This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name

Presents corrections to affiliation information in the above named paper.

The full-replication data storage mechanism, as commonly utilized in existing blockchains, is the barrier to the system's scalability, since it retains a copy of entire blockchain at each node so that the overall storage consumption per block is $O(n)$ with $n$ participants. Yet another drawback is that this mechanism may limit the throughput in permissioned blockchain. Moreover, due to the existence

In e-learning recommender systems, interpersonal information between learners is very scarce, which makes it difficult to apply collaborative filtering (CF) techniques to achieve recommendations. In this study, we propose a hybrid filtering recommendation approach ( $SI-IFL$SI-IFL ) combining learner influence model (LIM), self-organization based (SOB) recommendation strategy, and sequential pattern

Big sensing data is commonly encountered from various surveillance or sensing systems. Sampling and transferring errors are commonly encountered during each stage of sensing data processing. How to recover from these errors with accuracy and efficiency is quite challenging because of high sensing data volume and unrepeatable wireless communication environment. While Cloud provides a promising platform

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advances on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however

Active learning algorithms attempt to overcome the labeling bottleneck by asking queries from large collection of unlabeled examples. Existing batch mode active learning algorithms suffer from three limitations: (1) The methods that are based on similarity function or optimizing certain diversity measurement, in which may lead to suboptimal performance and produce the selected set with redundant examples

A large amount of research effort has been dedicated to adapting boosting for imbalanced classification. However, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class problems. This is because most of the existing solutions for handling class imbalance rely on expensive cost set tuning for determining the proper level of compensation. We show that the

Declarative approaches to control-flow modeling use logic-based languages to formalize a number of constraints that valid traces must satisfy. The most noticeable example is the Declare framework based on linear temporal logic. Despite the interest that Declare has been attracting, the current knowledge about its formal properties was rather limited. The goal of this paper is to fill this gap by: (i)

Entity resolution (ER) is the problem of identifying and merging records that refer to the same real-world entity. In many scenarios, raw records are stored under heterogeneous environment. Specifically, the schemas of records may differ from each other. To leverage such records better, most existing work assume that schema matching and data exchange have been done to convert records under different

Conformance checking enables organizations to automatically identify compliance violations based on the analysis of observed event data. A crucial requirement for conformance-checking techniques is that observed events can be mapped to normative process models used to specify allowed behavior. Without a mapping, it is not possible to determine if an observed event trace conforms to the specification

Knowledge graphs are useful for many AI tasks but often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. TransE is one of such models and the first translation-based method. TransE is well known because the principle of TransE can effectively capture the rules of a knowledge graph although it seems very simple. However, TransE has problems with its regularization

Constrained Concept Factorization (CCF) yields the enhanced representation ability over CF by incorporating label information as additional constraints, but it cannot classify and group unlabeled data appropriately. Minimizing the difference between the original data and its reconstruction directly can enable CCF to model a small noisy perturbation, but is not robust to gross sparse errors. Besides

Question answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in the literature. In this paper, we present two training algorithms for learning better QA and QG models through leveraging one another. The first algorithm extends Generative Adversarial Network (GAN), which selectively incorporates

A significant proportion of all search volume consists of local searches. As a result, search engines must be capable of finding relevant results combining both spatial proximity and textual relevance with high query throughput. We observe that existing techniques answering these spatial keyword queries use keyword aggregated indexing, which has several disadvantages on road networks. We propose K-SPIN

Quality control in crowdsourcing is challenging due to the heterogeneous nature of the workers. The state-of-the-art solutions attempt to address the issue from the technical perspective, which may be costly because they function as an additional procedure in crowdsourcing. In this paper, an economics based idea is adopted to embed quality control into the crowdsourcing process, where the requestor

Identification of travelers’ transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. In this paper, we aim to identify travelers’ transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments

This paper introduces how to infer trust relationships from billion-scale networked data to benefit Alibaba E-Commerce business. To effectively leverage the network correlations between labeled and unlabeled relationships to predict trust relationships, we formalize trust into multiple types and propose a graphical model to incorporate type-based dyadic and triadic correlations, namely eTrust. We also

In this paper, we focus on named entity boundary detection , which is to detect the start and end boundaries of an entity mention in text, without predicting its type. The detected entities are input to entity linking or fine-grained typing systems for semantic enrichment. We propose BdryBot , a recurrent neural network encoder-decoder framework with a pointer network to detect entity boundaries from

This paper explores Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to deal with unseen entities without annotation. A compromised solution

