title: BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic

abstract: Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observation that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system, BotSniffer, can capture this spatial-temporal correlation in network traffic and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated BotSniffer using many real-world network traces. The results show that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate.


title: Executive functions and self-regulation

abstract: Self-regulation is a core aspect of adaptive human behavior that has been studied, largely in parallel, through the lenses of social and personality psychology as well as cognitive psychology. Here, we argue for more communication between these disciplines and highlight recent research that speaks to their connection. We outline how basic facets of executive functioning (working memory operations, behavioral inhibition, and task-switching) may subserve successful self-regulation. We also argue that temporary reductions in executive functions underlie many of the situational risk factors identified in the social psychological research on self-regulation and review recent evidence that the training of executive functions holds significant potential for improving poor self-regulation in problem populations.


title: Subjective Cognitive Decline in Preclinical Alzheimer's Disease

abstract: Older adults with subjective cognitive decline (SCD) in the absence of objective neuropsychological dysfunction are increasingly viewed as at risk for non-normative cognitive decline and eventual progression to Alzheimer's disease (AD) dementia. The past decade has witnessed tremendous growth in research on SCD, which may reflect the recognition of SCD as the earliest symptomatic manifestation of AD. Yet methodological challenges associated with establishing common assessment and classification procedures hamper the construct. This article reviews essential features of SCD associated with preclinical AD and current measurement approaches, highlighting challenges in harmonizing study findings across settings. We consider the relation of SCD to important variables and outcomes (e.g., AD biomarkers, clinical progression). We also examine the role of self- and informant-reports in SCD and various psychological, medical, and demographic factors that influence the self-report of cognition. We conclude with a di...


title: Detecting human activities in retail surveillance using hierarchical finite state machine

abstract: Cashiers in retail stores usually exhibit certain repetitive and periodic activities when processing items. Detecting such activities plays a key role in most retail fraud detection systems. In this paper, we propose a highly efficient, effective and robust vision technique to detect checkout-related primitive activities, based on a hierarchical finite state machine (FSM). Our deterministic approach uses visual features and prior spatial constraints on the hand motion to capture particular motion patterns performed in primitive activities. We also apply our approach to the problem of retail fraud detection. Experimental results on a large set of video data captured from retail stores show that our approach, while much simpler and faster, achieves significantly better results than state-of-the-art machine learning-based techniques both in detecting checkout-related activities and in detecting checkout-related fraudulent incidents.


REDUCTION OF KETONES BY SODIUM BOROHYDRIDE IN THE ABSENCE OF PROTIC SOLVENTS. INTER- VERSUS INTRAMOLECULAR MECHANISM


Accurate Text-Enhanced Knowledge Graph Representation Learning


title: Behavioral differences among breeds of domestic dogs (Canis lupus familiaris): Current status of the science

abstract: Abstract In both popular media and scientific literature, it is commonly stated that breeds of dogs differ behaviorally in substantial, consistent and predictable ways. Since the mid-twentieth century, scientists have asked whether meaningful behavioral differences exist between breeds of dogs. Today, there are over 1000 identified dog breeds in the world, but to date, fewer than one-quarter of these are represented in studies investigating breed-specific behavioral differences. We review here scientific findings of breed differences in behavior from a wide range of methodologies with respect to both temperament traits and cognitive abilities to determine whether meaningful differences in behavior between breeds have been established. Although there is convincing scientific evidence for reliable differences between breeds and breed groups with respect to some behaviors (e.g., aggression, reactivity), the majority of studies that have measured breed differences in behavior have reported meaningful within-breed differences has well. These trends appear to be related to two main factors: the traits being assessed and the methodology used to assess those traits. In addition, where evidence for breed differences in behavior has been found, there is mixed consistency between empirical findings and the recognized breed standard. We discuss both the strengths and limitations of behavioral research in illuminating differences between dog breeds, highlight directions for future research, and suggest the integration of input from other disciplines to further contribute to our understanding of breed differences in behavior.


title: Lost in Space: Geolocation in Event Data

abstract: Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.


title: Evaluation of Remote Sensing Aerial Systems In Existing Transportation Practices

abstract: A low-cost aerial platform represents a flexible tool for acquiring high-resolution images for ground areas of interest. The geo-referencing of objects within these images could benefit civil engineers in a variety of research areas including, but not limited to, work zone management, traffic congestion, safety, and environmental impact studies. During the Phase II effort, a Remotely Controlled (R/C) aircraft based remote sensing platform was developed and flight tested at West Virginia University (WVU). Main components of the remote-sensing payload system include a high-resolution digital still camera, a 50 Hz global positioning system (GPS) receiver, a low-cost Inertial Navigation System (INS), a down-looking laser range finder, a custom-designed flight data recorder, and a wireless video transmission system. An extensive time-calibration and analysis effort for major measurement instruments was performed to assure that flight data were properly time-aligned. Additionally, an Unscented Kalman Filter (UKF) based 15-state GPS/INS sensor fusion algorithm was developed to estimate the aircraft attitude angles in flight. Based on the added range and orientation information for the camera, the geo-referencing software developed in Phase I effort was enhanced. The performance of the software was evaluated using a set of flight data and the known location of a fixed reference point on the ground. The flight data analysis shows an approximately 7.2 meter mean position estimation error was achieved with estimates from a single aerial image, after a set of lens distortion and the camera orientation corrections. Furthermore, a 0.5 meter position estimation error was achieved with the averaging of 15 individual estimates.


title: Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference

abstract: Recent years have witnessed a rapid adoption of mobile devices and a dramatic proliferation of mobile applications (Apps for brevity). However, the large number of mobile Apps makes it difficult for users to locate relevant Apps. Therefore, recommending Apps becomes an urgent task. Traditional recommendation approaches focus on learning the interest of a user and the functionality of an item (e.g., an App) from a set of user-item ratings, and they recommend an item to a user if the item's functionality well matches the user's interest. However, Apps could have privileges to access a user's sensitive resources ( e.g., contact, message, and location). As a result, a user chooses an App not only because of its functionality, but also because it respects the user's privacy preference. To the best of our knowledge, this paper presents the first systematic study on incorporating both interest-functionality interactions and users' privacy preferences to perform personalized App recommendations. Specifically, we first construct a new model to capture the trade-off between functionality and user privacy preference. Then we crawled a real-world dataset (16,344 users, 6,157 Apps, and 263,054 ratings) from Google Play and use it to comprehensively evaluate our model and previous methods. We find that our method consistently and substantially outperforms the state-of-the-art approaches, which implies the importance of user privacy preference on personalized App recommendations. Moreover, we explore the impact of different levels of privacy information on the performances of our method, which gives us insights on what resources are more likely to be treated as private by users and influence users' behaviors at selecting Apps.


title: Personalized query expansion for the web

abstract: The inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to improve such Web queries by expanding them with terms collected from each user's Personal Information Repository, thus implicitly personalizing the search output. We introduce five broad techniques for generating the additional query keywords by analyzing user data at increasing granularity levels, ranging from term and compound level analysis up to global co-occurrence statistics, as well as to using external thesauri. Our extensive empirical analysis under four different scenarios shows some of these approaches to perform very well, especially on ambiguous queries, producing a very strong increase in the quality of the output rankings. Subsequently, we move this personalized search framework one step further and propose to make the expansion process adaptive to various features of each query. A separate set of experiments indicates the adaptive algorithms to bring an additional statistically significant improvement over the best static expansion approach.


title: Automated Identification of Cryptographic Primitives in Binary Code with Data Flow Graph Isomorphism

abstract: Softwares use cryptographic algorithms to secure their communications and to protect their internal data. However the algorithm choice, its implementation design and the generation methods of its input parameters may have dramatic consequences on the security of the data it was initially supposed to protect. Therefore to assess the security of a binary program involving cryptography, analysts need to check that none of these points will cause a system vulnerability. It implies, as a first step, to precisely identify and locate the cryptographic code in the binary program. Since binary analysis is a difficult and cumbersome task, it is interesting to devise a method to automatically retrieve cryptographic primitives and their parameters. In this paper, we present a novel approach to automatically identify symmetric cryptographic algorithms and their parameters inside binary code. Our approach is static and based on DFG isomorphism. To cope with binary codes produced from different source codes and by different compilers and options, the DFG is normalized using code rewrite mechanisms. Our approach differs from previous works, that either use statistical criteria leading to imprecise results, or rely on heavy dynamic instrumentation. To validate our approach, we present experimental results on a set of synthetic samples including several cryptographic algorithms, binary code of well-known cryptographic libraries and reference source implementation compiled using different compilers and options.


