title: METRIC AND TOPO-GEOMETRIC PROPERTIES OF URBAN STREET NETWORKS : some convergences , divergences , and new results

text: The theory of cities, which has grown out of the use of space syntax techniques in urban studies, proposes a curious mathematical duality: that urban space is locally metric but globally topo-geometric. Evidence for local metricity comes from such generic phenomena as grid intensification to reduce mean trip lengths in live centres, the fall of movement from attractors with metric distance, and the commonly observed decay of shopping with metric distance from an intersection. Evidence for global topo-geometry come from the fact that we need to utilise both the geometry and connectedness of the larger scale space network to arrive at configurational measures which optimally approximate movement patterns in the urban network. It might be conjectured that there is some threshold above which human being use some geometrical and topological representation of the urban grid rather than the sense of bodily distance to making movement decisions, but this is unknown. The discarding of metric properties in the large scale urban grid has, however, been controversial. Here we cast a new light on this duality. We show first some phenomena in which metric and topo-geometric measures of urban space converge and diverge, and in doing so clarify the relation between the metric and topo-geometric properties of urban spatial networks. We then show how metric measures can be used to create a new urban phenomenon: the partitioning of the background network of urban space into a network of semi-discrete patches by applying metric universal distance measures at different metric radii, suggesting a natural spatial area-isation of the city at all scales. On this basis we suggest a key clarification of the generic structure of cities: that metric universal distance captures exactly the formally and functionally local patchwork properties of the network, most notably the spatial differentiation of areas, while the top-geometric measures identifying the structure which overcomes locality and links the urban patchwork into a whole at different scales. Introduction: the dual urban network The theory of cities, which has grown out of the use of space syntax techniques in urban studies, proposes that urban street networks have a dual form: a foreground network of linked centres at all scales, and a background network of primarily residential space in which the foreground network is embedded. (Hillier 2001/2) The theory also notes a mathematical duality. On the one hand, measures which express the geometric and topological properties of the network at an extended scale, such as integration and choice measures in axial maps or segment angular maps, are needed to capture structure-function relations such as natural movement patterns (Hillier & Iida 2005). We can call these measures topo-geometric. On the other, at a more localised level, an understanding of structure-function relations often requires an account of metric properties – for example the generic, but usually local, phenomenon of grid intensification to reduce mean trip lengths in live centres (Siksna 1997, Hillier 1999), the fall of movement rates with metric distance from attractors, and the commonly observed decay of shopping with metric distance from an intersection. In terms of understanding structure-function relations. urban space seems to be globally topo-geometric but locally metric. Here we propose to link these two dualities in a more thorough-going way. We show first that the large scale foreground network of space in cities, in spite of the claims of critics (Ratti 2004), really is not metric. On the contrary, the substitution of metric for topo-geometric measures in the analysis, has catastrophic effects on the ability of syntax to account for structure-function relations at this scale. At the same time, topo-geometric measures turn out to capture some interesting metric properties of the larger scale urban network. But we then show that the background network of space really is metric in a much more general sense than has been thought, in that metric measures at different radii can be used to partition the background network of urban space into a patchwork of semi-discrete areas, suggesting a natural metric area-isation of cities at all scales as a function of the placing, shaping and scaling of urban blocks. On this basis we suggest a clarification of the dual structure of cities: that metric ‘universal distance’ (distance from all points to all others – Hillier 1996) measures can capture the spatial differentiation of the background urban network into a patchwork of local areas, while the topo-geometric measures identify the structures which overcomes locality and links the urban patchwork into a whole at different scales. The patchwork theory is in effect a theory of block size and shape, picking up the local distortions in urban space induced by the placing and shaping of physical structures. More generally, we can say that the local-to-global topo-geometric structure reflects the visual, and so non-local effects of placing blocks in space, while the patchwork structure reflects metric and so local effects. The patchwork theory extends and generalises the concept of grid intensification, meaning the reduction of block size to reduce mean distance from all points to all others in a space network. As shown in (Hillier 2000), holding total land coverage and travellable distance in free space constant, a grid in which smaller blocks are placed at the centre and larger blocks at the edge has lower mean distance from all points to all others in the space network than a regular grid, while if larger blocks are placed at the centre and smaller blocks at the edge, then the mean distance from all points to others in the space network is higher than in a regular grid. This follows from the partitioning theory set out in Chapter 8 of Space is the Machine. (Hillier 1996) In general in urban grids, live centres and sub-centres (‘live’ in the sense of having movement dependent uses such as retail and catering) tend to the grid intensified form to maximise the inter-accessibility of the facilities within in the centre, residential areas tend to larger block sizes, reflecting the need to restrain and structure movement in the image of a spatial culture, while the linkages between centres tend to an even larger block size again, an effect of the directional structuring of routes, so that the network of linked centres which dominate the spatial structure of cities tend to oscillate between a relatively large and relative small block size, with the residential background occupying the middle range. This block size pattern is explained more fully in (Hillier 2001/2). In this paper we: – First review the duality of urban space in three ways: geometrically to establish its empirical existence as a key dimension of urban form, functionally to show its implications in terms of movement and land use patterns, and syntactically to show the relations between the two. – We then explore some of the suggestions that have been made about reducing the distance between syntax and more traditional metric approaches, in particular by examining the suggestion of Ratti that we should add metric weightings to the main syntax measures. We show the consequences of these suggestions for any theory which seeks to identify functionally meaningful structures in urban space – We then suggest a general method for showing the metric effect on space of block placing and shaping, both visually and in terms of patterns in scattergrams, by showing theoretical cases – We then apply this method to some cities and show its ability to identify if not natural spatial areas then at least a natural periodicity in city networks through which they tend to a natural spatial area-isation at all scales, reflecting the ways in which we talks about urban areas and regions at different scales. Metric and geometric properties of the grid First, we consider the urban duality geometrically by looking sections of metropolitan Tokyo and London, shown in Figure 1. As shown in (Hillier 2001/2) and later formalised in (Carvalho & Penn 2004), we must first remind ourselves of the fractal nature of urban least line networks: all are made up at all scales, from the local area to the city regions, of a small number of long lines and a large number of short lines. But there is more to be said. Longer and shorter lines form different kinds of geometric patterns. If we look for patterns in the section of Tokyo, the first thing the eye notes are line continuities. What we are seeing in effect is sequence of lines linked at their ends by nearly straight intersections with other lines, forming a visually dominant pattern in the network. But in general, the lines forming these nearly straight continuities as Figueredo calls them (Figueredo 2003) are longer than other nearby lines. This has the effect that if we find a locally longer line it is likely that at either end it will lead to another to which it will be connected by a nearly straight connection, and these lines will in turn be similarly connected. Probabilistically, we can say the longer the line, the more likely it is to end in a nearly straight connection, and taken together these alignments form a network of multi-directional sequences. Intuitively, the value of these in navigating urban grids is obvious, but here, following (Hillier 1999) we are making a structural point. Figure 1: sections of the Tokyo and London street networks What then of the shorter lines ? Again, in spite of a highly variable geometry, we find certain consistencies. First, shorter lines tend to form clusters, so that in the vicinity of each longer line there will be several shorter lines. These localised groups tend to form more grid-like local patterns, with lines either passing through each other, or ending on other lines, at near right angles. We can say then that the shorter the line, the more likely it is to


title: Current Status of Bioinks for Micro-Extrusion-Based 3D Bioprinting

text: Recent developments in 3D printing technologies and design have been nothing short of spectacular. Parallel to this, development of bioinks has also emerged as an active research area with almost unlimited possibilities. Many bioinks have been developed for various cells types, but bioinks currently used for 3D printing still have challenges and limitations. Bioink development is significant due to two major objectives. The first objective is to provide growth- and function-supportive bioinks to the cells for their proper organization and eventual function and the second objective is to minimize the effect of printing on cell viability, without compromising the resolution shape and stability of the construct. Here, we will address the current status and challenges of bioinks for 3D printing of tissue constructs for in vitro and in vivo applications.


