Reviewerly/exHarmony huggingface dataset:
Investigating the reviewer assignment problem: A systematic literature review

The assignment of appropriate reviewers to academic articles, known as the reviewer assignment problem (RAP), has become a crucial issue in academia. While there has been much research on RAP, there has not yet been a systematic literature review (SLR) examining the various approaches, techniques, algorithms and discoveries related to this topic. To conduct the SLR, we identified and evaluated relevant articles from four databases using defined inclusion and exclusion criteria. We analysed the selected articles and extracted information, and assessed their quality. Our review identified 67 articles on RAP published in conferences and journals up to mid-2022. As one of the main challenges in RAP is acquiring open data, we have studied the data sources used by researchers and found that most studies use real data from conferences, bibliographic databases and online academic search engines. RAP is divided into two main phases: (1) finding/recommending expert reviewers and (2) assigning reviewers to submitted manuscripts. In Phase 1, we have identified that decision support systems, recommendation systems, and machine learning-oriented approaches are more commonly used due to better results. In Phase 2, heuristics and metaheuristics are the approaches that present better results and are consequently more commonly used by researchers. Based on the analysed studies, we have identified potential areas for future research that could lead to improved results. Specifically, we suggest exploring the application of deep neural networks for calculating the degree of correspondence and using the Boolean satisfiability problem to optimise the attribution process.
Knowledge graph embedding by fusing multimodal content via cross-modal learning

Knowledge graph embedding aims to learn representation vectors for the entities and relations. Most of the existing approaches learn the representation from the structural information in the triples, which neglects the content related to the entity and relation. Though there are some approaches proposed to exploit the related multimodal content to improve knowledge graph embedding, such as the text description and images associated with the entities, they are not effective to address the heterogeneity and cross-modal correlation constraint of different types of content and network structure. In this paper, we propose a multi-modal content fusion model (MMCF) for knowledge graph embedding. To effectively fuse the heterogenous data for knowledge graph embedding, such as text description, related images and structural information, a cross-modal correlation learning component is proposed. It first learns the intra-modal and inter-modal correlation to fuse the multimodal content of each entity, and then they are fused with the structure features by a gating network. Meanwhile, to enhance the features of relation, the features of the associated head entity and tail entity are fused to learn relation embedding. To effectively evaluate the proposed model, we compare it with other baselines in three datasets, i.e., FB-IMG, WN18RR and FB15k-237. Experiment result of link prediction demonstrates that our model outperforms the state-of-the-art in most of the metrics significantly, implying the superiority of the proposed method.
Evolutionary backtracking search optimization for the dynamics of nonlinear benchmark physical systems

A novel application of bio-inspired computing heuristics with a backtracking search optimization algorithm (BSA) is presented for solving a system of nonlinear equations arising in a benchmark physical environment representing an interval arithmetic benchmark model, a kinematic application model, a neuro-physiology problem, combustion model and a chemical equilibrium system. The approximation in a mean square sense is exploited to formulate the residual error-based merit function for the systems of nonlinear equations. The design parameters of the merit function are calculated by using the strength of the optimization efficacy of BSA. Five variants of BSA are implemented to find the solution of benchmark nonlinear physical systems and comparative studies with state-of-the-art counterparts certify the worth of BSA in terms of precision, convergence, stability and complexity measures on single and multiple autonomous executions.
A study of malaria in West Bengal

Background: Malaria still occurs in West Bengal which is one of the states in the eastern part of India. Materials and Methods: The study design included an analysis of the 2017 and 2018 annual reports of the National Centre for Vector Borne Disease Control (NCVBDC). Results: The district of Kolkata had a high Annual Parasite Incidence (API) of Malaria of 3.72 during 2017 which decreased to 3.12 in 2018. Conclusion: If interventions such as the treatment of asymptomatic carriers take place, it is expected that the API will come down sooner in Kolkata district and in the rest of the state too.
LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant

The activated sludge process is a well-known method used to treat municipal and industrial wastewater. In this complex process, the oxygen concentration in the reactors plays a key role in the plant efficiency. This paper proposes the use of a Long Short-Term Memory (LSTM) network to identify an input–output model suitable for the design of an oxygen concentration controller. The model is identified from easily accessible measures collected from a real plant. This dataset covers almost a month of data collected from the plant. The performances achieved with the proposed LSTM model are compared with those obtained with a standard AutoRegressive model with eXogenous input (ARX). Both models capture the oscillation frequencies and the overall behavior (ARX Pearson correlation coefficient ρ = 0.833 , LSTM ρ = 0.921), but, while the ARX model fails to reach the correct amplitude (index of fitting FIT = 41.20%), the LSTM presents satisfactory performance (FIT = 60.56%).
Machine Learning and EPCA Methods for Extracting Lithology–Alteration Multi-Source Geological Elements: A Case Study in the Mining Evaluation of Porphyry Copper Ores in the Gondwana Metallogenic Belt

The location and development of porphyry copper deposits is a key issue for the mining industry. In this study, the Gondwana metallogenic belt was chosen as the study area to compare multiple methods for extracting multi-source geological elements to maximize the accuracy of the datasets used for mining evaluation and to use them to assess porphyry copper mineability. By comparison, a support vector machine (SVM) with an overall classification accuracy of 97.6573% and a Kappa coefficient of 0.9806 was used to extract the lithological distribution of the study area. Spectral feature-enhanced principal component analysis (EPCA) was combined with ASTER images to extract alteration information, with significant improvements in spatial aggregation and overall area compared to other alteration extraction methods, while a hierarchical alteration interpolation method was proposed to overcome the limitations of relying solely on remote sensing images to obtain surface alteration information and qualitatively extend deep alteration information. In addition, by overlaying various geoscientific factors affecting copper mineralization and mining, a Pearson correlation analysis is carried out in conjunction with currently proven or mined copper occurrences, and a weight of evidence approach is used to classify the study area into four mineability classes, which is important for narrowing down potential target areas for mineral exploration and assessing their mining value while contributing to an in-depth understanding of the role of geological elements in mineralization and development.
Logistic Model Tree Forest for Steel Plates Faults Prediction

Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset.
Gold deposits in the central part of the Aldan-Stanovoy shield in anomalies of geopotential filds

The analysis of potential geophysical filds of the central part of the Aldan-Stanovoy shield was carried out. Based on the modern automated program KOSCAD 3D, the following transformations of gravity and magnetic anomalies were carried out: a calculation of statistical and gradient characteristics, two-dimensional energy adaptive fitering, and a calculation of the data correlation coeffient. Based on the results of processing and interpretation of geological and geophysical materials, the block structure of the study area was clarifid and geophysical criteria for the localization of gold mineralization in the central part of the Aldan-Stanovoy shield were identifid.
Application of Hadoop cloud platform based on soft computing in financial accounting budget control

This article designs a Hadoop cloud platform system based on soft computing, and makes optimization research on its budget control application in financial accounting, stabilizing its solid position in financial accounting budget control. Based on this, the article improves the scheduling algorithm of the data migration module in the system, thereby optimizing the basic performance of the data migration module. By integrating the Hadoop cloud platform with the soft computing system, and using the Hadoop cloud platform to process big data, the system's query efficiency is fully demonstrated. This article also introduces the definition and theory of financial accounting budget control, combined with relevant budget control policies, and comprehensively summarizes the development status of a company's main business. Based on the era background of big data information technology innovation, we proposes an optimization strategy for comprehensive business budget management of an enterprise. The optimization plan addresses various issues mentioned above. By focusing on various aspects of enterprise budget management and strengthening the implementation of budget control processes, a comprehensive evaluation system for balanced budget management has been established, effectively resolving the above issues.
Deep neural network-based analysis of the impact of ambidextrous innovation and social networks on firm performance

The motivation for analyzing the impact of deep neural networks on enterprise performance is mainly due to the continuous deepening of enterprise information construction, shifting from traditional paper-based data acquisition methods to electronic data management. The data generated by the sales, production, logistics and other links of enterprises is also becoming increasingly large. How to scientifically and effectively process these massive amounts of data and extract valuable information has become an important issue that enterprises need to solve. The continuous and stable growth of China's economy has promoted the development and growth of enterprises, however, it has also made enterprises face a more complex competitive environment. The question of how to improve the performance of enterprises to enhance their competitiveness in the market has become a major issue to be addressed in the face of fierce competition and to ensure the long-term development of enterprises. In this paper, based on the research of firm performance evaluation, deep neural network is introduced to analyse the influence of ambidextrous innovation and social network on firm performance, and the theories of social network, ambidextrous innovation and deep neural network are sorted out and analysed, and a deep neural network-based firm performance evaluation model is established, and finally the sample data is obtained using crawler technology, and then the response values are analysed. Innovation and the improvement of the mean value of social networks are helpful to firm performance.
DenseTextPVT: Pyramid Vision Transformer with Deep Multi-Scale Feature Refinement Network for Dense Text Detection

