title: Identifying and Categorizing Anomalies in Retinal Imaging Data

abstract: The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for the observed data. A deep convolutional autoencoder is trained on healthy retinal images. The learned model generates a new feature representation, and the distribution of healthy retinal patches is estimated by a one-class support vector machine. Results demonstrate that we can identify pathologic regions in images without using expert annotations. A subsequent clustering categorizes findings into clinically meaningful classes. In addition the learned features outperform standard embedding approaches in a classification task.


title: Parameter Estimation Incorporating Modal Data and Boundary Conditions

abstract: A new error function is introduced to use natural frequencies and associated mode shapes measured at a selected subset of degrees of freedom for stiffness and mass parameter estimation at the element level. The incomplete mode shapes are used to form the “modal stiffness-based error function” by condensing out the mode shapes at the unmeasured degrees of freedom. Structural modeling errors cannot be completely avoided in any analytical procedure that relies on finite-element models; however, some errors may be controlled. Two elements are used for the first time in parameter estimation to more accurately capture the structural behavior at complex interfaces, thus reducing error. First, the soil-substructure superelement is introduced into the parameter estimation and is intended to capture stiffness and mass properties of the foundation. Second, the partially restrained frame element enables connections to be modeled as partially restrained, in addition to the more traditional fixed or pinned assumptions....


title: Exact optimization for markov random fields with convex priors

abstract: We introduce a method to solve exactly a first order Markov random field optimization problem in more generality than was previously possible. The MRF has a prior term that is convex in terms of a linearly ordered label set. The method maps the problem into a minimum-cut problem for a directed graph, for which a globally optimal solution can be found in polynomial time. The convexity of the prior function in the energy is shown to be necessary and sufficient for the applicability of the method.


title: School Readiness as a Longitudinal Predictor of Social-Emotional and Reading Performance Across the Elementary Grades:

abstract: This study is a brief psychometric report examining the Kindergarten Student Entrance Profile (KSEP). Multiple regression models were tested examining associations between kindergarten teachers’ ra...


Differential Representations for Mesh Processing


title: An Intelligent Hybrid MAC With Traffic-Differentiation-Based QoS for Wireless Sensor Networks

abstract: In this paper, we present the Intelligent Hybrid MAC (IH-MAC), a novel low power with quality of service guaranteed medium access control protocol for wireless sensor networks (WSNs). The IH-MAC achieves high energy efficiency under wide range of traffic load. It ensures shorter latency to critical and delay-sensitive packets. The IH-MAC protocol achieves high channel utilization during high traffic load without compromising energy efficiency. The IH-MAC does it by using the strength of CSMA and TDMA approach with intelligence. The novel idea behind the IH-MAC is that it uses both the broadcast scheduling and link scheduling. Depending on the network loads, the IH-MAC protocol dynamically switches from broadcast scheduling to link scheduling and vice versa in order to achieve better efficiency. The scheduling is done in the IH-MAC with a novel decentralized approach where the nodes locally use the clock arithmetic to find the time slot, allocated for it. Furthermore, the IH-MAC uses Request-To-Send, Clear-To-send handshakes with methods for adapting the transmit power to the minimum level necessary to reach the intended neighbor. Thus, the IH-MAC reduces energy consumption by suitably varying the transmit power. The IH-MAC also uses the concept of parallel transmission that further reduces delay. The analytical and simulation results corroborate the theoretical idea, and show the efficiency of our proposed protocol.


title: Effects of caffeine on mood and performance: a study of realistic consumption

abstract: Abstract ::: Rationale. There is a vast literature on the behavioural effects of caffeine. Many of the studies have involved single administration of a large dose of caffeine that is not representative of the way in which caffeine is usually ingested. Further information is required, therefore, on the behavioural effects of realistic patterns of consumption. ::: Objectives. The present study aimed to determine whether a realistic drinking regime (multiple small doses – 4×65 mg over a 5-h period) produced the same effects as a single large dose (200 mg). The smaller doses were selected so that the amount of caffeine present in the body after 5 h would be equivalent to that found with the single dose. ::: Methods. A double-blind, placebo-controlled, within-subjects experiment was, therefore, carried out. The participants (n=24) attended for four sessions. Each session started with a baseline measurement of mood and performance at 0930 hours. On two of the sessions, coffee was then consumed at 1000, 1100, 1200 and 1300 hours. In one of these sessions 65 mg caffeine was added to the de-caffeinated coffee. In the other two sessions, the participants consumed coffee at 1300 hours and 200 mg caffeine was added in one of the sessions. The volunteers completed the battery of tests again at 1500 hours. ::: Results. The results showed that in both consumption regimes caffeine led to increased alertness and anxiety and improved performance on simple and choice reactive tasks, a cognitive vigilance task, a task requiring sustained response and a dual task involving tracking and target detection. ::: Conclusions. These results suggest that previous findings from studies using a large single dose may be applicable to normal patterns of caffeine consumption.


Interpretable Deep Learning Framework for Predicting all-cause 30-day ICU Readmissions


title: Modeling Input Validation in UML

abstract: Security is an integral part of most software systems but it is not considered as an explicit part in the development process yet. Input validation is the most critical part of software security that is not covered in the design phase of software development life-cycle resulting in many security vulnerabilities. Our objective is to extend UML to new integrated framework for model driven security engineering leading to ideal way to design more secure software. Input validation in UML has not been addressed previously, hence we incorporate input validation into UML diagrams such as use case, class, sequence and activity. This approach has some advantages such as preventing from common input tampering attacks, having both security and convenience in software at high level of abstraction and ability of solving the problem of weak security background for developers.


title: An analysis of transformations

abstract: [Read at a RESEARCH METHODS MEETING of the SOCIETY, April 8th, 1964, Professor D. V. LINDLEY in the Chair] SUMMARY In the analysis of data it is often assumed that observations Yl, Y2, *-, Yn are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality, homoscedasticity and additivity to the transformation are separated. The relation of the present methods to earlier procedures for finding transformations is discussed. The methods are illustrated with examples.


title: A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations

abstract: This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as transitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple. That is, a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator. This idea is applied to TransE, TransR, and TransD to produce lppTransE, lppTransR, and lppTransD, respectively. According to the experimental results on link prediction and triple classification, the proposed logical property preserving embeddings show the state-of-the-art performance at both tasks. These results prove that it is critical to preserve logical properties of relations while embedding knowledge graphs, and the proposed method does it effectively.


title: Blockchain Security in Cloud Computing: Use Cases, Challenges, and Solutions

abstract: Blockchain has drawn attention as the next-generation financial technology due to its security that suits the informatization era. In particular, it provides security through the authentication of peers that share virtual cash, encryption, and the generation of hash value. According to the global financial industry, the market for security-based blockchain technology is expected to grow to about USD 20 billion by 2020. In addition, blockchain can be applied beyond the Internet of Things (IoT) environment; its applications are expected to expand. Cloud computing has been dramatically adopted in all IT environments for its efficiency and availability. In this paper, we discuss the concept of blockchain technology and its hot research trends. In addition, we will study how to adapt blockchain security to cloud computing and its secure solutions in detail.


title: Classification of gene microarrays by penalized logistic regression

abstract: SUMMARY Classification of patient samples is an important aspect of cancer diagnosis and treatment. The support vector machine (SVM) has been successfully applied to microarray cancer diagnosis problems. However, one weakness of the SVM is that given a tumor sample, it only predicts a cancer class label but does not provide any estimate of the underlying probability. We propose penalized logistic regression (PLR) as an alternative to the SVM for the microarray cancer diagnosis problem. We show that when using the same set of genes, PLR and the SVM perform similarly in cancer classification, but PLR has the advantage of additionally providing an estimate of the underlying probability. Often a primary goal in microarray cancer diagnosis is to identify the genes responsible for the classification, rather than class prediction. We consider two gene selection methods in this paper, univariate ranking (UR) and recursive feature elimination (RFE). Empirical results indicate that PLR combined with RFE tends to select fewer genes than other methods and also performs well in both cross-validation and test samples. A fast algorithm for solving PLR is also described.


