OpenRaiser/Intern-Atlas huggingface dataset:
ranx.fuse: A Python Library for Metasearch
Authors: Elias Bassani, Luca Romelli
Venue: CIKM
Year: 2022

This paper presents ranx.fuse, a Python library for Metasearch. Built following a user-centered design, it provides easy-to-use tools for combining the results of multiple search engines. ranx.fuse comprises 25 Metasearch algorithms implemented with Numba, a just-in-time compiler for Python code, for efficient vector operations and automatic parallelization. Moreover, in conjunction with the Metasearch algorithms, our library implements six normalization strategies that transform the search engines' result lists to make them comparable, a mandatory step for Metasearch. Finally, as many Metasearch algorithms require a training or optimization step, ranx.fuse offers a convenient functionality for their optimization that evaluates pre-defined hyper-parameters configurations via grid search. By relying on the provided functions, the user can optimally combine the results of multiple search engines in very few lines of code. ranx.fuse can also serve as a user-friendly tool for fusing the rankings computed by a first-stage retriever and a re-ranker, as a library providing several baselines for Metasearch, and as a playground to test novel normalization strategies.
Efficient and Effective SPARQL Autocompletion on Very Large Knowledge Graphs
Authors: Hannah Bast, Johannes Kalmbach, Theresa Klumpp, Florian Kramer, Niklas Schnelle
Venue: CIKM
Year: 2022

We show how to achieve fast autocompletion for SPARQL queries on very large knowledge graphs. At any position in the body of a SPARQL query, the autocompletion suggests matching subjects, predicates, or objects. The suggestions are context-sensitive and ranked by their relevance to the part of the query already typed. The suggestions can be narrowed down by prefix search on the names and aliases of the desired subject, predicate, or object. All suggestions are themselves obtained via SPARQL queries. For existing SPARQL engines, these queries are impractically slow on large knowledge graphs. We present various algorithmic and engineering improvements of an open-source SPARQL engine such that these queries are executed efficiently. We evaluate a variety of suggestion methods on three large knowledge graphs, including the complete Wikidata. We compare our results with two widely used SPARQL engines, Virtuoso and Blazegraph. Our code, benchmarks, and complete reproducibility materials are available on https://ad.cs.uni-freiburg.de/publications.
Federated Data Preparation, Learning, and Debugging in Apache SystemDS
Authors: Sebastian Baunsgaard, Matthias Boehm, Kevin Innerebner, Mito Kehayov, Florian Lackner, Olga Ovcharenko, Arnab Phani, Tobias Rieger, David Weissteiner, Sebastian Benjamin Wrede
Venue: CIKM
Year: 2022

Federated learning allows training machine learning (ML) models without central consolidation of the raw data. Variants of such federated learning systems enable privacy-preserving ML, and address data ownership and/or sharing constraints. However, existing work mostly adopt data-parallel parameter-server architectures for mini-batch training, require manual construction of federated runtime plans, and largely ignore the broad variety of data preparation, ML algorithms, and model debugging. Over the last years, we extended Apache SystemDS by an additional federated runtime backend for federated linear-algebra programs, federated parameter servers, and federated data preparation. In this paper, we share the system-level compiler and runtime integration, new features such as multi-tenant federated learning, selected federated primitives, multi-key homomorphic encryption, and our monitoring infrastructure. Our demonstrator showcases how composite ML pipelines can be compiled into federated runtime plans with low overhead.
exML: An Explainable Maximum Likelihood Tool for Proportion Estimation in DNA Data
Authors: Amit Bergman, Viviane Slon, Daniel Deutch
Venue: CIKM
Year: 2022

Estimating proportions of elements in a given set is a key problem in multiple scenarios. A particular use case of interest is the analysis of ancient DNA, where the goal is to estimate the proportion of species in a set of DNA reads extracted from sediments in archaeological sites. While there is a plethora of existing solutions for this type of problem, they lack explainability, which leads to challenges in their debugging and deployment as well as in downstream analysis tasks. To this end, we have developed exML, a Maximum Likelihood Estimator equipped with novel explanation methods. We propose to demonstrate exML in the context of analyzing ancient DNA samples. We will show use cases where the explanations generated by exML provide insights on otherwise ambiguous classification results.
CASA-Net: A Context-Aware Correlation Convolutional Network for Scale-Adaptive Crack Detection
Authors: Xin Bi, Shining Zhang, Yu Zhang, Lei Hu, Wei Zhang, Wenjing Niu, Ye Yuan, Guoren Wang
Venue: CIKM
Year: 2022