Detecting dense text in scene images is a challenging task due to the high variability, complexity, and overlapping of text areas. To adequately distinguish text instances with high density in scenes, we propose an efficient approach called DenseTextPVT. We first generated high-resolution features at different levels to enable accurate dense text detection, which is essential for dense prediction tasks. Additionally, to enhance the feature representation, we designed the Deep Multi-scale Feature Refinement Network (DMFRN), which effectively detects texts of varying sizes, shapes, and fonts, including small-scale texts. DenseTextPVT, then, is inspired by Pixel Aggregation (PA) similarity vector algorithms to cluster text pixels into correct text kernels in the post-processing step. In this way, our proposed method enhances the precision of text detection and effectively reduces overlapping between text regions under dense adjacent text in natural images. The comprehensive experiments indicate the effectiveness of our method on the TotalText, CTW1500, and ICDAR-2015 benchmark datasets in comparison to existing methods.
Cryptographically Secure PseudoRandom Bit Generator for Wearable Technology

This paper presents a prototype wearable Cryptographically Secure PseudoRandom Bit Generator CSPRBG (wearable CSPRBG). A vest prototype has been fabricated to which an evaluation board with a ZYBO (ZYnq BOard) Zynq Z-7010 has been mounted using tailoring technology. In this system, a seed generator and block cryptographic algorithms responsible for the generation of pseudo-random values were implemented. A microphone and an accelerometer recorded sound and acceleration during the use of the prototype vest, and these recordings were passed to the seed generator and used as entropy sources. Hardware implementations were made for several selected Block Cryptographic algorithms such as Advanced Encryption Standard (AES), Twofish and 3DES. The random binary values generated by the wearable CSPRBG were analyzed by National Institute of Standards and Technology (NIST) statistical tests as well as ENT tests to evaluate their randomness, depending on the configuration of the entropy sources used. The idea of possible development of the wearable CSPRBG as a System on Chip (SoC) solution is also presented.
Interpretation of Hyperspectral Shortwave Infrared Core Scanning Data Using SEM-Based Automated Mineralogy: A Machine Learning Approach

Understanding the mineralogy and geochemistry of the subsurface is key when assessing and exploring for mineral deposits. To achieve this goal, rapid acquisition and accurate interpretation of drill core data are essential. Hyperspectral shortwave infrared imaging is a rapid and non-destructive analytical method widely used in the minerals industry to map minerals with diagnostic features in core samples. In this paper, we present an automated method to interpret hyperspectral shortwave infrared data on drill core to decipher major felsic rock-forming minerals using supervised machine learning techniques for processing, masking, and extracting mineralogical and textural information. This study utilizes a co-registered training dataset that integrates hyperspectral data with quantitative scanning electron microscopy data instead of spectrum matching using a spectral library. Our methodology overcomes previous limitations in hyperspectral data interpretation for the full mineralogy (i.e., quartz and feldspar) caused by the need to identify spectral features of minerals; in particular, it detects the presence of minerals that are considered invisible in traditional shortwave infrared hyperspectral analysis.
Image Cryptosystem in Optical Gyrator Transform Domain Using Audio Keys

Two remote sensing image encryption algorithms based on the randomness of audio channel sequences are proposed and their robustness is verified by many attack experiments. The first encryption algorithm uses the randomness of audio to encrypt image pixels in RMD. Compared with traditional image encryption algorithms, this algorithm has high randomness and security and can resist stronger password attacks. However, the encryption algorithm takes a long time. Considering that information sometimes needs to be transmitted urgently, a second encryption algorithm is proposed. By combining vocal tract and random phase to form new public and private keys, the number of computation amount and dislocations is reduced. The second algorithm is short in time but low in security.
Track and field training information acquisition and feedback of the based wireless medical sensor network

