the data plane of network switches allowing intrusion detection at linerate. Although this enables fine-grained, scalable and timely analysis, outcomes are still subject to uncertainty, aggravated by limited data plane resources and language constraints of respective programs, and hence associated with higher risk of misclassification. Countering these deficiencies by leveraging increased computational capabilities at CPU level of the switches helps to obtain more sophisticated analysis decisions. However, exporting metadata for packet streams from data plane into the network operating system space to run downstream analysis is associated with additional delay and also limited regarding the amount of data that can be shared to ensure scalable processing. To address this trade-off, a certainty-based approach that selectively combines advantages of lower latency and data locality at the data plane with higher computational power and analysis complexity in the operating system is proposed. Therefore, a two-tier flow classification method integrates initial in-network inference on early subflow metadata with advanced decision support provided by a subsequent machine learning-based analysis step. In addition, load and flow monitoring is employed to track long-term and voluminous heavy hitters, supporting a flow detour and throttling mechanism to assist in controlling the volume and velocity of suspicious packet streams. Evaluations show that, first, the cooperative behavior using both traffic classification steps allows for improved accuracies while providing scalable, timely and certain decisions. Second, considered steps to handle potentially malicious heavy hitters allow traffic control to reduce negative impacts on benign flows.
Artin’s Theorem Towards the Existence of Algebraic Closures

Summary This is the first part of a two-part article formalizing existence and uniqueness of algebraic closures using the Mizar system [1], [2]. Our proof follows Artin’s classical one as presented by Lang in [3]. In this first part we prove that for a given field F there exists a field extension E such that every non-constant polynomial p ∈ F [ X ] has a root in E . Artin’s proof applies Kronecker’s construction to each polynomial p ∈ F [ X ] \F simultaneously. To do so we need the polynomial ring F [ X 1 , X 2 , ...] with infinitely many variables, one for each polynomal p ∈ F [ X ] \F . The desired field extension E then is F [ X 1 , X 2 , ...] \I , where I is a maximal ideal generated by all non-constant polynomials p ∈ F [ X ]. Note, that to show that I is maximal Zorn’s lemma has to be applied. In the second part this construction is iterated giving an infinite sequence of fields, whose union establishes a field extension A of F , in which every non-constant polynomial p ∈ A [ X ] has a root. The field of algebraic elements of A then is an algebraic closure of F . To prove uniqueness of algebraic closures, e.g. that two algebraic closures of F are isomorphic over F , the technique of extending monomorphisms is applied: a monomorphism F → A , where A is an algebraic closure of F can be extended to a monomorphism E → A , where E is any algebraic extension of F . In case that E is algebraically closed this monomorphism is an isomorphism. Note that the existence of the extended monomorphism again relies on Zorn’s lemma.
Load balancing strategy for workflow tasks using stochastic fractal search (SFS) in Cloud Computing

Infrastructure-as-a-service cloud environment provides infrastructure services per the user's workflow requirements using the pay-you-go model on demand. Workflow applications are highly used in IT industries, project management, supply chain management, large distributed management, and many more. Load-balanced workflow allocation to solve all the requirements of the cloud users is already proven NP-hard. In this scenario, meta-heuristic algorithms are one of the best candidates to solve the load balancing problem in the cloud to achieve an optimal allocation schedule. In this paper, we proposed a load-balanced workflow allocation method using stochastic fractal search (SFS) algorithms to maximize the resource utilization of the virtualized cloud resources. In this way, the load imbalance on virtual machines can be minimized. SFS is an evolutionary optimization method that uses the natural growth behavior in the search process. The performance analysis is conducted by implementing the workflow load balancer in MATLAB. The study shows that the solutions achieved by the proposed SFS strategy are far better than those obtained by particle swarm optimization (PSO).
Analysing RSA and PAILLIER homomorphic Property for security in Cloud

One of the critical goals of advanced cryptography is to protect data privacy. Although protecting sensitive data has always been essential, it has become much more due to the widespread use of cloud services. Conventional encryption schemes have one drawback: data must be decrypted before processing. Best practices have long recommended that data must be secured at rest and in motion when using public cloud services, i.e., we need encryption at the storage as well as at processing levels. Homomorphic encryption is an advanced cryptographic approach in which the processing of encrypted data is possible. In this paper, we have presented the insights required for understanding homomorphic encryption approaches. We examined and compared two partial homomorphic techniques, RSA and Pallier algorithm based on efficiency, base method, and homomorphic property.
Deep Contextualized Embeddings for Sentiment Analysis of Arabic Book's Reviews