title: Low-Cost Intelligent Carpet System for Footstep Detection

abstract: Recently, significant activities have been focused on the research of human identification and tracking systems in different environments. In this context, this paper describes a novel design and construction methodology for a low-cost intelligent carpet system for footstep detection. The carpet consists of activated carbon nonwoven material sandwiched between a pair of cross-aligned conductive wires to form in situ piezoresistive sensors. The assembly is externally covered with textiles to form a regular carpet. An electronic interface device and data treatment system is developed to monitor human footstep action-based piezoresistive changes observed within the carpet. The system is further practically validated for human footstep detection, localization, and counting. To demonstrate the potential application in human foot-step-based tracking, seven Gait features were extracted from piezoresistance change profile formed by human walking on the carpet and subsequently, a walking signature has been introduced. It is anticipated that such an intelligent carpet that retains its flexibility and textile aesthetics can be a good alternative for the conventional textile carpet with auxiliary functionality and added value.


title: State of the Art in Similarity Preserving Hashing Functions

abstract: 7 paginas, 1 tabla. Comunicacion presentada en: The 2014 International Conference on Security and Management (SAM’14), Worldcomp 2014, Las Vegas (USA), 21-24 July 2014.


title: Taking computer vision aloft – archaeological three‐dimensional reconstructions from aerial photographs with photoscan

abstract: Structure from motion (SFM) algorithms are known for their ability to reconstruct a sparse point cloud of scenes that were imaged by a series of overlapping photographs. When complemented by stereo-matching algorithms, detailed three-dimensional models can be built from such photograph collections in a fully automated way. Since 2010, a computer vision software package called PhotoScan has been available from the Russian manufacturer AgiSoft LLC. Even though the programme has a straightforward and simple interface, state-of-the-art SFM and other computer vision algorithms are implemented that enable PhotoScan to generate very dense and accurate three-dimensional meshes of various (archaeological) settings captured in two-dimensional imagery. Using a mixture of archaeological case studies, it will be shown that this low-cost application produces excellent results from aerial imagery in a minimum of time with almost no effort needed. Because of its solid and uncomplicated operation, it looks like this application could become an essential part of the aerial archaeologist's toolkit. Copyright © 2011 John Wiley & Sons, Ltd.


Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions


title: Artificial Intelligence and Consciousness

abstract: Consciousness is only marginally relevant to artificial intelligence (AI), because to most researchers in the field other problems seem more pressing. However, there have been proposals for how consciousness would be accounted for in a complete computational theory of the mind, from theorists such as Dennett, Hofstadter, McCarthy, McDermott, Minsky, Perlis, Sloman, and Smith. One can extract from these speculations a sketch of a theoretical synthesis, according to which consciousness is the property a system has by virtue of modeling itself as having sensations and making free decisions. Critics such as Harnad and Searle have not succeeded in demolishing a priori this or any other computational theory, but no such theory can be verified or refuted until and unless AI is successful in finding computational solutions of difficult problems such as vision, language, and locomotion.


title: Residual interpolation for color image demosaicking

abstract: A color difference interpolation technique is widely used for color image demosaicking. In this paper, we propose residual interpolation as an alternative to the color difference interpolation, where the residual is a difference between an observed and a tentatively estimated pixel value. We incorporate the proposed residual interpolation into the gradient based threshold free (GBTF) algorithm, which is one of current state-of-the-art demosaicking algorithms. Experimental results demonstrate that our proposed demosaicking algorithm using the residual interpolation can give state-of-the-art performance for the 30 images of Kodak and IMAX datasets.


title: Employing Program Semantics for Malware Detection

abstract: In recent years, malware has emerged as a critical security threat. In addition, malware authors continue to embed numerous anti-detection features to evade the existing malware detection approaches. Against this advanced class of malicious programs, dynamic behavior-based malware detection approaches outperform the traditional signature-based approaches by neutralizing the effects of obfuscation and morphing techniques.


title: Multi-Path TCP: A Joint Congestion Control and Routing Scheme to Exploit Path Diversity in the Internet

abstract: We consider the problem of congestion-aware multi-path routing in the Internet. Currently, Internet routing protocols select only a single path between a source and a destination. However, due to many policy routing decisions, single-path routing may limit the achievable throughput. In this paper, we envision a scenario where multi-path routing is enabled in the Internet to take advantage of path diversity. Using minimal congestion feedback signals from the routers, we present a class of algorithms that can be implemented at the sources to stably and optimally split the flow between each source-destination pair. We then show that the connection-level throughput region of such multi-path routing/congestion control algorithms can be larger than that of a single-path congestion control scheme.


title: MICROCONTROLLER BASED CAPACITANCE METER

abstract: A microcontroller based capacitance meter using 89C52 microcontroller for the measurement of capacitance has been design and developed. It is based on the principle of charging and discharging of the capacitor. Atmel's AT89C52 microcontroller is used in the present study. Further, an LCD module is interfaced with the microcontroller in 4-bit mode, which reduces the hardware complexity. Software is developed in C using Kiel's C-cross compiler. The instrument system covers a range 1pF to 1000 μF. The paper deals with the hardware and software details. This instrument has auto range selection and auto calibration facility i.e., auto reset facility. This instrument is provided with 400-value storage capacity. The system is quite successful in the measurement of capacitance with an accuracy of ±1 % and capacitance of whole construction is around 10 pF. The power consumption is less than 1W.


title: Service Quality and Perceived Value's Impact on Satisfaction, Intention and Usage of Short Message Service (SMS)

abstract: SMS, being an almost instantaneous communication medium that connects people, is now a phenomenon that has grown and spread around the globe at an amazing speed. Given the current trend of SMS usage and its potential growth, this paper will provide an insight of the extent to which how service quality and the value perceived by the SMS users have an impact on their extent of the SMS usage in the post SMS adoption phase. Specifically, this article will examine how service quality of the service providers and perceived value affect customer satisfaction and how customer satisfaction will affect their behavioural intention to continue to use SMS which in turn affects the extent of SMS usage in the local context. Using partial-least-squares, an analysis was conducted based on the 150 surveys collected to test for the proposed relationships. The results showed that the tangibles, empathy and assurance dimensions of service quality are antecedents of customer satisfaction and a positive relationship exists between customer satisfaction and customers' behavioural intentions to continue to use SMS. Additionally, the positive relationship between customers' behavioural intentions to continue to use SMS and the extent of SMS usage is also significant. These results were similar to the results shown by Cronin and Taylor (1992) studies. The perceived value/customer satisfaction relationship investigated in this research was in line with Fornell et al.(1996) and Cronin et al.(2000) where perceived value was one of the determinants of customer satisfaction. Specially, the results revealed that perceived value, together with tangibles, empathy and assurance aspects of the service quality, played an important role in determining customer satisfaction for SMS. Implications of the above results for research and practice are discussed.


title: Augmentation of obstacle sensation by enhancing low frequency component for horror game background sound

abstract: Computer games provide users with a mental stimulation that the real world cannot. Especially, horror games are a popular category. Current horror games can provide the user with a visible ghost and the stereo background sound to thrill the user. Inspired by obstacle sense - the ability of blind people localizing themselves only with hearing, a novel method to augment the sense of existence in the game background sound is proposed in this paper. We found that an effective sense can be created by decreasing high frequency component and increasing low frequency component simultaneously.


title: Accelerated Gradient Method for Multi-task Sparse Learning Problem

abstract: Many real world learning problems can be recast as multi-task learning problems which utilize correlations among different tasks to obtain better generalization performance than learning each task individually. The feature selection problem in multi-task setting has many applications in fields of computer vision, text classification and bio-informatics. Generally, it can be realized by solving a L-1-infinity regularized optimization problem. And the solution automatically yields the joint sparsity among different tasks. However, due to the nonsmooth nature of the L-1-infinity norm, there lacks an efficient training algorithm for solving such problem with general convex loss functions. In this paper, we propose an accelerated gradient method based on an ``optimal'' first order black-box method named after Nesterov and provide the convergence rate for smooth convex loss functions. For nonsmooth convex loss functions, such as hinge loss, our method still has fast convergence rate empirically. Moreover, by exploiting the structure of the L-1-infinity ball, we solve the black-box oracle in Nesterov's method by a simple sorting scheme. Our method is suitable for large-scale multi-task learning problem since it only utilizes the first order information and is very easy to implement. Experimental results show that our method significantly outperforms the most state-of-the-art methods in both convergence speed and learning accuracy.


