ACM Transactions on Intelligent Systems and Technology最新文献

{"title":"Explaining Neural News Recommendation with Attributions onto Reading Histories","authors":"Lucas Möller, Sebastian Padó","doi":"10.1145/3673233","DOIUrl":"https://doi.org/10.1145/3673233","url":null,"abstract":"<p>An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures which are optimized to predict clicks by matching candidate news items against users’ reading histories. Such systems achieve state-of-the-art click-prediction performance, but the rationale for their decisions is difficult to assess. At the same time, the economic and societal impact of these systems makes such insights very much desirable.</p><p>In this paper, we ask the question to what extent the recommendations of current news recommender systems are actually based on content-related evidence from reading histories. We approach this question from an explainability perspective. Building on the concept of integrated gradients, we present a neural news recommender that can accurately attribute individual recommendations to news items and words in input reading histories while maintaining a top scoring click-prediction performance.</p><p>Using our method as a diagnostic tool, we find that: (a), a substantial number of users’ clicks on news are not explainable from reading histories, and many history-explainable items are actually skipped; (b), while many recommendations are based on content-related evidence in histories, for others the model does not attend to reasonable evidence, and recommendations stem from a spurious bias in user representations. Our code is publicly available¹.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Social Cognition Ability Evaluation of LLMs: A Dynamic Gamified Assessment and Hierarchical Social Learning Measurement Approach","authors":"Qin Ni, Yangze Yu, Yiming Ma, Xin Lin, Ciping Deng, Tingjiang Wei, Mo Xuan","doi":"10.1145/3673238","DOIUrl":"https://doi.org/10.1145/3673238","url":null,"abstract":"<p>Large Language Model(LLM) has shown amazing abilities in reasoning tasks, theory of mind(ToM) has been tested in many studies as part of reasoning tasks, and social learning, which is closely related to theory of mind, are still lack of investigation. However, the test methods and materials make the test results unconvincing. We propose a dynamic gamified assessment(DGA) and hierarchical social learning measurement to test ToM and social learning capacities in LLMs. The test for ToM consists of five parts. First, we extract ToM tasks from ToM experiments and then design game rules to satisfy the ToM task requirement. After that, we design ToM questions to match the game’s rules and use these to generate test materials. Finally, we go through the above steps to test the model. To assess the social learning ability, we introduce a novel set of social rules (three in total). Experiment results demonstrate that, except GPT-4, LLMs performed poorly on the ToM test but showed a certain level of social learning ability in social learning measurement.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoT","authors":"Shucun Fu, Fang Dong, Dian Shen, Runze Chen, Jiangshan Hao","doi":"10.1145/3673237","DOIUrl":"https://doi.org/10.1145/3673237","url":null,"abstract":"The Artificial Intelligence of Things (AIoT) is an emerging technology that enables numerous AIoT devices to participate in big data analytics and machine learning (ML) model training, providing various customized intelligent services for industry manufacturing. Federated Learning (FL) empowers AIoT applications with privacy-preserving distributed model training without sharing raw data. However, due to IoT devices’ limited computing and memory resources, existing FL approaches for AIoT applications cannot support efficient large-scale model training. Federated synergy learning (FSyL) is a promising collaborative paradigm that alleviates the computation and communication overhead on resource-constrained AIoT devices via offloading part of the ML model to the edge server for end-to-edge collaborative training. Existing FSyL works neither efficiently address the inter-round device selection to improve model diversity nor determine the intra-round edge association to reduce the training cost, which hinders the applications of FSyL-enable AIoT. Motivated by this issue, this paper first investigates the bottlenecks of executing FSyL in AIoT. It builds an optimization model of joint inter-round device selection and intra-round edge association for balancing model diversity and training cost. To tackle the intractable coupling problem, we present a framework named Online DEvice SelectIon and EdGe AssociatioN for Cost-Diversity Trade-offs FSyL (DESIGN). First, the edge association subproblem is extracted from the original problem, and game theory determines the optimal association decision for an arbitrary device selection. Then, based on the optimal association decision, device selection is modeled as a combinatorial multi-armed bandit (CMAB) problem. Finally, we propose an online mechanism to obtain joint device selection and edge association decisions. The performance of DESIGN is theoretically analyzed and experimentally evaluated on real-world datasets. The results show that DESIGN can achieve up to (84.3%) in cost-saving with an accuracy improvement of (23.6%) compared with the state-of-the-art.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-based Abnormal Trajectory Gap Detection","authors":"Arun Sharma, Subhankar Ghosh, Shashi Shekhar","doi":"10.1145/3673235","DOIUrl":"https://doi.org/10.1145/3673235","url":null,"abstract":"Given trajectories with gaps (i.e., missing data), we investigate algorithms to identify abnormal gaps in trajectories which occur when a given moving object did not report its location, but other moving objects in the same geographic region periodically did. The problem is important due to its societal applications, such as improving maritime safety and regulatory enforcement for global security concerns such as illegal fishing, illegal oil transfers, and trans-shipments. The problem is challenging due to the difficulty of bounding the possible locations of the moving object during a trajectory gap, and the very high computational cost of detecting gaps in such a large volume of location data. The current literature on anomalous trajectory detection assumes linear interpolation within gaps, which may not be able to detect abnormal gaps since objects within a given region may have traveled away from their shortest path. In preliminary work, we introduced an abnormal gap measure that uses a classical space-time prism model to bound an object’s possible movement during the trajectory gap and provided a scalable memoized gap detection algorithm (Memo-AGD). In this paper, we propose a Space Time-Aware Gap Detection (STAGD) approach to leverage space-time indexing and merging of trajectory gaps. We also incorporate a Dynamic Region Merge-based (DRM) approach to efficiently compute gap abnormality scores. We provide theoretical proofs that both algorithms are correct and complete and also provide analysis of asymptotic time complexity. Experimental results on synthetic and real-world maritime trajectory data show that the proposed approach substantially improves computation time over the baseline technique.