title: Virtual Scrum: A teaching aid to introduce undergraduate software engineering students to scrum

abstract: Teaching agile practices is becoming a priority in Software engineering curricula as a result of the increasing use of agile methods (AMs) such as Scrum in the software industry. Limitations in time, scope, and facilities within academic contexts hinder students' hands-on experience in the use of professional AMs. To enhance students' exposure to Scrum, we have developed Virtual Scrum, an educational virtual world that simulates a Scrum-based team room through virtual elements such as blackboards, a Web browser, document viewers, charts, and a calendar. A preliminary version of Virtual Scrum was tested with a group of 45 students running a capstone project with and without Virtual Scrum support. Students' feedback showed that Virtual Scrum is a viable and effective tool to implement the different elements in a Scrum team room and to perform activities throughout the Scrum process. © 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 999: XX–XX, 2013; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21588 © 2013 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:147–156, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21588


title: Exploring Models and Mechanisms for Exchanging Resources in a Federated Cloud

abstract: One of the key benefits of Cloud systems is their ability to provide elastic, on-demand (seemingly infinite) computing capability and performance for supporting service delivery. With the resource availability in single data centres proving to be limited, the option of obtaining extra-resources from a collection of Cloud providers has appeared as an efficacious solution. The ability to utilize resources from multiple Cloud providers is also often mentioned as a means to: (i) prevent vendor lock in, (ii) to enable in house capacity to be combined with an external Cloud provider, (iii) combine specialist capability from multiple Cloud vendors (especially when one vendor does not offer such capability or where such capability may come at a higher price). Such federation of Cloud systems can therefore overcome a limit in capacity and enable providers to dynamically increase the availability of resources to serve requests. We describe and evaluate the establishment of such a federation using a CometCloud based implementation, and consider a number of federation policies with associated scenarios and determine the impact of such policies on the overall status of our system. CometCloud provides an overlay that enables multiple types of Cloud systems (both public and private) to be federated through the use of specialist gateways. We describe how two physical sites, in the UK and the US, can be federated in a seamless way using this system.


title: Faster R-CNN Scene Specialization with a Sequential Monte-Carlo Framework

abstract: The performance of the learning-based detector depends much on its training dataset and decreases rapidly when it is tested on a new scene. The reason is that in the large variations between the source training dataset and target scene. To solve this problem, we propose a novel approach to automatically specialize a generic detector to specific scene by utilizing the sequential Monte Carlo filter and the Faster R-CNN deep model. The main idea is to consider the Faster R-CNN as a function that generates realizations from the probability distribution of the object to be detected in the target sequence. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized Faster R-CNN estimated in a sequential Bayesian filter framework. The resulting algorithm is compared to the state of the art scene specialization methods on several challenging datasets, the results are very promising.


PROCESS : A Versatile Computational Tool for Observed Variable Mediation , Moderation , and Conditional Process Modeling 1


title: Components and Functions of Crowdsourcing Systems – A Systematic Literature Review

abstract: Many organizations are now starting to introduce crowdsourcing as a new model of business to outsource tasks, which are traditionally performed by a small group of people, to an undefined large workforce. While the utilization of crowdsourcing offers a lot of advantages, the development of the required system carries some risks, which are reduced by establishing a profound theo- retical foundation. Thus, this article strives to gain a better understanding of what crowdsourcing systems are and what typical design aspects are considered in the development of such systems. In this paper, the author conducted a sys- tematic literature review in the domain of crowdsourcing systems. As a result, 17 definitions of crowdsourcing systems were found and categorized into four perspectives: the organizational, the technical, the functional, and the human- centric. In the second part of the results, the author derived and presented com- ponents and functions that are implemented in a crowdsourcing system.


title: A High Power Factor, Electrolytic Capacitor-Less AC-Input LED Driver Topology With High Frequency Pulsating Output Current

abstract: Light emitting diode (LED) lamps with ac-input (50 or 60 Hz) usually require an electrolytic capacitor as the dc-link capacitor in the driver circuit to: 1) balance the energy between the input and output power, and 2) to minimize the low-frequency component of the output ripple across the LEDs. The lifetime of this capacitor, however, is much shorter than that of a LED. To maximize the potential lifetime of the LED lighting system, a new pulsating current driving LED driver that does not require any electrolytic capacitors or complicated control circuits to minimize the low-frequency (i.e., 100 or 120 Hz) output ripple is proposed in this paper. The proposed circuit is simple and a single-switch topology is designed to simplify the controller design. The proposed circuit is able to reduce the energy storage capacitance to a few microfarads range, so that film capacitor can be used to replace the unreliable electrolytic capacitor. The circuit operating principles and its theoretical analysis are provided in this paper. Simulation and experimental results are given on a 9-W LED lamp to highlight the merits of the proposed circuit.


title: Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art.

abstract: Gungor and Gupta [1999, Issues in environmentally conscious manufacturing and product recovery: a survey. Computers and Industrial Engineering, 36(4), 811–853] presented an important review of the development of research in Environmentally Conscious Manufacturing and Product Recovery (ECMPRO) and provided a state of the art survey of published work. However, that survey covered most papers published through 1998. Since then, a lot of activity has taken place in EMCPRO and several areas have become richer. Many new areas also have emerged. In this paper we primarily discuss the evolution of ECMPRO that has taken place in the last decade and discuss the new areas that have come into focus during this time. After presenting some background information, the paper systematically investigates the literature by classifying over 540 published references into four major categories, viz., environmentally conscious product design, reverse and closed-loop supply chains, remanufacturing, and disassembly. Finally, we conclude by summarizing the evolution of ECMPRO over the past decade together with the avenues for future research.


Refining Automatically Extracted Knowledge Bases Using Crowdsourcing


title: Outsourcing chemical synthesis in the drug discovery process.

abstract: The positive effects of outsourcing chemical synthesis are enhanced if the provider offers, as the strategic partner, unique expertise and complements the existing internal competencies of pharmaceutical companies. The emerging cooperation model of leased competence offers additional access to high-level specialist knowledge: external service providers are temporarily integrated into internal R&D teams and can support R&D projects flexibly and quickly. Practice examples show that this cooperation model supports the efficient realization of milestones and, in the long-term, helps to build up a high internal competence level, especially in small pharmaceutical companies.


title: Centralities in Large Networks: Algorithms and Observations

abstract: Node centrality measures are important in a large number of graph applications, from search and ranking to social and biological network analysis. In this paper we study node centrality for very large graphs, up to billions of nodes and edges. Various definitions for centrality have been proposed, ranging from very simple (e.g., node degree) to more elaborate. However, measuring centrality in billion-scale graphs poses several challenges. Many of the “traditional” definitions such as closeness and betweenness were not designed with scalability in mind. Therefore, it is very difficult, if not impossible, to compute them both accurately and efficiently. In this paper, we propose centrality measures suitable for very large graphs, as well as scalable methods to effectively compute them. More specifically, we propose effective closeness and LINERANK which are designed for billion-scale graphs. We also develop algorithms to compute the proposed centrality measures in MAPREDUCE, a modern paradigm for large-scale, distributed data processing. We present extensive experimental results on both synthetic and real datasets, which demonstrate the scalability of our approach to very large graphs, as well as interesting findings and anomalies.


title: MPI and UPC broadcast, scatter and gather algorithms in Xeon Phi

abstract: Summary ::: Accelerators have revolutionised the high performance computing (HPC) community. Despite their advantages, their very specific programming models and limited communication capabilities have kept them in a supporting role of the main processors. With the introduction of Xeon Phi, this is no longer true, as it can be programmed as the main processor and has direct access to the InfiniBand network adapter. Collective operations play a key role in many HPC applications. Therefore, studying its behaviour in the context of manycore coprocessors has great importance. This work analyses the performance of different algorithms for broadcast, scatter and gather, in a large-scale Xeon Phi supercomputer. The algorithms evaluated are those available in the reference message passing interface (MPI) implementation for Xeon Phi (Intel MPI), the default algorithm in an optimised MPI implementation (MVAPICH2-MIC), and a new set of algorithms, developed by the authors of this work, designed with modern processors and new communication features in mind. The latter are implemented in Unified Parallel C (UPC), a partitioned global address space language, leveraging one-sided communications, hierarchical trees and message pipelining. This study scales the experiments to 15360 cores in the Stampede supercomputer and compares the results to Xeon and hybrid Xeon + Xeon Phi experiments, with up to 19456 cores. Copyright © 2015 John Wiley & Sons, Ltd.


title: Mining Online Deal Forums for Hot Deals

abstract: Online deal forums are public places where participants share with each other news and information regarding "deals" such as sales promotion events by online stores. The large number of messages in the forums and their inherent uncertainty make it difficult for even seasoned users to identify useful deal information from the forums. We develop an intelligent deal alert service which assists ordinary Web surfers to find useful deals by mining online deal forums. It periodically crawls relevant deal forums to collect fresh message posts and responses, and evaluate them using a form of probabilistic text classification. Users may be notified of new, "potentially" useful deal messages via emails or they may browse them using their favorite Web browser. We train and evaluate the service using deal posts and responses collected from actual deal forums in the Web. The preliminary evaluation results show that the service is quite effective in reducing the time to find useful deals.


