Reviewerly/exHarmony huggingface dataset:
Towards Building Quantum Resistant Blockchain

The advent of quantum computing has generated apprehensions regarding the viability and security of conventional cryptographic algorithms. These algorithms safeguard the decentralized trust built into the blockchain, which relies on decentralized cryptographic consensus, instead of a central authority. Simultaneously, quantum computing has also unveiled unprecedented possibilities, opportunities and risks in the realm of technology. The significance of this is that numerous sub-routines employed within the blockchain, such as consensus protocols, data transmission, storage, and communication, heavily depend on conventional cryptographic algorithms like Rivest-Shamir-Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA). However, these algorithms are susceptible to quantum algorithms, which have the potential to effectively compromise them once quantum computers with sufficient computational capabilities are developed. Blockchain, although primarily recognized for its association with cryptocurrencies, is essentially a decentralized and trustless distributed ledger system. Its purpose is to enable decentralized communication, coordination, and the attainment of desired objectives through consensus mechanisms. A notable illustration of this concept is a blockchain transaction. The advent of quantum computing poses a significant threat to the security of blockchain systems, as the asymmetric algorithms employed within their many components will likely become obsolete. This jeopardizes the integrity of the blockchain system, which underpins a multi-billion dollar economy reliant on its inherent transparency and safety. This paper seeks to present an overview of the various threats faced by blockchain systems, examine the potential of quantum algorithms in offering cryptographic signatures for these systems, and provide a broad perspective on how to protect blockchain systems from quantum threats by utilizing quantum algorithms for cryptographic signatures. This study enhances the comprehension of existing vulnerabilities in blockchain systems, specifically focusing on the potential risks posed by quantum computing. Additionally, it sheds light on the ongoing efforts within the industry and current research endeavors aimed at addressing these weaknesses.
Wavelet-Based RGB Hand Gesture Recognition Architecture Using Transfer Learning, a Proof-of-Concept

Hand gesture recognition is an area of human-computer interaction that helps to recognize accustomed hand gestures to establish a communication interface between people and computers. sEMG-, camera-, and impedance-based hand gesture solutions are robust orientations but contain drawbacks such as user comfort, light sensitivity, and easy installation, as appropriate. Accelerometer data are a complementary focus for hand gestures and require only to be positioned over the object to provide positional information. This paper performs a hand gesture classification with a deep convolutional neural network from time-frequency domain transformed RGB (Red, Green, Blue) images of the X, Y, and Z accelerometer signals. We propose to compare GoogLeNet and ResNet pre-trained networks using the RGB approach of the accelerometer signals. Our database included 22 people containing 6 gestures with a record of 2000 samples acquired. We have found that the best classification rate was with GoogLeNet, which reached a general accuracy of 91.9% and more than 96% with some specific gestures. Furthermore, we have evidenced that knock-knock gestures possess user singularities that do not allow our classifier to generalize its recognition. For future work, we propose testing our RGB wavelet architecture with smartwatch accelerometer data to test its capacity for wrist gesture recognition.
Using Machine Learning To Detect Credit Card Fraud

Credit card fraud, a pressing concern in the digital age, necessitates advanced detection mechanisms. This research delves into machine learning solutions, particularly the Random Forest classifier, to discern fraudulent transactions. After addressing inherent class imbalances in transaction datasets, our model achieved remarkable accuracy, outperforming baseline models. Notably, our approach introduces novel methodologies in preprocessing and model interpretation, setting new industry benchmarks. The findings emphasize specific transactional features as key fraud indicators, offering financial institutions a refined lens for fraud detection.
Trust Management Framework for Securing Software-Defined Wireless Sensor Networks

The growing application of wireless sensor networks (WSN) has unveiled several opportunities for innovative services and data-driven applications. However, the WSN's inherent vulnerabilities and challenges have raised considerable concerns about data security and integrity despite inventions like software-defined networking. Therefore, this paper proposes a comprehensive trust management framework (TMF) to address Software-defined WSN (SDWSN) security challenges. The proposed TMF capitalizes on the dynamic and flexible nature of the SDWSN to establish trust relationships, both between sensors (S2S) and between controllers and sensors (C2S). Leveraging behavioural analysis, the framework evaluates packet forwarding ability for S2S trust based on reputation, while employing a recommendation-based scheme based on anomalous traffic detection for C2S trust. By analyzing the network traffic, the TMF enhances the trustworthiness assessment and decision-making process. The framework allows the efficient and reliable identification of malicious nodes, unauthorized access, and data tempering within the network. While the proposed TMF is currently in the development stage and awaits implementation and evaluation, its potential impact on improving network security in the SDWSNs is promising.
Analyzing Code Text Strings for Code Evaluation

