OpenRaiser/Intern-Atlas huggingface dataset:
Crime Linkage Based on Textual Hebrew Police Reports Utilizing Behavioral Patterns
Authors: Adir Solomon, Amit Magen, Simo Hanouna, Mor Kertis, Bracha Shapira, Lior Rokach
Venue: CIKM
Year: 2020

The identification of criminals' behavioral patterns can be helpful for solving crimes. Currently, in order to perform this task, police investigators manually extract criminals' behavioral patterns (also referred to as criminals' modus operandi) from a large corpus of police reports. These patterns are compared to the patterns observed in an ongoing criminal investigation to identify similarities that may link the suspect to other documented crimes. Due to the large number of historical cases, this manual process is time consuming, very costly in terms of police resources, and limits the investigators' ability to solve open cases. In this study, we propose an automatic and language independent method for extracting behavioral patterns from police reports. Relying on the extracted behavioral patterns as input, we utilize a Siamese neural network to identify burglaries committed by the same criminals. Experiments performed using a large dataset of police reports written in Hebrew provided by the Israel Police demonstrate the proposed method's high performance, achieving an AUC above 0.9. Using our method, we are also able to identify potential suspects for 22.41% of the open burglary cases in Israel.
DREAM: A Dynamic Relation-Aware Model for Social Recommendation
Authors: Liqiang Song, Ye Bi, Mengqiu Yao, Zhenyu Wu, Jianming Wang, Jing Xiao
Venue: CIKM
Year: 2020

Social connections play a vital role in improving the performance of recommendation systems (RS). However, incorporating social information into RS is challenging. Most existing models usually consider social influences in a given session, ignoring that both users? preferences and their friends? influences are evolving. Moreover, in real world, social relations are sparse. Modeling dynamic influences and alleviating data sparsity is of great importance. In this paper, we propose a unified framework named Dynamic RElation-Aware Model (DREAM) for social recommendation, which tries to model both users? dynamic interests and their friends? temporal influences. Specifically, we design temporal information encoding modules, because of which user representations are updated in each session. The updated user representations are transferred to relational-GAT modules, subsequently influence the operations on social networks. In each session, to solve social relation sparsity, we utilize glove-based method to complete social network with virtual friends. Then we employ relational-GAT module over completed social networks to update users? representations. In the extensive experiments on the public datasets, DREAM significantly outperforms the state-of-the-art solutions.
Carpe Diem, Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection
Authors: Hwanjun Song, Minseok Kim, Sundong Kim, Jae-Gil Lee
Venue: CIKM
Year: 2020

The accuracy of deep neural networks is significantly affected by how well mini-batches are constructed during the training step. In this paper, we propose a novel adaptive batch selection algorithm called Recency Bias that exploits the uncertain samples predicted inconsistently in recent iterations. The historical label predictions of each training sample are used to evaluate its predictive uncertainty within a sliding window. Then, the sampling probability for the next mini-batch is assigned to each training sample in proportion to its predictive uncertainty. By taking advantage of this design, Recency Bias not only accelerates the training step but also achieves a more accurate network. We demonstrate the superiority of Recency Bias by extensive evaluation on two independent tasks. Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20.97% in a fixed wall-clock training time. At the same time, it improved the training time by up to 59.32% to reach the same test error.
Do You Really Like Her Post?: Network-Based Analysis for Understanding Like Activities in SNS
Authors: Junho Song, Hyekyoung Park, Kyungsik Han, Sang-Wook Kim
Venue: CIKM
Year: 2020

As social network services (SNS) are expanding from friend-based to interest-based, users form a new type of relationships, namely interest-based relationships, with friends and others through social activities (e.g., likes, comments). Although such relationships are highlighted in the common-identity theory and have important values in theoretical and practical aspects, little evidence exists in the literature pertaining to the explanation of social activities as a central component for social network analysis and an association with friendship. In this paper, we build like networks in Instagram and analyze them through the lens of two salient aspects - friendship and interest - that constitute social networks. Our study results (1) show ambiguous interpretations of the like activities between users who are friends, based on the comparative analysis between friend- and non-friend-based like networks, and (2) demonstrate strong signals of the hashtag characterizing the interest-based relationships in users and content. Our research substantiates and gives insights on the common-identity theory applied in online social networks through data-driven, empirical analysis.
IoT Data Quality
Authors: Shaoxu Song, Aoqian Zhang
Venue: CIKM
Year: 2020