title: What’s in a name? Understanding the Interplay Between Titles, Content, and Communities in Social Media

abstract: Creating, placing, and presenting social media content is a difficult problem. In addition to the quality of the content itself, several factors such as the way the content is presented (the title), the community it is posted to, whether it has been seen before, and the time it is posted determine its success. There are also interesting \emph{interactions} between these factors. For example, the language of the title should be targeted to the community where the content is submitted, yet it should also highlight the distinctive nature of the content. In this paper, we examine how these factors interact to determine the popularity of social media content. We do so by studying resubmissions, i.e., content that has been submitted multiple times, with multiple titles, to multiple different communities. Such data allows us to `tease apart' the extent to which each factor influences the success of that content. The models we develop help us understand how to better target social media content: by using the right title, for the right community, at the right time.


title: Why people waive their Miranda rights: The power of innocence

abstract: In a laboratory experiment, 72 participants who were guilty or innocent of a mock theft were apprehended for investigation. Motivated to avoid prosecution and trial, they were confronted by a neutral, sympathetic, or hostile male “detective” who sought a waiver of their Miranda rights. Later, 72 other participants watched videotapes of these sessions and answered questions about the detective and suspect. Strikingly, results showed that although the detective's demeanor had no effect, participants who were truly innocent were significantly more likely to sign a waiver than those who were guilty. Naively believing in the power of their innocence to set them free, most waived their rights even in the hostile detective condition, where the risk of interrogation was apparent. The conceptual and policy implications of these results are discussed.


title: Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks

abstract: Location-Based Social Networks (LBSN) present so far the most vivid realization of the convergence of the physical and virtual social planes. In this work we propose a novel approach on modeling human activity and geographical areas by means of place categories. We apply a spectral clustering algorithm on areas and users of two metropolitan cities on a dataset sourced from the most vibrant LBSN, Foursquare. Our methodology allows the identification of user communities that visit similar categories of places and the comparison of urban neighborhoods within and across cities. We demonstrate how semantic information attached to places could be plausibly used as a modeling interface for applications such as recommender systems and digital tourist guides. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.


title: Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control

abstract: Prosthetic hand control based on the acquisition and processing of surface electromyography signals (sEMG) is a well-established method that makes use of the electric potentials evoked by the physiological contraction processes of one or more muscles. Furthermore intelligent mobile medical devices are on the brink of introducing safe and highly sophisticated systems to help a broad patient community to regain a considerable amount of life quality. The major challenges which are inherent in such integrated system's design are mainly to be found in obtaining a compact system with a long mobile autonomy, capable of delivering the required signal requirements for EMG based prosthetic control with up to 32 simultaneous acquisition channels and — with an eye on a possible future exploitation as a medical device — a proper perspective on a low priced system. Therefore, according to these requirements we present a wireless, mobile platform for acquisition and communication of sEMG signals embedded into a complete mobile control system structure. This environment further includes a portable device such as a laptop providing the necessary computational power for the control and a commercially available robotic hand-prosthesis. Means of communication among those devices are based on the Bluetooth standard. We show, that the developed low cost mobile device can be used for proper prosthesis control and that the device can rely on a continuous operation for the usual daily life usage of a patient.


title: Performance-aware server consolidation with adjustable interference levels

abstract: Virtualization technologies and server consolidation are the main drivers of high resource utilization and energy efficiency in modern Data Centers. However, some combinations of virtual machines into the same server may lead to severe performance degradation. This performance degradation is known as virtual machine interference. In a typical Data Center, different measures of virtual machine interference can be employed, depending on applications importance. Supporting a higher virtual machine interference may result in a higher consolidation, while strict low interference requirements may demand more resources. This paper presents an algorithm for server consolidation that uses an adjustable virtual machine interference threshold to map virtual machines into physical servers, allowing users to get a better trade off between amount of resources and performance according to their needs. Simulation results show that the solution succeeds in maintaining the interference levels below a defined threshold while also providing efficient server consolidation.


title: Definition of the Supraclavicular and Infraclavicular Nodes : Implications for Three-dimensional CT-based Conformal Radiation Therapy 1

abstract: PURPOSE: To delineate with computed tomography (CT) the anatomic regions containing the supraclavicular (SCV) and infraclavicular (IFV) nodal groups, to define the course of the brachial plexus, to estimate the actual radiation dose received by these regions in a series of patients treated in the traditional manner, and to compare these doses to those received with an optimized dosimetric technique. MATERIALS AND METHODS: Twenty patients underwent contrast material–enhanced CT for the purpose of radiation therapy planning. CT scans were used to study the location of the SCV and IFV nodal regions by using outlining of readily identifiable anatomic structures that define the nodal groups. The brachial plexus was also outlined by using similar methods. Radiation therapy doses to the SCV and IFV were then estimated by using traditional dose calculations and optimized planning. A repeated measures analysis of covariance was used to compare the SCV and IFV depths and to compare the doses achieved with the tradi...


title: Authorization mechanism for MQTT-based Internet of Things

abstract: This paper presents the design and implementation of an authorization mechanism for MQTT-based Internet of Things (IoT). The design is based on OAuth 1.0a, which is an open authorization standard for web applications. Some redesign and modification have been made to the based framework to fit it within the MQTT environment. Several considerations are taken into account, including limited node resources, lack of node's user interface, and key/secret distribution and management. The design is implemented on a real MQTT-based IoT service platform and demonstrated that it works as intended. Authorization delay and message overhead are minimal. Security issues are analyzed along with discussion for future work.


title: Auto-association by multilayer perceptrons and singular value decomposition

abstract: The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loève transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the rôle of the different parameters.


title: A Software Product Line Approach for Configuring Cloud Robotics Applications

abstract: The computational requirements of the increasingly sophisticated algorithms used in today's robotics software applications have outpaced the onboard processors of the average robot. Furthermore, the development and configuration of these applications are difficult tasks that require expertise in diverse domains, including software engineering, control engineering, and computer vision. As a solution to these problems, this paper extends and integrates our previous works, which are based on two promising techniques: Cloud Robotics and Software Product Lines. Cloud Robotics provides a powerful and scalable environment to offload the computationally expensive algorithms resulting in low-cost processors and light-weight robots. Software Product Lines allow the end user to deploy and configure complex robotics applications without dealing with low-level problems such as configuring algorithms and designing architectures. This paper discusses the proposed method in depth, and demonstrates its advantages with a case study.


title: Impairment of voice and face recognition in patients with hemispheric damage

abstract: Abstract Voice and face recognition were tested in 21 left- and 9 right-hemisphere-damaged patients. Test materials were photographs and recordings of famous political and entertainment personalities. Pathological face recognition (prosopagnosia) and voice recognition (phonagnosia) were both significantly more prevalent in the right-hemisphere group. Only one instance of prosopagnosia and one of phonagnosia were observed in the left-hemisphere group, all of whom were aphasic. Of the right-hemisphere cases, there were four instances of each agnosia, with three patients showing a dual impairment. These findings are discussed in relation to differential modes of processing by the two cerebral hemispheres.


title: ICE: Idiom and Collocation Extractor for Research and Education

abstract: Collocation and idiom extraction are well-known challenges with many potential applications in Natural Language Processing (NLP). Our experimental, open-source software system, called ICE, is a python package for flexibly extracting collocations and idioms, currently in English. It also has a competitive POS tagger that ::: can be used alone or as part of collocation/idiom extraction. ICE is available free of cost for research and educational uses in two user-friendly formats. This paper gives an overview of ICE and its perfor- ::: mance, and briefly describes the research underlying the extraction algorithms


title: RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting

abstract: We present the Mim-Solution's approach to the RecSys Challenge 2016, which ranked 2nd. The goal of the competition was to prepare job recommendations for the users of the website Xing.com. Our two phase algorithm consists of candidate selection followed by the candidate ranking. We ranked the candidates by the predicted probability that the user will positively interact with the job offer. We have used Gradient Boosting Decision Trees as the regression tool.


title: MOOC architecture model for computer programming courses

abstract: Computer programming courses are major contributor to non-passing rates and teaching the subject is significantly challenging. MOOC (Massive Open Online Course), a web-based LMS providing online courses, is a new popular learning media expected to facilitate better teaching-learning process for improving learning outcomes. Unfortunately, most of the current MOOCs are intended only for non-vocational (non-practical) learning and have set aside research on MOOC for computer programming courses. This study employed systematic literature review (SLR) method to summarize and review available functionalities as the core in designing a proposed MOOC architecture model. The proposed model covers application layers (user, system, and data layers) and adopts the theories of cognitive, constructive, and social situated learning. The proposed model is expected to provide guidance for institutions to develop their own MOOC and utilize it in blended learning processes to improve the outcome in computer programming courses.


