While diffusion models show promise for intent-based grasp generation, their isotropic noise schedules struggle with joint-specific sensitivity and task-aware variability. This limitation leads to grasps with suboptimal semantic alignment or physical feasibility. To address this challenge, we propose Semantic-guided Noise Scaling for grasp generation (SNS-Grasp), a novel framework that integrates two key innovations. First, the Semantic-guided Noise Scaling Diffusion (SNS-Diff) module generates intent-aware grasps by replacing isotropic noise with anisotropic modulation, dynamically adapting to task semantics and joint-specific sensitivity. Specifically, SNS-Diff leverages a pretrained Intent Recognizer to extract task-aware confidence scores and joint-specific gradient sensitivities from the interaction context. These signals adjust the noise scaling during denoising, downweighting perturbations for semantically critical joints to ensure semantic alignment. Second, the Fine-grained Grasp Refinement (FGR) module establishes dynamic joint-vertex coupling through fine-grained hand-object spatial relationships, enabling iterative optimization of physically executable grasps. Extensive experiments on OakInk and GRAB demonstrate SNS-Grasp's superior performance in semantic accuracy and physical feasibility, with robust generalization to unseen objects.

Abstract Understanding semantic intricacies and high‐level concepts is essential in image sketch generation, and this challenge becomes even more formidable when applied to the domain of videos. To address this, we propose a novel optimization‐based framework for sketching videos represented by the frame‐wise Bézier Curves. In detail, we first propose a cross‐frame stroke initialization approach to warm up the location and the width of each curve. Then, we optimize the locations of these curves by utilizing a semantic loss based on CLIP features and a newly designed consistency loss using the self‐decomposed 2D atlas network. Built upon these design elements, the resulting sketch video showcases notable visual abstraction and temporal coherence. Furthermore, by transforming a video into vector lines through the sketching process, our method unlocks applications in sketch‐based video editing and video doodling, enabled through video composition.

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects’ relationships. To enrich its information, objects in an HIN are typically associated with additional attributes. We call such an HIN anAttributed HINor AHIN. We study the problem of clustering objects in an AHIN, taking into account objects’ similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of amust-link setand acannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem, and two highly efficient variants, SCHAIN-PI and SCHAIN-IRAM, which employ thepower iteration based methodand theimplicitly restarted Arnoldi methodrespectively to compute eigenvectors of a matrix. We conduct extensive experiments comparing SCHAIN-based algorithms with other state-of-the-art clustering algorithms. Our results show that SCHAIN-IRAM outperforms other competitors in terms of clustering effectiveness and is highly efficient.

Live streaming platforms need to store a lot of recorded live videos on a daily basis. An important problem is how to automatically extract highlights (i.e., attractive short video clips) from these massive, long recorded live videos. However, algorithmic approaches are either domain-specific, which require experts to spend a long time to design, or resource-intensive, which require a lot of training data and/or computing resources. In this paper, we propose LIGHTOR, a novel implicit crowd-sourcing approach to overcome these limitations. The key insight is to collect users' natural interactions with a live streaming platform, and then leverage them to detect highlights. We recruit hundreds of users from Amazon Mechanical Turk, and evaluate the performance of LIGHTOR using two popular games in Twitch. The results show that LIGHTOR can achieve high extraction precision with a small set of training data and low computing resources.

In crowdsourcing, human workers are employed to tackle problems that are traditionally difficult for computers (e.g., data cleaning, missing value filling, and sentiment analysis). In this paper, we study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., What is the age of Jet Li?). Although this problem has been studied before, existing work often treats related attributes independently, leading to suboptimal performance. In this paper, we present T-Crowd, which is a crowdsourcing system that considers attribute relationships. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is used to guide task allocation to workers. Our solution seamlessly supports categorical and continuous attributes. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference and reducing the monetary cost of crowdsourcing.