title: Generative Approaches to Ergativity

abstract: This article surveys the principal generative syntactic analyses that have been proposed for ergativity, found primarily in Inuit, Austronesian, Mayan, and Pama-Nyungan language families. The main puzzle for generative grammar is how to analyze the behavior of ergative and absolutive arguments in terms of the grammatical functions of subject and object. I show in this article that early approaches tend to treat the absolutive uniformly as a subject or an object, while later analyses move toward disassociating case from grammatical function. Descriptively speaking, this article identifies two types of morphological ergativity, differing in how absolutive case is assigned. Morphological ergativity is also distinguished from syntactic ergativity, which is characterized primarily by a restriction that only absolutives can undergo A’-movement. In other aspects of the grammar, ergativity is not strikingly different from accusativity.


title: Political Bots and the Manipulation of Public Opinion in Venezuela

abstract: Social and political bots have a small but strategic role in Venezuelan political conversations. These automated scripts generate content through social media platforms and then interact with people. In this preliminary study on the use of political bots in Venezuela, we analyze the tweeting, following and retweeting patterns for the accounts of prominent Venezuelan politicians and prominent Venezuelan bots. We find that bots generate a very small proportion of all the traffic about political life in Venezuela. Bots are used to retweet content from Venezuelan politicians but the effect is subtle in that less than 10 percent of all retweets come from bot-related platforms. Nonetheless, we find that the most active bots are those used by Venezuela’s radical opposition. Bots are pretending to be political leaders, government agencies and political parties more than citizens. Finally, bots are promoting innocuous political events more than attacking opponents or spreading misinformation.


title: Deep Visual Attention Prediction

abstract: In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although convolutional neural networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve the CNN-based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark data sets demonstrate our method yields the state-of-the-art performance with competitive inference time. 1 1 Our source code is available at https://github.com/wenguanwang/deepattention .


title: Opportunities via extended networks for teens' informal learning

abstract: With the increasing use of the Internet and social media for knowledge and social connections, we might expect that people more easily expand their opportunities for new learning through social relationships online. Yet, this mixed methods study of teens' use of technologies (e.g. YouTube and social media) in informal learning contexts reveals that, in teens' preferred learning activities, few interact with new ties outside their immediate networks (school, family, and friends). Given the value of social interaction and weak ties for learning new knowledge, this research investigates teens' use of networked technologies with people and resources outside their immediate networks. Based on 23 semi-structured interviews, we describe teens' informal learning activities and technology practices, from which we identify design opportunities. To inform these, we examine teens' paths into extended networks, including the role of digital skills, technology access, intrinsic motivation, and sense of relatedness. We find that relatedness both motivated and inhibited teens to reach beyond immediate networks into extended networks for informal learning activities.


title: ESD Design Strategies for High-Speed Digital and RF Circuits in Deeply Scaled Silicon Technologies

abstract: Challenges of electrostatic discharge (ESD) protection in deeply scaled silicon technologies are addressed by improving design, characterization, and modeling of I/O MOSFETs, interconnect, ESD protection and power clamp devices. Recent progress on ESD protection design for both high-speed digital I/O and radiofrequency (RF) circuits are presented. Topological trade-offs are compared. High speed circuit protection techniques such as the T-coil based ESD design are reviewed in detail. Package- and wafer-level charged device model (CDM) correlation issues are discussed. I/O, ESD devices, and metal interconnect effects are examined using very fast transmission line pulses (VF-TLP) and TLP.


Submarine erosion on the anoxic sea floor: stratinomic, palaeoenvironmental, and temporal significance of reworked pyritebone deposits


title: Tensor object classification via multilinear discriminant analysis network

abstract: This paper proposes an multilinear discriminant analysis network (MLDANet) for the recognition of multidimensional objects, knows as tensor objects. The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms. The MLDANet consists of three parts: 1) The encoder learned by MLDA from tensor data. 2) Features maps obtained from decoder. 3) The use of binary hashing and histogram for feature pooling. A learning algorithm for MLDANet is described. Evaluations on UCF11 database indicate that the proposed MLDANet outperforms the PCANet, LDANet, MPCA+LDA, and MLDA in terms of classification for tensor objects.


title: Causal Consistency for Geo-Replicated Cloud Storage under Partial Replication

abstract: Data replication is a common technique used for fault-tolerance in reliable distributed systems. In geo-replicated systems and the cloud, it additionally provides low latency. Recently, causal consistency in such systems has received much attention. However, all existing works assume the data is fully replicated. This greatly simplifies the design of the algorithms to implement causal consistency. In this paper, we propose that it can be advantageous to have partial replication of data, and we propose two algorithms for achieving causal consistency in such systems where the data is only partially replicated. This is the first work that explores causal consistency for partially replicated geo-replicated systems. We also give a special case algorithm for causal consistency in the full-replication case.


title: Some from Here, Some from There: Cross-Project Code Reuse in GitHub

abstract: Code reuse has well-known benefits on code quality, coding efficiency, and maintenance. Open Source Software (OSS) programmers gladly share their own code and they happily reuse others'. Social programming platforms like GitHub have normalized code foraging via their common platforms, enabling code search and reuse across different projects. Removing project borders may facilitate more efficient code foraging, and consequently faster programming. But looking for code across projects takes longer and, once found, may be more challenging to tailor to one's needs. Learning how much code reuse goes on across projects, and identifying emerging patterns in past cross-project search behavior may help future foraging efforts. To understand cross-project code reuse, here we present an in-depth study of cloning in GitHub. Using Deckard, a clone finding tool, we identified copies of code fragments across projects, and investigate their prevalence and characteristics using statistical and network science approaches, and with multiple case studies. By triangulating findings from different methods, we find that cross-project cloning is prevalent in GitHub, ranging from cloning few lines of code to whole project repositories. Some of the projects serve as popular sources of clones, and others seem to contain more clones than their fair share. Moreover, we find that ecosystem cloning follows an onion model: most clones come from the same project, then from projects in the same application domain, and finally from projects in different domains. Our results show directions for new tools that can facilitate code foraging and sharing within GitHub.


title: Text Line Segmentation of Historical Documents: a Survey

abstract: There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade and dedicated to documents of historical interest.


title: Rate adaptation for directional multicast in IEEE 802.11ad networks

abstract: In this paper, we present a novel rate adaptation algorithm for directional multicast with a delay constraint in IEEE 802.11ad networks, which is an emerging communication technology over 60Ghz millimeter wave. The proposed algorithm seeks to find out appropriate transmission rates that preserve fairness among sectors and accommodate as many multicast devices as possible. Extensive simulation results are given to show the effectiveness of the proposed algorithm.


title: DBDC : Density Based Distributed Clustering

abstract: Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as many others. In most of these areas, the data are originally collected at different sites. In order to extract information from these data, they are merged at a central site and then clustered. In this paper, we propose a different approach. We cluster the data locally and extract suitable representatives from these clusters. These representatives are sent to a global server site where we restore the complete clustering based on the local representatives. This approach is very efficient, because the local clustering can be carried out quickly and independently from each other. Furthermore, we have low transmission cost, as the number of transmitted representatives is much smaller than the cardinality of the complete data set. Based on this small number of representatives, the global clustering can be done very efficiently. For both the local and the global clustering, we use a density based clustering algorithm. The combination of both the local and the global clustering forms our new DBDC (Density Based Distributed Clustering) algorithm. Furthermore, we discuss the complex problem of finding a suitable quality measure for evaluating distributed clusterings. We introduce two quality criteria which are compared to each other and which allow us to evaluate the quality of our DBDC algorithm. In our experimental evaluation, we will show that we do not have to sacrifice clustering quality in order to gain an efficiency advantage when using our distributed clustering approach.


title: Privacy and Anonymity in VANETs: A Contemporary Study.

abstract: Security and Privacy issues are among the most important concerns for Vehicular Ad hoc Network (VANET) users, and a number of papers have proposed various implementation schemes for one or the other, or both, in recent years. In this paper, we survey the existing work on security and privacy in VANETs from several distinct viewpoints, and classify them into distinct categories based on their cryptographic techniques, implementation methodologies, functionality, and other important characteristics. We identify the pertinent characteristics of different approaches and highlight their comparative advantages and disadvantages.


title: State-of-the-art autogenous ear reconstruction in cases of microtia.

