Reviewerly/exHarmony huggingface dataset:
Job Recommendation System Using NLP

Abstract: Recommender systems is one of the most successful machine learning applications. We are exposed to the products/information recommended by these systems everywhere in our daily life. This project will introduce several recommender systems in NLP, specifically in the domain of online recruitment. I will explain the important ideas behind the recommender systems in our web application, it might be also similar with those in recruiting platforms like LinkedIn, Xing. One of my main jobs is to design and develop recommender systems on the skill-based matching app. Before getting into this specific topic, I would like to go over the general concepts and methods of current recommender system in different domains. We have provided a brief overview of the approaches, techniques, and applications of recommendation systems in this paper. One significant application of recommendation systems is in the field of job recruitment, where candidates are chosen using online job recruitment portals based on their profiles, employment histories, and behavioral tendencies. According to a recent survey, this field has not been thoroughly investigated up to this point, and the current job recommender system has many flaws. They analyze resumes, profiles, and job descriptions, but because of the "cold start" problem, new job postings and candidate profiles are not properlymatched. In some cases, potential candidates lose their jobs as a result of inadequate job descriptions and education information in the ontology.
Standards in Digital Pathology: Status quo

Recent advancements in whole slide digital imaging technologies brought and will bring ignificantly altered traditional workflows within pathology laboratories. A diagnostic digital pathology requires the deep integration of pathology laboratory and imaging workflows, including order entry, result reporting, image storing, image manipulation, and image management. As far as different systems from different vendors are required for those integrated workflows, a common understanding for the workflow between the different partners is inevitable. Standards are the formalized representation of that common understanding. The status quo of four main classes of international reference standards for their use in digital pathology is described.
DHSEGATs: distance and hop-wise structures encoding enhanced graph attention networks

Numerous works prove that existing neighbor-averaging graph neural networks (GNNs) cannot efficiently catch structure features, and many works show that injecting structure, distance, position, or spatial features can significantly improve the performance of GNNs, however, injecting high-level structure and distance into GNNs is an intuitive but untouched idea. This work sheds light on this issue and proposes a scheme to enhance graph attention networks (GATs) by encoding distance and hop-wise structure statistics. Firstly, the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node. Secondly, the derived structure information, distance information, and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors. Thirdly, the derived embedding vectors are fed into GATs, such as GAT and adaptive graph diffusion network (AGDN) to get the soft labels. Fourthly, the soft labels are fed into correct and smooth (C&S) to conduct label propagation and get final predictions. Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks (DHSEGATs) achieve a competitive result.
A deep neural network based feature fusion panoramic segmentation method

Abstract Segmentation of complex and diverse scenes in the field of machine vision has the advantages of low cost and good robustness. However, in the real open intelligent driving scene, the segmented object will be affected by the change of proportion and illumination. The feature extraction of the interference algorithm causes some problems such as local information loss and feature background fusion in the process of execution. In this paper, deep neural network method is used for feature fusion panoramic segmentation. It focuses on the premise of lightweight, and improves the performance by improving the structure of semantic segmentation network layer and model calculation effect, and reducing the number of network parameters and calculations, so as to further improve the detection performance. At the same time, with the gradual deepening of deep learning network architecture, but most embedded devices can not bear a huge amount of computing workload. An attentional channel pruning based approach is proposed using a wide range of experimental and ablation studies to compare different data, including VOC2012 and ImageNet datasets. We show that the proposed method outperforms state of the art methods in terms of visual quality.
Dynamic collaborative optimization coverage algorithm of UAV based on trajectory prediction in Internet of Vehicles

Abstract This research presents a dynamic pre-deployment strategy based on vehicle trajectory prediction information to address the issues of coverage gaps of base stations and local traffic congestion in urban vehicle networking. Under the architecture of distributed federated learning and blockchain, numerous UAVs equipped with edge computing servers eliminate the central aggregation node and use an enhanced Raft method to train a unified Seq2Seq-GRU trajectory prediction model. In the training cycle, the scheme selects the nodes to perform parameter aggregation and model update based on the quantity of data given. Second, based on the model’s prediction findings, this study offers an enhanced virtual force guide deployment method that directs the UAV to dynamically deploy across each virtual force in order to increase the vehicle’s access rate and communication quality.
Factors Influencing Online Learning Students’ Satisfaction in UIN Fatmawati Bengkulu