title: Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data

text: Growing concern over traffic safety has led to research efforts directed towards predicting freeway crashes in Advanced Traffic Management and Information Systems (ATMIS) environment. This paper aims at developing a crash-likelihood prediction model using real-time traffic-flow variables (measured through series of underground sensors) and rain data (collected at weather stations) potentially associated with crash occurrence. Archived loop detector and rain data and historical crash data have been used to calibrate the model. This model can be implemented using an online loop and rain data to identify high crash potential in real-time. Principal component analysis (PCA) and logistic regression (LR) have been used to estimate a weather model that determines a rain index based on the rain readings at the weather station in the proximity of the freeway. A matched case-control logit model has also been used to model the crash potential based on traffic loop data and the rain index. The 5-min average occupancy and standard deviation of volume observed at the downstream station, and the 5-min coefficient of variation in speed at the station closest to the crash, all during 5-10 min prior to the crash occurrence along with the rain index have been found to affect the crash occurrence most significantly.


title: Data exfiltration: A review of external attack vectors and countermeasures

text: Context: One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective: This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method: We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results: We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion: This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in ‘in use’ state, therefore, future research needs to be directed towards securing data in ‘in rest’ and ‘in transit’ states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework.


title: Revisiting Cryptographic Accumulators, Additional Properties and Relations to Other Primitives

text: Cryptographic accumulators allow to accumulate a finite set of values into a single succinct accumulator. For every accumulated value, one can efficiently compute a witness, which certifies its membership in the accumulator. However, it is computationally infeasible to find a witness for any nonaccumulated value. Since their introduction, various accumulator schemes for numerous practical applications and with different features have been proposed. Unfortunately, to date there is no unifying model capturing all existing features. Such a model can turn out to be valuable as it allows to use accumulators in a black-box fashion. To this end, we propose a unified formal model for (randomized) cryptographic accumulators which covers static and dynamic accumulators, their universal features and includes the notions of undeniability and indistinguishability. Additionally, we provide an exhaustive classification of all existing schemes. In doing so, it turns out that most accumulators are distinguishable. Fortunately, a simple, light-weight generic transformation allows to make many existing dynamic accumulator schemes indistinguishable. As this transformation, however, comes at the cost of reduced collision freeness, we additionally propose the first indistinguishable scheme that does not suffer from this shortcoming. Finally, we employ our unified model for presenting a black-box construction of commitments from indistinguishable accumulators as well as a black-box construction of indistinguishable, undeniable universal accumulators from zero-knowledge sets. Latter yields the first universal accumulator construction that provides indistinguishability.


title: Motion Planning : The Essentials

text: This is the first installment of a two-part tutorial. The goal of the first part is to give the reader a basic understanding of the technical issues and types of approaches to solving the basic path planning or obstacle avoidance problem. The second installment will cover more advanced issues, including feedback, differential constraints, and uncert ainty. Note that is a brieftutorial, rather than a comprehensive surveyof methods. For the latter, consult recent textbooks [4], [9].


title: Customer churn prediction for retail business

text: Customer churn happens when a customer discontinues his or her interaction with a company. In retail business, a customer is treated to be churned once his/her transactions outdate a particular amount of time. Once a customer becomes a churn, the loss incurred by the company is not just the lost revenue due to the lost customer but also the costs involved in additional marketing in order to attract new customer. Reducing customer churn is a key business goal of every online business. In this project, we have considered data set from UCI Machine Learning repository. This dataset contains records of transactions that occurred between December 1, 2010 and December 1, 2011. This is recorded from an online retail gift store based in United Kingdom. The customers of this company are mainly wholesalers. This data set is pre-processed by removing NAs, validating numerical values, removing erroneous data points. We then perform aggregations on the data to generate invoice based and customer based data sets. A variable churn is attached to each data point. This churn value is determined based on the customer's transactions. Three algorithms are run on this customer aggregated dataset to predict churn value. They are Random forest, Support vector machines and Extreme gradient boosting. A comparative study is done on these three algorithms.


title: Detection of falls using accelerometers and mobile phone technology.

text:
OBJECTIVES
to study the sensitivity and specificity of fall detection using mobile phone technology.

DESIGN
an experimental investigation using motion signals detected by the mobile phone.

SETTING AND PARTICIPANTS
the research was conducted in a laboratory setting, and 18 healthy adults (12 males and 6 females; age = 29 ± 8.7 years) were recruited.

MEASUREMENT
each participant was requested to perform three trials of four different types of simulated falls (forwards, backwards, lateral left and lateral right) and eight other everyday activities (sit-to-stand, stand-to-sit, level walking, walking up- and downstairs, answering the phone, picking up an object and getting up from supine). Acceleration was measured using two devices, a mobile phone and an independent accelerometer attached to the waist of the participants.

RESULTS
Bland-Altman analysis shows a higher degree of agreement between the data recorded by the two devices. Using individual upper and lower detection thresholds, the specificity and sensitivity for mobile phone were 0.81 and 0.77, respectively, and for external accelerometer they were 0.82 and 0.96, respectively.

CONCLUSION
fall detection using a mobile phone is a feasible and highly attractive technology for older adults, especially those living alone. It may be best achieved with an accelerometer attached to the waist, which transmits signals wirelessly to a phone.


title: LiTell: robust indoor localization using unmodified light fixtures

text: Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.


title: A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment

text: Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.


title: Artificial Intelligence as an Effective Classroom Assistant

text: The field of artificial intelligence in education (AIED) uses techniques from AI and cognitive science to better understand the nature of learning and teaching and to build systems to help learners gain new skills or understand new concepts. This article studies metareviews and meta-analyses to make the case for blended learning, wherein the teacher can offload some work to AIED systems.


title: On Data Integrity Attacks Against Real-Time Pricing in Energy-Based Cyber-Physical Systems

text: In this paper, we investigate a novel real-time pricing scheme, which considers both renewable energy resources and traditional power resources and could effectively guide the participants to achieve individual welfare maximization in the system. To be specific, we develop a Lagrangian-based approach to transform the global optimization conducted by the power company into distributed optimization problems to obtain explicit energy consumption, supply, and price decisions for individual participants. Also, we show that these distributed problems derived from the global optimization by the power company are consistent with individual welfare maximization problems for end-users and traditional power plants. We also investigate and formalize the vulnerabilities of the real-time pricing scheme by considering two types of data integrity attacks: Ex-ante attacks and Ex-post attacks, which are launched by the adversary before or after the decision-making process. We systematically analyze the welfare impacts of these attacks on the real-time pricing scheme. Through a combination of theoretical analysis and performance evaluation, our data shows that the real-time pricing scheme could effectively guide the participants to achieve welfare maximization, while cyber-attacks could significantly disrupt the results of real-time pricing decisions, imposing welfare reduction on the participants.


