Reviewerly/exHarmony huggingface dataset:
Intelligent Recognition of Seismic Station Environmental Interference Based on YOLOv5

In recent years, human interference in seismic-station environments has posed challenges to the quality and accuracy of seismic signals, making data processing difficult. To accurately identify interference caused by personnel and ensure the reliability of seismic-network instrument detection data, it is necessary to track the detected targets across consecutive frames. Deep neural networks have made significant progress in this field. Therefore, an intelligent identification solution for environmental interference at seismic stations is proposed, which combines deep learning with multi-object tracking techniques. A centroid-matching tracking algorithm based on Kalman filtering is introduced to identify the entry/exit timestamps, alongside motion trajectories of interfering individuals, thereby marking the anomalous data caused by the presence of interfering personnel in seismic time-series data. Experimental results demonstrate that this research provides an effective solution for intelligent identification of environmental interference in seismic station environments.
Influence of Virtual Reality on User Evaluation of Prototypes in the Development Process—A Comparative Study with Control Rooms for Onshore Drilling Rigs

User evaluations of prototypes in virtual reality (VR) offer high potential for products that require resource-intensive prototype construction, such as drilling rigs. This study examined whether the user evaluation of a VR prototype for controlling an onshore drilling rigproduces results comparable to an evaluation in the real world. Using a between-subject design, 16 drilling experts tested a prototype in VR and reality. The experts performed three different work processes and evaluated their satisfaction based on task performance, user experience, and usability via standardized questionnaires. A test leader evaluated the effectiveness of the work process execution using a 3-level rating scheme. The number of user interactions and time on task were recorded. There were no significant differences in the effectiveness, number of interactions required, perceived usability, and satisfaction with respect to task performance. In VR, the drilling experts took significantly more time to complete tasks and rated the efficiency of the VR prototype significantly higher. Overall, the real-world evaluation provided more insights into prototype optimization. Nevertheless, several usability issues have been identified in VR. Therefore, user evaluations in VR are particularly suitable in the early development phases to identify usability issues, without the need to produce real prototypes.
DICE: a Dataset of Italian Crime Event news

Extracting events from news stories as the aim of several Natural Language Processing (NLP) applications (e.g., question answering, news recommendation, news summarization) is not a trivial task, due to the complexity of natural language and the fact that news reporting is characterized by journalistic style and norms. Those aspects entail scattering an event description over several sentences within one document (or more documents), applying a mechanism of gradual specification of event-related information. This implies a widespread use of co-reference relations among the textual elements, conveying non-linear temporal information. In addition to this, despite the achievement of state-of-the-art results in several tasks, high-quality training datasets for non-English languages are rarely available.
PEMBELAJARAN MENULIS TEKS EKSPLANASI MENGGUNAKAN MODEL PROBLEM BASED LEARNING BERBANTUAN MEDIA ARTICULATE STORYLINE DI KELAS VIII SMP BIMASENA SCHOOL

This study is motivated by the low skills and interest of students to write and Indonesian language learning becomes boring, less interesting, and seems monotonous because of the selection of models and media that are not appropriate. This research aims to describe: (1) how students learn to write explanatory texts in Class VIII SMP Bimasena School before using the problem-based learning model assisted by articulate storyline media; (2) how students learn to write explanatory texts in Class VIII SMP Bimasena School after using the problem-based learning model assisted by articulate storyline media; (3) the difference in the ability to write explanatory texts of students in Class VIII SMP Bimasena School in the experimental class and control class using the problem-based learning model assisted by articulate storyline media. The method used, namely pure experiment with pretest-posttest control group design. The results of this study, namely: (1) the value of experimental class students has not met the minimum completeness criteria that have been determined before applying the problem-based learning model assisted by articulate storyline media, this can be proven from the average value and recapitulation of the value that the average pretest of the experimental class is 17.3; (2) the value of experimental class students has met the predetermined minimum completeness criteria after applying the problem-based learning model assisted by articulate storyline media, this can be proven from the average value and recapitulation of the value that the average posttest of the experimental class is 86.5; (3) the experimental class has a higher value compared to the control class after receiving treatment, this can be proven from the recapitulation of the pretest and posttest and seen from the results of the independent sample t-test test, the Sig. (2-tailed) value is 0.001 &lt;0.05.
MSGAT-Based Sentiment Analysis for E-Commerce

