Reviewerly/exHarmony huggingface dataset:
An Improved Multi-Authority Attribute Access Control Scheme Base on Blockchain and Elliptic Curve for Efficient and Secure Data Sharing

With the rapid development of Internet of Things technology, sharing data safely and efficiently in different Internet of Things enterprises is becoming increasingly urgent. Traditional schemes usually use third-party centralized cloud storage and a single central authoritative organization to realize data storage and access management during data sharing. However, this centralized scheme design has the potential for a single point of failure. When the cloud storage platform is subjected to malicious attacks, it may lead to data loss or privacy leakage problems. Secondly, there is a trust crisis in the design of authoritative central organizations, and centralized rights management makes the data sharing process opaque. In order to address these shortcomings, an improved blockchain and elliptic curve-based multi-authority attribute access control scheme is proposed. Firstly, the interplanetary file system is used to store the ciphertext of symmetric encryption data to solve data leakage and tampering in centralized cloud storage. Secondly, the elliptic curve cryptography-based improved multi-authority ciphertext policy attribute-based encryption algorithm is used to encrypt the symmetric key. It can solve the single point of failure problem of user attribute management and significantly reduce the attribute encryption algorithm’s time and resource consumption. Thirdly, the data-related information is uploaded through the smart contract, and the attribute access threshold is set. Only qualified users can view the private information. Finally, the simulation experiments evaluate the efficiency and effectiveness of the scheme from three perspectives: data storage, smart contract, and attribute encryption.
Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.
Research on Improved Multi-Channel Image Stitching Technology Based on Fast Algorithms

The image registration and fusion process of image stitching algorithms entails significant computational costs, and the use of robust stitching algorithms with good performance is limited in real-time applications on PCs (personal computers) and embedded systems. Fast image registration and fusion algorithms suffer from problems such as ghosting and dashed lines, resulting in suboptimal display effects on the stitching. Consequently, this study proposes a multi-channel image stitching approach based on fast image registration and fusion algorithms, which enhances the stitching effect on the basis of fast algorithms, thereby augmenting its potential for deployment in real-time applications. First, in the image registration stage, the gridded Binary Robust Invariant Scalable Keypoints (BRISK) method was used to improve the matching efficiency of feature points, and the Grid-based Motion Statistics (GMS) algorithm with a bidirectional rough matching method was used to improve the matching accuracy of feature points. Then, the optimal seam algorithm was used in the image fusion stage to obtain the seam line and construct the fusion area. The seam and transition areas were fused using the fade-in and fade-out weighting algorithm to obtain smooth and high-quality stitched images. The experimental results demonstrate the performance of our proposed method through an improvement in image registration and fusion metrics. We compared our approach with both the original algorithm and other existing methods and achieved significant improvements in eliminating stitching artifacts such as ghosting and discontinuities while maintaining the efficiency of fast algorithms.
Entropy-Based Anomaly Detection for Gaussian Mixture Modeling

Gaussian mixture modeling is a generative probabilistic model that assumes that the observed data are generated from a mixture of multiple Gaussian distributions. This mixture model provides a flexible approach to model complex distributions that may not be easily represented by a single Gaussian distribution. The Gaussian mixture model with a noise component refers to a finite mixture that includes an additional noise component to model the background noise or outliers in the data. This additional noise component helps to take into account the presence of anomalies or outliers in the data. This latter aspect is crucial for anomaly detection in situations where a clear, early warning of an abnormal condition is required. This paper proposes a novel entropy-based procedure for initializing the noise component in Gaussian mixture models. Our approach is shown to be easy to implement and effective for anomaly detection. We successfully identify anomalies in both simulated and real-world datasets, even in the presence of significant levels of noise and outliers. We provide a step-by-step description of the proposed data analysis process, along with the corresponding R code, which is publicly available in a GitHub repository.
Being There, and Being Together: Avatar Appearance and Peer Interaction in VR Classrooms for Video-Based Learning

