Search

The ever-growing multi-modal images pose great challenges to local image storage and retrieval systems. Cloud computing provides a solution to large-scale image data storage but suffers from privacy issues and lacks the support for multi-modal image retrieval. To address these, a searchable encryption-empowered privacy-preserving multi-modal image retrieval method is proposed. First, we design a hybrid image retrieval framework that fuses visual features and textual features at a decision level and further supports similar image retrieval and multi-keyword image retrieval. Second, we construct a new hybrid inverted index structure to distinguish high-frequency terms from low-frequency terms and index them through hierarchical index trees and data blocks, respectively, which greatly improves query efficiency. Third, we design a prime encoding-based multi-keyword query method that converts mapping operations in bloom filters into inner product calculations, and further implements secure multi-keyword image query. Experiments against the Baseline schemes are conducted to verify the performance of the scheme in terms of high efficiency. [ABSTRACT FROM AUTHOR]

We present our anytime MaxSAT solver TT-Open-WBO-Inc, focusing on its evolution since the initial version that won both of the weighted incomplete tracks of MaxSAT Evaluation 2019 (MSE19). The solver's MSE20 version claimed victory in these tracks at MSE20 and secured second place in both unweighted incomplete tracks. The major innovation in the MSE20 version was the integration of SAT-based local search. The contributions of this paper include: (1) Introducing a previously unpublished variant of the SAT-based local search algorithm Polosat, Polosat-OBV, applied by default already in the MSE20 version, and showing its superiority for weighted solving; (2) Describing and analyzing TT-Open-WBO-Inc's unweighted component, not studied in previous work; (3) Demonstrating that integrating the local search algorithm SATLike into TT-Open-WBO-Inc as a preprocessor enables it to outperform the winners of MSE20 in all four incomplete tracks. [ABSTRACT FROM AUTHOR]

This paper tackles the asynchronous signature-free Byzantine consensus. One way to circumvent the FLP impossibility result consists in adding a Time-free assumption. This assumption is based on the pattern of messages that are exchanged. In the context of authenticated asynchronous Byzantine systems where at most t processes may exhibit a Byzantine behavior, Moumen and Mostéfaoui provide the main result. They assume at least one correct process p i , called ⋄ ⟨ t + 1 ⟩ -winning , and a set Q of t correct processes such that, eventually, for each query issued by p i , any process p j of Q receives a response from p i among the (n - t) first responses to that query. The main contribution of this paper is to show that a deterministic solution for the Signature-free Byzantine consensus problem is possible if the system model satisfies an additional assumption that relies on the pattern of exchanged messages. To solve the Consensus problem, we assume a correct process p i , called ⋄ ⟨ 2 t + 1 ⟩ -winning , and a set Q of (2 t + 1) correct processes (including p i itself) such that, eventually, for each query issued by p i , any process p j of Q receives a response from p i among the (n - t) first responses to that query. The processes of the set Q may change over time. Based on this assumption, a Signature-free Multivalued Byzantine consensus protocol is proposed. Whereas many time-free protocols have been designed for the consensus problem in the crash model and in the Byzantine Authenticated model, this is, to our knowledge, the first time-free deterministic solution to the Signature-free Byzantine consensus Problem. [ABSTRACT FROM AUTHOR]

It is commonly known that the conjunctive query entailment problem for certain extensions of (the well-known ontology language) ALC is computationally harder than their knowledge base satisfiability problem while for others the complexities coincide, both under the standard and the finite-model semantics. We expose a uniform principle behind this divide by identifying a wide class of (finitely) locally-forward description logics, for which we prove that (finite) query entailment problem can be solved by a reduction to exponentially many calls of the (finite) knowledge base satisfiability problem. Consequently, our algorithm yields tight ExpTime upper bounds for locally-forward logics with ExpTime-complete knowledge base satisfiability problem, including logics between ALC and μALCHbregQ (and more), as well as ALCSCC with global cardinality constraints, for which the complexity of querying remained open. Moreover, to make our technique applicable in future research, we provide easy-to-check sufficient conditions for a logic to be locally-forward based on several novel versions of the model-theoretic notion of unravellings. Together with existing results, this provides a nearly complete classification of the "benign" vs. "malign" primitive modelling features extending ALC, missing out only the Self operator. We then show a rather surprising result, namely that the conjunctive entailment problem for ALCSelf is exponentially harder than for ALC. This places the seemingly innocuous Self operator among the "malign" modelling features, like inverses, transitivity or nominals. [ABSTRACT FROM AUTHOR]

Query, expressing the user need and requirement, has an important role, in an information retrieval system, for reaching a high accuracy search. In this paper, we present an overview of the different refinement operations that the query may undergo, in the sake to enhance performance of an information retrieval system, such as: automatic query formulation through words prevision, query reformulation, query expansion, and query optimization. [ABSTRACT FROM AUTHOR]

