Reviewerly/exHarmony huggingface dataset:
Discovering Editing Rules by Deep Reinforcement Learning

Editing rules specify the conditions of applying high quality master data to repair low quality input data. Discovering editing rules, however, is challenging, since it considers not only the well curated master data but also the large-scale input data, an extremely large search space. A natural baseline, namely EnuMiner, costly enumerates the rules with possible conditions from both master and input data. Although several pruning strategies are enabled, the algorithm still takes a long time when the enumeration space is large. To avoid enumerating all candidate rules during mining, we argue to model the rule discovery process as a Markov Decision Process. Specifically, we discover editing rules by growing a rule tree where each node corresponds to a rule. The algorithm generates a new rule from the current node as a child node. We propose a reinforcement learning-based editing rule discovery algorithm, RLMiner, which trains an agent to wisely make decisions on branches when traversing the tree. Following the idea of evaluating rules, we design a reward function that is more in line with rule discovery scenarios and makes our algorithm perform effectively and efficiently. The experimental results show that our proposed RLMiner can mine high-utility editing rules like EnuMiner and scale well on the datasets with many attributes and large domains.
Optimizing Graph Partition by Optimal Vertex-Cut: A Holistic Approach

Graph partitioning is crucial in distributed graph-parallel computing systems, and it is challenging for graph partitioning to optimize the communication cost and load balancing together. Existing state-of-the-art works, such as Powerlyra and TopoX, optimize the load balancing by randomly distributing the edges of high-degree vertices, which inevitably brings a high communication cost that is unbounded. This paper proposes a graph partition model that can minimize communication cost while maximizing load balancing. More specifically, we model the graph partition as the combinatorial design problem. Our proposed model can provide high-quality partition that guarantees that the computing load can be evenly distributed to each worker and minimizes the communication cost with a near-optimal theoretical boundary.Based on the proposed model, we extend the hybrid-cut partitioning algorithm for the power-law graph and propose HCPD, a hybrid-cut partitioning algorithm based on combinatorial design. HCPD uses the proposed model to optimize the load balancing and communication cost simultaneously for high-degree vertices, and assigns the high-degree vertices and their low-degree neighbors to the same workers by label propagation to reduce the overall communication cost. In this way, we partition the low-degree and high-degree vertices holistically and further improve the partition quality, unlike Powerlyra and TopoX, which deal with the two parts independently. Our experiments show that HCPD outperforms Powerlyra on PageRank task by up to 2× faster on real-world power-law graphs with billions of edges.
Neural Node Matching for Multi-Target Cross Domain Recommendation

Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) simultaneously. Most existing multi-target CDR frameworks primarily rely on the existence of the majority of overlapped users across domains. However, general practical CDR scenarios cannot meet the strictly overlapping requirements and only share a small margin of common users across domains. Additionally, the majority of users have quite a few historical behaviors in such small-overlapping CDR scenarios. To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i.e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions. The present framework mainly contains two modules: (i) intra-to-inter node matching module, and (ii) intra node complementing module. Concretely, the first module conducts intra-knowledge fusion within each domain and subsequent inter-knowledge fusion across domains by fully connected user-user homogeneous graph information aggregating. By doing this, the knowledge of all users, especially the non-overlapping users, could be well extracted and transferred without relying heavily on overlapping users. The second module introduces user-item matching to complement the potential missing interactions for each user and correct his/her under-represented representations, especially for the users with observed sparse interactions. Essentially, companion objectives are also inserted into each module to guide the knowledge transferring procedures, which leads to positive effects on multiple domains simultaneously. Extensive experiments on four multi-target CDR tasks from both public and real-world large-scale financial industry datasets demonstrate the remarkable performance of our proposed approach. Our code is publicly available at the link: https://github.com/WujiangXu/NMCDRR.
Fair Group Summarization with Graph Patterns