The development of technology and the recent Covid-19 has moved a substantial part of education into online environments, and video-based learning has become a common format of online learning. An alternative to enhance the co-learning experience in video-based online learning is to make use of immersive virtual reality (VR) technology. The current study focused on how to build a sense of being together in an immersive VR classroom for video-based learning, when individual learners can schedule and pace their learning on their own. Two alternative in-class interaction mechanisms for video-based learning were proposed (i.e., real-time interaction and timeline-anchored interaction). The impact of in-class interaction design was investigated, together with the impact of different styles of visual embodiment of learners (i.e., realistic and cartoon-styled), through a laboratory experiment involving 48 participants, and the influences on learners’ immersion in a VR classroom, identification with the avatars, perceived social presence of peer learners, and learning outcomes were analyzed. The results showed that enabling in-class interaction with peer learners in VR classrooms enhances immersion in the learning environment and their identification of peer avatars, as well as helping learners to develop a stronger co-presence and more intimate perception of peers. Whereas real-time interaction improves perceived social presence the most, the reduced learning outcome suggests that it may distract learners from the video. The timeline-anchored interaction, on the other hand, improves social presence at no cost of learning performance. Appearance style, however, has no significant influence on learning experiences and outcomes.
GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers

Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has been paid to symbolic music generation using guitar tablatures (tabs) which can be used to encode multiple instruments. Tabs include information on expressive techniques and fingerings for fretted string instruments in addition to rhythm and pitch. In this work, we use the DadaGP dataset for guitar tab music generation, a corpus of over 26k songs in GuitarPro and token formats. We introduce methods to condition a Transformer-XL deep learning model to generate guitar tabs (GTR-CTRL) based on desired instrumentation (inst-CTRL) and genre (genre-CTRL). Special control tokens are appended at the beginning of each song in the training corpus. We assess the performance of the model with and without conditioning. We propose instrument presence metrics to assess the inst-CTRL model's response to a given instrumentation prompt. We trained a BERT model for downstream genre classification and used it to assess the results obtained with the genre-CTRL model. Statistical analyses evidence significant differences between the conditioned and unconditioned models. Overall, results indicate that the GTR-CTRL methods provide more flexibility and control for guitar-focused symbolic music generation than an unconditioned model.
An Efficient Phishing Attack Detection using Machine Learning Algorithms

Phishing is an illegal method which involves user's personal information at high risk. Phishing websites prey individuals, the cloud storage hosting companies and government agencies. Though there are various anti-phishing approaches like hardware as they are not cost effective and they don't choose these approaches. To overcome this, many software-based techniques are used. Zero-day phishing problem cannot be omitted with the existing models. To prevail over these issues and detect phishing attack an approach using heuristic methodology has been proposed. We classify whether a link is phishing or non-phishing based on the input features we take like Web Traffic and Uniform Resource Locator (URL). The proposed methodology is executed by retrieving datasets from phishing cases and Machine Learning model using algorithms like Random Forest, SVM, Genetic.
Home Automation using Augmented Reality

This article suggests integrating augmented reality and other updated platforms for intelligent appliance control. The proposed approach merges mobile AR and IoT to control household appliances. In recent years, smart home controllers have risen quickly, especially for IoT-enabled devices (IoT). Different apps can control different devices. Recently developed AR automates numerous technological equipment. When the mobile camera is focused at a target image, augmented reality (AR) enables virtual pop-ups. Pop-ups allow power on/off with a touch, boosting automation. Using processing, picture tracking, and communication, augmented reality drives applications.
Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications

Increased crime in packed events or lonely areas has made security a top priority in every industry. Computer Vision is used to find and fix anomalies. Increasing needs for security, privacy, and private property protection require video surveillance systems that can recognize and understand scene and anomalous situations. Monitoring such activities and recognizing antisocial behavior helps minimize crime and social offenses. Existing surveillance and control systems need human oversight. We're interested in detecting firearms quickly through photos and surveillance data. We recast the detection problem as decreasing false positives and solve it by building a data set guided by a deep CNN classifier and evaluating the best classification model using the region proposal approach. Our model uses Faster RCNN, YOLO.
Two efficient Kalman filter algorithms for measurement packet dropping systems under maximum correntropy criterion