Data quality issues have been widely recognized in IoT data, and prevent the downstream applications. In this tutorial, we review the state-of-the-art techniques for IoT data quality management. In particular, we discuss how the dedicated approaches improve various data quality dimensions, including validity, completeness and consistency. Among others, we further highlight the recent advances by deep learning techniques for IoT data quality. Finally, we indicate the open problems in IoT data quality management, such as benchmark or interpretation of data quality issues.
Continual Domain Adaptation for Machine Reading Comprehension
Authors: Lixin Su, Jiafeng Guo, Ruqing Zhang, Yixing Fan, Yanyan Lan, Xueqi Cheng
Venue: CIKM
Year: 2020

Machine reading comprehension (MRC) has become a core component in a variety of natural language processing (NLP) applications such as question answering and dialogue systems. It becomes a practical challenge that an MRC model needs to learn in non-stationary environments, in which the underlying data distribution changes over time. A typical scenario is the domain drift, i.e. different domains of data come one after another, where the MRC model is required to adapt to the new domain while maintaining previously learned ability. To tackle such a challenge, in this work, we introduce the Continual Domain Adaptation (CDA) task for MRC. So far as we know, this is the first study on the continual learning perspective of MRC. We build two benchmark datasets for the CDA task, by re-organizing existing MRC collections into different domains with respect to context type and question type, respectively. We then analyze and observe the catastrophic forgetting (CF) phenomenon of MRC under the CDA setting. To tackle the CDA task, we propose several BERT-based continual learning MRC models using either regularization-based methodology or dynamic-architecture paradigm. We analyze the performance of different continual learning MRC models under the CDA task and show that the proposed dynamic-architecture based model achieves the best performance.
LogBug: Generating Adversarial System Logs in Real Time
Authors: Jingyu Sun, Bingyu Liu, Yuan Hong
Venue: CIKM
Year: 2020

Log parsers first convert large-scale and unstructured system logs into structured data, and then cluster them into groups for anomaly detection and monitoring. However, the security vulnerabilities of the log parsers have not been unveiled yet. In this paper, to our best knowledge, we take the first step to propose a novel real-time black-box attack framework LogBug in which attackers slightly modify the logs to deviate the analysis result (i.e., evading the anomaly detection) without knowing the learning model and parameters of the log parser. We have empirically evaluated LogBug on five emerging log parsers using system logs collected from five different systems. The results demonstrate that LogBug can greatly reduce the accuracy of log parsers with minor perturbations in real time.
TABLE: A Task-Adaptive BERT-based ListwisE Ranking Model for Document Retrieval
Authors: Xingwu Sun, Hongyin Tang, Fuzheng Zhang, Yanling Cui, Beihong Jin, Zhongyuan Wang
Venue: CIKM
Year: 2020

Document retrieval (DR) is a crucial task in NLP. Recently, the pre-trained BERT-like language models have achieved remarkable success, obtaining a state-of-the-art result in DR. In this paper, we come up with a new BERT-based ranking model for DR task, named TABLE. In the pre-training stage of TABLE, we present a domain-adaptive strategy. More essentially, in the fine-tuning stage, we develop a two-phase task-adaptive process, i.e., type-adaptive pointwise fine-tuning and listwise fine-tuning. In the type-adaptive pointwise fine-tuning phase, the model can learn different matching patterns regarding different query types. In the listwise fine-tuning phase, the model matches documents with regard to a given query in a listwise fashion. This task-adaptive process makes the model more robust. In addition, a simple but effective exact matching feature is introduced in fine-tuning, which can effectively compute matching of out-of-vocabulary (OOV) words between a query and a document. As far as we know, we are the first who propose a listwise ranking model with BERT. This work can explore rich matching features between queries and documents. Therefore it substantially improves model performance in DR. Notably, our TABLE model shows excellent performance on the MS MARCO leaderboard.
AGATHA: Automatic Graph Mining And Transformer based Hypothesis Generation Approach
Authors: Justin Sybrandt, Ilya Tyagin, Michael Shtutman, Ilya Safro
Venue: CIKM
Year: 2020

Medical research is risky and expensive. Drug discovery requires researchers to efficiently winnow thousands of potential targets to a small candidate set. However, scientists spend significant time and money long before seeing the intermediate results that ultimately determine this smaller set. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions. We present AGATHA, a deep-learning hypothesis generation system that learns a data-driven ranking criteria to recommend new biomedical connections. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub-domains, and demonstrate AGATHA's predictive capacity across the twenty most popular relationship types. Furthermore, we perform an ablation study to examine the aspects of our semantic network that most contribute to recommendation quality. Overall, AGATHA achieves best-in-class recommendation quality when compared to other hypothesis generation systems built to predict across all available biomedical literature. Reproducibility: All code, experimental data, and pre-trained models are available online: sybrandt.com/2020/agatha.
ReQue: A Configurable Workflow and Dataset Collection for Query Refinement
Authors: Mahtab Tamannaee, Hossein Fani, Fattane Zarrinkalam, Jamil Samouh, Samad Paydar, Ebrahim Bagheri
Venue: CIKM
Year: 2020

