OpenRaiser/Intern-Atlas huggingface dataset:
Can Adversarial Training benefit Trajectory Representation?: An Investigation on Robustness for Trajectory Similarity Computation
Authors: Quanliang Jing, Shuo Liu, Xinxin Fan, Jingwei Li, Di Yao, Baoli Wang, Jingping Bi
Venue: CIKM
Year: 2022

Trajectory similarity computation as the fundamental problem for various downstream analytic tasks, such as trajectory classification and clustering, has been extensively studied in recent years. However, how to infer an accurate and robust similarity over two trajectories is difficult due to the some trajectory characteristics in practice, e.g. non-uniform sampling rate, nonmalignant fluctuation, and noise points, etc. To circumvent such challenges, we in this paper introduce the adversarial training idea into the trajectory representation learning for the first time to enhance the robustness and accuracy. Specifically, our proposed method AdvTraj2Vec has two novelties: i) it perturbs the weight parameters of embedding layers to learn a robust model to infer an accurate pairwise similarity over each two trajectories; and ii) it employs the GAN momentum to harness the perturbation extent to which an appropriate trajectory representation can be learned for the similarity computation. Extensive experiments using two real-world trajectory datasets Porto and Beijing validate our proposed AdvTraj2Vec on the robustness and accuracy aspects. The multi-facet results show that our AdvTraj2Vec significantly outperforms the stat-of-the-art methods in terms of different distortions, such as trajectory-point addition, deletion, disturbance, and outlier injection.
Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms
Authors: Carl Johnell, Morteza Haghir Chehreghani
Venue: CIKM
Year: 2022

We study Frank-Wolfe algorithms – standard, pairwise, and away-steps – for efficient optimization of Dominant Set Clustering. We present a unified and computationally efficient framework to employ the different variants of Frank-Wolfe methods, and we investigate its effectiveness via several experimental studies. In addition, we provide explicit convergence rates for the algorithms in terms of the so-called Frank-Wolfe gap. The theoretical analysis has been specialized to Dominant Set Clustering and covers consistently the different variants.
Workshop on Proactive and Agent-Supported Information Retrieval (PASIR)
Authors: Gareth J. F. Jones, Procheta Sen, Debasis Ganguly, Emine Yilmaz
Venue: CIKM
Year: 2022

Established information retrieval (IR) systems are generally reactive in that they respond to active entry of a search query by a user. Information is thus only provided to a user when they identify a need for information, and invest the effort to address this need using a search engine. As such, users end up spending more effort to satisfy their information need. Different from reactive systems, proactive information retrieval (PIR) systems seek to retrieve relevant content without the user explicitly submitting a query. A PIR system usually achieves this by using a combination of observed user activities, their contexts and user profile information firstly to automatically create search queries, and then perform search operations and present retrieved results. This new paradigm of PIR methodology thus presents opportunities for investigating a number of research questions for improving user satisfaction, such as when would be the right opportunities to deliver information to users, what should be the presentation mode of search results, establishing a conversational communication channel with the users to ask clarifying questions to refine the retrieved information, and even providing users with explanations for the retrieved information. The aim of the workshop thus is to solicit submissions from the research community targeted towards these topics, and consolidate the research directions in this domain via exchange of ideas.
Commonsense Knowledge Base Completion with Relational Graph Attention Network and Pre-trained Language Model
Authors: Jinhao Ju, Deqing Yang, Jingping Liu
Venue: CIKM
Year: 2022

Many commonsense knowledge graphs (CKGs) still suffer from incompleteness although they have been applied in many natural language processing tasks successfully. Due to the scale and sparsity of CKGs, existing knowledge base completion models are not still competent for CKGs. In this paper, we propose a commonsense knowledge base completion (CKBC) model which learns the structural representations and contextual representations of CKG nodes and relations, respectively by a relational graph attention network and a pre-trained language model. Based on these two types of representations, the scoring decoder in our model achieves a more accurate prediction for a given triple. Our empirical studies on the representative CKG ConceptNet demonstrate our model's superiority over the state-of-the-art CKBC models.
TrustLOG: The First Workshop on Trustworthy Learning on Graphs
Authors: Jian Kang, Shuaicheng Zhang, Bo Li, Jingrui He, Jian Pei, Dawei Zhou
Venue: CIKM
Year: 2022