title: An improved boosting based on feature selection for corporate bankruptcy prediction

text: With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction. 2013 Elsevier Ltd. All rights reserved.


title: Multimodal Convolutional Neural Networks for Matching Image and Sentence

text: In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.


title: A SICK cure for the evaluation of compositional distributional semantic models

text: Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.


title: IJ CS Differences in Individualistic and Collectivistic Tendencies among College Students in Japan and the United States Emiko

text: It is a worldwide stereotype that Japanese, compared to Americans, are oriented more toward collectivism. But this stereotypical notion of more collectivism among Japanese, which typically stems from a view that individualism and collectivism stand at opposite ends of a continuum, has been filled with dashed empirical findings, especially in a sample of college students. In the current study, following the view that individualism and collectivism are two separate concepts rather than one with two extremes, we test and compare both individualistic and collectivistic tendencies among college students in Japan and the United States. A review of theories and research on this dimension of cultural variability across the two diverse cultures and the literature on societal pressure of collectivity and on parents as primary socialization agents of culturally expected values lead to two hypotheses: 1) Japanese college students tend less toward individualism than do Americans, and 2) Japanese college students tend less toward collectivism than do Americans. Analysis of identical survey data from college students in Japan and in the United States provides strong support for both hypotheses.


title: Autonomous robotic system for bridge deck data collection and analysis

text: Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.


title: Effects of Harsh and Unpredictable Environments in Adolescence on Development of Life History Strategies: A Longitudinal Test of an Evolutionary Model.

text: The National Longitudinal Study of Adolescent Health data were used to test predictions from life history theory. We hypothesized that (1) in young adulthood an emerging life history strategy would exist as a common factor underlying many life history traits (e.g., health, relationship stability, economic success), (2) both environmental harshness and unpredictability would account for unique variance in expression of adolescent and young adult life history strategies, and (3) adolescent life history traits would predict young adult life history strategy. These predictions were supported. The current findings suggest that the environmental parameters of harshness and unpredictability have concurrent effects on life history development in adolescence, as well as longitudinal effects into young adulthood. In addition, life history traits appear to be stable across developmental time from adolescence into young adulthood.


title: Pattern Recognition Systems under Attack: Design Issues and Research Challenges

text: We analyze the problem of designing pattern recognition systems in adversarial settings, under an engineering viewpoint, motivated by their increasing exploitation in security-sensitive applications like spam and malware detection, despite their vulnerability to potential attacks has not yet been deeply understood. We first review previous work and report examples of how a complex system may be evaded either by leveraging on trivial vulnerabilities of its untrained components, e.g., parsing errors in the pre-processing steps, or by exploiting more subtle vulnerabilities of learning algorithms. We then discuss the need of exploiting both reactive and proactive security paradigms complementarily to improve the security by design. Our ultimate goal is to provide some useful guidelines for improving the security of pattern recognition in adversarial settings, and to suggest related open issues to foster research in this area.


title: Security, privacy and safety evaluation of dynamic and static fleets of drones

text: Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life — often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.


title: A retrospective of knowledge graphs

text: Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for machines, and even for humans. Knowledge graphs have become prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and finally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via logical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph systems and discuss the future research directions.


title: Sentiment analysis using Telugu SentiWordNet

text: In recent times, sentiment analysis in low resourced languages and regional languages has become emerging areas in natural language processing. Researchers have shown greater interest towards analyzing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, etc. In best of our knowledge, microscopic work has been reported till date towards Indian languages due to lack of annotated data set. In this paper, we proposed a two-phase sentiment analysis for Telugu news sentences using Telugu SentiWordNet. Initially, it identifies subjectivity classification where sentences are classified as subjective or objective. Objective sentences are treated as neutral sentiment as they don't carry any sentiment value. Next, Sentiment Classification has been done where the subjective sentences are further classified into positive and negative sentences. With the existing Telugu SentiWordNet, our proposed system attains an accuracy of 74% and 81% for subjectivity and sentiment classification respectively.


title: Combating VR sickness through subtle dynamic field-of-view modification

text: Virtual Reality (VR) sickness can cause intense discomfort, shorten the duration of a VR experience, and create an aversion to further use of VR. High-quality tracking systems can minimize the mismatch between a user's visual perception of the virtual environment (VE) and the response of their vestibular system, diminishing VR sickness for moving users. However, this does not help users who do not or cannot move physically the way they move virtually, because of preference or physical limitations such as a disability. It has been noted that decreasing field of view (FOV) tends to decrease VR sickness, though at the expense of sense of presence. To address this tradeoff, we explore the effect of dynamically, yet subtly, changing a physically stationary person's FOV in response to visually perceived motion as they virtually traverse a VE. We report the results of a two-session, multi-day study with 30 participants. Each participant was seated in a stationary chair, wearing a stereoscopic head-worn display, and used control and FOV-modifying conditions in the same VE. Our data suggests that by strategically and automatically manipulating FOV during a VR session, we can reduce the degree of VR sickness perceived by participants and help them adapt to VR, without decreasing their subjective level of presence, and minimizing their awareness of the intervention.


title: Advances in rapid detection methods for foodborne pathogens.

text: Food safety is increasingly becoming an important public health issue, as foodborne diseases present a widespread and growing public health problem in both developed and developing countries. The rapid and precise monitoring and detection of foodborne pathogens are some of the most effective ways to control and prevent human foodborne infections. Traditional microbiological detection and identification methods for foodborne pathogens are well known to be time consuming and laborious as they are increasingly being perceived as insufficient to meet the demands of rapid food testing. Recently, various kinds of rapid detection, identification, and monitoring methods have been developed for foodborne pathogens, including nucleic-acid-based methods, immunological methods, and biosensor-based methods, etc. This article reviews the principles, characteristics, and applications of recent rapid detection methods for foodborne pathogens.


title: Regression testing in the presence of non-code changes

text: Regression testing is an important activity performed to validate modified software, and one of its key tasks is regression test selection (RTS) -- selecting a subset of existing test cases to run on the modified software. Most existing RTS techniques focus on changes made to code components and completely ignore non-code elements, such as configuration files and databases, which can also change and affect the system behavior. To address this issue, we present a new RTS technique that performs accurate test selection in the presence of changes to non-code components. To do this, our technique computes traceability between test cases and the external data accessed by an application, and uses this information to perform RTS in the presence of changes to non-code elements. We present our technique, a prototype implementation of our technique, and a set of preliminary empirical results that illustrate the feasibility, effectiveness, and potential usefulness of our approach.


title: Evidence for van der Waals adhesion in gecko setae.

