Information Processing & Management最新文献

{"title":"Unsupervised feature selection using sparse manifold learning: Auto-encoder approach","authors":"Amir Moslemi ,&nbsp;Mina Jamshidi","doi":"10.1016/j.ipm.2024.103923","DOIUrl":"10.1016/j.ipm.2024.103923","url":null,"abstract":"<div><div>Feature selection techniques are widely being used as a preprocessing step to train machine learning algorithms to circumvent the curse of dimensionality, overfitting, and computation time challenges. Projection-based methods are frequently employed in feature selection, leveraging the extraction of linear relationships among features. The absence of nonlinear information extraction among features is notable in this context. While auto-encoder based techniques have recently gained traction for feature selection, their focus remains primarily on the encoding phase, as it is through this phase that the selected features are derived. The subtle point is that the performance of auto-encoder to obtain the most discriminative features is significantly affected by decoding phase. To address these challenges, in this paper, we proposed a novel feature selection based on auto-encoder to not only extracting nonlinear information among features but also decoding phase is regularized as well to enhance the performance of algorithm. In this study, we defined a new model of auto-encoder to preserve the topological information of reconstructed close to input data. To geometric structure of input data is preserved in projected space using Laplacian graph, and geometrical projected space is preserved in reconstructed space using a suitable term (abstract Laplacian graph of reconstructed data) in optimization problem. Preserving abstract Laplacian graph of reconstructed data close to Laplacian graph of input data affects the performance of feature selection and we experimentally showed this. Therefore, we show an effective approach to solve the objective of the corresponding problem. Since this approach can be mainly used for clustering aims, we conducted experiments on ten benchmark datasets and assessed our propped method based on clustering accuracy and normalized mutual information (NMI) metric. Our method obtained considerable superiority over recent state-of-the-art techniques in terms of NMI and accuracy.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103923"},"PeriodicalIF":7.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EvoPath: Evolutionary meta-path discovery with large language models for complex heterogeneous information networks","authors":"Shixuan Liu ,&nbsp;Haoxiang Cheng ,&nbsp;Yunfei Wang ,&nbsp;Yue He ,&nbsp;Changjun Fan ,&nbsp;Zhong Liu","doi":"10.1016/j.ipm.2024.103920","DOIUrl":"10.1016/j.ipm.2024.103920","url":null,"abstract":"<div><div>Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination. This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103920"},"PeriodicalIF":7.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A diachronic language model for long-time span classical Chinese","authors":"Yuting Wei,&nbsp;Meiling Li,&nbsp;Yangfu Zhu,&nbsp;Yuanxing Xu,&nbsp;Yuqing Li,&nbsp;Bin Wu","doi":"10.1016/j.ipm.2024.103925","DOIUrl":"10.1016/j.ipm.2024.103925","url":null,"abstract":"<div><div>Classical Chinese literature, with its long history spanning thousands of years, serves as an invaluable resource for historical and humanistic studies. Previous classical Chinese language models have achieved significant progress in semantic understanding. However, they largely neglected the dynamic evolution of language across different historical eras. In this paper, we introduce a novel diachronic pre-trained language model tailored for classical Chinese texts. This model utilizes a time-based transformer architecture that captures the continuous evolution of semantics over time. Moreover, it adeptly balances the contextual and temporal information, minimizing semantic ambiguities from excessive time-related inputs. A high-quality diachronic corpus for classical Chinese is developed for training. This corpus spans from the pre-Qin dynasty to the Qing dynasty and includes a diverse array of genres. We validate its effectiveness by enriching a well-known classical Chinese word sense disambiguation dataset with additional temporal annotations. The results demonstrate the state-of-the-art performance of our model in discerning classical Chinese word meanings across different historical periods. Our research helps linguists to rapidly grasp the extent of semantic changes across different periods from vast corpora.<span><span>¹</span></span></div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103925"},"PeriodicalIF":7.