Learning on graphs (LOG) plays a pivotal role in various high-impact application domains. The past decades have developed tremendous theories, algorithms, and open-source systems in answering what/who questions on graphs. However, recent studies reveal that the state-of-the-art techniques for learning on graphs (LOG) are often not trustworthy in practice with respect to several social aspects (e.g., fairness, transparency, security). A natural research question to ask is: how can we make learning algorithms on graphs trustworthy? To answer this question, we propose a paradigm shift, from answering what and who LOG questions to understanding how and why LOG questions. The TrustLOG workshop provides a venue for presenting, discussing, and promoting frontier research on trustworthy learning on graphs. Moreover, TrustLOG will serve as an impulse for the LOG community to identify novel research problems and shed new light on future directions.
Flurry: A Fast Framework for Provenance Graph Generation for Representation Learning
Authors: Maya Kapoor, Joshua Melton, Michael Ridenhour, Thomas Moyer, Siddharth Krishnan
Venue: CIKM
Year: 2022

Representation learning and deep learning on data provenance graphs has yielded insightful new angles for intrusion detection in cybersecurity systems and is rapidly expanding as a research topic in the scientific community. In order to train the learning models, system execution data must be created and captured which represent realistic cyberattacks of a wide variety. Furthermore, these graphs must contain equally authentic benign workflows relative to the host and its multi-faceted processes. In order to support dynamic generation of provenance graphs to rapidly train new provenance graph learning models, we present Flurry, an end-to-end framework which simulates attack and benign activity and generates provenance graphs in multiple formats exportable to graph learning systems. In this demonstration, we showcase Flurry's ability to simulate both pre-configured and user-defined cyberattacks as well as benign behavior, and convert these captures into provenance graphs. We investigate the spectral properties of these graphs and perform attack classification experiments comparing three graph neural network models. Our results are comparable to those in the original learning models' research papers, showing that the Flurry graphs provide an ideal baseline and an extensible framework for graph representation learning on provenance graphs. In our demo, we show that Flurry will bring brand-new expandability and proficiency to the provenance graph learning community's available data.
RaDaR: A Real-Word Dataset for AI powered Run-time Detection of Cyber-Attacks
Authors: Sareena Karapoola, Nikhilesh Singh, Chester Rebeiro, V. Kamakoti
Venue: CIKM
Year: 2022

Artificial Intelligence techniques on malware run-time behavior have emerged as a promising tool in the arms race against sophisticated and stealthy cyber-attacks. While data of malware run-time features are critical for research and benchmark comparisons, unfortunately, there is a dearth of real-world datasets due to multiple challenges to their collection. The evasive nature of malware, its dependence on connected real-world conditions to execute, and its potential repercussions pose significant challenges for executing malware in laboratory settings. Consequently, prior open datasets rely on isolated virtual sandboxes to run malware, resulting in data that is not representative of malware behavior in the wild. This paper presents RaDaR, an open real-world dataset for run-time behavioral analysis of Windows malware. RaDaR is collected by executing malware on a real-world testbed with Internet connectivity and in a timely manner, thus providing a close-to-real-world representation of malware behavior. To enable an unbiased comparison of different solutions and foster multiple verticals in malware research, RaDaR provides a multi-perspective data collection and labeling of malware activity. The multi-perspective collection provides a comprehensive view of malware activity across the network, operating system (OS), and hardware. On the other hand, the multi-perspective labeling provides four independent perspectives to analyze the same malware, including its methodology, objective, capabilities, and the information it exfiltrates. To date, RaDaR includes 7 million network packets, 11.3 million OS system call traces, and 3.3 million hardware events of 10,434 malware samples having different methodologies (3 classes) and objectives (9 classes), spread across 30 well-known malware families.
Building Next Best Action Engines for B2C and B2B Use Cases
Authors: Ilya Katsov
Venue: CIKM
Year: 2022