Information needs and information seeking processes depend on contextual factors. Identifying and paying attention to contextual factors such as work tasks can be helpful for personalization of information retrieval. This study aimed to determine the relationship between work task difficulty and interactive health information searching behaviour (HISB). The study was performed through an analysis of transaction logs to identify relation between the work task difficulty and HISB of postgraduate student health ambassadors (PSHB). Thirty participants were purposefully sampled and invited to complete four simulated work tasks. User perception of work task difficulty, satisfaction, and success of search process were measured using questionnaires. A total of 120 log files were analysed. The results showed that with increasing tasks difficulty, PSHB see more results pages, review more information items and finally save more documents. Also, with increasing difficulty, PSHB enter more and longer queries in information retrieval systems and, in addition to changing the query more, they use more keywords and longer queries to search. As the task difficulty increases, the rate of satisfaction and success, as well as the navigational speed decrease. Understanding how work task attributes affect HISB can be effective for designers of interactive information retrieval systems, and developers of personalized health information retrieval systems and recommendation systems based on contextual information. [ABSTRACT FROM AUTHOR]

This paper discusses the methods the course teacher used to teach the Information Processing and Retrieval course [Course Code: 19LISC302]. A few aspects, including Chain Indexing, KWIC, Boolean operators, queries, logical relations, and recall & precision, have been highlighted here for illustration. The course teacher was able to improve the students' knowledge of the mentioned areas by using teaching methods based on the Revised Bloom's Taxonomy. The methods also made it possible to follow the students' progress. Boolean operators, queries, logical relations, and recall & precision exercises are taken into consideration for the discussions here, but chain indexing and KWIC related classroom practice formats are only supplied at Annexure-i. The course teacher created the application that is connected to logical relations (Truth Table), and the students have used it in class to practice. Various multifaceted key phrases, a few queries, the Boolean operators AND, OR, and NOT, and the Truth Table are all included in the aforementioned programme. For students to successfully obtain pertinent data from any database, they must have a solid understanding of logical ideas. Meaningful results won't be produced by executing the queries and operators automatically or blindly. Additionally, it could need extra time from information seekers. Boolean operators are used in information retrieval systems to evaluate queries. The Boolean operators are typically linked to the classes in the queries. The evaluation for each class is either True or False. To effectively use the operators in search queries and extract the necessary data, learners need be familiar with the Truth Table-related concepts. In addition, for the aforementioned subjects other than Chain Indexing and KWIC, the Revised Bloom's Taxonomy based teaching and the students' evaluation techniques have also been presented here. Wherever needed, enough screenshots of the aforementioned programme are produced. Students used a learning approach based on Revised Bloom's Taxonomy to study the concepts linked to the aforementioned themes, and they have been duly documented below. With the use of the methodologies utilised in this study, the teacher was able to glean the essential information necessary for determining the pupils' degrees of learning ability and applying that information to the necessary charts. The charts show the students' competence levels according to themes, making it simple to see the students' skill status. Realizing their ability levels is aided by each level wise clearance and their overall clearance status. Chain indexing, KWIC, logical relations, queries with Boolean operators, database sample records format, RBT levels-based evaluation format, recall, and precision are the examples of exercise forms shown in Annexure-i. Further, overall progress charts, each level wise progress charts and Student performance evaluation report are given at Annexure-ii, Annexure-iii and Annexure-iv respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Science & Technology (JCS&T) is the property of Journal of Computer Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents a method for recommending augmentations against conceptual gaps in textbooks. Question Answer (QA) pairs from community question-answering (cQA) forums are noted to offer precise and comprehensive illustrations of concepts. Our proposed method retrieves QA pairs for a target concept to suggest two types of augmentations: basic and supplementary. Basic augmentations are suggested for the concepts on which a textbook lacks fundamental references. We identified such deficiencies by employing a supervised machine learning-based approach trained on 12 features concerning the textbook's discourse. Supplementary augmentations aiming for additional references are suggested for all the concepts. Retrieved QA pairs were filtered to ensure their comprehensiveness for the target students. The proposed augmentation system was deployed using a web-based interface. We collected 28 Indian textbooks and manually curated them to create gold standards for assessing our proposed system. Analyzing expert opinions and adopting an equivalent pretest-posttest setup for the students, the quality of these augmentations was quantified. We evaluated the usability of the interface from students' responses. Both system and human-based evaluations indicated that the suggested augmentations addressed the concept-specific deficiency and provided additional materials to stimulate learning interest. The learning interface was easy-to-use and showcased these augmentations effectively. [ABSTRACT FROM AUTHOR]