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mm-FERP: An effective method for human personality prediction via mm-wave radar using facial sensing","authors":"Naveed Imran ,&nbsp;Jian Zhang ,&nbsp;Zheng Yang ,&nbsp;Jehad Ali","doi":"10.1016/j.ipm.2024.103919","DOIUrl":"10.1016/j.ipm.2024.103919","url":null,"abstract":"<div><div>mm-FERP (millimeter wave Facial Expression Recognition for Personality) explores the use of mm-Wave radar technology, specifically the TI IWR1443, to assess personality traits based on the OCEAN model through facial expression analysis. This research uniquely combines psychological profiling with state-of-the-art technology to predict the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) personality traits by carefully analyzing facial muscle movements collected through mm-wave radar alongside detailed questionnaire analysis. Our advanced mm-FERP system employs mm-wave radar technology for the detection and analysis of facial expressions in a manner that is both non-intrusive and privacy-centric, handling the ethical and privacy concerns associated with traditional camera-based methods. Using a convolutional neural network (CNN), mm-FERP effectively analyzes the complex patterns in mm-wave signals. This approach enables the smooth transfer of model knowledge from extensive image-based (Scalograms) datasets to the detailed understanding of mm-wave radar signals, significantly enhancing the model’s predictive accuracy and efficiency in identifying personality traits via emotional behavior. Our in-depth evaluation reveals mm-FERP’s remarkable potential to predict personality traits through emotion recognition (Neutral, Smile, Angry, Sad, Amazed) with an impressive accuracy of 97% across distances up to 0.47 m. We experiment in a controlled environment with more than 50 participants from different age groups (18–35) including males and females of different continents to train our model on different facial symmetry. Each participant gives 50 samples 10 for each expression making a total of 2500 samples. We also collected a self-assessment report from the same participants of 64 questions related to psychological behavior to validate personality by correlating it with radar signal features on question value weight (0.5–1.5). mm-FERP achieve an average score of 97.8% in precision, 97.2% in Recall, and 97.2% of F1. These results show mm-FERP’s ability as an innovative approach for psychological behavioral analysis through mm-wave emotion recognition, improving user experience design, and paving the path for interactive technologies that are both personalized and psychologically insightful.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103919"},"PeriodicalIF":7.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IterSum: Iterative summarization based on document topological structure","authors":"Shuai Yu ,&nbsp;Wei Gao ,&nbsp;Yongbin Qin ,&nbsp;Caiwei Yang ,&nbsp;Ruizhang Huang ,&nbsp;Yanping Chen ,&nbsp;Chuan Lin","doi":"10.1016/j.ipm.2024.103918","DOIUrl":"10.1016/j.ipm.2024.103918","url":null,"abstract":"<div><div>Document structure plays a crucial role in understanding and analyzing document information. However, effectively encoding document structural features into the Transformer architecture faces significant challenges. This is primarily because different types of documents require the model to adopt varying structural encoding strategies, leading to a lack of a unified framework that can broadly adapt to different document types to leverage their structural properties. Despite the diversity of document types, sentences within a document are interconnected through semantic relationships, forming a topological semantic network. This topological structure is essential for integrating and summarizing information within the document. In this work, we introduce IterSum, a versatile text summarization framework applicable to various types of text. In IterSum, we utilize the document’s topological structure to divide the text into multiple blocks, first generating a summary for the initial block, then combining the current summary with the content of the next block to produce the subsequent summary, and continuing in this iterative manner until the final summary is generated. We validated our model on nine different types of public datasets, including news, knowledge bases, legal documents, and guidelines. Both quantitative and qualitative analyses were conducted, and the experimental results show that our model achieves state-of-the-art performance on all nine datasets measured by ROUGE scores. We also explored low-resource summarization, finding that even with only 10 or 100 samples in multiple datasets, top-notch results were obtained. Finally, we conducted human evaluations to further validate the superiority of our model.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103918"},"PeriodicalIF":7.