abstract: Ear reconstruction is considered to be a challenging form of surgery. In cases of microtia, surgeons must reconstruct complex missing contours, which necessitates the use of a support and skin remnants to cover this support. Although the use of synthetic material has been proposed in order to avoid harvesting and carving cartilage, the best long-term choice for reconstructing an ear is autologous rib cartilage. This procedure requires good understanding of the 3-dimensional architecture of the ear and learning the step-by-step construction of a harmonious framework (which with practice will become the most straightforward part of the procedure). Surgery, usually performed at the age of 9 or 10 years, is planned in 2 stages. In the first stage, the framework is placed under a skin pocket. Six months later, the sulcus is created using an additional cartilage graft for projection and a skin-grafted galeal fascial flap. In order to shorten the learning curve, a detailed carving process is described here, as well as a tool to enable training before surgery. Remnants of the microtic ear can have many different shapes; therefore, a comprehensive approach to skin management is proposed, providing a simple surgical classification for all types of microtia. Furthermore, some refinements of the cartilage framework and the construction of the retroauricular sulcus have improved results. Whenever possible, successful reconstruction of a microtic ear with autologous rib cartilage, as opposed to synthetic materials, is by far the best option.


title: Supporting Process Model Validation through Natural Language Generation

abstract: The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.


title: Shading-based refinement on volumetric signed distance functions

abstract: We present a novel method to obtain fine-scale detail in 3D reconstructions generated with low-budget RGB-D cameras or other commodity scanning devices. As the depth data of these sensors is noisy, truncated signed distance fields are typically used to regularize out the noise, which unfortunately leads to over-smoothed results. In our approach, we leverage RGB data to refine these reconstructions through shading cues, as color input is typically of much higher resolution than the depth data. As a result, we obtain reconstructions with high geometric detail, far beyond the depth resolution of the camera itself. Our core contribution is shading-based refinement directly on the implicit surface representation, which is generated from globally-aligned RGB-D images. We formulate the inverse shading problem on the volumetric distance field, and present a novel objective function which jointly optimizes for fine-scale surface geometry and spatially-varying surface reflectance. In order to enable the efficient reconstruction of sub-millimeter detail, we store and process our surface using a sparse voxel hashing scheme which we augment by introducing a grid hierarchy. A tailored GPU-based Gauss-Newton solver enables us to refine large shape models to previously unseen resolution within only a few seconds.


title: Capacity calculation for collaborative communication with imperfect phase synchronization, AWGN and fading in sensor networks

abstract: Collaborative communication produces high power gain and significantly mitigates the fading if both frequency and phase synchronization are achieved. In this paper, a procedure of the capacity calculation for collaborative communication system with imperfect phase synchronization that includes the influence of noise and Rayleigh fading is proposed, modeled, theoretically analyzed and simulated. The exact formula for channel capacity as a function of channel parameters and the number of collaborative nodes is derived. It was confirmed that using collaborative communication, the capacity of the network can be increased and energy consumption can be reduced.


title: A Composable Deadlock-Free Approach to Object-Based Isolation

abstract: A widely used principle in the design of concurrent programs is isolation – the property that a task can operate on shared data without interference from other tasks. In this paper, we introduce a new approach to object-based isolation that is guaranteed to be deadlock-free, while still retaining the rollback benefits of transactions. Further, our approach differentiates between read and write accesses in its concurrency control mechanisms. Finally, since the generality of our approach precludes the use of static ordering for deadlock avoidance, our runtime ensures deadlock-freedom by detecting and resolving deadlocks at runtime automatically, without involving the programmer.


title: RELATIONSHIPS BETWEEN GRADUATE TRAINING, PROFESSIONAL AFFILIATION, AND INDIVIDUAL PSYCHOLOGICAL ASSESSMENT PRACTICES FOR PERSONNEL DECISIONS

abstract: Survey data on 321 psychologists who conduct individual assessments were analyzed to compare practices and opinions of those with differing graduate training (I/O or non-I/O) and professional affiliation (SIOP or non-SIOP). Differences in practice on the basisof training and affiliation occur in licensing, types of assessments conducted, specific testing instruments used, and knowledge of legal guidelines governing selection practices. However, when purpose of assessment was controlled for by examining only managerial selection assessments, few practice differences related to training and affiliation emerged. Implications for the training of individual assessors are discussed, and several practice variations warranting further research are presented.


title: Characterization of electro-adhesives for robotic applications

abstract: Recent research in the field of climbing robots indicates that electro-adhesion may provide an attachment method for robots to adhere to a wide range of materials. This study compares the use of several different styles and shapes of electrodes while maintaining a similar electrode area. Electro-adhesion shear force testing was performed on electro-adhesive samples made with four different insulating polymers. These results are compared in order to indicate the best type of insulating material for electro-adhesives. Shear force adhesion tests are carried out on and compared for a wide range of common building materials.


title: Assignment Algorithms for Modeling Resource Contention in Multirobot Task Allocation

abstract: This paper considers multirobot task allocation problems where the estimated costs for performing tasks are interrelated, and the overall team objective need not be a standard sum-of-costs (or utilities) model, enabling straightforward treatment of the additional costs incurred by resource contention. In the model we introduce, a team may choose one of a set of shared resources to perform a task (e.g., several routes to reach a destination), and interference is modeled when multiple robots use the same resource. We show that the general problem is NP-hard, and investigate specialized subinstances with particular cost structures. For the general problem, we describe an exact algorithm which finds an optimal assignment in a reasonable time on small instances. Aiming at larger problems, we turn two particular subinstances, introducing an two algorithms that find assignments quickly even for problems of considerable size, the first being optimal, the second being an approximation algorithm but also producing high-quality solutions with bounded suboptimality.


title: Accuracy Study of Close Range 3D Object Reconstruction Based on Point Clouds

abstract: The paper presents the results of multi-view imagery 3D reconstruction of the close range building using the geodetic and photogrammetric networks and image based point clouds. DSLR camera was used to acquire the block of 28 terrestrial multiple convergent photos. To create the benchmark data set, the bundle block adjustment with camera self calibration in the Pictran software package was performed. For the 3D reconstruction the RealityCapture, Photoscan and Pix4D software were used. Software comparison was performed using mesh models. The analysis was concerned the comparison of processing parameters, checking the visual quality of the outputs and the evaluation of the 3D models accuracy. The study proved the highest usefulness of the RealityCapture application. The tested software allowed to the point matching on the control points with mean standard deviation lower than 0.5 pixel, mean adjustment RMS deviation lower than 3 mm and average points position error lower than 4 mm.


title: Nurse turnover: a literature review - an update.

abstract: BACKGROUND ::: Concerns related to the complex issue of nursing turnover continue to challenge healthcare leaders in every sector of health care. Voluntary nurse turnover is shown to be influenced by a myriad of inter-related factors, and there is increasing evidence of its negative effects on nurses, patients and health care organizations. ::: ::: ::: OBJECTIVES ::: The objectives were to conduct a comprehensive review of the related literature to examine recent findings related to the issue of nursing turnover and its causes and consequences, and to identify on methodological challenges and the implications of new evidence for future studies. ::: ::: ::: DESIGN ::: A comprehensive search of the recent literature related to nursing turnover was undertaken to summarize findings published in the past six years. ::: ::: ::: DATA SOURCES ::: Electronic databases: MEDLINE, CINAHL and PubMed, reference lists of journal publications. ::: ::: ::: REVIEW METHODS ::: Keyword searches were conducted for publications published 2006 or later that examined turnover or turnover intention in employee populations of registered or practical/enrolled or assistant nurses working in the hospital, long-term or community care areas. Literature findings are presented using an integrative approach and a table format to report individual studies. ::: ::: ::: RESULTS ::: From about 330 citations or abstracts that were initially scanned for content relevance, 68 studies were included in this summary review. The predominance of studies continues to focus on determinants of nurse turnover in acute care settings. Recent studies offer insight into generational factors that should be considered in strategies to promote stable staffing in healthcare organizations. ::: ::: ::: CONCLUSIONS ::: Nursing turnover continues to present serious challenges at all levels of health care. Longitudinal research is needed to produce new evidence of the relationships between nurse turnover and related costs, and the impact on patients and the health care team.