text: Geckos have evolved one of the most versatile and effective adhesives known. The mechanism of dry adhesion in the millions of setae on the toes of geckos has been the focus of scientific study for over a century. We provide the first direct experimental evidence for dry adhesion of gecko setae by van der Waals forces, and reject the use of mechanisms relying on high surface polarity, including capillary adhesion. The toes of live Tokay geckos were highly hydrophobic, and adhered equally well to strongly hydrophobic and strongly hydrophilic, polarizable surfaces. Adhesion of a single isolated gecko seta was equally effective on the hydrophobic and hydrophilic surfaces of a microelectro-mechanical systems force sensor. A van der Waals mechanism implies that the remarkable adhesive properties of gecko setae are merely a result of the size and shape of the tips, and are not strongly affected by surface chemistry. Theory predicts greater adhesive forces simply from subdividing setae to increase surface density, and suggests a possible design principle underlying the repeated, convergent evolution of dry adhesive microstructures in gecko, anoles, skinks, and insects. Estimates using a standard adhesion model and our measured forces come remarkably close to predicting the tip size of Tokay gecko seta. We verified the dependence on size and not surface type by using physical models of setal tips nanofabricated from two different materials. Both artificial setal tips stuck as predicted and provide a path to manufacturing the first dry, adhesive microstructures.


title: Evaluation of nutritional assessment techniques in elderly people newly admitted to municipal care

text: Objectives: To evaluate the Subjective Global Assessment (SGA) and the Mini Nutritional Assessment (MNA) with regard to validity using a combination of anthropometric and serum-protein measurements as standard criteria to assess protein-energy malnutrition (PEM).Design: Cross-sectional study with consecutive selection of residents aged ≥65 y.Setting: A municipality in the south of Sweden.Subjects: During a year, starting in October 1996, 148 females and 113 males, aged ≥65–104 y of age, newly admitted to special types of housing for the elderly, were included in the study.Results: According to SGA, 53% were assessed as malnourished or moderately malnourished on admission. The corresponding figure from MNA was 79% malnourished or at risk of malnutrition. Both tools indicated that anthropometric values and serum proteins were significantly lower in residents classified as being malnourished (P<0.05). Sensitivity in detecting PEM was in SGA 0.93 and in MNA 0.96 and specificity was 0.61 and 0.26, respectively. Using regression analysis, weight index and serum albumin were the best objective nutritional parameters in predicting the SGA- and MNA classifications. Item ‘muscle wasting’ in SGA and ‘self-experienced health status’ in MNA showed most predictive power concerning the odds of being assessed as malnourished.Conclusions: SGA was shown to be the more useful tool in detecting residents with established malnutrition and MNA in detecting residents who need preventive nutritional measures.


title: Youth texting: Help or hindrance to literacy?

text: An extensive amount of research has been performed in recent years into the widespread practice of text messaging in youth. As part of this broad area of research, the associations between youth texting and literacy have been investigated in a variety of contexts. A comprehensive, semi-systematic review of the literature into texting and literacy was conducted, with a particular focus on quantitative empirical studies. Media reports, teacher surveys, and qualitative studies were also taken into consideration as part of this wide-ranging examination of previous research into this phenomenon. There were no clear positive or negative links discovered between adolescent texting practices and literacy, with the research findings in this study area best summarized as mixed and inconclusive. More studies in this research area are required, especially of an experimental or longitudinal kind. In addressing the debate on the impact of texting on literacy, research analyses as well as media reports would benefit from a more balanced presentation of existing findings.


title: Deep Neural Networks for i-Vector Language Identification of Short Utterances in Cars

text: This paper is focused on the application of the Language Identification (LID) technology for intelligent vehicles. We cope with short sentences or words spoken in moving cars in four languages: English, Spanish, German, and Finnish. As the response time of the LID system is crucial for user acceptance in this particular task, speech signals of different durations with total average of 3.8s are analyzed. In this paper, the authors propose the use of Deep Neural Networks (DNN) to model effectively the i-vector space of languages. Both raw i-vectors and session variability compensated i-vectors are evaluated as input vectors to DNNs. The performance of the proposed DNN architecture is compared with both conventional GMM-UBM and i-vector/LDA systems considering the effect of durations of signals. It is shown that the signals with durations between 2 and 3s meet the requirements of this application, i.e., high accuracy and fast decision, in which the proposed DNN architecture outperforms GMM-UBM and i-vector/LDA systems by 37% and 28%, respectively.


title: Cloud RAN challenges and solutions

text: In this paper we take an overall look at key technical challenges in the evolution of the Radio Access Network (RAN) architecture towards Cloud RAN, and solutions to overcome them. To address fronthaul limitations, we examine the implications and tradeoffs enabled by functional splits on fronthaul needs, system performance, and centralization scale. We examine the architecture of algorithms for multi-cell coordination and implications in a Cloud RAN environment. To maximize the use of General-Purpose Processors (GPP) and operating systems such as Linux for Cloud RAN, we propose methods of achieving real-time performance suitable for RAN functions. To enable right-sizing the amount of compute used for various RAN functions based on the workload, we propose methods of pooling and elastic scaling for RAN functions that exploit the fact that certain RAN functions perform per-user operations while others perform per-cell operations. Cloud RAN also aims to use cloud management technologies such as virtualized infrastructure management (VIM) and orchestration for automating the instantiation and scaling of RAN functions. We identify special needs for RAN arising from real-time constraints and a mix of GPP and non-GPP hardware. Keywords—Cloud RAN, fronthaul, real-time, pooling, elastic scaling, multi-cell coordination, orchestration, NFV, 5G.


title: Cyberbullying classification using text mining

text: Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes method and Support Vector Machine (SVM) then applying n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. Naive Bayes yields an average accuracy of 92.81%, SVM with a poly kernel yields an average accuracy of 97.11% It can be concluded that SVM with poly kernel yields higher accuracy than SVM with other kernels, Naive Bayes, and Kelly Reynolds research method of decision tree (J48) and k-NN.


title: Compliance by design for artifact-centric business processes

text: Compliance to legal regulations, internal policies, or best practices is becoming a more and more important aspect in business processes management. Compliance requirements are usually formulated in a set of rules that can be checked during or after the execution of the business process, called compliance by detection. If noncompliant behavior is detected, the business process needs to be redesigned. Alternatively, the rules can be already taken into account while modeling the business process to result in a business process that is compliant by design. This technique has the advantage that a subsequent verification of compliance is not required. This paper focuses on compliance by design and employs an artifactcentric approach. In this school of thought, business processes are not described as a sequence of tasks to be performed (i. e., imperatively), but from the point of view of the artifacts that are manipulated during the process (i. e., declaratively). We extend the artifact-centric approach to model compliance rules and show how compliant business processes can be synthesized automatically.


title: Low Cost Dual Polarized Base Station Element for Long Term Evolution

text: A broadband low profile dual polarized patch antenna for fourth generation long term evolution (4G LTE) is presented in this communication. By employing two wideband feeding mechanisms for the two input ports, a dual polarized patch antenna element with the characteristics of high input port isolation and wide impedance bandwidth is successfully designed. A wide meandering probe (M-probe) and a pair of twin-L-probes are proposed to feed a patch for dual polarization. The proposed design with a wideband balun has impedance bandwidths of over 47% at the two input ports as well as over 37 dB isolation within the entire operating band. Over 8 dBi gain can be achieved at the two ports.