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CGCN: Context graph convolutional network for few-shot temporal action localization","authors":"Shihui Zhang ,&nbsp;Houlin Wang ,&nbsp;Lei Wang ,&nbsp;Xueqiang Han ,&nbsp;Qing Tian","doi":"10.1016/j.ipm.2024.103926","DOIUrl":"10.1016/j.ipm.2024.103926","url":null,"abstract":"<div><div>Localizing human actions in videos has attracted extensive attention from industry and academia. Few-Shot Temporal Action Localization (FS-TAL) aims to detect human actions in untrimmed videos using a limited number of training samples. Existing FS-TAL methods usually ignore the semantic context between video snippets, making it difficult to detect actions during the query process. In this paper, we propose a novel FS-TAL method named Context Graph Convolutional Network (CGCN) which employs multi-scale graph convolution to aggregate semantic context between video snippets in addition to exploiting their temporal context. Specifically, CGCN constructs a graph for each scale of a video, where each video snippet is a node, and the relationships between the snippets are edges. There are three types of edges, namely sequence edges, intra-action edges, and inter-action edges. CGCN establishes sequence edges to enhance temporal expression. Intra-action edges utilize hyperbolic space to encapsulate context among video snippets within each action, while inter-action edges leverage Euclidean space to capture similar semantics between different actions. Through graph convolution on each scale, CGCN enables the acquisition of richer and context-aware video representations. Experiments demonstrate CGCN outperforms the second-best method by 4.5%/0.9% and 4.3%/0.9% mAP on the ActivityNet and THUMOS14 datasets in one-shot/five-shot scenarios, respectively, at [email protected]. The source code can be found in <span><span>https://github.com/mugenggeng/CGCN.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103926"},"PeriodicalIF":7.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Few-shot cross domain event discovery in narrative text","authors":"Ruifang He ,&nbsp;Fei Huang ,&nbsp;Jinsong Ma ,&nbsp;Jinpeng Zhang ,&nbsp;Yongkai Zhu ,&nbsp;Shiqi Zhang ,&nbsp;Jie Bai","doi":"10.1016/j.ipm.2024.103901","DOIUrl":"10.1016/j.ipm.2024.103901","url":null,"abstract":"<div><div>Cross-domain event detection presents notable challenges in the form of data scarcity, and existing few-shot algorithms only consider events whose types are predefined, resulting in low coverage or excessive trivial identification results. To address this issue, this paper proposes the task <em>Few-shot Cross Domain Event Discovery</em>, which includes two subtasks: <em>Domain Event Discovery</em> and <em>Few-shot Domain Adaptation</em>. The former aims to identify the <em>type-agnostic event triggers</em>, and the latter completes domain adaptation with only a few annotated domain samples. Additionally, we introduce a positive–negative balanced sampling mechanism and a novel domain parameter adapter for these two subtasks, respectively. Extensive experiments on the DuEE dataset and the ACE2005 dataset show that our proposed method outperforms the current state-of-the-art method by 6.3% in Mix-F1 score on average. Moreover, we achieve SOTA performance in all domains of the DuEE dataset.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103901"},"PeriodicalIF":7.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landmark-v6: A stable IPv6 landmark representation method based on multi-feature clustering","authors":"Zhaorui Ma ,&nbsp;Xinhao Hu ,&nbsp;Fenlin Liu ,&nbsp;Xiangyang Luo ,&nbsp;Shicheng Zhang ,&nbsp;Wenxin Tai ,&nbsp;Guoming Ren ,&nbsp;Zheng Er ,&nbsp;Mingming Xu","doi":"10.1016/j.ipm.2024.103921","DOIUrl":"10.1016/j.ipm.2024.103921","url":null,"abstract":"<div><div>Highly reliable network entity landmarks are crucial for applications like geolocation-aware personalized service recommendations, traceability, and fraud detection. Traditionally, landmark acquisition methods have relied on data mining of rules or network behaviours to establish mappings between IP addresses and geolocation information. However, IPv6 address allocation policies, due to their dynamics and multi-homing phenomenon, pose a risk of IPv6 address deactivation for traditional IPv6 landmarks. To address the issues of reduced numbers and instability in traditional IPv6 landmarks, we propose a novel IPv6 landmark representation method, “landmark-v6”, which is grounded in multi-feature clustering. Firstly, IPv6 addresses are filtered based on multiple attributes derived from network entity fingerprints and routing features. Subsequently, a set of IPv6 addresses is associated with another set through multi-feature clustering. Second, the fine-grained IPv6 addresses are further refined by clustering based on precise physical spatial geolocation information, resulting in candidate landmarks that