Many data management and analytics tasks, such as entity resolution, cannot be solely addressed by automated processes. Crowdsourcing is an effective way to harness the human cognitive ability to process these computer-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon Mechanical Turk and CrowdFlower, we can easily involve hundreds of thousands of ordinary workers (i.e., the crowd) to address these computer-hard tasks. However it is rather inconvenient to interact with the crowdsourcing platforms, because the platforms require one to set parameters and even write codes. Inspired by traditional DBMS, crowdsourcing database systems have been proposed and widely studied to encapsulate the complexities of interacting with the crowd. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowdsourcing database systems. We first give an overview of crowdsourcing, and then summarize the fundamental techniques in designing crowdsourcing databases, including task design, truth inference, task assignment, answer reasoning and latency reduction. Next we review the techniques on designing crowdsourced operators, including selection, join, sort, top-k, max/min, count, collect, and fill. Finally, we discuss the emerging challenges.

We study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., what is the age of Jet Li?). Existing work often treats related attributes independently, leading to suboptimal performance. We present T-Crowd: a crowdsourcing system that considers attribute relationships. T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. Our solution seamlessly supports categorical and continuous attributes. Our experiments on real datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference.

Crowdsourcing is widely accepted as a means for resolving tasks that are hard for computers, e.g., entity resolution. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no proper quality control. Although previous studies attempt to eliminate workers by estimating workers' qualities via qualification tests or hidden tests, the qualities estimated may not be accurate, because workers may have diverse qualities across tasks. Thus, the quality of the results could be further improved by wisely assigning tasks to the workers who are specialized in the tasks and online task assignment is an effective way to achieve this goal. However, existing crowdsourcing platforms either do not support online task assignment (e.g., CrowdFlower) or are not user-friendly because they require to write complicated codes (e.g., Amazon MTurk). To address these limitations, we develop an online task assignment system, which can on-the-fly assign workers with appropriate tasks. We have deployed our system on top of MTurk. We demonstrate the following scenarios using our system. Firstly, requesters can easily utilize our system to enable online task assignment in order to improve answer quality. Moreover, requesters do not need to write any code. Secondly, our system can infer the quality of workers, and requesters can design and test their own task assignment algorithms using our proposed information. Thirdly, our system can monitor tasks and workers in real time, and the requesters can eliminate bad workers or terminate the crowdsourcing process to reduce the unnecessary cost.

Business analysts and data scientists today increasingly need to clean, standardize and transform diverse data sets, such as name, address, date time, phone number, etc., before they can perform analysis. These ad-hoc transformation problems are typically solved by one-off scripts, which is both difficult and time-consuming.

Today, business analysts and data scientists increasingly need to clean, standardize and transform diverse data sets, such as name, address, date time, and phone number, before they can perform analysis. This process of data transformation is an important part of data preparation, and is known to be difficult and time-consuming for end-users. Traditionally, developers have dealt with these longstanding transformation problems using custom code libraries. They have built vast varieties of custom logic for name parsing and address standardization, etc., and shared their source code in places like GitHub. Data transformation would be a lot easier for end-users if they can discover and reuse such existing transformation logic. We developed Transform-Data-by-Example ( TDE ), which works like a search engine for data transformations. TDE "indexes" vast varieties of transformation logic in source code, DLLs, web services and mapping tables, so that users only need to provide a few input/output examples to demonstrate a desired transformation, and TDE can interactively find relevant functions to synthesize new programs consistent with all examples. Using an index of 50K functions crawled from GitHub and Stackoverflow, TDE can already handle many common transformations not currently supported by existing systems. On a benchmark with over 200 transformation tasks, TDE generates correct transformations for 72% tasks, which is considerably better than other systems evaluated. A beta version of TDE for Microsoft Excel is available via Office store 1 . Part of the TDE technology also ships in Microsoft Power BI.