title: An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

abstract: Abstract Internet of Things (IoT) is the new technological revolution that aspires to connect all the everyday physical objects to the Internet, making a huge global network of uniquely things which can share information amongst each other and complete scheduled tasks, bringing significant benefits to users and companies of a Smart City (SC). A Smart City represents a new future framework, which integrates multiple information and communication technology (ICT) and Internet of Things (IoT) solutions, so as to improve the quality life of its citizens. However, there are many security and privacy issues which must be taken into account before the official launching of this new technological concept. Many methods which focus on media security of wireless sensor networks have been proposed and can be adopted in the new expandable network of IoT. In this paper, we describe the upcoming IoT network architecture and its security challenges and analyze the most important researches on media security and privacy in wireless sensor networks (WSNs). Subsequently, we propose an Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT network for Smart City Framework, which merges two algorithms introduced by other researchers for WSN packet routing and security, while it reclaims the new media compression standard, High Efficiency Video Coding (HEVC). Experimental analysis shows the efficacy of our proposed scheme in terms of users’ privacy, media security, and sensor node memory requirements. This scheme can be successfully integrated into the IoT network of the upcoming Smart City concept.


title: A general strategy for differential forensic analysis

abstract: The dramatic growth of storage capacity and network bandwidth is making it increasingly difficult for forensic examiners to report what is present on a piece of subject media. Instead, analysts are focusing on what characteristics of the media have changed between two snapshots in time. To date different algorithms have been implemented for performing differential analysis of computer media, memory, digital documents, network traces, and other kinds of digital evidence. This paper presents an abstract differencing strategy and applies it to all of these problem domains. Use of an abstract strategy allows the lessons gleaned in one problem domain to be directly applied to others. Published by Elsevier Ltd.


title: A learning-based approach for automatic image and video colorization

abstract: In this paper, we present a color transfer algorithm to colorize a broad range of gray images without any user intervention. The algorithm uses a machine learning-based approach to automatically colorize grayscale images. The algorithm uses the superpixel representation of the reference color images to learn the relationship between different image features and their corresponding color values. We use this learned information to predict the color value of each grayscale image superpixel. As compared to processing individual image pixels, our use of superpixels helps us to achieve a much higher degree of spatial consistency as well as speeds up the colorization process. The predicted color values of the gray-scale image superpixels are used to provide a 'micro-scribble' at the centroid of the superpixels. These color scribbles are refined by using a voting based approach. To generate the final colorization result, we use an optimization-based approach to smoothly spread the color scribble across all pixels within a superpixel. Experimental results on a broad range of images and the comparison with existing state-of-the-art colorization methods demonstrate the greater effectiveness of the proposed algorithm.


title: Design of the TUAT/Karlsruhe humanoid hand

abstract: The increasing demand for robotic applications in dynamic unstructured environments is motivating the need for dextrous end-effectors which can cope with the wide variety of tasks and objects encountered in these environments. The human hand is a very complex grasping tool that can handle objects of different sizes and shapes. Many research activities have been carried out to develop artificial robot hands with capabilities similar to the human hand. In this paper the mechanism and design of a new humanoid-type hand (called TUAT/Karlsruhe Humanoid Hand) with human-like manipulation abilities is discussed. The new hand is designed for the humanoid robot ARMAR which has to work autonomously or interactively in cooperation with humans and for an artificial lightweight arm for handicapped persons. The arm is developed as close as possible to the human arm and is driven by spherical ultrasonic motors. The ideal end-effector for such an artificial arm or a humanoid would be able to use the tools and objects that a person uses when working in the same environment. Therefore a new hand is designed for anatomical consistency with the human hand. This includes the number of fingers and the placement and motion of the thumb, the proportions of the link lengths and the shape of the palm. It can also perform most part of human grasping types. The TUAT/Karlsruhe Humanoid Hand possesses 20 DOF and is driven by one actuator which can be placed into or around the hand.


Inverted U-Shaped Dose-Response Curve of the Anxiolytic Effect of Cannabidiol during Public Speaking in Real Life


Depersonalization syndrome induced by fluoxetine.


Process Mining: Discovery, Conformance and Enhancement of Business Processes


title: On Summarization and Timeline Generation for Evolutionary Tweet Streams

abstract: Short-text messages such as tweets are being created and shared at an unprecedented rate. Tweets, in their raw form, while being informative, can also be overwhelming. For both end-users and data analysts, it is a nightmare to plow through millions of tweets which contain enormous amount of noise and redundancy. In this paper, we propose a novel continuous summarization framework called Sumblr to alleviate the problem. In contrast to the traditional document summarization methods which focus on static and small-scale data set, Sumblr is designed to deal with dynamic, fast arriving, and large-scale tweet streams. Our proposed framework consists of three major components. First, we propose an online tweet stream clustering algorithm to cluster tweets and maintain distilled statistics in a data structure called tweet cluster vector (TCV). Second, we develop a TCV-Rank summarization technique for generating online summaries and historical summaries of arbitrary time durations. Third, we design an effective topic evolution detection method, which monitors summary-based/volume-based variations to produce timelines automatically from tweet streams. Our experiments on large-scale real tweets demonstrate the efficiency and effectiveness of our framework.


The Concepts of Loneliness and Social Isolation Loneliness


FaRBIE: A Faceted Reactive Browsing Interface for Multi RDF Knowledge Graph Exploration


title: An empirical study of low-power wireless

abstract: We present empirical measurements of the packet delivery performance of the latest sensor platforms: Micaz and Telos motes. In this article, we present observations that have implications to a set of common assumptions protocol designers make while designing sensornet protocols—specifically—the MAC and network layer protocols. We first distill these common assumptions in to a conceptual model and show how our observations support or dispute these assumptions. We also present case studies of protocols that do not make these assumptions. Understanding the implications of these observations to the conceptual model can improve future protocol designs.


title: On-Line Flatness Measurement in the Steelmaking Industry

abstract: Shape is a key characteristic to determine the quality of outgoing flat-rolled products in the steel industry. It is greatly influenced by flatness, a feature to describe how the surface of a rolled product approaches a plane. Flatness is of the utmost importance in steelmaking, since it is used by most downstream processes and customers for the acceptance or rejection of rolled products. Flatness sensors compute flatness measurements based on comparing the length of several longitudinal fibers of the surface of the product under inspection. Two main different approaches are commonly used. On the one hand, most mechanical sensors measure the tensile stress across the width of the rolled product, while manufacturing and estimating the fiber lengths from this stress. On the other hand, optical sensors measure the length of the fibers by means of light patterns projected onto the product surface. In this paper, we review the techniques and the main sensors used in the steelmaking industry to measure and quantify flatness defects in steel plates, sheets and strips. Most of these techniques and sensors can be used in other industries involving rolling mills or continuous production lines, such as aluminum, copper and paper, to name a few. Encompassed in the special issue, State-of-the-Art Sensors Technology in Spain 2013, this paper also reviews the most important flatness sensors designed and developed for the steelmaking industry in Spain.


title: Automatic punctuation annotation in czech broadcast news speech

abstract: This paper reports our initial experiments with automatic punctuation annotation from speech. We have focused on Czech broadcast news speech. The task can be defined as a classification of each inter-word boundary into one of target classes. We considered comma, sentence boundary and “no punctuation” as the target classes. We employed two statistical models – prosodic model and language model. The prosodic model expresses relationships between prosodic quantities (such as pitch, speaking rate or loudness) and punctuation marks. We tested two implementations of this model – decision tree and multi-layer perceptron. Hidden-event N -gram models were employed for language modeling. Instead of using an ordinary word-based model, we replaced infrequent word forms by their morphological tags and trained a mixed model. Scores from both models can be combined. The model combining language model with the decision tree yielded superior results. Testing on true words we achieved classification accuracy 95.2% and F -measure 78.2%.


title: The role of the thalamus in the flow of information to the cortex

abstract: The lateral geniculate nucleus is the best understood thalamic relay and serves as a model for all thalamic relays. Only 5–10% of the input to geniculate relay cells derives from the retina, which is the driving input. The rest is modulatory and derives from local inhibitory inputs, descending inputs from layer 6 of the visual cortex, and ascending inputs from the brainstem. These modulatory inputs control many features of retinogeniculate transmission. One such feature is the response mode, burst or tonic, of relay cells, which relates to the attentional demands at the moment. This response mode depends on membrane potential, which is controlled effectively by the modulator inputs. The lateral geniculate nucleus is a firstorder relay, because it relays subcortical (i.e. retinal) information to the cortex for the first time. By contrast, the other main thalamic relay of visual information, the pulvinar region, is largely a higher-order relay, since much of it relays information from layer 5 of one cortical area to another. All thalamic relays receive a layer-6 modulatory input from cortex, but higher-order relays in addition receive a layer-5 driver input. Corticocortical processing may involve these corticothalamocortical ‘re-entry’ routes to a far greater extent than previously appreciated. If so, the thalamus sits at an indispensable position for the modulation of messages involved in corticocortical processing.


title: Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families

abstract: The apolipoprotein E type 4 allele (APOE-epsilon 4) is genetically associated with the common late onset familial and sporadic forms of Alzheimer9s disease (AD). Risk for AD increased from 20% to 90% and mean age at onset decreased from 84 to 68 years with increasing number of APOE-epsilon 4 alleles in 42 families with late onset AD. Thus APOE-epsilon 4 gene dose is a major risk factor for late onset AD and, in these families, homozygosity for APOE-epsilon 4 was virtually sufficient to cause AD by age 80.


