OpenRaiser/Intern-Atlas huggingface dataset:
MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
Authors: Yunjia Xi, Weiwen Liu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
Venue: CIKM
Year: 2024

Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most existing CRS models mainly focus on dialogue comprehension and preferences mining from the current dialogue session, overlooking user preferences in historical dialogue sessions. The preferences embedded in historical sessions and the current session exhibit continuity and sequentiality, and we refer to such CRSs as sequential CRSs. In this work, we leverage memory-enhanced LLMs to model the preference continuity, addressing two key issues: (1) redundancy and noise in historical dialogue sessions, and (2) the cold-start users problem. Thus, we propose a Memory-enhanced Conversational Recommender System Framework with Large Language Models (dubbed MemoCRS), consisting of user-specific memory and general memory. User-specific memory is tailored to each user's interests and uses an entity-based memory bank to refine preferences and retrieve relevant memory, thereby reducing the redundancy and noise of historical sessions. The general memory, encapsulating collaborative knowledge and reasoning guidelines, can provide shared knowledge for users, especially cold-start users. With the above memory, LLMs are empowered to deliver more precise and tailored recommendations for each user. Extensive experiments on Chinese and English datasets demonstrate MemoCRS's effectiveness.
Advancing Certified Robustness of Explanation via Gradient Quantization
Authors: Yang Xiao, Zijie Zhang, Yuchen Fang, Da Yan, Yang Zhou, Wei-Shinn Ku, Bo Hui
Venue: CIKM
Year: 2024

Explaining black-box models is fundamental to gaining trust and deploying these models in real applications. As existing explanation methods have been shown to lack robustness against adversarial perturbations, there has been a growing interest in generating robust explanations. However, existing works resort to empirical defense strategies and these heuristic methods fail against powerful adversaries. In this paper, we certify the robustness of explanations motivated by the success of randomized smoothing. Specifically, we compute a tight radius in which the robustness of the explanation is certified. While a challenge is how to formulate the robustness of the explanation mathematically, we quantize the explanation into discrete spaces to mimic classification in randomized smoothing. To address the high computational cost of randomized smoothing, we introduce randomized gradient smoothing. Also, we explore the robustness of the semantic explanation by certifying the robustness of capsules. In the experiment, we demonstrate the effectiveness of our method on benchmark datasets from the perspectives of post-hoc explanation and semantic explanation respectively. Our work is a promising step towards filling the gap between the theoretical robustness bound and empirical explanations. Our code has been released at https://github.com/NKUShaw/CertifiedExplanation.
Attentional Neural Integral Equation for Temporal Knowledge Graph Forecasting
Authors: Likang Xiao, Zijie Chen, Richong Zhang, Junfan Chen
Venue: CIKM
Year: 2024

Temporal Knowledge Graph Forecasting (TKGF) aims to forecast the missing entities or relations at a specific timestamp when only the historical information is observed. It is crucial to accurately identify the historical information of complex temporal relational graphs related to the query. Existing works, e.g., TANGO, have exploited the Neural Ordinary Differential Equation (NODE) to TKGF. However, TANGO encounters two limitations. First, TANGO observes historical facts with only one timestamp at each step, leading to a long-term forgetting problem. Second, TANGO gives the same weight to the entire history graph, including facts that are not relevant to the query. To tackle the above limitations, this paper utilizes Attentional Neural Integral Equation for TKGF (tIE), enabling the global interaction between query-related historical graph sequences. To achieve this, we employ the Relational Graph Convolutional Network and Fourier-type Transformer to model the graph structure and temporal evolution of TKG. The Iterative Integral Equation Solver is exploited to enhance the accuracy and robustness of numerical solutions. The proposed method outperforms baseline models regarding several metrics and inference speed on four benchmark datasets, especially on the long horizontal link forecasting task with irregular time intervals.
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks
Authors: Shuai Xiao, Zaifan Jiang
Venue: CIKM
Year: 2024

As the last stage of a typical recommendation system, collective recommendation aims to give the final touches to the recommended items and their layout so as to optimize overall objectives such as diversity and whole-page relevance. In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts' heuristics or simple models. To address this issue, we propose a div ersity-aware self-correcting sequential recommendation net works (DivNet) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. Experiments on both offline and online settings demonstrate that DivNet can achieve better results compared to baselines with or without collective recommendations.
Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing
Authors: Han Xie, Li Xiong, Carl Yang
Venue: CIKM
Year: 2024