Sentence-level sentiment analysis, as a research direction in natural language processing, has been widely used in various fields. In order to address the problem that syntactic features were neglected in previous studies on sentence-level sentiment analysis, a multiscale graph attention network (MSGAT) sentiment analysis model based on dependent syntax is proposed. The model adopts RoBERTa_WWM as the text encoding layer, generates graphs on the basis of syntactic dependency trees, and obtains sentence sentiment features at different scales for text classification through multilevel graph attention network. Compared with the existing mainstream text sentiment analysis models, the proposed model achieves better performance on both a hotel review dataset and a takeaway review dataset, with 94.8% and 93.7% accuracy and 96.2% and 90.4% F1 score, respectively. The results demonstrate the superiority and effectiveness of the model in Chinese sentence sentiment analysis.
A process calculus SMrCaIT for IoT

Abstract With the rapid popularization of smart devices, the applications and technologies of the Internet of Things (IoT) are in high demand worldwide, especially in improving development efficiency and ensuring system quality, reliability, and security. Formal methods have been successfully used to specify, verify, and analyze software and hardware systems, effectively alleviating the above problems in these systems. However, most of the existing works mainly focus on the practical applications of IoT, and there is a lack of research on modeling and analyzing IoT systems from the perspective of formal methods. In this paper, we first propose a secure mobile real‐time process calculus for specifying and reasoning about IoT systems, called SMrCaIT, which supports not only value‐passing communication but also name‐passing communication. In addition, this calculus can strictly separate process actions and mobility modeling by providing parametric mobility models. Subsequently, we present the operational semantics of this calculus, in particular, the rules on how to secure channel communication by closing the scope of a channel and handling scope extrusion. Taking vehicle ad hoc network (VANET) as a case, the application details of SMrCaIT and its operational semantics are fully demonstrated. By using the rewrite engine Real‐Time Maude, we further implement SMrCaIT and its operational semantics and verify some properties of the VANET case to indicate the effectiveness of our calculus in real‐world scenes.
Inconsistencies Between Information Security Policy Compliance and Shadow IT Usage

This study investigates the effects of self-efficacy on intentions toward information security policy compliance and behaviors in shadow information technology, with self-efficacy being divided into information technology self-efficacy and information security self-efficacy. An experiment was conducted and a total of 83 valid subjects were recruited in this study. Data were collected on the subjects’ behaviors during the experiment, and quantitative data were also collected using a posttest questionnaire. The findings indicated that ITSE and ISSE positively correlated with information security policy (ISP) compliance, suggesting that improving self-efficacy in either aspect will improve ISP compliance intention. However, ISP compliance did not correlate significantly with shadow IT usage, as subjects with high ISP compliance still used shadow IT. Therefore, there was a discrepancy between intention and actual behavior. In practical terms, organizations can use training and education to improve their employees’ self-efficacy in the technically challenging or unfamiliar aspects of IT and ISP compliance, improve their ISP compliance intention, and reduce the possibility of ISP violations, such as shadow IT usage.
RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration

The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission's first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole.
Practical Tracking Method based on Best Buddies Similarity

Visual tracking is a crucial skill for bionic robots to perceive the environment and control their movement. However, visual tracking is challenging when the target undergoes nonrigid deformation because of the perspective change from the camera mounted on the robot. In this paper, a real-time and scale-adaptive visual tracking method based on best buddies similarity (BBS) is presented, which is a state-of-the-art template matching method that can handle nonrigid deformation. The proposed method improves the original BBS in 4 aspects: (a) The caching scheme is optimized to reduce the computational overhead, (b) the effect of cluttered backgrounds on BBS is theoretically analyzed and a patch-based texture is introduced to enhance the robustness and accuracy, (c) the batch gradient descent algorithm is used to further speed up the method, and (d) a resample strategy is applied to enable the BBS to track the target in scale space. The proposed method on challenging real-world datasets is evaluated and its promising performance is demonstrated.
A Literature Review on Data Monetization using Smart Contracts