title: Sex Differences in Variability in General Intelligence: A New Look at the Old Question.

abstract: The idea that general intelligence may be more variable in males than in females has a long history. In recent years it has been presented as a reason that there is little, if any, mean sex differe...


title: The ( R ) evolution of Distributed Amplifiers

abstract: Broadband amplification of signals is desirable in many applications such as high-speed data communications, high-resolution imaging systems, optoelectronics, and instrumentation systems. Wide bandwidth is one of the prominent factors to be considered in designing such systems because it determines the system's ability to transfer high-data-rate information, transmit/receive short pulses, and process wide-band signals. Designing broadband amplifiers has always been challenging. The ever-increasing demand for higher data rates and low energy consumption in next-generation communication systems further complicates broadband amplifier design.


title: Age, gender, race, socioeconomic status, and birth cohort difference on the children's depression inventory: A meta-analysis.

abstract: A within-scale meta-analysis was performed on 310 samples of children (ages 8-16; N = 61,424) responding to the Children's Depression Inventory (CDI). Girls' depression scores stayed steady from ages 8 to 11 and then increased between ages 12 and 16. Boys' CDI scores were stable from ages 8 to 16 except for a high CDI score at age 12. Girls' scores were slightly lower than boys' during childhood, but girls scored higher beginning at age 13. There were no socioeconomic status effects and no differences between White and Black samples. However, Hispanic samples scored significantly higher on the CDI. Analyses for birth cohort showed a slight decrease in boys' CDI scores over time and no change for girls. Longitudinal studies demonstrated a marked testing effect.


title: Semantic interoperability in the Internet of Things: An overview from the INTER-IoT perspective

abstract: Abstract The Internet of Things (IoT) idea, explored across the globe, brings about an important issue: how to achieve interoperability among multiple existing (and constantly created) IoT platforms. In this context, in January 2016, the European Commission has funded seven projects that are to deal with various aspects of interoperability in the Internet of Things. Among them, the INTER-IoT project is aiming at the design and implementation of, and experimentation with, an open cross-layer framework and associated methodology to provide voluntary interoperability among heterogeneous IoT platforms. While the project considers interoperability across all layers of the software stack, we are particularly interested in answering the question: how ontologies and semantic data processing can be harnessed to facilitate interoperability across the IoT landscape. Henceforth, we have engaged in a “fact finding mission” to establish what is currently at our disposal when semantic interoperability is concerned. Since the INTER-IoT project is initially driven by two use cases originating from (i) ( e/m ) Health and (ii) transportation and logistics , these two application domains were used to provide context for our search. The paper summarizes our findings and provides foundation for developing methods and tools for supporting semantic interoperability in the INTER-IoT project (and beyond).


title: On the intrinsic thermal stability in HVDC cables

abstract: The paper deals with some important aspects of steady state thermal stability in the insulation of HVDC cables. The resistively graded electric field distribution and the temperature distribution, under steady state, in dc cables, are interrelated. The dc conductivity is known to be a function of stress as well as of temperature. Reasonably convincing theories of electric stress and temperature distribution are now available in the literature. Until recently, the thermal runaway was believed, primarily, to be a consequence of an inordinate run away in the sheath temperature due to dynamic interaction with the ambient. While this premise is partly true, there are other overriding factors controlling the stability. This paper presents a detailed investigation on this aspect and it is shown that the multi-factor dependent dc conductivity alone causes the thermal instability, boundary conditions at sheath having a marginal role.


Modeling relations between adolescents' perceptions of the school's social context, academic and social self-concept of ability beliefs, and student engagement


title: Discovering influencers for marketing in the blogosphere

abstract: Discovering influential bloggers will not only allow us to understand better the social activities taking place in the blogosphere, but will also provide unique opportunities for sales and advertising. In this paper, we develop an MIV (marketing influential value) model to evaluate the influential strength and identify the influential bloggers in the blogosphere. We analyze three dimensions of blog characteristics (network-based, content-based, and activeness-based factors) and utilize an artificial neural network (ANN) to discover potential bloggers. Based on peer and official evaluations, the experimental results show that the proposed framework outperforms two social-network-based methods (out-degree and betweenness centrality algorithms) and two content-based mechanisms (review rating and popular author approaches). The proposed framework can be effectively applied to support marketers or advertisers in promoting their products or services.


title: An optimal algorithm for stochastic strongly-convex optimization

abstract: We consider stochastic convex optimization with a strongly convex (but not necessarily smooth) objective. We give an algorithm which performs only gradient updates with optimal rate of convergence.


title: Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models

abstract: This paper describes a novel method to create a quantitative model of an educational content domain of related practice item-types using learning curves. By using a pairwise test to search for the relationships between learning curves for these item-types, we show how the test results in a set of pairwise transfer relationships that can be expressed in a Q-matrix domain model. Creating these Q-matrices for various test criteria we show that the new domain model results in consistently better learning curve fits as shown by cross- validation. Further, the Q-matrices produced can be used by educators or curriculum designers to gain a richer, more integrated perspective on concepts in the domain. The model may also have implications for tracing student knowledge more effectively to sequence practice in tutoring/training software.


title: Greening of the internet

abstract: In this paper we examine the somewhat controversial subject of energy consumption of networking devices in the Internet, motivated by data collected by the U.S. Department of Commerce. We discuss the impact on network protocols of saving energy by putting network interfaces and other router & switch components to sleep. Using sample packet traces, we first show that it is indeed reasonable to do this and then we discuss the changes that may need to be made to current Internet protocols to support a more aggressive strategy for sleeping. Since this is a position paper, we do not present results but rather suggest interesting directions for core networking research. The impact of saving energy is huge, particularly in the developing world where energy is a precious resource whose scarcity hinders widespread Internet deployment.


title: LSVT LOUD and LSVT BIG: Behavioral Treatment Programs for Speech and Body Movement in Parkinson Disease

abstract: Recent advances in neuroscience have suggested that exercise-based behavioral treatments may improve function and possibly slow progression of motor symptoms in individuals with Parkinson disease (PD). The LSVT (Lee Silverman Voice Treatment) Programs for individuals with PD have been developed and researched over the past 20 years beginning with a focus on the speech motor system (LSVT LOUD) and more recently have been extended to address limb motor systems (LSVT BIG). The unique aspects of the LSVT Programs include the combination of (a) an exclusive target on increasing amplitude (loudness in the speech motor system; bigger movements in the limb motor system), (b) a focus on sensory recalibration to help patients recognize that movements with increased amplitude are within normal limits, even if they feel “too loud” or “too big,” and (c) training self-cueing and attention to action to facilitate long-term maintenance of treatment outcomes. In addition, the intensive mode of delivery is consistent with principles that drive activity-dependent neuroplasticity and motor learning. The purpose of this paper is to provide an integrative discussion of the LSVT Programs including the rationale for their fundamentals, a summary of efficacy data, and a discussion of limitations and future directions for research.


title: Tutorial: Static Analysis and Dynamic Testing of Computer Software

abstract: Computer programs can be checked for errors statically and dynamically. Static analysis looks for structural and semantic faults in programs. Dynamic analysis affirms proper operation—and helps identify errors.


title: A one-to-many conditional generative adversarial network framework for multiple image-to-image translations

abstract: Image-to-Image translation was proposed as a general form of many image learning problems. While generative adversarial networks were successfully applied on many image-to-image translations, many models were limited to specific translation tasks and were difficult to satisfy practical needs. In this work, we introduce a One-to-Many conditional generative adversarial network, which could learn from heterogeneous sources of images. This is achieved by training multiple generators against a discriminator in synthesized learning way. This framework supports generative models to generate images in each source, so output images follow corresponding target patterns. Two implementations, hybrid fake and cascading learning, of the synthesized adversarial training scheme are also proposed, and experimented on two benchmark datasets, UTZap50K and MVOD5K, as well as a new high-quality dataset BehTex7K. We consider five challenging image-to-image translation tasks: edges-to-photo, edges-to-similar-photo translation on UTZap50K, cross-view translation on MVOD5K, and grey-to-color, grey-to-Oil-Paint on BehTex7K. We show that both implementations are able to faithfully translate from an image to another image in edges-to-photo, edges-to-similar-photo, grey-to-color, and grey-to-Oil-Paint translation tasks. The quality of output images in cross-view translation need to be further boosted.


title: The rise of intrusive online advertising and the response of user experience research at Yahoo!

abstract: Over the past five years, online advertising has shifted dramatically toward much greater levels of intrusiveness in an effort to increase advertising effectiveness. Advances in user experience through good design or improvements in usability in many products have been tarnished by forms of online advertising that have become increasingly annoying to users. Since advertising is often the primary source of revenue for many products, user experience design and research teams have had to accept online advertising as a "design constraint," with little influence on the advertising format selected. Here, we describe the emergence of a new user experience research role and our ongoing effort at Yahoo! to understand the nature and negative impacts of online advertising on user experience, with the goal of feeding this knowledge into the decision making process for ad formats, ad characteristics, and where ads are best placed within the Yahoo! network.


