Query: RTIC-C: A Big Data System for Massive Traffic Information Mining

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Real-time traffic sign recognition from video by class-specific discriminative features <=SEP=> Article history: Received 1 August 2007 Received in revised form 22 May 2009 Accepted 26 May 2009

Introduction to Algorithms <=SEP=> problems To understand the class of polynomial-time solvable proble ms, we must first have a formal notion of what a “problem” is. We define anbstract problemQ to be a binary relation on a set I of probleminstancesand a setS of problemsolutions. For example, an instance for SHORTEST-PATH is a triple consi sting of a graph and two vertices. A solution is a sequence of vertices in the g raph, with perhaps the empty sequence denoting that no path exists. The problem SHORTEST-PATH itself is the relation that associates each instance of a gra ph and two vertices with a shortest path in the graph that connects the two vertices. S ince shortest paths are not necessarily unique, a given problem instance may have mo r than one solution. This formulation of an abstract problem is more general than is required for our purposes. As we saw above, the theory of NP-completeness res tricts attention to decision problems : those having a yes/no solution. In this case, we can view an abstract decision problem as a function that maps the instan ce setI to the solution set {0, 1}. For example, a decision problem related to SHORTEST-PATH i s the problem PATH that we saw earlier. If i = 〈G,u, v,k〉 is an instance of the decision problem PATH, then PATH(i ) = 1 (yes) if a shortest path fromu to v has at mostk edges, and PATH (i ) = 0 (no) otherwise. Many abstract problems are not decision problems, but rather optimization problems , in which some value must be minimized or maximized. As we saw above, however, it is usual ly a simple matter to recast an optimization problem as a decision problem that is no harder. 1See Hopcroft and Ullman [156] or Lewis and Papadimitriou [20 4] for a thorough treatment of the Turing-machine model. 34.1 Polynomial time 973

Progressive skyline computation in database systems <=SEP=> The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.

Space/Time Trade-offs in Hash Coding with Allowable Errors <=SEP=> In this paper trade-offs among certain computational factors in hash coding are analyzed. The paradigm problem considered is that of testing a series of messages one-by-one for membership in a given set of messages. Two new hash-coding methods are examined and compared with a particular conventional hash-coding method. The computational factors considered are the size of the hash area (space), the time required to identify a message as a nonmember of the given set (reject time), and an allowable error frequency.
The new methods are intended to reduce the amount of space required to contain the hash-coded information from that associated with conventional methods. The reduction in space is accomplished by exploiting the possibility that a small fraction of errors of commission may be tolerable in some applications, in particular, applications in which a large amount of data is involved and a core resident hash area is consequently not feasible using conventional methods.
In such applications, it is envisaged that overall performance could be improved by using a smaller core resident hash area in conjunction with the new methods and, when necessary, by using some secondary and perhaps time-consuming test to “catch” the small fraction of errors associated with the new methods. An example is discussed which illustrates possible areas of application for the new methods.
Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.

The Skyline Operator <=SEP=> We propose to extend database systems by a Skyline operation. This operation $filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.

PEDOT:PSS "Wires" Printed on Textile for Wearable Electronics. <=SEP=> Herein, the fabrication of all-organic conductive wires is demonstrated by utilizing patterning techniques such as inkjet printing and sponge stencil to apply poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) onto nonwoven polyethylene terephthalate (PET) fabric. The coating of the conducting polymer is only present on the surface of the substrate (penetration depth ∼ 200 μm) to retain the functionality and wearability of the textile. The wires fabricated by different patterning techniques provide a wide range of resistance, i.e., tens of kΩ/□ to less than 2 Ω/□ that allows the resistance to be tailored to a specific application. The sheet resistance is measured to be as low as 1.6 Ω/□, and the breakdown current is as high as 0.37 A for a 1 mm wide line. The specific breakdown current exceeds the previously reported values of macroscopic carbon nanotube based materials. Simple circuits composed of the printed wires are demonstrated, and resistance of the circuit from the measurement agrees with the calculated value based on Kirchhoff's rules. Additionally, the printed PEDOT:PSS wires show less than 6.2% change in sheet resistance after three washing and drying cycles using detergent.

A neurocognitive approach to understanding the neurobiology of addiction <=SEP=> Recent concepts of addiction to drugs (e.g. cocaine) and non-drugs (e.g. gambling) have proposed that these behaviors are the product of an imbalance between three separate, but interacting, neural systems: an impulsive, largely amygdala-striatum dependent, neural system that promotes automatic, habitual and salient behaviors; a reflective, mainly prefrontal cortex dependent, neural system for decision-making, forecasting the future consequences of a behavior, and inhibitory control; and the insula that integrates interoception states into conscious feelings and into decision-making processes that are involved in uncertain risk and reward. These systems account for poor decision-making (i.e. prioritizing short-term consequences of a decisional option) leading to more elevated addiction risk and relapse. This article provides neural evidence for this three-systems neural model of addiction.

Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction <=SEP=> This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

You are what you include: large-scale evaluation of remote javascript inclusions <=SEP=> JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them.
 In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.

