title: Learning robust uniform features for cross-media social data by using cross autoencoders

abstract: Cross-media analysis exploits social data with different modalities from multiple sources simultaneously and synergistically to discover knowledge and better understand the world. There are two levels of cross-media social data. One is the element, which is made up of text, images, voice, or any combinations of modalities. Elements from the same data source can have different modalities. The other level of cross-media social data is the new notion of aggregative subject (AS)- a collection of time-series social elements sharing the same semantics (i.e., a collection of tweets, photos, blogs, and news of emergency events). While traditional feature learning methods focus on dealing with single modality data or data fused across multiple modalities, in this study, we systematically analyze the problem of feature learning for cross-media social data at the previously mentioned two levels. The general purpose is to obtain a robust and uniform representation from the social data in time-series and across different modalities. We propose a novel unsupervised method for cross-modality element-level feature learning called cross autoencoder (CAE). CAE can capture the cross-modality correlations in element samples. Furthermore, we extend it to the AS using the convolutional neural network (CNN), namely convolutional cross autoencoder (CCAE). We use CAEs as filters in the CCAE to handle cross-modality elements and the CNN framework to handle the time sequence and reduce the impact of outliers in AS. We finally apply the proposed method to classification tasks to evaluate the quality of the generated representations against several real-world social media datasets. In terms of accuracy, CAE gets 7.33% and 14.31% overall incremental rates on two element-level datasets. CCAE gets 11.2% and 60.5% overall incremental rates on two AS-level datasets. Experimental results show that the proposed CAE and CCAE work well with all tested classifiers and perform better than several other baseline feature learning methods.


title: Control Principles of Complex Networks

abstract: A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: It requires an accurate map of the network that governs the interactions between the system's components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in nonlinear dynamics and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these here we review recent advances on the controllability and the control of complex networks, exploring the intricate interplay between a system's structure, captured by its network topology, and the dynamical laws that govern the interactions between the components. We match the pertinent mathematical results with empirical findings and applications. We show that uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.


title: Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells

abstract: The task of segmenting cell nuclei and cytoplasm in pap smear images is one of the most challenging tasks in automated cervix cytological analysis due to specifically the presence of overlapping cells. This paper introduces a multi-pass fast watershed-based method (MPFW) to segment both nucleus and cytoplasm from large cell masses of overlapping cervical cells in three watershed passes. The first pass locates the nuclei with barrier-based watershed on the gradient-based edge map of a pre-processed image. The next pass segments the isolated, touching, and partially overlapping cells with a watershed transform adapted to the cell shape and location. The final pass introduces mutual iterative watersheds separately applied to each nucleus in the largely overlapping clusters to estimate the cell shape. In MPFW, the line-shaped contours of the watershed cells are deformed with ellipse fitting and contour adjustment to give a better representation of cell shapes. The performance of the proposed method has been evaluated using synthetic, real extended depth-of-field, and multi-layers cervical cytology images provided by the first and second overlapping cervical cytology image segmentation challenges in ISBI 2014 and ISBI 2015. The experimental results demonstrate superior performance of the proposed MPFW in terms of segmentation accuracy, detection rate, and time complexity, compared with recent peer methods.


title: Dry-Land Resistance Training Practices of Elite Swimming Strength and Conditioning Coaches

abstract: No research to date has investigated dry-land resistance (RT) training practices of elite swimming strength and conditioning coaches. This is the first comprehensive study exploring dry-land RT training practices in swimming. The aims of this study were to examine (a) the dry-land RT training practices and exercises used by elite swimming strength and conditioning coaches and (b) the rationale provided by coaches about their practices and prescription of specific dry-land RT training exercises. Twenty-three (n = 21 males, n = 2 females) elite swimming strength and conditioning coaches, from Ireland (n = 7), Great Britain (n = 5), Australia (n = 6), and the United States of America (n = 5) were recruited through their specific national governing bodies. Coaches completed an online questionnaire consisting of 7 sections; subject information, informed consent, coach's biography, coach education, current training commitments, dry-land RT training practices and exercises, and additional information. The results showed that coaches had varying levels of experience, education and worked with different level swimmers. A total of 95 dry-land RT training exercises were used by the coaches across 4 different dry-land RT training practices (warm-up, circuit training, traditional RT training and plyometrics). Traditional RT training (87%) was the most commonly practiced. The pull-up and squat were the most popular dry-land RT training exercises used by elite swimming strength and conditioning coaches. Future research needs to focus on exploring the specificity and the transfer of RT training exercises to swimming performance.


title: Internet Scale User-Generated Live Video Streaming: The Twitch Case

abstract: Twitch is a live video streaming platform used for broadcasting video gameplay, ranging from amateur players to eSports tournaments. This platform has gathered a substantial world wide community, reaching more than 1.7 million broadcasters and 100 million visitors every month. Twitch is fundamentally different from “static” content distribution platforms such as YouTube and Netflix, as streams are generated and consumed in real time. In this paper, we explore the Twitch infrastructure to understand how it manages live streaming delivery to an Internet-wide audience. We found Twitch manages a geo-distributed infrastructure, with presence in four continents. Our findings show that Twitch dynamically allocates servers to channels depending on their popularity. Additionally, we explore the redirection strategy of clients to servers depending on their region and the specific channel.


title: Effect of Stimulus Type and Motion on Smooth Pursuit in Adults and Children