This paper investigates the state estimation problem for the linear discrete-time system with both packet dropping and non-Gaussian noise. Specifically, maximum correntropy Kalman filter algorithms are derived under different packet dropping models. Both the cases with and without using the statistical characteristic of the packet dropping variable are discussed. For the state estimation using only the real-time known characteristic of the packet dropping variable, the maximum correntropy Kalman filter with packet dropping can be successfully derived by designing a coefficient matrix. Furthermore, it can degenerate to the Kalman filter with packet dropping when the kernel bandwidth tends to infinity. On the other hand, for the state estimation using both the real-time known and statistical characteristics of the packet dropping variable, the maximum correntropy suboptimal Kalman filter with packet dropping cannot achieve the degeneration due to the presence of mathematical expectation, but it still achieves an excellent estimation performance. Simulation results consistently indicate that the filter algorithm based on maximum correntropy criterion outperforms the one based on minimum mean square error criterion when the measurement packet dropping system is disturbed by non-Gaussian noise. This paper presents a novel idea and solution to the state estimation problem for the measurement packet dropping system with non-Gaussian noise.
How to develop and frame impactful review articles: key recommendations

Developing and framing an impactful review article at a top journal can be a daunting challenge, especially for graduate students and junior academics. With rising demands related to review types, methods, and contributions, guidance is sorely needed. In this short commentary, we draw on our experience as authors, reviewers, and editors of reviews, as well as instructors of a doctoral seminar on literature reviews. Our goal is to offer key recommendations on the elements of an impactful review paper and on the crucial role that feedback can play in the process. We believe these suggestions, together with the contributions of the special issue, are a useful resource for prospective authors of influential review papers.
A completed parted region local neighborhood energy pattern for texture classification

Binary pattern family has received unprecedented attention due to its primary role in texture representation. Texture images usually have strong region attributes and show dramatic changes from coarse regions to flats regions. However, most methods build texture descriptors by applying the same strategy to all pixels in the whole image, which ignore the difference between coarse regions and flat regions and fail to precisely represent the corresponding region attribute. To solve this problem, this paper proposes a novel completed parted region local neighborhood energy pattern which adaptively extracted discriminative texture information from coarse regions and flat regions. Firstly, we design an adaptive parted region division strategy for feature extraction. Secondly, to provide more rich and discriminative texture features, this paper further develops two novel operators based on the proposed strategy, parted region local difference energy pattern and parted region local pixel energy pattern. Finally, a completed parted region local neighborhood energy pattern is built by combining local sign pattern, parted region local difference energy pattern, and parted region local pixel energy pattern. Extensive experimental results on three popular texture databases demonstrate that the proposed descriptor outperforms recent state-of-the-art methods for texture classification tasks.
Zero-Inflated Patent Data Analysis Using Compound Poisson Models

A large part of big data consists of text documents such as papers, patents or articles. To analyze text data, we have to preprocess the text documents and build a structured data based on a document-word matrix using various text mining techniques. This is because statistics and machine learning algorithms used in text analysis require structured train data. The row and column of the matrix are document and word, respectively. The element of the matrix represents the frequency value of the word occurring in each document. In general, because the number of words is much larger than the number of documents, most elements have zero values. Due to the sparsity problem caused by inflated zeros, the performance of the predictive model has decreased. In this paper, we propose a method to solve the sparsity problem and improve the model performance in text data analysis. We perform compound Poisson linear modeling to make the proposed method. To show the performance of our proposed method, we collect and analyze the patent documents from patent databases. In our experimental results, we compared the value of the Akaike information criterion (AIC) of the proposed model with traditional models, such as linear model, generalized linear model and zero-inflated Poisson model. Additionally, we illustrated that the AIC value of our proposed model is smaller than others. Therefore, we verify the validity of this paper.
Federated Learning Based Fault Diagnosis Driven by Intra-Client Imbalance Degree

