title: Small-Variance Asymptotics for Hidden Markov Models

abstract: Small-variance asymptotics provide an emerging technique for obtaining scalable combinatorial algorithms from rich probabilistic models. We present a small-variance asymptotic analysis of the Hidden Markov Model and its infinite-state Bayesian nonparametric extension. Starting with the standard HMM, we first derive a "hard" inference algorithm analogous to k-means that arises when particular variances in the model tend to zero. This analysis is then extended to the Bayesian nonparametric case, yielding a simple, scalable, and flexible algorithm for discrete-state sequence data with a non-fixed number of states. We also derive the corresponding combinatorial objective functions arising from our analysis, which involve a k-means-like term along with penalties based on state transitions and the number of states. A key property of such algorithms is that— particularly in the nonparametric setting—standard probabilistic inference algorithms lack scalability and are heavily dependent on good initialization. A number of results on synthetic and real data sets demonstrate the advantages of the proposed framework.


title: Annotations and the Collaborative Digital Library: Effects of an Aligned Annotation Interface on Student Argumentation and Reading Strategies

abstract: Recent research on annotation interfaces provides provocative evidence that anchored, annotation-based discussion environments may lead to better conversations about a text. However, annotation interfaces raise complicated tradeoffs regarding screen real estate and positioning. It is argued that solving this screen real estate problem requires limiting the number of annotations displayed to users. In order to understand which annotations have the most learning value for students, this paper presents two complementary studies examining the effects of annotations on students performing a reading-to-write task. The first study used think-aloud protocols and a within-subjects methodology, finding that annotations appeared to provoke students to reflect more critically upon the primary text. This effect was particularly strong when students encountered pairs of annotations presenting different viewpoints on the same section of text. Student interviews suggested that annotations were most helpful when they caused the reader to consider and weigh conflicting viewpoints. The second study used a between-subjects methodology and a more naturalistic task to provide complementary evidence that annotations encourage more reflective responses to a text. This study found that students who received annotated materials both perceived themselves and were perceived by instructors as less reliant on unreflective summary strategies than students who received the same content but in a different format. These findings indicate that the learning value of an annotation lies in its ability to provoke students to consider and weigh new perspectives on the primary text. When selected effectively, annotations provide a critical scaffolding that can support students’ critical thinking and argumentation activities. Collaborative digital libraries and applications for the Web 2.0 should be designed with this learning framework in mind.


title: Linear Object Classes and Image Synthesis From a Single Example Image

abstract: The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, simpler techniques are applicable under restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by the authors (1992). For linear object classes, it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.


title: Self- perceived stress in relation to anxiety, depression and health-related quality of life among health professions students: A cross-sectional study from Bosnia and Herzegovina

abstract: Abstract Introduction The purpose of the present study was to examine self-perceived stress of health professions students at the Faculty of Medicine Foča, and to explore its association with anxiety, depression and health-related quality of life. Methods The cross-sectional study enrolled 451 students at the Faculty of Medicine (medicine, dentistry, nursing and speech therapy). Survey instruments were distributed at the conclusion of the spring semester during the last required lecture for each year and study programme class. Perceived stress was assessed using the 14-item Perceived Stress Scale. The students were evaluated for symptoms of depression and anxiety, using Zung’s self-assessment inventory for depression and the Spielberger State-Trait Anxiety Inventory (STAI). European Quality of Life-5 dimensions were used for describing and evaluating health. Multivariate analyses were carried out using logistic regression to examine the relationship between the outcome variable and selected determinant factors. Results A high degree of stress was reported by 1.6% of students, while the majority of students had either moderate (70.6%) or low degree (27.5%) of stress. The significant independent factors associated with perceived stress were anxiety score (OR, 0.339; CI 95%, 0.276-0.403) and EQ-5D score (OR, 0.044; CI 95%, 0.033-0.085). A high degree of perceived stress (OR, 0.624; CI 95%, 0.507-0.704), the presence of depression (OR, 0.800; CI 95%, 0.513-1.087), and low quality of life were associated with anxiety (OR, 0.073; CI 95%, 0.018-0.128). Conclusion Higher levels of perceived stress predispose health professions students for anxiety and lower quality of life. The study programme was not a significant determinant of perceived stress sore.


title: Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

abstract: Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge ( http://endovis.grand-challenge.org ) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.


Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition.


title: Attentional and non-attentional systems in the maintenance of verbal information in working memory: the executive and phonological loops

abstract: Working memory is the structure devoted to the maintenance of information at short term during concurrent processing activities. In this respect, the question regarding the nature of the mechanisms and systems fulfilling this maintenance function is of particular importance and has received various responses in the recent past. In the time-based resource-sharing model, we suggest that only two systems sustain the maintenance of information at the short term, counteracting the deleterious effect of temporal decay and interference. A non-attentional mechanism of verbal rehearsal, similar to the one described by Baddeley in the phonological loop model, uses language processes to reactivate phonological memory traces. Besides this domain-specific mechanism, an executive loop allows the reconstruction of memory traces through an attention-based mechanism of refreshing. The present paper reviews evidence of the involvement of these two independent systems in the maintenance of verbal memory items.


title: Person Recognition using Smartphones' Accelerometer Data

abstract: Smartphones have become quite pervasive in various aspects of our daily lives. They have become important links to a host of important data and applications, which if compromised, can lead to disastrous results. Due to this, today's smartphones are equipped with multiple layers of authentication modules. However, there still lies the need for a viable and unobtrusive layer of security which can perform the task of user authentication using resources which are cost-efficient and widely available on smartphones. In this work, we propose a method to recognize users using data from a phone's embedded accelerometer sensors. Features encapsulating information from both time and frequency domains are extracted from walking data samples, and are used to build a Random Forest ensemble classification model. Based on the experimental results, the resultant model delivers an accuracy of 0.9679 and Area under Curve (AUC) of 0.9822.


title: Malware fuzzy ontology for semantic web

abstract: Summary The term malware, in the area related to computer science, is used to define malicious code which is designed and written to execute attacks on software systems. In this paper, after a quick review of malact ontology, malware ontology is presented. Malwares include viruses, worms, botnets, spywares, backdoors, trojan horses, rootkits and exploits. In this paper malwares are grouped based on four attributes. These attributes include: Objective, operational status, establishment status and communication status. Then by analyzing malware and their characteristics, we propose malware ontology. The ontology is used to represent the concepts and their relationships in network security. One of the usages of ontology is information sharing and reuse in semantic web. In this paper, by proposing malware ontology, we presented the semantic relation map between concepts of this area which is used in semantic based search engines in incident area and CERT portals. Because malwares have similar characteristics, there is no clear boundary between their concepts so fuzzy logic is used to represent malware relationships. Malware relationships are presented in five categories: very weak relations, weak relations, moderate relations, good relations and very good relations and weights are assigned to them. If search is done on any concept (nodes) in the graph, the amount of the relationship with other concepts is calculated and based on the search demand and level of relationship, search is done on other related concepts.


Anomaly Detection in Text: The Value of Domain Knowledge.


title: Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement

abstract: We present a method called muscle synergy analysis, which can offer clinicians insight into both underlying neural strategies for movement and functional outcomes of muscle activity. Although neural dysfunction is central to many motor deficits, neural activity during movements is not directly measurable. Consequently, the majority of clinical tests focus on evaluating motor outputs at the behavioral and kinematic levels. However, altered behavioral or kinematic outcomes could be the result of multiple distinct neural abnormalities with very different muscle coordination patterns. Because muscle activity reflects motoneuron activity and generates the forces that produce behavioral outcomes, an analysis of muscle activity may provide a better understanding of the functional neural deficits in the impaired nervous system. Unfortunately electromyographic datasets can be large, highly variable, and difficult to interpret, precluding their clinical utility. Computational analyses can be used to extract muscle synergies from such datasets, revealing underlying patterns that may reflect different levels of neural function. These muscle synergies are hypothesized to represent motor modules recruited by the nervous system to flexibly perform biomechanical subtasks necessary for movement. For example, hemiparetic stroke patients exhibit differences in the number of muscle synergies, which may reflect disruptions in descending neural pathways and are correlated to deficits in motor function. Muscle synergy analysis may thus offer the clinician a better view of the neural structure underlying motor behaviors and how they change in motor deficits and rehabilitation. Such information could inform diagnostic tools and evidence-based interventions specifically targeted to a patient's deficits.


title: Gate Diffusion Input (GDI) logic in standard CMOS nanoscale process

abstract: In this paper CMOS compatible Gate Diffusion Input (GDI) design technique is proposed. The GDI method enables the implementation of a wide range of complex logic functions using only two transistors. This method is suitable for the design of low-power logic gates, with a much smaller area than Static CMOS and existing PTL techniques. As opposite to our originally proposed GDI logic, the modified GDI logic is fully compatible for implementation in a standard CMOS process. Simulations of basic GDI gates under process and temperature corners in 40nm CMOS process are shown and compared to similar CMOS gates. We show that while having the same delay, GDI gates achieve leakage and active power reduction of up to 70% and 50%, respectively.