Surface cracks in infrastructure are a key indicator of structural safety and degradation. Visual-based crack detection is a critical task for the enormous application demands of infrastructure industries. Convolution operations have been widely deployed due to the strong feature learning abilities. However, global feature dependencies of multi-scale cracks are ignored due to the limited receptive field.In addition, the detection of cracks with low contrast suffers a serious performance loss.Therefore, to address the scale-adaptive crack detection problem, we propose a context-aware correlation convolutional network for scale-adaptive crack detection named CASA-Net. CASA-Net is capable of extracting multi-scale crack features for distinguishing between cracks and surface backgrounds, and evaluating feature correlations to capture global contexts. CASA-Net is composed of the multi-scale distinguishing feature extraction (MDFE) module and the context-aware feature correlation (CAFC) module. Specifically, the MDFE module consists of multiple cascaded convolutional layers and distinguishing feature extraction layers (DFLayers). The CAFC module consists of a mapping block and cascaded correlators to capture the context-aware features for long-range interactions. The performance of CASA-Net is evaluated on a benchmark crack dataset. The experimental results indicate that CASA-Net outperforms rival methods by achieving an F1-Score of 0.65 and an AP50 of 63.9%.
AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence
Authors: Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas
Venue: CIKM
Year: 2022

Recent technological advances rely on accurate decision support systems that can be perceived as black boxes due to their overwhelming complexity. This lack of transparency can lead to technical, ethical, legal, and trust issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. The General Data Protection Regulation (GDPR), approved by the European Parliament in 2018, suggests that individuals should be able to obtain explanations of the decisions made from their data by automated processing, and to challenge those decisions. All these reasons have given rise to the domain of interpretable and explainable AI. AIMLAI aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable ML and interpretable AI. The workshop encourages interdisciplinary collaborations, with particular emphasis in knowledge management, Infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters. AIMLAI envisions to become a discussion venue for the advent of novel interpretable algorithms and explainability modules that mediate the communication between complex ML/AI systems and users.
Marine-tree: A Large-scale Marine Organisms Dataset for Hierarchical Image Classification
Authors: Tanya Boone-Sifuentes, Asef Nazari, Imran Razzak, Mohamed Reda Bouadjenek, Antonio Robles-Kelly, Daniel Ierodiaconou, Elizabeth S. Oh
Venue: CIKM
Year: 2022

This paper presents Marine-tree, a large-scale hierarchical annotated dataset for marine organism classification. Marine-tree contains more than 160k annotated images divided into 60 classes organised in a hierarchy-tree structure using an adapted CATAMI (Collaborative and Automated Tools for the Analysis of Marine Imagery and video) classification scheme. Images were meticulously collected by scuba divers using the RLS (Reef Life Survey) methodology and later annotated by experts in the field. We also propose a hierarchical loss function that can be applied to any multi-level hierarchical classification model, which takes into account the parent-child relationship between predictions and uses it to penalize inconsistent predictions. Experimental results demonstrate thatMarine-tree and the proposed hierarchical loss function are a good contribution for both research in underwater imagery and hierarchical classification.
On the Mining of Time Series Data Counterfactual Explanations using Barycenters
Authors: Soukaïna Filali Boubrahimi, Shah Muhammad Hamdi
Venue: CIKM
Year: 2022

EXplainable Artificial Intelligence (XAI) methods are increasingly accepted as effective tools to trace complex machine learning models' decision-making processes. There are two underlying XAI paradigms: (1) traditional factual methods and (2) emerging counterfactual models. The first family of methods uses feature attribution techniques that alter the feature space and observe the impact on the decision function. Counterfactual models aim at providing the smallest possible change to the feature vector that can change the prediction outcome. In this paper, we propose TimeX, a new model-agnostic time series counterfactual explanation algorithm that provides sparse, interpretable, and contiguous explanations. We validate our model using real-world time series datasets and show that our approach can generate explanations with up to 20% fewer outliers in comparison with other state-of-the-art competing baselines.
DocSemMap 2.0: Semantic Labeling based on Textual Data Documentations Using Seq2Seq Context Learner
Authors: Andreas Burgdorf, Alexander Paulus, André Pomp, Tobias Meisen
Venue: CIKM
Year: 2022