In this article, we solve the ancestry-labeling scheme problem, which aims at assigning the shortest possible labels (bit strings) to nodes of rooted trees, so ancestry queries between any two nodes can be answered by inspecting their assigned labels only. This problem was introduced more than 20 years ago by Kannan et al. [1988] and is among the most well-studied problems in the field of informative labeling schemes. We construct an ancestry-labeling scheme for n-node trees with label size log2 n+O(log log n) bits, thus matching the log2 n+Ω (log log n) bits lower bound given by Alstrup et al. [2003]. Our scheme is based on a simplified ancestry scheme that operates extremely well on a restricted set of trees. In particular, for the set of n-node trees with a depth of at most d, the simplified ancestry scheme enjoys label size of log2 n+ 2 log2 d + O(1) bits. Since the depth of most XML trees is at most some small constant, such an ancestry scheme may be of practical use. In addition, we also obtain an adjacency-labeling scheme that labels n-node trees of depth d with labels of size log2 n+3 log2 d+O(1) bits. All our schemes assign the labels in linear time, and guarantee that any query can be answered in constant time. Finally, our ancestry scheme finds applications to the construction of small universal partially ordered sets (posets). Specifically, for any fixed integer k, it enables the construction of a universal poset of size Õ(nk) for the family of n-element posets with a tree dimension of at most k. Up to lower-order terms, this bound is tight thanks to a lower bound of nk-o(1) by to Alon and Scheinerman [1988]. [ABSTRACT FROM AUTHOR]

Visual geolocalisation has remained as a challenge in the research community: Given a query image, and a geo‐tagged reference database, the goal is to derive a location estimate for the query image. We propose an approach to tackling the geolocalisation problem in a four‐step manner. Essentially, our approach focuses on re‐ranking the candidate images after image retrieval, by considering the visual similarity of the candidate and its neighbouring images, to the query image. By introducing the neighbouring images, the visual information of a candidate location has been enriched. The evaluation has been conducted on three street view datasets, where our approach outperforms three baseline approaches, in terms of location estimation accuracy on two datasets. We provide discussions related to, firstly, whether using deep features for image retrieval helps improve location estimation accuracy, and the effectiveness of geographical neighbourhoods; secondly, using different deep architectures for feature extraction, and its impact on estimation accuracy; thirdly, investigating if our approach consistently outperforms the classic 1‐NN approach, on two datasets with significant difference in visual elements. [ABSTRACT FROM AUTHOR]

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed. [ABSTRACT FROM AUTHOR]

In this paper, we present results of employing DBpedia and YAGO as lexical databases for answering questions formulated in the natural language. The proposed solution has been evaluated for answering class 1 and class 2 questions (out of 5 classes defined by Moldovan for TREC conference). Our method uses dependency trees generated from the user query. The trees are browsed for paths leading from the root of the tree to the question subject. We call those paths fibers and they represent the user intention. The question analysis consists of three stages: query analysis, query breakdown and information retrieval. The aim of those stages is the detection of the entities of interest and its attributes, indicating the users' domain of interest. The user query is then converted into a SPARQL query and sent to the DBpedia and YAGO databases. The baseline and the extended methods are presented and the quality of the solution is evaluated and discussed. [ABSTRACT FROM AUTHOR]

Motivated by a recent conjecture concerning the expressiveness of declarative networking, we propose a formal computation model for "eventually consistent" distributed querying, based on relational transducers. A tight link has been conjectured between coordination-freeness of computations, and monotonicity of the queries expressed by such computations. Indeed, we propose a formal definition of coordination-freeness and confirm that the class of monotone queries is captured by coordination-free transducer networks. Coordination-freeness is a semantic property, but the syntactic class of "oblivious" transducers we define also captures the same class of monotone queries. Transducer networks that are not coordination-free are much more powerful. [ABSTRACT FROM AUTHOR]

In this article, we demonstrate that, ignoring computational constraints, it is possible to release synthetic databases that are useful for accurately answering large classes of queries while preserving differential privacy. Specifically, we give a mechanism that privately releases synthetic data useful for answering a class of queries over a discrete domain with error that grows as a function of the size of the smallest net approximately representing the answers to that class of queries. We show that this in particular implies a mechanism for counting queries that gives error guarantees that grow only with the VC-dimension of the class of queries, which itself grows at most logarithmically with the size of the query class. We also show that it is not possible to release even simple classes of queries (such as intervals and their generalizations) over continuous domains with worst-case utility guarantees while preserving differential privacy. In response to this, we consider a relaxation of the utility guarantee and give a privacy preserving polynomial time algorithm that for any halfspace query will provide an answer that is accurate for some small perturbation of the query. This algorithm does not release synthetic data, but instead another data structure capable of representing an answer for each query. We also give an efficient algorithm for releasing synthetic data for the class of interval queries and axis-aligned rectangles of constant dimension over discrete domains. [ABSTRACT FROM AUTHOR]