Sniffing out significant “Pee values”: genome wide association study of asparagus anosmia


title: Convolutional neural networks with intra-layer recurrent connections for scene labeling

abstract: Scene labeling is a challenging computer vision task. It requires the use of both local discriminative features and global context information. We adopt a deep recurrent convolutional neural network (RCNN) for this task, which is originally proposed for object recognition. Different from traditional convolutional neural networks (CNN), this model has intra-layer recurrent connections in the convolutional layers. Therefore each convolutional layer becomes a two-dimensional recurrent neural network. The units receive constant feed-forward inputs from the previous layer and recurrent inputs from their neighborhoods. While recurrent iterations proceed, the region of context captured by each unit expands. In this way, feature extraction and context modulation are seamlessly integrated, which is different from typical methods that entail separate modules for the two steps. To further utilize the context, a multi-scale RCNN is proposed. Over two benchmark datasets, Standford Background and Sift Flow, the model outperforms many state-of-the-art models in accuracy and efficiency.


title: Simple Anchoring of the Penopubic Skin to the Prepubic Deep Fascia in Surgical Correction of Buried Penis

abstract: Purpose ::: The aim of this study was to categorize concealed penis and buried penis by preoperative physical examination including the manual prepubic compression test and to describe a simple surgical technique to correct buried penis that was based on surgical experience and comprehension of the anatomical components.


title: Gender differences in personality: A meta-analysis.

abstract: Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations. Do men and women differ in personality characteristics? Research on gender differences was begun by scientists who believed that individual differences in traits were biologically determined, and that findings of gender differences supported their view (Fausto-Sterling, 1985; Feingold, 1992d; Shields, 1975). Contemporary research on gender differences has focused on cognitive abilities (for recent reviews, see Feingold, 1993a; Linn, 1992; Wilder & Powell, 1989) and social behavior (e.g., Feingold, 1988b, 1992c; see also review by Eagly, 1987), including mate selection preferences (e.g., Buss, 1989; Feingold, 1990, 1991, 1992b). Yet, gender differences in personality (operationally defined as "what personality scales measure"1) are as worthy of inquiry as cognitive gender differences (and may be a proximal cause of gender differences in social behavior), but they have received scant attention. Interest in gender differences in personality initially followed the formulation of theories of personality and the development of methods of assessment. Personality inventories are self-report, pencil-and-paper questionnaires on which respondents report their own feelings and behaviors, generally by responding to a series of yes-or-no or true-or-false test items. Personality inventories yield one or more scores that measure such traits as assertiveness, extraversion, and anxiety, and gender differences


title: Inferring Undiscovered Public Knowledge by Using Text Mining-driven Graph Model

abstract: Due to the recent development of Information Technology, the number of publications is increasing exponentially. In response to the increasing number of publications, there has been a sharp surge in the demand for replacing the existing manual text data processing by an automatic text data processing. Swanson proposed ABC model [1] on the top of text mining as a part of literature-based knowledge discovery for finding new possible biomedical hypotheses about three decades ago. The following clinical scholars proved the effectiveness of the possible hypotheses found by ABC model [2]. Such effectiveness let scholars try various literature-based knowledge discovery approaches [3, 4, 5]. However, their trials are not fully automated but hybrids of automatic and manual processes. The manual process requires the intervention of experts. In addition, their trials consider a single perspective. Even trials involving network theory have difficulties in mal-understanding the entire network structure of the relationships among concepts and the systematic interpretation on the structure [6, 7]. Thus, this study proposes a novel approach to discover various relationships by extending the intermediate concept B to a multi-leveled concept. By applying a graph-based path finding method based on co-occurrence and the relational entities among concepts, we attempt to systematically analyze and investigate the relationships between two concepts of a source node and a target node in the total paths. For the analysis of our study, we set our baseline as the result of Swanson [8]'s work. This work suggested the intermediate concept or terms between Raynaud's disease and fish oils as blood viscosity, platelet aggregability, and vasconstriction. We compared our results of intermediate concepts with these intermediate concepts of Swanson's. This study provides distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.


title: Powergraph: Distributed graphparallel computation on natural graphs

abstract: Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly skewed power-law degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability. ::: ::: In this paper, we characterize the challenges of computation on natural graphs in the context of existing graph-parallel abstractions. We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs. We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graph-parallel systems. Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on large-scale real-world problems demonstrating order of magnitude gains.


title: Deep Supervised Hashing for Fast Image Retrieval

abstract: In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning robust image representation on various vision tasks, this paper proposes a novel Deep Supervised Hashing (DSH) method to learn compact similarity-preserving binary code for the huge body of image data. Specifically, we devise a CNN architecture that takes pairs of images (similar/dissimilar) as training inputs and encourages the output of each image to approximate discrete values (e.g. +1/-1). To this end, a loss function is elaborately designed to maximize the discriminability of the output space by encoding the supervised information from the input image pairs, and simultaneously imposing regularization on the real-valued outputs to approximate the desired discrete values. For image retrieval, new-coming query images can be easily encoded by propagating through the network and then quantizing the network outputs to binary codes representation. Extensive experiments on two large scale datasets CIFAR-10 and NUS-WIDE show the promising performance of our method compared with the state-of-the-arts.


title: Digital imaging in pathology: whole-slide imaging and beyond.

abstract: Digital imaging in pathology has undergone an exponential period of growth and expansion catalyzed by changes in imaging hardware and gains in computational processing. Today, digitization of entire glass slides at near the optical resolution limits of light can occur in 60 s. Whole slides can be imaged in fluorescence or by use of multispectral imaging systems. Computational algorithms have been developed for cytometric analysis of cells and proteins in subcellular locations by use of multiplexed antibody staining protocols. Digital imaging is unlocking the potential to integrate primary image features into high-dimensional genomic assays by moving microscopic analysis into the digital age. This review highlights the emerging field of digital pathology and explores the methods and analytic approaches being developed for the application and use of these methods in clinical care and research settings.