Methods for automated semantic labeling of data are an indispensable basis for increasing the usability of data. On the one hand, they contribute to the homogenization of the annotations and thus to the increase in quality; on the other hand, they reduce the modeling effort, provided that the quality of the used methodology is sufficient. In the past, research has focused primarily on data- and label-based methods. Another approach that has received recent attention is the incorporation of textual data documentations to support the automatic mapping of datasets to a knowledge graph. However, upon deeper analysis, our recent approach called DocSemMap gives away potential in a number of places. In this paper, we extend the current state of the art approach by uncovering existing shortcomings and presenting our own improvements. Using a sequence-to-sequence model (Seq2Seq), we exploit the context of datasets. An additional introduced classifier provides the linkage of documentation and labels for prediction. Our extended approach achieves a sustainable improvement in comparison to the reference approach.
GALGO: Scalable Graph Analytics with a Parallel DBMS
Authors: Wellington Cabrera, Xiantian Zhou, Ladjel Bellatreche, Carlos Ordonez
Venue: CIKM
Year: 2022

We present GALGO, a system for large scale graph analytics. GALGO provides complex graph analytics in a parallel cluster, exploiting a parallel database system as a computation engine. In this demonstration we show that fundamental graph algorithms including all pairs shortest path, single source shortest path, PageRank, triangle counting, connected components and reachability can be solved completely with queries, dynamically generated by our system. Our system presents performance that is very competitive to state-of-the-art graph systems. Furthermore, our out-of-core graph computation can process graphs larger than available main memory, without compromising performance.
Memory Graph with Message Rehearsal for Multi-Turn Dialogue Generation
Authors: Xiaoyu Cai, Yao Fu, Hong Zhao, Weihao Jiang, Shiliang Pu
Venue: CIKM
Year: 2022

Multi-turn dialogue system has attracted increasing attention in both academic and industry community. Multi-turn dialogue generation task is a challenging work as the relations among words, utterances and external knowledge are extremely complex. However, the existing methods only focus on constructing the relations between current utterance and historical utterances, and they even oversimplify the relation mining process. Moreover, with the accumulation of dialogue information, the deep semantic information is difficult to understand so that it needs a mechanism with the ability of reasoning and digesting information repeatedly, which is ignored by previous methods. In order to solve the above problems, we propose a Memory Graph with Message Rehearsal (MGMR) for dialogue generation based on the cognitive process of human memory. MGMR contains three main modules: sensory memory, short-term memory and long-term memory. Sensory memory converts the current utterance into embeddings from both word-level and sentence-level. We design a message rehearsal module in short-term memory to extract valuable information of current utterance deeply and repeatedly combined with the relative historical dialogue information and external knowledge stored in long-term memory. Furthermore, we innovatively design a memory graph in long-term memory to construct the relations among words, utterances and knowledge. The memory graph achieves three goals: extracting accurate relations between current utterance and historical utterances, updating the historical dialogue information, and achieving knowledge precipitation by expanding memory graph with the key words and relevant external knowledge of current utterance. We evaluate our model on real-world datasets and achieve better performance compared with the existing state-of-the-art methods.
Deep Ordinal Neural Network for Length of Stay Estimation in the Intensive Care Units
Authors: Derun Cai, Moxian Song, Chenxi Sun, Baofeng Zhang, Shenda Hong, Hongyan Li
Venue: CIKM
Year: 2022

Length of Stay (LoS) estimation is important for efficient healthcare resource management. Since the distribution of LoS is highly skewed, some previous works frame the LoS estimation as a multi-class classification problem by dividing the range of LoS into buckets. However, they ignore the ordinal relationship between labels. The distribution of bucketed LoS, with a heavy head and a heavy tail, is still imbalanced since the long tail is grouped into the last bucket. This paper proposes a Deep Ordinal neural network for Length of stay Estimation in the intensive care units (DOSE). DOSE can exploit the ordinal relationship and mitigate the skewness. The ordinal classification problem is decomposed into a series of binary classification sub-problems by using multiple binary classifiers. To maintain consistency among binary classifiers, the monotonicity constraint penalty is proposed. The number of samples whose labels are higher or lower than a given threshold is at the same level due to the heavy head and tail of the distribution. Therefore, the training data of each binary classifier are balanced. Experiments are conducted on the real-world healthcare dataset. DOSE outperforms all baseline methods in all metrics. The distribution of the prediction of DOSE is more aligned with the ground truth.
Contrastive Cross-Domain Sequential Recommendation
Authors: Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, Bin Wang
Venue: CIKM
Year: 2022

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. Additionally, we point out a serious information leak issue in prior datasets. We correct this issue and release the corrected datasets. Extensive experiments demonstrate the effectiveness of our approach C2DSR.
A Context-Enhanced Transformer with Abbr-Recover Policy for Chinese Abbreviation Prediction
Authors: Kaiyan Cao, Deqing Yang, Jingping Liu, Jiaqing Liang, Yanghua Xiao, Feng Wei, Baohua Wu, Quan Lu
Venue: CIKM
Year: 2022