title: An Automatic Three-Dimensional Scene Reconstruction System Using Crowdsourced Geo-Tagged Videos

abstract: Automatic 3-D scene reconstruction is a useful technique in modern intelligent systems. Scene reconstruction from video sequences requires a selection of representative video frames. Most previous works employ the content-based techniques to automatically extract key frames. These methods take no frame geo-information into account and may be computationally heavy. In this paper, we propose a new key frame selection scheme based on the video geographic cues. Currently, an increasing number of user-generated videos are collected, which is a trend driven by the popularity of smartphones. In addition, it is convenient to acquire and fuse various sensor data (e.g., the geo-spatial metadata) for creating the geo-tagged mobile videos. Nowadays, large repositories of media content are automatically geo-tagged. Our proposed technique utilizes these underlying geo-metadata to select the most representative frames. We first eliminate irrelevant frames in which the candidate 3-D object does not appear. Then, a fixed number of key frames are selected. The criterion is that the selected key frames can maximally cover the candidate 3-D object/scene. Comprehensive experiments demonstrate the high quality of the reconstructed 3-D objects. Moreover, the execution time is reduced by 90%.


title: Novice Programmers and the Problem Description Effect

abstract: It is often debated whether a problem presented in a straightforward minimalist fashion is better, or worse, for learning than the same problem presented with a "real-life" or "concrete" context. The presentation, contextualization, or "problem description" has been well studied over several decades in disciplines such as mathematics education and psychology; however, little has been published in the field of computing education. In psychology it has been found that not only the presence of context, but the type of context can have dramatic results on problem success. In mathematics education it has been demonstrated that there are non-mathematical factors in problem presentation that can affect success in solving the problem and learning. The contextual background of a problem can also impact cognitive load, which should be considered when evaluating the effects of context. Further, it has been found that regarding cognitive load, computer science has unique characteristics compared to other disciplines, with the consequence that results from other disciplines may not apply to computer science, thus requiring investigation within computer science. This paper presents a multi-national, multi-institutional study of the effects of problem contextualization on novice programmer success in a typical CS1 exercise.


title: The Shifting Sands of Creative Thinking: Connections to Dual Process Theory and Implications for Creativity Training

abstract: Dual-process models of cognition suggest that there are two types of thought: autonomous Type 1 processes and working memory dependent Type 2 processes that support hypothetical thinking. Models of creative thinking also distinguish between two sets of thinking processes: those involved in the generation of ideas and those involved with their refinement, evaluation, and/or selection. Here we review dual-process models in both these literatures and delineate the similarities and differences. Both generative creative processing and evaluative creative processing involve elements that have been attributed to each of the dual processes of cognition. We explore the notion that creative thinking may rest upon the nature of a shifting process between Type 1 and Type 2 dual processes. We suggest that a synthesis of the evidence bases on dual-process models of cognition and of creative thinking, together with developing time-based approaches to explore the shifting process, could better inform the development of i...


title: An Underwater Color Image Quality Evaluation Metric

abstract: Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.


title: Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt

abstract: Abstract Urban growth is a worldwide phenomenon but the rate of urbanization is very fast in developing country like Egypt. It is mainly driven by unorganized expansion, increased immigration, rapidly increasing population. In this context, land use and land cover change are considered one of the central components in current strategies for managing natural resources and monitoring environmental changes. In Egypt, urban growth has brought serious losses of agricultural land and water bodies. Urban growth is responsible for a variety of urban environmental issues like decreased air quality, increased runoff and subsequent flooding, increased local temperature, deterioration of water quality, etc. Egypt possessed a number of fast growing cities. Mansoura and Talkha cities in Daqahlia governorate are expanding rapidly with varying growth rates and patterns. In this context, geospatial technologies and remote sensing methodology provide essential tools which can be applied in the analysis of land use change detection. This paper is an attempt to assess the land use change detection by using GIS in Mansoura and Talkha from 1985 to 2010. Change detection analysis shows that built-up area has been increased from 28 to 255 km 2 by more than 30% and agricultural land reduced by 33%. Future prediction is done by using the Markov chain analysis. Information on urban growth, land use and land cover change study is very useful to local government and urban planners for the betterment of future plans of sustainable development of the city.


title: Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform.

abstract: Rapid advances in sequencing technology have changed the experimental landscape of microbial ecology. In the last 10 years, the field has moved from sequencing hundreds of 16S rRNA gene fragments per study using clone libraries to the sequencing of millions of fragments per study using next-generation sequencing technologies from 454 and Illumina. As these technologies advance, it is critical to assess the strengths, weaknesses, and overall suitability of these platforms for the interrogation of microbial communities. Here, we present an improved method for sequencing variable regions within the 16S rRNA gene using Illumina's MiSeq platform, which is currently capable of producing paired 250-nucleotide reads. We evaluated three overlapping regions of the 16S rRNA gene that vary in length (i.e., V34, V4, and V45) by resequencing a mock community and natural samples from human feces, mouse feces, and soil. By titrating the concentration of 16S rRNA gene amplicons applied to the flow cell and using a quality score-based approach to correct discrepancies between reads used to construct contigs, we were able to reduce error rates by as much as two orders of magnitude. Finally, we reprocessed samples from a previous study to demonstrate that large numbers of samples could be multiplexed and sequenced in parallel with shotgun metagenomes. These analyses demonstrate that our approach can provide data that are at least as good as that generated by the 454 platform while providing considerably higher sequencing coverage for a fraction of the cost.


title: Voluntary motion support control of Robot Suit HAL triggered by bioelectrical signal for hemiplegia

abstract: Our goal is to enhance the quality of life of patients with hemiplegia by means of an active motion support system that assists the impaired motion such as to make it as close as possible to the motion of an able bodied person. We have developed the Robot Suit HAL (Hybrid Assistive Limb) to actively support and enhance the human motor functions. The purpose of the research presented in this paper is to propose the required control method to support voluntarily motion using a trigger based on patient's bioelectrical signal. Clinical trials were conducted in order to investigate the effectiveness of the proposed control method. The first stage of the trials, described in this paper, involved the participation of one hemiplegic patient who is not able to bend his right knee. As a result, the motion support provided by the HAL moved the paralyzed knee joint according to his intention and improved the range of the subject's knee flexion. The first evaluation of the control method with one subject showed promising results for future trials to explore the effectiveness for a wide range of types of hemiplegia.


title: 3D model-based tracking for UAV position control

abstract: This paper presents a 3D model-based tracking suitable for indoor position control of an unmanned aerial vehicle (UAV). Given a 3D model of the edges of its environment, the UAV locates itself thanks to a robust multiple hypothesis tracker. The pose estimation is then fused to inertial data to provide the translational velocity required for the control. A hierarchical control is used to achieve positioning tasks. Experiments on a quad-rotor aerial vehicle validate the proposed approach.


LEXOR, the LCG-2 Executor for the ATLAS DC2 Production System


title: GENETIC DIVERSITY OF Pyricularia grisea, THE CAUSAL AGENT OF RICE BLAST BY SRR

abstract: Pyricularia grisea, the rice blast fungus is the main pathological threats to rice crop in Iran and worldwide. In this research was evaluated the genetic diversity of P. grisea collected from different fields of Guilan province by using of 14 microsatellite primers. These primers produced 64 polymorphic bands by an average of 4.57 bands for each marker. An average of polymorphic information content in whole primers was 0.734, an average of effective number of alleles was 2.68, an average of Nei's expected het- erozygosity was 0.734 and an average of Shannon's information index was 1.05. Primer SSR43,44 had the most polymorphic information content (PIC = 0.85), observed number of alleles (na = 8), effective number of alleles (ne = 3.76), Nei's expected heterozygosity (Ne = 0.861) and Shannon's information index (I = 1.38). This marker was the best primer between 14 used primers for evaluation the genetic diversity of P. grisea. Cluster analysis was done with simple matching similarity matrix and UPGMA method. The re- sults showed that the studied isolates were classified into 3 lineages by cutting off the dendrogram at 0.76 similar linkage level. Number 1 was the major group and represented most of those isolates. Results of principal coordinate analysis also divided the isolates into three groups exactly similar to obtained with cluster analysis. Overall, our results confirmed that microsatellite primers were good and suitable markers for analyzing struc- ture of P. grisea.