Native accent classification via I-Vectors and speaker compensation fusion


title: Pain sensitivity and vasopressin analgesia are mediated by a gene-sex-environment interaction

abstract: The authors find that the Avpr1a gene, encoding the vasopressin-1A receptor, is responsible for strain-dependent pain sensitivity of mice to formalin and capsaicin. In humans, a single nucleotide polymorphism in AVPR1A was found to affect capsaicin pain and desmopressin analgesia. In both species, the effects were male specific and dependent on stress levels at the time of testing.


title: CASH: A Cost Asymmetric Secure Hash Algorithm for Optimal Password Protection

abstract: An adversary who has obtained the cryptographic hash of a user's password can mount an offline attack to crack the password by comparing this hash value with the cryptographic hashes of likely password guesses. This offline attacker is limited only by the resources he is willing to invest to crack the password. Key-stretching techniques like hash iteration and memory hard functions have been proposed to mitigate the threat of offline attacks by making each password guess more expensive for the adversary to verify. However, these techniques also increase costs for a legitimate authentication server. We introduce a novel Stackelberg game model which captures the essential elements of this interaction between a defender and an offline attacker. In the game the defender first commits to a key-stretching mechanism, and the offline attacker responds in a manner that optimizes his utility (expected reward minus expected guessing costs). We then introduce Cost Asymmetric Secure Hash (CASH), a randomized key-stretching mechanism that minimizes the fraction of passwords that would be cracked by a rational offline attacker without increasing amortized authentication costs for the legitimate authentication server. CASH is motivated by the observation that the legitimate authentication server will typically run the authentication procedure to verify a correct password, while an offline adversary will typically use incorrect password guesses. By using randomization we can ensure that the amortized cost of running CASH to verify a correct password guess is significantly smaller than the cost of rejecting an incorrect password. Using our Stackelberg game framework we can quantify the quality of the underlying CASH running time distribution in terms of the fraction of passwords that a rational offline adversary would crack. We provide an efficient algorithm to compute high quality CASH distributions for the defender. Finally, we analyze CASH using empirical data from two large scale password frequency datasets. Our analysis shows that CASH can significantly reduce (up to 50%) the fraction of password cracked by a rational offline adversary.


title: Semantic Search Meets the Web

abstract: While semantic search technologies have been proven to work well in specific domains, they still have to confront two main challenges to scale up to the Web in its entirety. In this work we address this issue with a novel semantic search system that a) provides the user with the capability to query Semantic Web information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance keyword search technologies.


title: Analysis of the Structure of Surgical Activity for a Suturing and Knot-Tying Task

abstract: Background ::: Surgical tasks are performed in a sequence of steps, and technical skill evaluation includes assessing task flow efficiency. Our objective was to describe differences in task flow for expert and novice surgeons for a basic surgical task.


title: Social snapshots: digital forensics for online social networks

abstract: Recently, academia and law enforcement alike have shown a strong demand for data that is collected from online social networks. In this work, we present a novel method for harvesting such data from social networking websites. Our approach uses a hybrid system that is based on a custom add-on for social networks in combination with a web crawling component. The datasets that our tool collects contain profile information (user data, private messages, photos, etc.) and associated meta-data (internal timestamps and unique identifiers). These social snapshots are significant for security research and in the field of digital forensics. We implemented a prototype for Facebook and evaluated our system on a number of human volunteers. We show the feasibility and efficiency of our approach and its advantages in contrast to traditional techniques that rely on application-specific web crawling and parsing. Furthermore, we investigate different use-cases of our tool that include consensual application and the use of sniffed authentication cookies. Finally, we contribute to the research community by publishing our implementation as an open-source project.


title: A Compact High Gain Antenna for Small Satellite Applications

abstract: The development of a circularly-polarized compact high-gain antenna to be used for the European Student Earth Orbiter small satellite mission is described. The antenna is realized stacking two coaxial shorted annular patches and placing the top one at half a wavelength from the lower one. At variance of other similar configurations proposed in the past, the one presented in this paper has several important features which can be extremely useful for application in the space environment. In particular, it can be easily fabricated employing suspended technology and its radiation characteristics are to some extent controllable by opportunely varying the geometry. In the following, a straightforward design procedure will be presented detailing the criteria to optimize top and bottom radiating elements. Both numerical and measured results will show that the proposed design satisfies the specifications and presents characteristics comparable to other larger and heavier solutions usually more difficult to design. Specifically, it will be shown that with this configuration it is possible to obtain gain values higher than 12 dB using ground plane of moderate size and that a further improvement up to 15dB can be obtained employing larger ground planes


title: Churn prediction analysis using various clustering algorithms in KNIME analytics platform

abstract: In data mining techniques, Clustering is a performed by grouping objects based on similarity of its characteristics to provide patterns and knowledge of given user data. Different types of clustering algorithms called partitioning, hierarchical and grid based clustering methods. Here k-means clustering, k-medoids clustering, Hierarchical clustering, DBSCAN and Fuzzy c means clustering. Clustering algorithms are used for customer churn analysis; one of the important reasons is that the cost of increasing a new customer is much higher than retaining an existing customer by using customer churn analysis. Initially KNIME analytics platform is used to analyse and visualization of data and later it is used to create model, rules and interactive views ofdata.


title: Metamaterial based compact antenna design for UWB applications

abstract: In this paper, a compact Ultrawideband (UWB) high gain microstrip patch antenna using planar metamaterial structures is proposed. The antenna has two layers of metamaterial structure which are made by etching π-shaped slot on main radiating patch and crossed shaped slots on the ground plane. The inductances and capacitances developed due to the ground plane and patterned radiating patch leads to the left handed behavior of the metamaterial. The proposed antenna has compact size 27.6×30.8mm2 with height 1.6mm and designed on low cost FR4 substrate. The impedance bandwidth (−10dB) of the antenna is 3–12GHz with average gain of 4dBi and the peak gain 5.8 dBi at 9.5 GHz. The results obtained from the simulation studies shows that the antenna has good radiation characteristics for the UWB applications.


title: Does ResNet Learn Good General Purpose Features?

abstract: ResNet with hundreds or even thousands of layers has become the most successful image recognition model in the computer vision community. However, we do not know whether this deeper architecture has better transferability than the traditional models. In this paper, we systematically investigate the well-known ResNet features for different computer vision tasks without fine-tuning. The experimental results show that ResNet did not learn good general purpose features in image retrieval and visual object tracking tasks. GoogLeNet high-level features and VGGNet middle-level features achieve better performance for transfer learning. Therefore, it is not a good idea to choose ResNet model in other computer vision tasks without fine-tuning.


Detachable Balloon Technique for Transvenous Closure of Patent Ductus Arteriosus


title: Distributed Gaussian Processes

abstract: To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-theart sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or variational parameters. The key idea is to recursively distribute computations to independent computational units and, subsequently, recombine them to form an overall result. Efficient closed-form inference allows for straightforward parallelisation and distributed computations with a small memory footprint. The rBCM is independent of the computational graph and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters. With sufficient computing resources our distributed GP model can handle arbitrarily large data sets.


A Systematic Mapping Study on Software Engineering Education


title: A regenerative microchannel device for recording multiple single-unit action potentials in awake, ambulatory animals

abstract: Despite significant advances in robotics, commercially advanced prosthetics provide only a small fraction of the functionality of the amputated limb that they are meant to replace. Peripheral nerve interfacing could provide a rich controlling link between the body and these advanced prosthetics in order to increase their overall utility. Here, we report on the development of a fully integrated regenerative microchannel interface with 30 microelectrodes and signal extraction capabilities enabling evaluation in an awake and ambulatory rat animal model. In vitro functional testing validated the capability of the microelectrodes to record neural signals similar in size and nature to those that occur invivo. In vitro dorsal root ganglia cultures revealed striking cytocompatibility of the microchannel interface. Finally, invivo, the microchannel interface was successfully used to record a multitude of single-unit action potentials through 63% of the integrated microelectrodes at the early time point of 3weeks. This marks a significant advance in microchannel interfacing, demonstrating the capability of microchannels to be used for peripheral nerve interfacing.


