title: Access Control Models

abstract: Access control, or authorization, is arguably the most fundamental and most pervasive security mechanism in use today in computer systems. In computer systems, to grant authorization is to determine whether a subject can access resources. Informally speaking it is to decide “who can do what.” Access control is critical to enforce confidentiality (only authorized users can read information) and integrity (only authorized users can alter information) in computer systems, preventing hackers and cyber-terrorists from reading and modifying sensitive files. Several access control models have been proposed since 1960 up today: from simple access matrix to task based access control through military models. Each one providing a different way to organize and express users’ privileges. For example, the role based access control model aggregate privileges thanks to the concept of role: all users receive permissions only through the roles to which they are assigned. We first introduce the purpose of access control, then we describe models in use today, their specificities and the mechanisms which they rely on. The end of the this chapter is dedicated to current issues on access control.


title: Evaluating the Usability of Optimizing Text-based CAPTCHA Generation

abstract: A CAPTCHA is a test that can, automatically, tell human and computer programs apart. It is a mechanism widely used nowadays for protecting web applications, interfaces, and services from malicious users and automated spammers. Usability and robustness are two fundamental aspects with CAPTCHA, where the usability aspect is the ease with which humans pass its challenges, while the robustness is the strength of its segmentation-resistance mechanism. The collapsing mechanism, which is removing the space between characters to prevent segmentation, has been shown to be reasonably resistant to known attacks. On the other hand, this mechanism drops considerably the human-solvability of text-based CAPTCHAs. Accordingly, an optimizer has previously been proposed that automatically enhances the usability of a CAPTCHA generation without sacrificing its robustness level. However, this optimizer has not yet been evaluated in terms of improving the usability. This paper, therefore, evaluates the usability of this optimizer by conducting an experimental study. The results of this evaluation showed that a statistically significant enhancement is found in the usability of text-based CAPTCHA generation.


title: Identifying and Improving Dataset References in Social Sciences Full Texts

abstract: Scientific full text papers are usually stored in separate places than their underlying research datasets. Authors typically make references to datasets by mentioning them for example by using their titles and the year of publication. However, in most cases explicit links that would provide readers with direct access to referenced datasets are missing. Manually detecting references to datasets in papers is time consuming and requires an expert in the domain of the paper. In order to make explicit all links to datasets in papers that have been published already, we suggest and evaluate a semi-automatic approach for finding references to datasets in social sciences papers. Our approach does not need a corpus of papers (no cold start problem) and it performs well on a small test corpus (gold standard). Our approach achieved an F-measure of 0.84 for identifying references in full texts and an F-measure of 0.83 for finding correct matches of detected references in the dara dataset registry.


Review on Text Mining Algorithms


title: A reference model for designing an e-commerce curriculum

abstract: The electronic commerce (e-commerce) revolution has arrived. E-commerce business-to-business revenue is projected to rise from $43 billion in 1998 to $1 trillion in 2003, a 23-fold increase. Consumer purchases online are expected to be worth $380 billion in 2003, up from an estimated $31.2 billion in 1999. The US Internet economy generated a revenue of $301 billion and was responsible for 1.2 million jobs in 1998. The Internet economy has already surpassed traditional sectors, such as energy and telecommunications, and is comparable with the automobile sector. The growth of e-commerce activities has been phenomenal and is expected to increase even further. As a way to structure the analysis of and discussions about e-commerce systems, I propose a reference model for e-commerce. I've used this model to develop a proposal for a graduate program in e-commerce that will help students meet the challenges of e-commerce.


title: Self-Regulation in Academic Writing Tasks

abstract: This study investigated writing beliefs, self-regulatory behaviors, and epistemology beliefs of preservice teachers in academic writing tasks. Students completed self-report measures of selfregulation, epistemology, and beliefs about writing. Both knowledge and regulation of cognition were positively related to writing enjoyment, and knowledge of cognition was negatively related to beliefs of ability as a fixed entity. Enjoyment of writing was related to learnability and selfassessment. It may be that students who are more self-regulated during writing also believe they can learn to improve their writing skills. It may be, however, that students who believe writing is learnable will exert the effort to self-regulate during writing. Student beliefs and feelings about learning and writing play an important and complex role in their self-regulation behaviors. Suggestions for instruction are included, and continued research of students’ beliefs and selfregulation in naturalistic contexts is recommended.


title: Unmanned Aircraft Capture and Control Via GPS Spoofing

abstract: The theory and practice of unmanned aerial vehicle UAV capture and control via Global Positioning System GPS signal spoofing are analyzed and demonstrated. The goal of this work is to explore UAV vulnerability to deceptive GPS signals. Specifically, this paper 1 establishes the necessary conditions for UAV capture via GPS spoofing, and 2 explores the spoofer's range of possible post-capture control over the UAV. A UAV is considered captured when a spoofer gains the ability to eventually specify the UAV's position and velocity estimates. During post-capture control, the spoofer manipulates the true state of the UAV, potentially resulting in the UAV flying far from its flight plan without raising alarms. Both overt and covert spoofing strategies are considered, as distinguished by the spoofer's attempts to evade detection by the target GPS receiver and by the target navigation system's state estimator, which is presumed to have access to non-GPS navigation sensor data. GPS receiver tracking loops are analyzed and tested to assess the spoofer's capability for covert capture of a mobile target. The coupled dynamics of a UAV and spoofer are analyzed and simulated to explore practical post-capture control scenarios. A field test demonstrates capture and rudimentary control of a rotorcraft UAV, which results in unrecoverable navigation errors that cause the UAV to crash.


title: Facial expression recognition using radial encoding of local Gabor features and classifier synthesis

abstract: Primarily motivated by some characteristics of the human visual cortex (HVC), we propose a new facial expression recognition scheme, involving a statistical synthesis of hierarchical classifiers. In this scheme, the input images of the database are first subjected to local, multi-scale Gabor-filter operations, and then the resulting Gabor decompositions are encoded using radial grids, imitating the topographical map-structure of the HVC. The codes are fed to local classifiers to produce global features, representing facial expressions. Experimental results show that such a hybrid combination of the HVC structure with a hierarchical classifier significantly improves expression recognition accuracy when applied to wide-ranging databases in comparison with the results in the literature. Furthermore, the proposed system is not only robust to corrupted data and missing information, but can also be generalized to cross-database expression recognition.


title: Credit card fraud detection system

abstract: The use of credit cards is prevalent in modern day society. But it is obvious that the number of credit card fraud cases is constantly increasing in spite of the chip cards worldwide integration and existing protection systems. This is why the problem of fraud detection is very important now. In this paper the general description of the developed fraud detection system and comparisons between models based on using of artificial intelligence are given. In the last section of this paper the results of evaluative testing and corresponding conclusions are considered.


title: 10 Watt High Efficiency GaAs MMIC Power Amplifier for Space Applications

abstract: This paper describes the design of a GaAs monolithic high power amplifier at Ku band. The chip delivers about 40 dBm of saturated output power, in CW operating conditions, at 11.7 GHz central frequency, with 17% of bandwidth. The saturated power gain is 12.4 dB with 2 dB gain flatness across the application bandwidth while the chip power added efficiency is estimated between 33% to 47%. The amplifier is designed to be used as final stage of a downlink satellite transmitter for Tracking Telemetry & Command system. A commercial power p-HEMT process capable of handling a power density higher than 1 W/mm has been selected for the MMIC design. Due to the space application, special attention must be put on the process and MMIC reliability: to this aim performances must be guaranteed in de-rated conditions respect to the process maximum ratings and, in addition, the channel temperature of the active devices must be kept within the value established by Space Requirements and carefully controlled. This makes the design objective very tight. The MMIC power amplifier design and some measurement results are presented in the paper.


title: Low cost RF MEMS switches using LCP substrate

abstract: In this paper, we present an RF MEMS switch developed on a low cost, flexible liquid crystal polymer (LCP) substrate. LCP's very low water absorption (0.04%), low dielectric loss and multi-layer circuit capability make it very appealing for RF systems-on-a-package (SOP). Here we present for the first time capacitive MEMS switch fabrication on an LCP substrate and its characterization and properties up to 40 GHZ.


title: Multi-Hop Ride Sharing

abstract: We study the problem of ride sharing in road networks. Current approaches to this problem focus on simple bulletin board like services or on algorithms that do not allow to transfer. In this work, we present a solution with an arbitrary number of transfers that respects personal preferences of the users. We engineer the ride sharing problem by searching a graph that represents a timetable network similar to those used for train networks. Our experimental analysis shows that our solution provides good performance and that it is significantly faster than a naive search. The algorithm achieves about an order of magnitude higher speedups over Dijkstra’s algorithm than what could be expected from previous work.