DW MODELS Data Warehousing Battle of the Giants : Comparing the Basics of the Kimball and Inmon Models


title: Efficiently Implementing Episodic Memory

abstract: Endowing an intelligent agent with an episodic memory affords it a multitude of cognitive capabilities. However, providing efficient storage and retrieval in a task-independent episodic memory presents considerable theoretical and practical challenges. We characterize the computational issues bounding an episodic memory. We explore whether even with intractable asymptotic growth, it is possible to develop efficient algorithms and data structures for episodic memory systems that are practical for real-world tasks. We present and evaluate formal and empirical results using Soar-EpMem: a task-independent integration of episodic memory with Soar 9, providing a baseline for graph-based, task-independent episodic memory systems.


title: Learning tasks from a single demonstration

abstract: Learning a complex dynamic robot manoeuvre from a single human demonstration is difficult. This paper explores an approach to learning from demonstration based on learning an optimization criterion from the demonstration and a task model from repeated attempts to perform the task, and using the learned criterion and model to compute an appropriate robot movement. A preliminary version of the approach has been implemented on an anthropomorphic robot arm using a pendulum swing up task as an example.


title: Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition

abstract: This paper presents a new unsupervised learning approach with stacked autoencoder (SAE) for Arabic handwritten digits categorization. Recently, Arabic handwritten digits recognition has been an important area due to its applications in several fields. This work is focusing on the recognition part of handwritten Arabic digits recognition that face several challenges, including the unlimited variation in human handwriting and the large public databases. Arabic digits contains ten numbers that were descended from the Indian digits system. Stacked autoencoder (SAE) tested and trained the MADBase database (Arabic handwritten digits images) that contain 10000 testing images and 60000 training images. We show that the use of SAE leads to significant improvements across different machine-learning classification algorithms. SAE is giving an average accuracy of 98.5%.


title: Robust Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction

abstract: We study the problem of recovering an underlying 3D shape from a set of images. Existing learning based approaches usually resort to recurrent neural nets, e.g., GRU, or intuitive pooling operations, e.g., max/mean poolings, to fuse multiple deep features encoded from input images. However, GRU based approaches are unable to consistently estimate 3D shapes given different permutations of the same set of input images as the recurrent unit is permutation variant. It is also unlikely to refine the 3D shape given more images due to the long-term memory loss of GRU. Commonly used pooling approaches are limited to capturing partial information, e.g., max/mean values, ignoring other valuable features. In this paper, we present a new feed-forward neural module, named AttSets, together with a dedicated training algorithm, named FASet, to attentively aggregate an arbitrarily sized deep feature set for multi-view 3D reconstruction. The AttSets module is permutation invariant, computationally efficient and flexible to implement, while the FASet algorithm enables the AttSets based network to be remarkably robust and generalize to an arbitrary number of input images. We thoroughly evaluate FASet and the properties of AttSets on multiple large public datasets. Extensive experiments show that AttSets together with FASet algorithm significantly outperforms existing aggregation approaches.


title: Invited Editorial: A Natural-Resource-Based View of the Firm Fifteen Years After

abstract: The authors revisit Hart’s natural-resource-based view (NRBV) of the firm and summarize progress that has been made in testing elements of that theory and reevaluate the NRBV in light of a number of important developments that have emerged in recent years in both the resource-based view literature and in research on sustainable enterprise. First, the authors consider how the NRBV can both benefit from recent work in dynamic capabilities and can itself inform such work. Second, they review recent research in the areas of clean technology and business at the base of the pyramid and suggest how the NRBV can help inform research on the resources and capabilities needed to enter and succeed in these domains.


Assortment and Pricing with Demand Learning


title: RoboSantral: An autonomous mobile guide robot

abstract: RoboSantral, An autonomous mobile robot which has been designed and realized in order to guide the visitors through a university campus, is presented in this paper. This robot accompanies guests through the campus and gives presentations on predefined locations. Location data is obtained from GPS sensors. Targets such as faculty buildings, museums etc… are recognized by the image processing of pre-defined tags. As microprocessor and microcontroller, Raspberry Pi and Arduino are used respectively.


title: Extracting the informative constraints for semi-supervised marginal projections in multimodal dimensionality reduction

abstract: This paper discusses the semi-supervised marginal projection problems learning from partial constrained data. Two effective multimodal dimensionality reduction (DR) algorithms, which we call semi-supervised marginal projections (SSMP) and orthogonal SSMP (OSSMP), are proposed. By specifying the types of similarity pairs with the pairwise constraints (PC), our techniques can preserve the global structures of all points as well as local geometrical and discriminant structures embedded in the PC. SSMP in singular case is also discussed. Because in all the PC guided methods, extracting the informative constraints is difficult and random constraints greatly affect the learning performance of techniques, this work also presents an effective and efficient methodology of optimally selecting the informative constraints for learning. The analytic form of the marginal projections can be effectively obtained by eigen-decomposition. The connections between this present work and the related semi-supervised algorithms are also detailed. The effectiveness of our proposed informative constraint selection method and algorithms are evaluated by benchmark problems. Results show our methods are capable of delivering competitive results with some widely used state-of-the-art semi-supervised algorithms.


title: Restoring occluded regions using FW-PCA for face recognition

abstract: Occlusions in face images such as eyeglasses, hairs and whiskers decrease the performance of face recognition algorithms. Addressing this problem, this paper proposes a method for restoring occluded regions in face images. The proposed method employs Fast Weighted Principal Component Analysis (FW-PCA), which computes PCA only with effective pixels. The use of FW-PCA makes it possible to detect and restore occluded regions in face images. Through a set of experiments using public face databases, we demonstrate the effectiveness of the proposed method compared with the conventional methods.


title: Outsourcing Software Testing: A Case Study in the Oulu Area

abstract: Global software engineering increases coordination, communication, and control challenges in software development. The testing phase in this context is not a widely researched subject. In this paper, we study the outsourcing of software testing in the Oulu area, research the ways in which it is used, and determine the observable benefits and obstacles. The companies that participated in this study were found to use the outsourcing possibility of software testing with good efficiency and their testing process was considered to be mature. The most common benefits, in addition to the companies' cost savings, included the utilization of time zone differences for around-the-clock productivity, a closer proximity to the market, an improved record of communication and the tools that record the audit materials. The most commonly realized difficulties consisted of teamwork challenges, a disparate tool infrastructure, tool expense, and often-elevated coordination costs. We utilized in our study two matrices that consist in one dimension of the three distances, control, coordination, and communication, and in another dimension of four distances, temporal, geographical, socio-cultural and technical. The technical distance was our extension to the matrix that has been used as the basis for many other studies about global software development and outsourcing efforts. Our observations justify the extension of matrices with respect to the technical distance.


title: Hashtag recommendation for hyperlinked tweets

abstract: Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link. In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.


title: AGILE SOFTWARE DEVELOPMENT: A SURVEY OF EARLY ADOPTERS

abstract: In the past few years, anecdotal evidence of mostly positive experiences with the implementation of agile development methods has emerged from case studies. We used a survey strategy, which enables data gathering from a larger sample of organizations, to learn about the factors driving the adoption and use of agile practices, and their benefits and challenges as percieved by early adopters of this software development methodology. Our survey reveals that personal interest is the driving force behind agile adoption, and conversely, organizational resistance and managerial apathy are key inhibitors. The ability to meet client needs and the delivery of quality software products on time are significant benefits of agile development, while a steep learning curve and its unsuitability for projects characterized by distributed environments and large development teams are identified as minor concerns. This article reports these and other findings accompanied by relevant interpretive analysis.


title: On optimizing an SQL-like nested query

abstract: SQL is a high-level nonprocedural data language which has received wide recognition in relational databases. One of the most interesting features of SQL is the nesting of query blocks to an arbitrary depth. An SQL-like query nested to an arbitrary depth is shown to be composed of five basic types of nesting. Four of them have not been well understood and more work needs to be done to improve their execution efficiency. Algorithms are developed that transform queries involving these basic types of nesting into semantically equivalent queries that are amenable to efficient processing by existing query-processing subsystems. These algorithms are then combined into a coherent strategy for processing a general nested query of arbitrary complexity.


title: A framework for the classification of joint hypermobility and related conditions

abstract: In the last decade, growing attention has been placed on joint hypermobility and related disorders. The new nosology for Ehlers–Danlos syndrome (EDS), the best-known and probably the most common of the disorders featuring joint hypermobility, identifies more than 20 different types of EDS, and highlights the need for a single set of criteria to substitute the previous ones for the overlapping EDS hypermobility type and joint hypermobility syndrome. Joint hypermobility is a feature commonly encountered in many other disorders, both genetic and acquired, and this finding is attracting the attention of an increasing number of medical and non-medical disciplines. In this paper, the terminology of joint hypermobility and related disorders is summarized. Different types of joint hypermobility, its secondary musculoskeletal manifestations and a simplified categorization of genetic syndromes featuring joint hypermobility are presented. The concept of a spectrum of pathogenetically related manifestations of joint hypermobility intersecting the categories of pleiotropic syndromes with joint hypermobility is introduced. A group of hypermobility spectrum disorders is proposed as diagnostic labels for patients with symptomatic joint hypermobility but not corresponding to any other syndromes with joint hypermobility. © 2017 Wiley Periodicals, Inc.