Traditional machine learning methods used in marketing and digital commerce applications, including propensity scoring and many recommendation algorithms, are usually focused on improving short-term outcomes such as click-through rates. In many environments, however, long-term customer engagement can be more important than immediate outcomes. In this paper, we describe several real-world case studies on building personalization engines that address this problem using reinforcement learning (RL) methods. We also discuss the design patterns used to create such solutions.
Convolutional Transformer Networks for Epileptic Seizure Detection
Authors: Nan Ke, Tong Lin, Zhouchen Lin, Xiao-Hua Zhou, Taoyun Ji
Venue: CIKM
Year: 2022

Epilepsy is a chronic neurological disease that affects many people in the world. Automatic epileptic seizure detection based on electroencephalogram (EEG) signals is of great significance and has been widely studied. The current deep learning epilepsy detection algorithms are often designed to be relatively simple and seldom consider the characteristics of EEG signals. In this paper, we propose a promising epilepsy detection model based on convolutional transformer networks. We demonstrate that integrating convolution and transformer modules can achieve higher detection performance. Our convolutional transformer model is composed of two branches: one extracts time-domain features from multiple inputs of channel-exchanged EEG signals, and the other handle frequency-domain representations. Experiments on two EEG datasets show that our model offers state-of-the-art performance. Particularly on the CHB-MIT dataset, our model achieves 96.02% in average sensitivity and 97.94% in average specificity, outperforming other existing methods with clear margins.
Mining Entry Gates for Points of Interest
Authors: Tanya Khanna, Abhinav Ganesan, Jose Mathew, Kranthi Mitra Adusimilli
Venue: CIKM
Year: 2022

In this paper, we propose two algorithms for identifying entry gates for Points of Interest (PoIs) using polygon representations of the PoIs (PoI polygons) and the Global Positioning System (GPS) trajectories of the Delivery Partners (DPs) obtained from their smartphones in the context of online food delivery platforms. PoIs include residential complexes, office complexes, and educational institutes where customers can order from. Identifying entry gates of PoIs helps avoid delivery hassles by routing the DPs to the nearest entry gates for customers within the PoIs. The DPs mark 'reached' on their smartphone applications when they reach the entry gate or the parking spot of the PoI. However, it is not possible to ensure compliance, and the 'reached' locations are dispersed throughout the PoI. The first algorithm is based on density-based clustering of GPS traces where the DPs mark 'reached'. The clusters that overlap with the PoI polygon as measured by a metric that we propose, namely Cluster Fraction in Polygon (CFIP), are declared as entry gate clusters. The second algorithm obtains the entry gate clusters as density-based clustering of intersections of GPS trajectories of the DPs with the PoI polygon edges. The entry gates are obtained as median centroids of the entry gate clusters for both the algorithms which are then snapped to the nearest polygon edge in the case of the first algorithm. We evaluate the algorithms for a few thousand PoIs across 9 large cities in India using appropriately defined precision and recall metrics. For single-gate PoIs, we obtain a mean precision of 84%, a mean recall of 77%, and an average haversine distance error of 14.7 meters for the first algorithm. For the second algorithm, the mean precision is the same as the first algorithm while the recall obtained is 78% and the average haversine distance error is 14.3 meters. The algorithmically identified gates were evaluated by manual validation. To the best of our knowledge, this is the first published work with metrics that solves for a ''last-last mile" entity of digital maps for India, i.e., the entry gates.
Bootstrapped Knowledge Graph Embedding based on Neighbor Expansion
Authors: Jun Seon Kim, Seong-Jin Ahn, Myoung Ho Kim
Venue: CIKM
Year: 2022