abstract: Purpose. This study presents a two-degree customized animated stimulus developed to evaluate smooth pursuit in children ::: and investigates the effect of its predetermined characteristics (stimulus type and size) in an adult population. Then, the ::: animated stimulus is used to evaluate the impact of different pursuit motion paradigms in children. ::: Methods. To study the effect of animating a stimulus, eye movement recordings were obtained from 20 young adults while the ::: customized animated stimulus and a standard dot stimulus were presentedmoving horizontally at a constant velocity. To study the ::: effect of using a larger stimulus size, eye movement recordings were obtained from 10 young adults while presenting a standard ::: dot stimulus of different size (1- and 2-) moving horizontally at a constant velocity. Finally, eye movement recordings were ::: obtained from 12 children while the 2- customized animated stimulus was presented after three different smooth pursuit motion ::: paradigms. Performance parameters, including gains and number of saccades, were calculated for each stimulus condition. ::: Results. The animated stimulus produced in young adults significantly higher velocity gain (mean: 0.93; 95% CI: 0.90Y0.96; ::: P = .014), position gain (0.93; 0.85Y1; P = .025), proportion of smooth pursuit (0.94; 0.91Y0.96, P = .002), and fewer saccades ::: (5.30; 3.64Y6.96, P = .008) than a standard dot (velocity gain: 0.87; 0.82Y0.92; position gain: 0.82; 0.72Y0.92; proportion ::: smooth pursuit: 0.87; 0.83Y0.90; number of saccades: 7.75; 5.30Y10.46). In contrast, changing the size of a standard dot ::: stimulus from1- to 2- did not have an effect onsmooth pursuit in young adults (P 9.05). Finally, smooth pursuit performance did ::: not significantly differ in children for the different motion paradigms when using the animated stimulus (P 9 .05). ::: Conclusions. Attention-grabbing and more dynamic stimuli, such as the developed animated stimulus, might potentially be ::: useful for eye movement research. Finally, with such stimuli, children perform equally well irrespective of the motion ::: paradigm used.


title: Evolving Space-Time Neural Architectures for Videos

abstract: We present a new method for finding video CNN architectures that more optimally capture rich spatio-temporal information in videos. Previous work, taking advantage of 3D convolutions, obtained promising results by manually designing CNN video architectures. We here develop a novel evolutionary algorithm that automatically explores models with different types and combinations of layers to jointly learn interactions between spatial and temporal aspects of video representations. We demonstrate the generality of this algorithm by applying it to two meta-architectures. Further, we propose a new component, the iTGM layer, which more efficiently utilizes its parameters to allow learning of space-time interactions over longer time horizons. The iTGM layer is often preferred by the evolutionary algorithm and allows building cost-efficient networks. The proposed approach discovers new diverse and interesting video architectures that were unknown previously. More importantly they are both more accurate and faster than prior models, and outperform the state-of-the-art results on four datasets: Kinetics, Charades, Moments in Time and HMDB. We will open source the code and models, to encourage future model development.


title: Big Data and supply chain management: a review and bibliometric analysis

abstract: As Big Data has undergone a transition from being an emerging topic to a growing research area, it has become necessary to classify the different types of research and examine the general trends of this research area. This should allow the potential research areas that for future investigation to be identified. This paper reviews the literature on ‘Big Data and supply chain management (SCM)’, dating back to 2006 and provides a thorough insight into the field by using the techniques of bibliometric and network analyses. We evaluate 286 articles published in the past 10 years and identify the top contributing authors, countries and key research topics. Furthermore, we obtain and compare the most influential works based on citations and PageRank. Finally, we identify and propose six research clusters in which scholars could be encouraged to expand Big Data research in SCM. We contribute to the literature on Big Data by discussing the challenges of current research, but more importantly, by identifying and proposing these six research clusters and future research directions. Finally, we offer to managers different schools of thought to enable them to harness the benefits from using Big Data and analytics for SCM in their everyday work.


title: WHAT MAKES A USEFUL MATURITY MODEL? A FRAMEWORK OF GENERAL DESIGN PRINCIPLES FOR MATURITY MODELS AND ITS DEMONSTRATION IN

abstract: Since the Software Engineering Institute has launched the Capability Maturity Model almost twenty years ago, hundreds of maturity models have been proposed by researchers and practitioners across multiple application domains. With process orientation being a central paradigm of organizational design and continuous process improvement taking top positions on CIO agendas, maturity models are also prospering in business process management. Although the application of maturity models is increasing in quantity and breadth, the concept of maturity models is frequently subject to criticism. Indeed, numerous shortcomings have been disclosed referring to both maturity models as design products and the process of maturity model design. Whereas research has already substantiated the design process, there is no holistic understanding of the principles of form and function – that is, the design principles – maturity models should meet. We therefore propose a pragmatic, yet well-founded framework of general design principles justified by existing literature and grouped according to typical purposes of use. The framework is demonstrated using an exemplary set of maturity models related to business process management. We finally give a brief outlook on implications and topics for further research.


title: The Document Components Ontology (DoCO)

abstract: The availability in machine-readable form of descriptions of the structure of documents, as well as of the document ::: discourse (e.g. the scientific discourse within scholarly articles), is crucial for facilitating semantic publishing and the overall ::: comprehension of documents by both users and machines. In this paper we introduce DoCO, the Document Components Ontology, ::: an OWL 2 DL ontology that provides a general-purpose structured vocabulary of document elements to describe both structural and ::: rhetorical document components in RDF. In addition to describing the formal description of the ontology, this paper showcases its ::: utility in practice in a variety of our own applications and other activities of the Semantic Publishing community that rely on DoCO ::: to annotate and retrieve document components of scholarly articles.


title: Some considerations in the development of color rendering and gamut mapping algorithms

abstract: Newly developed standard terminology enables improved communication about color processing objectives and research goals. By clarifying specific color processing tasks, one observes that there are de-facto standard practices for many processing steps, that can be used as a baseline recommendation for implementers and future development work. The more explicit descriptions of development goals can also serve to focus research on the desired objectives. This increased clarity leads to work being both more effective at meeting design objectives, and also more apparently relevant to commercial applications. Differences in objectives and requirements for the development of color rendering and gamut mapping algorithms are discussed and contrasted. In some cases, these differences can explain the reasons for different approaches, enabling broader consensus and understanding. Differences between color appearance and reproduction models are also discussed, along with the impact of these differences on their use for imaging applications. The above concepts are related to important color reproduction considerations, such as the preference and media capabilities. If the more explicit terminology is widely adopted, it could accelerate the advance of digital color understanding across both product manufacturers and users, and enable significantly more effective research, development, and use.