Feasibility of Information Interoperability in the Humanitarian Domain.


title: Learning Lightprobes for Mixed Reality Illumination

abstract: This paper presents the first photometric registration pipeline for Mixed Reality based on high quality illumination estimation using convolutional neural networks (CNNs). For easy adaptation and deployment of the system, we train the CNNs using purely synthetic images and apply them to real image data. To keep the pipeline accurate and efficient, we propose to fuse the light estimation results from multiple CNN instances and show an approach for caching estimates over time. For optimal performance, we furthermore explore multiple strategies for the CNN training. Experimental results show that the proposed method yields highly accurate estimates for photo-realistic augmentations.


title: Rich360: optimized spherical representation from structured panoramic camera arrays

abstract: This paper presents Rich360, a novel system for creating and viewing a 360° panoramic video obtained from multiple cameras placed on a structured rig. Rich360 provides an as-rich-as-possible 360° viewing experience by effectively resolving two issues that occur in the existing pipeline. First, a deformable spherical projection surface is utilized to minimize the parallax from multiple cameras. The surface is deformed spatio-temporally according to the depth constraints estimated from the overlapping video regions. This enables fast and efficient parallax-free stitching independent of the number of views. Next, a non-uniform spherical ray sampling is performed. The density of the sampling varies depending on the importance of the image region. Finally, for interactive viewing, the non-uniformly sampled video is mapped onto a uniform viewing sphere using a UV map. This approach can preserve the richness of the input videos when the resolution of the final 360° panoramic video is smaller than the overall resolution of the input videos, which is the case for most 360° panoramic videos. We show various results from Rich360 to demonstrate the richness of the output video and the advancement in the stitching results.


title: DexterJS: robust testing platform for DOM-based XSS vulnerabilities

abstract: DOM-based cross-site scripting (XSS) is a client-side vulnerability that pervades JavaScript applications on the web, and has few known practical defenses. In this paper, we introduce DEXTERJS, a testing platform for detecting and validating DOM-based XSS vulnerabilities on web applications. DEXTERJS leverages source-to source rewriting to carry out character-precise taint tracking when executing in the browser context—thus being able to identify vulnerable information flows in a web page. By scanning a web page, DEXTERJS produces working exploits that validate DOM-based XSS vulnerability on the page. DEXTERJS is robust, has been tested on Alexa’s top 1000 sites, and has found a total of 820 distinct zero-day DOM-XSS confirmed exploits automatically.


title: CarTel: a distributed mobile sensor computing system

abstract: CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors located on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally before delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive.CarTel provides a simple query-oriented programming interface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network connectivity. CarTel nodes rely primarily on opportunistic wireless (e.g., Wi-Fi, Bluetooth) connectivity to the Internet, or to "data mules" such as other CarTel nodes, mobile phone flash memories, or USB keys-to communicate with the portal. CarTel applications run on the portal, using a delay-tolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delay-tolerant network stack, CafNet, to communicat.CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi deployments, and for automotive diagnostics.


title: Memory-efficient Radix-2 FFT processor using CORDIC algorithm

abstract: In this paper, a new memory-efficient architecture is presented for Radix-2 FFT processor using CORDIC algorithm. In the proposed memory-efficient FFT processor, an address decoding scheme is used to generate real time angles for the pipelined Radix-2 butterfly. This eliminates the need for storing twiddle factors and angles. This also results in significant area savings with no negative impact on performance. An efficient addressing scheme is implemented to realize the “in-place” Serial-in and Serial-out memory accessing. Here, the synthesis results match the theoretical analysis and it can be observed that a significant reduction can be achieved in total memory logic.


title: A scalable active framework for region annotation in 3D shape collections

abstract: Large repositories of 3D shapes provide valuable input for data-driven analysis and modeling tools. They are especially powerful once annotated with semantic information such as salient regions and functional parts. We propose a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations. Given a shape collection and a user-specified region label our goal is to correctly demarcate the corresponding regions with minimal manual work. Our active framework achieves this goal by cycling between manually annotating the regions, automatically propagating these annotations across the rest of the shapes, manually verifying both human and automatic annotations, and learning from the verification results to improve the automatic propagation algorithm. We use a unified utility function that explicitly models the time cost of human input across all steps of our method. This allows us to jointly optimize for the set of models to annotate and for the set of models to verify based on the predicted impact of these actions on the human efficiency. We demonstrate that incorporating verification of all produced labelings within this unified objective improves both accuracy and efficiency of the active learning procedure. We automatically propagate human labels across a dynamic shape network using a conditional random field (CRF) framework, taking advantage of global shape-to-shape similarities, local feature similarities, and point-to-point correspondences. By combining these diverse cues we achieve higher accuracy than existing alternatives. We validate our framework on existing benchmarks demonstrating it to be significantly more efficient at using human input compared to previous techniques. We further validate its efficiency and robustness by annotating a massive shape dataset, labeling over 93,000 shape parts, across multiple model classes, and providing a labeled part collection more than one order of magnitude larger than existing ones.


Cross-Lingual Named Entity Recognition via Wikification


title: Discoloration of dye solutions induced by solar photolysis of ferrioxalate in aqueous solutions

abstract: The discoloration of four dyes, C.I. reactive red 2, C.I. reactive black 8, C.I. basic red 13 and C.I. basic yellow 2, were induced by solar photolysis of ferrioxalate in aqueous solutions. The discoloration of the dye solutions were all pseudo-first order reactions. The discoloration rates of the dye solutions in sunlight/ferrioxalate system is about the same as in UV/ferrioxalate system, while much higher than in sunlight/Fe(III)-hydroxy system.


title: Improving recommendation lists through topic diversification

abstract: In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.


title: Joint inference in information extraction

abstract: The goal of information extraction is to extract database records from text or semi-structured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computationally efficient, this approach is suboptimal, because it ignores the fact that segmenting one candidate record can help to segment similar ones. For example, resolving a well-segmented field with a less-clear one can disambiguate the latter's boundaries. In this paper we propose a joint approach to information extraction, where segmentation of all records and entity resolution are performed together in a single integrated inference process. While a number of previous authors have taken steps in this direction (eg., Pasula et al. (2003), Wellner et al. (2004)), to our knowledge this is the first fully joint approach. In experiments on the CiteSeer and Cora citation matching datasets, joint inference improved accuracy, and our approach outperformed previous ones. Further, by using Markov logic and the existing algorithms for it, our solution consisted mainly of writing the appropriate logical formulas, and required much less engineering than previous ones.


A brief introduction to mixed effects modelling and multi-model inference in ecology


title: Analysis of Oscillators Locked by Large Injection Signals: Generalized Adler's Equation and Geometrical Interpretation

abstract: Using the hard-limiting characteristics of transconductors, a new model for injection-locking, applicable for any strong and weak injection, is proposed. Backed by simulations, examples of the powerfulness of this new model are enumerated as proof of the concept.


title: A UML based approach for Modeling ETL processes in data warehouses

abstract: Data warehouses (DWs) are complex computer systems whose main goal is to facilitate the decision making process of knowledge workers. ETL (Extraction-Transformation-Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into DWs. ETL processes are a key component of DWs because incorrect or misleading data will produce wrong business decisions, and therefore, a correct design of these processes at early stages of a DW project is absolutely necessary to improve data quality. However, not much research has dealt with the modeling of ETL processes. In this paper, we present our approach, based on the Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of these ETL processes. We provide the necessary mechanisms for an easy and quick specification of the common operations defined in these ETL processes such as, the integration of different data sources, the transformation between source and target attributes, the generation of surrogate keys and so on. Another advantage of our proposal is the use of the UML (standardization, ease-of-use and functionality) and the seamless integration of the design of the ETL processes with the DW conceptual schema.


title: Procedural Content Generation Using Patterns as Objectives

abstract: In this paper we present a search-based approach for procedural generation of game levels that represents levels as sequences of micro-patterns and searched for meso-patterns. The micro-patterns are “slices” of original human-designed levels from an existing game, whereas the meso-patters are abstractions of common design patterns seen in the same levels. This method generates levels that are similar in style to the levels from which the original patterns were extracted, while still allowing for considerable variation in the geometry of the generated levels. The evolutionary method for generating the levels was tested extensively to investigate the distribution of micro-patterns used and meso-patterns found.


title: Theoretical Analyses and Design of Circular Array to Generate Orbital Angular Momentum

abstract: Uniform circular array (UCA) is a pioneering approach to generate radio twisted beams carrying orbital angular momentum (OAM). However, faulty antenna elements in a UCA could render the array into an imperfect UCA (IUCA), consequently creating distortion for OAM-based communication or radar systems. To analyze the effects of an IUCA on generating OAM, elementary wave function and wave transformation are derived to obtain field equations of a general circular array. Further derivation of IUCA shows that the field of an IUCA is a superposition of OAM modes, which is caused by polluted orthogonalities of OAM modes. To achieve pure OAM modes, the orthogonal matching pursuit algorithm, the convex optimization tool, and the analytical solution are employed to design a feeding network to retrieve the orthogonal property at the cost of strong gain loss. Then, the reduced constraints method is proposed for the IUCA to achieve high-purity and low-loss OAM mode. Finally, four circular arrays with different shapes are explored for limited installation space to generate twisted beams.