title: Sparse Bilingual Word Representations for Cross-lingual Lexical Entailment

abstract: We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language. We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations. As commonly done in the monolingual setting, we assume a worde entails a wordf if the prominent context features of e are a subset of those of f . To address the challenge of comparing contexts across languages, we propose a novel method for inducing sparse bilingual word representations from monolingual and parallel texts. Our approach yields an Fscore of 70%, and significantly outperforms strong baselines based on translation and on existing word representations.


title: What is wrong with the Budeanu concept of reactive and distortion power and why it should be abandoned

abstract: The Budeanu definitions of reactive and distortion power in circuits with nonsinusoidal waveforms have been widely used for almost 60 years. There have been objections, concerned mainly with the questions of whether these powers should be defined in the frequency domain and whether they can be measured as defined. The main drawbacks of these definitions include the fact that the Budeanu reactive and distortion powers do not possess any attributes which might be related to the power phenomena in the circuit; that their values do not provide any information which would allow the design of compensating circuits; and that the distortion power value does not provide any information about waveform distortion. It is concluded that Budeanu's concept has led the power theory of circuits with nonsinusoidal waveforms into a blind alley.


title: Beamforming Lens Antenna on a High Resistivity Silicon Wafer for 60 GHz WPAN

abstract: Wafer-scale beamforming lenses for future IEEE802.15.3c 60 GHz WPAN applications are presented. An on-wafer fabrication is of particular interest because a beamforming lens can be fabricated with sub-circuits in a single process. It means that the beamforming lens system would be compact, reliable, and cost-effective. The Rotman lens and the Rotman lens with antenna arrays were fabricated on a high-resistivity silicon (HRS) wafer in a semiconductor process, which is a preliminary research to check the feasibility of a Rotman lens for a chip scale packaging. In the case of the Rotman lens only, the efficiency is in the range from 50% to 70% depending on which beam port is excited. Assuming that the lens is coupled with ideal isotropic antennas, the synthesized beam patterns from the S-parameters shows that the beam directions are -29.3°, -15.1°, 0.2°, 15.2°, and 29.5 °, and the beam widths are 15.37°, 15.62°, 15.46°, 15.51°, and 15.63°, respectively. In the case of the Rotman lens with antenna array, the patterns were measured by using on-wafer measurement setup. It shows that the beam directions are -26.6°, -21.8°, 0°, 21.8°, and 26.6° . These results are in good agreement with the calculated results from ray-optic. Thus, it is verified that the lens antenna implemented on a wafer can be feasible for the system-in-package (SiP) and wafer-level package technologies.


title: Perceived usefulness, user involvement and behavioral intention: an empirical study of ERP implementation

abstract: Many firms have implemented enterprise resource planning (ERP) systems in the past few years. The expensive nature of these systems requires that effective usage of these systems be attained in order for an organization to derive the expected benefits from the technology. This study looks at the influence that perceived usefulness, user involvement, argument for change, prior usage and ease of use have on the behavioral intention to use an ERP system. A mail survey was used to collect data in an organization that was implementing an ERP system. A total of 571 responses were obtained. The results indicate that users perception of the perceived usefulness, ease of use of the technology, and the users' level of intrinsic involvement all affect their intention to use the technology. The results seem to suggest that managerial efforts aimed at increasing the users' perceptions of the usefulness and personal relevance of the technology will contribute to implementation success, where success is defined as effectual usage of the technology. The paper ends with possible extensions to this study.


title: Attachment and emotion regulation in substance addictions and behavioral addictions

abstract: Background Risky behaviors have been related to emotional regulation and attachment, which may constitute risk factors for developing an addictive behavior. However, there may also be differences between substance and non-substance-related addictions. Aims This study aimed to examine the relationship of emotional regulation and attachment, with substance (alcohol and drug abuse), and non-substance-related addictions (gambling disorder, video game addiction, and problematic Internet use) in adolescents and emerging adults. The study also aimed to examine gender differences for such predictors. Methods The sample comprised 472 students aged 13-21 years recruited from high schools and vocational education centers. Results Findings demonstrated that emotion regulation was predictive of all addictive behaviors assessed in this study (alcohol and drug abuse, gambling disorder, video game addiction, and problematic Internet use), whereas attachment predicted non-substance-related addictions (gambling disorder, video game addiction, and problematic Internet use). In addition, gender differences were found, with females scoring significantly higher in maternal and peer attachment, whereas males scored significantly higher in gambling disorder and video game addiction. Conclusion The findings may be useful for preventive and clinical interventions conducted with youth regarding addictive behaviors.


A SECURE MULTIMATCHER SYSTEM FOR FINGERPRINT VERIFICATION


title: Fair Two-Party Computations via Bitcoin Deposits

abstract: We show how the Bitcoin currency system (with a small modification) can be used to obtain fairness in any two-party secure computation protocol in the following sense: if one party aborts the protocol after learning the output then the other party gets a financial compensation (in bitcoins). One possible application of such protocols is the fair contract signing: each party is forced to complete the protocol, or to pay to the other one a fine.


Breaking the barrier of transactions: mining inter-transaction association rules


title: Flow-based scatterplots for sensitivity analysis

abstract: Visualization of multi-dimensional data is challenging due to the number of complex correlations that may be present in the data but that are difficult to be visually identified. One of the main causes for this problem is the inherent loss of information that occurs when high-dimensional data is projected into 2D or 3D. Although 2D scatterplots are ubiquitous due to their simplicity and familiarity, there are not a lot of variations on their basic metaphor. In this paper, we present a new way of visualizing multidimensional data using scatterplots. We extend 2D scatterplots using sensitivity coefficients to highlight local variation of one variable with respect to another. When applied to a scatterplot, these sensitivities can be understood as velocities, and the resulting visualization resembles a flow field. We also present a number of operations, based on flow-field analysis, that help users navigate, select and cluster points in an efficient manner. We show the flexibility and generality of this approach using a number of multidimensional data sets across different domains.


title: Vlfeat: an open and portable library of computer vision algorithms

abstract: VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.


title: Neural Program Synthesis with Priority Queue Training

abstract: We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs so far. Then, we synthesize new programs and add them to the priority queue by sampling from the RNN. We benchmark our algorithm, called priority queue training (or PQT), against genetic algorithm and reinforcement learning baselines on a simple but expressive Turing complete programming language called BF. Our experimental results show that our simple PQT algorithm significantly outperforms the baselines. By adding a program length penalty to the reward function, we are able to synthesize short, human readable programs.


title: AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments

abstract: In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide recommendations of learning resources in near real-time. Furthermore, AFEL-REC can cope with any kind of data that is present in social learning environments such as resource metadata, user interactions or social tags. We provide a preliminary evaluation of three recommendation use cases implemented in AFEL-REC and we find that utilizing social data in form of tags is helpful for not only improving recommendation accuracy but also coverage. This paper should be valuable for both researchers and practitioners interested in providing resource recommendations in social learning environments.


They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking


title: Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review

abstract: Dynamic difficulty adjustment (DDA) is a method of automatically modifying a game’s features, behaviors, and scenarios in real-time, depending on the player’s skill, so that the player, when the game is very simple, does not feel bored or frustrated, when it is very difficult. The intent of the DDA is to keep the player engrossed till the end and to provide him/her with a challenging experience. In traditional games, difficulty levels increase linearly or stepwise during the course of the game. The features such as frequency, starting levels, or rates can be set only at the beginning of the game by choosing a level of difficulty. This can, however, result in a negative experience for players as they try to map a predecided learning curve. DDA attempts to solve this problem by presenting a customized solution for the gamers. This paper provides a review of the current approaches to DDA.


title: Security attacks in IoT: A survey

abstract: Internet of Things (IoT) is one of the most buzzing and discussed topic in research field today. Some of the researchers are also looking future of the world in this technology. Since then significant research and development have taken place on IoT, however various vulnerabilities are observed which shall keep IoT as a technology in danger. As a result, there are so many attacks on IoT have been invented before actual commercial implementation of it. The present study discusses about various IoT attacks happening, classify them, its countermeasures and finding the most prominent attacks in IoT. A state of the art survey about the various attacks have been presented and compared including their efficiency and damage level in IoT.