Carotid Artery Disease and Lower Extremities Artery Disease in Patients with Chronic Obstructive Pulmonary Disease


title: Recent developments in the MAFFT multiple sequence alignment program

abstract: The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reasonable MSA, we developed the initial version of the MAFFT program in 2002. MSA software is now facing greater challenges in both scalability and accuracy than those of 5 years ago. As increasing amounts of sequence data are being generated by large-scale sequencing projects, scalability is now critical in many situations. The requirement of accuracy has also entered a new stage since the discovery of functional noncoding RNAs (ncRNAs); the secondary structure should be considered for constructing a high-quality alignment of distantly related ncRNAs. To deal with these problems, in 2007, we updated MAFFT to Version 6 with two new techniques: the PartTree algorithm and the Four-way consistency objective function. The former improved the scalability of progressive alignment and the latter improved the accuracy of ncRNA alignment. We review these and other techniques that MAFFT uses and suggest possible future directions of MSA software as a basis of comparative analyses. MAFFT is available at http://align.bmr.kyushu-u.ac.jp/mafft/software/.


title: Selective Attention and Health Anxiety: Ill-Health Stimuli are Distracting for Everyone

abstract: Psychological theories of anxiety are increasingly referring to information processing paradigms in order to understand the cognitive processes which underlie these disorders. Numerous studies of anxiety have demonstrated an attentional bias towards anxiety relevant stimuli. In addition, there is consistent evidence that it is more difficult to process absent than present information. Prior research has suggested that both these processing biases contribute to the maintenance of health anxiety. The present study investigated differences in attentional processing between participants high and low in health anxiety, using two visual search tasks. The visual search tasks used either letters (domain free stimuli), or words (anxiety-related stimuli). Both low and high health anxious participants demonstrated poorer performance for target absent than target present trials. In addition, all participants showed an attentional bias towards ill-health words over good-health, negative, positive, or neutral words. These results suggest that concern about health is relevant to all people regardless of level of health anxiety.


title: Crafting technology: Reimagining the processes, materials, and cultures of electronics

abstract: This article examines the practice of electronics building in the context of other crafts. We compare the experience of making electronics with the experiences of carving, sewing, and painting. Our investigation is grounded in a survey of 40 practicing craftspeople who are working in each of these disciplines. We then use this survey as a foundation for a discussion of hybrid craft—integrations of electronics with carving, sewing, and painting. We present examples of hybrid craft and discuss the ways in which blended practices can enrich and diversify technology.


title: Domain-Sensitive Recommendation with User-Item Subgroup Analysis

abstract: In this paper, we propose a Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. The proposed framework of DsRec includes three components: a matrix factorization model for the observed rating reconstruction, a bi-clustering model for the user-item subgroup analysis, and two regularization terms to connect the above two components into a unified formulation. Extensive experiments on three real-world datasets show that our method achieves the better performance over some state-of-the-art methods.


title: Speaker Diarization: A Review of Recent Research

abstract: Speaker diarization is the task of determining "who spoke when?" in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher-level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.


title: An oral hygiene protocol improves oral health for patients in inpatient stroke rehabilitation

abstract: THE OBJECTIVE ::: To determine whether a simple oral hygiene protocol improves the oral health of inpatients in stroke rehabilitation. ::: ::: ::: THE BACKGROUND DATA DISCUSSING THE PRESENT STATUS OF THE FIELD ::: Poor oral health can lead to serious complications, such as pneumonia. The comorbidities associated with stroke, such as dysphagia, hemiparesis and cognitive impairment, can further impede independent oral care. International stroke guidelines recommend routine oral care but stop short of detailing specific regimes. ::: ::: ::: MATERIALS AND METHODS ::: The oral health assessment tool (OHAT) was conducted by speech-language pathologists with 100 patients with and without dysphagia in three metropolitan inpatient stroke rehabilitation facilities. A simple nurse-led oral hygiene regime was then implemented with all participants, which included twice daily tooth brushing and mouth rinsing after lunch, and oral health was measured again one week later. ::: ::: ::: RESULTS ::: Initially, dysphagia was negatively associated with OHAT scores, and independence for oral hygiene was positively associated with oral health. After one week of a simple oral hygiene regime, the OHAT scores available for 89 participants indicated an improvement on average for all participants. In particular, 59% of participants with dysphagia had an improvement of 1 or more points. None of the participants developed pneumonia. ::: ::: ::: CONCLUSION ::: A simple, inexpensive oral hygiene regime resulted in positive outcomes for patients with and without dysphagia in inpatient stroke rehabilitation settings. Oral health assessments and oral hygiene regimes that are simple to implement by the interdisciplinary team can be incorporated into standard stroke care with positive effect.


title: Code club: bringing programming to UK primary schools through scratch

abstract: Code Club is a network of after-school programming clubs for primary (US: elementary) schoolchildren, run by technically-competent volunteers in conjunction with (generally technically-unskilled) teachers. The main motivation of Code Club is to inspire children with a sense of fun and achievement for programming and digital creativity. This paper reports on the first year of Code Club in 1000 UK schools. The results were extremely positive, but some children had difficulty understanding the concepts behind the projects.


title: Proposed wall climbing robot with permanent magnetic tracks for inspecting oil tanks

abstract: This paper presents a proposed research on the wall climbing robot with permanent magnetic tracks. A brief review about the wall climbing robot is given, different prototypes of wall climbing robot are compared, and the different application fields are introduced. A proposed wall climbing robot with permanent magnetic adhesion mechanism for inspecting oil tanks is put forward. The mechanical system architecture is detailed in the paper. By analyzing the robot's workspace, permanent magnetic adhesion mechanism is chosen for the robot. Also, tracked locomotion mechanism is applied to the robot. By static and dynamic force analysis of the robot, design parameters about adhesion and locomotion mechanism are derived. In addition, safety constraints for the robot are obtained. Finally, the electrical system architecture for the robot is offered. To improve the efficiency, hierarchy control architecture is employed in the robot system. An embedded system is used and installed in the robot to manage multiple sensors and to communicate with the master computer through the wireless link. And the Web-based teleoperation for the robot is illustrated in the paper.


English, Devnagari and Urdu Text Identification


title: MusicMixer: computer-aided DJ system based on an automatic song mixing

abstract: In this paper, we present MusicMixer, a computer-aided DJ system that helps DJs, specifically with song mixing. MusicMixer continuously mixes and plays songs using an automatic music mixing method that employs audio similarity calculations. By calculating similarities between song sections that can be naturally mixed, MusicMixer enables seamless song transitions. Though song mixing is the most fundamental and important factor in DJ performance, it is difficult for untrained people to seamlessly connect songs. MusicMixer realizes automatic song mixing using an audio signal processing approach; therefore, users can perform DJ mixing simply by selecting a song from a list of songs suggested by the system, enabling effective DJ song mixing and lowering entry barriers for the inexperienced. We also propose personalization for song suggestions using a preference memorization function of MusicMixer.


title: Content Adaptive Representations of Omnidirectional Videos for Cinematic Virtual Reality

abstract: Cinematic virtual reality provides an immersive visual experience by presenting omnidirectional videos of real-world scenes. A key challenge is to develop efficient representations of omnidirectional videos in order to maximize coding efficiency under resource constraints, specifically, number of samples and bitrate. We formulate the choice of representation as a multi-dimensional, multiple-choice knapsack problem and show that the resulting representations adapt well to varying content. We also show that separation of the sampling and bit allocation constraints leads to a computationally efficient solution using Lagrangian optimization with only minor performance loss. Results across images and videos show significant coding gains over standard representations.


title: Structured Learning of Tree Potentials in CRF for Image Segmentation

abstract: We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some predefined parametric models, and then, methods, such as structured support vector machines, are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests—ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn classwise decision trees for each object that appears in the image. Experimental results on several public segmentation data sets demonstrate the power of the learned nonlinear nonparametric potentials.


title: A modal control for active/semi-active suspension systems

abstract: Active/semi-active suspension systems are getting widely accepted for their potential to improve ride comfort of passenger cars with respect to traditional suspension systems. A feedback controller has been designed allowing to control separately the vehicle body motions of heave, pitch and roll taking into account the dynamics of the active/semi-active actuator. Simulations results stated that significant improvements of ride comfort can be achieved respect to passive shock absorbers.


title: Lyotropic liquid crystal systems in drug delivery.

abstract: Lyotropic liquid crystal systems, such as reversed bicontinuous cubic and hexagonal mesophases, are attracting more and more attention because of their unique microstructures and physicochemical properties. Various bioactive molecules such as chemical drugs, peptides and proteins can be solubilized in either aqueous or oil phase and be protected from hydrolysis or oxidation. Furthermore, several studies have demonstrated sustained release of bioactive molecules from reversed cubic and hexagonal mesophases. This article gives an overview of recent advances and current status of reversed cubic and hexagonal mesophases, especially with respect to their preparation methods and applications in the field of drug delivery. In addition, potential problems and possible future research directions are highlighted.