In this paper, we implement and publicly share a configurable software workflow and a collection of gold standard datasets for training and evaluating supervised query refinement methods. Existing datasets such as AOL and MS MARCO, which have been extensively used in the literature for this purpose, are based on the weak assumption that users' input queries improve gradually within a search session, i.e., the last query where the user ends her information seeking session is the best reconstructed version of her initial query. In practice, such an assumption is not necessarily accurate for a variety of reasons, e.g., topic drift. The objective of our work is to enable researchers to build gold standard query refinement datasets without having to rely on such weak assumptions. Our software workflow, which generates such gold standard query datasets, takes three inputs: (1) a dataset of queries along with their associated relevance judgements (e.g. TREC topics), (2) an information retrieval method (e.g., BM25), and (3) an evaluation metric (e.g., MAP), and outputs a gold standard dataset. The produced gold standard dataset includes a list of revised queries for each query in the input dataset, each of which effectively improves the performance of the specified retrieval method in terms of the desirable evaluation metric. Since our workflow can be used to generate gold standard datasets for any input query set, in this paper, we have generated and publicly shared gold standard datasets for TREC queries associated with Robust04, Gov2, ClueWeb09, and ClueWeb12. The source code of our software workflow, the generated gold datasets, and benchmark results for three state-of-the-art supervised query refinement methods over these datasets are made publicly available for reproducibility purposes.
OHEA: Secure Data Aggregation in Wireless Sensor Networks against Untrusted Sensors
Authors: Li Tang, Haibo Hu
Venue: CIKM
Year: 2020

Data aggregation is a key problem in wireless sensor networks (WSNs). To secure the aggregation results, researchers have proposed to adopt homomorphic encryptions. Since aggregation is conducted in the ciphertext space without decryption, both the confidentiality and integrity can be protected against untrusted or compromised aggregators. However, such techniques cannot protect against untrusted or compromised sources, i.e., wireless sensors, as homomorphic encryptions require all sources to share a common encryption key. Since wireless sensor networks are often vulnerable to physical or network attacks, new secure aggregation schemes that can protect against compromised sources are needed. This paper proposes Onion Homomorphic Encryption-based Aggregation (OHEA), where sources form groups with their dedicated encryption keys, a.k.a., the group keys. OHEA has a nice property that group keys themselves can be aggregated, so it can work recursively with any level of aggregation hierarchy. By security analysis, we show that even if multiple aggregators or sources are compromised, an adversary is still unable to compromise the data of other nodes in the same or upper levels of the hierarchy. Furthermore, the experimental results show that OHEA incurs low computation and communication cost, and is thus scalable to large WSNs.
DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation
Authors: Md. Mehrab Tanjim, Hammad A. Ayyubi, Garrison W. Cottrell
Venue: CIKM
Year: 2020

Recently convolutional networks have shown significant promise for modeling sequential user interactions for recommendations. Critically, such networks rely on fixed convolutional kernels to capture sequential behavior. In this paper, we argue that all the dynamics of the item-to-item transition in session-based settings may not be observable at training time. Hence we propose DynamicRec, which uses dynamic convolutions to compute the convolutional kernels on the fly based on the current input. We show through experiments that this approach significantly outperforms existing convolutional models on real datasets in session-based settings.
Generating Clarifying Questions in Conversational Search Systems
Authors: Leila Tavakoli
Venue: CIKM
Year: 2020

Asking a clarifying question can be a key element improving the performance of information seeking systems, particularly conversational search systems due to their limited bandwidth interfaces. While generating and asking clarifying questions is important; get-ting an answer for the clarifying question is also essential as a clarifying question without an answer is useless. Therefore, as the first step in current research, we analysed human-generated clarifying questions in a Community Question Answering website as a sample of conversation. This helped us to gain a better insight into how users interact with clarification. We investigated the clarifying questions in terms of whether they add any information to the question and the accepted answer. We further discovered the patterns and types of such clarifying questions. The next phase of this research will then generate clarifying questions in conversational search systems. We will then employ neural network models to generate clarifying questions to maximise clarification in questions. The proposed model will be trained using the MIMICS data collection in addition to our collected dataset. We will also attempt to consider the recognised patterns from the analysis conducted in the first step to enhance the chance that a user will interact with the clarifying questions. Finally, we will aim to minimise the interaction between the search system and the user to reduce the risk of dropping the conversation by the user due to asking too many clarifying questions.
Schema-Agnostic Entity Matching using Pre-trained Language Models
Authors: Kai Sheng Teong, Lay-Ki Soon, Tin Tin Su
Venue: CIKM
Year: 2020