Chinese abbreviation prediction is very important for various natural language processing tasks such as query understanding and entity linking, since people tend to use the concise abbreviation rather than the full form (name) to mention an entity. The existing models achieve their predictions through sequence labeling, i.e., the binary classification for each character (token) of the full form. However, they only leverage the semantics of the entity itself, overlooking the label dependencies between the tokens, and the rich information of the entity-related texts. In this paper we proposed a Context-Enhanced Transformer with Abbr-Recover policy, namely CETAR, for Chinese abbreviation prediction. CETAR predicts the abbreviation sequence mainly through an iterative decoding process, of which each round consists of an abbreviation and recovery operation. Our extensive experiments upon both general field and specific domain datasets justify that CETAR outperforms the state-of-the-art baselines including sequence labeling models and sequence generation models. Moreover, we have successfully constructed a Chinese abbreviation dataset from the famous tour website Fliggy, and we also shared it at https://github.com/tolerancecky/abbr-0731. The online A/B test on the Fliggy search system shows that 2.03% of conversion rate improvement has been achieved with the predicted abbreviations.
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
Authors: Yue Cao, Xiaojiang Zhou, Jiaqi Feng, Peihao Huang, Yao Xiao, Dayao Chen, Sheng Chen
Venue: CIKM
Year: 2022

Rich user behavior data has been proven to be of great value for Click-Through Rate (CTR) prediction applications, especially in industrial recommender, search, or advertising systems. However, it's non-trivial for real-world systems to make full use of long-term user behaviors due to the strict requirements of online serving time. Most previous works adopt the retrieval-based strategy, where a small number of user behaviors are retrieved first for subsequent attention. However, the retrieval-based methods are sub-optimal and would cause information losses, and it's difficult to balance the effectiveness and efficiency of the retrieval algorithm. In this paper, we propose SDIM (Sampling-based Deep Interest Modeling), a simple yet effective sampling-based end-to-end approach for modeling long-term user behaviors. We sample from multiple hash functions to generate hash signatures of the candidate item and each item in the user behavior sequence, and obtain the user interest by directly gathering behavior items associated with the candidate item with the same hash signature. We show theoretically and experimentally that the proposed method performs on par with standard attention-based models on modeling long-term user behaviors, while being sizable times faster. We also introduce the deployment of SDIM in our system. Specifically, we decouple the behavior sequence hashing, which is the most time-consuming part, from the CTR model by designing a separate module named BSE (Behavior Sequence Encoding). BSE is latency-free for the CTR server, enabling us to model extremely long user behaviors. Both offline and online experiments are conducted to demonstrate the effectiveness of SDIM. SDIM now has been deployed online in the search system of Meituan APP.
Predicting Guiding Entities for Entity Aspect Linking
Authors: Shubham Chatterjee, Laura Dietz
Venue: CIKM
Year: 2022

Entity linking can disambiguate mentions of an entity in text. However, there are many different aspects of an entity that could be discussed but are not differentiable by entity links, for example, the entity "oyster'' in the context of "food'' or "ecosystems''. Entity aspect linking provides such fine-grained explicit semantics for entity links by identifying the most relevant aspect of an entity in the given context. We propose a novel entity aspect linking approach that outperforms several neural and non-neural baselines on a large-scale entity aspect linking test collection. Our approach uses a supervised neural entity ranking system to predict relevant entities for the context. These entities are then used to guide the system to the correct aspect.
Efficient Second-Order Optimization for Neural Networks with Kernel Machines
Authors: Yawen Chen, Yile Chen, Jian Chen, Zeyi Wen, Jin Huang
Venue: CIKM
Year: 2022

Second-order optimization has been recently explored in neural network training. However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training. There have been some attempts to address this issue by approximation on the Hessian matrix, which unfortunately degrades the performance of the neural models. In order to tackle this issue, we propose Kernel Stochastic Gradient Descent (Kernel SGD) which solves the optimization problem in a space transformed by the Hessian matrix of the kernel machine. Kernel SGD eliminates the Hessian matrix recomputation in the training and requires a much smaller memory cost which can be controlled via the mini-batch size. We show that Kernel SGD optimization is theoretically guaranteed to converge. Our experimental results on tabular, image and text data confirm that Kernel SGD converges up to 30 times faster than the existing second-order optimization techniques, and achieves the highest test accuracy on all the tasks tested. Kernel SGD even outperforms the first-order optimization baselines in some problems tested in our experiments.