In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users’ preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate

Dense subgraph discovery has proven useful in various applications of temporal networks. We focus on a special class of temporal networks whose nodes and edges are kept fixed, but edge weights regularly vary with timestamps. However, finding dense subgraphs in temporal networks is non-trivial, and its state of the art solution uses a filter-and-verification framework that is not scalable on large temporal

We propose an embedded/integrated feature selection method based on neural networks with Group Lasso penalty. Group Lasso regularization is considered to produce sparsity on the inputs to the network, i.e., for selection of useful features. Lasso based feature selection using a multi-layer perceptron usually requires an additional set of weights, while our Group Lasso formulation does not require that

Event recommendation is an essential means to enable people to find attractive upcoming social events, such as party, exhibition, and concert. While growing line of research has focused on suggesting events to individuals, making event recommendation for a group of users has not been well studied. In this paper, we aim to recommend upcoming events for a group of users. We formalize group recommendation

Summarization systems for various applications, such as opinion mining, online news services, and answering questions, have attracted increasing attention in recent years. These tasks are complicated, and a classic representation using bag-of-words does not adequately meet the comprehensive needs of applications that rely on sentence extraction. In this paper, we focus on representing sentences as

Recently, spatial crowdsourcing has been drawing increasing attention with its great potential in collecting geographical knowledge. The system throughput (number of assigned tasks) and workers’ travel distance are two of many important factors in spatial crowdsourcing, and the improvement to one of them usually means the sacrifice of the other. However, most existing works resolve the trade-off between

Encryption alone is not enough to protect data privacy, because access pattern leaks some sensitive information. Oblivious RAM (ORAM), the solution to this problem, is still far from practical deployment for heavy storage and communication/ computation overhead. To reduce them, an insightful idea was proposed to utilize non-colluding clouds to shift client computation and client-cloud communication

Breast cancer is now considered as one of the leading causes of deaths among women all over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions, computer-aided diagnosis (CAD) system is of increasing interest in breast cancer detection and analysis nowadays. In this paper, a novel computer-aided diagnosis scheme with human-in-the-loop is proposed to help clinicians

As a website evolves to align with users’ changing information needs and interests, its structure can outgrow the original design, accumulating links and pages in unanticipated places. This increases complexity to both web pages and the navigation structure, which could cause difficulty in locating relevant links and information. Though the increasing complexity of website and its impact on users’

While the majority of methods for community detection produce disjoint communities of nodes, most real-world networks naturally involve overlapping communities. In this paper, a scalable method for the detection of overlapping communities in large networks is proposed. The method is based on an extension of the notion of normalized cut to cope with overlapping communities. A spectral clustering algorithm

We study the problem of similarity self-join and similarity join size estimation in a streaming setting where the goal is to estimate, in one scan of the input and with sublinear space in the input size, the number of record pairs that have a similarity within a given threshold. The problem has many applications in data cleaning and query plan generation, where the cost of a similarity join may be

In this paper, we study a new type of spatial crowdsourcing, namely competitive detour tasking, where workers can make detours from their original travel paths to perform multiple tasks, and each worker is allowed to compete for preferred tasks by strategically claiming his/her detour costs. The objective is to make suitable task assignment by maximizing the social welfare of crowdsourcing systems

Graph clustering plays an important role in data mining. Based on an input data graph, data points are partitioned into clusters. However, most existing methods keep the data graph fixed during the clustering procedure, so they are limited to exploit the implied data manifold and highly dependent on the initial graph construction. Inspired by the recent development on manifold learning, this paper

In the era of open data, a single data source rarely contains all of the attributes we need for inference in specific applications. For example, a marketing department may aim to integrate retailer-specific purchase data with separate demographic data for purposes of targeted advertising – a capability not possible with either dataset alone. In this work, we address two key desiderata of an automated

Non-negative Matrix Factorization (NMF) has been one of the most popular clustering techniques in machine leaning, and involves various real-world applications. Most existing works perform matrix factorization on high-dimensional data directly. However, the intrinsic data structure is always hidden within the low-dimensional subspace. And, the redundant features within the input space may affect the

We present a new unsupervised dimensionality reduction technique, called LN-SNE, for anomaly detection. LN-SNE generates a parametric embedding by means of Restricted Boltzmann Machines and uses a heavy-tail distribution to project data to a lower dimensional space such that dissimilarities between normal data and anomalies are preserved or strengthened. We compare LN-SNE to several benchmark dimensionality