title: GraphTwist: Fast Iterative Graph Computation with Two-tier Optimizations

abstract: Large-scale real-world graphs are known to have highly skewed vertex degree distribution and highly skewed edge weight distribution. Existing vertex-centric iterative graph computation models suffer from a number of serious problems: (1) poor performance of parallel execution due to inherent workload imbalance at vertex level; (2) inefficient CPU resource utilization due to short execution time for low-degree vertices compared to the cost of in-memory or on-disk vertex access; and (3) incapability of pruning insignificant vertices or edges to improve the computational performance. In this paper, we address the above technical challenges by designing and implementing a scalable, efficient, and provably correct two-tier graph parallel processing system, GraphTwist. At storage and access tier, GraphTwist maximizes parallel efficiency by employing three graph parallel abstractions for partitioning a big graph by slice, strip or dice based partitioning techniques. At computation tier, GraphTwist presents two utility-aware pruning strategies: slice pruning and cut pruning, to further improve the computational performance while preserving the computational utility defined by graph applications. Theoretic analysis is provided to quantitatively prove that iterative graph computations powered by utility-aware pruning techniques can achieve a very good approximation with bounds on the introduced error.


title: Smart surveillance: applications, technologies and implications

abstract: Smart surveillance, is the use of automatic video analysis technologies in video surveillance applications. This paper attempts to answer a number of questions about smart surveillance: What are the applications of smart surveillance? What are the system architectures for smart surveillance? What are the key technologies? What are the some of the key technical challenges? and What are the implications of smart surveillance, both to security and privacy?.


title: Dependency-based convolutional neural network for drug-drug interaction extraction

abstract: Drug-drug interactions (DDIs) are crucial for healthcare. Besides DDIs reported in medical knowledge bases such as DrugBank, a large number of latest DDI findings are also reported in unstructured biomedical literature. Extracting DDIs from unstructured biomedical literature is a worthy addition to the existing knowledge bases. Currently, convolutional neural network (CNN) is a state-of-the-art method for DDI extraction. One limitation of CNN is that it neglects long distance dependencies between words in candidate DDI instances, which may be helpful for DDI extraction. In order to incorporate the long distance dependencies between words in candidate DDI instances, in this work, we propose a dependency-based convolutional neural network (DCNN) for DDI extraction. Experiments conducted on the DDIExtraction 2013 corpus show that DCNN using a public state-of-the-art dependency parser achieves an F-score of 70.19%, outperforming CNN by 0.44%. By analyzing errors of DCNN, we find that errors from dependency parsers are propagated into DCNN and affect the performance of DCNN. To reduce error propagation, we design a simple rule to combine CNN with DCNN, that is, using DCNN to extract DDIs in short sentences and CNN to extract DDIs in long distances as most dependency parsers work well for short sentences but bad for long sentences. Finally, our system that combines CNN and DCNN achieves an F-score of 70.81%, outperforming CNN by 1.06% and DNN by 0.62% on the DDIExtraction 2013 corpus.


title: Electromyographic Activity and Applied Load During Shoulder Rehabilitation Exercises Using Elastic Resistance

abstract: Muscle activity (measured by electromyography) and applied load were measured during seven shoulder rehabilitation exercises done with an elastic resistance device. Nineteen men with no shoulder abnormalities performed seven exercises: external and internal rotation, forward punch, shoulder shrug, and seated rowing with a narrow, middle, and wide grip. Qualitative video (60 Hz) was synchronized with the electromyography data from eight muscles (2000 Hz). Fine-wire intramuscular electrodes were inserted into the supraspinatus and subscapularis muscles, and surface electrodes were placed over the anterior deltoid, infraspinatus, pectoralis major, latissimus dorsi, serratus anterior, and trapezius muscles. Ten trials per subject were analyzed for average and peak amplitude, and the results were expressed as a percentage of maximum voluntary contractions. The peak loads for all exercises ranged from 21 to 54 N. The muscle activity patterns suggest that these shoulder rehabilitation exercises incorporating elastic resistance, controlled movements, and low initial loading effectively target the rotator cuff and supporting musculature and are appropriate for postinjury and postoperative patients.


title: Relationships among androgyny, self-esteem, and trait coping style of Chinese university students

abstract: Chinese university undergraduates’ personality type was investigated from the perspective of androgyny as the ideal gender role type. Based on Bem’s 4 gender role types (1974), 434 undergraduates were classified as masculine, feminine, androgynous, or undifferentiated. The relationships among personality type, trait coping style, and level of self-esteem were examined. Results showed that the students were mainly in two gender role types, androgynous and undifferentiated. Gender and grade had a significant impact on the distribution of gender role types. The androgynous group had the highest self-esteem level and tended to use positive coping strategies. Androgyny was assessed as an ideal gender role type, relative to the three other types.


title: DESIGN OF BROADBAND CIRCULAR POLARIZATION TRUNCATED HORN ANTENNA WITH SINGLE FEED

abstract: This paper presents the design of a broadband circular polarization truncated horn antenna with single feed. It does not require any complex feeding structure and uses only a coaxial feed extended with a simple electric fleld coupling probe. The corners of the horn are truncated to generate circular polarization modes, and a broad axial ratio bandwidth which is insensitive to the probe feed dimension is achieved. Simulated and measured results of an S band truncated horn antenna are presented. The antenna has a broad 3dB axial ratio bandwidth of 26% with aperture e-ciency of 60%.


title: Microstructure near corners of continuous-cast steel slabs showing three-dimensional frozen meniscus and hooks

abstract: Frozen meniscus features near the slab corners in continuous-cast ultra-low-carbon steel samples were investigated using special etching reagents and optical microscopy. The three-dimensional (3D) continuous shapes of hook defects along the oscillation marks around the slab corners were constructed from a set of micrographs taken at different vertical sections. The hook depth variation was traced around the slab perimeter. The maximum hook depth was observed at the corners and concave curvature was observed on a 45° vertical section from the corner. Horizontal cross-sections through the oscillation marks near the corners provide evidence that liquid steel overflow caused the oscillation mark. Shrinkage of the corner allows the overflowing steel to penetrate deeper into the larger corner gaps, giving rise to oscillation marks that point down in the casting direction at the corner. These results also explain the complex, 3D subsurface microstructures observed near the corners of the slab and quantify the hook depth and shape.


4–8 Subdivision


title: Common Elements Wideband MIMO Antenna System for WiFi/LTE Access-Point Applications

abstract: A wideband multiple-input-multiple-output (MIMO) antenna system with common elements suitable for WiFi/2.4 GHz and Long Term Evolution (LTE)/2.6 GHz wireless access point (WAP) applications is presented. The proposed MIMO antenna system consists of four wideband microstrip feedline printed monopole antennas with common radiating element and a ring-shaped ground plane. The radiator of the MIMO antenna system is designed as the shape of a modified rectangle with a four-stepped line at the corners to enhance the impedance bandwidth. According to the common elements structure of the MIMO antenna system, isolation between the antennas (ports) can be challenging. Therefore, the ground plane is modified by introducing four slots in each corner to reduce the mutual coupling. For an antenna efficiency of more than 60%, the measured impedance bandwidth for reflection coefficients below -10 dB was observed to be 1100 MHz from 1.8 to 2.9 GHz. Measured isolation is achieved greater than 15 dB by using a modified ground plane. Also, a low envelope correlation coefficient (ECC) less than 0.1 and polarization diversity gain of about 10 dB with the orthogonal mode of linear polarization and quasi-omnidirectional pattern during the analysis of radiation characteristic are achieved. Therefore, the proposed design is a good candidate for indoor WiFi and LTE WAP applications due to the obtained results.


title: On the Ontology of MMORPG Beings: A Theorethical Model for Research

abstract: In the Game Studies field, the study of MMORPG’s have been approached by four mayor research traditions: social aspects tradition, individual player issues, narratological themes and ludological themes. Although these are not mutually exclusive, focusing in one or two aspects of MMORPG’s usually means omitting elements of other themes. One fundamental theme that has been usually ommited is that of the essential characteristics, or ontology, of the online beings that inhabit MMORPGs. This paper focuses on this aspect, by providing a practical example of how, by applying a theoretical model that has at its vertex the Ontology of MMORPG Being, all the other themes and levels of analysis can be taken into account in one single theoretical framework.


