Inf. Comput.最新文献_第3页

Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model 利用轻量级机器学习模型预防业余运动员深蹲运动损伤的智能穿戴设备

Inf. Comput. Pub Date : 2023-07-14 DOI: 10.3390/info14070402

Ricardo P. Arciniega-Rocha, Vanessa C. Erazo-Chamorro, P. Rosero-Montalvo, G. Szabó

引用次数: 1

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques 利用边缘检测技术增强基于csi的人体活动识别

Inf. Comput. Pub Date : 2023-07-14 DOI: 10.3390/info14070404

Hossein Shahverdi, M. Nabati, P. Moshiri, R. Asvadi, S. Ghorashi

{"title":"Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques","authors":"Hossein Shahverdi, M. Nabati, P. Moshiri, R. Asvadi, S. Ghorashi","doi":"10.3390/info14070404","DOIUrl":"https://doi.org/10.3390/info14070404","url":null,"abstract":"Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85185591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Exploitation of Vulnerabilities: A Topic-Based Machine Learning Framework for Explaining and Predicting Exploitation 漏洞利用:基于主题的机器学习框架，用于解释和预测漏洞利用

Inf. Comput. Pub Date : 2023-07-14 DOI: 10.3390/info14070403

Konstantinos Charmanas, N. Mittas, L. Angelis

{"title":"Exploitation of Vulnerabilities: A Topic-Based Machine Learning Framework for Explaining and Predicting Exploitation","authors":"Konstantinos Charmanas, N. Mittas, L. Angelis","doi":"10.3390/info14070403","DOIUrl":"https://doi.org/10.3390/info14070403","url":null,"abstract":"Security vulnerabilities constitute one of the most important weaknesses of hardware and software security that can cause severe damage to systems, applications, and users. As a result, software vendors should prioritize the most dangerous and impactful security vulnerabilities by developing appropriate countermeasures. As we acknowledge the importance of vulnerability prioritization, in the present study, we propose a framework that maps newly disclosed vulnerabilities with topic distributions, via word clustering, and further predicts whether this new entry will be associated with a potential exploit Proof Of Concept (POC). We also provide insights on the current most exploitable weaknesses and products through a Generalized Linear Model (GLM) that links the topic memberships of vulnerabilities with exploit indicators, thus distinguishing five topics that are associated with relatively frequent recent exploits. Our experiments show that the proposed framework can outperform two baseline topic modeling algorithms in terms of topic coherence by improving LDA models by up to 55%. In terms of classification performance, the conducted experiments—on a quite balanced dataset (57% negative observations, 43% positive observations)—indicate that the vulnerability descriptions can be used as exclusive features in assessing the exploitability of vulnerabilities, as the “best” model achieves accuracy close to 87%. Overall, our study contributes to enabling the prioritization of vulnerabilities by providing guidelines on the relations between the textual details of a weakness and the potential application/system exploits.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87459400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Application of Z-Numbers in Fuzzy Decision Making: The State of the Art z数在模糊决策中的应用:研究现状

Inf. Comput. Pub Date : 2023-07-13 DOI: 10.3390/info14070400

N. Alam, K. Khalif, N. Jaini, A. Gegov

{"title":"The Application of Z-Numbers in Fuzzy Decision Making: The State of the Art","authors":"N. Alam, K. Khalif, N. Jaini, A. Gegov","doi":"10.3390/info14070400","DOIUrl":"https://doi.org/10.3390/info14070400","url":null,"abstract":"A Z-number is very powerful in describing imperfect information, in which fuzzy numbers are paired such that the partially reliable information is properly processed. During a decision-making process, human beings always use natural language to describe their preferences, and the decision information is usually imprecise and partially reliable. The nature of the Z-number, which is composed of the restriction and reliability components, has made it a powerful tool for depicting certain decision information. Its strengths and advantages have attracted many researchers worldwide to further study and extend its theory and applications. The current research trend on Z-numbers has shown an increasing interest among researchers in the fuzzy set theory, especially its application to decision making. This paper reviews the application of Z-numbers in decision making, in which previous decision-making models based on Z-numbers are analyzed to identify their strengths and contributions. The decision making based on Z-numbers improves the reliability of the decision information and makes it more meaningful. Another scope that is closely related to decision making, namely, the ranking of Z-numbers, is also reviewed. Then, the evaluative analysis of the Z-numbers is conducted to evaluate the performance of Z-numbers in decision making. Future directions and recommendations on the applications of Z-numbers in decision making are provided at the end of this review.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76344453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Addiction and Spending in Gacha Games Gacha游戏中的成瘾和消费