Most Knowledge Graph(KG) embedding models require negative sampling to learn the representations of KG by discriminating the differences between positive and negative triples. Knowledge representation learning tasks such as link prediction are heavily influenced by the quality of negative samples. Despite many attempts, generating high-quality negative samples remains a challenge. In this paper, we propose a novel framework, Bootstrapped Knowledge graph Embedding based on Neighbor Expansion (BKENE), which learns representations of KG without using negative samples. Our model avoids using augmentation methods that can alter the semantic information when creating the two semantically similar views of KG. In particular, we generate an alternative view of KG by aggregating the information of the expanded neighbor of each node with multi-hop relation. Experimental results show that our BKENE outperforms the state-of-the-art methods for link prediction tasks.
Context-aware Traffic Flow Forecasting in New Roads
Authors: Namhyuk Kim, Dong-Kyu Chae, Jung Ah Shin, Sang-Wook Kim, Duen Horng Chau, Sunghwan Park
Venue: CIKM
Year: 2022

This paper focuses on the problem of forecasting daily traffic of new roads, where very little data is available for prediction. We propose a novel prediction model based on Generative Adversarial Networks (GAN) that learns the subtle patterns of the changes in the traffic flow according to the various contextual factors. Then the trained generator makes a prediction via generating a realistic traffic flow data of a target new road given its weather and day type. Both the quantitative and qualitative results of our extensive experiments indicate the effectiveness of our method.
SWAG-Net: Semantic Word-Aware Graph Network for Temporal Video Grounding
Authors: Sunoh Kim, Taegil Ha, Kimin Yun, Jin Young Choi
Venue: CIKM
Year: 2022

In this paper, to effectively capture non-sequential dependencies among semantic words for temporal video grounding, we propose a novel framework called Semantic Word-Aware Graph Network (SWAG-Net), which adopts graph-guided semantic word embedding in an end-to-end manner. Specifically, we define semantic word features as node features of semantic word-aware graphs and word-to-word correlations as three edge types (i.e., intrinsic, extrinsic, and relative edges) for diverse graph structures. We then apply Semantic Word-aware Graph Convolutional Networks (SW-GCNs) to the graphs for semantic word embedding. For modality fusion and context modeling, the embedded features and video segment features are merged into bi-modal features, and the bi-modal features are aggregated by incorporating local and global contextual information. Leveraging the aggregated features, the proposed method effectively finds a temporal boundary semantically corresponding to a sentence query in an untrimmed video. We verify that our SWAG-Net outperforms state-of-the-art methods on Charades-STA and ActivityNet Captions datasets.
Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
Authors: Minseok Kim, Jinoh Oh, Jaeyoung Do, Sungjin Lee
Venue: CIKM
Year: 2022

Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions. However, little attention was paid to GNN's vulnerability to exposure bias: users are exposed to a limited number of items so that a system only learns a biased view of user preference to result in suboptimal recommendation quality. Although inverse propensity weighting is known to recognize and alleviate exposure bias, it usually works on the final objective with the model outputs, whereas GNN can also be biased during neighbor aggregation. In this paper, we propose a simple but effective approach, neighbor aggregation via inverse propensity (NAVIP) for GNNs. Specifically, given a user-item bipartite graph, we first derive propensity score of each user-item interaction in the graph. Then, inverse of the propensity score with Laplacian normalization is applied to debias neighbor aggregation from exposure bias. We validate the effectiveness of our approach through our extensive experiments on two public and Amazon Alexa datasets where the performance enhances up to 14.2%.
EEG-Oriented Self-Supervised Learning and Cluster-Aware Adaptation
Authors: Wonjun Ko, Heung-Il Suk
Venue: CIKM
Year: 2022

Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have gained widespread attention to monitor a user's clinical condition or identify his/her intention/emotion. Nevertheless, the existing methods mostly model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, thus suffering from representing complex spatio-spectro-temporal patterns as well as inter-subject variability. In this work, we propose novel EEG-oriented self-supervised learning methods to discover complex and diverse patterns of spatio-spectral characteristics and spatio-temporal dynamics. Combined with the proposed self-supervised representation learning, we also devise a feature normalization strategy to resolve an inter-subject variability problem via clustering. We demonstrated the validity of the proposed framework on three publicly available datasets by comparing with state-of-the-art methods.
MM-evocat: A Tool for Modelling and Evolution Management of Multi-Model Data
Authors: Pavel Koupil, Jáchym Bártík, Irena Holubová
Venue: CIKM
Year: 2022