title: A Wide Tuning Range 69 GHz Push-Push VCO Using 0.18 $\mu{\rm m}$ CMOS Technology

abstract: A wide tuning range V-band push-push CMOS voltage controlled oscillator (VCO) is proposed in this study. A new core complementary Colpitts structure was adopted in a 0.18 μm CMOS process to reduce dc power consumption and to improve tuning range owing to the reduction weighting of FET induced capacitance of L-C tank. The designed VCO oscillates from 64.2 to 69.4 GHz with a 5.2 GHz tuning range under a control voltage range of 1.2 V. The measured phase noise at 1 MHz offset is -76.23 dBc/Hz at 69.39 GHz. The power consumption of the VCO core is only 27.52 mW.


title: Design and optimization of artificial magnetic conductor for aperture coupled SatCom antenna

abstract: In this study, we propose a single antenna structure that is capable of transmit and receive operations for the entire Ku band, eliminating the use of separate antennas for communication. However, such broadband system demand careful ground plane design for optimum antenna characteristics. We propose an aperture coupled microstrip antenna (ACMA) using artificial magnetic conductor (AMC). In general, AMC structure can be used to reduce antenna profile, enhance gain, radiation efficiency and bandwidth of the antenna, if designed properly. Structure of the AMC and its parameter optimization is carried out under plane wave excitation, i.e., far field conditions, but the design is further optimized for antenna performance where near field effects are vital for antenna operation. Antenna performances with and without AMC loaded ground are compared to each other. Simulations are performed using CST Microwave Studio, 3D electromagnetic field solver. Simulation results show that ground with loaded AMC structure has better radiation efficiency, gain and wider impedance bandwidth than standalone PEC ground. Antenna has impedance bandwidth measured as VSWR < 2.5 : 1 range from 10.5–14.65 GHz (32.8% fractional bandwidth) covering entire receive and transmit bands at Ku band with an average 8.5 dBi broadside gain.


title: Language, Thought and Reference

abstract: How should we best analyse the meaning of proper names, indexicals, ::: demonstratives, both simple and complex, and definite descriptions? In what relation ::: do such expressions stand to the objects they designate? In what relation do they stand ::: to mental representations of those objects? Do these expressions form a semantic ::: class, or must we distinguish between those that arc referential and those that are ::: quantificational? Such questions have constituted one of the core research areas in the ::: philosophy of language for much of the last century, yet consensus remains elusive: ::: the field is still divided, for instance, between those who hold that all such expressions ::: are semantically descriptive and those who would analyse most as the natural ::: language counterparts of logical individual constants. ::: The aim of this thesis is to cast new light on such questions by approaching them from ::: within the cognitive framework of Sperber and Wilson's Relevance Theory. ::: Relevance Theory offers not just an articulated pragmatics but also a broad ::: conception of the functioning of natural language which differs radically from that ::: presupposed within (most of) the philosophy of language. The function of linguistic ::: expressions, on this conception, is not to determine propositional content, but rather to ::: provide inferential premises which, in parallel with context and general pragmatic ::: principles, will enable a bearer to reach the speaker's intended interpretation. ::: Working within this framework, I shall argue that the semantics of the expressions ::: discussed should best be analysed not in terms of their relation to those objects which, ::: on occasions of use, they may designate, but rather in terms of the indications they ::: offer a hearer concerning the mental representation which constitutes the content of a ::: speaker's informative intention. Such an analysis can, I shall claim, capture certain ::: key data on reference which have proved notoriously problematic, while respecting a ::: broad range of apparently conflicting intuitions.


title: Trust and Reputation Systems

abstract: There are currently very few practical methods for assessing the quality of resources or the reliability of other entities in the online environment. This makes it difficult to make decisions about which resources can be relied upon and which entities it is safe to interact with. Trust and reputation systems are aimed at solving this problem by enabling service consumers to reliably assess the quality of services and the reliability of entities before they decide to use a particular service or to interact with or depend on a given entity. Such systems should also allow serious service providers and online players to correctly represent the reliability of themselves and the quality of their services. In the case of reputation systems, the basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive its reputation score. In the case of trust systems, the basic idea is to analyse and combine paths and networks of trust relationships in order to derive measures of trustworthiness of specific nodes. Reputation scores and trust measures can assist other parties in deciding whether or not to transact with a given party in the future, and whether it is safe to depend on a given resource or entity. This represents an incentive for good behaviour and for offering reliable resources, which thereby tends to have a positive effect on the quality of online markets and communities. This chapter describes the background, current status and future trend of online trust and reputation systems.


title: Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

abstract: Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a neural network architecture which processes input sequences and questions, forms episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the inputs and the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN can be trained end-to-end and obtains state-of-the-art results on several types of tasks and datasets: question answering (Facebook's bAbI dataset), text classification for sentiment analysis (Stanford Sentiment Treebank) and sequence modeling for part-of-speech tagging (WSJ-PTB). The training for these different tasks relies exclusively on trained word vector representations and input-question-answer triplets.


title: Requirements Engineering process for Software-as-a-Service (SaaS) cloud environment

abstract: This paper deals with the important topic of Requirements Engineering in cloud computing, specifically on Software as a Services (SaaS) cloud model and presents a new methodology for Requirements Engineering of SaaS by identifying two key questions to handle Requirements in SaaS. The paper evolves by considering a CMMI modification by adding a new element in such a process, specifically devoted to SaaS and proposed a modification in the traditional Requirements Engineering process. The paper also identifies a set of requirement categories that must be considered in SaaS, which may not always be considered in the development process of traditional software.


title: Ensemble of Example-Dependent Cost-Sensitive Decision Trees

abstract: Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.


title: Visualising 3D porous media fluid interaction using X-ray CT data and Smooth Particles Hydrodynamics modelling

abstract: We propose the use of the Lagrangian method Smoothed particle Hydrodynamics (SPH) to model the behaviour of fluids through a pore structure of a volcanic soil core sample. Such studies are of importance to simulate preferential flows which are essential in the leeching of organic and chemical compounds in underground aquifers. Our approach combines X-ray Computed Tomography imaging, and image processing techniques to extract the 3D porous structure of the media studied. Smoothed particle Hydrodynamics modelling simulates water transport in the reconstructed 3D pore network using Navier Stokes governing laws. An isolated pore was considered as a case-based study. We are currently extending our simulation to a full sized pore network.


title: Events Can Make Sense

abstract: Tame is a new event-based system for managing concurrency in network applications. Code written with Tame abstractions does not suffer from the "stack-ripping" problem associated with other event libraries. Like threaded code, tamed code uses standard control flow, automatically-managed local variables, and modular interfaces between callers and callees. Tame's implementation consists of C++ libraries and a source-to-source translator; no platform-specific support or compiler modifications are required, and Tame induces little runtime overhead. Experience with Tame in real-world systems, including a popular commercial Web site, suggests it is easy to adopt and deploy.


title: Warehousing and Protecting Big Data: State-Of-The-Art-Analysis, Methodologies, Future Challenges

abstract: This paper proposes a comprehensive critical survey on the issues of warehousing and protecting big data, which are recognized as critical challenges of emerging big data research. Indeed, both are critical aspects to be considered in order to build truly, high-performance and highly-flexible big data management systems. We report on state-of-the-art approaches, methodologies and trends, and finally conclude by providing open problems and challenging research directions to be considered by future efforts.


title: SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries

abstract: How can we analyze very large real-world tensors where additional information is coupled with certain modes of tensors? Coupled matrix-tensor factorization is a useful tool to simultaneously analyze matrices and a tensor, and has been used for important applications including collaborative filtering, multi-way clustering, and link prediction. However, existing single machine or distributed algorithms for coupled matrix-tensor factorization do not scale for tensors with billions of elements in each mode. In this paper, we propose SCOUT, a large-scale coupled matrix-tensor factorization algorithm running on the distributed MAPREDUCE platform. By carefully reorganizing operations, and reusing intermediate data, SCOUT decomposes up to 100× larger tensors than existing methods, and shows linear scalability for order and machines while other methods are limited in scalability. We also apply SCOUT on real world tensors and discover interesting hidden patterns like seasonal spike, and steady attentions for healthy food on Yelp dataset containing user-business-yearmonth tensor and two coupled matrices.


title: Towards an spiking deep belief network for face recognition application

abstract: Understanding brain mechanisms and its problem solving techniques is the motivation of many emerging brain inspired computation methods. In this paper, respecting deep architecture of the brain and spiking model of biological neural networks, we propose a spiking deep belief network to evaluate ability of the deep spiking neural networks in face recognition application on ORL dataset. To overcome the change of using spiking neural networks in a deep learning algorithm, Siegert model is utilized as an abstract neuron model. Although there are state of the art classic machine learning algorithms for face detection, this work is mainly focused on demonstrating capabilities of brain inspired models in this era, which can be serious candidate for future hardware oriented deep learning implementations. Accordingly, the proposed model, because of using leaky integrate-and-fire neuron model, is compatible to be used in efficient neuromorphic platforms for accelerators and hardware implementation.