Federated learning on graphs (a.k.a., federated graph learning - FGL) has recently received increasing attention due to its capacity to enable collaborative learning over distributed graph datasets without compromising local clients' data privacy. In previous works, clients of FGL typically represent institutes or organizations that possess sets of entire graphs (e.g., molecule graphs in biochemical research) or parts of a larger graph (e.g., sub-user networks of e-commerce platforms). However, another natural paradigm exists where clients act as remote devices retaining the graph structures of local neighborhoods centered around the device owners (i.e., ego-networks), which can be modeled for specific graph applications such as user profiling on social ego-networks and infection prediction on contact ego-networks. FGL in such novel yet realistic ego-network settings faces the unique challenge of incomplete neighborhood information for non-ego local nodes since they likely appear and have different sets of neighbors in multiple ego-networks. To address this challenge, we propose an FGL method for distributed ego-networks in which clients obtain complete neighborhood information of local nodes through sharing node embeddings with other clients. A contrastive learning mechanism is proposed to bridge the gap between local and global node embeddings and stabilize the local training of graph neural network models, while a secure embedding sharing protocol is employed to protect individual node identity and embedding privacy against the server and other clients. Comprehensive experiments on various distributed ego-network datasets successfully demonstrate the effectiveness of our proposed embedding sharing method on top of different federated model sharing frameworks, and we also provide discussions on the potential efficiency and privacy drawbacks of the method as well as their future mitigation.
Image-text Retrieval with Main Semantics Consistency
Authors: Yi Xie, Yangtao Wang, Yanzhao Xie, Xin Tan, Jingjing Li, Xiaocui Li, Weilong Peng, Maobin Tang, Meie Fang
Venue: CIKM
Year: 2024

Image-text retrieval (ITR) has been one of the primary tasks in cross-modal retrieval, serving as a crucial bridge between computer vision and natural language processing. Significant progress has been made to achieve global alignment and local alignment between images and texts by mapping images and texts into a common space to establish correspondences between these two modalities. However, the rich semantic content contained in each image may bring false matches, resulting in the matched text ignoring the main semantics but focusing on the secondary or other semantics of this image. To address this issue, this paper proposes a semantically optimized approach with a novel Main Semantics Consistency (MSC) loss function, which aims to rank the semantically most similar images (or texts) corresponding to the given query at the top position during the retrieval process. First, in each batch of image-text pairs, we separately compute (i) the image-image similarity, i.e., the similarity between every two images, (ii) the text-text similarity, i.e., the similarity between a group of texts (that belong to a certain image) and another group of texts (that belong to another image), and (iii) the image-text similarity, i.e., the similarity between each image and each text. Afterward, our proposed MSC effectively aligns the above image-image, image-text, and text-text similarity, since the main semantics of every two images will be highly close if their text descriptions remain highly semantically consistent. By this means, we can capture the main semantics of each image to be matched with its corresponding texts, prioritizing the semantically most related retrieval results. Extensive experiments on MSCOCO and FLICKR30K verify the superior performance of MSC compared with the SOTA image-text retrieval methods. The source code of this project is released at GitHub: https://github.com/xyi007/MSC.
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models
Authors: Wenjia Xie, Rui Zhou, Hao Wang, Tingjia Shen, Enhong Chen
Venue: CIKM
Year: 2024

Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models, especially diffusion models, which have achieved significant results in fields like image and audio, hold considerable promise in the field of sequential recommendation. However, existing sequential recommendation methods based on diffusion models are constrained by a prior distribution limited to Gaussian distribution, hindering the possibility of introducing user-specific information for each recommendation and leading to information loss. To address these issues, we introduce the Schrödinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. This allows us to replace the Gaussian prior of the diffusion model with the user's current state, directly modeling the process from a user's current state to the target recommendation. Additionally, to better utilize collaborative information in recommendations, we propose an extended version of SdifRec called con-SdifRec, which utilizes user clustering information as a guiding condition to further enhance the posterior distribution. Finally, extensive experiments on multiple public benchmark datasets have demonstrated the effectiveness of SdifRec and con-SdifRec through comparison with several state-of-the-art methods. Further in-depth analysis has validated their efficiency and robustness.
GetCom: An Efficient and Generalizable Framework for Community Detection
Authors: Kaiyu Xiong, Yucheng Jin, Yun Xiong, Jiawei Zhang
Venue: CIKM
Year: 2024