This paper presents a comprehensive investigation into the collection and organization of the LeetCode 70K human-submitted dataset, aimed at providing a valuable resource for assessing code quality, encompassing correctness and performance considerations. We introduce methods for effective code evaluation and annotation, resulting in a labeled dataset. We compare the performance of two widely used deep learning models, Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT), for classification. The BERT model demonstrates exceptional performance, positioning it as a reliable classifier for code quality assessment. Beyond classification, our research advances by constructing and evaluating the dataset using regression models to establish a code quality score, a comprehensive metric for intrinsic code assessment. This score offers insights into code quality and efficiency, informing coding practices without even executing code. Importantly, the implications of the code quality score extend beyond code evaluation, identifying and addressing code security issues. Leveraging the code quality score enhances vulnerability detection, fortifying software against cyber threats. The amalgamation of AI-driven code assessment and cybersecurity marks a pivotal advancement, shaping robust and secure software development practices. Our research underscores the pivotal role of AI-human cooperation in software development’s future and cybersecurity landscape.
Multi-Frequency and Multi-Snapshot Gaussian Processes for Matched-Field Processing Source Localization

To interpolate the sparsely sampled vertical array signal into a denser one, Gaussian process regression is employed by considering the noise-free field as a Gaussian process. This interpolated signal is then combined with matched-field processing (MFP) for accurate localization of the source. The success of Gaussian process regression depends on the selected kernel function and the hyperparameters obtained from the type-II maximum likelihood estimation. In this paper, to improve the stability of the hyperparametric search process, two methods, using multiple frequencies and multiple snapshots, are employed. These methods increase the accuracy of the Gaussian process for MFP. By densifying and denoising the received sound field, the sidelobes of the MFP ambiguity surfaces are reduced, and the probability of correct localization is increased. Compared to conventional processing methods, the improved Gaussian process-based processor performs better to some extent at a range of signal-to-noise ratios. Moreover, the probability of correct localization is further increased as the number of frequency points and snapshots increase.
The Optimal Closed-Form Linear Canonical Wigner Distribution

This study devotes to Heisenberg's uncertainty inequalities under the closed-form instantaneous cross-correlation function Wigner distribution (CICFWD), whose generalizability enables it to be a representative Wigner distribution (WD) in the linear canonical transform (LCT) domain. A relation between the spread in the time-CICFWD domain and the spreads and moments in LCT domains is established. We then obtain two versions of lower bounds on the uncertainty product of spreads in time-CICFWD and LCT-CICFWD domains. The first is the tightest universal lower bound for all signals, and the other is a lower bound for complex-valued signals. As a result, uncertainty principles for the traditional WD are revisited. The optimal LCT free parameters selection problems of the CICFWD are also well-addressed from the perspectives of the highest time-frequency resolution and its best estimation accuracy, respectively.
Innovative Exploration of Smart University Archives Management Based on Learning Algorithms

This article proposes a design method for an intelligent management system for university archives based on learning algorithms. This system utilizes advanced learning algorithms and artificial intelligence technology to intelligently manage and classify university archive data, improving the efficiency and accuracy of archive management. This article provides a detailed introduction to the design and implementation process of the system, including functional modules, data flow, and key technologies. Through experimental verification, this system can effectively improve the performance and efficiency of university archives management, providing strong support for the construction of smart campuses in universities. And through experimental simulation, it can also be seen that the management system based on learning algorithms proposed in this article has much higher operational efficiency than ordinary management systems.
An Automated System for Detecting and Preventing Phishing Attempts on Steam Accounts

In recent years Steam has become a giant in the gaming industry. The Valve-owned digital distribution platform held an estimated 75% of market share in 2013, and continues to grow. However, with such a large user base comes the ever-increasing threat of security breaches in both the Steam database and individual Steam user accounts. While Steam does make use of a mobile authentication app, users are still susceptible to malware and/or phishing attempts that can cause users to lose access to their accounts through manipulation or gaining access to recovery email accounts. This project aims to investigate how the authentication process can be improved or if a solution can be created to help prevent the worst-case scenario when users become victims of phishing attacks.
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach

As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency. Pruning is one of the most popular network compression techniques. In this paper, we propose a novel unstructured pruning pipeline, Attention-based Simultaneous sparse structure and Weight Learning (ASWL). In ASWL, an efficient algorithm is proposed to calculate the pruning ratios layer-wisely from attentions, and both weights for the dense network and the sparse network are tracked so that the pruned structure is simultaneously learned from randomly initialized weights. Our experiments on MNIST, Cifar10, and ImageNet show that ASWL achieves superior pruning results in terms of accuracy, pruning ratio and operating efficiency when compared with state-of-the-art network pruning methods.
Hand Sign Language Detection Using Deep Learning and Computer Vision

This work presents an innovative system/app designed to assist individuals with visual or hearing impairments in communicating with the outside world using American Sign Language (ASL). By leveraging machine learning and image processing techniques, the proposed system captures and processes hand gestures, enabling the optimal transliteration of sign language alphabets and numbers into machine-readable English text. The system employs deep learning or random forest algorithms to classify the gestures, followed by mapping them to specific alphabets or numbers. The resulting text is presented audibly, bridging the communication gap and facilitating interactions between ASL users and those unfamiliar with sign language. Through the integration of technology, this work aims to enhance inclusivity and accessibility for individuals having disabilities, fostering greater engagement and interaction with the wider world.
Leveraging Uncertainty Quantification for Reducing Data for Recommender Systems

The recent California Consumer Privacy Act (CCPA) requires that personal data shall be limited to what is necessary for business purposes. Business services shall “implement technical safeguards that prohibit re-identification of the consumer to whom the information may pertain”. For recommender systems, we believe the legal concepts of limitation and technical safeguard are not specific enough to operationalize in practice. This study makes efforts to map the legislative challenges to practice of reducing personal data. More importantly, we borrowed the notion of uncertainty from the machine learning community, and added it as another aspect of recommendation utility, in addition to recommendation accuracy, to guide the data reduction process. The benefit of using uncertainty is that we have more comprehensive consideration while reducing the personal data. In addition, two major types of uncertainty in machine learning models: aleatoric uncertainty and epistemic uncertainty, helped us formulate two groups of data reduction strategies: within-user and between-user. We conducted a series of analyses regarding uncertainty change and accuracy loss caused by different data reduction strategies. We found that at the aggregate level, data reduction is feasible with certain data reduction strategies. At the individual level, the recommendation utility (both uncertainty and accuracy) loss incurred by data reduction disparately impacts different users — a finding which has implications for fairness and transparency of AI models. Our results reveal the difficulty and intricacy of the data reduction problem in the context of recommender systems.
Spectrum Recovery Method of Adjacent Harmonic Signals with Extremely Short Sample Time

In some engineering scenarios, detecting harmonics signals with extremely short sample time is necessary but a considerable challenge. Due to the lack of sample points, the insufficient frequency resolution can not separate different harmonic components with adjacent frequencies. In this paper, the sparsity of the harmonic spectrum is considered, and the sine (cosine) basis is adopted to represent the harmonic signal sparsely. Moreover, the group sparsity of the amplitude of the sine (cosine) basis is constrained by the block L1 norm. The simulation results show that the proposed method can effectively separate harmonic components and has high separation accuracy.
Research on the Digital Model of Substation Inspection System Based on Digital Twin Technology

This research paper explores the application of digital twin technology in the development of a substation inspection system. The study aims to enhance the efficiency, accuracy, and effectiveness of substation inspection and maintenance processes by leveraging real-time data, advanced analytics, and simulation capabilities offered by digital twin technology. Validation and testing procedures are carried out to evaluate the performance of the digital twin-based system. Five case studies are conducted to assess the system's performance in substation inspection, maintenance, fault detection, and diagnosis. The results demonstrate the superiority of the digital twin-based system in terms of accuracy, efficiency, cost-effectiveness, and maintenance time reduction. A comparison with traditional approaches highlights the limitations of these methods and emphasizes the benefits of the digital twin-based system. The research provides valuable insights for industry professionals, researchers, and policymakers seeking to adopt and implement digital twin technology in the power sector. The findings contribute to advancing substation inspection and maintenance practices and showcase the potential of digital twin technology in optimizing substation performance and reliability.
A Privacy-Preserving Semi-Decentralized Personalized Recommendation System