Given a set of node groups in a graph (e.g., gender or race groups), how to succinctly summarize their neighbors, and meanwhile ensure a "fair" representation to mitigate under- or over-representation of a certain group? We propose a novel framework to compute concise summaries of node groups with fairness guarantees. (1) We introduce a pattern-correction structure called r-summaries. An r-summary uses a graph pattern set to specify representative nodes and an auxiliary edge correction set to losslessly describe their r-hop neighbors. (2) We formulate the fair group summarization problem, which is to compute an r-summary that can select and accurately describe high quality nodes and their neighbors with small edge corrections, and meanwhile guarantee a desirable coverage for each group. The need for generating such summaries is evident in social recommendation, healthcare and graph search. We show that the problem is $\Sigma _2^p$-complete with the verification problem already NP-complete. (3) We present approximation algorithms that can generate r-summaries with (a) guaranteed quality and coverage properties, and (b) relative approximations on optimal edge correction costs. For large groups, we introduce an efficient algorithm that interleaves node selection and localized pattern discovery to reduce unnecessary computation. In addition, we introduce an algorithm to incrementally maintain the r-summaries over dynamic graphs with evolving edges. Using real-world data, we experimentally verify the efficiency and effectiveness of our algorithms and verify their applications.
HyTGraph: GPU-Accelerated Graph Processing with Hybrid Transfer Management

Processing large graphs with memory-limited GPU needs to resolve issues of host-GPU data transfer, which is a key performance bottleneck. Existing GPU-accelerated graph processing frameworks reduce the data transfers by managing the active subgraph transfer at runtime. Some frameworks adopt explicit transfer management approaches based on explicit memory copy with filter or compaction. In contrast, others adopt implicit transfer management approaches based on on-demand access with zero-copy or unified-memory. Having made intensive analysis, we find that as the active vertices evolve, the performance of the two approaches varies in different workloads. Due to heavy redundant data transfers, high CPU compaction overhead, or low bandwidth utilization, adopting a single approach often results in suboptimal performance.In this work, we propose a hybrid transfer management approach to take the merits of both the two approaches at runtime, with an objective to achieve the shortest execution time in each iteration. Based on the hybrid approach, we present HyTGraph, a GPU-accelerated graph processing framework, which is empowered by a set of effective task scheduling optimizations to improve the performance. Our experimental results on real-world and synthesized graphs demonstrate that HyTGraph achieves up to 10.27X speedup over existing GPU-accelerated graph processing systems including Grus, Subway, and EMOGI.
A Competition-Aware Approach to Accurate TV Show Recommendation

As the number of TV shows increases, designing recommendation systems to provide users with their favorable TV shows becomes more important. In a TV show domain, watching a TV show (i.e., giving implicit feedback to the show) among the TV shows broadcast at the same time frame implies that the currently watching show is the winner in the competition with others (i.e., losers). However, in previous studies, such a notion of limited competitions has not been considered in estimating the user’s preferences for TV shows. In this paper, we propose a new recommendation framework to take this new notion into account based on pair-wise models. Our framework is composed of the following ideas: (i) identify winners and losers by determining pairs of competing TV shows; (ii) learn the pairs of competing TV shows based on the confidence for the pair-wise preference between the winner and the loser; (iii) recommend the most favorable TV shows by considering the time factors with respect to users and TV shows. Using a real-world TV show dataset, our experimental results show that our proposed framework consistently improves the accuracy of recommendation by up to 38%, compared with the best state-of-the-art method. The code and datasets of our framework are available in an external link (https://github.com/hongkyun-bae/tvshow_rs).
A Survey on Spark Ecosystem: Big Data Processing Infrastructure, Machine Learning, and Applications (Extended abstract)

With the explosive increase of big data in industry and academic fields, it is important to apply large-scale data processing systems to analyze Big Data. Arguably, Spark is the state-of-the-art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed Dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Additionally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark.
Privacy vs Utility analysis when applying Differential Privacy on Machine Learning Classifiers