title: Outcomes of Conservative Treatment of 134 Cases of Umbilical Pilonidal Sinus

abstract: BACKGROUND ::: There are different opinions about the treatment of umbilical pilonidal sinus. The aim of the present study was to evaluate the outcome of conservative treatment of this disease. ::: ::: ::: METHODS ::: All patients with umbilical pilonidal sinus who attended our private surgical clinic in Erbil, Kurdistan, from January 1997 to August 2011, were treated by conservative treatment. Umbilectomy was performed for those who did not respond to the conservative treatment after three sessions. ::: ::: ::: RESULTS ::: A total of 134 patients were seen; 121 (90.3 %) were men. Only 105 (78.35 %) patients came back for the second visit. Eighty of them (76.19 %) responded to the treatment. The other 25 (23.8 %) patients were retreated by a second session of conservative treatment. Of these 25 patients, 19 (76 %) came back for the third visit and only three (15.78 %) patients did not respond to the treatment. One patient insisted on umbilectomy. The other two patients were treated but did not respond to the third session treatment. Umbilectomy was performed for them. Hair was found in the sinus after excision in these three patients. The follow-up period ranged from 9 months to 6.5 years for 46 (34.32 %) patients and revealed no recurrence. ::: ::: ::: CONCLUSIONS ::: Conservative treatment should be regarded as the first choice and the main method of treatment in the management of umbilical pilonidal sinus. Improper extraction of hair is the main cause of failure of conservative treatment. Recurrence can be prevented by giving the patients instructions on preventive measures.


title: A flexible network on-chip router for data-flow monitoring

abstract: The Network on-Chip (NoC) is considered as an emerging technology for distributed embedded systems which is proposed as an alternative interconnection solution in Mutli-Processors System on-Chip (MPSoC). This paper proposes a graphical toll that allows researchers to generate and configure quickly their Network on-Chip in the hope to evaluate easily their contributions in no time. Unlike major existing simulators which are developed in hardware language description and make debugging very difficult, this high-level framework helps software designer, who need simpler means, to properly build mapping and scheduler algorithm then validate their contributions. This proposed toll is open source and extensible. The provided conceptual details allow users to efficiently create, validate their real scheduler and mapper as well as integrate it easily.


title: A Simple Algorithm for Subgraph Queries in Big Graphs.

abstract: Subgraph queries also known as subgraph isomorphism search is a fundamental problem in querying graph-like structured data. It consists to enumerate the subgraphs of a data graph that match a query graph. This problem arises in many real-world applications related to query processing or pattern recognition such as computer vision, social network analysis, bioinformatic and big data analytic. Subgraph isomorphism search knows a lot of investigations and solutions mainly because of its importance and use but also because of its NP-completeness. Existing solutions use filtering mechanisms and optimise the order within witch the query vertices are matched on the data vertices to obtain acceptable processing times. However, existing approaches are iterative and generate several intermediate results. They also require that the data graph is loaded in main memory and consequently are not adapted to large graphs that do not fit into memory or are accessed by streams. To tackle this problem, we propose a new approach based on concepts widely different from existing works. Our approach distills the semantic and topological information that surround a vertex into a simple integer. This simple vertex encoding that can be computed and updated incrementally reduces considerably intermediate results and avoid to load the entire data graph into main memory. We evaluate our approach on several real-word datasets. The experimental results show that our approach is efficient and scalable.


title: A Real-time Complex Event Discovery Platform for Cyber-Physical-Social Systems

abstract: We are living in the Internet of Things (IoT) era where all the (smart) objects around us are connected and communicated with each other to serve our life better without the need of explicit instruction. Soon we have to cope with trillions of heterogeneous data streams coming from IoT. Since data is not information, methods for discovering useful and correlative information from data and utilising them for the better life, in real-time mode, are the utmost requirements. In order to tackle this problem, we introduce an Event Information Management platform (EvIM) that can be used to develop applications run as cyber-physical-social systems. EvIM includes two components (1) EventWarehouse: is built for harvesting, storing, and analysing data coming from large scale heterogeneous sensors, and (2) EventShop: plays as a real-time complex spatio-temporal event processing system. Differ from conventional systems that use data-driven or pre-defined event-based approaches, the proposed platform can alleviate the burden of human intervention meanwhile increase the scalability, robustness, feasibility, and applicability by offering series of services that not only automatically discover correlations among sensors' data to extract useful information but also can help users design and monitor situations visually, efficiently and effectively, under on-fly mode.


title: An inexpensive scheme for calibration of a colour monitor in terms of CIE standard coordinates

abstract: The Commission Internationale d'Eclairage system of colorimetry is a method of measuring colours that has been standardized, and is widely used by industries involved with colour. Knowing the CIE coordinates of a colour allows it to be reproduced easily and exactly in many different media. For this reason graphics installations which utilize colour extensively ought to have the capability of knowing the CIE coordinates of displayed colours, and of displaying colours of given CIE coordinates. Such a capability requires a function which transforms video monitor gun voltages (RGB colour space) into CIE coordinates (XYZ colour space), and vice versa. The function incorporates certain monitor parameters. The purpose of this paper is to demonstrate the form that such a function takes, and to show how the necessary monitor parameters can be measured using little more than a simple light meter. Because space is limited, and because each user is likely to implement the calibration differently, few technical details are given, but principles and methods are discussed in sufficient depth to allow the full use of the system. In addition, several visual checks which can be used for quick verification of the integrity of the calibration are described. The paper begins with an overview of the CIE system of colorimetry. It continues with a general discussion of transformations from RGB colour space to XYZ colour space, after which a detailed step-by-step procedure for monitor calibration is presented.


title: VM Placement Strategies for Cloud Scenarios

abstract: The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP)that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSPtechnique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.


title: "Sandwich" Bone Augmentation Technique: Rationale and Report of Pilot Cases

abstract: The aim of this article is to present a new technique for augmentation of deficient alveolar ridges and/or correction of osseous defects around dental implants. Current knowledge regarding bone augmentation for treatment of osseous defects prior to and in combination with dental implant placement is critically appraised. The "sandwich" bone augmentation technique is demonstrated step by step. Five pilot cases with implant dehiscence defects averaging 10.5 mm were treated with the technique. At 6 months, the sites were uncovered, and complete defect fill was noted in all cases. Results from this pilot case study indicated that the sandwich bone augmentation technique appears to enhance the outcomes of bone augmentation by using the positive properties of each applied material (autograft, DFDBA, hydroxyapatite, and collagen membrane). Future clinical trials for comparison of this approach with other bone augmentation techniques and histologic evaluation of the outcomes are needed to validate these findings.