The use of e-learning in education can increase efficiency and motivation in learning. The success of this online learning platform is influenced by student satisfaction. Students who are satisfied then they will be comfortable with using online learning. The success of the online learning system can be seen in student satisfaction when using the LMS and online learning software. So, this study aims to analyse student satisfaction with the online learning system. The sample of this research is students of the Tarbiyah Faculty of UIN Sukarno Fatmawati Bengkulu. The sampling technique used is convenience sampling. The independent variables used were computer self-efficacy (CSE), perceived usefulness (PU), and perceived ease of use (PEU). The dependent variable in this study was student satisfaction (SAT). This data analysis technique used Structural Equation Modelling (SEM) with SmartPLS software. The results of this study indicated that the computer self-efficacy had a significant positive effect on the perceived ease of use and the perceived usefulness. The perceived ease of use and perceived usefulness significantly positively affected the student’s satisfaction. Keywords: Online Learning Higher Education, Students’ Satisfaction, Structural Equation Modeling
The right to encryption: Privacy as preventing unlawful access

Encryption technologies are a fundamental building block of modern digital infrastructure, but plans to curb these technologies continue to spring up. Even in the European Union, where their application is by now firmly embedded in legislation, lawmakers are again calling for measures which would impact these technologies. One of the most important arguments in this debate are human rights, most notably the rights to privacy and to freedom of expression. And although some authors have in the past explored how encryption technologies support human rights, this connection is not yet firmly grounded in an analysis of European human rights case law. This contribution aims to fill this gap, developing a framework for assessing restrictions of encryption technologies under the rights to privacy and freedom of expression as protected under the European Convention of Human Rights (the Convention) and the Charter of Fundamental rights in the European Union (the Charter). In the first section, the relevant function of encryption technologies, restricting access to information (called confidentiality), is discussed. In the second section, an overview of some governmental policies and practices impacting these technologies is provided. This continues with a discussion of the case law on the rights to privacy, data protection and freedom of expression, arguing that these rights are not only about ensuring lawful access by governments to protected information, but also about preventing unlawful access by others. And because encryption technologies are an important technology to reduce the risk of this unlawful access, it is then proposed that this risk is central to the assessment of governance measures in the field of encryption technologies. The article concludes by recommending that states perform an in-depth assessement of this when proposing new measures, and that courts when reviewing them also place the risk of unlawful access central to the analysis of interference and proportionality.
Pose-aware Multi-position Feature Network for Driver Distraction Recognition

Abstract This paper introduce pose-aware multi-position feature network for driver distraction recognition, taking into account the high association between the visual and geometric features of neighboring human key points. This method first uses the object detector to detect the driver’s body, and then uses the pose estimation to detect the key points of the human body. Finally, the key points of the human body are deconstructed to obtain key visual features and spatial features.In order to create the visual representation that corresponds to each key point, all the chosen visual representations are sent into a convolutional neural network. The geometric locations of key points on the upper body are used to build the spatial reasoning module, which is then transmitted to the neural network to get spatial features. The visual appearance features are fused with geometric features to predict corresponding driver action through linear layers.The experimental result shows that our methods achieve comparative performance on our own HY Large Vehicle Driver Dataset and the public AUC Driver Distracted Dataset.
Robust Image Hashing Using Histogram Reconstruction for Improving Content Preservation Resistance and Discrimination

This paper proposes a new image hashing method, which uses histogram reconstruction to solve the problem of the histogram not being sensitive to the change of pixel position, while ensuring the robustness of the hashing algorithm against common content preservation attacks (such as blurring, noise addition and rotation). The proposed algorithm can resist arbitrary angles of rotation, possibly because the reconstructed histogram leverages the rotational symmetry and its own invariance to rotation operations. We measure the similarity between different images by calculating the Hamming distance of the hash vectors of different images. Our experiments show that the proposed method performs well in robustness and discrimination compared with other established algorithms. In addition, we conduct a receiver operating characteristic curve analysis to further verify the superior overall performance of our image hash method.
Contrastive Learning-Based Anomaly Detection for Actual Corporate Environments