title: Mitigating DoS attacks against signature-based authentication in VANETs

text: Vehicular ad hoc networks (VANETs) are supposed to improve traffic safety and drivers' experiences. In a typical VANET, a vehicle broadcasts safety messages to its neighbors. Since behaviors based on safety messages could be life-critical, authentication of these messages must be guaranteed. Many signature-based schemes have been proposed for authentication in VANETs, but few of them have addressed the problem of denial of service (DoS) attacks against signature-based authentication. In such a DoS attack, attackers can broadcast forged messages with invalid signatures to force the receiving vehicles to perform lots of unnecessary signature verifications, and thus the benign vehicles cannot verify the messages from other legitimate vehicles. Our scheme features a pre-authentication process before signature verifying process to deal with this kind of DoS attack. The pre-authentication process takes advantage of the one-way hash chain and a group rekeying scheme. Evaluations show that the proposed scheme mitigates such DoS attacks effectively.


title: Pareto Optimal Security Resource Allocation for Internet of Things

text: In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution.


title: Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images

text: The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.


title: Control Knowledge to Improve Plan Quality

text: Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledge has been aimed at improving the efficiency of planning; this work has been termed “speed-up learning”. This paper focuses on learning control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.


title: Multi-Scale Convolutional Neural Networks for Time Series Classification

text: Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls short of classification accuracy and efficiency. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, based on which an off-the-shelf classifier can be applied. These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance. Plus, most existing methods fail to take into account the fact that time series often have features at different time scales. To address these problems, we propose a novel end-to-end neural network model, Multi-scale Convolutional Neural Network (MCNN), which incorporates feature extraction and classification in a single framework. Leveraging a novel multi-branch layer and learnable convolutional layers, MCNN automatically extracts features at different scales and frequencies, leading to superior feature representation. MCNN is also computationally efficient, as it naturally leverages GPU computing. We conduct comprehensive empirical evaluation with various existing methods on a large number of benchmark datasets, and show that MCNN advances the state-of-the-art by achieving superior accuracy performance than other leading methods.


title: Mobile commerce adoption in China and the United States: a cross-cultural study

text: Mobile communication technologies have penetrated consumer markets throughout the world. Mobile commerce is likely to make a strong influence on business activities, consumer behavior, as well as national and global markets. Thus the identification of factors that influence mobile commerce adoption has significant value. In a global context, this study identified nine factors affecting mobile commerce adoption based on published research in MIS. These factors were investigated in China and the United States, and a comparative examination was conducted. A survey was conducted on 190 individual mobile commerce users in China and USA. Results show that there are several significant differences among the antecedents and their impacts on consumer intention to use mobile commerce in the two cultural settings. The study provides a number of practical insights and informs vendors seeking to enter the Chinese and the US marketplace with specific information on user perspectives.


title: The effects of perceived risk and technology type on users' acceptance of technologies

text: Previous studies on technology adoption disagree regarding the relative magnitude of the effects of perceived usefulness and perceived ease of use. However these studies did not consider moderating variables. We investigated four potential moderating variables – perceived risk, technology type, user experience, and gender – in users’ technology adoption. Their moderating effects were tested in an empirical study of 161 subjects. Results showed that perceived risk, technology type, and gender were significant moderating variables. However the effects of user experience were marginal after the variance of errors was removed. # 2007 Published by Elsevier B.V. www.elsevier.com/locate/im Available online at www.sciencedirect.com Information & Management 45 (2008) 1–9


title: Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet

text: This study examines the utility of three prospective models for understanding the continued IT usage behavior. The three models include: Expectation-Confirmation Model in IT Domain (ECM-IT), Technology Acceptance Model (TAM), and a hybrid model integrating TAM and ECM-IT (extended ECM-IT). Based on a survey of 1826 mobile Internet users, the LISREL analysis shows that all three models meet the various goodness-of-fit criteria. When compared using special indices for differentiating among alternative good models, TAM has the best fit to the data followed by ECM-IT, and the extended ECM-IT. In terms of variance explained for intention to continue IT usage, the extended ECM-IT has the highest R (67%) followed by TAM (63%), and ECM-IT (50%). We conclude that TAM is the most parsimonious and generic model that can be used to study both initial and continued IT adoption; the extended ECM-IT explains continued IT usage behavior as well as TAM; and both the ECM-IT and extended ECM-IT models provide additional information to increase our understanding of continued IT usage. © 2006 Elsevier B.V. All rights reserved.


title: Trust and TAM in Online Shopping: An Integrated Model

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


title: A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data

text: Recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. Thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. This model is built as micro-clusters using semi-supervised clustering technique and classification is performed with kappa-nearest neighbor algorithm. An ensemble of these models is used to classify the unlabeled data. Empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training, outperforms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.


title: Chess Q & A : Question Answering on Chess Games

text: We introduce a new dataset1 for the evaluation of models addressing reasoning tasks. For a position in a chess game, we provide question and answer pairs, an image of the board, and the sequence of moves up to that position. We hope this synthetic task will improve our understanding in memory based Deep Learning with posed challenges.


title: NEUROSCIENTIFIC INVE STIGATIONS OF MUSICAL RHYTHM

text: Music occurs in every human society, unfolds over time, and enables synchronized movements. The neural mechanisms underlying the perception, cognition, and production of musical rhythm have been investigated using a variety of methods. FMRI studies in particular have shown that the motor system is crucially involved in rhythm and beat perception. Studies using other methods demonstrate that oscillatory neural activity entrains to regularities in musical rhythm, and that motor system excitability is modulated by listening to musical rhythm. This review paper describes some of the recent neuroscientific findings regarding musical rhythm, and especially the perception of a regular beat. INTRODUCTION The temporal structure of music enables synchronized movement, such as tapping one’s foot, clapping, or dancing to the ‘beat’ of musical rhythms. Such movement is precisely timed to align with the periodic, salient beats in the music, and with the movements of other individuals. Given this relationship between musical rhythm and movement, it is perhaps unsurprising that the brain’s motor system is heavily involved in the neural processing of auditory rhythms. However, it is a relatively recent discovery that the motor system is involved even in the absence of movement – subtle differences in the temporal structure or context of an auditory rhythm can elicit robust differences in motor system activity. These discoveries are the topic of this review paper, with a focus on findings from functional magnetic resonance imaging (fMRI). FMRI measures the change in oxygenated blood levels following neural activity [see 1, 2]. This ‘blood-oxygen-level dependent’ (or BOLD) signal is considered to be an indirect measure of brain activity, and therefore increases in BOLD are termed ‘activations’ in this review. Findings from patient studies, as well as electroencephalography (EEG), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) studies will also be discussed. Although much theoretic and empirical work has sought to explain why certain temporal patterns elicit movement (e.g., dancing) while others do not [3-5], and the evolutionary basis for human sensitivity to musical rhythm [6-7], this review will focus on the neural substrates of rhythm perception and the role of individual differences, expertise, and sensory modality. RHYTHM AND BEAT IN THE BRAIN When human participants listen to rhythms (i.e., auditory sequences) with or without a beat, widespread activity is observed in the cortical motor system, especially in the supplementary motor area (SMA) and premotor cortex (PMC), as well as subcortical regions such as the basal ganglia and cerebellum [8-14]. Rhythms that are composed of intervals that are integer ratios of one another, and have accents occurring at regular intervals, tend to elicit the perception of a regular, emphasized beat, and beats are usually organized in a metre (a temporal structure determined by the cyclical pattern of strong and weak beats; see Figure 1). Compared to rhythms without a beat, listening to beat-based rhythms elicits more activity in the SMA and the basal ganglia [10]. The importance of the basal ganglia in beat perception was highlighted in a study demonstrating that patients with Parkinson’s disease have impaired


