J. Data Intell.最新文献_第2页

Expert Opinions on Smart Retailing Technologies and Their Impacts 智能零售技术及其影响的专家意见

J. Data Intell. Pub Date : 2022-05-01 DOI: 10.26421/jdi3.2-5

Alexander Voelz, Patrick Hafner, C. Strauss

{"title":"Expert Opinions on Smart Retailing Technologies and Their Impacts","authors":"Alexander Voelz, Patrick Hafner, C. Strauss","doi":"10.26421/jdi3.2-5","DOIUrl":"https://doi.org/10.26421/jdi3.2-5","url":null,"abstract":"The share of e-commerce has been rising uninterrupted for many years, but the largest share of sales is still achieved in stationary retail. Nevertheless, many retailers are forced to address the benefits that consumers have gotten used to through digital shopping experiences. Smart Retailing (SR) is seen as a possible solution for brick-and-mortar stores to answer the challenges created by the rise of e-commerce. This study aims to present the current state of research in SR and evaluate its potential to confront the challenges that have arisen in the industry. To do so, we built upon insights from previous works, in which we derived specific areas of impact that need to be considered when implementing those technologies. In this work, we substantiate our developed assessment with empirical data generated through the analysis of exploratory expert interviews. The textit{gain of valuable data} and the textit{personalization of the shopping experience} are confirmed among the positive impact factors. At the same time, textit{privacy concerns} and textit{customer acceptance risk} were the most discussed negative impact factors in the interviews. Finally, the interview findings were used to adapt the Technology Acceptance Model (TAM) to the specific context of SR. Overall, this work provides more profound insight into the essential factors that need to be considered by brick-and-mortar retailers to face the increasing pressure imposed by online competition.","PeriodicalId":232625,"journal":{"name":"J. Data Intell.","volume":"41 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133385942","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: 3

SigmaLaw PBSA - A Deep Learning Approach For Aspect Based Sentiment Analysis in Legal Opinion Texts SigmaLaw PBSA -一种基于方面的法律意见文本情感分析的深度学习方法

J. Data Intell. Pub Date : 2022-02-01 DOI: 10.26421/jdi3.1-1

Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Ratnayaka, Andrea Perera

{"title":"SigmaLaw PBSA - A Deep Learning Approach For Aspect Based Sentiment Analysis in Legal Opinion Texts","authors":"Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Ratnayaka, Andrea Perera","doi":"10.26421/jdi3.1-1","DOIUrl":"https://doi.org/10.26421/jdi3.1-1","url":null,"abstract":"When lawyers and legal officers are working on a new legal case, they are supposed have properly studied prior cases similar to the current case, as the prior cases can provide valuable information which can have a direct impact on the outcomes of the current court case. Therefore, developing methodologies which are capable of automatically extracting information from legal opinion texts related to previous court cases can be considered as an important tool when it comes to the legal technology ecosystem. In this study, we focus on finding advantageous and disadvantageous facts or arguments in court cases, which is one of the most critical and time-consuming tasks in court case analysis. The Aspect-based Sentiment Analysis concept is used as the base of this study to perform legal information extraction. In this paper, we introduce a solution to predict sentiment value of sentences in legal documents in relation to its legal parties. The proposed approach employs a fine-grained sentiment analysis (Aspect-Based Sentiment Analysis) technique to achieve this task. Sigmalaw PBSA is a novel deep learning-based model for ABSA which is specifically designed for legal opinion texts. We evaluate the Sigmalaw PBSA model and existing ABSA models on the SigmaLaw-ABSA dataset which consists of 2000 legal opinion texts fetched from a public online data base. Experiments show that our model outperforms the state-of-the-art models. We also conduct an ablation study to identify which methods are most effective for legal texts.","PeriodicalId":232625,"journal":{"name":"J. Data Intell.","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121490892","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

Predicting pre-trial detention outcomes in the Brazilian Supreme Court 预测巴西最高法院审前拘留结果

J. Data Intell. Pub Date : 2022-02-01 DOI: 10.26421/jdi3.1-2

Thiago Raulino Dal Pont, Isabela Cristina Sabo, Pablo Ernesto Vigneaux Wilton, Victor Araújo de Menezes, Rafael Copetti, Luciano Zambrota, Pablo Procópio Martins, Edjandir Corrêa Costa, Edimeia Liliani Schnitzler, Paloma Maria Santos, Rodrigo Rafael Cunha, Gerson Bovi Kaster, Aires José Rover

{"title":"Predicting pre-trial detention outcomes in the Brazilian Supreme Court","authors":"Thiago Raulino Dal Pont, Isabela Cristina Sabo, Pablo Ernesto Vigneaux Wilton, Victor Araújo de Menezes, Rafael Copetti, Luciano Zambrota, Pablo Procópio Martins, Edjandir Corrêa Costa, Edimeia Liliani Schnitzler, Paloma Maria Santos, Rodrigo Rafael Cunha, Gerson Bovi Kaster, Aires José Rover","doi":"10.26421/jdi3.1-2","DOIUrl":"https://doi.org/10.26421/jdi3.1-2","url":null,"abstract":"Brazil has a large prison population, which places it as the third country in the world with the most incarceration rate. In addition, the criminal caseload is increasing in Brazilian Judiciary, which is encouraging AI usage to advance in e-Justice. Within this context, the paper presents a case study with a dataset composed of 2,200 judgments from the Supreme Federal Court (STF) about pre-trial detention. These are cases in which a provisional prisoner requests for freedom through habeas corpus. We applied Machine Learning (ML) and Natural Language Processing (NLP) techniques to predict whether STF will release or not the provisional prisoner (text classification), and also to find a reliable association between the judgment outcome and the prisoners' crime and/or the judge responsible for the case (association rules). We obtained satisfactory results in both tasks. Classification results show that, among the models used, Convolutional Neural Network (CNN) is the best, with 95% accuracy and 0.91 F1-Score. Association results indicate that, among the rules generated, there is a high probability of drug law crimes leading to a dismissed habeas corpus (which means the maintenance of pre-trial detention). We concluded that STF has not interfered in first degree decisions about pre-trial detention and that it is necessary to discuss drug criminalization in Brazil. The main contribution of the paper is to provide models that can support judges and pre-trial detainees.","PeriodicalId":232625,"journal":{"name":"J. Data Intell.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754675","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