title: Risk factors for involvement in cyber bullying: Victims, bullies and bully–victims ☆

abstract: Abstract Objectives The purpose of the current study was to examine the frequency of cyber bullying among youth by distinguishing among the three categories of involvement in cyber bullying: victims, bullies, and bully–victims, to compare these to a fourth category of students who are not involved in the three categories of cyber bullying and to explore the factors that contribute to involvement in cyber bullying. Method This study utilized a large and diverse sample of 2186 middle and high school students, who completed self report questionnaires during class time. We performed a Multinomial Logistic Regression to examine the relationship between the cyber bullying categories and our independent variables (gender, age, technology use, parental involvement and safety). Results Over 30% of the students in this study identified as involved in cyber bullying, as victims or perpetrators, and one in four of the students (25.7%) reported having been involved in cyber bullying as both bully and victim during the previous three months. Students who were involved in cyber bullying were more likely than others to report perpetration of violence toward peers, to use computers for more hours a day, and to give their password to friends. Other risk factors, such as gender, age and safety, were found to be specific only for one category of cyber bullying. Conclusion The findings revealed that students are highly involved in cyber bullying. Several unique characteristics emerged regarding the frequency and risk factors of students' involvement in cyber bullying. In traditional bullying the category of bully–victims represents the smallest and most vulnerable group of children, whereas in the current study the bully–victims category emerged as common. In addition, females were more likely than males to be bully–victims, in contrast to research on traditional bullying, in which more males than females are typically involved as bully–victims. In addition, several risk factors were common among the three groups of children, including the amount of hours per day students use the computer, and giving passwords to a friend. These results point to the need for further examination and to focus on the risk factors for students' cyber bullying involvement in each of the three categories.


title: Design of an Intelligent Agent for Personalization of Moodle's Contents

abstract: This paper presents the architecture of an Intelligent Learning Management System (ILMS) applied to Moodle. Specifically, we present the design and implementation of an agent that select the teaching strategy according to the student's learning style. The chosen teaching strategy is used to filter the learning objects that are displayed to the students.


Communicative Actions for Artificial Agents


title: A Review of the Status of Brain Structure Research in Transsexualism

abstract: The present review focuses on the brain structure of male-to-female (MtF) and female-to-male (FtM) homosexual transsexuals before and after cross-sex hormone treatment as shown by in vivo neuroimaging techniques. Cortical thickness and diffusion tensor imaging studies suggest that the brain of MtFs presents complex mixtures of masculine, feminine, and demasculinized regions, while FtMs show feminine, masculine, and defeminized regions. Consequently, the specific brain phenotypes proposed for MtFs and FtMs differ from those of both heterosexual males and females. These phenotypes have theoretical implications for brain intersexuality, asymmetry, and body perception in transsexuals as well as for Blanchard’s hypothesis on sexual orientation in homosexual MtFs. Falling within the aegis of the neurohormonal theory of sex differences, we hypothesize that cortical differences between homosexual MtFs and FtMs and male and female controls are due to differently timed cortical thinning in different regions for each group. Cross-sex hormone studies have reported marked effects of the treatment on MtF and FtM brains. Their results are used to discuss the early postmortem histological studies of the MtF brain.


title: Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks

abstract: Computer-aided polyp detection in colonoscopy videos has been the subject of research for over the past decade. However, despite significant advances, automatic polyp detection is still an unsolved problem. In this paper, we propose a new polyp detection method based on a unique 3-way image presentation and convolutional neural networks. Our method learns a variety of polyp features such as color, texture, shape, and temporal information in multiple scales, enabling a more accurate polyp localization. Given a polyp candidate, a set of convolution neural networks — each specialized in one type of features — are applied in the vicinity of the candidate and then their results are aggregated to either accept or reject the candidate. Our experimental results based on our collection of videos, which to our knowledge is the largest annotated polyp database, shows a remarkable performance improvement over the state-of-the-art, significantly reducing the number of false positives in nearly all operating points. In addition, we propose a new performance curve, demonstrating that our new method significantly decreases polyp detection latency, which is defined as the time from the first appearance of a polyp in the video to the time of its first detection by our method.


title: EARNPIPE: A Testbed for Smart Water Pipeline Monitoring Using Wireless Sensor Network☆

abstract: Abstract Large quantities of water are wasted daily due to leakages in pipelines. In order to decrease this waste and preserve water, advanced systems could be used. In this context, a Wireless Sensor Network (WSN) is increasingly required to optimize the reliability of the inspection and improve the accuracy of the water pipeline monitoring. A WSN solution is proposed in this paper with a view to detecting and locating leaks for long distance pipelines. It combines powerful leak detection and localization algorithms and an efficient wireless sensor node System on Chip (SoC) architecture. In fact, a novel hybrid Water Pipeline Monitoring (WPM) method has been proposed using Leak detection Predictive Kalman Filter (LPKF) and Modified Time Difference of Arrival (TDOA) method based on pressure measurements. The data collected from sensors are filtered, analyzed and compressed with the same Kalman Filter (KF) based algorithm instead of using various algorithms that deeply damage the battery of the node. The local low power pre-processing is efficient to save the power of the sensor nodes. Moreover, a laboratory testbed has been constructed using plumbing components and validated by an ARM-based prototyping platform with pressure sensors.


title: Split-transistor compensation: Analytical model

abstract: Split-transistor compensation (STC) is introduced as a new compensation technique applicable to power management of Internet-of-Things (IoT) devices. An extension of the split-length compensation (SLC) technique [1]-[3], STC significantly reduces the area of the required on-chip compensation capacitor, while maintaining the two major benefits of SLC: reduced minimum supply voltage and no additional power consumption. We develop equations to model split-transistor behavior in a compensation network and verify the proposed novel analytical model via simulations using foundry transistor models.


title: RAISE: a raw images dataset for digital image forensics

abstract: Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.


Organismically-inspired robotics : homeostatic adaptation and teleology beyond the closed sensorimotor loop


title: Action2Vec: A Crossmodal Embedding Approach to Action Learning

abstract: We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train our embedding using a joint loss that combines classification accuracy with similarity to Word2Vec semantics. We evaluate Action2Vec by performing zero shot action recognition and obtain state of the art results on three standard datasets. In addition, we present two novel analogy tests which quantify the extent to which our joint embedding captures distributional semantics. This is the first joint embedding space to combine verbs and action videos, and the first to be thoroughly evaluated with respect to its distributional semantics.


title: Personal Data Management with the Databox: What's Inside the Box?

abstract: We are all increasingly the subjects of data collection and processing systems that use data generated both about and by us to provide and optimise a wide range of services. Means for others to collect and process data that concerns each of us -- often referred to possessively as "your data" -- are only increasing with the long-heralded advent of the Internet of Things just the latest example. As a result, means to enable personal data management is generally recognised as a pressing societal issue. We have previously proposed that one such means might be realised by the Databox, a collection of physical and cloud-hosted software components that provide for an individual data subject to manage, log and audit access to their data by other parties. In this paper we elaborate on this proposal, describing the software architecture we are developing, and the current status of a prototype implementation. We conclude with a brief discussion of Databox's limitations.


title: An Information Retrieval Approach to Short Text Conversation

abstract: Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.


title: Clustering with Lower Bound on Similarity ⋆

abstract: We propose a new method, called SimClus, for clustering with lower bound on similarity. Instead of accepting k the number of clusters to find, the alternative similarity-based approach imposes a lower bound on the similarity between an object and its corresponding cluster representative (with one representative per cluster). SimClus achieves a O (logn ) approximation bound on the number of clusters, whereas for the best previous algorithm the bound can be as poor as O (n ). Experiments on real and synthetic datasets show that our algorithm produces more than 40% fewer representative objects, yet offers the same or better clustering quality. We also propose a dynamic variant of the algorithm, which can be effectively used in an on-line setting.


title: Investigation of electrical field grading of bushings with microvaristor filled epoxy resin components

abstract: High electric field intensities along the composite insulator surface of high voltage bushings, in particular around the area of the grounded electrode, can result in electrical partial discharges on the silicone rubber surface. The microvaristor filled epoxy resin components are applied to bushings in order to reduce the electric field intensity locally. Using this nonlinear component makes it possible to reduce the diameter of bushings, while still having a reasonable electric field distribution. This new compact design is analyzed using numerical simulations. The corresponding prototypes are experimentally tested in the high voltage laboratory.