In this paper we present how Differential Privacy (DP), which is the state of the art in privacy preserving technologies in recent times, plays a role with different ML classifiers.Preserving privacy while serving the utility needs it is supposed to do is a challenge for each ML implementation.In order to study the effects of different DP implementations , we perturb data at different phases of the ML cycle such as perturbing data at its origin (Differential Privacy Method 1 -DPM1), during the training process (DPM2) or the parameters of the ML model generated (DPM3) by adding noise at these phases and see the effect of privacy preservation on ML model utility.Further we have tested with different perturbation methods such as Laplace mechanism, Gaussian mechanism, Analytic Gaussian mechanism, Snapping mechanism and Staircase mechanism for DPM1 and analysed the results to know which one works better for numerical data.We have used privacy attacks such as Membership Inference Attack (MIA) and Attribute Inference Attack (AIA) to see the effect of DP in protecting the privacy of data.Based on the results of our experiment we found perturbing at later stages of an ML method provides better utility and when considering different DPM1 mechanisms, improved versions of Laplace and Gaussian works better in terms of utility while preserving privacy.
HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract)

Sequential recommendation aims to identify and recommend the next few items of users’ interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and Data Engineering. Please refer to the full manuscript [4] for more details.
Efficient and Effective Path Compression in Large Graphs

A path in a graph is a walk from one vertex to the other via edges. Many tasks for graph analytics may produce numerous paths, which record critical intermediate information or results. On the platform of Alibaba Cloud, a transaction (e.g., user purchase and money transfer) usually involves network communication via multiple servers. The server communication history is recorded as a path, where each vertex is an IP address. It is of significance to record such paths in Alibaba Cloud for daily maintenance tasks, such as anomaly server detection and network routing optimization. Motivated by the considerable data scale of IP paths, this paper proposes a compression method Overlap-Free Frequent Subpath (OFFS) to reduce the overall size. Meanwhile, the compressed paths should allow retrievals of any individual path, which is required by applications in our scenarios. We build a lookup table to match a series of frequent common subpaths to supernodes. Each path is shortened by replacing subpaths with corresponding supernodes in the table. We adopt a bottom-up framework to construct the lookup table in given iterations. Several optimizations are proposed to improve the compression ratio and speed. We conduct extensive experiments to show our effectiveness and efficiency based on several real datasets from Alibaba Cloud.
A Deep Neural Network for Crossing-City POI Recommendations : (Extended Abstract)

THE popularity of location-aware devices such as smart phones makes users freely share their activities through various location-based social networks (LBSNs), such as Foursquare and Yelp. A large amount of user-contributed data enable to develop effective point-of-interest (POI) recommender systems. It not only guides users to explore more interesting attractions, but also helps the location service providers deliver targeted advertising. Now most of existing studies focus on recommending POIs in the same city or region, named as traditional POI recommender systems. However, they fail to deal with the increasingly popular case: users travel to new cities to explore more attractions. This raises the problem that how we shall recommend POIs in a target city to a new visitor based on her/his check-in records in source cities. We refer to this problem as crossing-city POI recommendations. Compared with traditional POI recommender systems, crossing-city POI recommender systems are more challenging due to the following aspects:
EdgeVisionBench: A Benchmark of Evolving Input Domains for Vision Applications at Edge

Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods overlook evolving domains and under represent their shifts in label and feature distributions. To address this gap, we present EdgeVisionBench, a benchmark seeking to generate evolving domains of various types and reflect their realistic label and feature shifts encountered by edge-based vision applications. To facilitate evaluating domain adaptation methods on edge devices, we provide an open-source package that automates workload generation, contains popular DNN models and compression techniques, and standardizes evaluations with interactive interfaces. Code and datasets are available at https://github.com/LINC-BIT/EdgeVisionBench.
On Sharding Across Heterogeneous Blockchains