title: Detecting Faces in Images : A Survey

abstract: Images containing faces are essential to intelligent vision-based human-computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face, regardless of its 3D position, orientation and lighting conditions. Such a problem is challenging because faces are non-rigid and have a high degree of variability in size, shape, color and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.


title: Linking riparian vegetation spatial structure in Australian tropical savannas to ecosystem health indicators: semi-variogram analysis of high spatial resolution satellite imagery

abstract: Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone health. The objective of this work was to determine if the structural attributes of savanna riparian zones in northern Australia can be detected from commercially available remotely sensed image data. Two QuickBird images and coincident field data covering sections of the Daly River and the South Alligator River – Barramundie Creek in the Northern Territory were used. Semi-variograms were calculated to determine the characteristic spatial scales of riparian zone features, both vegetative and landform. Interpretation of semi-variograms showed that structural dimensions of riparian environments could be detected and estimated from the QuickBird image data. The results also show that selecting the correct spatial resolution and spectral bands is essential to maximize the accuracy of mapping spatial characteristics of savanna rip...


title: Querying Inconsistent Description Logic Knowledge Bases under Preferred Repair Semantics

abstract: Recently several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. Most of these semantics rely on the notion of a repair, defined as an inclusion-maximal subset of the facts (ABox) which is consistent with the ontology (TBox). In this paper, we study variants of two popular inconsistency-tolerant semantics obtained by replacing classical repairs by various types of preferred repair. We analyze the complexity of query answering under the resulting semantics, focusing on the lightweight logic DL-LiteR. Unsurprisingly, query answering is intractable in all cases, but we nonetheless identify one notion of preferred repair, based upon priority levels, whose data complexity is "only" coNP-complete. This leads us to propose an approach combining incomplete tractable methods with calls to a SAT solver. An experimental evaluation of the approach shows good scalability on realistic cases.


title: Systematic floor coverage of unknown environments using rectangular regions and localization certainty

abstract: We address the problem of systematically covering all accessible floor space in an unknown environment by a mobile robot. Our approach uses rectangular regions that are swept across the environment. In the first stage, the robot covers each region using the classic boustrophedon pattern and planning paths to uncovered areas within the region while keeping track of its position uncertainty. The region is then moved sideways to cover the next part of the environment until all accessible space has been visited. In the second stage, the robot revisits the perimeter around the obstacles. We compare our method in terms of total trajectory length to 5 off-line methods including the distance transformation by Zelinsky et al. [1] in a standard test environment as well as in multi-room homes. The presented method has been employed in our Mint cleaning robot [2] for autonomously sweeping and mopping the floors.


title: Non-Myopic Learning in Repeated Stochastic Games

abstract: In repeated stochastic games (RSGs), an agent must quickly adapt to the behavior of previously unknown associates, who may themselves be learning. This machine-learning problem is particularly challenging due, in part, to the presence of multiple (even infinite) equilibria and inherently large strategy spaces. In this paper, we introduce a method to reduce the strategy space of two-player general-sum RSGs to a handful of expert strategies. This process, called Mega, effectually reduces an RSG to a bandit problem. We show that the resulting strategy space preserves several important properties of the original RSG, thus enabling a learner to produce robust strategies within a reasonably small number of interactions. To better establish strengths and weaknesses of this approach, we empirically evaluate the resulting learning system against other algorithms in three different RSGs.


title: An Empirical Evaluation of Visuo-Haptic Feedback on Physical Reaching Behaviors During 3D Interaction in Real and Immersive Virtual Environments

abstract: In an initial study, we characterized the properties of human reach motion in the presence or absence of visuo-haptic feedback in real and Immersive Virtual Environments (IVEs) or virtual reality within a participant’s maximum arm reach. Our goal is to understand how physical reaching actions to the perceived location of targets in the presence or absence of visuo-haptic feedback are different between real and virtual viewing conditions. Typically, participants reach to the perceived location of objects in the three-dimensional (3D) environment to perform selection and manipulation actions during 3D interaction in applications such as virtual assembly or rehabilitation. In these tasks, participants typically have distorted perceptual information in the IVE as compared to the real world, in part due to technological limitations such as minimal visual field of view, resolution, latency, and jitter. In an empirical evaluation, we asked the following questions: (i) how do the perceptual differences between virtual and real world affect our ability to accurately reach to the locations of 3D objects, and (ii) how do the motor responses of participants differ between the presence or absence of visual and haptic feedback? We examined factors such as velocity and distance of physical reaching behavior between the real world and IVE, both in the presence or absence of visuo-haptic information. The results suggest that physical reach responses vary systematically between real and virtual environments, especially in situations involving the presence or absence of visuo-haptic feedback. The implications of our study provide a methodological framework for the analysis of reaching motions for selection and manipulation with novel 3D interaction metaphors and to successfully characterize visuo-haptic versus non-visuo-haptic physical reaches in virtual and real-world situations.


title: From Node Embedding To Community Embedding

abstract: Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space. Despite the success of embedding individual nodes for graph analytics, we notice that an important concept of embedding communities (i.e., groups of nodes) is missing. Embedding communities is useful, not only for supporting various community-level applications, but also to help preserve community structure in graph embedding. In fact, we see community embedding as providing a higher-order proximity to define the node closeness, whereas most of the popular graph embedding methods focus on first-order and/or second-order proximities. To learn the community embedding, we hinge upon the insight that community embedding and node embedding reinforce with each other. As a result, we propose ComEmbed, the first community embedding method, which jointly optimizes the community embedding and node embedding together. We evaluate ComEmbed on real-world data sets. We show it outperforms the state-of-the-art baselines in both tasks of node classification and community prediction.


title: A large annotated corpus for learning natural language inference

abstract: Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.


title: Multi-Objective Optimization of 50 kW/85 kHz IPT System for Public Transport

abstract: Inductive power transfer (IPT) is an attractive solution for the automated battery charging of public transport electric vehicles (EVs) with low maintenance requirements. This paper presents the design of a 50 kW/85 kHz contactless EV charger, with a focus on the IPT transmitter and receiver coils. The multi-objective magnetics optimization reveals the Pareto tradeoffs and performance limitations in terms of high efficiency, high power density, and low stray field for high-power IPT systems without moving mechanical parts. A full-scale hardware prototype is realized and experimentally investigated. The dc-dc conversion efficiency, including all the power electronics stages, is measured as 95.8% at 50 kW power transfer across an air gap of 160 mm (coil dimensions $410\times 760\times 60$ mm3). With 150 mm coil misalignment in the worst case direction, the dc-dc efficiency drops to 92%. The measurements of the magnetic stray field show compliance with ICNIRP 2010 at 800 mm distance from the IPT coil center.


title: Anonymizing transaction databases for publication

abstract: This paper considers the problem of publishing "transaction data" for research purposes. Each transaction is an arbitrary set of items chosen from a large universe. Detailed transaction data provides an electronic image of one's life. This has two implications. One, transaction data are excellent candidates for data mining research. Two, use of transaction data would raise serious concerns over individual privacy. Therefore, before transaction data is released for data mining, it must be made anonymous so that data subjects cannot be re-identified. The challenge is that transaction data has no structure and can be extremely high dimensional. Traditional anonymization methods lose too much information on such data. To date, there has been no satisfactory privacy notion and solution proposed for anonymizing transaction data. This paper proposes one way to address this issue.