Community detection plays a pivotal role in network analysis, with applications in recommendation systems, anomaly detection, and biochemistry. However, traditional methods, while computationally efficient, often fall short in managing the complexities of real-world network structures. In contrast, deep learning approaches enhance accuracy but require substantial computational resources and task-specific architectures. This paper introduce GetCom, a novel three-phase "pre-train, generate, prompt" framework that integrates traditional methods and deep learning techniques. In the pre-training phase, GetCom acquires comprehensive understanding of community structures, which provides a solid foundation for the subsequent phases. During the generation phase, traditional community detection methods are employed to efficiently identify potential communities, which are subsequently refined in the prompt learning phase. This integration offers an efficient, accurate, and generalizable solution for community detection. Experiments on five real-world network datasets demonstrate that GetCom achieves state-of-the-art performance, with strong efficiency and generalization capabilities across diverse datasets and tasks.
Transforming Digital Forensics with Large Language Models: Unlocking Automation, Insights, and Justice
Authors: Eric Xu, Wenbin Zhang, Weifeng Xu
Venue: CIKM
Year: 2024

In the pursuit of justice and accountability in the digital age, the integration of Large Language Models (LLMs) with digital forensics holds immense promise. This half-day tutorial provides a comprehensive exploration of the transformative potential of LLMs in automating digital investigations and uncovering hidden insights. Through a combination of real-world case studies, interactive exercises, and hands-on labs, participants will gain a deep understanding of how to harness LLMs for evidence analysis, entity identification, and knowledge graph reconstruction. By fostering a collaborative learning environment, this tutorial aims to empower professionals, researchers, and students with the skills and knowledge needed to drive innovation in digital forensics. As LLMs continue to revolutionize the field, this tutorial will have far-reaching implications for enhancing justice outcomes, promoting accountability, and shaping the future of digital investigations.
Towards Seamless User Query to REST API Conversion
Authors: Han Xu
Venue: CIKM
Year: 2024

Integrating Large Language Models (LLMs) with external tools and APIs is essential for fields such as information retrieval and knowledge management. While LLMs have made significant strides, their effective integration with external APIs-essential for real-world applications-remains challenging. This paper introduces RESTful-Llama, a novel method designed to empower open-source LLMs to accurately convert natural language instructions into well-formed RESTful API calls. Moreover, RESTful-Llama utilizes DOC-Prompt, a newly proposed technique for generating fine-tuning datasets from publicly available API documentation. Initial experiments demonstrate that RESTful-Llama significantly enhances the accuracy of generated REST API requests.
Post-Quantum Searchable Encryption Supporting User-Authorization for Outsourced Data Management
Authors: Shiyuan Xu, Yibo Cao, Xue Chen, Yu Guo, Yuer Yang, Fangda Guo, Siu-Ming Yiu
Venue: CIKM
Year: 2024

With the widespread development of database systems, data security has become crucial when it comes to sharing among users and servers. A straightforward approach involves using searchable encryption to ensure the confidentiality of shared data. However, in certain scenarios, varying user tiers are granted disparate data searching privileges, and administrators need to restrict the searchability of ciphertexts to select users exclusively. To address this issue, public key encryption with authorized keyword search (PEAKS) was proposed, wherein solely authorized users possess the ability to conduct targeted keyword searches. Nonetheless, it is vulnerable to resist quantum computing attacks. As a result, research focusing on authorizing users to search for keywords while achieving quantum security is far-reaching. In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. Unlike existing schemes, our design incorporates identity-based encryption (IBE) to overcome the bottleneck of public key management. Besides, we utilize several lattice sampling algorithms to defend against attacks from quantum adversaries. Specifically, each authorized user must obtain a search privilege from an authority. The authority distributes an authorized token to the user within a specific time period, and the user generates a trapdoor for any authorized keywords. Our scheme is proven to be secure against IND-sID-CKA and T-EUF security in a quantum setting. We also conduct comprehensive evaluations on a commodity machine to assess completeness and provide theoretical complexity comparisons with existing state-of-the-art schemes.
CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers via POI Feature Matching
Authors: Shuai Xu, Donghai Guan
Venue: CIKM
Year: 2024