title: COGEX: A Logic Prover For Question Answering

abstract: Recent TREC results have demonstrated the need for deeper text understanding methods. This paper introduces the idea of automated reasoning applied to question answering and shows the feasibility of integrating a logic prover into a Question Answering system. The approach is to transform questions and answer passages into logic representations. World knowledge axioms as well as linguistic axioms are supplied to the prover which renders a deep understanding of the relationship between question text and answer text. Moreover, the trace of the proofs provide answer justifications. The results show that the prover boosts the performance of the QA system on TREC questions by 30%.


title: A review of Augmented Reality and its application in context aware library system

abstract: Augmented Reality has revolutionized the way of looking at actuality. The colossal developments in this area have led to visualizations beyond desktop. Unreal objects can be augmented on real surfaces. Augmented Reality has converged the knowledge of computer vision, virtual reality, image-processing, human-to-computer interaction and more such areas. It has found applications in various expanses like Games, Entertainment, Military, Navigation and many more. One such application could be in Library Management System. Earlier systems for book tracking involved immobile computers that only had the shelf numbers nested with book id to locate a book. A borrower needed to ask the Librarian/support staff to look up the shelf number from the system so as to find a book or simply do it manually by himself by searching each shelf. The application of Augmented Reality in Library Administration is useful in automating the proximity location tracking of a book and providing alternatives to the borrower in the books context. The borrower is navigated through the library till he/she reaches the shelf where the book is kept. The spine of the book serves as the index of the required book and contextual information is augmented once the book is located. Thus Augmented Reality can prove immensely helpful for Library Administration.


title: Yawning Detection for Monitoring Driver Fatigue

abstract: Fatigue driving is an important reason of traffic accidents. Yawning is an evidence of driver fatigue. This paper proposes to locate and track a driver's mouth movement using a CCD camera to study on monitoring and recognizing a driver's yawning. Firstly detecting drivers' faces uses Gravity-Center template, then detecting drivers' left and right mouth corners by grey projection, and extracting texture features of drivers' mouth corners (left and right) using Gabor wavelets. Finally LDA is applied to classify feature vectors to detect yawning. The method is tested on 400 images from twenty videos. In contrast, yawning is also detected by the ratio of mouth height and width. The experiment results show that Gabor coefficients are more powerful than geometric features to detect yawning and the average recognition rate is 95% which has more than 20% improvement.


title: A Modular Architecture for Adaptive ChatBots

abstract: We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate.


title: A comparative linkage map of oilseed rape and its use for QTL analysis of seed oil and erucic acid content

abstract: We have developed a new DH mapping population for oilseed rape, named TNDH, using genetically and phenotypically diverse parental lines. We used the population in the construction of a high stringency genetic linkage map, consisting of 277 loci, for use in quantitative genetic analysis. A proportion of the markers had been used previously in the construction of linkage maps for Brassica species, thus permitting the alignment of maps. The map includes 68 newly developed Sequence Tagged Site (STS) markers targeted to the homologues of defined genes of A. thaliana. The use of these markers permits the alignment of our linkage map with the A. thaliana genome sequence. An additional 74 loci (31 newly developed STS markers and 43 loci defined by SSR and RFLP markers that had previously been used in published linkage maps) were added to the map. These markers increased the resolution of alignment of the newly constructed linkage map with existing Brassica linkage maps and the A. thaliana genome sequence. We conducted field trials with the TNDH population at two sites, and over 2 years, and identified reproducible QTL for seed oil content and erucic acid content. The results provide new insights into the genetic control of seed oil and erucic acid content in oilseed rape, and demonstrate the utility of the linkage map and population.


Credibility: A multidisciplinary framework


title: Probable benefits of green tea with genetic implications

abstract: Tea is produced from the Camellia sinensis plant and can generally be divided into categories based on how they are processed. In general, green tea that is unfermented C. sinensis has been considered superior to black tea in health benefits. It contains a unique set of catechins that possess biological activity as antioxidant, anti-inflammatory and antiproliferative, which is potentially significant to the prevention and treatment of various forms of diseases. Oral cavity oxidative stress and inflammation, consequent cigarettes due to nicotine and acrolein, may be reduced in the presence of green tea polyphenols. In addition, green tea polyphenols can close down halitosis through modification of odorant sulfur components. Usually, green tea defends healthy cells from malignant transformation and locally has the ability to induce apoptosis in oral cancer cells. In unison, there is an increasing implication in the health benefits of green tea in the field of oral health. This review will cover recent findings on the therapeutic properties and anticancer health benefits of green tea.


title: Optical background measurement in a potential site for the NEMO KM undersea neutrino telescope

abstract: We describe the measurement of the deep sea optical background in some sites south of Capo Passero, Sicily. A continuous flux of about 440 photons cm -2 s -1 in the wavelength interval 440-550 nm is estimated due to the decays of the 40K contained in the sea water. Bioluminescence light bursts are also observed and an example of time evolution is reported. All the measurements were accomplished using a deep sea module also described in this article.


title: 5-Methyldeoxycytidine monophosphate deaminase and 5-methylcytidyl-DNA deaminase activities are present in human mature sperm cells

abstract: Abstract Human mature sperm cells have a high nuclease and 5-methyldeoxycytidine monophosphate (5-mdCMP) deaminase activity. The deaminase converts the nuclease degradation product 5-mdCMP into dTMP which is further cleaved into thymine and the abasic sugar-phosphate. Both 5-methylcytidine 5′ and 3′ monophosphates are good substrates for the deaminase. 5-methylcytidine is not a good deaminase substrate and 5-methylcytosine (5mC) is not a substrate. A purified fraction of the deaminase free of nucleases deaminates 5mC present in intact methylated double-stranded DNA. 5-mdCMP deaminase co-purifies on SDS–PAGE with dCMP deaminase and has an apparent molecular weight of 25 kDa. The enzyme requires no divalent cations and has a Km of 1.4×10−7 M for 5-mdCMP and a Vmax of 7×10−11 mol/h/μg protein. The possible biological implications of the deaminase’s activities in the present system are discussed.


title: Vicarious pain responders and emotion: Evidence for distress rather than mimicry†‡

abstract: Up to a third of the population experiences pain when seeing another in pain. The mechanisms underlying such vicarious sensory experiences are thought to reflect hyperactive mirror systems (threshold theory) or dysfunctional processing and representation of oneself versus others (self/other theory). This study investigated whether the tendency to experience vicarious pain corresponds to disinhibited physiological reactivity toward other's emotions, and/or greater empathic mimicry of other's physiological state (respiratory behavior) during fear, pain, and positive emotion. Fifty healthy individuals aged 18–55 years (23 vicarious pain responders) completed empathy- and anxiety-related questionnaires, and a film task. Respiration was measured noninvasively with piezoelectric respiration belts while participants viewed emotional film clips depicting three emotions (fear/pain/positive) interspersed with neutral clips. The emotional stimuli depicted scenes in which the characters showed increases or decreases in respiration. The results suggest that vicarious pain responders do not mimic emotional respiratory behavior. Rather, vicarious pain responders had a significantly slower respiration rate for all emotional stimuli (MDiff = 1.40 respiratory cycles, SE = .68), compared to nonresponders. However, this was associated with heightened trait anxiety. The findings suggest vicarious pain is associated with acute distress, rather than empathic mimicry of the emotional states of another.


title: EVIDENCE!BASED RESISTANCE TRAINING RECOMMENDATIONS FOR MUSCULAR HYPERTROPHY

abstract: Objective: There is considerable interest in attaining muscular hypertrophy in recreational gym-goers, bodybuilders, older adults, and persons suffering from immunodeficiency conditions. Multiple review articles have suggested guidelines for the most efficacious training methods to obtain muscular hypertrophy. Unfortunately these included articles that inferred hypertrophy markers such as hormonal measurements, used older techniques that might not be valid (e.g. circumference) and failed to appropriately consider the complexity of training variables. Methods: The present commentary provides a narrative review of literature, summarising main areas of interest and providing evidence-based guidelines towards training for muscular hypertrophy. Conclusions: Evidence supports that persons should train to the highest intensity of effort, thus recruiting as many motor units and muscle fibres as possible, self-selecting a load and repetition range, and performing single sets for each exercise. No specific resistance type appears more advantageous than another, and persons should consider the inclusion of concentric, eccentric and isometric actions within their training regime, at a repetition duration that maintains muscular tension. Between set/exercise rest intervals appear not to affect hypertrophy, and in addition the evidence suggests that training through a limited range of motion might stimulate similar results to full range of motion exercise. The performance of concurrent endurance training appears not to negatively affect hypertrophy, and persons should be advised not to expect uniform muscle growth both along the belly of a muscle or for individual muscles within a group. Finally evidence suggests that short (~3 weeks) periods of detraining in trained persons does not incur significant muscular atrophy and might stimulate greater hypertrophy upon return to training.