Abstract The real‐time embedded system is a computer‐based special‐purpose intelligent computer system that meets the strict requirements of the application system for power consumption. With the development of the Internet era, people are increasingly using the Internet to obtain relevant information, while traditional data collection mainly relies on manual operations. Therefore, the collection, storage and transmission of track and field training information based on existing equipment and databases has become a research hotspot. Based on wireless medical sensor network, this paper proposes an efficient track and field training information collection and feedback system communication protocol for real‐time transmission of various data of track and field athletes, and a real‐time embedded system for data collection and storage. Through research and analysis, it is found that the collection and feedback of track and field training information based on wireless medical sensor network can improve the accuracy of collection by 8.625. This shows that the collection and feedback of sports training information based on wireless medical sensor network is feasible.
Exploiting Misconfiguration Vulnerabilities in Microsoft’s Azure Active Directory for Privilege Escalation Attacks

Cloud services provided by Microsoft are growing rapidly in number and importance. Azure Active Directory (AAD) is becoming more important due to its role in facilitating identity management for cloud-based services. However, several risks and security issues have been associated with cloud systems due to vulnerabilities associated with identity management systems. In particular, misconfigurations could severely impact the security of cloud-based systems. Accordingly, this study identifies and experimentally evaluates exploitable misconfiguration vulnerabilities in Azure AD which can eventually lead to the risk of privilege escalation attacks. The study focuses on two scenarios: dynamic group settings and the activation of the Managed Identity feature on virtual devices. Through experimental evaluation, the research demonstrates the successful execution of these attacks, resulting in unauthorized access to sensitive information. Finally, we suggest several approaches to prevent such attacks by isolating sensitive systems to minimize the possibility of damage resulting from a misconfiguration accident and highlight the need for further studies.
Counteracting sociocultural barriers in global software engineering using group activities

Abstract In modern times, internationally organized teams face a number of coordination problems owing to their different physical operating locations. These challenges usually come in temporal, cultural, and linguistic forms. To resolve some of these issues, we need more coordination, teamwork, and shared understanding in the requirements engineering phase. Many approaches have been introduced to overcome these challenges associated with global software engineering (GSE). The objective of this research study is to introduce amateurs to GSE and improve their understanding of its associated challenges through an activity‐based learning approach. Our method is primarily targeted toward students who already have theoretical knowledge on the topic but require first‐hand experience with GSE. With the aforementioned motivation in mind, we propose, designe, and empirically evaluate two different activities that can help enhance awareness of GSE challenges. For each activity, we simulate an environment wherein participants are made to go through various constructed coordination challenges related to communication, time management, team mistrust, linguistic barriers, cultural barriers, and distribution of tasks. The effectiveness of our proposed activities, captured by the extent to which participants were able to deal with GSE challenges, was judged through various techniques including (i) observation, (ii) post activities survey questionnaire, and (iii) brainstorming and discussion. We show that the proposed activities were effective in helping students learn and further their understanding of GSE concepts. In particular, discussion sessions and survey questionnaire results reflect their ability to identify critical GSE challenges (specifically related to teams) in a simulated scenario.
Dynamic path-speed planning algorithm for autonomous driving on structured roads

Aiming at the problem of local path planning for structured roads, this paper proposes a framework of local path-speed planning for autonomous driving, which takes safety as the premise and improves driving efficiency. The framework simulates human driving thinking and divides the local path planning of autonomous driving into two parts: lane decision and path-speed planning. In the part of the lane decision, a lane decision algorithm based on driving risk field and safe distance is proposed, which can ensure driving efficient and ensure that the planning vehicle is always in a low-risk driving environment. In the part of the lane change path-speed planning, a candidate path generation algorithm based on uniform sampling of lane change time and a cost function considering lane change timeliness, driving safety, speed smoothness, and path continuity are proposed to achieve optimal path selection and speed planning. In the experiment part, there are six different driving tasks. In six scenes, the local path-speed planning framework proposed in this paper can plan a safe, efficient, and smooth driving path and a safe planning speed. Taking the scenario of detouring low-speed obstacles as an example, the path-speed planning algorithm proposed is compared with the path-speed planning algorithm based on discrete optimization in Hu et al. It has been verified that the algorithm proposed can ensure that planner is always at low environmental risks and drive with high driving efficiency.