In this paper, we focus on the problem of evolution management of multi-model data. With the changing user requirements, the schema and the data need to be adapted to preserve the expected functionality of a multi-model application. We introduce a tool MM-evocat based on utilising the category theory. We show that the core of the tool, i.e., the categorical representation of multi-model data, enables us to grasp all the specifics of the individual models and their possible combinations. Its simple but powerful formal basis enables unique and robust support for evolution management.
POTATO: exPlainable infOrmation exTrAcTion framewOrk
Authors: Ádám Kovács, Kinga Gémes, Eszter Iklódi, Gábor Recski
Venue: CIKM
Year: 2022

We present POTATO, a task- and language-independent framework for human-in-the-loop (HITL) learning of rule-based text classifiers using graph-based features. POTATO handles any type of directed graph and supports parsing text into Abstract Meaning Representations (AMR), Universal Dependencies (UD), and 4lang semantic graphs. A web-based user interface allows users to build rule systems from graph patterns, provides real-time evaluation based on ground truth data, and suggests rules by ranking graph features using interpretable machine learning models. Users can also provide patterns over graphs using regular expressions, and POTATO can recommend refinements of such rules. POTATO is applied in projects across domains and languages, including classification tasks on German legal text and English social media data. All components of our system are written in Python, can be installed via pip, and are released under an MIT License on GitHub.
PAVE: Lazy-MDP based Ensemble to Improve Recall of Product Attribute Extraction Models
Authors: Kushal Kumar, Anoop S. V. K. K. Saladi
Venue: CIKM
Year: 2022

E-commerce stores face the challenge of missing and inconsistent attribute values in the product detail pages and have to impute them on behalf of their vendors. Traditional approaches formulate the problem of attribute extraction(AE) from product profiles as natural language tasks such as information extraction or text classification. Such models typically operate at high precision but may yield low recall especially on attributes with an open vocabulary due to 1) missing or incorrect information in product profiles, 2) generalization errors due to lack of contextual understanding, and 3) confidence thresholding to operate at high precision. In this work, we present PAVE: Product Attribute Value Ensemble, a novel reinforcement learning model that usesLazy-MDP formalism to solve for low recall by aggregating information from a sequence of product neighbors. We train a policy network usingProximal Policy Optimization that learns to choose the correct value from the sequence. We observe consistent improvement in recall across all open attributes compared to traditionalAE models with an average lift of 10.3% with no drop in precision. Our method surpasses simple aggregation methods like nearest neighbor, majority vote and binary classifier ensembles and even outperformsAE models for closed attributes. Our approach is scalable, robust to noisy product neighbors and generalizes well on unseen attributes.
Neuron Specific Pruning for Communication Efficient Federated Learning
Authors: Gaurav Kumar, Durga Toshniwal
Venue: CIKM
Year: 2022

Federated Learning (FL) is a distributed training framework where a model is collaboratively trained over a set of clients without communicating their private data to the central server. However, each client shares the parameters of its local model. The first challenge faced by the FL is high communication cost due to the size of Deep Neural Network (DNN) models. Pruning is an efficient technique to reduce the number of parameters in DNN models, in which insignificant neurons are removed from the model. This paper introduces a federated pruning method based on Neuron Importance Scope Propagation (NISP) algorithm. The importance scores of output layer neurons are back-propagated layer-wise to every neuron in the network. The central server iteratively broadcasts the sparsified weights to all selected clients. Then, each participating client intermittently downloads the mask vector and reconstructs the weights in their original form. The locally updated model is pruned using the mask vector and shared with the server. After receiving model updates from each participating client, the server reconstructs and aggregates the weights. Experiments on MNIST and CIFAR10 datasets demonstrate that the proposed approach achieves accuracy close to Federated Averaging (FedAvg) algorithm with less communication cost.