Fog computing: Security issues, solutions and robust practices <=SEP=> Fog Computing (FC) has extended the services of cloud computing to the edge of the network. It inherits some of the characteristics from cloud computing but FC also have some distinguished features such as geo-distribution, location awareness and low latency. Along with the inherited characteristics, it also inherits the issues and problems of cloud computing like energy efficiency, resource management and security issues. This paper presents the critical analysis of the fog architecture with respect to security. The state of the art work done since 2012 is critical analyzed on the bases of security techniques and security threats. We grouped the existing security techniques on the bases of security goals achieved by each. It will provide a clear and comprehensive distinction between the security areas explored and those, which still need researchers' attention.

Specification Inference from Demonstrations <=SEP=> Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent’s behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent’s actions. The system is often modeled as a Markov decision process (Puterman 2014), that is, the next state depends only on the current state and agent’s action, and the the agent’s choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.

From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework <=SEP=> Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined.

Comparative study of trust and reputation systems for wireless sensor networks <=SEP=> Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small-sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust-based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.

Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands. <=SEP=> Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.

InverseRenderNet: Learning single image inverse rendering <=SEP=> We show how to train a fully convolutional neural network to perform inverse rendering from a single, uncontrolled image. The network takes an RGB image as input, regresses albedo and normal maps from which we compute lighting coefficients. Our network is trained using large uncontrolled image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision: 1. We learn a statistical natural illumination prior, 2. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering.

Design of ultra high-speed CMOS CML buffers and latches <=SEP=> A comprehensive study of ultra high-speed current-mode logic (CML) buffers and regenerative CML latches will be illustrated. A new design procedure to systematically design a chain of tapered CML buffers is proposed. Next, a new 20GHz regenerative latch circuit will be introduced. Experimental results show a higher performance for the new latch architecture compared to a conventional CML latch circuit at ultra high-frequencies. It is also shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed low-voltage applications.

Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images <=SEP=> Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

Online crowdsourcing: Rating annotators and obtaining cost-effective labels <=SEP=> Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing services like Amazon Mechanical Turk. How can one trust the labels obtained from such services? We propose a model of the labeling process which includes label uncertainty, as well a multi-dimensional measure of the annotators' ability. From the model we derive an online algorithm that estimates the most likely value of the labels and the annotator abilities. It finds and prioritizes experts when requesting labels, and actively excludes unreliable annotators. Based on labels already obtained, it dynamically chooses which images will be labeled next, and how many labels to request in order to achieve a desired level of confidence. Our algorithm is general and can handle binary, multi-valued, and continuous annotations (e.g. bounding boxes). Experiments on a dataset containing more than 50,000 labels show that our algorithm reduces the number of labels required, and thus the total cost of labeling, by a large factor while keeping error rates low on a variety of datasets.

Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization <=SEP=> Can one parallelize complex exploration– exploitation tradeoffs? As an example, consider the problem of optimal highthroughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate function mapping stimulus to response and identify the maximum of the function. We formalize the task as a multiarmed bandit problem, where the unknown payoff function is sampled from a Gaussian process (GP), and instead of a single arm, in each round we pull a batch of several arms in parallel. We develop GP-BUCB, a principled algorithm for choosing batches, based on the GP-UCB algorithm for sequential GP optimization. We prove a surprising result; as compared to the sequential approach, the cumulative regret of the parallel algorithm only increases by a constant factor independent of the batch size B. Our results provide rigorous theoretical support for exploiting parallelism in Bayesian global optimization. We demonstrate the effectiveness of our approach on two real-world applications.

Using Computer Programming to Enhance Science Learning for 5th Graders in Taipei <=SEP=> The purpose of this study is to evaluate the effectiveness of using Scratch Programming in science learning for 5th graders in Taipei. A quasi-experimental design with a single group was used in this study. 96 fifth graders at an elementary school in Taipei participated in 15 weeks of Scratch Programming activities to enrich computer learning during their science learning. Three research instruments were used in this study, including a logical thinking test, a problem solving test, and a learning questionnaire. The results were subjected to t-test and frequency analysis. The findings of this study include (1) the outcomes of the logical thinking test and the problem solving test indicate that students had better performance in logical thinking and problem solving, (2) feedback from the study questionnaire indicates that more than 71.4% of students favored using Scratch Programming in their computer learning, and more than 58.4% of students favored designing Scratch Programming in their science learning, (3) more than 54.7% of students reported that they are willing to do more similar computer programming projects for other science units in the future. In view of the above findings, this study concludes that Scratch Programming is effective for 5th graders in Taipei.

New E-Commerce User Interest Patterns <=SEP=> The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.