Nowadays, sentiment analysis has become a vital research field involving various research fields, including data mining, computational linguistics, and natural language processing. It seeks to identify and extract the sentiment polarity of written textual data (post, tweet, review, etc.). Context-sensitive representations produced by the language model ELMo are generally aided with the polysemy and capturing words with diverse sentiment valence, allowing for more effective handling of the Arabic language's ambiguity and complexity. Recently, deep learning has shown robust data modelling capabilities for coping with complex and massive datasets. In various language models, it is considered the most advanced-based model. In this work, firstly, we investigate the effectiveness of deep learning models with context-sensitive representations. Finally, we develop a bidirectional LSTM deep model for sentiment analysis on Arabic book reviews with the ability to capture the polysemy in the context. This study aims to determine whether book reviews can be categorised as displaying positive or negative sentiments. The experiments prove that our proposed method considerably outperforms the state-of-the-art sentiment analysis tailored to Arabic book reviews.
Blockchain Base Community Cluster-Federated Learning for Secure Aggregation of Healthcare Data

Federated machine learning is a decentralized/ distributive method to train machine learning models by keeping the datasets of the collaborators private and safe at their respective sites. The collaborators send their model updates to a central aggregator which merges the local models into a global model and then sends the model updates to all the contributing parties. Thus, only model updates are sent ex-situ while the data remains in-situ. This method of working assumes the aggregator to be safe from malicious gradient tampering, poisoning attacks, and the introduction of backdoors, which is not always the case. So, in this work, a Blockchain-based federated learning method is proposed for the secure aggregation of private data. In the proposed blockchain-based model, aggregation is carried out as the clients submit their respective local updates, resulting in accuracy comparable to the traditional centralized models. Moreover, for large datasets, the model provides lower latency, and thus, it can be a good solution for real-time and near-real-time applications.
Using Deep Packet Inspection Data to Examine Subscribers on the Network

This article proposes the creation of the deep packet inspection (DPI) dataset to study subscribers’ behavior on the network, applying ensemble learning to this dataset, and comparing it with the UGRansome dataset. The subscriber can be thought of as a person or a group of users using a network service or connectivity. The DPI features represent the subscriber network usage, and the ensemble learning approach is implemented on the DPI dataset to predict the subscriber's service category on the network. The classification and prediction problem addressed on the DPI dataset reached a precision of 100%. The paper predicts that the web and streaming categories with Netflix, Facebook, and YouTube services will be the most utilized in the next few years. This study will lead to a better understanding of the idiosyncratic behavior of active subscribers on the network, exposing novel network anomalies and facilitating the development of novel DPI systems.
Quantum Thermalization and Thermal Entanglement in the Open Quantum Rabi Model

Abstract Quantum thermalization and thermal entanglement in the open quantum Rabi model (QRM), in which a two‐level system and a single‐mode bosonic field are coupled to either two individual heat baths or a common heat bath, are studied. By treating the QRM as an effective multilevel system and deriving global quantum master equations in the eigenstate representation of the QRM, the physical conditions for quantum thermalization of the QRM is studied. It is found that, in the individual heat‐bath case, the QRM can only be thermalized when either the two heat baths have the same temperature or the QRM is only coupled to one of the two baths. In the common heat‐bath case, differently, the QRM can always be thermalized. Thermal entanglement of the QRM in both the resonant‐ and non‐resonant coupling cases is also studied. The logarithmic negativity for the thermal state of the QRM is obtained in a wide parameter space, ranging from the low‐ to high‐temperature limits, and from the weak‐ to deep‐strong‐coupling regimes. This work paves the way toward the study of quantum effects in nonequilibrium ultrastrongly‐coupled light‐matter systems.
Application of a Genetic Algorithm for the Selection of the Optimal Composition of Protection Tools of the Information and Educational System of the University