Most existing research works involving deep learning focus on performance improvement by developing new architectures or regularizers. However, in this paper we study the modeling of uncertainty in recurrent networks for the application of student response modeling, more specifically, knowledge tracing. Knowledge tracing is an application of time series machine learning. It consists of inferring the mastery level of a skill for a student as they navigate a question bank, thus adjusting curriculum for efficient learning. Deep Knowledge Tracing (DKT) takes the deep learning approach for knowledge tracing and has achieved better results than models like Bayesian Knowledge Tracing (BKT) and Performance Factor Analysis (PFA). However, the opaqueness of these deep knowledge tracing models also brings some criticisms. Providing an uncertainty score for each prediction helps mitigate this opaqueness. To investigate uncertainty modeling in DKT, we first examine a popular way of modeling data dependent uncertainties using Monte Carlo and show how it is insufficient to model variance in data. Second, we show how to incorporate sensible uncertainties by explicitly regularizing the cross entropy loss function. Third, we evaluate our method both in three different real datasets and in a more controlled way using synthetic data. Using synthetic data allows us to quantitatively understand the generated uncertainties. The results show that our method provides comparable results with standard deep knowledge tracing models as well as meaningful prediction uncertainties. [ABSTRACT FROM AUTHOR]

Privacy-preserving in mobile business supplying location-based services (LBS) has the potential to become a primary concern for clients and service providers. In m-business providing LBS services, a client sends its exact locations to service providers. This data may involve sensitive and private personal information. Therefore, the misuse of location information by service providers creates privacy issues for clients. Moreover, the query must not be linked to the mobile client, even if the location information is exposed willingly by her/him to obtain specific services. Thus, there are location cloaking algorithms that allow the protection of the location privacy of mobile users. Hence, many temporal and spatial approaches to cloaking a specific user's location have been proposed. Different from the existing methods, the current works define location and query privacy separately. Therefore, in this paper, we investigate the issues related to the mobile client privacy. Mainly, we aim to preserve the client location privacy as well as the continuous queries privacy, where mobile clients continuously emit different queries during their travels. It's on this premise that we propose a new clique-based cloaking algorithm named Mobile Clique Cloak (MCC) to preserve the mobile client's privacy in the M-business providing LBS services. Also, to build the cloaking region in our approach, we take into account the similarity of client velocity and direction to obtain a right balance between quality of service QoS and privacy. Furthermore, we generate different realistic dummies instead of dropping the query; thus, all queries will be processed even in the case of k−1 other mobile clients' queries cannot be found. Moreover, our work deals with a series of attacks in the same cloaking process (location attack, tracking attack, query sampling attacks and homogenous attack). We evaluate our approach from three aspects: privacy guaranty, quality of service and performance. Experimental evaluation of our algorithms on a real world map shows that our approach ensures total privacy for clients and protects the privacy of clients during the entire query period whiles allowing clients' choice of privacy requirements. Besides, we compare our algorithm with existing privacy protection algorithms such as V-DCA, D-TC and GCA. According to the evaluation results and a comparison of the algorithms, our algorithm MCC can make a good balance between quality of service, performance and privacy. [ABSTRACT FROM AUTHOR]

Continuously finding the most relevant (shortly, top-k) answer of a query that joins streaming and distributed data is getting a growing attention. In recent years, this is in particular happening in Social Media and IoT. It is well known that, in those settings, remaining reactive can be challenging, because accessing the distributed data can be highly time consuming as well as rate-limited. In this paper, we investigate the problem of continuous top-k query evaluation over a data stream joined with a distributed dataset in even a more extreme situation: the distributed data evolves. We propose the Topk+N algorithm and the AcquaTop framework. They keep up to date a local replica of the distributed dataset and guarantees reactiveness by construction, but to do so they may need to approximate the result. Therefore, we propose two maintenance policies to update the replica: the Top Selection Maintenance (AT-TSM) policy maximizes the relevancy, while the Border Selection Maintenance (AT-BSM) policy maximizes the accuracy of the top-k result. We contribute a theoretical proof of the correctness of Topk+N algorithm and we study its complexity. Moreover, we provide empirical evidence that the proposed policies within AcquaTop framework produce more relevant and accurate results than the state of the art. [ABSTRACT FROM AUTHOR]