Information systems play an important role in business management, especially in personnel, budget, and financial management. If an anomaly ensues in an information system, all operations are paralyzed until their recovery. In this study, we propose a method for collecting and labeling datasets from actual operating systems in corporate environments for deep learning. The construction of a dataset from actual operating systems in a company's information system involves constraints. Collecting anomalous data from these systems is challenging because of the need to maintain system stability. Even with data collected over a long period, the training dataset may have an imbalance of normal and anomalous data. We propose a method that utilizes contrastive learning with data augmentation through negative sampling for anomaly detection, which is particularly suitable for small datasets. To evaluate the effectiveness of the proposed method, we compared it with traditional deep learning models, such as the convolutional neural network (CNN) and long short-term memory (LSTM). The proposed method achieved a true positive rate (TPR) of 99.47%, whereas CNN and LSTM achieved TPRs of 98.8% and 98.67%, respectively. The experimental results demonstrate the method's effectiveness in utilizing contrastive learning and detecting anomalies in small datasets from a company's information system.
Low-Cost Object Detection Models for Traffic Control Devices through Domain Adaption of Geographical Regions

Automated inspection systems utilizing computer vision technology are effective in managing traffic control devices (TCDs); however, they face challenges due to the limited availability of training datasets and the difficulty in generating new datasets. To address this, our study establishes a benchmark for cost-effective model training methods that achieve the desired accuracy using data from related domains and YOLOv5, a one-stage object detector known for its high accuracy and speed. In this study, three model cases were developed using distinct training approaches: (1) training with COCO-based pre-trained weights, (2) training with pre-trained weights from the source domain, and (3) training with a synthesized dataset mixed with source and target domains. Upon comparing these model cases, this study found that directly applying source domain data to the target domain is unfeasible, and a small amount of target domain data is necessary for optimal performance. A model trained with fine-tuning-based domain adaptation using pre-trained weights from the source domain and minimal target data, proved to be the most resource-efficient approach. These results contribute valuable guidance for practitioners aiming to develop TCD models with limited data, enabling them to build optimal models while conserving resources.
Digital Skills through the Lens of Gamified Activities in Language Mediation. The DIAL4U Erasmus+Project

The current study intended to analyse the perception of foreign language higher education instructors and higher education students in Romania regarding the increase of their digital skills and the use of digital tools in the COVID-19 pandemic. Online instruction provided a multitude of challenges and opportunities for designing instructional content. Our respondents concluded that language learning games and gamified instruction represent valuable tools in reconfiguring the scenario within language classes. By exploring these opportunities that increase students’ participation, interactivity and accessibility, the teaching and learning experience can be improved and adapted to more complex and ever-changing technological advances.
Influence of Mineral Deposition on the Retention of Potentially Hazardous Elements in Geothermal Spring Sediments

Geothermal springs are known to contain a variety of potentially hazardous elements (PHEs), which may threaten human health. Their release on Earth’s surface is largely dependent on the retention by the sediments at the spring outflux. In this study, the hot spring waters and the sediments at the corresponding sites were collected from the Nagqu geothermal field on the Tibetan Plateau. The water geochemistry and sediment mineralogy were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), X-ray fluorescence (XRF), and X-ray diffraction (XRD). The association of PHEs with minerals was analyzed. The results indicate that while the concentrations of PHEs were highly elevated, Be, As, and Tl in some hot springs exceeded the criteria for class III groundwater in China by up to 2, 2, and 19 times, respectively. Cs occurred at relatively high levels, up to 776 μg/L. As, Co, Se, Tl, and U in the hot spring waters were probably captured by iron sulfide minerals in the sediments, while Be and Cs were strongly retained by the carbonate minerals. The releases of V and Cr were apparently regulated by the clay minerals. Overall, the mobility of PHEs from the geothermal springs is controlled by the deposition of minerals at the spring vents.
Requirements‐related fault prevention during the transformation from formal specifications to programs

Abstract Software faults are costly to find and remove from programs. It is better to avoid inserting the faults in the first place. The authors identify requirements‐related faults that can arise during the transformation of formal specifications into programs, analyse each to discover its causes, and propose corresponding fault prevention methods. The approach works by analysing a specification, decomposing it into specification fragments with different levels like components, determining the correct implementation order among the components, and enabling automated code generation for the components and other low‐level specification fragments. The approach seeks to reduce the faults in programs significantly by guiding programmers to work effectively and productively. The authors evaluate the approach's performance experimentally and compare it to the performance of a fault‐prevention method with industry applications.
A Method for Predicting the Visual Attention Area in Real-Time Using Evolving Neuro-Fuzzy Models