title: Usability Analysis of Visual Programming Environments: A 'Cognitive Dimensions' Framework

abstract: Abstract The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other. The purpose of this paper is to propose the framework as an evaluation technique for visual programming environments. We apply it to two commercially-available dataflow languages (with further examples from other systems) and conclude that it is effective and insightful; other HCI-based evaluation techniques focus on different aspects and would make good complements. Insofar as the examples we used are representative, current VPLs are successful in achieving a good ‘closeness of match’, but designers need to consider the ‘viscosity ’ (resistance to local change) and the ‘secondary notation’ (possibility of conveying extra meaning by choice of layout, colour, etc.).


title: CPW to waveguide transition with tapered slotline probe

abstract: A new CPW to waveguide transition is developed based on the concept of tapered slot antenna and E-plane probe coupling. The transition consists of a tapered slotline probe and a slotline to CPW matching section. The current design has the advantages of broad bandwidth, compact size, low fabrication cost, and high reliability. The characteristics of a prototype transition are investigated by numerical simulation on Duroid substrate and WR-90 waveguide. The back-to-back combination is measured to verify the agreement with the simulated results and realization of this design.


title: Recognition of Static Hand Gestures of Alphabet in Bangla Sign Language

abstract: This paper presents a system for recognizing static hand gestures of alphabet in Bangla Sign Language (BSL). A BSL finger spelling and an alphabet gesture recognition system was designed with Artificial Neural Network (ANN) and constructed in order to translate the BSL alphabet into the corresponding printed Bangla letters. The proposed ANN is trained with features of sign alphabet using feed-forward back- propagation learning algorithm. Logarithmic sigmoid (logsig) function is chosen as transfer function. This ANN model demonstrated a good recognition performance with the mean square error values in this training function. This recognition system does not use any gloves or visual marking systems. This system only requires the images of the bare hand for the recognition. The Simulation results show that this system is able to recognize 36 selected letters of BSL alphabet with an average accuracy of 80.902%.


Effect of PGPR on Improving the Germination of Durum Wheat (Triticum durum Desf.) under pH Stress Condition


title: Generation Y Adoption of Instant Messaging: An Examination of the Impact of Social Usefulness and Media Richness on Use Richness

abstract: By integrating Media Richness Theory, Channel Expansion Theory, and the Technology Acceptance Model (TAM), we study the postadoption use behavior of instant messaging. We developed the construct “use richness” as a measure of the extent to which users use the media communication capacity after adoption and proposed a conceptual model of the antecedents of use richness. Through a field survey with 272 valid responses and structural equation modeling, we empirically tested our model and found that use richness is positively affected by perceived media richness, perceived usefulness, and perceived social usefulness.


title: Multi-decade GaN HEMT Cascode-distributed power amplifier with baseband performance

abstract: This paper reports on multi-decade bandwidth GaN HEMT Cascode-distributed power amplifier designs which achieve performance from base-band to over 20 GHz. The GaN MMICs are based on a 0.2um AlGaN/GaN low noise T-gate HEMT technology with an fT ∼ 75 GHz. To increase the MMIC power capability of this low noise GaN technology, a cascode DA design approach was employed which can operate at twice the recommended Vds voltage. The resulting amplifiers achieve 1–4 Watts of saturated CW power from 100MHz to over 20GHz at an operating voltage of 30V. Typical OIP3 ≫ 40 dBm and NF of 3 dB were also achieved. Compared to equivalent designs in a similar 0.15um GaAs PHEMT low noise technology fabricated in the same foundry, these multi-decade GaN HEMT MMIC DAs obtain 6 dB higher output power and 5.8–6.6dB higher OIP3 while achieving comparable gain, noise figure, and bandwidth. These are believed to be the first multi-decade GaN power distributed amplifiers that have been demonstrated and can enable future ultra-wideband frequency agile and software defined radio systems that require baseband to microwave frequency operation.


title: GPU-Accelerated Bulk Computation of the Eigenvalue Problem for Many Small Real Non-symmetric Matrices

abstract: The main contribution of this paper is to present a very efficient GPU implementation of bulk computation of eigenvalues for a large number of small non-symmetric real matrices. This work is motivated by the necessity of such bulk computation in design of control systems, which requires to compute the eigenvalues of hundreds of thousands non-symmetric real matrices of size up to 30x30. In our GPU implementation, we considered programming issues of the GPU architecture including warp divergence, coalesced access of the global memory, bank conflict of the shared memory, etc. In particular, we present three types of assignments of GPU threads to matrices and introduce three memory arrangements in the global memory. The experimental results on NVIDIA GeForce GTX TITAN X show that our GPU implementation for 500000 matrices of size 5x5 to 30x30 attains a speed-up factor of approximately 15 over the CPU implementation on Intel Core i7-4790.


title: Bio‐field array: a dielectrophoretic electromagnetic toroidal excitation to restore and maintain the golden ratio in human erythrocytes

abstract: Erythrocytes must maintain a biconcave discoid shape in order to efficiently deliver oxygen (O2 ) molecules and to recycle carbon dioxide (CO2 ) molecules. The erythrocyte is a small toroidal dielectrophoretic (DEP) electromagnetic field (EMF) driven cell that maintains its zeta potential (ζ) with a dielectric constant (ԑ) between a negatively charged plasma membrane surface and the positively charged adjacent Stern layer. Here, we propose that zeta potential is also driven by both ferroelectric influences (chloride ion) and ferromagnetic influences (serum iron driven). The Golden Ratio, a function of Phi φ, offers a geometrical mathematical measure within the distinct and desired curvature of the red blood cell that is governed by this zeta potential and is required for the efficient recycling of CO2 in our bodies. The Bio-Field Array (BFA) shows potential to both drive/fuel the zeta potential and restore the Golden Ratio in human erythrocytes thereby leading to more efficient recycling of CO2 . Live Blood Analyses and serum CO2 levels from twenty human subjects that participated in immersion therapy sessions with the BFA for 2 weeks (six sessions) were analyzed. Live Blood Analyses (LBA) and serum blood analyses performed before and after the BFA immersion therapy sessions in the BFA pilot study participants showed reversal of erythrocyte rheological alterations (per RBC metric; P = 0.00000075), a morphological return to the Golden Ratio and a significant decrease in serum CO2 (P = 0.017) in these participants. Immersion therapy sessions with the BFA show potential to modulate zeta potential, restore this newly defined Golden Ratio and reduce rheological alterations in human erythrocytes.


title: Cloud Computing: Opportunities and Challenges

abstract: We live and operate in the world of computing and computers. The Internet has drastically changed the computing world from the concept of parallel computing to distributed computing to grid computing and now to cloud computing. Cloud computing is a new wave in the field of information technology. Some see it as an emerging field in computer science. It consists of a set of resources and services offered through the Internet. Hence, "cloud computing" is also called "Internet computing." The word "cloud" is a metaphor for describing the Web as a space where computing has been preinstalled and exists as a service. Operating systems, applications, storage, data, and processing capacity all exist on the Web, ready to be shared among users. Figure 1 shows a conceptual diagram of cloud computing.


SYNTHETIC APERTURE RADAR IMAGE FORMATION SIMULATOR FOR EDUCATIONAL PURPOSE


SemEval 2018 Task 4: Character Identification on Multiparty Dialogues


title: CloudMix: Generating Diverse and Reducible Workloads for Cloud Systems

abstract: The prosperity of cloud computing offers commoninfrastructures to a wide range of applications. Understandingthese applications’ workload behaviors is the premise of designing,managing, and optimizing cloud systems. Considering theheterogeneity and diversity of cloud workloads, for the sake offairness, cloud benchmarks must be able to accurately replicatetheir behaviors in cloud systems, including both the usages ofcloud resources and the micro-architectural behaviors beyondthe virtualization layer. Furthermore, workloads spanning longdurations are usually required to achieve representativeness inevaluation. Hence the more challenging issue is to significantlyreduce the evaluation duration while still preserving their workloadcharacteristics.This paper presents our efforts towards generating cloudworkloads of diverse behaviors and reducible durations. Ourbenchmark tool, CloudMix, employs a repository of reducibleworkload blocks (RWBs) as the high level abstraction of workloadbehaviors, including usages of the two most important cloud resources(CPU and memory) and their pairing micro-architecturaloperations. CloudMix further introduces an efficient methodologyto combine RWBs to synthesize and replicate diverse cloudworkloads in real-world traces. The effectiveness of CloudMixis demonstrated by generating a variety of reducible workloadsaccording to a Google cluster trace and by applying theseworkloads in job scheduling optimization on Hadoop YARN.The evaluation results show: (i) when the workload durationsare reduced by 100 times, the replication errors of workloadbehaviors are smaller than 2.08%; (ii) when providing fastevaluations (workload durations are reduced by 10 to 100 times)to recommend the optimal setting in YARN job scheduling,the performance degradation in the recommended setting isjust 0.69% compared to that of the actual optimal setting.CloudMix is publicly available from the projec


title: Using Synthetic Data to Improve Facial Expression Analysis with 3D Convolutional Networks

abstract: Over the past few years, neural networks have made a huge improvement in object recognition and event analysis. However, due to a lack of available data, neural networks were not efficiently applied in expression analysis. In this paper, we tackle the problem of facial expression analysis using deep neural network by generating a realistic large scale synthetic labeled dataset. We train a deep 3-dimensional convolutional network on the generated dataset and empirically show how the presented method can efficiently classify facial expressions. Our method addresses four fundamental issues: (i) generating a large scale facial expression dataset that is realistic and accurate, (ii) a rich spatial representation of expressions, (iii) better spatiotemporal feature learning compared to recent techniques and (iv) with a simple linear classifier our learned features outperform state-of-the-art methods.