Inf. Comput. Pub Date : 2023-07-13 DOI: 10.3390/info14070399

N. Lakic, A. Bernik, Andrej Čep

引用次数: 0

Inf. Comput. Pub Date : 2023-07-12 DOI: 10.3390/info14070398

Dmitry Vesnin, D. Levshun, A. Chechulin

{"title":"Trademark Similarity Evaluation Using a Combination of ViT and Local Features","authors":"Dmitry Vesnin, D. Levshun, A. Chechulin","doi":"10.3390/info14070398","DOIUrl":"https://doi.org/10.3390/info14070398","url":null,"abstract":"The origin of the trademark similarity analysis problem lies within the legal area, specifically the protection of intellectual property. One of the possible technical solutions for this issue is the trademark similarity evaluation pipeline based on the content-based image retrieval approach. CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area has been transitioning from CNNs to a new architecture, namely, Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre-, post-processing, and pre-training on big datasets. We propose the enhancement of the trademark similarity evaluation pipeline by joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on the METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves a mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the usage of an enhanced trademark similarity evaluation pipeline allows for the improvement of the protection of intellectual property with the help of artificial intelligence methods. Moreover, this approach enables one to identify cases of unfair use of such data and form an evidence base for litigation.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80848616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Satisfiability Modulo Theory Solvers for Verification of Neural Networks in Predictive Maintenance Applications 利用可满足模理论求解器验证预测性维护应用中的神经网络

Inf. Comput. Pub Date : 2023-07-12 DOI: 10.3390/info14070397

Dario Guidotti, L. Pandolfo, Luca Pulina

引用次数: 1

Measuring and Understanding Crowdturfing in the App Store 衡量和理解App Store中的众筹

Inf. Comput. Pub Date : 2023-07-11 DOI: 10.3390/info14070393

Qi-Qi Hu, Xiaomei Zhang, Fangqi Li, Zhushou Tang, Shilin Wang

{"title":"Measuring and Understanding Crowdturfing in the App Store","authors":"Qi-Qi Hu, Xiaomei Zhang, Fangqi Li, Zhushou Tang, Shilin Wang","doi":"10.3390/info14070393","DOIUrl":"https://doi.org/10.3390/info14070393","url":null,"abstract":"Application marketplaces collect ratings and reviews from users to provide references for other consumers. Many crowdturfing activities abuse user reviews to manipulate the reputation of an app and mislead other consumers. To understand and improve the ecosystem of reviews in the app market, we investigate the existence of crowdturfing based on the App Store. This paper reports a measurement study of crowdturfing and its reviews in the App Store. We use a sliding window to obtain the relationship graph between users and the community detection method to binary classify the detected communities. Then, we measure and analyze the crowdturfing obtained from the classification and compare them with genuine users. We analyze several features of crowdturfing, such as ratings, sentiment scores, text similarity, and common words. We also investigate which apps crowdturfing often appears in and reveal their role in app ranking. These insights are used as features in machine learning models, and the results show that they can effectively train classifiers and detect crowdturfing reviews with an accuracy of up to 98.13%. This study reveals malicious crowdfunding practices in the App Store and helps to strengthen the review security of app marketplaces.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74220166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic 3D Building Model Generation from Airborne LiDAR Data and OpenStreetMap Using Procedural Modeling 使用程序建模的机载LiDAR数据和OpenStreetMap自动生成3D建筑模型