Purging Compliance from Database Backups by Encryption 通过加密清除数据库备份的合规性

J. Data Intell. Pub Date : 2022-02-01 DOI: 10.26421/jdi3.1-4

Nick Scope, A. Rasin, B. Lenard, J. Wagner, K. Heart

引用次数: 2

Government as a Platform? The Power of Platforms to S upport Personalization of Public Services 政府是一个平台?平台助力公共服务个性化

J. Data Intell. Pub Date : 2022-02-01 DOI: 10.26421/jdi3.1-5

Benedict Bender, Moreen Heine

引用次数: 0

Transformer Models in the Home Improvement Domain 家装领域中的变压器模型

J. Data Intell. Pub Date : 2022-02-01 DOI: 10.26421/jdi3.1-3

Macedo Maia, M. Endres

引用次数: 0

Making Use of More Reviews Skillfully in Explaninable Recommendation Gerneration 在可解释推荐生成中巧妙地利用更多评论

J. Data Intell. Pub Date : 2021-11-01 DOI: 10.26421/jdi2.4-3

Shunsuke Kido, Ryuji Sakamoto, M. Aritsugi

引用次数: 0

Attention-Based Context Boosted Cyberbullying Detection in Social Media 基于注意力的情境促进了社交媒体中的网络欺凌检测

J. Data Intell. Pub Date : 2021-11-01 DOI: 10.26421/jdi2.4-2

Nabi Rezvani, A. Beheshti

{"title":"Attention-Based Context Boosted Cyberbullying Detection in Social Media","authors":"Nabi Rezvani, A. Beheshti","doi":"10.26421/jdi2.4-2","DOIUrl":"https://doi.org/10.26421/jdi2.4-2","url":null,"abstract":"Cyberbullying detection is a rising research topic due to its paramount impact on social media users, especially youngsters and adolescents. While there has been an enormous amount of progress in utilising efficient machine learning and NLP techniques for tackling this task, recent methods have not fully addressed contextualizing the textual content to the highest possible extent. The textual content of social media posts and comments is normally long, noisy and mixed with lots of irrelevant tokens and characters, and therefore utilizing an attention-based approach that can focus on more relevant parts of the text can be quite pertinent. Moreover, social media information is normally multi-modal in nature and may contain various metadata and contextual information that can contribute to enhancing the Cyberbullying prediction system. In this research, we propose a novel machine learning method that, (i) fine tunes a variant of BERT, a deep attention-based language model, which is capable of detecting patterns in long and noisy bodies of text; (ii)~extracts contextual information from multiple sources including metadata information, images and even external knowledge sources and uses these features to complement the learner model; and (iii) efficiently combines textual and contextual features using boosting and a wide-and-deep architecture. We compare our proposed method with state-of-the-art methods and highlight how our approach significantly outperforming the quality of results compared to those methods in most cases.","PeriodicalId":232625,"journal":{"name":"J. Data Intell.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129146176","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

Modeling Personality Effect in Trust Prediction 人格效应在信任预测中的建模

J. Data Intell. Pub Date : 2021-11-01 DOI: 10.26421/jdi2.4-1

S. Ghafari, A. Beheshti, Aditya Joshi, Cécile Paris, S. Yakhchi, M. Orgun, A. Jolfaei, Quan.Z Sheng

{"title":"Modeling Personality Effect in Trust Prediction","authors":"S. Ghafari, A. Beheshti, Aditya Joshi, Cécile Paris, S. Yakhchi, M. Orgun, A. Jolfaei, Quan.Z Sheng","doi":"10.26421/jdi2.4-1","DOIUrl":"https://doi.org/10.26421/jdi2.4-1","url":null,"abstract":"Trust among users in online social networks is a key factor in determining the amount of information that is perceived as reliable. Compared to the number of users in online social networks, user-specified trust relations are very sparse. This makes the pair-wise trust prediction a challenging task. Social studies have investigated trust and why people trust each other. The relation between trust and personality traits of people who established those relations, has been proved by social theories. In this work, we attempt to alleviate the effect of the sparsity of trust relations by extracting implicit information from the users, in particular, by focusing on users' personality traits and seeking a low-rank representation of users. We investigate the potential impact on the prediction of trust relations, by incorporating users' personality traits based on the Big Five factor personality model. We evaluate the impact of similarities of users' personality traits and the effect of each personality trait on pair-wise trust relations. Next, we formulate a new unsupervised trust prediction model based on tensor decomposition. Finally, we empirically evaluate this model using two real-world datasets. Our extensive experiments confirm the superior performance of our model compared to the state-of-the-art approaches.","PeriodicalId":232625,"journal":{"name":"J. Data Intell.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122653052","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

Discover Relations in the Industry 4.0 Standards Via Unsupervised Learning on Knowledge Graph Embeddings 通过知识图嵌入的无监督学习发现工业4.0标准中的关系

J. Data Intell. Pub Date : 2021-09-01 DOI: 10.26421/JDI2.3-2

Ariam Rivas, Irlán Grangel-González, D. Collarana, Jens Lehmann, Maria-Esther Vidal

引用次数: 2