title: Behavioral detection of malware on mobile handsets

abstract: A novel behavioral detection framework is proposed to detect mobile worms, viruses and Trojans, instead of the signature-based solutions currently available for use in mobile devices. First, we propose an efficient representation of malware behaviors based on a key observation that the logical ordering of an application's actions over time often reveals the malicious intent even when each action alone may appear harmless. Then, we generate a database of malicious behavior signatures by studying more than 25 distinct families of mobile viruses and worms targeting the Symbian OS - the most widely-deployed handset OS - and their variants. Next, we propose a two-stage mapping technique that constructs these signatures at run-time from the monitored system events and API calls in Symbian OS. We discriminate the malicious behavior of malware from the normal behavior of applications by training a classifier based on Support Vector Machines (SVMs). Our evaluation on both simulated and real-world malware samples indicates that behavioral detection can identify current mobile viruses and worms with more than 96% accuracy. We also find that the time and resource overheads of constructing the behavior signatures from low-level API calls are acceptably low for their deployment in mobile devices.


title: Fast generation of result snippets in web search

abstract: The presentation of query biased document snippets as part of results pages presented by search engines has become an expectation of search engine users. In this paper we explore the algorithms and data structures required as part of a search engine to allow efficient generation of query biased snippets. We begin by proposing and analysing a document compression method that reduces snippet generation time by 58% over a baseline using the zlib compression library. These experiments reveal that finding documents on secondary storage dominates the total cost of generating snippets, and so caching documents in RAM is essential for a fast snippet generation process. Using simulation, we examine snippet generation performance for different size RAM caches. Finally we propose and analyse document reordering and compaction, revealing a scheme that increases the number of document cache hits with only a marginal affect on snippet quality. This scheme effectively doubles the number of documents that can fit in a fixed size cache.


title: The Fusiform Face Area is Part of a Network that Processes Faces at the Individual Level

abstract: According to modular models of cortical organization, many areas of the extrastriate cortex are dedicated to object categories. These models often assume an early processing stage for the detection of category membership. Can functional imaging isolate areas responsible for detection of members of a category, such as faces or letters? We consider whether responses in three different areas (two selective for faces and one selective for letters) support category detection. Activity in these areas habituates to the repeated presentation of one exemplar more than to the presentation of different exemplars of the same category, but only for the category for which the area is selective. Thus, these areas appear to play computational roles more complex than detection, processing stimuli at the individual level. Drawing from prior work, we suggest that face-selective areas may be involved in the perception of faces at the individual level, whereas letter-selective regions may be tuning themselves to font information in order to recognize letters more efficiently.


title: Modulation Techniques to Reduce Leakage Current in Three-Phase Transformerless H7 Photovoltaic Inverter

abstract: Recently, reduced common-mode voltage (CMV) pulsewidth modulation (RCMV-PWM) methods have been proposed to reduce the leakage current in three-phase transformerless photovoltaic (PV) systems. However, most of these studies only focus on leakage current elimination and neglect the overall performance of the PV systems on issues such as cost, voltage linearity, dc-link current ripples, and harmonic distortion. In this paper, a three-phase transformerless inverter, adapted from the single-phase H5 topology, is investigated. Since the H5 topology has been conventionally developed for a single-phase system, its adaptation to the three-phase system requires the development of corresponding three-phase modulation techniques. Hence, modulation techniques are proposed based on conventional PWM. The performances of the proposed PWM, in terms of CMV, leakage current, voltage linearity, output current ripples, dc-link current ripples, and harmonic distortion are studied and discussed via simulation and experiment. It is proven that the proposed topology is able reduce the leakage current without sacrificing the overall performance of the system.


title: External localization system for mobile robotics

abstract: We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems.


title: Generation of synthetic training data for an HMM-based handwriting recognition system

abstract: A perturbation model for generating synthetic text lines from existing cursively handwritten lines of text produced by human writers is presented. Our purpose is to improve the performance of an HMM-based off-line cursive handwriting recognition system by providing it with additional synthetic training data. Two kinds of perturbations are applied, geometrical transformations and thinning/thickening operations. The proposed perturbation model is evaluated under different experimental conditions.


title: Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion

abstract: Large and dense mobile ad hoc networks often meet scalability problems, the hierarchical structures are needed to achieve performance of network such as cluster control structure. Clustering in mob...


title: Efficient Markov logic inference for natural language semantics

abstract: Using Markov logic to integrate logical and distributional information in natural-language semantics results in complex inference problems involving long, complicated formulae. Current inference methods for Markov logic are ineffective on such problems. To address this problem, we propose a new inference algorithm based on SampleSearch that computes probabilities of complete formulae rather than ground atoms. We also introduce a modified closed-world assumption that significantly reduces the size of the ground network, thereby making inference feasible. Our approach is evaluated on the recognizing textual entailment task, and experiments demonstrate its dramatic impact on the efficiency of inference.


title: Embodied cognition and linguistic comprehension.

abstract: Abstract Traditionally, the language faculty was supposed to be a device that maps linguistic inputs to semantic or conceptual representations. These representations themselves were supposed to be distinct from the representations manipulated by the hearer’s perceptual and motor systems. Recently this view of language has been challenged by advocates of embodied cognition. Drawing on empirical studies of linguistic comprehension, they have proposed that the language faculty reuses the very representations and processes deployed in perceiving and acting. I review some of the evidence and arguments in favor of the embodied view of language comprehension, and argue that none of it is conclusive. Moreover, the embodied view itself blurs two important distinctions: first, the distinction between linguistic comprehension and its typical consequences; and second, the distinction between representational content and vehicles. Given that these distinctions are well-motivated, we have good reason to reject the embodied view of linguistic understanding.


title: The Investment Model of Commitment Processes

abstract: The investment model of commitment processes is rooted in interdependence theory and emerged from the broader scientific zeitgeist of the 1960s and 1970s that sought to understand seemingly irrational persistence in social behavior. The investment model was developed originally to move social psychology beyond focusing only on positive affect in predicting persistence in a close interpersonal relationship. As originally tested, the investment model holds that commitment to a target is influenced by three independent factors: satisfaction level, quality of alternatives, and investment size. Commitment, in turn, is posited to mediate the effects of these three bases of dependence on behavior, including persistence. Commitment is presumed to bring about persistence by influencing a host of relationship maintenance phenomena. The investment model has proven to be remarkably generalizable across a range of commitment targets, including commitment toward both interpersonal (e.g., abusive relationships, friendships) and non-interpersonal (e.g., job, sports participation, support for public policies) targets. Empirical support for the investment model is presented as well as a review of recent applications of the model and a proposed extension of it.