Heterogeneous blockchains are expected to be increasingly deployed in real-world applications, making cross-chain transaction confirmations essential. Currently, confirmations for cross-chain transactions are usually accomplished through an intermediary, such as a relay chain, which may well become a performance bottleneck. Sharding has been widely used to improve the blockchain throughput through parallel transaction validations by distributing transactions into multiple sub-communities, Yet, when sharding technique is directly applied over a relay chain, it results in an excessive number of cross-shard transactions, offsetting the throughput improvement.In this paper, we propose Sliver, a novel transaction distribution mechanism specifically designed for improving the relay chain throughput for the first time. We first capture and leverage the unique characterization of transaction dependency on the relay chain, and place those transactions with dependency into one shard. Consequently, this completely eliminates cross-shard transactions. However, due to the varying nature of transaction dependency, such a transaction placement can lead to a highly skewed distribution in terms of the number of transactions (i.e., shard size) to be validated in different shards, which negatively affects the relay chain throughput. We proceed to formulate the transaction distribution as an integer optimization problem with a lexicographical minimization objective for achieving a balanced shard size. While such a problem is proved to be NP-hard, we are able to mathematically transform it to a linear programming (LP) formulation by incorporating several unique properties in the integer optimization formulation, which can then be efficiently solved using off-the-shelf LP solvers. Theoretical and experimental analyses show that Sliver is extremely efficient in solving the assignment problem and the throughput can be 5 × that of the state-of-the-art under various configurations.
Automatic Synonym Extraction and Context-based Query Reformulation for Points-of-Interest Search

In modern search engines, synonyms are widely used for query reformulation to improve search recall and relevance. The search query is reformulated based on the synonymous terms that are semantically related to the original query. The reformulated queries are used for improving or augmenting the original query to retrieve more relevant results. However, there are four main challenges in production environments: (1) How to obtain high-quality synonyms and validate their effectiveness, especially for low-resource languagesƒ (2) How to prevent search intent drift caused by over-reformulating the correct queryƒ (3) How to efficiently keep the synonyms and models up-to-date for large-scale production systemsƒ (4) How to ensure the latency introduced by query reformulation does not affect user’s search experienceƒ In this paper, we address these challenges by introducing a context-based query reformulation system for Points-of-Interest (POI) search based on the synonyms automatically extracted from search logs and language models. The synonyms are automatically validated using historical query samples. We also propose a lightweight term identification model to prevent over-reformulation by considering query context during reformulation. The proposed methods are unsupervised/self-supervised that can be easily applied to large-scale production systems. We deploy our system to eight Southeast Asia countries that have both English and low-resource languages. Both offline evaluations and online A/B tests show that our system enhances search recall and relevance significantly.
Transferable Feature Selection for Unsupervised Domain Adaptation : Extended Abstract

Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. Since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. In this paper, we propose to find a feature subset that is both transferable and discriminative, so that both the domain discrepancy and the classification loss measured on the selected features can be reduced. To achieve this, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method.
Cougar: A General Framework for Jobs Optimization In Cloud

In the cloud environment, different kinds of jobs (Flink, PyTorch, TensorFlow, AI-Serving) are running in the same cluster with different service-level agreements (SLA). To manage large amounts of jobs in a cloud environment efficiently, it is critical to build a system to optimize the job performance in consideration of multiple predefined objectives. For example, one kind of optimization target is improving the resource utilization of jobs, other kinds of objectives are to guarantee the system SLA (e.g., system throughput, response time, and so on). Currently, most of the existing frameworks are working on one aspect of optimization, and can not support different kinds of optimization targets via a uniform framework or system. In Antgroup, we have designed and implemented a general framework (named Cougar) to improve jobs and cluster performance to meet such requirements. Cougar provides the ability to support different optimization scenarios like the initial and runtime optimization for one job, and cross-job optimization for multiple jobs. Nowadays, Cougar has widely used in the production environment of Antgroup including 110,000 jobs and 800,000 Pods daily, and has successfully improved the CPU/Memory/GPU utilization by more than 20% and performance (i.e., throughput or completion time or latency) by around 10%. In the end, we also like to share our best practice on how to tune Flink and Deep Learning Job (GPU collocate) in the production environment.
Learning from Noisy Crowd Labels with Logics

This paper explores the integration of symbolic logic knowledge into deep neural networks for learning from noisy crowd labels. We introduce Logic-guided Learning from Noisy Crowd Labels (Logic-LNCL), an EM-alike iterative logic knowledge distillation framework that learns from both noisy labeled data and logic rules of interest. Unlike traditional EM methods, our framework contains a "pseudo-E-step" that distills from the logic rules a new type of learning target, which is then used in the "pseudo-M-step" for training the classifier. Extensive evaluations on two real-world datasets for text sentiment classification and named entity recognition demonstrate that the proposed framework improves the state-of-the-art and provides a new solution to learning from noisy crowd labels.