title: Box Refinement: Object Proposal Enhancement and Pruning

abstract: Object proposal generation has been an important preprocessing step for object detectors in general and the convolutional neural network (CNN) detectors in particular. Recently, people start to use the CNN to generate object proposals but most of these methods suffer from the localization bias problem, like other objectness-based methods. Since contours offer a powerful cue for accurate localization, we propose a box refinement method by searching for the optimal contour for each initial bounding box that minimizes the contour cost. Experiments on the PASCAL VOC2007 test dataset show that our box refinement method can significantly improve the object recall at a high overlapping threshold while maintaining a similar recall at a loose one. Given 1000 proposals, the average recall of multiple existing methods is increased by more than 5% with our box refinement process integrated.


title: Co-creation strategies for learning analytics

abstract: In order to further the field of learning analytics (LA), researchers and experts may need to look beyond themselves and their own perspectives and expertise to innovate LA platforms and interventions. We suggest that by co-creating with the users of LA, such as educators and students, researchers and experts can improve the usability, usefulness, and draw greater understanding from LA interventions. Within this article we discuss the current LA issues and barriers and how co-creation strategies can help address many of these challenges. We further outline the considerations, both pre- and during interventions, which support and foster a co-created strategy for learning analytics interventions.


title: Real-Time Deep Learning Method for Abandoned Luggage Detection in Video

abstract: Recent terrorist attacks in major cities around the world have brought many casualties among innocent citizens. One potential threat is represented by abandoned luggage items (that could contain bombs or biological warfare) in public areas. In this paper, we describe an approach for real-time automatic detection of abandoned luggage in video captured by surveillance cameras. The approach is comprised of two stages: (i) static object detection based on background subtraction and motion estimation and (ii) abandoned luggage recognition based on a cascade of convolutional neural networks (CNN). To train our neural networks we provide two types of examples: images collected from the Internet and realistic examples generated by imposing various suitcases and bags over the scene's background. We present empirical results demonstrating that our approach yields better performance than a strong CNN baseline method.


title: Fact-based question decomposition in DeepQA

abstract: Factoid questions often contain more than one fact or assertion about their answers. Question-answering (QA) systems, however, typically do not use such fine-grained distinctions because of the need for deep understanding of the question in order to identify and separate the facts. We argue that decomposing complex factoid questions is beneficial to QA systems, because the more facts that support an answer candidate, the more likely it is to be the correct answer. We broadly categorize decomposable questions into two types: parallel and nested. Parallel decomposable questions contain subquestions that can be evaluated independent of each other. Nested questions require decompositions to be processed in sequence, with the answer to an "inner" subquestion plugged into an Bouter[ subquestion. In this paper, we present a novel question decomposition framework capable of handling both decomposition types, built on top of the base IBM Watsoni QA system for Jeopardy!™. The framework contains a suite of decomposition rules that use predominantly lexico-syntactic features to identify facts within complex questions. It also contains a question-rewriting component and a candidate re-ranker, which uses machine learning and heuristic selection strategies to generate a final ranked answer list, taking into account answer confidences from the base QA system. We apply our decomposition framework to the particularly challenging domain of Final Jeopardy! questions, which are found to be difficult even for qualified Jeopardy! players, and we show a statistically significant improvement in the performance of our baseline QA system.


title: Content-based retinal image retrieval using dual-tree complex wavelet transform

abstract: Content-based image retrieval methods aid physicians in diagnosing the early detection of diabetic retinopathy for preventing blindness. In this work, the combination of two dimensional dual-tree complex wavelet transform (DT-CWT) and generalized Gaussian density (GGD) model is used for feature extraction. Kull-back Leibler Divergence (KLD) computes the similarity measure between two feature set. Experimental dataset includes 1200 images from MESSIDOR database. The mean precision rates at five retrieved images are obtained as 78.23% and 53.70% for Macular Edema and Retinopathy classes, respectively. Results are promising and give an indication that the dual-tree complex wavelet transform is efficient for content-based retinal image retrieval problem.


title: Assessing COTS integration risk using cost estimation inputs

abstract: Most risk analysis tools and techniques require the user to enter a good deal of information before they can provide useful diagnoses. In this paper, we describe an approach to enable the user to obtain a COTS glue code integration risk analysis with no inputs other than the set of glue code cost drivers the user submits to get a glue code integration effort estimate with the COnstructive COTS integration cost estimation (COCOTS) tool. The risk assessment approach is built on a knowledge base with 24 risk identification rules and a 3-level risk probability weighting scheme obtained from an expert Delphi analysis. Each risk rule is defined as one critical combination of two COCOTS cost drivers that may cause certain undesired outcome if they are both rated at their worst case ratings. The 3-level nonlinear risk weighting scheme represents the relative probability of risk occurring with respect to the individual cost driver ratings from the input. Further, to determine the relative risk impact, we use the productivity range of each cost driver in the risky combination to reflect the cost consequence of risk occurring. We also develop a prototype called COCOTS Risk Analyzer to automate our risk assessment method. The evaluation of our approach shows that it has done an effective job of estimating the relative risk levels of both small USC e-services and large industry COTS-based applications.


title: Web Content Analysis: Expanding the Paradigm

abstract: Are established methods of content analysis (CA) adequate to analyze web content, or should new methods be devised to address new technological developments? This article addresses this question by contrasting narrow and broad interpretations of the concept of web content analysis. The utility of a broad interpretation that subsumes the narrow one is then illustrated with reference to research on weblogs (blogs), a popular web format in which features of HTML documents and interactive computer-mediated communication converge. The article concludes by proposing an expanded Web Content Analysis (WebCA) paradigm in which insights from paradigms such as discourse analysis and social network analysis are operationalized and implemented within a general content analytic framework.


title: Distributed discrete-event simulation

abstract: Traditional discrete-event simulations employ an inherently sequential algorithm. In practice, simulations of large systems are limited by this sequentiality, because only a modest number of events can be simulated. Distributed discrete-event simulation (carried out on a network of processors with asynchronous message-communicating capabilities) is proposed as an alternative; it may provide better performance by partitioning the simulation among the component processors. The basic distributed simulation scheme, which uses time encoding, is described. Its major shortcoming is a possibility of deadlock. Several techniques for deadlock avoidance and deadlock detection are suggested. The focus of this work is on the theory of distributed discrete-event simulation.


Finger Gesture Recognition Using Microphone Arrays


title: CP-decomposition with Tensor Power Method for Convolutional Neural Networks compression

abstract: Convolutional Neural Networks (CNNs) has shown a great success in many areas including complex image classification tasks. However, they need a lot of memory and computational cost, which hinders them from running in relatively low-end smart devices such as smart phones. We propose a CNN compression method based on CP-decomposition and Tensor Power Method. We also propose an iterative fine tuning, with which we fine-tune the whole network after decomposing each layer, but before decomposing the next layer. Significant reduction in memory and computation cost is achieved compared to state-of-the-art previous work with no more accuracy loss.


Sub-linear Privacy-preserving Search with Unsecured Server and Semi-honest Parties


title: HKIA SAS: A Constraint-Based Airport Stand Allocation System Developed with Software Components

abstract: SAS is an AI application developed for the Hong Kong International Airport (HKIA) at Chek Lap Kok. SAS uses constraint-programming techniques to assign parking stands to aircraft and schedules tow movements based on a set of business and operational constraints. The system provides planning, real-time operation, and problem solving capabilities. SAS generates a stand allocation plan that finely balances the objectives of the airlines/handling agents, the convenience of passengers, and the operational constraints of the airport. The system ensures a high standard of quality in customer service, airport safety, and utilization of stand resources. This paper also describes our experience in developing an AI system using standard off-the-shelf software components. SAS is an example of how development methodologies used to construct modern AI applications have become fully inline with mainstream practices.


title: A Large-scale RGB-D Database for Arbitrary-view Human Action Recognition

abstract: Current researches mainly focus on single-view and multiview human action recognition, which can hardly satisfy the requirements of human-robot interaction (HRI) applications to recognize actions from arbitrary views. The lack of databases also sets up barriers. In this paper, we newly collect a large-scale RGB-D action database for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The database includes action samples captured in 8 fixed viewpoints and varying-view sequences which covers the entire 360 view angles. In total, 118 persons are invited to act 40 action categories, and 25,600 video samples are collected. Our database involves more articipants, more viewpoints and a large number of samples. More importantly, it is the first database containing the entire 360? varying-view sequences. The database provides sufficient data for cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance.


title: Part-based R-CNNs for Fine-grained Category Detection

abstract: Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-normalized representations have been proposed, but generally presume bounding box annotations at test time due to the difficulty of object detection. We propose a model for fine-grained categorization that overcomes these limitations by leveraging deep convolutional features computed on bottom-up region proposals. Our method learns whole-object and part detectors, enforces learned geometric constraints between them, and predicts a fine-grained category from a pose-normalized representation. Experiments on the Caltech-UCSD bird dataset confirm that our method outperforms state-of-the-art fine-grained categorization methods in an end-to-end evaluation without requiring a bounding box at test time.