Employing U.S. Military Families to Provide Business Process Outsourcing Services: A case study of Impact Sourcing and Reshoring


title: Tracking People in Highly Dynamic Industrial Environments

abstract: To date, the majority of positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper, we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker’s mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e., new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.


title: Time-Agnostic Prediction: Predicting Predictable Video Frames

abstract: Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks---while we cannot predict the precise trajectory of a robot arm between being at rest and holding an object up, we can be certain that it must have picked the object up. To exploit this, we decouple visual prediction from a rigid notion of time. While conventional approaches predict frames at regularly spaced temporal intervals, our time-agnostic predictors (TAP) are not tied to specific times so that they may instead discover predictable "bottleneck" frames no matter when they occur. We evaluate our approach for future and intermediate frame prediction across three robotic manipulation tasks. Our predictions are not only of higher visual quality, but also correspond to coherent semantic subgoals in temporally extended tasks.


title: Cloud computing adoption by SMEs in the north east of England: A multi-perspective framework

abstract: Purpose – The purpose of this paper is to contribute to a growing body of research on cloud computing, by studying the small to medium‐sized enterprise (SME) adoption process. If SMEs have access to scalable technologies they could potentially deliver products and services that in the past only large enterprises could deliver, flattening the competitive arena.Design/methodology/approach – By adopting the Technological, Organisational and Environmental (TOE) framework as a theoretical base, this qualitative exploratory study used semi‐structured interviews to collect data in 15 different SMEs and service providers in the north east of England. The north east of England was selected as it is a region that aspires to become home to innovative digital firms and most of the companies in the region are SMEs.Findings – The main factors that were identified as playing a significant role in SME adoption of cloud services were: relative advantage, uncertainty, geo‐restriction, compatibility, trialability, size, top...


title: Giraphx: Parallel Yet Serializable Large-Scale Graph Processing

abstract: Bulk Synchronous Parallelism (BSP) provides a good model for parallel processing of many large-scale graph applications, however it is unsuitable/inefficient for graph applications that require coordination, such as graph-coloring, subcoloring, and clustering. To address this problem, we present an efficient modification to the BSP model to implement serializability (sequential consistency) without reducing the highly-parallel nature of BSP. Our modification bypasses the message queues in BSP and reads directly from the worker's memory for the internal vertex executions. To ensure serializability, coordination is performed--implemented via dining philosophers or token ring-- only for border vertices partitioned across workers. We implement our modifications to BSP on Giraph, an open-source clone of Google's Pregel. We show through a graph-coloring application that our modified framework, Giraphx, provides much better performance than implementing the application using dining-philosophers over Giraph. In fact, Giraphx outperforms Giraph even for embarrassingly parallel applications that do not require coordination, e.g., PageRank.


title: Challenges of Using Edge Devices in IoT Computation Grids

abstract: Internet of Things (IoT) has the potential to become a technology revolution with a vision of creating very large scale network, comprising of unprecedented number of connected devices. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, embedded computers. There has been sustained research work and standardization effort from different IoT perspectives like integration of sensor and RFID devices to the Internet. With the increasing trend of gathering business insights from unstructured data, the high volume of data generated by such devices is also of interest. Cloud based data mining platforms are suitable for analyses of such data and researchers have proposed architectures where personal mobile phones can act as Edge Gateway between the sensor network and cloud analytics platform. It seems that the surge in the volume of data generated by huge number of Smart Items can only be matched if a large percentage of mobile users start sharing the computation capability of their personal devices and work together towards true Participatory Computing in the IoT systems. In this work we try to understand the challenges associated with running computation jobs on the mobile devices using different types of workload often observed in IoT applications. Based on the insights gained from experiments performed by us, we propose a scheme where mobile phones, residential gateways and other edge devices offer free slots to servers in a cloud based data analytics system. Based on the free time slots offered by the mobile phones, if commensurately sized computational jobs can be scheduled, the unpredictability associated with using mobile phones as grid resources can be solved.


Blood Glucose Monitoring Using Non Invasive Optical Method : Design Limitations and Challenges


title: SOLAR MAXIMUM POWER POINT TRACKING SYSTEM AND ITS APPLICATION TO GREENHOUSE

abstract: 3 ABSTRACT: - In this paper a low voltage, low cost & high efficiency based solar maximum power point tracking system for green house applications is presented. The main controlling element is the micro controller programme in c language in order to extract maximum solar power point. In addition we use buck converter for MPPT. Further this maximum power is utilized to drive pumps and fans of green house.


title: Softwarization and virtualization in 5G mobile networks: Benefits, trends and challenges

abstract: Abstract The promise behind the effective deployment of 5G networks is an architecture able to provide flexibility, reconfigurability and programmability in order to support, with fine granularity, a wide and heterogeneous set of 5G use cases. This dictates a radical change in the design of mobile systems which, being usually based on the use of static deployment of vendor equipment characterized by monolithic functionality deployed at specific network locations, fail in providing the above mentioned features. By decoupling network functionalities from the underlying hardware, softwarization and virtualization are two disruptive paradigms considered to be at the basis of the design process of 5G networks. This paper analyses and summarizes the role of these two paradigms in enhancing the network architecture and functionalities of mobile systems. With this aim, we analyze several 5G application scenarios in order to derive and classify the requirements to be taken into account in the design process of 5G network. We provide an overview on the recent advances by standardization bodies in considering the role of softwarization and virtualization in the next-to-come mobile systems. We also survey the proposals in literature by underlining the recent proposals exploiting softwarization and virtualization for the network design and functionality implementation of 5G networks. Finally, we conclude the paper by suggesting a set of research challenges to be investigated.


title: Factors influencing intention of mobile application use

abstract: The current study examined how task performance enhancement, easiness, important others' opinions, entertainment motivation, information seeking motivation, and social connection motivation would predict use intention of mobile applications. Based on a motivational communication theory and the technology acceptance model, a web-based survey was administered among mobile application users. The results of hypothesised model testing show that easiness is the key factor influencing continuance intention of mobile application use. In addition, this study reveals that mobile application users consider human connection and social utility to be more important than entertainment in creating task performance, easiness, social influence, and use intention. This study combined motivations with technology adoption factors for mobile application use in an integrated model. This theoretical attempt strengthened the explanatory power of the technology adoption model for mobile application use. Mobile application vendors and developers must consider the user friendliness of their applications and how many purposes they satisfy.


title: Signs of REM sleep dependent enhancement of implicit face memory: a repetition priming study

abstract: Faces are processed and stored in distinct neuroanatomical systems. Based on evidence of a critical role of sleep in memory processes, we investigated the impact of nocturnal sleep on implicit memories for faces in healthy men. Face repetition effects in reaction times were compared across sleep periods early in the night, which are dominated by slow wave sleep (SWS), and late in the night, where rapid eye movement (REM) sleep prevails, as well as across corresponding nocturnal intervals of wakefulness. An inverse priming effect was found selectively across REM sleep rich late sleep, as indicated by distinctly prolonged response latencies to previously presented faces compared with novel faces after this period of sleep (P<0.05). We assumed this inverse priming to reflect a facilitated identification of previously presented faces after extended REM sleep periods, thereby producing interference with the response generation in our task which did not require face identification but rather required recognizing formal features of the faces. This interpretation was supported by a supplementary experiment where enhanced positive repetition priming was found across late, REM sleep dominated sleep in a task requiring face identification. Together, these findings indicate that implicit face memories particularly benefit from REM sleep associated brain mechanisms.


title: Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges

abstract: In recent years, deep neural networks (DNNs) have received increased attention, have been applied to different applications, and achieved dramatic accuracy improvements in many tasks. These works rely on deep networks with millions or even billions of parameters, and the availability of graphics processing units (GPUs) with very high computation capability plays a key role in their success. For example, Krizhevsky et al. [1] achieved breakthrough results in the 2012 ImageNet Challenge using a network containing 60 million parameters with five convolutional layers and three fully connected layers. Usually, it takes two to three days to train the whole model on the ImagetNet data set with an NVIDIA K40 machine. In another example, the top face-verification results from the Labeled Faces in the Wild (LFW) data set were obtained with networks containing hundreds of millions of parameters, using a mix of convolutional, locally connected, and fully connected layers [2], [3]. It is also very time-consuming to train such a model to obtain a reasonable performance. In architectures that only rely on fully connected layers, the number of parameters can grow to billions [4].


title: Transfer of Training between Music and Speech: Common Processing, Attention, and Memory

abstract: After a brief historical perspective of the relationship between language and music, we review our work on transfer of training from music to speech that aimed at testing the general hypothesis that musicians should be more sensitive than nonmusicians to speech sounds. In light of recent results in the literature, we argue that when long-term experience in one domain influences acoustic processing in the other domain, results can be interpreted as common acoustic processing. But when long-term experience in one domain influences the building-up of abstract and specific percepts in another domain, results are taken as evidence for transfer of training effects. Moreover, we also discuss the influence of attention and working memory on transfer effects and we highlight the usefulness of the Event-Related Potentials method to disentangle the different processes that unfold in the course of music and speech perception. Finally, we give an overview of an on-going longitudinal project with children aimed at testing transfer effects from music to different levels and aspects of speech processing.