This research paper presents the prediction of the visual attention area on a visual display using an evolving rule-based fuzzy model: evolving Takagi–Sugeno (eTS). The evolving fuzzy model is feasible for predicting the visual attention area because of its non-iterative, recursive, online, and real-time nature. Visual attention area prediction through a web camera is a problem that requires online adaptive systems with higher accuracy and greater performance. The proposed approach using an evolving fuzzy model to predict the eye-gaze attention area on a visual display in an ambient environment (to provide further services) mimics the human cognitive process and its flexibility to generate fuzzy rules without any prior knowledge. The proposed Visual Attention Area Prediction using Evolving Neuro-Fuzzy Systems (VAAPeNFS) approach can quickly generate compact fuzzy rules from new data. Numerical experiments conducted in a simulated environment further validate the performance and accuracy of the proposed model. To validate the model, the forecasting results of the eTS model are compared with DeTS and ANFIS. The result shows high accuracy, transparency and flexibility achieved by applying the evolving online versions compared to other offline techniques. The proposed approach significantly reduces the computational overhead, which makes it suitable for any sort of AmI application. Thus, using this approach, we achieve reusability, robustness, and scalability with better performance with high accuracy.
Relational graph location network for multi-view image localization

In multi-view image localization task, the features of the images captured from different views should be fused properly. This paper considers the classification-based image localization problem. We propose the relational graph location network (RGLN) to perform this task. In this network, we propose a heterogeneous graph construction approach for graph classification tasks, which aims to describe the location in a more appropriate way, thereby improving the expression ability of the location representation module. Experiments show that the expression ability of the proposed graph construction approach outperforms the compared methods by a large margin. In addition, the proposed localization method outperforms the compared localization methods by around 1.7% in terms of meter-level accuracy.
Efficient Autonomous Exploration and Mapping in Unknown Environments

Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the regional legacy issues, i.e., the great impact of smaller unexplored regions on the whole exploration process, which results in a dramatic reduction in their later exploration efficiency. To this end, this paper proposes a Local-and-Global Strategy (LAGS) algorithm that combines a local exploration strategy with a global perception strategy, which considers and solves the regional legacy issues in the autonomous exploration process to improve exploration efficiency. Additionally, we further integrate Gaussian process regression (GPR), Bayesian optimization (BO) sampling, and deep reinforcement learning (DRL) models to efficiently explore unknown environments while ensuring the robot's safety. Extensive experiments show that the proposed method could explore unknown environments with shorter paths, higher efficiencies, and stronger adaptability on different unknown maps with different layouts and sizes.
Hybrid Low-Rank Tensor CP and Tucker Decomposition with Total Variation Regularization for HSI Noise Removal

The acquired hyperspectral images (HSIs) are affected by a mixture of several types of noise, which often suffer from information missing. Corrupted HSIs limit the precision of the subsequent processing. In this paper, the weighted Total Variation-regularized Hybrid Model of CP and Tucker (WTV-HMCT) is proposed to accurately identify the intrinsic structures of the clean HSIs. By jointly minimizing CP rank and Tucker rank in the low-rank tensor approximation, WTV-HMCT fully exploits the high-dimensional structure correlations of HSI. To ensure the piecewise smoothness of the recovered image, the hybrid low-rank tensor decomposition approach integrates the weighted spatial spectral total variation regularization for the separation of the noise-free HSI and mixed noise. By the Alternating Direction Method of Multipliers (ADMM), the optimization model is transformed into two subproblems. Finally, an efficient proximal alternating minimization algorithm is developed to optimize the proposed hybrid low-rank tensor decomposition efficiently. The experimental results show that the proposed model effectively handles Gauss noise, striping noise, and mixed noise and that it outperforms the most advanced methods in terms of evaluation metrics and visual evaluation.
Changes in the Behaviour of Scientific Information Consumers as the Basis of Interaction between Researcher and Scientific Library

In this article are reflected the results of research on the behaviour of scientists within the information system, in the process of searching and using scientific information, also the factors that influence behaviour and the possibility of changing them. The authors analyse the current trends in the process of working with scientific information. The barriers preventing the implementation of scientific activities and the publication of scientific results are highlighted. Directions to optimize the interaction between the scientific library and scientists from the Republic of Moldova are proposed. Their collaboration should be based on constant dialog, personalization of services and individualized adaptation to the needs of each researcher. Constant monitoring of the external and internal environment of the scientific library functioning, rapid adaptation to changes in the information market, the study of trends in the development, systematization and management of scientific communications are becoming priorities of library activities. <!-- se va indica textul rezumatului-->