Modelling the Domain of Digital Preservation in Wikidata


title: Corporate lobbying's new frontier: from influencing policy-making to shaping public debate

abstract: The private sector plays a pivotal role in fighting corruption worldwide. Transparency International's Global Corruption Report 2009 documents in unique detail the many corruption risks for businesses, ranging from small entrepreneurs in Sub-Saharan Africa to multinationals from Europe and North America. More than 75 experts examine the scale, scope and devastating consequences of a wide range of corruption issues, including bribery and policy capture, corporate fraud, cartels, corruption in supply chains and transnational transactions, emerging challenges for carbon trading markets, sovereign wealth funds and growing economic centres, such as Brazil, China and India. The Report also discusses the most promising tools to tackle corruption in business, identifies pressing areas for reform and outlines how companies, governments, investors, consumers and other stakeholders can contribute to raising corporate integrity and meeting the challenges that corruption poses to sustainable economic growth and development.


title: Sentic Computing for social media marketing

abstract: In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.


title: Pereskiaand the Origin of the Cactus Life‐Form

abstract: The cactus life-form is cited as an example of a tight relationship between organism form and function: a succulent, long-lived, photosynthetic stem allows cacti to survive long periods of drought while maintaining a positive tissue water status. Pereskia (Cactaceae) comprises 17 species of leafy shrubs and trees that are thought to represent the original cactus condition. Recent phylogenetic work has shown that there are two separate clades of Pereskia species, which are basal and paraphyletic with respect to the rest of the cacti. We selected seven Pereskia species, representing both clades, and characterized their water relations by measuring a suite of physiological traits in wild populations. Additionally, we estimated basic climate parameters from collection localities for all 17 Pereskia species. Extant Pereskia species exhibit ecological water use patterns that are very similar to those of the leafless, stem-succulent cacti. Ancestral trait reconstruction for the physiological and environmental data provides a preliminary assessment of the ecology and water relations of the earliest cacti and suggests that several key elements of the cactus ecological niche were established before the evolution of the cactus life-form. We interpret these ecological traits as potentially important drivers of evolutionary innovation in the cacti.


title: Approximation Algorithms

abstract: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field. He gives clear, lucid explanations of key results and ideas, with intuitive proofs, and provides critical examples and numerous illustrations to help elucidate the algorithms. Many of the results presented have been simplified and new insights provided. Of interest to theoretical computer scientists, operations researchers, and discrete mathematicians.


title: Behavior wizard: a method for matching target behaviors with solutions

abstract: We present a method for matching target behaviors with solutions for achieving those behaviors. Called the Behavior Wizard, this method first classifies behavior change targets into one of 15 types. Later stages focus on triggers for the target behaviors and on relevant theories and techniques. This new approach to persuasive design, as well as the terminology we propose, can lead to insights into the patterns of behavior change. The Behavior Wizard can also increase success rates in academic studies and commercial products. The most current version of this method is at www.BehaviorWizard.org.


title: Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem

abstract: In this paper, we review some fuzzy linear programming methods and techniques from a practical point of view. In the rst part, the general history and the approach of fuzzy mathematical programming are introduced. Using a numerical example, some models of fuzzy linear programming are described. In the second part of the paper, fuzzy mathematical programming approaches are compared to stochastic programming ones. The advantages and disadvantages of fuzzy mathematical programming approaches are exemplied in the setting of an optimal portfolio selection problem. Finally, some newly developed ideas and techniques in fuzzy mathematical programming are briey reviewed. c 2000 Elsevier Science B.V. All rights reserved.


title: Graph measures and network robustness

abstract: Network robustness research aims at finding a measure to quantify network robustness. Once such a measure has been established, we will be able to compare networks, to improve existing networks and to design new networks that are able to continue to perform well when it is subject to failures or attacks. In this paper we survey a large amount of robustness measures on simple, undirected and unweighted graphs, in order to offer a tool for network administrators to evaluate and improve the robustness of their network. The measures discussed in this paper are based on the concepts of connectivity (including reliability polynomials), distance, betweenness and clustering. Some other measures are notions from spectral graph theory, more precisely, they are functions of the Laplacian eigenvalues. In addition to surveying these graph measures, the paper also contains a discussion of their functionality as a measure for topological network robustness.


title: Second-order ΔΣAD modulator with novel feedforward architecture

abstract: This paper proposes novel feedforward architecture of a second-order DeltaSigmaAD modulator with single DAC- feedback topology. DeltaSigmaAD modulator realizes high resolution by oversampling and noise shaping technique. However, its SNDR (signal to noise and distortion ratio) is limited by the dynamic range of the input signal and non-idealities of building blocks, particularly by the harmonic distortion in amplifier circuits. Compared with a feed backed DeltaSigmaAD modulator, in a full feedforward DeltaSigmaAD modulator structure, the signal transfer function is unity under ideal circumstances. It means that the signal swings through the loop filter become smaller. Therefore, the harmonic distortion generated inside the loop filter can be significantly reduced because the effect of non-idealities in amplifiers can be suppressed when signal swing is small. Moreover, the reduction of the internal signal swings also relaxes output swing requirement for amplifiers in low-voltage design. However, in conventional feedforward DeltaSigmaAD modulator, an analog adder is needed before quantizer. Especially in a multibit modulator, an additional amplifier is necessary to realize the summation of feedforward signals, which leads to large chip area and extra power dissipation. In this paper, we propose a novel architecture of a feedforward DeltaSigmaAD modulator. It realizes the summation of feedforward signals without additional amplifier that is equivalent to the conventional one but smaller chip area and low-power dissipation. We also conducted MATLAB and SPICE simulations to verify the proposed architecture and modulator circuits.


title: I2R: Three Systems for Word Sense Discrimination, Chinese Word Sense Disambiguation, and English Word Sense Disambiguation

abstract: This paper describes the implementation of our three systems at SemEval-2007, for task 2 (word sense discrimination), task 5 (Chinese word sense disambiguation), and the first subtask in task 17 (English word sense disambiguation). For task 2, we applied a cluster validation method to estimate the number of senses of a target word in untagged data, and then grouped the instances of this target word into the estimated number of clusters. For both task 5 and task 17, We used the label propagation algorithm as the classifier for sense disambiguation. Our system at task 2 achieved 63.9% F-score under unsupervised evaluation, and 71.9% supervised recall with supervised evaluation. For task 5, our system obtained 71.2% micro-average precision and 74.7% macro-average precision. For the lexical sample subtask for task 17, our system achieved 86.4% coarse-grained precision and recall.


title: A genome-wide linkage scan for genes controlling variation in urinary albumin excretion in type II diabetes.

abstract: The main hallmark of diabetic nephropathy is elevation in urinary albumin excretion. We performed a genome-wide linkage scan in 63 extended families with multiple members with type II diabetes. Urinary albumin excretion, measured as the albumin-to-creatinine ratio (ACR), was determined in 426 diabetic and 431 nondiabetic relatives who were genotyped for 383 markers. The data were analyzed using variance components linkage analysis. Heritability (h2) of ACR was significant in diabetic (h2=0.23, P=0.0007), and nondiabetic (h2=0.39, P=0.0001) relatives. There was no significant difference in genetic variance of ACR between diabetic and nondiabetic relatives (P=0.16), and the genetic correlation (rG=0.64) for ACR between these two groups was not different from 1 (P=0.12). These results suggested that similar genes contribute to variation in ACR in diabetic and nondiabetic relatives. This hypothesis was supported further by the linkage results. Support for linkage to ACR was suggestive in diabetic relatives and became significant in all relatives for chromosome 22q (logarithm of odds, LOD=3.7) and chromosome 7q (LOD=3.1). When analyses were restricted to 59 Caucasian families, support for linkage in all relatives increased and became significant for 5q (LOD=3.4). In conclusion, genes on chromosomes 22q, 5q and 7q may contribute to variation in urinary albumin excretion in diabetic and nondiabetic individuals.


title: Characterizing resource usage for mobile web browsing

abstract: Multiple entities in the smartphone ecosystem employ various methods to provide better web browsing experience. In this paper, we take a first comprehensive examination of the resource usage of mobile web browsing by focusing on two important types of resources: bandwidth and energy. Using a novel traffic collection and analysis tool, we examine a wide spectrum of important factors including protocol overhead, TCP connection management, web page content, traffic timing dynamics, caching efficiency, and compression usage, for the most popular 500 websites. Our findings suggest that that all above factors at different layers can affect resource utilization for web browsing, as they often poorly interact with the underlying cellular networks. Based on our findings, we developed novel recommendations and detailed best practice suggestions for mobile web content, browser, network protocol, and smartphone OS design, to make mobile web browsing more resource efficient.


title: Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression

abstract: This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression. While local anomaly is typically detected as a 3D pattern matching problem, we are more interested in global anomaly that involves multiple normal events interacting in an unusual manner such as car accident. To simultaneously detect local and global anomalies, we formulate the extraction of normal interactions from training video as the problem of efficiently finding the frequent geometric relations of the nearby sparse spatio-temporal interest points. A codebook of interaction templates is then constructed and modeled using Gaussian process regression. A novel inference method for computing the likelihood of an observed interaction is also proposed. As such, our model is robust to slight topological deformations and can handle the noise and data unbalance problems in the training data. Simulations show that our system outperforms the main state-of-the-art methods on this topic and achieves at least 80% detection rates based on three challenging datasets.