Multi-objective spatial keyword query aims to find a set of objects that are reasonably distributed in spatial, with all query objectives to be satisfied. However, existing approaches mainly take the coverage of query keywords into account, while leaving the semantics of the textual data to be largely ignored. This limits us to return those rational results that are synonyms but morphologically different. To address this problem, this paper studies the problem of multi-objective spatial keyword query with semantics, and targets to return the object set that is optimum regarding to both spatial proximity and semantic relevance. Specifically, we take advantage of the probabilistic topic model and locality sensitive hashing (LSH), so that all query objectives can be satisfied in terms of their semantics. Afterwards, a novel indexing structure called LIR-tree is designed to integrate the spatial and semantic information of all objects in a balanced way. On top of the LIR-tree, we further propose a distance-owner based query processing algorithm, which provides tight bounds to achieve superb pruning effect in the searching phase. To speed up the processing, a distance owners based replacement strategy can be used to conduct approximate querying more efficiently. Empirical study based on a real dataset demonstrates the good effectiveness and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Museums play a crucial role in preserving cultural heritage. However, the forms in which they display cultural heritage might not be the most effective at piquing visitors' interest. Therefore, museums tend to integrate different technologies that aim to create engaging and memorable experiences. In this context, the emerging Internet of Things (IoT) technology results particularly promising due to the possibility of implementing smart objects in museums, granting exhibits advanced interaction capabilities. Gamification techniques are also a powerful technique to draw visitors' attention. These often rely on interactive question-based games. A drawback of such games is that questions must be periodically regenerated, and this is a time-consuming task. To confront these challenges, this paper proposes a low-maintenance gamified smart object platform that automates the creation of questions by exploiting semantic web technologies. The platform has been implemented in a real-life scenario. The results obtained encourage the use of the platform in the museum considered. Therefore, it appears to be a promising work that could be extrapolated and adapted to other kinds of museums or cultural heritage institutions. [ABSTRACT FROM AUTHOR]

Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end Big Data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines. [ABSTRACT FROM AUTHOR]

Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user's browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data. [ABSTRACT FROM AUTHOR]

The assessment of the fairness of health resource allocation is an important part of the study for the fairness of social development. The data used in most of the existing assessment methods comes from statistical yearbooks or field survey sampling. These statistics are generally based on administrative areas and are difficult to support a fine-grained evaluation model. In response to these problems, the evaluation method proposed in this paper is based on the query statistics of the geographic grid of the target area, which are more accurate and efficient. Based on the query statistics of hot words in the geographic grids, this paper adopts the maximum likelihood estimation method to estimate the population in the grid region. Then, according to the statistical yearbook data of Hunan province, the estimated number and actual number of hospitals in each grid are analyzed and compared to measure the fairness of health resource allocation in the target region. Experiments show that the geographical grid population assessment based on hot words is more accurate and close to the actual value. The estimated average error is only about 17.8 percent. This method can assess the fairness of health resource allocation in any scale, and is innovative in data acquisition and evaluation methods. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction. [ABSTRACT FROM AUTHOR]

Street-level landmarks are an important basis for street-level IP geolocation, and the web-based landmark is one of the main sources of street-level landmarks. Considering the existing street-level landmark evaluation methods having low accuracy and strict constraints, this paper analyses the causes and evaluation idea of invalid web-based candidate landmarks and proposes Evaluator, a web-based landmark evaluation approach. Evaluator adopts the idea of the decision tree to filter invalid landmarks layer by layer and comprehensively estimates the quantitative reliability of candidate landmarks with public data and services to obtain reliable landmarks. This paper proposes the domain name system (DNS) distributed query algorithm to effectively resolve all IP addresses of a domain name, which provides data support for Evaluator to filter candidate landmarks. Meanwhile, this paper also proposes a reverse verification algorithm to obtain all domain names of an IP address, which provides an important reference to calculate the reliability of a reliable landmark. In addition, gradient descent is used to assess the parameters of the reliability estimating model, which effectively improves the robustness of Evaluator. Experiments show that reliable landmarks from Evaluator reduce the geolocation error of 100 targets in Hong Kong from 7.30 km to 3.91 km, compared with the landmark verifying method (LVM), one of the latest web-based landmark evaluation methods. Moreover, Evaluator significantly improves the evaluation coverage based on the same geolocation accuracy with street-level landmark evaluation (SLE), one of the latest landmark evaluation methods. [ABSTRACT FROM AUTHOR]

The proliferation of semantic big data has resulted in a large amount of content published over the Linked Open Data (LOD) cloud. Semantic Web applications consume these data by issuing SPARQL queries. One of the main challenges faced by querying the LOD web cloud on account of the inherent distributed nature of LOD is its high search latency and lack of tools to connect the SPARQL endpoints. In this paper, we propose an Adaptive Cache Replacement strategy (ACR) that aims to accelerate the overall query processing of the LOD cloud. ACR alleviates the burden on SPARQL endpoints by identifying subsequent queries learned from clients historical query patterns and caching the result of these queries. For cache replacement, we propose an exponential smoothing forecasting method to replace the less valuable cache content. In the experimental study, we evaluate the performance of the proposed approach in terms of hit rates, query time and overhead. The proposed approach is found to outperform existing state-of-the-art approaches, increase hit rates by 5.46%, and reduce the query times by 6.34%. [ABSTRACT FROM AUTHOR]

Background: With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication. Methods: In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT). Results: In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov–Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset. Conclusion: Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects. [ABSTRACT FROM AUTHOR]