Non-parametric density estimation methods are more flexible than parametric methods, due to the fact that they do not assume any specific shape or structure for the data. Most non-parametric methods, like Kernel estimation, require tuning of parameters to achieve good data smoothing, a non-trivial task, even in low dimensions. In higher dimensions, sparsity of data in local neighborhoods becomes a

Data series similarity search is a core operation for several data series analysis applications across many different domains. Nevertheless, even state-of-the-art techniques cannot provide the time performance required for large data series collections. We propose ParIS and ParIS+, the first disk-based data series indices carefully designed to inherently take advantage of multi-core architectures,

Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously

Set similarity search is a fundamental operation in a variety of applications. While many previous studies focus on threshold based set similarity search and join, few efforts have been paid for KNN set similarity search. In this paper, we propose a transformation based framework to solve the problem of KNN set similarity search, which given a collection of set records and a query set, returns k results

ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals

This paper presents a general inductive graph representation learning framework called DeepGL for learning deep node and edge features that generalize across-networks. In particular, DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands

Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a very challenging problem, affected by multiple complex factors, such as spatial correlations between different locations, temporal correlations among different

Keystroke data collected from smart devices includes various sensitive information about users. Collecting and analyzing such data raise serious privacy concerns. Google and Apple have recently applied local differential privacy (LDP) to address privacy issue on learning new words from users' keystroke data. However, these solutions require multiple LDP reports for a single word, which result in inefficient

We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we give a much simpler approach for solving the

Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this

To fully utilize the resources of a main memory database cluster, we additionally take the independent parallelism into account to parallelize multiple pipelines of one query. However, scheduling resources to multiple pipelines is an intractable problem. Traditional static approaches to this problem may lead to a serious waste of resources and suboptimal execution order of pipelines, because it is

Retweeting is a powerful driving force of information propagation on microblogging. How to identify effective retweeters of a message (called "key retweeter prediciton" problem) has then become a significant research topic. Conventional approaches addressed this topic mainly from two aspects by analyzing either the personal attributes of microblogging users or the network structure of user graphs.

User Interfaces (UI) are the bridge to connect Business Processes (BPs) and end users. The implementation of UIs normally needs a lot of manual efforts of developers. Aiming to resolve this issue, this work proposes a UI derivation method with a role-enriched BP (REBP) model as its foundation. This process model has the capability to present the details of task control flow and data operations in tasks

Real-time/online activity recognition (AR) is an important technology in smart Internet of Things (IoT) systems where users are assisted by smart devices in their daily activities. How to generate appropriate feature representation from sensor event streaming is a challenging issue for accurate and efficient real-time AR. Previous AR models that rely on explicit domain knowledge are not appropriate

Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss

In recent years, we have witnessed an increasing demand to process big data in numerous applications. It is observed that there often exist substantial amounts of repetitive data in different portions of a big data repository/dataset for applications such as genome sequence analyses. In this paper, we present a novel method, called the VA-Store, to reduce the large space requirement for repetitive

Subspace clustering assumes that the data is separable into separate subspaces. Such a simple assumption, does not always hold. We assume that, even if the raw data is not separable into subspaces, one can learn a representation (transform coefficients) such that the learnt representation is separable into subspaces. To achieve the intended goal, we embed subspace clustering techniques (locally linear

With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management. In this paper, we analyze the impact of 5G on both traditional and emerging technologies and project our view on future research challenges and opportunities. With a predicted increase of 10-100x in bandwidth and 5-10x decrease in latency, 5G is

Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting

Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size parameter of the neighborhood. In this paper, we propose a new kernel function to estimate samples’ local densities and propose a weighted neighborhood

Massive data broadcast by GPS-equipped vehicles provide unprecedented opportunities. One of the main tasks in order to optimize our transportation networks is to build data-driven real-time decision support systems. However, the dynamic environments where the networks operate disallow the traditional assumptions required to put in practice many off-the-shelf supervised learning algorithms, such as

As 98 percent of shoppers do not make a purchase on the first visit, we study the problem of predicting whether they would come back for a purchase later (i.e., conversion prediction). This problem is important for strategizing “retargeting”, for example, by sending coupons for customers who are likely to convert. For this goal, we study the following two problems, prediction of market and predictability

As flash-based solid-state drive (SSD) becomes more prevalent because of the rapid fall in price and the significant increase in capacity, customers expect better data services than traditional disk-based systems. However, the order of magnitude performance provided and new characteristics of flash require a rethinking of data services. For example, backup and recovery is an important service in a