Review on Fundus Image Acquisition Techniques with Data base Reference to Retinal Abnormalities in Diabetic Retinopathy


title: Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm

abstract: The error backpropagation learning algorithm (BP) is generally considered biologically implausible because it does not use locally available, activation-based variables. A version of BP that can be computed locally using bidirectional activation recirculation (Hinton and McClelland 1988) instead of backpropagated error derivatives is more biologically plausible. This paper presents a generalized version of the recirculation algorithm (GeneRec), which overcomes several limitations of the earlier algorithm by using a generic recurrent network with sigmoidal units that can learn arbitrary input/output mappings. However, the contrastive Hebbian learning algorithm (CHL, also known as DBM or mean field learning) also uses local variables to perform error-driven learning in a sigmoidal recurrent network. CHL was derived in a stochastic framework (the Boltzmann machine), but has been extended to the deterministic case in various ways, all of which rely on problematic approximations and assumptions, leading some to conclude that it is fundamentally flawed. This paper shows that CHL can be derived instead from within the BP framework via the GeneRec algorithm. CHL is a symmetry-preserving version of GeneRec that uses a simple approximation to the midpoint or second-order accurate Runge-Kutta method of numerical integration, which explains the generally faster learning speed of CHL compared to BI. Thus, all known fully general error-driven learning algorithms that use local activation-based variables in deterministic networks can be considered variations of the GeneRec algorithm (and indirectly, of the backpropagation algorithm). GeneRec therefore provides a promising framework for thinking about how the brain might perform error-driven learning. To further this goal, an explicit biological mechanism is proposed that would be capable of implementing GeneRec-style learning. This mechanism is consistent with available evidence regarding synaptic modification in neurons in the neocortex and hippocampus, and makes further predictions.


title: Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

abstract: Due to rapid development in software industry, it was necessary to reduce time and efforts in the ::: software development process. Software Reusability is an important measure that can be applied ::: to improve software development and software quality. Reusability reduces time, effort, errors, ::: and hence the overall cost of the development process. Reusability prediction models are established ::: in the early stage of the system development cycle to support an early reusability assessment. ::: In Object-Oriented systems, Reusability of software components (classes) can be obtained ::: by investigating its metrics values. Analyzing software metric values can help to avoid developing ::: components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order ::: to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to ::: cluster datasets of CK metrics values that were extracted from three different java-based systems. ::: The goal was to find the relationship between CK metrics values and the reusability level of the ::: class. The reusability level of the class was classified into three main categorizes (High Reusable, ::: Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that ::: were used to achieve the experiments. The proposed methodology succeeds in classifying classes ::: to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments ::: show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide ::: different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and ::: Number of Children (NOC) metrics dominated the clustering process, so these two metrics were ::: discarded from the experiments to achieve a successful clustering. The most efficient SOM topology ::: [2 × 2] grid size is used to predict the reusability of classes.


title: Search Result Diversity Evaluation Based on Intent Hierarchies

abstract: Search result diversification aims at returning diversified document lists to cover different user intents of a query. Existing diversity measures assume that the intents of a query are disjoint, and do not consider their relationships. In this paper, we introduce intent hierarchies to model the relationships between intents, and present four weighing schemes. Based on intent hierarchies, we propose several hierarchical measures that take into account the relationships between intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections and by using NTCIR-11 IMine test collection. Our main experimental findings are: (1) Hierarchical measures are more discriminative and intuitive than existing measures. In terms of intuitiveness, it is preferable for hierarchical measures to use the whole intent hierarchies than to use only the leaf nodes. (2) The types of intent hierarchies used affect the discriminative power and intuitiveness of hierarchical measures. We suggest the best type of intent hierarchies to be used according to whether the nonuniform weights are available. (3) To measure the benefits of the diversification algorithms which use automatically mined hierarchical intents, it is important to use hierarchical measures instead of existing measures.


title: CNN based region proposals for efficient object detection

abstract: In computer vision, object detection is addressed as one of the most challenging problems as it is prone to localization and classification error. The current best-performing detectors are based on the technique of finding region proposals in order to localize objects. Despite having very good performance, these techniques are computationally expensive due to having large number of proposed regions. In this paper, we develop a high-confidence region-based object detection framework that boosts up the classification performance with less computational burden. In order to formulate our framework, we consider a deep network that activates the semantically meaningful regions in order to localize objects. These activated regions are used as input to a convolutional neural network (CNN) to extract deep features. With these features, we train a set of class-specific binary classifiers to predict the object labels. Our new region-based detection technique significantly reduces the computational complexity and improves the performance in object detection. We perform rigorous experiments on PASCAL, SUN, MIT-67 Indoor and MSRC datasets to demonstrate that our proposed framework outperforms other state-of-the-art methods in recognizing objects.


title: Investigating bank failures using text mining

abstract: We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at public annual reports for a large sample of U.S. banks in the 2000-2014 period, we identify 52 public bank holding companies that were associated with bank failures during the global financial crisis. Utilizing sentiment dictionaries designed for financial context, we find that negative and positive sentiments discriminate between failed and non-failed banks 88% and 79%, respectively, of the time. However, we find that positive sentiment contains stronger predictive power than negative sentiment; out of ten failed banks, on average positive sentiment can identify six true events, while negative sentiment identifies five failed banks at most. While one would link financial soundness with more positive sentiment, it appears that failed banks exhausted more positive sentiment than their non-failed peers, whether ex-ante in anticipation of good news or ex-post to conceal financial distress.


title: Hierarchical reinforcement learning and central pattern generators for modeling the development of rhythmic manipulation skills

abstract: The development of manipulation skills is a fundamental process for young primates as it leads them to acquire the capacity to modify the world to their advantage. As other motor skills, manipulation develops on the basis of motor babbling processes which are initially heavily based on the production of rhythmic movements. We propose a computational bio-inspired model to investigate the development of functional rhythmic hand skills from initially unstructured movements. The model is based on a hierarchical reinforcement-learning actor-critic model that searches the parameters of a set of central pattern generators (CPGs) having different degrees of sophistication. The model is tested with a simulated robotic hand engaged in rotating bottle cap-like objects having different shape and size. The results show that the model is capable of developing skills based on different combinations of CPGs so as to suitably manipulate the different objects. Overall, the model shows to be a valuable tool for the study of the development of rhythmic manipulation skills in primates.


title: Foamy slag fundamentals and their practical application to electric furnace steelmaking

abstract: Using a mass-balance approach and isothermal saturation diagrams, steelmakers not only can monitor their existing slag practices, but design foamy slags to better suit their operations.


title: Wide Ku-Band Microstrip patch antenna using defected patch and ground

abstract: New Simple and compact wide Ku-band Microstrip patch antenna is proposed. Defected patch and ground design is used to achieve wide band. The proposed antenna is designed on FR-4 substrate with dielectric constant 4.9 and thickness 1 mm and its overall size is 17 × 17 × 1.07 mm3 with patch size 13 × 11 × 0.035 mm3. The results of proposed antennas like return loss, VSWR and radiation pattern (gain, directivity and efficiency) are simulated and analyzed using commercial computer simulation technology microwave studio (CST MWS). By simulating this antenna structure using above EM tool we are getting one wide band from 15.27 to 16.51 GHz with resonant frequency at 15.8 GHz. Return loss at this frequency is −25 dB. Gain and directivity at the resonant frequency are 4.45 dB and 5.17 dBi respectively. Radiation efficiency of this antenna is 84.8%. The VSWR ≤ 1.1 for the resonant frequency. The low profile nature and simple configuration of antennas show the way to easy fabrication. The proposed antenna is operating in wide range of Ku-band which includes different satellite applications (Fixed satellite, Mobile satellite, aeronautical radio navigation, space research, radiolocation etc.)