The article discusses the possibility of using a genetic algorithm (GA) in the search for the optimal composition of information security tools (IST) for the information and educational environment of the university (IEEU). In the process of research, there was developed an optimization model for choosing the structure and composition of the information security system for IEEU nodes. The model made it possible to refine the objective function based on minimizing the costs of creating an information security system (IS) of the IEEU. The corresponding computational experiments were carried out, which confirmed the efficiency of the model. The practical significance of the conducted research lies in the fact that the proposed approach significantly reduces the time for calculating the optimal composition of the information security system for IEEU. This, in turn, allows the protection side to quickly respond to the changing landscape of cyber threats for IEEU. The proposed approaches, in our opinion, will lead to an improvement in their technical and economic indicators of the IS of IEEU system, reduce time and labor costs for designing an effective IS system
Textual Cyberbullying detection using Ensemble of Machine Learning models

Nowadays Cyberbullying on social media has become a major problem. Cyberbullying may cause many serious and negative mental, emotional and physical impacts on a person's life. However, Cyberbullying leaves a record that can demonstrate value and give proof to help stop digital abuse. The early detection of Cyberbullying on social media becomes crucial to moving the effect on the social media user. Numerous studies are being done to automatically identify cyberbullying content in this trend. The absence of linguistic resources, especially for recently developed languages, is the main issue and gap in Cyberbullying detection measures. Romanized Urdu is a recently developed and frequently used language in Asian nations on social networking platforms. Using Machine Learning or Deep Learning with Natural Language Processing (NLP) techniques to automatically detect cyberbullying is the best way to stop it. Current research develops an efficient framework to detect Cyberbullying, using NLP tools with Machine Learning models. Using different preprocessing techniques, the proposed study is validated on a roman-Urdu-abusive-comment-detector (RUACD) dataset. Five machine learning models Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) are evaluated on the RUACD dataset. From experiments, the current study finds that the SVM, LR, and DT outperformed and achieved promising results. In last, an ensemble of these outperformed models is formed and achieved 95.9% of test accuracy.
Acoustic Based Drone Detection Via Machine Learning

Artificial Intelligence, data-driven decision techniques, and the use of drones as swarm technology have rendered drones as a potential weapon for mass destruction. Their small size and ability of autonomous flight let them evade Radar, Radio Frequency, and Vision-based detection techniques, hence, increasing their threat to military sensitive areas. However, Acoustic Based Drone Detection has been proven to remain effective as it utilizes drones' own acoustic signature to detect it. This paper proposes a low-cost and highly reliable Acoustic-Based Drone Detection System that employs machine learning at its core. The drone database was acquired from GitHub, and it contained acoustic signatures of 7 different drones. The environmental signals were collected from the BBC sound database and YouTube. 26 Mel Frequency Cepstral Coefficients (MFCCs) were extracted from the audio signals and the data was labeled as ‘Drone’ and ‘No-Drone’. Prior to training, clustering was performed to provide ground truth that the drone database contains acoustic signatures from 7 different drones. MFCCs were fed into Random Forest and MLP algorithms. The preliminary results of both algorithms were comparable with a combined average F-score of 0.92 (on training data) which indicates that our proposed system can reliably detect drones from their acoustic signature.
A Survey of Extractive Question Answering

Extractive question answering is one of the most important tasks in natural language processing (NLP) which has high research value. In order to sort out its development process, in this paper, it was divided into two categories roughly in this paper: single-span extractive question answering and multispan extractive question answering, which are divided according to the number of spans. Studies on the two classifications are briefly described in the following context, following a certain logical relationship. And single-span extractive question is further divided into four categories to make a more detailed introduction. In addition, some works that can be done in the future is mentioned.
Software Implementation of AES-128: Side Channel Attacks Based on Power Traces Decomposition

Side Channel Attacks (SCAs), an attack that exploits the physical information generated when an encryption algorithm is executed on a device to recover the key, has become one of the key threats to the security of encrypted devices. Recently, with the development of deep learning, deep learning techniques have been applied to SCAs with good results on publicly available dataset experiences. In this paper, we propose a power traces decomposition method that divides the original power traces into two parts, where the data-influenced part is defined as data power traces (T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data</inf> ) and the other part is defined as device constant power traces, and use the T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data</inf> for training the network model, which has more obvious advantages than using the original power traces for training the network model. To verify the effectiveness of the approach, we evaluated the ATXmega128D4 microcontroller by capturing the power traces generated when implementing AES-128. Experimental results show that network models trained using T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data</inf> outperform network models trained using raw power traces (T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">raw</inf> ) in terms of classification accuracy, training time, cross-subkey recovery key, and cross-device recovery key.