title: Very-low-profile, stand-alone, tri-band WLAN antenna design for laptop-tablet computer with complete metal-backed cover

abstract: A very-low-profile, tri-band, planar inverted-F antenna (PIFA) with a height of 3.7 mm only for wireless local area network (WlAN) applications in the 2.4 GHz (2400∼2484 MHz), 5.2 GHz (5150∼5350 MHz), and 5.8 GHz (5725∼5825 MHz) bands for tablet-laptop computer with a metal back cover is presented. The antenna was made of a flat plate of size 18.7 mm × 32.5 mm and bent into a compact structure with the dimensions 3.7 mm × 7 mm × 32.5 mm. The PIFA comprised a radiating top patch, a feeding/shorting portion, and an antenna ground plane. The top patch was placed horizontally above the ground with the feeding/shorting portion connecting perpendicularly therebetween. The antenna was designed in a way that the radiating patch consisted of three branches, in which each branch controlled the corresponding resonant mode in the 2.4/5.2/5.8 GHz bands.


title: The state of the art in online handwriting recognition

abstract: This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. >


title: Regulating the New Information Intermediaries as Gatekeepers of Information Diversity

abstract: Purpose – The purposes of this paper are to deal with the questions: because search engines, social networks and app-stores are often referred to as gatekeepers to diverse information access, what is the evidence to substantiate these gatekeeper concerns, and to what extent are existing regulatory solutions to control gatekeeper control suitable at all to address new diversity concerns? It will also map the different gatekeeper concerns about media diversity as evidenced in existing research before the background of network gatekeeping theory critically analyses some of the currently discussed regulatory approaches and develops the contours of a more user-centric approach towards approaching gatekeeper control and media diversity. Design/methodology/approach – This is a conceptual research work based on desk research into the relevant and communications science, economic and legal academic literature and the relevant laws and public policy documents. Based on the existing evidence as well as on applying t...


title: Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification

abstract: Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different passages. To address this problem, we propose an end-to-end neural model that enables those answer candidates from different passages to verify each other based on their content representations. Specifically, we jointly train three modules that can predict the final answer based on three factors: the answer boundary, the answer content and the cross-passage answer verification. The experimental results show that our method outperforms the baseline by a large margin and achieves the state-of-the-art performance on the English MS-MARCO dataset and the Chinese DuReader dataset, both of which are designed for MRC in real-world settings.


title: Software Engineering for Smart Cyber-Physical Systems -- Towards a Research Agenda: Report on the First International Workshop on Software Engineering for Smart CPS

abstract: Cyber-Physical Systems (CPS) are large interconnected softwareintensive systems that influence, by sensing and actuating, the physical world. Examples are traffic management and power grids. One of the trends we observe is the need to endow such systems with the "smart" capabilities, typically in the form of selfawareness and self-adaptation, along with the traditional qualities of safety and dependability. These requirements combined with specifics of the domain of smart CPS -- such as large scale, the role of end-users, uncertainty, and open-endedness -- render traditional software engineering (SE) techniques not directly applicable; making systematic SE of smart CPS a challenging task. This paper reports on the results of the First International Workshop on Software Engineering of Smart Cyber-Physical Systems (SEsCPS 2015), where participants discussed characteristics, challenges and opportunities of SE for smart CPS, with the aim to outline an agenda for future research in this important area.


title: Collective Vertex Classification Using Recursive Neural Network

abstract: Collective classification of vertices is a task of assigning categories to each vertex in a graph based on both vertex attributes and link structure. Nevertheless, some existing approaches do not use the features of neighbouring vertices properly, due to the noise introduced by these features. In this paper, we propose a graph-based recursive neural network framework for collective vertex classification. In this framework, we generate hidden representations from both attributes of vertices and representations of neighbouring vertices via recursive neural networks. Under this framework, we explore two types of recursive neural units, naive recursive neural unit and long short-term memory unit. We have conducted experiments on four real-world network datasets. The experimental results show that our frame- work with long short-term memory model achieves better results and outperforms several competitive baseline methods.


title: Dynamic Word Embeddings via Skip-Gram Filtering

abstract: We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec [Mikolov, 2013]. These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms---skip-gram smoothing and skip-gram filtering---that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.


title: Multi-task Multi-modal Models for Collective Anomaly Detection

abstract: This paper proposes a new framework for anomaly detection when collectively monitoring many complex systems. The prerequisite for condition-based monitoring in industrial applications is the capability of (1) capturing multiple operational states, (2) managing many similar but different assets, and (3) providing insights into the internal relationship of the variables. To meet these criteria, we propose a multi-task learning approach based on a sparse mixture of sparse Gaussian graphical models (GGMs). Unlike existing fused- and group-lasso-based approaches, each task is represented by a sparse mixture of sparse GGMs, and can handle multi-modalities. We develop a variational inference algorithm combined with a novel sparse mixture weight selection algorithm. To handle issues in the conventional automatic relevance determination (ARD) approach, we propose a new l0-regularized formulation that has guaranteed sparsity in mixture weights. We show that our framework eliminates well-known issues of numerical instability in the iterative procedure of mixture model learning. We also show better performance in anomaly detection tasks on real-world data sets. To the best of our knowledge, this is the first proposal of multi-task GGM learning allowing multi-modal distributions.


title: iLPR: an Indian license plate recognition system

abstract: One of the major concerns of Integrated Traffic Management System (ITMS) in India is the identification of vehicles violating the stop-line at a road crossing. A large number of Indian vehicles do not stop at the designated stop-line and pose serious threat to the pedestrians crossing the roads. The current work reports the technicalities of the i$$ i $$LPR (Indian License Plate Recognition) system implemented at five busy road-junctions in one populous metro city in India. The designed system is capable of localizing single line and two-line license plates of various sizes and shapes, recognizing characters of standard/ non-standard fonts and performing seamlessly in varying weather conditions. The performance of the system is evaluated with a large database of images for different environmental conditions. We have published a limited database of Indian vehicle images in http://code.google.com/p/cmaterdb/ for non-commercial use by fellow researchers. Despite unparallel complexity in the Indian city-traffic scenario, we have achieved around 92 % plate localization accuracy and 92.75 % plate level recognition accuracy over the localized vehicle images.