title: Image Retrieval by Examples

text: A currently relevant research field in information sciences is the management of nontraditional distributed multimedia databases. Two related key issues are to achieve an efficient content-basedquery by exampleretrieval and a fast response time. This paper presents the architecture of a distributed image retrieval system which provides novel solutions to these key issues. In particular, a way to quantify the effectiveness of low level visual descriptors in database query tasks is presented. The results are also used to improve the system response time, which is an important issue when querying very large databases. A new mechanism to adapt system query strategies to user behavior is also introduced in order to improve the effectiveness of relevance feedback and overall system response time. Finally, the issue of browsing multiple distributed databases is considered and a solution is proposed using multidimensional scaling techniques.


title: Caring for a child with autism spectrum disorder and parents' quality of life: application of the CarerQol.

text: This study describes the impact of caregiving on parents of children with autism spectrum disorders (ASDs). Secondly, we investigate construct validation of the care-related quality of life instrument (CarerQol) measuring impact of caregiving. Primary caregivers of children with ASDs were included. Many parents experienced considerable problems combining daily activities with care, had financial problems or suffered from depressive mood. Validity tests showed that a higher impact of caring on the CarerQol was positively associated with higher subjective burden and lower family quality of life. Most of the associations between CarerQol scores and background characteristics confirmed previous research. The CarerQol validly measures the impact of caregiving for children with ASDs on caregivers in our sample. The CarerQol may therefore be useful for including parent outcomes in research on ASDs.


title: Image recognition of plant diseases based on principal component analysis and neural networks

text: Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.


title: Damage in the anca-associated vasculitides: long-term data from the European vasculitis study group (EUVAS) therapeutic trials.

text:
OBJECTIVES
To describe short-term (up to 12 months) and long-term (up to 7 years) damage in patients with newly diagnosed antineutrophil-cytoplasm antibody-associated vasculitis (AAV).

METHODS
Data were combined from six European Vasculitis Study group trials (n=735). Long-term follow-up (LTFU) data available for patients from four trials (n=535). Damage accrued was quantified by the Vasculitis Damage Index (VDI). Sixteen damage items were defined a priori as being potentially treatment-related.

RESULTS
VDI data were available for 629 of 735 patients (85.6%) at baseline, at which time 217/629 (34.5%) had ≥1 item of damage and 32 (5.1%) ≥5 items, reflecting disease manifestations prior to diagnosis and trial enrolment. LTFU data were available for 467/535 (87.3%) at a mean of 7.3 years postdiagnosis. 302/535 patients (56.4%) had VDI data at LTFU, with 104/302 (34.4%) having ≥5 items and only 24 (7.9%) no items of damage. At 6 months and LTFU, the most frequent items were proteinuria, impaired glomerular filtration rate, hypertension, nasal crusting, hearing loss and peripheral neuropathy. The frequency of damage, including potentially treatment-related damage, rose over time (p<0.01). At LTFU, the most commonly reported items of treatment-related damage were hypertension (41.5%; 95% CI 35.6 to 47.4%), osteoporosis (14.1%; 9.9 to 18.2%), malignancy (12.6%; 8.6 to 16.6%), and diabetes (10.4%; 6.7 to 14.0%).

CONCLUSIONS
In AAV, renal, otolaryngological and treatment-related (cardiovascular, disease, diabetes, osteoporosis and malignancy) damage increases over time, with around one-third of patients having ≥5 items of damage at a mean of 7 years postdiagnosis.


title: A Software Defined Networking architecture for the Internet-of-Things

text: The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.


title: Multi-Task Label Embedding for Text Classification

text: Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless onehot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semisupervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.


title: WebRTC technology overview and signaling solution design and implementation

text: This paper describes the WebRTC technology and implementation of WebRTC client, server and signaling. Main parts of the WebRTC API are described and explained. Signaling methods and protocols are not specified by the WebRTC standards, therefore in this study we design and implement a novel signaling mechanism. The corresponding message sequence chart of the WebRTC communication behavior describes a communication flow between peers and the server. The server application is implemented as a WebSocket server. The client application demonstrates the use of the WebRTC API for achieving real-time communication. Benefits and future development of the WebRTC technology are mentioned.


title: Leadership for safety: industrial experience.

text: The importance of leadership for effective safety management has been the focus of research attention in industry for a number of years, especially in energy and manufacturing sectors. In contrast, very little research into leadership and safety has been carried out in medical settings. A selective review of the industrial safety literature for leadership research with possible application in health care was undertaken. Emerging findings show the importance of participative, transformational styles for safety performance at all levels of management. Transactional styles with attention to monitoring and reinforcement of workers' safety behaviours have been shown to be effective at the supervisory level. Middle managers need to be involved in safety and foster open communication, while ensuring compliance with safety systems. They should allow supervisors a degree of autonomy for safety initiatives. Senior managers have a prime influence on the organisation's safety culture. They need to continuously demonstrate a visible commitment to safety, best indicated by the time they devote to safety matters.


title: How to handle ARP in a software-defined network

text: The Address Resolution Protocol (ARP) enables communication between IP-speaking nodes in a local network by reconstructing the hardware (MAC) address associated with the IP address of an interface. This is not needed in a Software-Defined Network (SDN), because each device can forward packets without the need to learn this association. We tackle the interoperability problem arising between standard network devices (end systems, routers), that rely on ARP, and SDN datapaths, that do not handle ARP packets natively. In particular, we propose a general approach to handle ARP in a SDN, that is applicable in several network scenarios, is transparent for existing devices, and can coexist with any packet forwarding logic implemented in the controller. Our approach reduces ARP traffic by confining it to the edge of SDNs and requires a minimal set of flow entries in the datapaths. We argument about its applicability and confirm it with experiments performed on SDN datapaths from a range of different vendors.


title: RASCAL: A Domain Specific Language for Source Code Analysis and Manipulation

text: Many automated software engineering tools require tight integration of techniques for source code analysis and manipulation. State-of-the-art tools exist for both, but the domains have remained notoriously separate because different computational paradigms fit each domain best. This impedance mismatch hampers the development of new solutions because the desired functionality and scalability can only be achieved by repeated and ad hoc integration of different techniques. Rascal is a domain-specific language that takes away most of this boilerplate by integrating source code analysis and manipulation at the conceptual, syntactic, semantic and technical level. We give an overview of the language and assess its merits by implementing a complex refactoring.