title: Are Cyberbullies Less Empathic? Adolescents' Cyberbullying Behavior and Empathic Responsiveness

abstract: Abstract Meta-analyses confirm a negative relationship between aggressive behavior and empathy, that is, the ability to understand and share the feelings of others. Based on theoretical considerations, it was, therefore, hypothesized that a lack of empathic responsiveness may be characteristic for cyberbullies in particular. In the present study, 2.070 students of Luxembourg secondary schools completed an online survey that included a cyberbullying questionnaire4 and a novel empathy short scale. According to the main hypothesis, analyses of variances indicated that cyberbullies demonstrated less empathic responsiveness than non-cyberbullies. In addition, cyberbullies were also more afraid of becoming victims of cyberbullying. The findings confirm and substantially extend the research on the relationship between empathy and aggressive behavior. From an educational point of view, the present findings suggest that training of empathy skills might be an important tool to decrease cyberbullying.


title: Attack-defense trees based cyber security analysis for CPSs

abstract: Cyber-physical system (CPS) is the fuse of cyber world and the dynamic physical world and it is being widely used in areas closely related to people's livelihood. Therefore, the security issues of CPS have drawn a global attention and an appropriate risk assessment for CPS is in urgent need. The existing proposals using attack trees for risk assessment mainly focus on depicting the possible intrusions, not for interactions between threats and defenses. In this paper, a risk assessment idea for cyber-physical system with the use of attack-defense tree (ADTree) is proposed, considering the effect of both the attack cost and defense cost. The effectiveness of the proposed approach is evaluated by a set of metrics like probability of success, attack and defense cost and the impact of an attack. In addition, we introduce two economic factors (ROA and ROI) to evaluate the performance of ADTree. Finally, an illustration case of threat risk analysis in SCADA system is given to demonstrate our approach. Overall, our approach provides an effective means of risk assessment and countermeasures evaluation in the evolutional process of security management for cyber-physical system security.


title: Chemical Inhibitors and microRNAs (miRNA) Targeting the Mammalian Target of Rapamycin (mTOR) Pathway: Potential for Novel Anticancer Therapeutics

abstract: The mammalian target of rapamycin (mTOR) is a critical regulator of many fundamental features in response to upstream cellular signals, such as growth factors, energy, stress and nutrients, controlling cell growth, proliferation and metabolism through two complexes, mTORC1 and mTORC2. Dysregulation of mTOR signalling often occurs in a variety of human malignant diseases making it a crucial and validated target in the treatment of cancer. Tumour cells have shown high susceptibility to mTOR inhibitors. Rapamycin and its derivatives (rapalogs) have been tested in clinical trials in several tumour types and found to be effective as anticancer agents in patients with advanced cancers. To block mTOR function, they form a complex with FKBP12 and then bind the FRB domain of mTOR. Furthermore, a new generation of mTOR inhibitors targeting ATP-binding in the catalytic site of mTOR showed potent and more selective inhibition. More recently, microRNAs (miRNA) have emerged as modulators of biological pathways that are essential in cancer initiation, development and progression. Evidence collected to date shows that miRNAs may function as tumour suppressors or oncogenes in several human neoplasms. The mTOR pathway is a promising target by miRNAs for anticancer therapy. Extensive studies have indicated that regulation of the mTOR pathway by miRNAs plays a major role in cancer progression, indicating a novel way to investigate the tumorigenesis and therapy of cancer. Here, we summarize current findings of the role of mTOR inhibitors and miRNAs in carcinogenesis through targeting mTOR signalling pathways and determine their potential as novel anti-cancer therapeutics.


title: Intelligent Semantic Web Search Engines: A Brief Survey

abstract: The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.


Hyperspectral , Multispectral , and Multimodal ( HMM ) Imaging : Acquisition , Algorithms , and Applications


title: A single-phase current source solar inverter with reduced-size DC link

abstract: This paper presents a new current source converter topology that is primarily intended for single-phase photovoltaic (PV) applications. In comparison against the existing PV inverter technology, the salient features of the proposed topology are: a) the low frequency (double of line frequency) ripple that is common to single-phase inverter has been eliminated; b) the absence of low frequency ripple enables significantly reduced-size passive components to achieve necessary stiffness; and c) improved maximum-power-point-tracking performance is readily achieved due to the tightened current ripple even with reduced-size passive components. This paper presents the proposed topology and its working principle backed up with numerical verifications.


Cutaneous myiasis due to Dermatobia hominis : A case report from eastern Sudan


title: Serotonin Transporter Occupancy of Five Selective Serotonin Reuptake Inhibitors at Different Doses: An [11C]DASB Positron Emission Tomography Study

abstract: OBJECTIVE: Minimum therapeutic doses of paroxetine and citalopram produce 80% occupancy for the serotonin (5-HT) transporter (5-HTT). The authors used [11C]DASB positron emission tomography to measure occupancies of three other selective serotonin reuptake inhibitors (SSRIs) at minimum therapeutic doses. The relationship between dose and occupancy was also investigated. METHOD: Striatal 5-HTT binding potential was measured in 77 subjects before and after 4 weeks of medication administration. Binding potential is proportional to the density of receptors not blocked by medication. Subjects received citalopram, fluoxetine, sertraline, paroxetine, or extended-release venlafaxine. Healthy subjects received subtherapeutic doses; subjects with mood and anxiety disorders received therapeutic doses. Percent reduction in 5-HTT binding potential for each medication and dose was calculated. To obtain test-retest data, binding potential was measured before and after 4 weeks in six additional healthy subjects. RESULTS:...


title: Multi-layer study of wave propagation in sunspots

abstract: We analyze the propagation of waves in sunspots from the photosphere to the chromosphere using time series of co-spatial Ca II H intensity spectra (including its line blends) and polarimetric spectra of Si I λ10,827 and the He I λ10,830 multiplet. From the Doppler shifts of these lines we retrieve the variation of the velocity along the line of sight at several heights. Phase spectra are used to obtain the relation between the oscillatory signals. Our analysis reveals standing waves at frequencies lower than 4 mHz and a continuous propagation of waves at higher frequencies, which steepen into shocks in the chromosphere when approaching the formation height of the Ca II H core. The observed nonlinearities are weaker in Ca II H than in He I lines. Our analysis suggests that the Ca II H core forms at a lower height than the He I λ10,830 line: a time delay of about 20 s is measured between the Doppler signal detected at both wavelengths. We fit a model of linear slow magnetoacoustic wave propagation in a stratified atmosphere with radiative losses according to Newton's cooling law to the phase spectra and derive the difference in the formation height of the spectral lines. We show that the linear model describes well the wave propagation up to the formation height of Ca II H, where nonlinearities start to become very important.


title: An actuator with physically variable stiffness for highly dynamic legged locomotion

abstract: Running is a complex dynamical task which places strict design requirements on both the physical components and software control systems of a robot. This paper explores some of those requirements and illustrates how a variable compliance actuation system can satisfy them. We present the design, analysis, simulation, and benchtop experimental validation of such an actuator system. We demonstrate, through simulation, the application of our prototype actuator to the problem of biped running.


title: Search Engines - Information Retrieval in Practice

abstract: KEY BENEFIT: Written by a leader in the field of information retrieval, this text provides the background and tools needed to evaluate, compare and modify search engines. KEY TOPICS: Coverage of the underlying IR and mathematical models reinforce key concepts. Numerous programming exercises make extensive use of Galago, a Java-based open source search engine. MARKET: A valuable tool for search engine and information retrieval professionals.


title: Materials and Mechanics for Stretchable Electronics

abstract: Recent advances in mechanics and materials provide routes to integrated circuits that can offer the electrical properties of conventional, rigid wafer-based technologies but with the ability to be stretched, compressed, twisted, bent, and deformed into arbitrary shapes. Inorganic and organic electronic materials in microstructured and nanostructured forms, intimately integrated with elastomeric substrates, offer particularly attractive characteristics, with realistic pathways to sophisticated embodiments. Here, we review these strategies and describe applications of them in systems ranging from electronic eyeball cameras to deformable light-emitting displays. We conclude with some perspectives on routes to commercialization, new device opportunities, and remaining challenges for research.