Federated learning is an effective means to combine model information from different clients to achieve joint optimization when the model of a single client is insufficient. In the case when there is an inter-client data imbalance, it is significant to design an imbalanced federation aggregation strategy to aggregate model information so that each client can benefit from the federation global model. However, the existing method has failed to achieve an efficient federation strategy in the case when there is an imbalance mode mismatch between clients. This paper aims to design a federated learning method guided by intra-client imbalance degree to ensure that each client can receive the maximum benefit from the federation model. The degree of intra-client imbalance, measured by gain of a class-by-class model update on the federation model based on a small balanced dataset, is used to guide the designing of federation strategy. An experimental validation for the benchmark dataset of rolling bearing shows that a 23.33% improvement of fault diagnosis accuracy can be achieved in the case when the degree of imbalance mode mismatch between clients is prominent.
Context-Based Aspect-Oriented Requirement Engineering Model

Mobile applications are context-oriented systems that involve the use of context information while operating. Mobile applications demand tackling context information in the early phase of software engineering. A context-aware system demands a different approach to handling the influence of the context on a system's requirements. Aspect-oriented Requirement Engineering separates concerns throughout requirements, called crosscutting concerns, in the early phase of software development to improve the modularity of complex applications. Capturing requirements embedded within context is a challenging procedure. This study aimed to identify such contextual characteristics of requirements in the early phase of software engineering, using natural language processing techniques, by proposing Context-Based Aspect-Oriented Requirement Engineering (CB-AORE) to visualize the existence of crosscutting concerns. CB-AORE performs context modeling to analyze the context dependency with base requirements and helps the analyst to visualize the correlation of functional and non-functional requirements with context. A case study analyzed the identification of context and its use to identify crosscutting concerns.
Cd isotope constraints on metal sources of the Zhugongtang Zn–Pb deposit, NW Guizhou, China

The Sichuan–Yunnan–Guizhou metallogenic province (SYGMP) includes > 400 Zn − Pb deposits and prospects, eight of which are large-scale deposits with large reserves of critical metals such as Cd and Ge, including the Huize Zn − Pb − Cd − Ge and Daliangzi Zn–Pb–Ge–Cd deposits. The newly discovered Zhugongtang Zn − Pb deposit is a super-large deposit with Zn–Pb reserves of > 3 Mt. Its geochemical features are similar to those of the Huize deposit, with similar sulfide δ34S values and concentrations of critical elements in sphalerite (e.g., Cd and Ge). However, the two deposits have different host strata, and it remains unclear as to whether they have similar oregenesis. In this study, δ114/110Cd values and major- and trace-element compositions of sphalerites collected from a drill-core and tunnels of the Zhugongtang deposit were determined in an investigation of metal sources. Drill-core samples were impure and exhibited strong correlation (R2 = 0.89) between Zn and Cd contents. For samples from mining tunnels, the Cd and Fe contents of selected sphalerites were positively correlated, especially yellow sphalerites (R2 = 0.76). Cadmium is likely hosted in sphalerite by the substitution mechanism of (Fe2+, Cd2+) ↔ Zn2+. The δ114/110Cd values of all samples ranged from − 0.43 ‰ to 0.06 ‰. Based on Zn/Cd ratios, and excluding geochemical processes that may have caused the variable Cd isotopic compositions, we suggest that the metal sources of the deposit were derived from the mixing of sedimentary and basement rocks. This model is supported by the strong relationship between the δ114/110Cd and 1/Cd values of sphalerites from 11 typical Zn–Pb deposits in the SYGMP (R2 = 0.81). The quantification of metal contributions of source rocks indicates that deposits derived mainly from sedimentary rocks generally have relatively low sphalerite Ge contents and small Ge reserves, whereas those derived mainly from basement rocks have higher Ge contents and larger Ge reserves. This study provides a new model for explaining the enrichment of critical metals in Zn–Pb deposits of the SYGMP, thus extending the applications of Cd isotopes in hydrothermal systems.