title: Robust Audio Hashing for Content Identification

text: Nowadays most audio content identification systems are based on watermarking technology. In this paper we present a different technology, referred to as robust audio hashing. By extracting robust features and translating them into a bit string, we get an object called a robust hash. Content can then be identified by comparing hash values of a received audio clip with the hash values of previously stored original audio clips. A distinguishing feature of the proposed hash scheme is its ability to extract a bit string for every so many milliseconds. More precisely, for every windowed time interval a hash value of 32 bits is computed by thresholding energy differences of several frequency bands. A sequence of 256 hash values, corresponding to approximately 3 seconds of audio, can uniquely identify a song. Experimental results show that the proposed scheme is robust against severe compression, but bit errors do occur. This implies that searching and matching is a non-trivial task for large databases. For instance, a brute force search approach is already prohibitive for databases containing hash values of more than 100 songs. Therefore we propose a scheme that exploits a structured search algorithm that allows searching databases containing over 100,000 songs.


title: License plate detection and character recognition system for commercial vehicles based on morphological approach and template matching

text: Though license plate detection and recognition system has a lot of applications but a very little work has been done on Bangla license plate recognition. Variations among the license plate patterns and complex background of Bangladeshi license plates make it more difficult to use the existing algorithms. Because of this, we propose a solution for Bangla license plate detection and recognition. Firstly, the position of the vehicle is determined. In our country, commercial License Plates have the unique color (green) of its own. That's why; we select the portions of green color with the matching RGB intensity of the plate. A boundary based contour algorithm, area and aspect ratio have been proposed to track down the license plate in the vehicle region. License plate rows containing registration information have been separated by using horizontal projection with threshold. The characters and the digits of the rows have been segmented using vertical projection with threshold value. Finally, template matching has been used for recognizing the characters and the digits of the Bangla license plate. We tested our algorithms for over 180 still images captured from the road. We achieve93% success in license plate detection, 98.1% success in segmentation and 88.8% success rate in Bangla license plate recognition.


title: Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain

text: This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.


title: ServerSwitch: A Programmable and High Performance Platform for Data Center Networks

text: As one of the fundamental infrastructures for cloud computing, data center networks (DCN) have recently been studied extensively. We currently use pure software-based systems, FPGA based platforms, e.g., NetFPGA, or OpenFlow switches, to implement and evaluate various DCN designs including topology design, control plane and routing, and congestion control. However, software-based approaches suffer from high CPU overhead and processing latency; FPGA based platforms are difficult to program and incur high cost; and OpenFlow focuses on control plane functions at present. In this paper, we design a ServerSwitch to address the above problems. ServerSwitch is motivated by the observation that commodity Ethernet switching chips are becoming programmable and that the PCI-E interface provides high throughput and low latency between the server CPU and I/O subsystem. ServerSwitch uses a commodity switching chip for various customized packet forwarding, and leverages the server CPU for control and data plane packet processing, due to the low latency and high throughput between the switching chip and server CPU. We have built our ServerSwitch at low cost. Our experiments demonstrate that ServerSwitch is fully programmable and achieves high performance. Specifically, we have implemented various forwarding schemes including source routing in hardware. Our in-network caching experiment showed high throughput and flexible data processing. Our QCN (Quantized Congestion Notification) implementation further demonstrated that ServerSwitch can react to network congestions in 23us. ∗Correspondent author. †This work was performed when Zhiqiang Zhou was a visiting student at Microsoft Research Asia.


title: Generalized velocity obstacles

text: We address the problem of real-time navigation in dynamic environments for car-like robots. We present an approach to identify controls that will lead to a collision with a moving obstacle at some point in the future. Our approach generalizes the concept of velocity obstacles, which have been used for navigation among dynamic obstacles, and takes into account the constraints of a car-like robot. We use this formulation to find controls that will allow collision free navigation in dynamic environments. Finally, we demonstrate the performance of our algorithm on a simulated car-like robot among moving obstacles.


title: Plant pathogens and integrated defence responses to infection

text: Plants cannot move to escape environmental challenges. Biotic stresses result from a battery of potential pathogens: fungi, bacteria, nematodes and insects intercept the photosynthate produced by plants, and viruses use replication machinery at the host's expense. Plants, in turn, have evolved sophisticated mechanisms to perceive such attacks, and to translate that perception into an adaptive response. Here, we review the current knowledge of recognition-dependent disease resistance in plants. We include a few crucial concepts to compare and contrast plant innate immunity with that more commonly associated with animals. There are appreciable differences, but also surprising parallels.


title: Trait conscientiousness and the personality meta-trait stability are associated with regional white matter microstructure

text: Establishing the neural bases of individual differences in personality has been an enduring topic of interest. However, while a growing literature has sought to characterize grey matter correlates of personality traits, little attention to date has been focused on regional white matter correlates of personality, especially for the personality traits agreeableness, conscientiousness and openness. To rectify this gap in knowledge we used a large sample (n > 550) of older adults who provided data on both personality (International Personality Item Pool) and white matter tract-specific fractional anisotropy (FA) from diffusion tensor MRI. Results indicated that conscientiousness was associated with greater FA in the left uncinate fasciculus (β = 0.17, P < 0.001). We also examined links between FA and the personality meta-trait 'stability', which is defined as the common variance underlying agreeableness, conscientiousness, and neuroticism/emotional stability. We observed an association between left uncinate fasciculus FA and stability (β = 0.27, P < 0.001), which fully accounted for the link between left uncinate fasciculus FA and conscientiousness. In sum, these results provide novel evidence for links between regional white matter microstructure and key traits of human personality, specifically conscientiousness and the meta-trait, stability. Future research is recommended to replicate and address the causal directions of these associations.


title: How Regulatory Fit Affects Value in Consumer Choices and Opinions

text: Vol. XLIII (February 2006), 1–10 1 © 2006, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) *Tamar Avnet is Assistant Professor of Business, Rotman School of Management, University of Toronto (e-mail: tavnet@rotman.utoronto.ca). E. Tory Higgins is Professor of Psychology, Department of Psychology, and Professor of Management, Columbia School of Business, Columbia University (e-mail: tory@psych.columbia.edu). The research reported in this article was supported by a grant (No. 39429) from the National Institute of Mental Health to E. Tory Higgins. TAMAR AVNET and E. TORY HIGGINS*


title: Elastic Distributed Bayesian Collaborative Filtering

text: In this paper, we consider learning a Bayesian collaborative filtering model on a shared cluster of commodity machines. Two main challenges arise: (1) How can we parallelize and distribute Bayesian collaborative filtering? (2) How can our distributed inference system handle elasticity events common in a shared, resource managed cluster, including resource ramp-up, preemption, and stragglers? To parallelize Bayesian inference, we adapt ideas from both matrix factorization partitioning schemes used with stochastic gradient descent and stale synchronous programming used with parameter servers. To handle elasticity events we offer a generalization of previous partitioning schemes that gives increased flexibility during system disruptions. We additionally describe two new scheduling algorithms to dynamically route work at runtime. In our experiments, we compare the effectiveness of both scheduling algorithms and demonstrate their robustness to system failure.


title: Exploring the link between self-compassion and body image in university women.