S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES <=SEP=> Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “ ||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

A requirements monitoring framework for enterprise systems <=SEP=> Requirements compliant software is becoming a necessity. Fewer and fewer organizations will run their critical transactions on software that has no visible relationship to its requirements. Businesses wish to see their software being consistent with their policies. Moreover, partnership agreements are pressuring less mature organizations to improve their systems. Businesses that rely on web services, for example, are vulnerable to the problems of their web service providers. While electronic commerce has increased the speed of on-line transactions, the technology for monitoring requirements compliance—especially for transactions—has lagged behind. To address the requirements monitoring problem for enterprise information systems, we integrate techniques for requirements analysis and software execution monitoring. Our framework assists analysts in the development of requirements monitors for enterprise services. The deployed system raises alerts when services succeed or fail to satisfy their specified requirements, thereby making software requirements visible. The framework usage is demonstrated with an analysis of ebXML marketplace specifications. An analyst applies goal analysis to discover potential service obstacles, and then derives requirements monitors and a distributed monitoring system. Once deployed, the monitoring system provides alerts when obstacles occur. A summary of the framework implementation is presented, along with analysis of two monitor component implementations. We conclude that the approach implemented in the framework, called ReqMon, provides real-time feedback on requirements satisfaction, and thereby provides visibility into requirements compliance of enterprise information systems.

Differential Dataflow <=SEP=> Existing computational models for processing continuously changing input data are unable to efficiently support iterative queries except in limited special cases. This makes it difficult to perform complex tasks, such as social-graph analysis on changing data at interactive timescales, which would greatly benefit those analyzing the behavior of services like Twitter. In this paper we introduce a new model called differential computation, which extends traditional incremental computation to allow arbitrarily nested iteration, and explain—with reference to a publicly available prototype system called Naiad—how differential computation can be efficiently implemented in the context of a declarative dataparallel dataflow language. The resulting system makes it easy to program previously intractable algorithms such as incrementally updated strongly connected components, and integrate them with data transformation operations to obtain practically relevant insights from real data streams.

ioCane: A Smart-Phone and Sensor-Augmented Mobility Aid for the Blind <=SEP=> We present the design, implementation, and early results of ioCane, a mobility aid for blind cane users that uses detachable cane-mounted ultrasonic sensors connected to a circuit board to send contextual data wirelessly to an Android phone application. The system uses the built-in mobile phone modalities of vibrations and chimes to alert the user to object height and proximity. We believe this plug-and-play solution for visually impaired users has the potential to enhance user mobility and object avoidance with a minimal learning curve. A pilot study testing the performance of the ioCane with blind cane users showed a 47.3% improvement in obstacle avoidance. To our knowledge, the ioCane is the first sensor-based mobility assistance system to integrate natively with a mobile phone without any modifications to the phone or the system, as well as the first system of its kind to be evaluated by actual visually-impaired cane users. ACM Classification

"Soft" Exoskeletons for Upper and Lower Body Rehabilitation - Design, Control and Testing <=SEP=> The basic concepts for exoskeletal systems have been suggested for some time with applications ranging from construction, manufacturing and mining to rescue and emergency services. In recent years, research has been driven by possible uses in medical/rehabilitation and military applications. Yet there are still significant barriers to the effective use and exploitation of this technology. Among the most pertinent of these factors is the power and actuation system and its impact of control, strength, speed and, perhaps most critically, safety. This work describes the design, construction and testing of an ultra low-mass, full-body exoskeleton system having seven degrees of freedom (DOFs) for the upper limbs and five degrees of freedom (DOFs) for each of the lower limbs. This low mass is primarily due to the use of a new range of pneumatic muscle actuators as the power source for the system. The work presented will show how the system takes advantage of the inherent controllable compliance to produce a unit that is powerful, providing a wide range of functionality (motion and forces over an extended range) in a manner that has high safety integrity for the user. The general layout of both the upper and the lower body exoskeleton is presented together with results from preliminary experiments to demonstrate the potential of the device in limb retraining, rehabilitation and power assist (augmentation) operations.

Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation <=SEP=> In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation. Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax. Absolute person center depth is estimated by a 1D heatmap prediction head. The coordinates are back-projected to 3D camera space, where we minimize the L1 loss. Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset. In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark when considering methods that use no extra pose datasets in training. Code for applying synthetic occlusions is availabe at https://github.com/ isarandi/synthetic-occlusion.

Text Document Categorization by Term Association <=SEP=> A goodtext classifieris a classifierthat efficiently categorizeslarge setsof text documentsin a reasonabletime frameand with an acceptableaccuracy, and that provides classificationrules that are humanreadablefor possible fine-tuning. If the training of the classifier is also quick, this couldbecomein someapplicationdomainsa goodasset for the classifier . Many techniquesand algorithmsfor automatictext categorizationhavebeendevised.According to publishedliterature, somearemoreaccuratethanothers, andsomeprovidemore interpretableclassificationmodels thanothers. However, nonecancombineall thebeneficial propertiesenumer atedabove. In this paper, we presenta novel approach for automatictext categorizationthat borrowsfrom market basket analysistechniquesusingassociation rule miningin thedata-miningfield. We focuson two major problems:(1) findingthebesttermassociationrules in a textual databaseby generating and pruning; and (2) using the rules to build a text classifier . Our text categorization methodprovesto beefficientandeffective, andexperimentsonwell-knowncollectionsshowthattheclassifier performswell. In addition,trainingaswell asclassification arebothfastandthegeneratedrulesarehumanreadable.

The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea <=SEP=> We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective. © 2013 Elsevier Ltd. All rights reserved.

DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging <=SEP=> Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec—two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.