Column-oriented stores, known for their scalability and flexibility, are a common NoSQL database implementation and are increasingly used in big data management. In column-oriented stores, a “full-scan” query strategy is inefficient and the search space can be reduced if data is well partitioned or indexed; however, there is no pre-defined schema for building and maintaining partitions and indexes at lower cost. We leverage an accumulative and high-dimensional data model, a sophisticated linearization algorithm, and an efficient query algorithm, to solve the challenge of how a pre-defined and well-partitioned data model can be applied to flexible and time-varied key-value data. We adapt a high-dimensional array as the data model to partition the key-value data without additional storage and massive calculation; improve the Z-order linearization algorithm, which map multidimensional data to one dimension while preserving locality of the data points, for flexibility; efficiently build an expansion mechanism for the data model to support time-varied data. The result is Haery, a column-oriented store, based on a distributed file system and computing framework. In experiments, Haery is compared with Hive, HBase, Cassandra, MongoDB, PostgresXL, and HyperDex in terms of query performance. With results indicating Haery on average performs 4.57x, 4.23x, 3.55x, 1.79x, 1.82x, and 120.6x faster, respectively. [ABSTRACT FROM AUTHOR]

Determining the optimal targets of genomic subsampling for phylogenomics, phylogeography, and population genomics remains a challenge for evolutionary biologists. Of the available methods for subsampling the genome, hybrid enrichment (sequence capture) has become one of the primary means of data collection for systematics, due to the flexibility and cost efficiency of this approach. Despite the utility of this method, information is lacking as to what genomic targets are most appropriate for addressing questions at different evolutionary scales. In this study, first, we compare the benefits of target loci developed for deep- and shallow scales by comparing these loci at each of three taxonomic levels: within a genus (phylogenetics), within a species (phylogeography), and within a hybrid zone (population genomics). Specifically, we target evolutionarily conserved loci that are appropriate for deeper phylogenetic scales and more rapidly evolving loci that are informative for phylogeographic and population genomic scales. Second, we assess the efficacy of targeting multiple-locus sets for different taxonomic levels in the same hybrid enrichment reaction, an approach we term hierarchical hybrid enrichment. Third, we apply this approach to the North American chorus frog genus Pseudacris to answer key evolutionary questions across taxonomic and temporal scales. We demonstrate that in this system the type of genomic target that produces the most resolved gene trees differs depending on the taxonomic level, although the potential for error is substantially lower for the deep-scale loci at all levels. We successfully recover data for the two different locus sets with high efficiency. Using hierarchical data targeting deep and shallow levels: we 1) resolve the phylogeny of the genus Pseudacris and introduce a novel visual and hypothesis testing method that uses nodal heat maps to examine the robustness of branch support values to the removal of sites and loci; 2) estimate the phylogeographic history of Pseudacris feriarum , which reveals up to five independent invasions leading to sympatry with congener Pseudacris nigrita to form replicated reinforcement contact zones with ongoing gene flow into sympatry; and 3) quantify with high confidence the frequency of hybridization in one of these zones between P. feriarum and P. nigrita , which is lower than microsatellite-based estimates. We find that the hierarchical hybrid enrichment approach offers an efficient, multitiered data collection method for simultaneously addressing questions spanning multiple evolutionary scales. [Anchored hybrid enrichment; heat map; hybridization; phylogenetics; phylogeography; population genomics; reinforcement; reproductive character displacement.] [ABSTRACT FROM AUTHOR]

Using mobile devices to support the process and products of learning activities is a trend in inquiry-based learning, which is culminating in the formation of the subfield of mobile inquiry-based learning (m-IBL). However, even though mobile devices have been widely used in IBL activities in recent years, evaluative evidence for their substantial contributions to IBL is still deficient. Therefore, this study collected and analyzed 40 peer-reviewed journal articles published during 2001 to 2017 involving 3,542 participants and further discovered that m-IBL has produced meaningful improvements for IBL with an overall mean effect size of 0.803, with a 95% confidence interval [0.61, 0.99]. In addition, moderator variables such as control group types, learning outcomes, inquiry types, teaching methods, and domain subjects were related to different effect sizes. Finally, the results of the meta-analysis were discussed along with their implications for future research and practice. [ABSTRACT FROM AUTHOR]

Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool. These integrated solutions are also a fertile ground of theoretically fundamental and challenging problems at the intersection of relational and statistical data models. This article introduces a unified framework for training and evaluating a class of statistical learning models over relational databases. This class includes ridge linear regression, polynomial regression, factorization machines, and principal component analysis. We show that, by synergizing key tools from database theory such as schema information, query structure, functional dependencies, recent advances in query evaluation algorithms, and from linear algebra such as tensor and matrix operations, one can formulate relational analytics problems and design efficient (query and data) structure-aware algorithms to solve them. This theoretical development informed the design and implementation of the AC/DC system for structureaware learning. We benchmark the performance of AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting and advertisement planning applications, AC/DC can learn polynomial regression models and factorization machines with at least the same accuracy as its competitors and up to three orders of magnitude faster than its competitors whenever they do not run out of memory, exceed 24-hour timeout, or encounter internal design limitations. [ABSTRACT FROM AUTHOR]