Blockchain technology is poised to change nearly every facet of our digital lives. Blockchain can be said as an immutable, and decentralized database. Data stored in the blockchain cannot be tampered, making it secure. Also, being decentralized, no central entity controls the blockchain, ensuring reliability. So, the data can be stored publicly, such that anyone could read the data. This vast availability of data could enable data scientists to perform various analytics over the large amount of data. This could result in many useful insights in many fields. But, when sensitive data such as healthcare data and reports are to be stored in the blockchain, it could raise several privacy issues. Medical reports or personal information cannot be stored in a way that anyone could access them. Thus, this paper suggests a way to store and perform analytics over sensitive data in blockchain. In this paper homomorphic encryption is used to store the sensitive data in blockchain. Computed results from homomorphic encryption on ciphertexts are encrypted. When the encrypted result is decoded, it produces a result that is identical to what would have happened if the operations had been carried out in plaintext. Thus, critical data are encrypted and stored in the blockchain, data analytics are performed over them, without knowing the actual data. Thus, the proposed system provides privacy of the data stored publicly and could also profit the data scientists with access to large amount of real time data directly from the owners of the data
Using Generative Adversarial Networks in Classification Tasks with Very Small Amounts of Training Data

Deep learning algorithms are incredibly powerful and have achieved impressive results in various classification tasks. However, one of their limitations is their dependence on large amounts of training data. When there is limited training data available, the standard approach is to increase the dataset size by using data augmentation and training a neural network on the expanded dataset. This method can be effective, but it requires significant computational resources and may not always be feasible. In our work, we proposed a new approach to address the problem of limited training data. Instead of relying solely on increasing the dataset size, we train Generative Adversarial Networks (GANs) to generate distributions of individual categories. The classification of the unknown element is then performed using distributions generated by trained GAN networks. We proposed four methods that compare the unknown element with the elements generated by trained GAN networks and establish estimates of the conditional probabilities of the unknown element belonging to individual categories. These conditional probabilities are then used for the classification of the unknown element into individual categories. This approach enables us to make informed decisions and achieve accurate classification results even when dealing with limited training data.
Researcher Network Visualization Using Matrix Researcher2vec

In this study, we introduce a system called Matrix Researcher2vec (MResearcher2vec) which generates researcher embedding vectors from their papers and research projects in researchmap and KAKENHI databases. The system includes data on 276,841 researchers, 6,161,592 papers, and research projects. Utilizing natural language processing techniques, the MResearcher2vec model extracts researcher vectors from the papers and research project summaries of KAKENHI grant recipients. The similarity between reseachers is then computed to visualize inter-researcher relationships. The machine learning results have been integrated into a web service, providing a novel approach for academic relationship mining. It can be applied in the matching of research contents and researchers in evaluation of industry-government-academia collaboration and joint research. It contributes in four aspects: (1) exchanges between researchers, (2) creation of opportunities for researchers and companies to connect, (3) further promotion of interdisciplinary research, and (4) reduction of lost opportunities for research institutions to acquire talents.
Formation of Students’ Legal Consciousness and Civic Position in Higher Education Institutions

Introduction. The principles and values, which form and keep functioning the civil society, should be a priority for all the present and future generations of citizens of the country. At the same time, the theoretical understanding of these categories should be backed up by their practical implementation. Therefore, the article aims at conducting the experimental pedagogical study of the problematic aspects of legal consciousness development for enabling a comprehensive assessment of the civic position maturity level of higher education teachers and students. Materials and Methods. The pedagogical activity management process in a Higher Education Institution and the work of tutors served the material for the present research. The research was conducted using a set of general scientific and specific scientific methods targeted at analysis and systematisation of the relevant materials, as well as at practical and experimental investigation. Results. The content of educators’ and students’ work on development of self-consciousness was considered during both in-class and extracurricular activities. Among the fundamental principles of the pedagogical activity management, the principles of legal consciousness and citizenship, democracy, as well as tolerance and civic actions engagement experience were defined. The features of tutors' work targeted at students’ legal consciousness and civic position formation were also analysed. In addition, the article focused at the need to form and strengthen partnerships with the general public, educational institutions, authorities and business. Discussion and Conclusions. The manageability of the civic self-consciousness development process has been revealed as the main result of the authors’ analytical research. The essential pedagogical requirements for this process have been distinguished: setting up interaction among all participants of this process both during educational as well as extracurricular activities, considering student professional training specifics and involvement of the general public into the process of self-consciousness development. The need to sensitize the teaching methods fostering students’ legal consciousness and citizenship formation has also been mentioned among the pedagogical requirements. The efficiency has been proved of setting up the temporary pedagogical communities of experts working directly with students and elaborating the specialised approaches designated to form the above-mentioned qualities in students through promotion of the special topics and issues within the curricula courses, the study of which can multiply the efficiency of the civil legal consciousness development efforts. The tutor activities programme aimed at students’ civic position formation has been worked out and suggested; step-by-step monitoring of students’ civic position formation process has also been envisaged.
Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections

The quantitative effect of seasonality on the prevalence of infectious diseases has been widely studied in epidemiological models. However, its influence in clinical prediction models has not been analyzed in great depth. In this work, we study the different approaches that can be employed to deal with seasonality when using white-box models related to infections, including two new proposals based on sliding windows and ensembles. We additionally consider the effects of class imbalance and high dimensionality, as they are common problems that must be confronted when building clinical prediction models. These approaches were tested with four datasets: two created synthetically and two extracted from the MIMIC-III database. Our proposed methods obtained the best results in the majority of the experiments, although traditional approaches attained good results in certain cases. On the whole, our results corroborate the theory that clinical prediction models for infections can be improved by considering the effect of seasonality, although the techniques employed to obtain the best results are highly dependent on both the dataset and the modeling technique considered.
One Stop Shop for Question-Answering Dataset Selection

In this paper, we offer a new visualization tool -- Dataset Statistical View (DSV), to lower the barrier of research entry by providing easy access to the question-answering (QA) datasets that researchers can build their work upon. Our target users are new researchers to the QA domain with no prior knowledge nor programming skills. The system is populated with multiple QA datasets, which covers a wide range of QA tasks. It allows researchers to explore and compare existing QA datasets at a one-stop website. The system shows statistical graphs for each QA dataset to offer an overview and a visual comparison between datasets. Although this paper focuses mainly at the syntactic level comparison, integrating bias and semantic level analysis is our ongoing work. We believe our DSV system is a valuable contribution to the advancement of the QA field, as it provides a solid starting point for new researchers and practitioners. An overview of the framework is demonstrated in this paper and the introduction of the application system is available at https://cnchuy.github.io/images/demo.mp4.
A Social Media Knowledge Retrieval Method Based on Knowledge Demands and Knowledge Supplies

In large social media knowledge retrieval systems, employing a keyword-based fuzzy matching method to obtain knowledge presents several challenges, such as irrelevant, inaccurate, disorganized, or non-systematic knowledge results. Therefore, this paper proposes a knowledge retrieval method capable of returning hierarchical, systematized knowledge results. The method can match the knowledge demands according to the keyword input by users and then present the knowledge supplies corresponding to the knowledge demands as results to the users. Firstly, a knowledge structure named Knowledge Demand is designed to represent the genuine needs of social media users. This knowledge structure measures the popularity of topic combinations in the Topic Map, so the topic combinations with high popularity are regarded as the main content of the Knowledge Demands. Secondly, the proposed method designs a hierarchical and systematic knowledge structure, named Knowledge Supply, which provides Knowledge Solutions matched with the Knowledge Demands. The Knowledge Supply is generated based on the Knowledge Element Repository, using the BLEU similarity matrix to retrieve Knowledge Elements with high similarity, and then clustering these Knowledge Elements into several knowledge schemes to extract the Knowledge Solutions. The organized Knowledge Elements and Knowledge Solutions are the presentation of each Knowledge Supply. Finally, this research crawls posts in the “Autohome Forum” and conducts an experiment by simulating the user’s actual knowledge search process. The experiment shows that the proposed method is an effective knowledge retrieval method, which can provide users with hierarchical and systematized knowledge.