A Survey on Churn Prediction Techniques in Communication Sector


title: A hierarchical model for object-oriented design quality assessment

abstract: The paper describes an improved hierarchical model for the assessment of high-level design quality attributes in object-oriented designs. In this model, structural and behavioral design properties of classes, objects, and their relationships are evaluated using a suite of object-oriented design metrics. This model relates design properties such as encapsulation, modularity, coupling, and cohesion to high-level quality attributes such as reusability, flexibility, and complexity using empirical and anecdotal information. The relationship or links from design properties to quality attributes are weighted in accordance with their influence and importance. The model is validated by using empirical and expert opinion to compare with the model results on several large commercial object-oriented systems. A key attribute of the model is that it can be easily modified to include different relationships and weights, thus providing a practical quality assessment tool adaptable to a variety of demands.


title: Model-based clustering for social networks

abstract: Network models are widely used to represent relations between interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by attributes of the actors or dyads, and clustering. Interest often focuses on finding clusters of actors or ties, and the number of groups in the data is typically unknown. We propose a new model, the "latent position cluster model", under which the probability of a tie between two actors depends on the distance between them in an unobserved Euclidean 'social space', and the actors' locations in the latent social space arise from a mixture of distributions, each corresponding to a cluster. We propose two estimation methods: a two-stage maximum likelihood method and a fully Bayesian method that uses Markov chain Monte Carlo sampling. The former is quicker and simpler, but the latter performs better. We also propose a Bayesian way of determining the number of clusters that are present by using approximate conditional Bayes factors. Our model represents transitivity, homophily by attributes and clustering simultaneously and does not require the number of clusters to be known. The model makes it easy to simulate realistic networks with clustering, which are potentially useful as inputs to models of more complex systems of which the network is part, such as epidemic models of infectious disease. We apply the model to two networks of social relations. A free software package in the R statistical language, latentnet, is available to analyse data by using the model. Copyright 2007 Royal Statistical Society.


title: A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition

abstract: This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labeling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve realtime semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.


title: Geodesic weighted Bayesian model for salient object detection

abstract: In recent years, a variety of salient object detection methods under Bayesian framework have been proposed and many achieved state of the art. However, those ignore spatial relationships and thus background regions similar to the objects are also highlighted. In this paper, we propose a novel geodesic weighted Bayesian model to address this issue. We consider spatial relationships by attaching more importance to regions which are more likely to be parts of a salient object, thus suppressing background regions. First, we learn a combined similarity via multiple features to measure similarity of adjacent regions. Then, we apply the combined similarity as edge weight to construct an undirected weighted graph and compute geodesic distance. Last, we utilize the geodesic distance to weight the observation likelihood to infer a more precise saliency map. Experiments on several benchmark datasets demonstrate the effectiveness of our model.


title: PGS: Parking Guidance System based on wireless sensor network

abstract: In this paper, we present PGS, a parking guidance system based on wireless sensor network(WSN) which guides a driver to an available parking lot. The system consists of a WSN-based VDS(vehicle detection sub-system) and a management sub-system. The WSN based VDS gathers information on the availability of each parking lot and the management sub-system processes the information and refines them and guides the driver to the available parking lot by controlling a VMS(Variable Messaging System). The paper describes the overall system architecture of PGS from the hardware platform to the application software in the view point of a WSN. We implemented the WSN based VDS of PGS and experimented on the system with several kinds of cars. The experimental results show that PGS succeeds in detecting various kinds of cars and the predicted battery life-time using measured current profiles is over 5 years.


title: An Algorithm for Real-Time Pulse Waveform Segmentation and Artifact Detection in Photoplethysmograms

abstract: Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts’ annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.


title: Development of an omni-directional mobile robot with 3 DOF decoupling drive mechanism

abstract: A new driving mechanism for holonomic omnidirectional mobile robots is designed, which enables 3 DOF motion control by three correspondent actuators in a decoupled manner with no redundancy. A prototype of the omnidirectional mobile robot with the driving mechanism is developed including a parallel link suspension mechanism. The kinematics of the omnidirectional mobile robot is also analyzed, and simulation for velocity control of the robot is performed by a method for velocity modulation with interpolation to achieve the given target position and velocity.


An Extensive Empirical Evaluation of Character-Based Morphological Tagging for 14 Languages


title: Development and Psychometric Evaluation of the Emotional Metric Outcomes (EMO) Questionnaire

abstract: This article describes the development and psychometric evaluation of the Emotional Metric Outcomes (EMO) questionnaire—a new questionnaire designed to assess the emotional outcomes of interaction, especially the interaction of customers with service-provider personnel or software. The EMO is a concise multifactor standardized questionnaire that provides an assessment of transaction-driven personal and relationship emotional outcomes, both positive and negative. The primary purpose of the EMO is to move beyond traditional assessment of satisfaction to achieve a more effective measurement of customers’ emotional responses to products and processes. Psychometric evaluation showed that the EMO and its component scales had high reliability and concurrent validity with loyalty and overall experience metrics in a variety of measurement contexts. Concurrent measurement with the System Usability Scale (SUS) indicated that reported significant correlation of the SUS with likelihood-to-recommend ratings are probabl...


What are the Relationships Between Job and Organizational Tenure, and Job Performance?


title: A Survey on Social Media Anomaly Detection

abstract: Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large amount of work have been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection. In this paper, we present a survey on existing approaches to address this problem. We focus on the new type of anomalous phenomena in the social media and review the recent developed techniques to detect those special types of anomalies. We provide a general overview of the problem domain, common formulations, existing methodologies and potential directions. With this work, we hope to call out the attention from the research community on this challenging problem and open up new directions that we can contribute in the future.


title: AN ANALYTICAL SOLUTION TO THE PERSPECTIVE-N-POINT PROBLEM FOR COMMON PLANAR CAMERA AND FOR CATADIOPTRIC SENSOR

abstract: The Perspective-N-Point problem (PNP) is a notable problem in computer vision. It consists of given N points known in an object coordinate space and their projection onto the image, estimating the distance between the video camera and the set of points. By the use of an unusual formulation, we propose a method to get a strictly analytical solution based on the resolution of linear systems. This solution can be computed instantly and is well adapted to real time computer vision applications. Our approach is general enough to work with a nonlinear sensor like a catadioptric panoramic sensor. To improve the localization accuracy, we also provide a technique to correct geometrical distortion. This algorithm also corrects little errors on intrinsic and extrinsic parameters. Well implemented, this correction can be performed in real time.


title: Wideband measurement of the dielectric constant of an FR4 substrate using a parallel-coupled microstrip resonator

abstract: We have made a wideband measurement of the real part of the dielectric constant of flame retardant #4 epoxy (FR4), a common high-frequency printed-circuit-board insulator. We designed a novel test circuit, an electrically long parallel-cou- pled microstrip resonator, which was etched on a 0.014-in FR4 substrate, manufactured by NELCO, Melville, NY. We used a computer model of the resonator to extract the dielectric constant at the frequencies of zeroes in its measured transmission response. By adjusting the model's dielectric constant, we tuned the fre- quency of each zero to match the measured frequency, yielding the dielectric constant at that frequency. To validate our method and results, we present a simple, but original proof that the frequencies of zeroes in the resonator's transmission response are insensitive to input and output mismatches. Additionally, we compare the measured and predicted response of a two-stub filter designed with our measured data. The fabricated filter's measured return loss and insertion loss from 3 to 12 GHz are within 1% of the predictions of Agilent Technology's Momentum. Index Terms—Dielectric materials, measurement, microstrip resonators, permittivity measurement, printed circuits.


title: Enhanced flexible LoRaWAN node for industrial IoT

abstract: The Industrial Internet of Things (IIoT) is introducing the IoT approach in the industrial automation world, paving the way to innovative services for improving efficiency, reliability and availability of industrial processes and products. The IIoT takes advantage of the collection of large amount of data by means of (wireless) links connecting smart sensors attached to the system of interest. Low Power Wide Area Networks emerged as a viable solution for implementing private cellular like communications. In this paper, the LoRaWAN technology is addressed, thanks to the wide acceptance it received in both industrial and academic worlds. In particular, an enhanced node is proposed as a building block of IIoT-enabled industrial wireless networks. It offers new features: it behaves as a regular node; it can act as a gateway toward legacy/different (wired) networks; and it can extend LoRaWAN coverage acting as a range extender (i.e. a single hop forwarder). After a brief overview of LoRa and LoRaWAN, the paper deals with the features of the realized node, exploiting commercially available hardware. The experimental results show the feasibility of the proposed approach. In particular, the range extender capability of transmitting replicas of an incoming messages is tested for different transmission delays.


title: Models for Optimization of Supply Chain Network Design Integrating the Cost of Quality: A Literature Review

abstract: After focusing on Cost of Quality (CoQ), the companies must now move into integrating the CoQ into Supply Chain upstream and downstream, to address CoQ issues across Supply Chain Network Design (SCND) modeling. In this preliminary study, we propose a state of the art of the published works on models and classifications of SCND incorporating the CoQ existing in the literature. In the second part of this paper, we will present a review by studying and analyzing the application of some meta-heuristics to solve supply chain models. The reviewed meta-heuristics include: the genetic algorithm (GA), and simulated annealing (SA). Finally, we suggest directions for future research.