The ranking and recommendation of regions of interest are increasingly important in recent years. In this light, we propose and study a novel and interesting problem of inferring region significance using multi-source spatiotemporal data. In our study, POIs, locations, regions, trajectories, and spatial networks are taken into account. Given a set of regions R and a set of trajectories T, we seek for the top-k most attractive regions to users, i.e., regions with the top-k highest spatial-density correlations to the trajectories of travelers. This study is useful in many mobile applications such as urban computing, region recommendation, and location-based service in general. This problem is challenging due to two reasons: (1) how to model the spatial-density correlation effectively and practically and (2) how to process the problem in interactive time. To overcome the challenges, we design a novel spatial-density correlation function to evaluate the relationship between regions and trajectories, and the density of POIs and network distance are taken into account. Then, we develop a series of optimization techniques to accelerate the query efficiency. Furthermore, we develop a parallel mechanism to support big spatial data. Finally, we conduct extensive experiments on real and synthetic spatial data sets to show the efficiency and effectiveness of developed algorithms. [ABSTRACT FROM AUTHOR]

In order to maintain a trade-off between preserved privacy and enhanced utility of data publishing, a partition and publishing method for location big data is proposed based on sampling and adjustment. Firstly, the sampling with fixed time interval is used to simulate the publishing process of location big data, and differential processing method is designed to reduce the temporal and spatial redundancy of adjacent snapshots. Then, data update status at the current time is determined by the result of differential processing. Corresponding adjustment methods are designed for the grid-based and tree-based partition structure, and Laplace noise is added to the adjusted structure in order to realize differential privacy protection for the published data. Experiments show that the proposed partition and publishing method has larger advantages in improving regional query accuracy and the efficiency of algorithm. [ABSTRACT FROM AUTHOR]

We consider variable selection in high-dimensional sparse multiresponse linear regression models, in which a q-dimensional response vector has a linear relationship with a p-dimensional covariate vector through a sparse coefficient matrix B ∈ R p × q . We propose a consistent procedure for the purpose of identifying the nonzeros in B. The procedure consists of two major steps, where the first step focuses on the detection of all the nonzero rows in B, the latter aims to further discover its individual nonzero cells. The first step is an extension of Orthogonal Matching Pursuit (OMP) and the second step adopts the bootstrap strategy. The theoretical property of our proposed procedure is established. Extensive numerical studies are presented to compare its performances with available representatives. [ABSTRACT FROM AUTHOR]

Question plays a leading role for Visual Question Answering (VQA) because it specifies the particular visual objects or conjures vivid visual that the machine should attend. However, existing approaches predominantly predict the answer by utilizing the question and the whole image without considering the leading role of the question. Also, recent object spatial inference is usually conducted on pixel level instead of object level. Therefore, we propose a novel but simple framework, namely Question-Led Object Attention (QLOB), to improve the VQA performance by exploring question semantics, fine-grained object information, and the relationship between those two modalities. First, we extract sentence semantics by a question model, and utilize the efficient object detection network to obtain a global visual feature and local features from top r object region proposals. Second, our QLOB attention mechanism selects those question-related object regions. Third, we optimize question model and QLOB attention by a softmax classifier to predict the final answer. Extensive experimental results on three public VQA datasets demonstrate that our QLOB outperforms the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Top- k query processing is an important building block for ranked retrieval, with various applications. In this paper, we consider two interesting top- k retrieval problems. In the first problem, we consider the r -combinations of a set S of n real numbers. Precisely, on any input set S , any positive integers k and r , our target is to generate the k best size- r subsets of S efficiently, whose sum of elements is maximized. Our method defines a novel metadata structure G for n and r , even before S and k are known, so that we can later use G to report the top- k r -combinations efficiently when S is available. In the second problem, we consider the top- k procurement decision problem in a multi-item auction, where the input consists of (i) a set of items, where each item is partitioned into equal number of shares, (ii) a set of suppliers, and (iii) for each supplier, her prices of selling different shares of each item; our target is to find k procurements with the least total costs. Our solution extends the novel metadata structure G of the first problem to speed up the reporting steps. For both of the above problems, we further show that the metadata structure can be generated on the fly, thereby saving a considerable amount of storage space. [ABSTRACT FROM AUTHOR]