title: Dual Control for Jerk-Driven Robotics in Rehabilitative Planar Applications

abstract: This study compares a set of strategies to plan and control the trajectory of a robotic device in a planar workspace. These strategies are based on an affective application of jerk-laws able to indicate undesirable conditions (e.g., vibrations) facilitating the device control. The jerk is the time derivative of acceleration, and this solution provides an indirect means to control the variation rate of the actuator torques, while avoiding the complex robot dynamic models and their algorithms for computing the dynamics. In order to obtain a smooth trajectory, a regulator to control a robotic device has been developed and validated. It consists of the implementation of two control modules able to i) generate the predefined trajectory and ii) guarantee the path tracking, reducing unwanted effects. In this case a simple S-shaped path has been originated by the “trajectory generator module” as a reference movement to rehabilitate upper limb functionality. The numerical simulation and the results of preliminary tests show the efficacy of the proposed approach through the vibration smoothness appraisal associated with the motion profile.


title: On Building Decision Trees from Large-scale Data in Applications of On-line Advertising

abstract: Decision trees have been used for several decades as simple and effective solutions to supervised learning problems. Their success extends to tasks across a variety of areas. Yet, data collected today through web-domains such as on-line advertising presents many new challenges: sheer size, the prevalence of high-arity categorical features, unknown feature-values, "cold starts", sparse training instances, and imbalance in the class labels. We argue that decision trees remain an ideal choice for applications of on-line advertising as they naturally construct higher-order conjunctive features; we then contribute two ideas to improve tree-building accordingly. First, to handle high-arity categorical features, we introduce a method to cluster feature-values based on their output responses. The result is more "data-dense" trees with relatively small branching factors. Second, we employ cross-validation as a principled approach to derive splitting and stopping criteria: thereby we identify splits that generalize well, and also curb overfitting. Evaluated on three distinct probability-estimation tasks in on-line advertising, our method, "CCDT", shows significant improvements in the accuracy of prediction.


A Distributed Multiplatform Architecture for Traffic Generation


title: A Study of MAC Address Randomization in Mobile Devices and When it Fails

abstract: MAC address randomization is a privacy technique whereby mobile devices rotate through random hardware addresses in order to prevent observers from singling out their traffic or physical location from other nearby devices. Adoption of this technology, however, has been sporadic and varied across device manufacturers. In this paper, we present the first wide-scale study of MAC address randomization in the wild, including a detailed breakdown of different randomization techniques by operating system, manufacturer, and model of device. ::: We then identify multiple flaws in these implementations which can be exploited to defeat randomization as performed by existing devices. First, we show that devices commonly make improper use of randomization by sending wireless frames with the true, global address when they should be using a randomized address. We move on to extend the passive identification techniques of Vanhoef et al. to effectively defeat randomization in ~96% of Android phones. Finally, we show a method that can be used to track 100% of devices using randomization, regardless of manufacturer, by exploiting a previously unknown flaw in the way existing wireless chipsets handle low-level control frames.


Discovering Implicit Knowledge with Unary Relations


Regional Attention Based Deep Feature for Image Retrieval


title: Sign Language Production using Neural Machine Translation and Generative Adversarial Networks

abstract: We present a novel approach to automatic Sign Language Production using stateof- ::: the-art Neural Machine Translation (NMT) and Image Generation techniques. Our ::: system is capable of producing sign videos from spoken language sentences. Contrary to ::: current approaches that are dependent on heavily annotated data, our approach requires ::: minimal gloss and skeletal level annotations for training. We achieve this by breaking ::: down the task into dedicated sub-processes. We first translate spoken language sentences ::: into sign gloss sequences using an encoder-decoder network. We then find a data driven ::: mapping between glosses and skeletal sequences. We use the resulting pose information ::: to condition a generative model that produces sign language video sequences. We ::: evaluate our approach on the recently released PHOENIX14T Sign Language Translation ::: dataset. We set a baseline for text-to-gloss translation, reporting a BLEU-4 score of ::: 16.34/15.26 on dev/test sets. We further demonstrate the video generation capabilities ::: of our approach by sharing qualitative results of generated sign sequences given their ::: skeletal correspondence.


title: Speech Emotion Classification Using Machine Learning Algorithms

abstract: The recognition of emotional states is a relatively new technique in the field of machine learning. The paper presents the study and the performance results of a system for emotion classification using the architecture of a distributed speech recognition system (DSR). The features used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202-211 V1.1.1 standard. On the basis of the time trend of these parameters, over 3800 statistical parameters were extracted to characterize semantic units of varying length (sentences and words). Using the WEKA (Waikato Environment for Knowledge Analysis) software the most significant parameters for the classification of emotional states were selected and the results of various classification techniques were analysed. The results, obtained using both the Berlin Database of Emotional Speech (EMO-DB) and the Speech Under Simulated and Actual Stress (SUSAS) corpus, showed that the best performance is achieved using a support vector machine (SVM) trained with the sequential minimal optimization (SMO) algorithm, after normalizing and discretizing the input statistical parameters.


title: Learning to Refine Source Representations for Neural Machine Translation

abstract: Neural machine translation (NMT) models generally adopt an encoder-decoder architecture for modeling the entire translation process. The encoder summarizes the representation of input sentence from scratch, which is potentially a problem if the sentence is ambiguous. When translating a text, humans often create an initial understanding of the source sentence and then incrementally refine it along the translation on the target side. Starting from this intuition, we propose a novel encoder-refiner-decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a strategy, leveraging the power of reinforcement learning models, to decide when to refine at specific decoding steps. Experimental results on both Chinese-English and English-German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoder-decoder framework. Furthermore, when refining strategy is applied, results still show reasonable improvement over the baseline without much decrease in decoding speed.


title: A Markov Chain Model Checker

abstract: Markov chains are widely used in the context of performance and reliability evaluation of systems of various nature. Model checking of such chains with respect to a given (branching) temporal logic formula has been proposed for both the discrete and the continuous time setting. In this paper, we describe a prototype model checker for discrete and continuous-time Markov chains, the Erlangen-Twente Markov Chain Checker (E ⊢ MC2), where properties are expressed in appropriate extensions of CTL. We illustrate the general benefits of this approach and discuss the structure of the tool. Furthermore we report on first successful applications of the tool to non-trivial examples, highlighting lessons learned during development and application of E ⊢ MC2.