Current studies mainly rely on overlapping users (who leave trajectories in both cities) as a medium to learn travelers' preference in the target city, however it is unrealistic to find overlapping users when two cities are far apart, thus a severe data scarcity issue exists for this problem. Besides, due to the mixture of mobility pattern from both cities, directly applying the model trained in the source city may lead to negative transfer in the target city. To tackle these issues, in this paper, we conceive and implement a novel framework called CrossPred to predict the cross-city mobility of long-distance travelers in the target city. Specifically, POI features including popularity, textual description, spatial distribution as well as sequential pattern are considered for cross-city POI matching, which further acts as a vital link for jointly modeling native user mobility preference in both source and target cities. Maximum Mean Discrepancy (MMD) is adopted to strengthen the shared POI features among cities and weaken the unique POI features, thereby promoting cross-city POI feature matching. Extensive experiments on real-world datasets demonstrate the effectiveness and superiority of the proposed framework.
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Authors: Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian, Wei Li, Tieying Li, Yiping Ke, Miao Qiao
Venue: CIKM
Year: 2024

Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from functional magnetic resonance imaging (fMRI) data. Despite the prevalence of Graph Neural Networks (GNNs) and Graph Transformers in various domains, applying them to brain networks faces challenges. Specifically, the datasets are severely impacted by the noises caused by distribution shifts across sub-populations and the neglect of node identities, both obstruct the identification of disease-specific patterns. To tackle these challenges, we propose Contrasformer, a novel contrastive brain network Transformer. It generates a prior-knowledge-enhanced contrast graph to address the distribution shifts across sub-populations by a two-stream attention mechanism. A cross attention with identity embedding highlights the identity of nodes, and three auxiliary losses ensure group consistency. Evaluated on 4 functional brain network datasets over 4 different diseases, Contrasformer outperforms the state-of-the-art methods for brain networks by achieving up to 10.8% improvement in accuracy, which demonstrates its efficacy in neurological disorder identification. Case studies illustrate its interpretability, especially in the context of neuroscience. This paper provides a solution for analyzing brain networks, offering valuable insights into neurological disorders. Our code is available at https://github.com/AngusMonroe/Contrasformer.
scACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data
Authors: Siwei Xu, Junhao Liu, Jing Zhang
Venue: CIKM
Year: 2024

Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regulatory interconnections explorations via in-silico perturbations. To test its performance, we applied scACT on diverse single-cell datasets and found it outperformed existing methods in all three tasks. Finally, we have developed scACT as an individual open-source software package to advance single-cell omics data processing and analysis within the research community.
Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources
Authors: Yipei Xu, Dakuan Lu, Jiaqing Liang, Jin Zhao, Xintao Wang, Hengkui Wu, Ken Chen, Liujiang Liu, Yingsi Xin, Xuepeng Liu, Yanghua Xiao, Zhixu Li
Venue: CIKM
Year: 2024

Pre-trained language models (PLMs) have established the new paradigm in the field of NLP. For more powerful PLMs, one of the most popular and successful ways is to continuously scale up sizes of the models and the pre-training corpora. These large corpora, typically obtained by converging smaller ones from multiple sources, are thus growing increasingly diverse. However, colossal converged corpora don't always enhance PLMs' performance. In this paper, we identify the disadvantage of heterogeneous corpora from multiple sources for pre-training PLMs. Towards coordinated pre-training on diverse corpora, we further propose Source Prompt (SP), which explicitly prompt the model with the source of data at the pre-training and fine-tuning stages. Extensive experimental results show that pre-training PLMs with SP on diverse corpora significantly improves performance in various downstream tasks.
Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data
Authors: Liqiang Xu, Yuuki Nishiyama, Kota Tsubouchi, Kaoru Sezaki
Venue: CIKM
Year: 2024

As the capabilities of smart sensing and mobile technologies continue to evolve and expand, storing diverse sensor data on smartphones and cloud servers becomes increasingly challenging. Effective data compression is crucial to alleviate these storage pressures. Compressed sensing (CS) offers a promising approach, but traditional CS methods often struggle with the unique characteristics of sensor data-like variability, dynamic changes, and different sampling rates-leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. This framework is designed to manage the complexities of smartphone sensor data, ensuring high-quality reconstruction across diverse conditions. We developed a mobile application to collect data from 30 volunteers over one month, including accelerometer, gyroscope, barometer, and other sensor measurements. Comparative analysis reveals that Mob-ISTA-1DNet not only enhances reconstruction accuracy but also significantly reduces processing time, consistently outperforming other methods in various scenarios.