consist of IPv6 prefixes and geolocation data. Finally, the reliability of these landmarks is determined and evaluated using the voting resolution mechanism in the Candidate Landmark Evaluation task. Our experimental evaluation, spanning 10 months and conducted in three real-world areas, Zhengzhou, Hong Kong, and Shanghai, demonstrates the effectiveness of landmark-v6. Specifically, landmark-v6 obtains 933, 746, and 859 IPv6 prefix landmarks in Zhengzhou, Hong Kong, and Shanghai, respectively. These results surpass those obtained with existing rule or network behaviour-based methods such as Structon, SVMM, and SLG. Landmark-v6 offers a more robust and accurate approach to acquiring IPv6 landmarks, making it well-suited for various applications that necessitate reliable geolocation information. It effectively tackles the challenges posed by the dynamic nature of IPv6 addresses, enhancing both the stability.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103921"},"PeriodicalIF":7.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TAAD: Time-varying adversarial anomaly detection in dynamic graphs","authors":"Guanghua Liu ,&nbsp;Jia Zhang ,&nbsp;Peng Lv ,&nbsp;Chenlong Wang ,&nbsp;Huan Wang ,&nbsp;Di Wang","doi":"10.1016/j.ipm.2024.103912","DOIUrl":"10.1016/j.ipm.2024.103912","url":null,"abstract":"<div><div>The timely detection of anomalous nodes that can cause significant harm is essential in real-world networks. One challenge for anomaly detection in dynamic graphs is the identification of abnormal nodes at newly emerged moments. Unfortunately, existing methods tend to learn nontransferable features from historical moments that do not generalize well to newly emerged moments. In response to this challenge, we propose Time-varying Adversarial Anomaly Detection (TAAD), a generalizable model to learn transferable features from historical moments, which can transfer prior anomaly knowledge to newly emerged moments. It comprises four components: the feature extractor, the anomaly detector, the time-varying discriminator and the score generator. The time-varying discriminator cooperates with the feature extractor to conduct adversarial training, which decreases the distributional differences in the feature representations of nodes between historical and newly emerged moments to learn transferable features. The score generator measures the distributional differences of feature representations between normal and abnormal nodes, and further learns discriminable features. Extensive experiments conducted with four different datasets present that the proposed TAAD outperforms state-of-the-art methods.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103912"},"PeriodicalIF":7.4,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRoT-FL: A privacy-preserving and robust Training Manager for Federated Learning","authors":"Idoia Gamiz ,&nbsp;Cristina Regueiro ,&nbsp;Eduardo Jacob ,&nbsp;Oscar Lage ,&nbsp;Marivi Higuero","doi":"10.1016/j.ipm.2024.103929","DOIUrl":"10.1016/j.ipm.2024.103929","url":null,"abstract":"<div><div>Federated Learning emerged as a promising solution to enable collaborative training between organizations while avoiding centralization. However, it remains vulnerable to privacy breaches and attacks that compromise model robustness, such as data and model poisoning. This work presents PRoT-FL, a privacy-preserving and robust Training Manager capable of coordinating different training sessions at the same time. PRoT-FL conducts each training session through a Federated Learning scheme that is resistant to privacy attacks while ensuring robustness. To do so, the model exchange is conducted by a “Private Training Protocol” through secure channels and the protocol is combined with a public blockchain network to provide auditability, integrity and transparency. The original contribution of this work includes: (i) the proposal of a “Private Training Protocol” that breaks the link between a model and its generator, (ii) the integration of this protocol into a complete system, PRoT-FL, which acts as an orchestrator and manages multiple trainings and (iii) a privacy, robustness and performance evaluation. The theoretical analysis shows that PRoT-FL is suitable for a wide range of scenarios, being capable of dealing with multiple privacy attacks while maintaining a flexible selection of methods against attacks that compromise robustness. The experimental results are conducted using three benchmark datasets and compared with traditional Federated Learning using different robust aggregation rules. The results show that those rules still apply to PRoT-FL and that the accuracy of the final model is not degraded while maintaining data privacy.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103929"},"PeriodicalIF":7.4,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}