Software Frameworks for Deep Learning at Scale


title: Buffer Management for Aggregated Streaming Data with Packet Dependencies

abstract: In many applications the traffic traversing the network has inter-packet dependencies due to application-level encoding schemes. For some applications, e.g., multimedia streaming, dropping a single packet may render useless the delivery of a whole sequence. In such environments, the algorithm used to decide which packet to drop in case of buffer overflows must be carefully designed, to avoid goodput degradation. We present a model that captures such inter-packet dependencies, and design algorithms for performing packet discards. Traffic consists of an aggregation of multiple streams, each of which consists of a sequence of inter-dependent packets. We provide two guidelines for designing buffer management algorithms for this problem, and demonstrate the effectiveness of these criteria. We devise an algorithm according to these guidelines and evaluate its performance analytically, using competitive analysis. We also present a simulation study that shows that the performance of our algorithm is within a small fraction of the performance of the best offline algorithm.


title: A Comparative Study on Steganography Digital Images: A Case Study of Scalable Vector Graphics (SVG) and Portable Network Graphics (PNG) Images Formats

abstract: Today image steganography plays a key role for exchanging a secret data through the internet. However, the optimal choice of images formats for processing steganography is still an open issue; therefore, this research comes into a table. This research conducts a comparative study between Scalable Vector Graphics (SVG) image format and Portable Network Graphics (PNG) image format. As results show, SVG image format is more efficient than PNG image format in terms of capacity and scalability before and after processing steganography. As well, SVG image format helps to increase simplicity and performance for processing steganography, since it is an XML text file. Our comparative study provides significant results between SVG and PNG images, which have not been seen in the previous related studies.


title: A statistical method for robust 3D surface reconstruction from sparse data

abstract: General information about a class of objects, such as human faces or teeth, can help to solve the otherwise ill-posed problem of reconstructing a complete surface from sparse 3D feature points or 2D projections of points. We present a technique that uses a vector space representation of shape (3D morphable model) to infer missing vertex coordinates. Regularization derived from a statistical approach makes the system stable and robust with respect to noise by computing the optimal tradeoff between fitting quality and plausibility. We present a direct, noniterative algorithm to calculate this optimum efficiently, and a method for simultaneously compensating unknown rigid transformations. The system is applied and evaluated in two different fields: (1) reconstruction of 3D faces at unknown orientations from 2D feature points at interactive rates, and (2) restoration of missing surface regions of teeth for CAD-CAM production of dental inlays and other medical applications.


title: Predictive-State Decoders: Encoding the Future into Recurrent Networks

abstract: Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution. We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations. Predictive-State Decoders are simple to implement and easily incorporated into existing training pipelines via additional loss regularization. We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning. In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.


title: Condition monitoring of dc-link capacitor utilizing zero state of solar PV H5 inverter

abstract: Single phase direct grid feeding solar photovoltaic (PV) inverters are used to interface with the grid. Aluminum Electrolytic Capacitors (AECs) are used to suppress dc-link voltage oscillation in these inverter. Life of solar PV inverter system is limited due to the use of these capacitors. With ageing its capacitance decreases, which may lead to increased voltage ripple, thereby reduced average power extraction from solar PV and/or damage to semiconductor switches due to overvoltage. Further, prolonged used of aged capacitor could lead to open circuit of the dc-link due to degradation of oxide layer in the AEC. Therefore, to improve the reliability of system, health monitoring of capacitor is desirable. In this paper, a technique for condition monitoring of dc-link capacitor is proposed for the solar PV H5 inverter. Proposed technique estimates capacitance by utilizing the sampled values of solar PV voltage and current during zero states of the inverter. Key advantage is that the existing voltage and current sensors and digital processor/controller are used for estimation of capacitance. Further, the proposed technique is also applicable to other full bridge based, single phase inverters. To validate the efficacy of proposed technique, detailed simulation studies are performed using MATLAB-Simulink. Further, error analysis in the estimation of capacitance values using proposed technique under different levels of solar radiation over the day is provided. Practical aspects of the proposed technique are also discussed in this paper.


title: Transgenic Brassica napus L. lines carrying a two gene construct demonstrate enhanced resistance against Plutella xylostella and Sclerotinia sclerotiorum

abstract: Plant diseases and insect pests are serious threat to the growth and yield of oilseed rape. In this study, a binary vector carrying sporamin and chitinase PjChi-1 genes in tandem was introduced into Brassica napus cv. ZS 758 via Agrobacterium tumefaciens for dual resistance against disease and insect attack. Thirty-two regenerated plantlets exhibiting hygromycin resistance were selected following Agrobacterium-mediated transformation of 600 leaf petiole explants. Of these, 27 transformants were confirmed to carry the two transgenes as detected by polymerase chain reaction (PCR) with 4.5% transformation efficiency. Eight plantlets were randomly selected for further confirmation by Southern and northern blot hybridization analyses. Four plants carried single copy of the transgenes, while the remaining four plants carried either two or three copies of the transgenes. Moreover, expression of the sporamin transgene was detected by northern blot hybridization in transgenic lines, but not in wild-type plants. These eight T0 plants were grown in vitro, and inoculated with the Lepidoptera larvae of Plutella xylostella and with spores of the fungal pathogen of Sclerotinia sclerotiorum. Transgenic plants exhibited high levels of resistance to P. xylostella and S. sclerotiorum when compared to untransformed wild-type plants. Genetic analysis of T1 progeny confirmed Mendelian segregation of the introduced genes. Therefore, these transgenic lines demonstrate a promising potential for variety development of oilseed rape lines with enhanced resistance against both P. xylostella and S. sclerotiorum.


title: EVALUATION OF FAMILY STRUCTURES WITH FIGURE PLACEMENT TECHNIQUES

abstract: The relevance of figure placement techniques for analyzing family cohesion and hierarchy structures in various settings is described and the completion of further studies including these instruments is suggested.


title: Using artificial neural network in intrusion detection systems to computer networks

abstract: The constant growth in the use of computer networks has demanded some concerns regarding disponibility, vulnerability and security. Intrusion Detection Systems (IDS) have been considered essential in keeping network security and therefore have been commonly adopted by network administrators. A possible disadvantage is the fact that such systems are usually based on signature systems, which make them strongly dependent on updated database and consequently inefficient against novel attacks (unknown attacks). The research presented in this paper proposes an IDS system based on artificial neural network (ANN) and the KDDCUP'99 dataset. Experimental results clearly show that the proposed system can reach an overall accuracy of 99.9% regarding the classification of pre-defined classes of intrusion attacks with, which is a very satisfactory result when compared to traditional methods.