title: Efficient Ranking from Pairwise Comparisons

abstract: The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking, biological applications and others. In many practical situations the true pairwise comparisons cannot be actively measured, but a subset of all n(n-1)/2 comparisons is passively and noisily observed. Optimization algorithms (e.g., the SVM) could be used to predict a ranking with fixed expected Kendall tau distance, while achieving an Ω (n) lower bound on the corresponding sample complexity. However, due to their centralized structure they are difficult to extend to online or distributed settings. In this paper we show that much simpler algorithms can match the same Ω (n) lower bound in expectation. Furthermore, if an average of O(n log(n)) binary comparisons are measured, then one algorithm recovers the true ranking in a uniform sense, while the other predicts the ranking more accurately near the top than the bottom. We discuss extensions to online and distributed ranking, with benefits over traditional alternatives.


title: Comparison of different implementations of MFCC

abstract: The performance of the Mel-Frequency Cepstrum Coefficients (MFCC) may be affected by (1) the number of filters, (2) the shape of filters, (3) the way in which filters are spaced, and (4) the way in which the power spectrum is warped. In this paper, several comparison experiments are done to find a best implementation. The traditional MFCC calculation excludes the 0th coefficient for the reason that it is regarded as somewhat unreliable. According to the analysis and experiments, the authors find that it can be regarded as the generalized frequency band energy (FBE) and is hence useful, which results in the FBE-MFCC. The authors also propose a better analysis, namely the auto-regressive analysis, on the frame energy, which outperform its 1st and/or 2nd order differential derivatives. Experiments with the “863” Speech Database show that, compared with the traditional MFCC with its corresponding auto-regressive analysis coefficients, the FBE-MFCC and the frame energy with their corresponding auto-regressive analysis coefficients form the best combination, reducing the Chinese syllable error rate (CSER) by about 10%, while the FBE-MFCC with the corresponding auto-regressive analysis coefficients reduces CSER by 2.5%. Comparison experiments are also done with a quite casual Chinese speech database, named Chinese Annotated Spontaneous Speech (CASS) corpus. The FBE-MFCC can reduce the error rate by about 2.9% on an average.


title: Record-Replay Architecture as a General Security Framework

abstract: Hardware security features need to strike a careful balance between design intrusiveness and completeness of methods. In addition, they need to be flexible, as security threats continuously evolve. To help address these requirements, this paper proposes a novel framework where Record and Deterministic Replay (RnR) is used to complement hardware security features. We call the framework RnR-Safe. RnR-Safe reduces the cost of security hardware by allowing it to be less precise at detecting attacks, potentially reporting false positives. This is because it relies on on-the-fly replay that transparently verifies whether the alarm is a real attack or a false positive. RnR-Safe uses two replayers: an always-on, fast Checkpoint replayer that periodically creates checkpoints, and a detailed-analysis Alarm replayer that is triggered when there is a threat alarm. As an example application, we use RnR-Safe to thwart Return Oriented Programming (ROP) attacks, including on the Linux kernel. Our design augments the Return Address Stack (RAS) with relatively inexpensive hardware. We evaluate RnR-Safe using a variety of workloads on virtual machines running Linux. We find that RnR-Safe is very effective. Thanks to the judicious RAS hardware extensions and hypervisor changes, the checkpointing replayer has an execution speed comparable to the recorded execution. Also, the alarm replayer needs to handle very few false positives.


title: How to customize a bona fide psychotherapy for generalized anxiety disorder? A two-arms, patient blinded, ABAB crossed-therapist randomized clinical implementation trial design [IMPLEMENT 2.0]

abstract: Background ::: Bona fide psychotherapy approaches are effective treatments for generalized anxiety disorder (GAD) compared to no-treatment conditions. Treatment manuals and protocols allow a relatively high degree of freedom for the way therapists implement these overall treatment packages and there is a systematic lack of knowledge on how therapists should customize these treatments. The present study experimentally examines two implementation strategies of customizing a bona fide psychotherapy approach based on a 16 session time-limited cognitive-behavioral therapy (CBT) protocol and their relation to the post-session and ultimate treatment outcomes.


title: A Novel Growing Device Inspired by Plant Root Soil Penetration Behaviors

abstract: Moving in an unstructured environment such as soil requires approaches that are constrained by the physics of this complex medium and can ensure energy efficiency and minimize friction while exploring and searching. Among living organisms, plants are the most efficient at soil exploration, and their roots show remarkable abilities that can be exploited in artificial systems. Energy efficiency and friction reduction are assured by a growth process wherein new cells are added at the root apex by mitosis while mature cells of the root remain stationary and in contact with the soil. We propose a new concept of root-like growing robots that is inspired by these plant root features. The device penetrates soil and develops its own structure using an additive layering technique: each layer of new material is deposited adjacent to the tip of the device. This deposition produces both a motive force at the tip and a hollow tubular structure that extends to the surface of the soil and is strongly anchored to the soil. The addition of material at the tip area facilitates soil penetration by omitting peripheral friction and thus decreasing the energy consumption down to 70% comparing with penetration by pushing into the soil from the base of the penetration system. The tubular structure provides a path for delivering materials and energy to the tip of the system and for collecting information for exploratory tasks.


title: Improving human body part detection using deep learning and motion consistency

abstract: Body part segmentation and detection in videos is a useful analysis for many computer vision tasks such as action recognition and video search. Conventional methods mainly focus on body part detection assuming upright posture of the human body. Recently, a body part detection framework was proposed to include non-upright postures. This method consists of 2 parts, initial segmentation and computation of body part likelihood score for each segment. In this paper, we propose improvements to this approach. Firstly, we propose a novel motion based body part segmentation using kinematic features to identify segments which undergo similar motion in the video based on a consistency or error measure. Secondly, we replace the Extreme Learning Machine classifier in the original work with deep learning to investigate it's performance. For accurate detection, deep learning requires a lot of training data and it has so far been used only in high resolution images. Here we apply deep learning for body part detection in low resolution cases. We conduct experiments to study and analyse the effect of the improvements proposed.


title: Deep Learning Driven Visual Path Prediction from a Single Image

abstract: Capabilities of inference and prediction are the significant components of visual systems. Visual path prediction is an important and challenging task among them, with the goal to infer the future path of a visual object in a static scene. This task is complicated as it needs high-level semantic understandings of both the scenes and underlying motion patterns in video sequences. In practice, cluttered situations have also raised higher demands on the effectiveness and robustness of models. Motivated by these observations, we propose a deep learning framework, which simultaneously performs deep feature learning for visual representation in conjunction with spatiotemporal context modeling. After that, a unified path-planning scheme is proposed to make accurate path prediction based on the analytic results returned by the deep context models. The highly effective visual representation and deep context models ensure that our framework makes a deep semantic understanding of the scenes and motion patterns, consequently improving the performance on visual path prediction task. In experiments, we extensively evaluate the model’s performance by constructing two large benchmark datasets from the adaptation of video tracking datasets. The qualitative and quantitative experimental results show that our approach outperforms the state-of-the-art approaches and owns a better generalization capability.


title: LIFESTYLE SEGMENTATION OF WOMEN CONSUMERS AN EMPRICAL STUDY

abstract: Psychographics or lifestyle measures have become popular means for identifying consumers and describing them along the psychological dimension rather than using the traditional demographic measures. This study focuses on segmenting the women consumers based on their lifestyle (AIO Measure) characteristics. The women were clustered into three segments viz quality conscious women, home loving women and variety seeking women. These segments were profiled based on their demographic characteristics. The purchase behaviour in terms of the impact of influencers (family members, company salespersons and dealers) and their brand choice behaviour of these segments have been discussed .


title: A framework for dynamically instrumenting GPU compute applications within GPU Ocelot

abstract: In this paper we present the design and implementation of a dynamic instrumentation infrastructure for PTX programs that procedurally transforms kernels and manages related data structures. We show how performing instrumentation within the GPU Ocelot dynamic compiler infrastructure provides unique capabilities not available to other profiling and instrumentation toolchains for GPU computing. We demonstrate the utility of this instrumentation capability with three example scenarios - (1) performing workload characterization accelerated by a GPU, (2) providing load imbalance information for use by a resource allocator, and (3) providing compute utilization feedback to be used online by a simulated process scheduler that might be found in a hypervisor. Additionally, we measure both (1) the compilation overheads of performing dynamic compilation and (2) the increases in runtimes when executing instrumented kernels. On average, compilation overheads due to instrumentation consisted of 69% of the time needed to parse a kernel module, in the case of the Parboil benchmark suite. Slowdowns for instrumenting each basic block ranged from 1.5x to 5.5x, with the largest slowdowns attributed to kernels with large numbers of short, compute-bound blocks.


title: Organizing programs without classes

abstract: All organizational functions carried out by classes can be accomplished in a simple and natural way by object inheritance in classless languages, with no need for special mechanisms. A single model—dividing types into prototypes and traits—supports sharing of behavior and extending or replacing representations. A natural extension, dynamic object inheritance, can model behavioral modes. Object inheritance can also be used to provide structured name spaces for well-known objects. Classless languages can even express "class-based" encapsulation. These stylized uses of object inheritance become instantly recognizable idioms, and extend the repertory of organizing principles to cover a wider range of programs.