Crowd-powered database systems can leverage the crowd's ability to address machine-hard problems, e.g., data integration. Existing crowdsourcing systems adopt the traditional tree model to select a good query plan. However, the tree model can optimize the I/O cost but cannot optimize the monetary cost, latency and quality, which are three important optimization goals in crowdsourcing. To address this limitation, we demonstrate CDB, a crowd-powered database system. CDB proposes a new graph-based model that adopts a fine-grained tuple-level optimization model which significantly outperforms existing coarse-grained tree-based optimization models. Moreover, CDB provides a unified framework to simultaneously optimize the monetary cost, quality and latency. We have deployed CDB on well-known crowd-sourcing platforms and users can easily use our system to deploy their applications. We will demonstrate how to use CDB to address real-world applications, including web table integration and entity collection.

We study the classical kNN queries on road networks. Existing solutions mostly focus on reducing query processing time. In many applications, however, system throughput is a more important measure. We devise a mathematical model that describes throughput in terms of a number of system characteristics. We show that query time is only one of the many parameters that impact throughput. Others include update time and query/update arrival rates. We show that the traditional approach of improving query time alone is generally inadequate in optimizing throughput. Moreover, existing solutions lack flexibility in adapting to environments of different characteristics. We propose Toain, which is a very flexible algorithm that can be easily trained to adapt to a given environment for maximizing query throughput. We conduct extensive experiments on both real and synthetic data and show that Toain gives significantly higher throughput compared with existing solutions.

Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs ), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.

Crowdsourcing platforms have been widely deployed to solve many computer-hard problems, e.g., image recognition and entity resolution. Quality control is an important issue in crowdsourcing, which has been extensively addressed by existing quality-control algorithms, e.g., voting-based algorithms and probabilistic graphical models. However, these algorithms cannot ensure quality under sybil attacks, which leverages a large number of sybil accounts to generate results for dominating answers of normal workers. To address this problem, we propose a sybil defense framework for crowdsourcing, which can help crowdsourcing platforms to identify sybil workers and defense the sybil attack. We develop a similarity function to quantify worker similarity. Based on worker similarity, we cluster workers into different groups such that we can utilize a small number of golden questions to accurately identify the sybil groups. We also devise online algorithms to instantly detect sybil workers to throttle the attacks. Our method also has ability to detect multi-attackers in one task. To the best of our knowledge, this is the first framework for sybil defense in crowdsourcing. Experimental results on real-world datasets demonstrate that our method can effectively identify and throttle sybil workers.

Many important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring is an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality, (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost, (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. We survey and synthesize a wide spectrum of existing studies on crowdsourced data management.

Crowdsourcing database systems have been proposed to leverage crowd-powered operations to encapsulate the complexities of interacting with the crowd. Existing systems suffer from two major limitations. Firstly, in order to optimize a query, they often adopt the traditional tree model to select an optimized table-level join order. However, the tree model provides a coarse-grained optimization, which generates the same order for different joined tuples and limits the optimization potential that different joined tuples can be optimized by different orders. Secondly, they mainly focus on optimizing the monetary cost. In fact, there are three optimization goals (i.e., smaller monetary cost, lower latency, and higher quality) in crowdsourcing, and it calls for a system to enable multi-goal optimization.

Many important data management and analytics tasks cannot be completely addressed by automated processes. Crowdsourcing is an effective way to harness human cognitive abilities to process these computer-hard tasks, such as entity resolution, sentiment analysis, and image recognition. Crowdsourced data management has been extensively studied in research and industry recently. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowdsourced data management. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data management. Next we review crowdsourced operators, including selection, collection, join, top-k, sort, categorize, aggregation, skyline, planning, schema matching, mining and spatial crowdsourcing. We also discuss crowdsourcing optimization techniques and systems. Finally, we provide the emerging challenges.

A heterogeneous information network (HIN) is one whose nodes model objects of different types and whose links model objects' relationships. In many applications, such as social networks and RDF-based knowledge bases, information can be modeled as HINs. To enrich its information content, objects (as represented by nodes) in an HIN are typically associated with additional attributes. We call such an HIN an Attributed HIN or AHIN. We study the problem of clustering objects in an AHIN, taking into account objects' similarities with respect to both object attribute values and their structural connectedness in the network. We show how supervision signal, expressed in the form of a must-link set and a cannot-link set, can be leveraged to improve clustering results. We put forward the SCHAIN algorithm to solve the clustering problem. We conduct extensive experiments comparing SCHAIN with other state-of-the-art clustering algorithms and show that SCHAIN outperforms the others in clustering quality.