title: Facebook as a Platform for Health Information and Communication: A Case Study of a Diabetes Group

abstract: As one of the largest social networking sites in the world, Facebook holds a great potential for promoting health. In this exploratory study, we analyzed 1352 messages posted to an active Facebook diabetes group to identify the characteristics of the group. The results revealed that the group was international in nature. Users overcame language barriers to communicate with people with similar conditions. Users’ interactions were structured around information, emotion, and community building. They exchanged medical and lifestyle information, and highly valued their peers’ personal experiences, opinions, and advice. They also demonstrated a positive attitude toward the reality of living with diabetes and generously provided encouragements and affirmations to one another. Great efforts were made to maintain the proper operation of the community by the administrator and a group of core members. As a result, the group was shaped as a social network where peer users share social support, cultivate companionship, and exert social influence. Based on the results, we discussed future directions for research of health communities in a highly connected world.


title: Pulsar Candidate Selection by Assembling Positive Sample Emphasized Classifiers

abstract: Pulsar candidate selection identifies prospective observations of modern radio pulsar surveys for further inspection in search of real pulsars. Typically, human experts visually select valuable candidates and eliminate radio frequency interference or other noises. Recently, machine learning methods are adopted to automate this task, which saves human labor and makes it possible for processing millions of observations efficiently. Considering the number of positive training samples are relatively too small and the cost of incorrectly labeling a real pulsar candidate as negative is large, we propose a novel hierarchical candidate-sifting model by emphasizing the cost of incorrect prediction of positive samples and assembling multiple classifiers trained with different weighting parameters. Experiments on three pulsar selection datasets demonstrate our proposed method improves the pulsar-sifting performance a lot according to several standard evaluation metrics.


title: VAE with a VampPrior

abstract: Many different methods to train deep generative models have been introduced in the past. In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new type of prior which we call "Variational Mixture of Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture distribution (e.g., a mixture of Gaussians) with components given by variational posteriors conditioned on learnable pseudo-inputs. We further extend this prior to a two layer hierarchical model and show that this architecture with a coupled prior and posterior, learns significantly better models. The model also avoids the usual local optima issues related to useless latent dimensions that plague VAEs. We provide empirical studies on six datasets, namely, static and binary MNIST, OMNIGLOT, Caltech 101 Silhouettes, Frey Faces and Histopathology patches, and show that applying the hierarchical VampPrior delivers state-of-the-art results on all datasets in the unsupervised permutation invariant setting and the best results or comparable to SOTA methods for the approach with convolutional networks.


title: ADVANCES IN TURBO ENGINE REAL-TIME SIMULATION FOR MODERN CONTROL SYSTEM DEVELOPMENT

abstract: The available computational power is constantly increasing. This facilitates detailed simulations of turbo engine dynamical behavior in real-time with a high sampling rate. These simulations can include secondary effects as well as failure cases. This paper first gives an overview and a comparison of the most common simulation methods. Special real-time issues will be discussed. Following this, examples will be given of how real-time engine simulations can be used for the development of engine control systems. One main application described is rapid prototyping of control systems for helicopter engines as performed at the Chair of Flight Propulsion. The other application described is the design of control systems including onboard engine models. The advantages of such an approach for highly integrated hypersonic engines, to be used in the lower stage of a planned two-stage-to-orbit vehicle, will be discussed.


title: Greedy Feature Selection for Subspace Clustering

abstract: Unions of subspaces provide a powerful generalization to linear subspace models for collections of high-dimensional data. To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace structures present in the data. Recently, sparse recovery methods have been shown to provide a provable and robust strategy for exact feature selection (EFS)--recovering subsets of points from the ensemble that live in the same subspace. In parallel with recent studies of EFS with L1-minimization, in this paper, we develop sufficient conditions for EFS with a greedy method for sparse signal recovery known as orthogonal matching pursuit (OMP). Following our analysis, we provide an empirical study of feature selection strategies for signals living on unions of subspaces and characterize the gap between sparse recovery methods and nearest neighbor (NN)-based approaches. In particular, we demonstrate that sparse recovery methods provide significant advantages over NN methods and the gap between the two approaches is particularly pronounced when the sampling of subspaces in the dataset is sparse. Our results suggest that OMP may be employed to reliably recover exact feature sets in a number of regimes where NN approaches fail to reveal the subspace membership of points in the ensemble.


Large-scale characterization of Mandarin pronunciation errors made by native speakers of European languages.


Design and Development of Credit Scoring Model for the Commercial banks of Pakistan : Forecasting Creditworthiness of Individual Borrowers Asia


title: Interactive software maps for web-based source code analysis

abstract: Software maps -- linking rectangular 3D-Treemaps, software system structure, and performance indicators -- are commonly used to support informed decision making in software-engineering processes. A key aspect for this decision making is that software maps provide the structural context required for correct interpretation of these performance indicators. In parallel, source code repositories and collaboration platforms are an integral part of today's software-engineering tool set, but cannot properly incorporate software maps since implementations are only available as stand-alone applications. Hence, software maps are 'disconnected' from the main body of this tool set, rendering their use and provisioning overly complicated, which is one of the main reasons against regular use. We thus present a web-based rendering system for software maps that achieves both fast client-side page load time and interactive frame rates even with large software maps. We significantly reduce page load time by efficiently encoding hierarchy and geometry data for the net transport. Apart from that, appropriate interaction, layouting, and labeling techniques as well as common image enhancements aid evaluation of project-related quality aspects. Metrics provisioning can further be implemented by predefined attribute mappings to simplify communication of project specific quality aspects. The system is integrated into dashboards to demonstrate how our web-based approach makes software maps more accessible to many different stakeholders in software-engineering projects.