title: Hashtag-centric Immersive Search on Social Media

abstract: Social media information distributes in different Online Social Networks (OSNs). This paper addresses the problem integrating the cross-OSN information to facilitate an immersive social media search experience. We exploit hashtag, which is widely used to annotate and organize multi-modal items in different OSNs, as the bridge for information aggregation and organization. A three-stage solution framework is proposed for hashtag representation, clustering and demonstration. Given an event query, the related items from three OSNs, Twitter, Flickr and YouTube, are organized in cluster-hashtag-item hierarchy for display. The effectiveness of the proposed solution is validated by qualitative and quantitative experiments on hundreds of trending event queries.


title: A model of neuronal responses in visual area MT

abstract: Electrophysiological studies indicate that neurons in the middle temporal (MT) area of the primate brain are selective for the velocity of visual stimuli. This paper describes a computational model of MT physiology, in which local image velocities are represented via the distribution of MT neuronal responses. The computation is performed in two stages, corresponding to neurons in cortical areas V1 and MT. Each stage computes a weighted linear sum of inputs, followed by rectification and divisive normalization. V1 receptive field weights are designed for orientation and direction selectivity. MT receptive field weights are designed for velocity (both speed and direction) selectivity. The paper includes computational simulations accounting for a wide range of physiological data, and describes experiments that could be used to further test and refine the model.


title: High-performance determinism with total store order consistency

abstract: We present Consequence, a deterministic multi-threading library. Consequence achieves deterministic execution via store buffering and strict ordering of synchronization operations. To ensure high performance under a wide variety of conditions, the ordering of synch operations is based on a deterministic clock [25], and store buffering is implemented using version-controlled memory [23]. Recent work on deterministic concurrency [14, 19] has proposed relaxing the consistency model beyond total store order (TSO). Through novel optimizations, Consequence achieves the same or better performance on the Phoenix, PARSEC and SPLASH-2 benchmark suites, while retaining TSO memory consistency. Across 19 benchmark programs, Consequence incurs a worst-case slowdown of 3.9× vs. pthreads, with 14 out of 19 programs at or below 2.5×. We believe this performance improvement takes parallel programming one step closer to "determinism by default".


title: Social support and the likelihood of maintaining and improving levels of physical activity: the Whitehall II Study

abstract: Background: Evidence on the association between social support and leisure time physical activity (LTPA) is scarce and mostly based on cross-sectional data with different types of social support collapsed into a single index. The aim of this study was to investigate whether social support from the closest person was associated with LTPA. Methods: Prospective cohort study of 5395 adults (mean age 55.7 years, 3864 men) participating in the British Whitehall II study. Confiding/emotional support and practical support were assessed at baseline in 1997–99 using the Close Persons Questionnaire. LTPA was assessed at baseline and follow-up in (2002–04). Baseline covariates included socio-demographics, self-rated health, long-standing illnesses, physical functioning and common mental disorders. Results: Among participants who reported recommended levels of LTPA at baseline, those who experienced high confiding/emotional support were more likely to report recommended levels of LTPA at follow-up [odds ratio (OR): 1.39, 95% confidence interval (CI): 1.12–1.70 in a model adjusted for baseline covariates]. Among those participants who did not meet the recommended target of LTPA at baseline, high confiding/emotional support was not associated with improvement in activity levels. High practical support was associated with both maintaining (OR: 1.34, 95% CI: 1.10–1.63) and improving (OR: 1.25, 95% CI: 1.02–1.53) LTPA levels. Conclusion: These findings suggest that emotional and practical support from the closest person may help the individual to maintain the recommended level of LTPA. Practical support also predicted a change towards a more active lifestyle.


title: Modeling churn using customer lifetime value

abstract: The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a product-centric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable.


title: An attraction-selection-attrition theory of online community size and resilience

abstract: Online discussion communities play an important role in the development of relationships and the transfer of knowledge within and across organizations. Their underlying technologies enhance these processes by providing infrastructures through which group-based communication can occur. Community administrators often make decisions about technologies with the goal of enhancing the user experience, but the impact of such decisions on how a community develops must also be considered. To shed light on this complex and under-researched phenomenon, we offer a model of key latent constructs influenced by technology choices and possible causal paths by which they have dynamic effects on communities. Two important community characteristics that can be impacted are community size (number of members) and community resilience (membership that is willing to remain involved with the community in spite of variability and change in the topics discussed). To model community development, we build on attraction-selection-attrition (ASA) theory, introducing two new concepts: participation costs (how much time and effort are required to engage with content provided in a community) and topic consistency cues (how strongly a community signals that topics that may appear in the future will be consistent with what it has hosted in the past). We use the proposed ASA theory of online communities (OCASA) to develop a simulation model of community size and resilience that affirms some conventional wisdom and also has novel and counterintuitive implications. Analysis of the model leads to testable new propositions about the causal paths by which technology choices affect the emergence of community size and community resilience, and associated implications for community sustainability.


title: New Learning Algorithm for High-Quality Velocity Measurement and Control When Using Low-Cost Optical Encoders

abstract: A new compensation method that can greatly reduce the slit errors (i.e., transition location errors) due to nonidealities in optical incremental encoders caused by manufacturing limitations in the code wheel, optical components, and analog circuitry is presented. An M/T-type constant sample-time digital tachometer (CSDT), which involves pulse-count and high-frequency timer measurement, effectively time-stamps the encoder transitions. Using CSDT-based data, encoder compensation techniques that improve velocity measurement accuracy are presented. These do not require precise knowledge of the shaft velocity, thereby eliminating the need for high-accuracy reference equipment. During the initial learning stage (possibly performed in situ ), slit errors are calculated through pseudoinverse-based solutions of simple approximate linear equations or an iterative method that requires very little memory storage. Subsequent operation of the motion system utilizes adjusted slit positions for more accurate velocity calculation. The performance improvement in velocity measurement is experimentally demonstrated using motor drive systems, each of which includes a field-programmable gate array (FPGA) and a digital signal processor (DSP). Results from open-loop velocity measurement and closed-loop servo control applications are given, with the latter highlighting the resultant reduction in high-frequency motor torque. Slit error reductions in the range of 60%-86% are obtained (typically approximately 80%) with a similar improvement in velocity measurement error.


title: A Comparison of Blocking Methods for Record Linkage

abstract: Record linkage seeks to merge databases and to remove duplicates when unique identifiers are not available. Most approaches use blocking techniques to reduce the computational complexity associated with record linkage. We review traditional blocking techniques, which typically partition the records according to a set of field attributes, and consider two variants of a method known as locality sensitive hashing, sometimes referred to as “private blocking.” We compare these approaches in terms of their recall, reduction ratio, and computational complexity. We evaluate these methods using different synthetic datafiles and conclude with a discussion of privacy-related issues.


title: Analysis of Phase-Locked Loop Low-Frequency Stability in Three-Phase Grid-Connected Power Converters Considering Impedance Interactions

abstract: Synchronous reference frame (SRF) phase-locked loop (PLL) is a critical component for the control and grid synchronization of three-phase grid-connected power converters. The PLL behaviors, especially its low-frequency dynamics, influenced by different grid and load impedances as well as operation mode have not been investigated yet, which may not be captured by conventional linear PLL models. In this paper, we propose a state-feedback quasi-static SRF-PLL model, which can identify and quantify the inherent frequency self-synchronization mechanism in the converter control system. This self-synchronization effect is essentially due to the converter interactions with grid impedance and power flow directions. The low-frequency nonlinear behaviors of the PLL under different grid impedance conditions are then analyzed, which forms the framework of evaluating the impacts of the large penetration level of distributed generation units, weak grid, microgrid, and large reactive power consumption in terms of the frequency stability of PLL. Specifically, the PLL behavior of the converter system under islanded condition is investigated to explain the PLL instability issues and the related islanding-detection methods in early publications and industry reports.


title: Cryopreservation and its clinical applications

abstract: Abstract Cryopreservation is a process that preserves organelles, cells, tissues, or any other biological constructs by cooling the samples to very low temperatures. The responses of living cells to ice formation are of theoretical interest and practical relevance. Stem cells and other viable tissues, which have great potential for use in basic research as well as for many medical applications, cannot be stored with simple cooling or freezing for a long time because ice crystal formation, osmotic shock, and membrane damage during freezing and thawing will cause cell death. The successful cryopreservation of cells and tissues has been gradually increasing in recent years, with the use of cryoprotective agents and temperature control equipment. Continuous understanding of the physical and chemical properties that occur in the freezing and thawing cycle will be necessary for the successful cryopreservation of cells or tissues and their clinical applications. In this review, we briefly address representative cryopreservation processes, such as slow freezing and vitrification, and the available cryoprotective agents. In addition, some adverse effects of cryopreservation are mentioned.