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates addressing problems that are hard for computers, e.g., entity resolution and sentiment analysis. However, due to the openness of crowdsourcing, workers may yield low-quality answers, and a redundancy-based method is widely employed, which first assigns each task to multiple workers and then infers the correct answer (called truth ) for the task based on the answers of the assigned workers. A fundamental problem in this method is Truth Inference , which decides how to effectively infer the truth. Recently, the database community and data mining community independently study this problem and propose various algorithms. However, these algorithms are not compared extensively under the same framework and it is hard for practitioners to select appropriate algorithms. To alleviate this problem, we provide a detailed survey on 17 existing algorithms and perform a comprehensive evaluation using 5 real datasets. We make all codes and datasets public for future research. Through experiments we find that existing algorithms are not stable across different datasets and there is no algorithm that outperforms others consistently. We believe that the truth inference problem is not fully solved, and identify the limitations of existing algorithms and point out promising research directions.

In recent years, query recommendation algorithms have been designed to provide related queries for search engine users. Most of these solutions, which perform extensive analysis of users' search history (or query logs), are largely insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study a new solution, which makes use of a knowledge base (or KB), such as YAGO and Freebase. A KB is a rich information source that describes how real-world entities are connected. We extract entities from a query, and use these entities to explore new ones in the KB. Those discovered entities are then used to suggest new queries to the user. As shown in our experiments, our approach provides better recommendation results for long-tail queries than existing solutions.

A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Objects are often associated with properties such as labels. In many applications, such as curated knowledge bases for which object labels are manually given, only a small fraction of the objects are labeled. Studies have shown that transductive classification is an effective way to classify and to deduce labels of objects, and a number of transductive classifiers have been put forward to classify objects in an HIN. We study the performance of a few representative transductive classification algorithms on HINs. We identify two fundamental properties, namely, cohesiveness and connectedness, of an HIN that greatly influence the effectiveness of transductive classifiers. We define metrics that measure the two properties. Through experiments, we show that the two properties serve as very effective indicators that predict the accuracy of transductive classifiers. Based on cohesiveness and connectedness we derive (1) a black-box tester that evaluates whether transductive classifiers should be applied for a given classification task and (2) an active learning algorithm that identifies the objects in an HIN whose labels should be sought in order to improve classification accuracy.

Identifying the labels of points of interest (POIs), aka POI labelling, provides significant benefits in location-based services. However, the quality of raw labels manually added by users or generated by artificial algorithms cannot be guaranteed. Such low-quality labels decrease the usability and result in bad user experiences. In this paper, by observing that crowdsourcing is a best-fit for computer-hard tasks, we leverage crowdsourcing to improve the quality of POI labelling. To our best knowledge, this is the first work on crowdsourced POI labelling tasks. In particular, there are two sub-problems: (1) how to infer the correct labels for each POI based on workers' answers, and (2) how to effectively assign proper tasks to workers in order to make more accurate inference for next available workers. To address these two problems, we propose a framework consisting of an inference model and an online task assigner. The inference model measures the quality of a worker on a POI by elaborately exploiting (i) worker's inherent quality, (ii) the spatial distance between the worker and the POI, and (iii) the POI influence, which can provide reliable inference results once a worker submits an answer. As workers are dynamically coming, the online task assigner judiciously assigns proper tasks to them so as to benefit the inference. The inference model and task assigner work alternately to continuously improve the overall quality. We conduct extensive experiments on a real crowdsourcing platform, and the results on two real datasets show that our method significantly outperforms state-of-the-art approaches.