Criterion vs. Norm-referenced Testing.


title: ADADELTA: An Adaptive Learning Rate Method

abstract: We present a novel per-dimension learning rate method for gradient descent called ADADELTA. The method dynamically adapts over time using only first order information and has minimal computational overhead beyond vanilla stochastic gradient descent. The method requires no manual tuning of a learning rate and appears robust to noisy gradient information, different model architecture choices, various data modalities and selection of hyperparameters. We show promising results compared to other methods on the MNIST digit classification task using a single machine and on a large scale voice dataset in a distributed cluster environment.


title: The development of regular and irregular verb inflection in Spanish child language

abstract: We present morphological analyses of verb inflections produced by 15 Spanish-speaking children (age range: 1;7 to 4;7) taken from longitudinal and cross-sectional samples of spontaneous speech and narratives. Our main observation is the existence of a dissociation between regular and irregular processes in the distribution of errors: regular suffixes and unmarked (non-alternating) stems are over-extended to irregulars in children's inflection errors, but not vice versa . We also found that overregularization errors at all ages are only a small minority of the children's irregular verbs, that the period of overregularization is preceded by a stage without errors, and that the onset of overregularizations is connected to the emergence of obligatory finiteness markings. These findings are explained in terms of the dual-mechanism model of inflection.


title: A new perspective on processing-in-memory architecture design

abstract: As computation becomes increasingly limited by data movement and energy consumption, exploiting locality throughout the memory hierarchy becomes critical for maintaining the performance scaling that many have come to expect from the computing industry. Moving computation closer to main memory presents an opportunity to reduce the overheads associated with data movement. We explore the potential of using 3D die stacking to move memory-intensive computations closer to memory. This approach to processing-in-memory addresses some drawbacks of prior research on in-memory computing and appears commercially viable in the foreseeable future. We show promising early results from this approach and identify areas that are in need of research to unlock its full potential.


title: Removal of Fine Powders from Film Surface. I. Effect of Electrostatic Force on the Removal Efficiency

abstract: A novel fine particle removal system composed of a corona-discharge neutralizer, a pulse-jet air unit and an image processing system has been developed. First of all, adhesion force between particle and film was directly measured and effect of electrostatic force on the adhesion force was calculated experimentally and theoretically. The electrostatic force was found to be significant, leading to the suggestion that the countermeasure for the electrostatic force was required to effectively remove fine particles. This system was then applied to the removal of fine particles from surface of a gelatin film used for conventional capsule material. The number of particles removed by the system was calculated by an image processing system and number base removal efficiency was computed with and without the elimination of electrostatic charge by the neutralizer. It was found that the difference between the removal efficiency of particles with elimination of electrostatic charge and that of without the elimination showed linear relationship with the electrostatic adhesion force. The data confirmed the necessity of electrostatic charge elimination for the effective removal of fine particles.


title: A pooling based scene text proposal technique for scene text reading in the wild

abstract: Abstract Automatic reading texts in scenes has attracted increasing interest in recent years as texts often carry rich semantic information that is useful for scene understanding. In this paper, we propose a novel scene text proposal technique aiming for accurate reading texts in scenes. Inspired by the pooling layer in the deep neural network architecture, a pooling based scene text proposal technique is developed. A novel score function is designed which exploits the histogram of oriented gradients and is capable of ranking the proposals according to their probabilities of being text. An end-to-end scene text reading system has also been developed by incorporating the proposed scene text proposal technique where false alarms elimination and words recognition are performed simultaneously. Extensive experiments over several public datasets show that the proposed technique can handle multi-orientation and multi-language scene texts and obtains outstanding proposal performance. The developed end-to-end systems also achieve very competitive scene text spotting and reading performance.


title: Epileptic seizure detection for multi-channel EEG with deep convolutional neural network

abstract: A new epileptic seizure detection method based on deep convolutional network is proposed. The proposed network is designed for multi-channel EEG signals and considers spatio-temporal correlation, a feature in epileptic seizure detection, using 1D and 2D convolutional layers. 1D convolutional layer considers temporal evolution of the EEG signal of each channel and 2D convolutional layers considers spatial relationships between EEG channels. We make datasets for training and test by extracting the EEG segments from CHB-MIT EEG Scalp database and SNUH-HYU EEG database: the recordings of long-term EEG monitoring at Seoul National University Hospital and Children's Hospital Boston. Our model is trained and tested using the EEG segments with varying durations. We also investigate the effect of artifact elimination on epileptic seizure detection by applying a low-pass filter to the EEG signals. Our model achieves 90.5% prediction accuracy with SNUH-HYU EEG dataset.


title: Multimedia content processing through cross-modal association

abstract: Multimodal information processing has received considerable attention in recent years. The focus of existing research in this area has been predominantly on the use of fusion technology. In this paper, we suggest that cross-modal association can provide a new set of powerful solutions in this area. We investigate different cross-modal association methods using the linear correlation model. We also introduce a novel method for cross-modal association called Cross-modal Factor Analysis (CFA). Our earlier work on Latent Semantic Indexing (LSI) is extended for applications that use off-line supervised training. As a promising research direction and practical application of cross-modal association, cross-modal information retrieval where queries from one modality are used to search for content in another modality using low-level features is then discussed in detail. Different association methods are tested and compared using the proposed cross-modal retrieval system. All these methods achieve significant dimensionality reduction. Among them CFA gives the best retrieval performance. Finally, this paper addresses the use of cross-modal association to detect talking heads. The CFA method achieves 91.1% detection accuracy, while LSI and Canonical Correlation Analysis (CCA) achieve 66.1% and 73.9% accuracy, respectively. As shown by experiments, cross-modal association provides many useful benefits, such as robust noise resistance and effective feature selection. Compared to CCA and LSI, the proposed CFA shows several advantages in analysis performance and feature usage. Its capability in feature selection and noise resistance also makes CFA a promising tool for many multimedia analysis applications.


title: The Definition of Components and the Use of Formal Indexes are Key Steps for a Successful Application of Network Analysis in Personality Psychology

abstract: Conceiving personality as a network provides an interesting theoretical framework and a promising methodological perspective. The application of network analysis to personality psychology however is not straightforward, and some issues require careful consideration. We argue that the definition of components within networks cannot be limited to single items, and more work is needed to reflect the inherently hierarchical and indefinite nature of components. Additionally, we argue that formal empirical indexes must be clearly defined and consistently used to describe properties of personality networks. Copyright © 2012 John Wiley & Sons, Ltd.


title: Deep classifier: automatically categorizing search results into large-scale hierarchies

abstract: Organizing Web search results into hierarchical categories facilitates users' browsing through Web search results, especially for ambiguous queries where the potential results are mixed together. Previous methods on search result classification are usually based on pre-training a classification model on some fixed and shallow hierarchical categories, where only the top-two-level categories of a Web taxonomy is used. Such classification methods may be too coarse for users to browse, since most search results would be classified into only two or three shallow categories. Instead, a deep hierarchical classifier must provide many more categories. However, the performance of such classifiers is usually limited because their classification effectiveness can deteriorate rapidly at the third or fourth level of a hierarchy. In this paper, we propose a novel algorithm known as Deep Classifier to classify the search results into detailed hierarchical categories with higher effectiveness than previous approaches. Given the search results in response to a query, the algorithm first prunes a wide-ranged hierarchy into a narrow one with the help of some Web directories. Different strategies are proposed to select the training data by utilizing the hierarchical structures. Finally, a discriminative naíve Bayesian classifier is developed to perform efficient and effective classification. As a result, the algorithm can provide more meaningful and specific class labels for search result browsing than shallow style of classification. We conduct experiments to show that the Deep Classifier can achieve significant improvement over state-of-the-art algorithms. In addition, with sufficient off-line preparation, the efficiency of the proposed algorithm is suitable for online application


title: Stairs Detection for Enhancing Wheelchair Capabilities Based on Radar Sensors

abstract: Powered wheelchair users encounter barriers to their mobility everyday. Entering a building with non barrier-free areas can massively impact a persons mobility related activities. There are a few commercial devices and some experimental that can climb stairs using for instance adaptive wheels with joints or caterpillar drive. These systems rely on the use for sensing and control. For safe automated obstacle crossing, a robust and environment invariant detection of the surrounding is necessary. Radar may prove to be a suitable sensor for its capability to handle harsh outdoor environmental conditions. In this paper, we introduce a mirror based two dimensional Frequency-Modulated Continuous-Wave (FMCW) radar scanner for stair detection. A radar image based stair dimensioning approach is presented and tested under laboratory and realistic conditions.