Zero-shot Relation Classification as Textual Entailment


title: Audio-Visual Speech Recognition with a Hybrid CTC/Attention Architecture

abstract: Recent works in speech recognition rely either on connectionist temporal classification (CTC) or sequence-to-sequence models for character-level recognition. CTC assumes conditional independence of individual characters, whereas attention-based models can provide nonsequential alignments. Therefore, we could use a CTC loss in combination with an attention-based model in order to force monotonic alignments and at the same time get rid of the conditional independence assumption. In this paper, we use the recently proposed hybrid CTC/attention architecture for audio-visual recognition of speech in-the-wild. To the best of our knowledge, this is the first time that such a hybrid architecture architecture is used for audio-visual recognition of speech. We use the LRS2 database and show that the proposed audio-visual model leads to an 1.3% absolute decrease in word error rate over the audio-only model and achieves the new state-of-the-art performance on LRS2 database (7% word error rate). We also observe that the audio-visual model significantly outperforms the audio-based model (up to 32.9% absolute improvement in word error rate) for several different types of noise as the signal-to-noise ratio decreases.


title: A noise-tolerant algorithm for robotic hand-eye calibration with or without sensor orientation measurement

abstract: An iterative algorithm for calibration of a robotic hand-eye relationship is presented. The hand-eye calibration can be performed by solving a system of homogeneous transformation equations of the form A/sub i/X=XB/sub i/, where X is the unknown sensor position relative to the robot wrist, A/sub i/ is the ith robot motion, and B/sub i/ is the ith sensor motion. Unlike existing approaches, the algorithm presented solves the kinematic parameters of X in one stage, thus eliminating error propagation and improving noise sensitivity. Furthermore, with the iterative algorithm, the parameters of X can be computed even when the rotational part of B/sub i/ is unknown. This is important since position is easier to measure than orientation. Comparative simulation studies show that the performance of the iterative algorithm is usually better than that of noniterative two-stage algorithms, regardless of whether the orientation part of B/sub i/ is used or not. This paper also discusses the application of the proposed method to calibration of a tool mounted on a robot manipulator. >


title: Better Driving and Recall When In-car Information Presentation Uses Situationally-Aware Incremental Speech Output Generation

abstract: It is established that driver distraction is the result of sharing cognitive resources between the primary task (driving) and any other secondary task. In the case of holding conversations, a human passenger who is aware of the driving conditions can choose to interrupt his speech in situations potentially requiring more attention from the driver, but in-car information systems typically do not exhibit such sensitivity. We have designed and tested such a system in a driving simulation environment. Unlike other systems, our system delivers information via speech (calendar entries with scheduled meetings) but is able to react to signals from the environment to interrupt when the driver needs to be fully attentive to the driving task and subsequently resume its delivery. Distraction is measured by a secondary short-term memory task. In both tasks, drivers perform significantly worse when the system does not adapt its speech, while they perform equally well to control conditions (no concurrent task) when the system intelligently interrupts and resumes.


title: Enterprise resource planning: A taxonomy of critical factors

abstract: Abstract This paper presents a novel taxonomy of the critical success factors in enterprise resource planning (ERP) implementation process. ERP benefits cannot be fully realised unless a strong alignment and reconciliation mechanism is established between technical and organisational imperatives based on the principles of process orientation. It is suggested in the taxonomy that measurement takes place in a balanced perspective, and for the purpose of providing useful information that can enable the decision making process and, which can help deliver the corporate objectives and therefore lead the business competitively forward. Upon this premise, the taxonomy is based on a comprehensive analysis of ERP literature combining research studies and organisational experiences. The taxonomy reflects the essential features of ERP systems, as being built based on the principles of business process management. Furthermore, it illustrates that ERP benefits are realised when a tight link is established between implementation approach and business process performance measures.


title: 2D/3D Sensor Exploitation and Fusion for Enhanced Object Detection

abstract: This paper describes a method for object (e.g., vehicles, pedestrians) detection and recognition using a combination of 2D and 3D sensor data. Detection of individual data modalities is carried out in parallel, and then combined using a fusion scheme to deliver the final results. Specifically, we first apply deformable part based object detection in the 2D image domain to obtain initial estimates of candidate object regions. Meanwhile, 3D blobs (i.e., clusters of 3D points) containing potential objects are extracted from the corresponding input point cloud in an unsupervised manner. A novel morphological feature set Morph166 is proposed to characterize each of these 3D blobs, and only blobs matched to predefined object models are kept. Based on the individual detections from the aligned 2D and 3D data, we further develop a fusion scheme to boost object detection and recognition confidence. Experimental results with the proposed method show good performance.


title: The Dunning – Kruger Effect : On Being Ignorant of One ’ s Own Ignorance

abstract: Abstract In this chapter, I provide argument and evidence that the scope of people's ignorance is often invisible to them. This meta-ignorance (or ignorance of ignorance) arises because lack of expertise and knowledge often hides in the realm of the “unknown unknowns” or is disguised by erroneous beliefs and background knowledge that only appear to be sufficient to conclude a right answer. As empirical evidence of meta-ignorance, I describe the Dunning–Kruger effect, in which poor performers in many social and intellectual domains seem largely unaware of just how deficient their expertise is. Their deficits leave them with a double burden—not only does their incomplete and misguided knowledge lead them to make mistakes but those exact same deficits also prevent them from recognizing when they are making mistakes and other people choosing more wisely. I discuss theoretical controversies over the interpretation of this effect and describe how the self-evaluation errors of poor and top performers differ. I also address a vexing question: If self-perceptions of competence so often vary from the truth, what cues are people using to determine whether their conclusions are sound or faulty?


title: Representing Business Strategy through Goal Modeling

abstract: This paper focuses on the representation of business strategy through goal modeling. Traditional approaches to goal modeling focus on capturing the business goals into an accurate representation. Business goals originate from the vision and strategy of the company being modeled. By restraining to model the business goals, traditional approached often fail to capture the meaning of goals and the managers’ vision of business. By capturing some of the concepts underlying management theories such as the Balanced Scorecard, a new approach to goal modeling is presented. This approach aims at providing a modeling language that is closer to the manager’s and business needs.


title: A Framework for Flexible Access Control in Digital Library Systems

abstract: Traditional access control models are often found to be inadequate for digital libraries. This is because the user population for digital libraries is very dynamic and not completely known in advance. In addition, the objects stored in a digital library are characterized by fine-grained behavioral interfaces and highly-contextualized access restrictions that require a user's access privileges to be updated dynamically. These motivate us to propose a trust-based authorization model for digital libraries. Access privileges can be associated with both objects and content classes. Trust levels associated with these specify the minimum acceptable level of trust needed of a user to allow access to the objects. We use a vector trust model to calculate the system's trust about a user. The model uses a number of different types of information about a user, for example, prior usage history, credentials, recommendations etc., to calculate the trust level in a dynamic manner and thus achieve a fine-grained access control.


title: Manipulator Inverse Kinematic Solutions Based on Vector Formulations and Damped Least-Squares Methods

abstract: Inverse kinematic solutions are used in manipulator controllers to determine corrective joint motions for errors in end-effector position and orientation. Previous formulations of these solutions, based on the Jacobian matrix, are inefficient and fail near kinematic singularities. Vector formulations of inverse kinematic problems are developed that lead to efficient computer algorithms. To overcome the difficulties encountered near kinematic singularities, the exact inverse problem is reformulated as a damped least-squares problem, which balances the error in the solution against the size of the solution. This yields useful results for all manipulator configurations.


title: An automated behavioral measure of mind wandering during computerized reading

abstract: Mind wandering is a ubiquitous phenomenon in which attention shifts from task-related to task-unrelated thoughts. The last decade has witnessed an explosion of interest in mind wandering, but research has been stymied by a lack of objective measures, leading to a near-exclusive reliance on self-reports. We addressed this issue by developing an eye-gaze-based, machine-learned model of mind wandering during computerized reading. Data were collected in a study in which 132 participants reported self-caught mind wandering while reading excerpts from a book on a computer screen. A remote Tobii TX300 or T60 eyetracker recorded their gaze during reading. The data were used to train supervised classification models to discriminate between mind wandering and normal reading in a manner that would generalize to new participants. We found that at the point of maximal agreement between the model-based and self-reported mind-wandering means (smallest difference between the group-level means: M model = .310, M self = .319), the participant-level mind-wandering proportional distributions were similar and were significantly correlated (r = .400). The model-based estimates were internally consistent (r = .751) and predicted text comprehension more strongly than did self-reported mind wandering (r model = −.374, r self = −.208). Our results also indicate that a robust strategy of probabilistically predicting mind wandering in cases with poor or missing gaze data led to improved performance on all metrics, as compared to simply discarding these data. Our findings demonstrate that an automated objective measure might be available for laboratory studies of mind wandering during reading, providing an appealing alternative or complement to self-reports.