title: Uniplanar multiband MIMO antennas for laptop computer applications

abstract: This paper proposes a uniplanar 2 × 2 MIMO (Multi-Input Multi-Output) antenna suitable for laptop computer applications. The proposed 2×2 MIMO antenna is composed of two identical antenna elements, and each antenna element is composed of a loop structure and a parasitic element. The size of each antenna element is 38 mm × 7 mm, and it has a small ground plane of size 38 mm × 6 mm. The 6-dB impedance bandwidths of each antenna element are able to cover 1677 — 1905 MHz, 2132 — 2587 MHz and 4860 — 6360 MHz, and the isolation between two antenna elements is greater than 17dB. Furthermore, the measured efficiency is more than 45%.


title: Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm

abstract: With the evolving IoT scenario, computing has spread to the most minuscule everyday activities, leading to a momentous shift in the way applications are developed and deployed. With the volume of impact increasing exponentially, a coherent approach of deploying these applications is critical for an efficient utilization of the network infrastructure. A typical IoT application consists of various modules running together with active interdependencies; traditionally running on the Cloud hosted in global data centres. In this paper, we present a Module Mapping Algorithm for efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. With Fog computing into picture, computation is dynamically distributed across the Fog and Cloud layer, and the modules of an application can thus be deployed closer to the source on devices in the Fog layer. The result of this work can serve as a Micro-benchmark in studies/research related with IoT and Fog Computing, and can be used for Quality of Service (QoS) and Service Level Objective benchmarking for IoT applications. The approach is generic, and applies to a wide range of standardized IoT applications over varied network topologies irrespective of load.


title: Neuroimaging in anxiety disorders

abstract: Over the last few years, neuroimaging techniques have contributed greatly to the identification of the structural and functional neuroanatomy of anxiety disorders. The amygdala seems to be a crucial structure for fear and anxiety, and has consistently been found to be activated in anxiety-provoking situations. Apart from the amygdala, the insula and anterior cinguiate cortex seem to be critical, and ail three have been referred to as the "fear network." In the present article, we review the main findings from three major lines of research. First, we examine human models of anxiety disorders, including fear conditioning studies and investigations of experimentally induced panic attacks. Then we turn to research in patients with anxiety disorders and take a dose look at post-traumatic stress disorder and obsessive-compulsive disorder. Finally, we review neuroimaging studies investigating neural correlates of successful treatment of anxiety, focusing on exposure-based therapy and several pharmacological treatment options, as well as combinations of both.


title: Prediction analysis of risky credit using Data mining classification models

abstract: Gaining as many good credit scores are beneficial for customers in numerous ways and it also allows banks to analyse their clients and to give credit loans to them accordingly. In this paper, we look whether data mining techniques are useful to predict and classify the customer's credit score (good/bad) to overcome the future risks giving loans to clients who cannot repay. We use historical given dataset of a bank for our predictive modelling (general models), banks can use them for the better outcome of their overall credit system. For example, if a customer is assigned a bad credit score after applying these predictive classification models, then the bank will not allow giving that customer a future credit and will quickly analyse all the other risky credits.


title: A SVM based character recognition system

abstract: Character recognition is an art of detecting and recognizing both alphabets and numbers from input images thereby making tremendous advancements in automation process. There are many methods available for character recognition namely OCR by matrix matching, fuzzy logic etc. In this paper, concepts of machine learning are used to develop an efficient algorithm for character recognition. Different feature extraction techniques are designed so as to train the character recognition network. The proposed classifier yields high accuracy than the existing techniques and can be used in high accuracy end to end system.


title: Learning 3D mesh segmentation and labeling

abstract: This paper presents a data-driven approach to simultaneous segmentation and labeling of parts in 3D meshes. An objective function is formulated as a Conditional Random Field model, with terms assessing the consistency of faces with labels, and terms between labels of neighboring faces. The objective function is learned from a collection of labeled training meshes. The algorithm uses hundreds of geometric and contextual label features and learns different types of segmentations for different tasks, without requiring manual parameter tuning. Our algorithm achieves a significant improvement in results over the state-of-the-art when evaluated on the Princeton Segmentation Benchmark, often producing segmentations and labelings comparable to those produced by humans.


title: Hiding stars with fireworks: location privacy through camouflage

abstract: Individuals face privacy risks when providing personal location data to potentially untrusted location based services (LBSs). We develop and demonstrate CacheCloak, a system that enables realtime anonymization of location data. In CacheCloak, a trusted anonymizing server generates mobility predictions from historical data and submits intersecting predicted paths simultaneously to the LBS. Each new predicted path is made to intersect with other users' paths, ensuring that no individual user's path can be reliably tracked over time. Mobile users retrieve cached query responses for successive new locations from the trusted server, triggering new prediction only when no cached response is available for their current locations. A simulated hostile LBS with detailed mobility pattern data attempts to track users of CacheCloak, generating a quantitative measure of location privacy over time. GPS data from a GIS-based traffic simulation in an urban environment shows that CacheCloak can achieve realtime location privacy without loss of location accuracy or availability.


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title: Research on three-layer collaborative filtering recommendation for Online Judge

abstract: Online Judge is widespread for the undergraduate to study programming. The users usually feel confused while locating the problems they prefer from the massive ones. This paper proposes a specialized recommendation model for the online judge systems in order to present the alternative problems to the users which they may be interested in potentially. In this model, a three-level collaborative filtering recommendation method is referred to and redesigned catering for the specific interaction mode of Online Judge. This method is described in detail in this paper and implemented in our demo system which demonstrates its availability.


title: Neural Coding and Decoding: Communication Channels and Quantization

abstract: We present a novel analytical approach for studying neural encoding. As a first step we model a neural sensory system as a communication channel. Using the method of typical sequence in this context, we show that a coding scheme is an almost bijective relation between equivalence classes of stimulus/response pairs. The analysis allows a quantitative determination of the type of information encoded in neural activity patterns and, at the same time, identification of the code with which that information is represented. Due to the high dimensionality of the sets involved, such a relation is extremely difficult to quantify. To circumvent this problem, and to use whatever limited data set is available most efficiently, we use another technique from information theory--quantization. We quantize the neural responses to a reproduction set of small finite size. Among many possible quantizations, we choose one which preserves as much of the informativeness of the original stimulus/response relation as possible, through the use of an information-based distortion function. This method allows us to study coarse but highly informative approximations of a coding scheme model, and then to refine them automatically when more data become available.


title: Information-preserving hybrid data reduction based on fuzzy-rough techniques

abstract: Data reduction plays an important role in machine learning and pattern recognition with a high-dimensional data. In real-world applications data usually exists with hybrid formats, and a unified data reducing technique for hybrid data is desirable. In this paper, an information measure is proposed to computing discernibility power of a crisp equivalence relation or a fuzzy one, which is the key concept in classical rough set model and fuzzy-rough set model. Based on the information measure, a general definition of significance of nominal, numeric and fuzzy attributes is presented. We redefine the independence of hybrid attribute subset, reduct, and relative reduct. Then two greedy reduction algorithms for unsupervised and supervised data dimensionality reduction based on the proposed information measure are constructed. Experiments show the reducts found by the proposed algorithms get a better performance compared with classical rough set approaches.


title: Data quality in ontology-based data access: the case of consistency

abstract: Ontology-based data access (OBDA) is a new paradigm aiming at accessing and managing data by means of an ontology, i.e., a conceptual representation of the domain of interest in the underlying information system. In the last years, this new paradigm has been used for providing users with abstract (independent from technological and system-oriented aspects), effective, and reasoning-intensive mechanisms for querying the data residing at the information system sources. In this paper we argue that OBDA, besides querying data, provides the right principles for devising a formal approach to data quality. In particular, we concentrate on one of the most important dimensions considered both in the literature and in the practice of data quality, namely consistency. We define a general framework for data consistency in OBDA, and present algorithms and complexity analysis for several relevant tasks related to the problem of checking data quality under this dimension, both at the extensional level (content of the data sources), and at the intensional level (schema of the data sources).