title: A hybrid finite automaton for practical deep packet inspection

abstract: Deterministic finite automata (DFAs) are widely used to perform regular expression matching in linear time. Several techniques have been proposed to compress DFAs in order to reduce memory requirements. Unfortunately, many real-world IDS regular expressions include complex terms that result in an exponential increase in number of DFA states. Since all recent proposals use an initial DFA as a starting-point, they cannot be used as comprehensive regular expression representations in an IDS. In this work we propose a hybrid automaton which addresses this issue by combining the benefits of deterministic and non-deterministic finite automata. We test our proposal on Snort rule-sets and we validate it on real traffic traces. Finally, we address and analyze the worst case behavior of our scheme and compare it to traditional ones.


title: A framework for developing EFL reading vocabulary. Reading in a Foreign Language

abstract: Effective second language vocabulary acquisition is particularly important for English as a foreign language (EFL) learners who frequently acquire impoverished lexicons despite years of formal study. This paper comprehensively reviews and critiques second language (L2) reading vocabulary research and proposes that EFL teachers and administrators adopt a systematic framework in order to speed up lexical development. This framework incorporates two approaches: 1) promoting explicit lexical instruction and learning strategies; and 2) encouraging the use of implicit lexical instruction and learning strategies. The three most crucial explicit lexical instruction and learning strategies are acquiring decontextualized lexis, using dictionaries and inferring from context. Implicit lexical instruction and learning can take many forms including the use of integrated task sets and narrow reading; however, this framework emphasizes extensive reading, which is arguably the primary way that EFL learners can build their reading vocabulary to an advanced level. The principal notion underlying this framework is that the most effective and efficient lexical development will occur in multifaceted curriculums that achieve a pedagogically sound balance between explicit and implicit activities for L2 learners at all levels of their development. keywords: EFL reading, reading curriculum, vocabulary acquisition, explicit and implicit instruction


title: A Novel Compact Butler Matrix Without Phase Shifter

abstract: A novel compact 4 $\times$ 4 Butler matrix using only microstrip couplers and a crossover is proposed in this letter. Compared with the conventional Butler matrix, the proposed one avoids the interconnecting mismatch loss and imbalanced amplitude introduced by the phase shifter. The measurements show accurate phase differences of $45\pm 0.8^{\circ}$ and $-135\pm 0.9^{\circ}$ with an amplitude imbalance less than 0.4 dB. The 10 dB return loss bandwidth is 20.1%.


title: MobileMiner: mining your frequent patterns on your phone

abstract: Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.


title: Energy-efficient radio resource and power allocation for device-to-device communication underlaying cellular networks

abstract: Device-to-device (D2D) communication underlaying cellular networks brings significant benefits to resource utilization, improving user's throughput and extending battery life of user equipments. However, the allocation of radio resources and power to D2D communication needs elaborate coordination, as D2D communication causes interference to cellular networks. In this paper, we propose a novel joint radio resource and power allocation scheme to improve the performance of the system in the uplink period. Energy efficiency is considered as our optimization objective since devices are handheld equipments with limited battery life. We formulate the the allocation problem as a reverse iterative combinatorial auction game. In the auction, radio resources occupied by cellular users are considered as bidders competing for D2D packages and their corresponding transmit power. We propose an algorithm to solve the allocation problem as an auction game. We also perform numerical simulations to prove the efficacy of the proposed algorithm.


title: Real-time Distracted Driver Posture Classification

abstract: In this paper, we present a new dataset for "distracted driver" posture estimation. In addition, we propose a novel system that achieves 95.98% driving posture estimation classification accuracy. The system consists of a genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show that a weighted ensemble of classifiers using a genetic algorithm yields in better classification confidence. We also study the effect of different visual elements (i.e. hands and face) in distraction detection and classification by means of face and hand localizations. Finally, we present a thinned version of our ensemble that could achieve a 94.29% classification accuracy and operate in a realtime environment.


title: Dual-Domain Compressed Sensing Method for Oceanic Environmental Elements Collection with Underwater Sensor Networks

abstract: Bandwidth and energy constraints of underwater wireless sensors networks necessitate an efficient data transmission between sensor nodes and the fusion center. This paper considers the data gathering underwater networks for monitoring oceanic environmental elements (e.g. temperature, salinity) and only a portion of measurements from sensors allows for oceanic information map reconstruction under compressed sensing (CS) theory. By utilizing the spatial sparsity of active sensors’ data, we introduce an activity and data detection based on CS at the receiver side resulting in an efficient data communication by avoiding the necessity of conveying identity information. For an interleave division multiple access (IDMA) sporadic transmission, CS-CBC detection that combines the benefits from chip-by-chip (CBC) multi-user detection and CS detection is proposed. Further, by successively exploring the sparsity of sensor data in spatial and frequency domain, we propose a novel efficient data gathering scheme named Dual-domain compressed sensing (DCS). Simulation results validate the effectiveness of the proposed scheme compared to IDMA-CS scheme and an optimal sensing probability problem related to minimum reconstruction error is explored.


title: Models of Metaphor in NLP

abstract: Automatic processing of metaphor can be clearly divided into two subtasks: metaphor recognition (distinguishing between literal and metaphorical language in a text) and metaphor interpretation (identifying the intended literal meaning of a metaphorical expression). Both of them have been repeatedly addressed in NLP. This paper is the first comprehensive and systematic review of the existing computational models of metaphor, the issues of metaphor annotation in corpora and the available resources.


title: A novel fabrication and assembly method for synchronous reluctance machines

abstract: In this paper a comprehensive manufacturing and assembly process of Synchronous Reluctance Machines (SynRMs) is presented. One stator and two 7.5 hp transversally laminated anisotropic (TLA) SynRM rotors are manufactured. Different stages of manufacturing and assembly process as well as specialized components used at various stages of the process are described. Performance characteristics such as static torque-angle curves are obtained experimentally as well as analyzed in Finite Element Analysis (FEA) to verify the accuracy of design, manufacturing, and assembly.


title: Optimal control of nonlinear inverted pendulum dynamical system with disturbance input using PID controller & LQR

abstract: Optimal response of the controlled dynamical systems is desired hence for that is the optimal control. Linear quadratic regulator (LQR), an optimal control method, and PID control which are generally used for control of the linear dynamical systems have been used in this paper to control the nonlinear dynamical system. The inverted pendulum, a highly nonlinear unstable system is used as a benchmark for implementing the control methods. In this paper the modeling and control design of nonlinear inverted pendulum-cart dynamic system with disturbance input using PID control & LQR have been presented. The nonlinear system states are fed to LQR which is designed using linear state-space model. Here PID & LQR control methods have been implemented to control the cart position and stabilize the inverted pendulum in vertically upright position. The MATLAB-SIMULINK models have been developed for simulation of the control schemes. The simulation results justify the comparative advantages of LQR control methods.


title: Inertial navigation system aided by aircraft dynamics

abstract: In this work the possibility of using the model of aircraft dynamics as a means for aiding an inertial navigation system is studied. The method is of particular interest for low-grade inertial navigating system (INS). The aiding formulation is introduced, its corresponding mathematical model is derived and used in the design of an appropriate extended Kalman filter. Sensitivity analysis of the errors caused by perturbations in the parameters of the aircraft dynamics demonstrates the robustness of the scheme. The importance of maneuvers for observability is also presented. Simulations show that the use of aircraft dynamics for aiding a low-grade inertial navigation system yields a navigation system whose performance is considerably better than that of the pure INS. This improvement is made possible by the fact that the errors in the aircraft dynamics model are observable when performing appropriate calibration maneuvers.