In the present era of big data, recommendation systems play a crucial role in our daily lives by assisting us in making quicker and more informed decisions from a vast array of choices. The concept of personalized recommendations has gained widespread popularity, offering suggestions based on user profiles, preferences, and/or interests. Although many existing systems centralize data for making recommendations, the revelation of sensitive data poses a privacy concern, as research indicates the potential to de-identify anonymous users. For instance, sensitive information such as political views or sexual orientations can be inferred from seemingly non-sensitive data like product review and ratings. In this paper, we present a privacy-preserving personalized recommendation system named P2RecSys to address these privacy issues. Our system takes a semi-decentralized approach by treating each node in the network as an agent. Data are distributed to each agent within trusted networks, and the recommendation service provider only collects obfuscated data from agents using a differential-privacy mechanism. Consequently, data in P2RecSys are either safeguarded within local trusted networks or obfuscated outside of these networks. The final recommendation is then generated by combining local suggestions from the trusted network with obfuscated global suggestions from the service provider. The emphasis on local suggestions allows for highly personalized recommendations. Evaluation results demonstrate that P2RecSys achieves high accuracy in recommendations while effectively safeguarding user privacy.
Application of Random Forest Algorithm in Agricultural Meteorological Disaster Statistics

Currently, meteorological departments simply categorize agrometeorological disasters into general meteorological disasters, major meteorological disasters and extreme meteorological disasters. When using big data techniques to analyze and access disaster classification data, it becomes clear that disasters are closely related to economic losses, population and crop damage, and must follow certain rules. Agrometeorological databases on natural hazards contain a large amount of data. These data contain many patterns and rules of research interest that can be used to prevent disasters and reduce economic losses. Random forest algorithm is a method used for data classification. It can effectively solve the problems in traditional artificial weather forecasting models and provide new ideas. In this paper, we will introduce two basic models, random forest and forward distribution, and discuss their applications in weather disaster prediction, early warning and other aspects. Meanwhile, its advantages and disadvantages are analyzed and its applicability is compared. Finally, the measured and actual loss rates (i.e., probability density functions) and standard deviations under several sets of different parameters are given through numerical examples. From the above results, it can be concluded that the model obtained by using the above method can classify the data, and the Random Forest algorithm outperforms the traditional algorithm in terms of average prediction accuracy, Random Forest classification error rate, inconsistency index and so on.
Bridging the Gap: Real-Time Detection and Interpretation of Sign Language Using Convolutional Neural Networks

Sign languages enable communication and interaction for speech-impaired and hearing-impaired individuals. However, their usage is often limited to situations where visual signs can be understood, posing challenges for widespread communication. In the digital era, written communication using standardized languages predominates, which are easily recognizable. Converting signed languages into a computationally interpretable format is complex due to their nuanced, 3-dimensional nature. This paper proposes a computer vision system based on a convolutional neural network (CNN) to detect and interpret manual elements in sign language, simplifying their representation. Real-time object detection models including YOLOv3, YOLOv3- Tiny, YOLOv4, YOLOv4- Tiny, and SSD MobileNet were trained and evaluated. The YOLOv4 ASL model achieved an accuracy of 98.20% at 31.71 frames per second. This approach bridges the gap between sign language and written characters, advancing sign language interpretation.
The Application of Structuration Theory in Software Testing within the Organisation

Software testing is the most essential part of software development life cycle. It involves the execution of a software component to evaluate one or more properties of interest. Software testing also helps to identify errors (defects), gaps or missing requirements in conflicting with the actual requirements. It can be either conducted manually or through the use of automated tools. The testing of software assures and provides confidence to business about the readiness of software. If it is bypassed or not conducted properly, the software may not function as expected in production. Therefore, poor quality software may not serve its purpose and it may hinder the business to continue. As result, the organization may lose its customers, potential customers as well as loss in profits. The image of the organization may also be tainted. The interpretivist approach was applied in this study. Semi-structured technique was used for data collection from the qualitative approach perspective. The duality of structure from Structuration theory was employed as lens to guide analysis of the data. Based on the findings, challenges encountered during software testing were identified.