The journey of the database started with a relational database followed by transactional, eXtensible Markup Language (XML)[10,11], Not only SQL (NoSQL), and Hybrid Transactional/Analytical Processing (HTAP) database[2]. Analytic functions in SQL are used to process analytical databases, XQuery is used to process XML databases and NewSQL queries are used to process Hybrid Transactional and Analytical Processing (HTAP) databases. In the current scenario, the research related to the XML database and analytics on the XML database is an important area of concern. Numerous enterprises use XML as a data exchange format. Considering all the benefits of XML and its use in the field of web applications, various works elaborate to adopt it as a data storage technology. In this work, an attempt has been made to perform analytical functions using XQuery on an XML data and compare its performance with Analytical SQL queries in terms of execution time. A sample set of queries has been performed on a sample dataset. According to the results, it is concluded that Analytic SQL is dominating XQuery to perform analytical functions. In experimentation, we explained the persistence of XML data (electronic health records dataset) in eXist-DB and BaseX and executing queries on that data. The finding is that the performance of XML data in a health care environment is better if we use BaseX instead of eXist-DB. [ABSTRACT FROM AUTHOR]

We introduce a new family of string processing problems. Given two or more strings, we are asked to compute a factor common to all strings that preserves a specific property and has maximal length. We consider three fundamental string properties: square-free factors, periodic factors, and palindromic factors under three different settings, one per property. In the first setting, we are given a string x and we are asked to construct a data structure over x answering the following type of online queries: given a string y , find a longest square-free factor common to x and y. In the second setting, we are given k strings and an integer 1 < k ′ ≤ k and we are asked to find a longest periodic factor common to at least k ′ strings. In the third one, we are given two strings and we are asked to find a longest palindromic factor common to the two strings. We present linear-time solutions for all settings. This is a full and extended version of a paper from SPIRE 2018. [ABSTRACT FROM AUTHOR]

Time-resolved electronic spectroscopy has grown into a technique that provides hundreds to thousands of electronic spectra with femtosecond time resolution. This enables complex questions to be interrogated, with an obvious cost that the data are more detailed and thus require accurate modelling to be properly reproduced. Analysis of these data comes in a variety of forms, starting with a variety of assumptions about how the data may be decomposed. Here, four different types of analysis commonly used are discussed: band-shape analysis, global kinetic analysis, lifetime distribution models, and soft-modelling. This review provides a 'user's guide' to these various methods of data analysis, and attempts to elucidate their successes, domains in which they may be useful, and potential pitfalls in their usage. [ABSTRACT FROM AUTHOR]

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k -nearest neighbour (kNN) queries. [ABSTRACT FROM AUTHOR]

Titles play an important role in genre analysis. Cross-genre studies show that research paper and thesis titles have distinctive features. However, thesis and dissertation titles in the field of dentistry have thus far received little attention. Objective: To analyze the syntactic structures and their functions in English-language thesis and dissertation titles in dentistry. Methodology: We randomly chose 413 titles of English-language dentistry theses or dissertations presented at universities in 12 countries between January 2000 and June 2019. The resulting corpus of 5,540 running words was then analyzed both qualitatively and quantitatively, the two complementary focuses being grammatical structures and their functions. Results: The average title length was 13.4 words. Over half of the titles did not include any punctuation marks. For compound titles, we found that colons, dashes, commas, and question marks were used to separate the different components, colons being the most frequent. Four syntactic structures (nominal phrase, gerund phrase, full-sentence, and prepositional phrase) were identified for single-unit titles. Single-unit nominal phrase titles constituted the most frequent structure in the corpus, followed by compound titles. Four particular rhetorical combinations of compound title components were found to be present throughout the corpus. Conclusions: Titles of dentistry theses and dissertation in English echo the content of the text body and make an important contribution to fulfilling the text's communicative purposes. Thus, teaching research students about the linguistic features of thesis titles would be beneficial to help them write effective titles and also facilitate assessment by teachers. [ABSTRACT FROM AUTHOR]

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well‐recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools. [ABSTRACT FROM AUTHOR]

Deep learning models have been shown to have great advantages in answer selection tasks. The existing models, which employ encoder-decoder recurrent neural network (RNN), have been demonstrated to be effective. However, the traditional RNN-based models still suffer from limitations such as 1) high-dimensional data representation in natural language processing and 2) biased attentive weights for subsequent words in traditional time series models. In this study, a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory (Bi-LSTM) and attention mechanism. The proposed model is able to generate the more effective question-answer pair representation. Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 3.8% over the classical LSTM model in terms of mean average precision. [ABSTRACT FROM AUTHOR]

Scientific data is frequently stored across geographically distributed data repositories. Although there have been recent efforts to query scientific datasets using structured query operators, they have not yet supported joins across distributed data repositories. This paper describes a framework that supports join-like operations over multi-dimensional array datasets that are spread across multiple sites. More specifically, we first formally define join operations over array datasets and establish how they arise in the context of scientific data analysis. We then describe a methodology for optimizing such operations—components of our approach include enumeration algorithms for candidate plans, methods for pruning plans before they are enumerated, and a detailed cost model for selecting the best (cheapest) plan. We evaluate our approach using candidate queries, and show that the optimization effort is practical and profitable—query performance was improved significantly using our approach. [ABSTRACT FROM AUTHOR]