The Impact of Content Delivery Networks on the Internet Ecosystem


title: Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection

abstract: Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall systems. The success of NIDS is highly dependent on the performance of the algorithms and improvement methods used to increase the classification accuracy and decrease the training and testing times of the algorithms. We propose an effective deep learning approach, self-taught learning (STL)-IDS, based on the STL framework. The proposed approach is used for feature learning and dimensionality reduction. It reduces training and testing time considerably and effectively improves the prediction accuracy of support vector machines (SVM) with regard to attacks. The proposed model is built using the sparse autoencoder mechanism, which is an effective learning algorithm for reconstructing a new feature representation in an unsupervised manner. After the pre-training stage, the new features are fed into the SVM algorithm to improve its detection capability for intrusion and classification accuracy. Moreover, the efficiency of the approach in binary and multiclass classification is studied and compared with that of shallow classification methods, such as J48, naive Bayesian, random forest, and SVM. Results show that our approach has accelerated SVM training and testing times and performed better than most of the previous approaches in terms of performance metrics in binary and multiclass classification. The proposed STL-IDS approach improves network intrusion detection and provides a new research method for intrusion detection.


title: Stability analysis and parasitic effects of negative impedance converter circuits

abstract: Stability is a major obstacle preventing practical applications of negative impedance converters (NIC) such as electrically small antenna matching. Return ratio method with a mesh or nodal analysis in frequency domain can provide an insight of the overall stability and performance of a NIC. This paper studies the stability of two types of NICs, based on tunneling diode and cross-coupled pair MOSFET. The influence of device capacitances, load impedance, and bias network are discussed.


title: Reduced-complexity entropy coding of transform coefficient levels using truncated golomb-rice codes in video compression

abstract: In hybrid video coding, the difference between the intra or inter prediction signal and the original signal is transmitted using block-based transform coding. The state-of-the-art in coding the quantized transform coefficients is the approach specified in H.264/AVC for context-adaptive binary arithmetic coding. It has, however, been shown that the number of binary symbols that have to be arithmetically coded for the transform coefficients can become very large, making the concept less attractive for high rate applications. To overcome this issue, we propose a combination of simple variable-length codes and context-adaptive binary coding, which yields the same coding efficiency as the H.264/AVC transform coefficient coding at a lower complexity level and which has been adopted into the HEVC test model (HM).


title: Promoting recommendations: An attack on collaborative filtering

abstract: The growth and popularity of Internet applications has reinforced the need for effective information filtering techniques. The collaborative filtering approach is now a popular choice and has been implemented in many on-line systems. While many researchers have proposed and compared the performance of various collaborative filtering algorithms, one important performance measure has been omitted from the research to date - that is the robustness of the algorithm. In essence, robustness measures the power of the algorithm to make good predictions in the presence of noisy data. In this paper, we argue that robustness is an important system characteristic, and that it must be considered from the point-of-view of potential attacks that could be made on a system by malicious users. We propose a definition for system robustness, and identify system characteristics that influence robustness. Several attack strategies are described in detail, and experimental results are presented for the scenarios outlined.


title: Open source robotic 3D mapping framework with ROS — Robot Operating System, PCL — Point Cloud Library and Cloud Compare

abstract: We propose an open source robotic 3D mapping framework based on Robot Operating System, Point Cloud Library and Cloud Compare software extended by functionality of importing and exporting datasets. The added value is an integrated solution for robotic 3D mapping and new publicly available datasets (accurate 3D maps with geodetic precision) for evaluation purpose Datasets were gathered by mobile robot in stop scan fashion. Presented results are a variety of tools for working with such datasets, for task such as: preprocessing (filtering, down sampling), data registration (ICP, NDT), graph optimization (ELCH, LUM), tools for validation (comparison of 3D maps and trajectories), performance evaluation (plots of various outputs of algorithms). The tools form a complete pipeline for 3D data processing. We use this framework as a reference methodology in recent work on SLAM algorithms.


title: A low-volume multi-phase interleaved Dc-Dc converter for high step-down applications with auto-balancing of phase currents

abstract: This paper introduces a multi-phase interleaved dc-dc converter, targeted for high-step down applications including voltage regulator modules (VRM) for microprocessors, where low volume and high efficiency are the key priorities. The introduced architecture, reducing the voltage swing at the switching node, along with doubling the effective switching frequency of the inductor current ripple, allows reducing the size of the inductors by up to 4 times, resulting in superior dynamic regulation, while maintaining high power processing efficiency of above 90%. Along with the interleaved 2-phase operation of the introduced converter, practical implementation details, including gate driver implementation, startup, and additional features such as automatic phase current balancing, are also addressed. Experimental verifications with a 12-to-1.2 V, 10 A, 250 kHz prototype show proper functionality of the introduced converter.


title: Seven ways that HetNets are a cellular paradigm shift

abstract: Imagine a world with more base stations than cell phones: this is where cellular technology is headed in 10-20 years. This mega-trend requires many fundamental differences in visualizing, modeling, analyzing, simulating, and designing cellular networks vs. the current textbook approach. In this article, the most important shifts are distilled down to seven key factors, with the implications described and new models and techniques proposed for some, while others are ripe areas for future exploration.


title: To catch a chorus: using chroma-based representations for audio thumbnailing

abstract: An important application for use with multimedia databases is a browsing aid, which allows a user to quickly and efficiently preview selections from either a database or from the results of a database query. Methods for facilitating browsing, though, are necessarily media dependent. We present one such method that produces short, representative samples (or "audio thumbnails") of selections of popular music. This method attempts to identify the chorus or refrain of a song by identifying repeated sections of the audio waveform. A reduced spectral representation of the selection based on a chroma transformation of the spectrum is used to find repeating patterns. This representation encodes harmonic relationships in a signal and thus is ideal for popular music, which is often characterized by prominent harmonic progressions. The method is evaluated over a sizable database of popular music and found to perform well, with most of the errors resulting from songs that do not meet our structural assumptions.


The Role of Information Quality in Healthcare Organizations: A Multi-Disciplinary Literature Review


title: Suppression by Selecting Wavelets for Feature Compression in Distributed Speech Recognition

abstract: Distributed speech recognition (DSR) splits the processing of data between a mobile device and a network server. In the front-end, features are extracted and compressed to transmit over a wireless channel to a back-end server, where the incoming stream is received and reconstructed for recognition tasks. In this paper, we propose a feature compression algorithm termed suppression by selecting wavelets (SSW) to achieve the two main goals of DSR: Minimizing memory and device requirements while also maintaining or even improving the recognition performance. The SSW approach first applies the discrete wavelet transform (DWT) to filter the incoming speech feature sequence into two temporal subsequences at the client terminal. Feature compression is achieved by keeping the low (modulation) frequency subsequence while discarding the high frequency counterpart. The low-frequency subsequence is then transmitted across the remote network for specific feature statistics normalization. Wavelets are favorable for resolving the temporal properties of the feature sequence, and the down-sampling process in DWT achieves data compression by reducing the amount of data at the terminal prior to transmission across the network. Once the compressed features have arrived at the server, the feature sequence can be enhanced by statistics normalization, reconstructed with inverse DWT, and compensated with a simple post filter to alleviate any over-smoothing effects from the compression stage. Results on a standard robustness task (Aurora-4) and on a Mandarin Chinese news corpus showed SSW outperforms conventional noise-robustness techniques while also providing nearly a 50% compression rate during the transmission stage of DSR systems.


title: Structural Neighborhood Based Classification of Nodes in a Network

abstract: Classification of entities based on the underlying network structure is an important problem. Networks encountered in practice are sparse and have many missing and noisy links. Statistical learning techniques have been used in intra-network classification; however, they typically exploit only the local neighborhood, so may not perform well. In this paper, we propose a novel structural neighborhood-based classifier learning using a random walk. For classifying a node, we take a random walk from the node and make a decision based on how nodes in the respective k^th-level neighborhood are labeled. We observe that random walks of short length are helpful in classification. Emphasizing role of longer random walks may cause the underlying Markov chain to converge to a stationary distribution. Considering this, we take a lazy random walk based approach with variable termination probability for each node, based on the node's structural properties including its degree. Our experimental study on real world datasets demonstrates the superiority of the proposed approach over the existing state-of-the-art approaches.


title: Temporal Rules Discovery for Web Data Cleaning

abstract: Declarative rules, such as functional dependencies, are widely used for cleaning data. Several systems take them as input for detecting errors and computing a "clean" version of the data. To support domain experts, in specifying these rules, several tools have been proposed to profile the data and mine rules. However, existing discovery techniques have traditionally ignored the time dimension. Recurrent events, such as persons reported in locations, have a duration in which they are valid, and this duration should be part of the rules or the cleaning process would simply fail. ::: ::: In this work, we study the rule discovery problem for temporal web data. Such a discovery process is challenging because of the nature of web data; extracted facts are (i) sparse over time, (ii) reported with delays, and (iii) often reported with errors over the values because of inaccurate sources or non robust extractors. We handle these challenges with a new discovery approach that is more robust to noise. Our solution uses machine learning methods, such as association measures and outlier detection, for the discovery of the rules, together with an aggressive repair of the data in the mining step itself. Our experimental evaluation over real-world data from Recorded Future, an intelligence company that monitors over 700K Web sources, shows that temporal rules improve the quality of the data with an increase of the average precision in the cleaning process from 0.37 to 0.84, and a 40% relative increase in the average F-measure.