Inf. Comput. Pub Date : 2023-07-11 DOI: 10.3390/info14070394

R. Zupan, Adam Vinković, Rexhep Nikçi, Bernarda Pinjatela

{"title":"Automatic 3D Building Model Generation from Airborne LiDAR Data and OpenStreetMap Using Procedural Modeling","authors":"R. Zupan, Adam Vinković, Rexhep Nikçi, Bernarda Pinjatela","doi":"10.3390/info14070394","DOIUrl":"https://doi.org/10.3390/info14070394","url":null,"abstract":"This research is primarily focused on utilizing available airborne LiDAR data and spatial data from the OpenStreetMap (OSM) database to generate 3D models of buildings for a large-scale urban area. The city center of Ljubljana, Slovenia, was selected for the study area due to data availability and diversity of building shapes, heights, and functions, which presented a challenge for the automated generation of 3D models. To extract building heights, a range of data sources were utilized, including OSM attribute data, as well as georeferenced and classified point clouds and a digital elevation model (DEM) obtained from openly available LiDAR survey data of the Slovenian Environment Agency. A digital surface model (DSM) and digital terrain model (DTM) were derived from the processed LiDAR data. Building outlines and attributes were extracted from OSM and processed using QGIS. Spatial coverage of OSM data for buildings in the study area is excellent, whereas only 18% have attributes describing external appearance of the building and 6% describing roof type. LASTools software (rapidlasso GmbH, Friedrichshafener Straße 1, 82205 Gilching, GERMANY) was used to derive and assign building heights from 3D coordinates of the segmented point clouds. Various software options for procedural modeling were compared and Blender was selected due to the ability to process OSM data, availability of documentation, and low computing requirements. Using procedural modeling, a 3D model with level of detail (LOD) 1 was created fully automated. After analyzing roof types, a 3D model with LOD2 was created fully automated for 87.64% of buildings. For the remaining buildings, a comparison of procedural roof modeling and manual roof editing was performed. Finally, a visual comparison between the resulting 3D model and Google Earth’s model was performed. The main objective of this study is to demonstrate the efficient modeling process using open data and free software and resulting in an enhanced accuracy of the 3D building models compared to previous LOD2 iterations.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89759239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Incorporating an Unsupervised Text Mining Approach into Studying Logistics Risk Management: Insights from Corporate Annual Reports and Topic Modeling 将无监督文本挖掘方法纳入物流风险管理研究:来自公司年度报告和主题建模的见解

Inf. Comput. Pub Date : 2023-07-11 DOI: 10.3390/info14070395

David L. Olson, Bongsug Chae

{"title":"Incorporating an Unsupervised Text Mining Approach into Studying Logistics Risk Management: Insights from Corporate Annual Reports and Topic Modeling","authors":"David L. Olson, Bongsug Chae","doi":"10.3390/info14070395","DOIUrl":"https://doi.org/10.3390/info14070395","url":null,"abstract":"This study examined the Security and Exchange Commission (SEC) annual reports of selected logistics firms over the period from 2006 through 2021 for risk management terms. The purpose was to identify which risks are considered most important in supply chain logistics operations. Section 1A of the SEC reports includes risk factors. The COVID-19 pandemic has had a heavy impact on global supply chains. We also know that trucking firms have long had difficulties recruiting drivers. Fuel price has always been a major risk for airlines but also can impact shipping, trucking, and railroads. We were especially interested in pandemic, personnel, and fuel risks. We applied topic modeling, enabling us to identify some of the capabilities of unsupervised text mining as applied to SEC reports. We demonstrate the identification of terms, the time dimension, and correlation across topics by the topic model. Our analysis confirmed expectations about COVID-19’s impact, personnel shortages, and fuel. It also revealed common themes regarding the risks involved in international trade and perceived regulatory risks. We conclude with the supply chain management risks identified and discuss means of mitigation.","PeriodicalId":13622,"journal":{"name":"Inf. Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90314817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0