Entity matching (EM) is the process of linking records from different data sources. While extensive research has been done in various aspects of EM, many of these studies generally assume EM tasks as schema-specific, which attempt to match record pairs at attributes level. Unfortunately, in the real-world, tables that undergo EM may not have an aligned schema, and often, the schema or metadata of the table and attributes are not known beforehand.In view of this challenge, this paper presents an effective approach for schema-agnostic EM, where having schema-aligned tables is not compulsory. The proposed method stemmed from the idea of treating tuples in tables for EM similar to sentence pair classification problem in natural language processing (NLP). A pre-trained language model, BERT is adopted by fine-tuning it using labeled dataset. The proposed method was experimented using benchmark datasets and compared against two state-of-the-art approaches,namely DeepMatcher and Magellan. The experimental results show that our proposed solution outperforms by an average of 9% in F1 score. The performance is in fact consistent across different types of datasets, showing significant improvement of 29.6% for one of dirty datasets. These prove that our proposed solution is versatile for EM.
QSAN: A Quantum-probability based Signed Attention Network for Explainable False Information Detection
Authors: Tian Tian, Yudong Liu, Xiaoyu Yang, Yuefei Lyu, Xi Zhang, Binxing Fang
Venue: CIKM
Year: 2020

False information detection on social media is challenging as it commonly requires tedious evidence-collecting but lacks available comparative information. Clues mined from user comments, as the wisdom of crowds, could be of considerable benefit to this task. However, it is non-trivial to capture the complex semantics from the contents and comments in consideration of their implicit correlations. Although deep neural networks have good expressive power, one major drawback is the lack of explainability. In this paper, we focus on how to learn from the post contents and related comments in social media to understand and detect the false information more effectively, with explainability. We thus propose a Quantum-probability based Signed Attention Network (QSAN) that integrates the quantum-driven text encoding and a novel signed attention mechanism in a unified framework. QSAN is not only able to distinguish important comments from the others, but also can exploit the conflicting social viewpoints in the comments to facilitate the detection. Moreover, QSAN is advantageous with its explainability in terms of transparency due to quantum physics meanings and the attention weights. Extensive experiments on real-world datasets show that our approach outperforms state-of-the-art baselines and can provide different kinds of user comments to explain why a piece of information is detected as false.
Exploiting Common Neighbor Graph for Link Prediction
Authors: Hao Tian, Reza Zafarani
Venue: CIKM
Year: 2020

Link prediction aims to predict whether two nodes in a network are likely to get connected. Motivated by its applications, e.g., in friend or product recommendation, link prediction has been extensively studied over the years. Most link prediction methods are designed based on specific assumptions that may or may not hold in different networks, leading to link prediction methods that are not generalizable. Here, we address this problem by proposing general link prediction methods that can capture network-specific patterns. Most link prediction methods rely on computing similarities between between nodes. By learning a γ-decaying model, the proposed methods can measure the pairwise similarities between nodes more accurately, even when only using common neighbor information, which is often used by current techniques.
Quaternion-Based Self-Attentive Long Short-term User Preference Encoding for Recommendation
Authors: Thanh Tran, Di You, Kyumin Lee
Venue: CIKM
Year: 2020

Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting. Unfortunately, most of the current recommender systems rely on real-valued representations in Euclidean space to model either user's long-term or short-term interests. In this paper, we fully utilize Quaternion space to model both user's long-term and short-term preferences. We first propose a QUaternion-based self-Attentive Long term user Encoding (QUALE) to study the user's long-term intents. Then, we propose a QUaternion-based self-Attentive Short term user Encoding (QUASE) to learn the user's short-term interests. To enhance our models' capability, we propose to fuse QUALE and QUASE into one model, namely QUALSE, by using a Quaternion-based gating mechanism. We further develop Quaternion-based Adversarial learning along with the Bayesian Personalized Ranking (QABPR) to improve our model's robustness. Extensive experiments on six real-world datasets show that our fused QUALSE model outperformed 11 state-of-the-art baselines, improving 8.43% at HIT@1 and 10.27% at NDCG@1 on average compared with the best baseline.
E-Commerce Dispute Resolution Prediction
Authors: David Tsurel, Michael Doron, Alexander Nus, Arnon Dagan, Ido Guy, Dafna Shahaf
Venue: CIKM
Year: 2020

E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.