A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures can be used in various applications, including entity resolution, recommendation, and information retrieval. Several studies have investigated the use of HIN information for relevance computation, however, most of them only utilize simple structure, such as path, to measure the similarity between objects. In this paper, we propose to use meta structure, which is a directed acyclic graph of object types with edge types connecting in between, to measure the proximity between objects. The strength of meta structure is that it can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we further design an algorithm with data structures proposed to support their evaluation. Our extensive experiments on YAGO and DBLP show that meta structure-based relevance is more effective than state-of-the-art approaches, and can be efficiently computed.

Crowdsourcing is a new computing paradigm that harnesses human effort to solve computer-hard problems, such as entity resolution and photo tagging. The crowd (or workers) have diverse qualities and it is important to effectively model a worker's quality. Most of existing worker models assume that workers have the same quality on different tasks. In practice, however, tasks belong to a variety of diverse domains, and workers have different qualities on different domains. For example, a worker who is a basketball fan should have better quality for the task of labeling a photo related to ' Stephen Curry ' than the one related to ' Leonardo DiCaprio '. In this paper, we study how to leverage domain knowledge to accurately model a worker's quality. We examine using knowledge base (KB), e.g., Wikipedia and Freebase, to detect the domains of tasks and workers. We develop Domain Vector Estimation , which analyzes the domains of a task with respect to the KB. We also study Truth Inference , which utilizes the domain-sensitive worker model to accurately infer the true answer of a task. We design an Online Task Assignment algorithm, which judiciously and efficiently assigns tasks to appropriate workers. To implement these solutions, we have built DOCS, a system deployed on the Amazon Mechanical Turk. Experiments show that DOCS performs much better than the state-of-the-art approaches.

Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.

Recent advances in crowdsourcing technologies enable computa-tionally challenging tasks (e.g., sentiment analysis and entity reso-lution) to be performed by Internet workers, driven mainly by mon-etary incentives. A fundamental question is: how should work-ers be selected, so that the tasks in hand can be accomplished successfully and economically? In this paper, we study the Jury Selection Problem (JSP): Given a monetary budget, and a set of decision-making tasks (e.g., “Is Bill Gates still the CEO of Mi-crosoft now?”), return the set of workers (called jury), such that their answers yield the highest “Jury Quality ” (or JQ). Existing JSP solutions make use of the Majority Voting (MV) strategy, which uses the answer chosen by the largest number of workers. We show that MV does not yield the best solution for JSP. We further prove that among all voting strategies (including deterministic and ran-domized strategies), Bayesian Voting (BV) can optimally solve JSP. We then examine how to solve JSP based on BV. This is technically challenging, since computing the JQ with BV is NP-hard. We solve this problem by proposing an approximate algorithm that is com-putationally efficient. Our approximate JQ computation algorithm is also highly accurate, and its error is proved to be bounded within 1%. We extend our solution by considering the task owner’s “be-lief ” (or prior) on the answers of the tasks. Experiments on syn-thetic and real datasets show that our new approach is consistently better than the best JSP solution known. 1.

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform for a large number of questions to be answered by Internet workers. Such systems have been shown to be useful to solve problems that are difficult for computers, including entity resolution, sentiment analysis, and image recognition. In this paper, we investigate the online task assignment problem: Given a pool of n questions, which of the k questions should be assigned to a worker? A poor assignment may not only waste time and money, but may also hurt the quality of a crowdsourcing application that depends on the workers' answers. We propose to consider quality measures (also known as evaluation metrics) that are relevant to an application during the task assignment process. Particularly, we explore how Accuracy and F-score, two widely-used evaluation metrics for crowdsourcing applications, can facilitate task assignment. Since these two metrics assume that the ground truth of a question is known, we study their variants that make use of the probability distributions derived from workers' answers. We further investigate online assignment strategies, which enables optimal task assignments. Since these algorithms are expensive, we propose solutions that attain high quality in linear time. We develop a system called the Quality-Aware Task Assignment System for Crowdsourcing Applications (QASCA) on top of AMT. We evaluate our approaches on five real crowdsourcing applications. We find that QASCA is efficient, and attains better result quality (of more than 8% improvement) compared with existing methods.