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special Issue on Responsible Recommender Systems Part 1","authors":"Lina Yao, Julian McAuley, Xianzhi Wang, D. Jannach","doi":"10.1145/3663528","DOIUrl":"https://doi.org/10.1145/3663528","url":null,"abstract":"yet promising field of responsible recommender systems. They represent some of the most recent progress in advancing responsible recommender systems research in four directions below","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141337078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Text: Multimodal Credibility Assessment Approaches for Online User-Generated Content","authors":"Monika Choudhary, S. Chouhan, Santosh Singh Rathore","doi":"10.1145/3673236","DOIUrl":"https://doi.org/10.1145/3673236","url":null,"abstract":"User-Generated Content (UGC) is increasingly becoming prevalent on various digital platforms. The content generated on social media, review forums, and question-answer platforms impacts a larger audience and influences their political, social, and other cognitive abilities. Traditional credibility assessment mechanisms involve assessing the credibility of the source and the text. However, with the increase in how user content can be generated and shared (audio, video, images), multimodal representation of User-Generated Content has become increasingly popular. This paper reviews the credibility assessment of UGC in various domains, particularly identifying fake news, suspicious profiles, and fake reviews and testimonials, focusing on both textual content and the source of the content creator. Next, the concept of multimodal credibility assessment is presented, which also includes audio, video, and images in addition to text. After that, the paper presents a systematic review and comprehensive analysis of work done in the credibility assessment of UGC considering multimodal features. Additionally, the paper provides extensive details on the publicly available multimodal datasets for the credibility assessment of UGC. In the end, the research gaps, challenges, and future directions in assessing the credibility of multimodal user-generated content are presented.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141339802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-grained Courier Delivery Behavior Recovery with a Digital Twin Based Iterative Calibration Framework","authors":"Fudan Yu, Guozhen Zhang, Haotian Wang, Depeng Jin, Yong Li","doi":"10.1145/3663484","DOIUrl":"https://doi.org/10.1145/3663484","url":null,"abstract":"Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers’ fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time, and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec.","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141346854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Misinformation Resilient Search Rankings with Webgraph-based Interventions","authors":"Peter Carragher, Evan M. Williams, Kathleen M. Carley","doi":"10.1145/3670410","DOIUrl":"https://doi.org/10.1145/3670410","url":null,"abstract":"<p>The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is in their control), generality (label/fact-check agnostic), targeted (increase the cost of adversarial behavior), and scalability (works at webscale). We refine our methods on small-scale webdata as a testbed and then generalize the interventions to a large-scale webgraph containing 93.9M domains and 1.6B edges. We demonstrate that our methods penalize unreliable domains far more than reliable domains in both settings and we explore multiple avenues to mitigate unintended effects on both the small-scale and large-scale webgraph experiments. These results indicate the potential of our approach to reduce the spread of misinformation and foster a more reliable online information ecosystem. This research contributes to the development of targeted strategies to enhance the trustworthiness and quality of search engine results, ultimately benefiting users and the broader digital community.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks","authors":"Yash Mahajan, Jin-Hee Cho, Ing-Ray Chen","doi":"10.1145/3670411","DOIUrl":"https://doi.org/10.1145/3670411","url":null,"abstract":"<p>As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team’s diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals’ privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a team formation (TF) algorithm in the context of OSNs that can maximize team performance and preserve team members’ privacy under different types of tasks. Our proposed <underline>PR</underline>iv<underline>A</underline>cy-<underline>D</underline>iversity-<underline>A</underline>ware <underline>T</underline>eam <underline>F</underline>ormation framework, called <monospace>PRADA-TF</monospace>, is based on trust relationships between users in OSNs where trust is measured based on a user’s expertise and privacy preference levels. The PRADA-TF algorithm considers the team members’ domain expertise, privacy preferences, and the team’s expertise diversity in the process of team formation. Our approach employs game-theoretic principles <i>Mechanism Design</i> to motivate self-interested individuals within a team formation context, positioning the mechanism designer as the pivotal team leader responsible for assembling the team. We use two real-world datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team members. We evaluate the effectiveness of our proposed <monospace>PRADA-TF</monospace> scheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics for studying OSNs: social welfare, privacy loss, and team diversity.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ranking the Transferability of Adversarial Examples","authors":"Moshe Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky","doi":"10.1145/3670409","DOIUrl":"https://doi.org/10.1145/3670409","url":null,"abstract":"<p>Adversarial transferability in blackbox scenarios presents a unique challenge: while attackers can employ surrogate models to craft adversarial examples, they lack assurance on whether these examples will successfully compromise the target model. Until now, the prevalent method to ascertain success has been trial and error—testing crafted samples directly on the victim model. This approach, however, risks detection with every attempt, forcing attackers to either perfect their first try or face exposure.</p><p>Our paper introduces a ranking strategy that refines the transfer attack process, enabling the attacker to estimate the likelihood of success without repeated trials on the victim’s system. By leveraging a set of diverse surrogate models, our method can predict transferability of adversarial examples. This strategy can be used to either select the best sample to use in an attack or the best perturbation to apply to a specific sample.</p><p>Using our strategy, we were able to raise the transferability of adversarial examples from a mere 20%—akin to random selection—up to near upper-bound levels, with some scenarios even witnessing a 100% success rate. This substantial improvement not only sheds light on the shared susceptibilities across diverse architectures but also demonstrates that attackers can forego the detectable trial-and-error tactics raising increasing the threat of surrogate-based attacks.</p>","PeriodicalId":48967,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141256351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}