title: Multi-factor Authentication Security Framework in Cloud Computing

abstract: Data Security is the most critical issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Traditional password authentication does not provide enough security for information in cloud computing environment to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. In this paper the features of various access control mechanisms are discussed and a novel framework of access control is proposed for cloud computing, which provides a multi -step and multifactor authentication of a user. The model proposed is well-organized and provably secure solution of access control for externally hosted applications.


title: Separation of Instrument Sounds using Non-negative Matrix Factorization with Spectral Envelope Constraints

abstract: Spectral envelope is one of the most important features that characterize the timbre of an instrument sound. However, it is difficult to use spectral information in the framework of conventional spectrogram decomposition methods. We overcome this problem by suggesting a simple way to provide a constraint on the spectral envelope calculated by linear prediction. In the first part of this study, we use a pre-trained spectral envelope of known instruments as the constraint. Then we apply the same idea to a blind scenario in which the instruments are unknown. The experimental results reveal that the proposed method outperforms the conventional methods.


Online Robust Low-Rank Tensor Learning


title: Link prediction on evolving data using tensor factorization

abstract: Within the last few years a lot of research has been done on large social and information networks. One of the principal challenges concerning complex networks is link prediction. Most link prediction algorithms are based on the underlying network structure in terms of traditional graph theory. In order to design efficient algorithms for large scale networks, researchers increasingly adapt methods from advanced matrix and tensor computations. ::: ::: This paper proposes a novel approach of link prediction for complex networks by means of multi-way tensors. In addition to structural data we furthermore consider temporal evolution of a network. Our approach applies the canonical Parafac decomposition to reduce tensor dimensionality and to retrieve latent trends. ::: ::: For the development and evaluation of our proposed link prediction algorithm we employed various popular datasets of online social networks like Facebook and Wikipedia. Our results show significant improvements for evolutionary networks in terms of prediction accuracy measured through mean average precision.


title: Ultrasonic Fingerprint Sensor With Transmit Beamforming Based on a PMUT Array Bonded to CMOS Circuitry

abstract: In this paper, we present a single-chip 65 ×42 element ultrasonic pulse-echo fingerprint sensor with transmit (TX) beamforming based on piezoelectric micromachined ultrasonic transducers directly bonded to a CMOS readout application-specific integrated circuit (ASIC). The readout ASIC was realized in a standard 180-nm CMOS process with a 24-V high-voltage transistor option. Pulse-echo measurements are performed column-by-column in sequence using either one column or five columns to TX the ultrasonic pulse at 20 MHz. TX beamforming is used to focus the ultrasonic beam at the imaging plane where the finger is located, increasing the ultrasonic pressure and narrowing the 3-dB beamwidth to [Formula: see text], a factor of 6.4 narrower than nonbeamformed measurements. The surface of the sensor is coated with a poly-dimethylsiloxane (PDMS) layer to provide good acoustic impedance matching to skin. Scanning laser Doppler vibrometry of the PDMS surface was used to map the ultrasonic pressure field at the imaging surface, demonstrating the expected increase in pressure, and reduction in beamwidth. Imaging experiments were conducted using both PDMS phantoms and real fingerprints. The average image contrast is increased by a factor of 1.5 when beamforming is used.


title: Compassion fatigue and satisfaction: A cross‐sectional survey among US healthcare workers

abstract: Professional quality of life among healthcare providers can impact the quality and safety of patient care. The purpose of this research was to investigate compassion satisfaction and compassion fatigue levels as measured by the Professional Quality of Life Scale self-report instrument in a community hospital in the United States. A cross-sectional survey study examined differences among 139 RNs, physicians, and nursing assistants. Relationships among individual and organizational variables were explored. Caregivers for critical patients scored significantly lower on the Professional Quality of Life subscale of burnout when compared with those working in a noncritical care unit. Linear regression results indicate that high sleep levels and employment in critical care areas are associated with less burnout. Identification of predictors can be used to design interventions that address modifiable risks.


title: ICT & Economic Growth: A Comparison Between Developed & Developing Countries

abstract: Information and Communication technology (ICT) plays a very important role in macroeconomics of both developed as well as developing countries. The impact of ICT usage on promotion innovation, raising productivity and increasing economic growth and therefore economic well-being of human beings are being emphasized in recent studies especially during the last decade of twentieth and the first decade of twenty first century. The purpose of this paper is to compare the impact of ICT on economic growth in developed and developing countries. To do so, we have used a sample of both countries for which the necessary data were available for the period 2001-2012. Having concentrate on the usage aspect of ICT to deal with its impact on economic growth we have used a new composite index of ICT called Digital Opportunity Index (DOI) covering 3 dimensions including overall opportunity, infrastructure and application. Our findings based on panel data regression models indicate that in general significance and positive relationship between ICT and economic growth exists in both developed and developing countries. However, our results indicated a stronger impact for the developing countries. This may be due to a relatively more recent usage of ICT in developing countries .I other words; these countries experience the early stage of using ICT as compared to developed countries. Therefore, policies to promote usage of ICT in all countries especially in developing countries are suggested.


title: Effects of 12 weeks of block periodization on performance and performance indices in well-trained cyclists.

abstract: The purpose of this study was to compare the effects of two different methods of organizing endurance training in trained cyclists during a 12-week preparation period. One group of cyclists performed block periodization (BP; n = 8), wherein every fourth week constituted five sessions of high-intensity aerobic training (HIT), followed by 3 weeks of one HIT session. Another group performed a more traditional organization (TRAD; n = 7), with 12 weeks of two weekly HIT sessions. The HIT was interspersed with low-intensity training (LIT) so that similar total volumes of both HIT and LIT were performed in the two groups. BP achieved a larger relative improvement in VO2max than TRAD (8.8 ± 5.9% vs 3.7 ± 2.9%, respectively, P < 0.05) and a tendency toward larger increase in power output at 2 mmol/L [la−] (22 ± 14% vs 10 ± 7%, respectively, P = 0.054). Mean effect size (ES) of the relative improvement in VO2max, power output at 2 mmol/L [la−], hemoglobin mass, and mean power output during 40-min all-out trial revealed moderate superior effects of BP compared with TRAD training (ES range was 0.62–1.12). The present study suggests that BP of endurance training has superior effects on several endurance and performance indices compared with TRAD.


title: Study of security and effectiveness of DoIP in vehicle networks

abstract: As the number of Electronic Control Unit increased in automotive system, its memory capability and functionalities have also increased in large scale. These large number of high functional Electronic Control Units (ECU) in vehicle system demand for high data rate connectivity to external network and within in-vehicle network. This high data rate requirement lead to the introduction of Ethernet Technology to the vehicle network system, which also provide the remote vehicle access and remote diagnostic functionalities to the vehicle network. Diagnostics over IP (DoIP) have standardized as international standard for diagnostics over IP which is documented in ISO 13400 DIS. This survey studies different technologies in vehicle network and are compared based on the data rate for diagnostics and a study of different Communication scenarios along with safety and security requirements of DoIP protocol is examined.


title: Adoption, motivation and success factors of social media for team collaboration in SMEs

abstract: In team collaboration we deal with collaboration among team members that may either belong to the same enterprise (intra-enterprise collaboration) or among team members belonging to different companies (e.g., in research projects). Social media provide an efficient and accessible means of encouraging and supporting team members working together on shared objects, i.e., performing collaborative tasks within these teams. Due to ease of use and access, especially small or medium-sized enterprises (SMEs) may profit a lot from social media in team collaboration. Based on a number of case studies on the application of social media in SMEs, we analyze the adoption and implementation, the motivation of team members and their benefit, and success factors of the utilization of social media for team collaboration. To provide a set of case studies that allow for detailed comparison and in-depth analysis of multiple case studies, a standardized case study structure is introduced.