Goal-Directed and Stimulus-Driven Determinants of Attentional Control


title: Rotor flux-barrier design for torque ripple reduction in synchronous reluctance motors

abstract: The torque produced by a synchronous reluctance machine (including the PM assisted configuration) is studied analytically, to the aim of characterizing the effect of the position of the flux-barriers on the torque ripple. It is verified that the position of the flux-barrier ends highly influences the torque waveform. To the aim of reducing the torque harmonic contents, a new strategy is proposed based on the choice of couples of flux- barriers of different shapes. The couples of the flux? barriers are chosen so as to obtain a compensation between the torque harmonics produced by each couple of them. Experimental results on two prototypes confirm the analytical prediction.


title: Mutations in ARID2 are associated with intellectual disabilities

abstract: The etiology of intellectual disabilities (ID) remains unknown for the majority of patients. Due to reduced reproductive fitness in many individuals with ID, de novo mutations account for a significant portion of severe ID. The ATP-dependent SWI/SNF chromatin modifier has been linked with neurodevelopmental disorders including ID and autism. ARID2 is an intrinsic component of polybromo-associated BAF (PBAF), the SWI/SNF subcomplex. In this study, we used clinical whole exome sequencing (WES) in proband-parent-trios to identify the etiology of ID. We identified four independent, novel, loss of function variants in ARID2 gene in four patients, three of which were confirmed to be de novo. The patients all have ID and share other clinical characteristics including attention deficit hyperactivity disorder, short stature, dysmorphic facial features, and Wormian bones. All four novel variants are predicted to lead to a premature termination with the loss of the two conservative zinc finger motifs. This is the first report of mutations in ARID2 associated with developmental delay and ID.


title: Gastrointestinal abnormalities in children with autistic disorder.

abstract: OBJECTIVES ::: Our aim was to evaluate the structure and function of the upper gastrointestinal tract in a group of patients with autism who had gastrointestinal symptoms. ::: ::: ::: STUDY DESIGN ::: Thirty-six children (age: 5.7 +/- 2 years, mean +/- SD) with autistic disorder underwent upper gastrointestinal endoscopy with biopsies, intestinal and pancreatic enzyme analyses, and bacterial and fungal cultures. The most frequent gastrointestinal complaints were chronic diarrhea, gaseousness, and abdominal discomfort and distension. ::: ::: ::: RESULTS ::: Histologic examination in these 36 children revealed grade I or II reflux esophagitis in 25 (69.4%), chronic gastritis in 15, and chronic duodenitis in 24. The number of Paneth's cells in the duodenal crypts was significantly elevated in autistic children compared with non-autistic control subjects. Low intestinal carbohydrate digestive enzyme activity was reported in 21 children (58.3%), although there was no abnormality found in pancreatic function. Seventy-five percent of the autistic children (27/36) had an increased pancreatico-biliary fluid output after intravenous secretin administration. Nineteen of the 21 patients with diarrhea had significantly higher fluid output than those without diarrhea. ::: ::: ::: CONCLUSIONS ::: Unrecognized gastrointestinal disorders, especially reflux esophagitis and disaccharide malabsorption, may contribute to the behavioral problems of the non-verbal autistic patients. The observed increase in pancreatico-biliary secretion after secretin infusion suggests an upregulation of secretin receptors in the pancreas and liver. Further studies are required to determine the possible association between the brain and gastrointestinal dysfunctions in children with autistic disorder.


title: High-Performance Long NoC Link Using Delay-Insensitive Current-Mode Signaling

abstract: High-performance long-range NoC link enables efficient implementation of network-on-chip topologies which inherently require high-performance long-distance point-to-point communication such as torus and fat-tree structures. In addition, the performance of other topologies, such as mesh, can be improved by using high-performance link between few selected remote nodes. We presented novel implementation of high-performance long-range NoC link based on multilevel current-mode signaling and delay-insensitive two-phase 1-of-4 encoding. Current-mode signaling reduces the communication latency of long wires significantly compared to voltage-mode signaling, making it possible to achieve high throughput without pipelining and/or using repeaters. The performance of the proposed multilevel current-mode interconnect is analyzed and compared with two reference voltage mode interconnects. These two reference interconnects are designed using two-phase 1-of-4 encoded voltage-mode signaling, one with pipeline stages and the other using optimal repeater insertion. The proposed multilevel current-mode interconnect achieves higher throughput and lower latency than the two reference interconnects. Its throughput at 8 mm wire length is 1.222 GWord/s which is 1.58 and 1.89 times higher than the pipelined and optimal repeater insertion interconnects, respectively. Furthermore, its power consumption is less than the optimal repeater insertion voltage-mode interconnect, at 10 mm wire length its power consumption is 0.75 mW while the reference repeater insertion interconnect is 1.066 mW. The effect of crosstalk is analyzed using four-bit parallel data transfer with the best-case and worst-case switching patterns and a transmission line model which has both capacitive coupling and inductive coupling.


title: CB1 Receptor Signaling in the Brain: Extracting Specificity from Ubiquity

abstract: Endocannabinoids (eCBs) are amongst the most ubiquitous signaling molecules in the nervous system. Over the past few decades, observations based on a large volume of work, first examining the pharmacological effects of exogenous cannabinoids, and then the physiological functions of eCBs, have directly challenged long-held and dogmatic views about communication, plasticity and behavior in the central nervous system (CNS). The eCBs and their cognate cannabinoid receptors exhibit a number of unique properties that distinguish them from the widely studied classical amino-acid transmitters, neuropeptides, and catecholamines. Although we now have a loose set of mechanistic rules based on experimental findings, new studies continue to reveal that our understanding of the eCB system (ECS) is continuously evolving and challenging long-held conventions. Here we will briefly summarize findings on the current canonical view of the ‘ECS’ and will address novel aspects that reveal how a nearly ubiquitous system can determine highly specific functions in the brain. In particular, we will focus on findings that push for an expansion of our ideas around long-held beliefs about eCB signaling that, while clearly true, may be contributing to an oversimplified perspective on how cannabinoid signaling at the microscopic level impacts behavior at the macroscopic level.


title: Classification of Protein Crystallization X-Ray Images Using Major Convolutional Neural Network Architectures

abstract: The generation of protein crystals is necessary for the study of protein molecular function and structure. This is done empirically by processing large numbers of crystallization trials and inspecting them regularly in search of those with forming crystals. To avoid missing the hard-gained crystals, this visual inspection of the trial X-ray images is done manually as opposed to the existing less accurate machine learning methods. To achieve higher accuracy for automation, we applied some of the most successful convolutional neural networks (ResNet, Inception, VGG, and AlexNet) for 10-way classification of the X-ray images. We showed that substantial classification accuracy is gained by using such networks compared to two simpler ones previously proposed for this purpose. The best accuracy was obtained from ResNet (81.43%), which corresponds to a missed crystal rate of 5.9%. This rate could be lowered to less than 0.1% by using a top-3 classification strategy. Our dataset consisted of 486,000 internally annotated images, which was augmented to more than a million to address class imbalance. We also provide a label-wise analysis of the results, identifying the main sources of error and inaccuracy.


title: Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

abstract: A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a "prior" that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations. We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.


ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge


title: The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

abstract: Online learning algorithms often have to operate in the presence of concept drift (i.e., the concepts to be learned can change with time). This paper presents a new categorization for concept drift, separating drifts according to different criteria into mutually exclusive and nonheterogeneous categories. Moreover, although ensembles of learning machines have been used to learn in the presence of concept drift, there has been no deep study of why they can be helpful for that and which of their features can contribute or not for that. As diversity is one of these features, we present a diversity analysis in the presence of different types of drifts. We show that, before the drift, ensembles with less diversity obtain lower test errors. On the other hand, it is a good strategy to maintain highly diverse ensembles to obtain lower test errors shortly after the drift independent on the type of drift, even though high diversity is more important for more severe drifts. Longer after the drift, high diversity becomes less important. Diversity by itself can help to reduce the initial increase in error caused by a drift, but does not provide the faster recovery from drifts in long-term.


title: Lock-on range of infrared heat seeker missile

abstract: The lock-on range, R, of infrared (IR) heat seeker missile has been studied in details. An analytical expression form of R has been derived in term of target (assumed as aircraft hot metal tailpipe), atmosphere and infrared (IR) detector parameters. The R is represented in term of “Lambert W function” and as a function of target temperature, T, and atmosphere extinction coefficient, α. Assuming clear sky background, the simulation results show: firstly, R increases as the target temperature increases. High value of R is achieved when the temperature of aircraft engine is high especially in takeoff case where the engine thrust is large. Secondly, R can be decreased by (1) increasing the attenuation coefficient of the propagation medium, i. e. by cooling the aircraft hot engine or any other obscurants that absorb or scatter the IR radiation (2) or by decreasing emissivity , e, of the target surface which depends on the applied coatings / paintings. The decreasing of R will increase the probability of aircraft survivability assuming effective aircraft early warning system where the detecting range of the latter should be larger than the missile lock-on range.


title: DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

abstract: Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder–decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves $F$ -measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.