text: The purpose of the present research was to examine the relationships between self-compassion and women's body image. In Study 1, female undergraduates (N=142) completed three measures of body image and measures of self-esteem and self-compassion. Results showed that high self-compassion predicted fewer body concerns independently of self-esteem. Moreover, when both self-compassion and self-esteem were included as predictors, self-compassion accounted for unique variance in body preoccupation and weight concerns whereas self-esteem did not. In Study 2, this finding was partially replicated with one component (self-judgment) of self-compassion uniquely predicting body preoccupation in undergraduate women (N=187). High scores on self-compassion also predicted less eating guilt independent of self-esteem. Additionally, self-compassion was shown to partially mediate the relationship between body preoccupation and depressive symptoms. The findings highlight the possibility that a consideration of self-compassion for body image may contribute to identifying who is most at risk for body/shape concerns.


title: Self-compassion versus global self-esteem: two different ways of relating to oneself.

text: This research examined self-compassion and self-esteem as they relate to various aspects of psychological functioning. Self-compassion entails treating oneself with kindness, recognizing one's shared humanity, and being mindful when considering negative aspects of oneself. Study 1 (N=2,187) compared self-compassion and global self-esteem as they relate to ego-focused reactivity. It was found that self-compassion predicted more stable feelings of self-worth than self-esteem and was less contingent on particular outcomes. Self-compassion also had a stronger negative association with social comparison, public self-consciousness, self-rumination, anger, and need for cognitive closure. Self-esteem (but not self-compassion) was positively associated with narcissism. Study 2 (N=165) compared global self-esteem and self-compassion with regard to positive mood states. It was found that the two constructs were statistically equivalent predictors of happiness, optimism, and positive affect. Results from these two studies suggest that self-compassion may be a useful alternative to global self-esteem when considering what constitutes a healthy self-stance.


title: Are improvements in shame and self-compassion early in eating disorders treatment associated with better patient outcomes?

text: Compassion-focused therapy (CFT; Gilbert, 2005, 2009) is a transdiagnostic treatment approach focused on building self-compassion and reducing shame. It is based on the theory that feelings of shame contribute to the maintenance of psychopathology, whereas self-compassion contributes to the alleviation of shame and psychopathology. We sought to test this theory in a transdiagnostic sample of eating disorder patients by examining whether larger improvements in shame and self-compassion early in treatment would facilitate faster eating disorder symptom remission over 12 weeks. Participants were 97 patients with an eating disorder admitted to specialized day hospital or inpatient treatment. They completed the Eating Disorder Examination-Questionnaire, Experiences of Shame Scale, and Self-Compassion Scale at intake, and again after weeks 3, 6, 9, and 12. Multilevel modeling revealed that patients who experienced greater decreases in their level of shame in the first 4 weeks of treatment had faster decreases in their eating disorder symptoms over 12 weeks of treatment. In addition, patients who had greater increases in their level of self-compassion early in treatment had faster decreases in their feelings of shame over 12 weeks, even when controlling for their early change in eating disorder symptoms. These results suggest that CFT theory may help to explain the maintenance of eating disorders. Clinically, findings suggest that intervening with shame early in treatment, perhaps by building patients' self-compassion, may promote better eating disorders treatment response.


title: The Body Appreciation Scale: development and psychometric evaluation.

text: Body image has been conceptualized and assessed almost exclusively in terms of its negative dimensions. Therefore, a measure reflecting body appreciation, an aspect of positive body image, was developed and evaluated via four independent samples of college women. Study 1 (N = 181) supported the Body Appreciation Scale's (BAS) unidimensionality and construct validity, as it was related as expected to body esteem, body surveillance, body shame, and psychological well-being. Study 2 (N = 327) cross-validated its unidimensionality. Study 3 (N = 424) further upheld the construct validity of the BAS, as it was: (a) related as expected to appearance evaluation, body preoccupation, body dissatisfaction, and eating disorder symptomatology and (b) unrelated to impression management. Studies 1 and 3 also indicated that the BAS predicted unique variance in psychological well-being above and beyond extant measures of body image. Study 4 (N = 177) demonstrated that its scores were stable over a 3-week period. All studies supported the internal consistency reliability of its scores. The BAS should prove useful for researchers and clinicians interested in positive body image assessment.


title: On a Feasible-Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch

text: We explore data-driven methods for gaining insight into the dynamics of a two population genetic algorithm (GA), which has been effective in tests on constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective function values. Infeasible solutions are selected and bred to reduce their constraint violations. Interbreeding between populations is completely indirect, that is, only through their offspring that happen to migrate to the other population. We introduce an empirical measure of distance, and apply it between individuals and between population centroids to monitor the progress of evolution. We find that the centroids of the two populations approach each other and stabilize. This is a valuable characterization of convergence. We find the infeasible population influences, and sometimes dominates, the genetic material of the optimum solution. Since the infeasible population is not evaluated by the objective function, it is free to explore boundary regions, where the optimum is likely to be found. Roughly speaking, the No Free Lunch theorems for optimization show that all blackbox algorithms (such as Genetic Algorithms) have the same average performance over the set of all problems. As such, our algorithm would, on average, be no better than random search or any other blackbox search method. However, we provide two general theorems that give conditions that render null the No Free Lunch results for the constrained optimization problem class we study. The approach taken here thereby escapes the No Free Lunch implications, per se.


title: Robotics Component Verification on ISS ROKVISS - Preliminary Results for Telepresence

text: ROKVISS, Germany's newest space robotics technology experiment, was successfully installed outside at the Russian Service Module of the International Space Station (ISS) during an extravehicular space walk at the end of January 2005. Since February 2005 a two joint manipulator is operated from ground via a direct radio link. The aim of ROKVISS is the in flight verification of highly integrated modular robotic joints as well as the demonstration of different control modes, reaching from high system autonomy to force feedback teleoperation (telepresence mode). The experiment will be operated for at least one year in free space to evaluate and qualify intelligent light weight robotics components under realistic circumstances for maintenance and repair tasks as foreseen in upcoming manned and unmanned space applications in near future. This paper focuses in the telepresence control mode, its technology and first results from the space experiment ROKVISS


title: No data left behind: real-time insights from a complex data ecosystem

text:
The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query processing, state-of-the-art data analysis approaches solely operate on top of the local, historical data in the data lake, and ignore the fresh tail end of data that resides in the original remote sources. However, as many business operations depend on real-time analytics, this approach is no longer viable. The alternative is hand-crafting the analysis task to explicitly consider the characteristics of the various data sources and identify optimization opportunities, rendering the overall analysis non-declarative and convoluted.
 Based on our experiences operating in data lake environments, we design System-PV, a real-time analytics system that masks the complexity of dealing with multiple data sources while offering minimal response times. System-PV extends Spark with a sophisticated data virtualization module that supports multiple applications - from SQL queries to machine learning. The module features a location-aware compiler that considers source complexity, and a two-phase optimizer that produces and refines the query plans, not only for SQL queries but for all other types of analysis as well. The experiments show that System-PV is often faster than Spark by more than an order of magnitude. In addition, the experiments show that the approach of accessing both the historical and the remote fresh data is viable, as it performs comparably to solely operating on top of the local, historical data.