SEPARATION AND IDENTIFICATION OF SPECIFIC PROTEINS IN LEAVES OF GRAFTED CUCUMBER UNDER NACL STRESS


title: Acquiring Vocabulary Through Story- Songs 1

abstract: It is common practice for teachers of first and second language learners to read stories to children. By engaging in this practice, teachers not only model literacy skills, but they also cultivate listening skills and promote vocabulary acquisition. One particular type of story, the "story-song" is frequently used by educators of young children. The story-song is basically a poem with a story-line woven through it. Furthermore, because it has been set to music it can be sung rather than spoken. Yet, are story-songs simply novelty items or does the melodic element make an important instructional contribution? An empirical investigation of a group of 48 third grade second language learners measured the amount of vocabulary acquisition which was produced when two variables were investigated: illustrations and music. Thus subjects either heard a story which was (1) sung and illustrated, (2) sung, but not illustrated, (3) spoken and illustrated, or (4) spoken yet not illustrated. Subjects were exposed to their respective treatments for three minutes a day for three consecutive days. Apart from being pre-tested, they were post tested immediately after their last treatment and again two weeks later. The statistical analysis revealed that the illustrated spoken and sung stories were equally effective means of supporting language acquisition. However, close examination of the descriptive data revealed a definite bias in favor of the illustrated story-song. That is, second language learners who listened to the illustrated sung stories acquired an average of 1.5 words by the end of the treatment period, while those who heard the illustrated spoken rendition of the story acquired an average of 1.0 words. This difference of .5 words grew after two weeks. At that time, the average vocabulary acquired by the illustrated story-song group was 1.75 as opposed to the illustrated spoken story group's 1.08. As expected, the effects of music were greatest with the subjects who knew the least amount of vocabulary initially. After two weeks, beginners in the music-illustration group acquired an average of 3.33 vocabulary while low proficiency level learners in the no-music-illustration group averaged 1.5 vocabulary words. Although this research was conducted on a relatively small number of subjects, it points to the positive effects which music may have upon language acquisition. More specifically, it suggests that illustrated story-songs may produce greater vo


title: Features Extraction of Electromyography Signals in Time Domain on Biceps Brachii Muscle

abstract: Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Since EMG signals contain a wealth of information about muscle functions, there are many approaches in analyzing the EMG signals. It is important to know the features that can be extracting from the EMG signal. The ideal feature is important for the achievement in EMG analysis. Hence, the objective of this paper is to evaluate the features extraction of time domain from the EMG signal. The experiment was setup according to surface electromyography for noninvasive assessment of muscle (SENIAM). The recorded data was analyzed in time domain to get the features. Based on the analysis, three features have been considered based on statistical features. The features was then been evaluate by getting the percentage error of each feature. The less percentage error determines the ideal feature. The results shows that the extracted features of the EMG signals in time domain can be implement in signal classification. These findings could be integrated to design a signal classification based on the features extraction.


title: Volume comparison of DC-DC converters for electric vehicles

abstract: One of the main problems in autonomous electric vehicles is the volume of the electrical systems, because bulky components carry additional mass and high cost to the total system. Consequently, Interleaving phases and magnetic coupling techniques have been reported as effective methods for increasing the power density of the DC-DC converters that interface the storage unit with the electric motor. However, there are several converter topologies that use these techniques. Therefore, a volume assessment of these topologies is required in order to have a complete understanding when an electric power train is designed. In this paper, a volume modeling methodology is introduced with the purpose of comparing four different DC-DC converter topologies: Single-Phase Boost, Two-Phase Interleaved with non-coupled inductor, Loosely Coupled Inductor (LCI) and Integrated Winding Coupled Inductor (IWCI). This analysis considers the volume of magnetic components, power devices (conventional and next-generation), cooling devices and capacitors. As a result, interleaving phases and magnetic coupling techniques were validated as effective to downsize power converters. In particular, it was found that LCI and IWCI converters offer lower volume in comparison with other topologies.


title: Domain Aware Neural Dialog System

abstract: We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains. The domain, in this case, is the topic or theme of the conversation. To achieve this, we present DOM-Seq2Seq, a domain aware neural network model based on the novel technique of using domain-targeted sequence-to-sequence models (Sutskever et al., 2014) and a domain classifier. The model captures features from current utterance and domains of the previous utterances to facilitate the formation of relevant responses. We evaluate our model on automatic metrics and compare our performance with the Seq2Seq model.


UAV Formation Flight Based on Nonlinear Model Predictive Control


title: DART: A Practical Reconstruction Algorithm for Discrete Tomography

abstract: In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.


title: Physiological-Based Affect Event Detector for Entertainment Video Applications

abstract: In this paper, we propose a methodology to build a real-time affect detector dedicated to video viewing and entertainment applications. This detector combines the acquisition of traditional physiological signals, namely, galvanic skin response, heart rate, and electromyogram, and the use of supervised classification techniques by means of Gaussian processes. It aims at detecting the emotional impact of a video clip in a new way by first identifying emotional events in the affective stream (fast increase of the subject excitation) and then by giving the associated binary valence (positive or negative) of each detected event. The study was conducted to be as close as possible to realistic conditions by especially minimizing the use of active calibrations and considering on-the-fly detection. Furthermore, the influence of each physiological modality is evaluated through three different key-scenarios (mono-user, multi-user and extended multi-user) that may be relevant for consumer applications. A complete description of the experimental protocol and processing steps is given. The performances of the detector are evaluated on manually labeled sequences, and its robustness is discussed considering the different single and multi-user contexts.


title: IncobotulinumtoxinA in aesthetics: Russian multidisciplinary expert consensus recommendations

abstract: Background ::: Although there are various international consensus recommendations on the use of botulinum neurotoxin type A (BoNT/A) in facial aesthetics, there are no global or Russian guidelines on the optimal dose of incobotulinumtoxinA, free from complexing proteins, within specific aesthetic indications. This article reports the outcomes of two expert consensus meetings, conducted to review and analyze efficacy and tolerability data for incobotulinumtoxinA in various facial aesthetic indications and to give expert consensus recommendations to ensure best clinical practice among Russian clinicians.


title: Osteoclasts in arthritis and Th17 cell development.

abstract: Bone destruction associated with rheumatoid arthritis (RA) is mainly attributed to the abnormal activation of osteoclasts, which are terminally differentiated cells of monocyte/macrophage lineage that resorb bone matrix. Studies on the immune regulation of osteoclasts in RA have promoted the new research field of "osteoimmunology", which investigates the interplay of the skeletal and immune systems at the molecular level. This interdisciplinary field is proving to be crucial to advances in the treatment of diseases associated with the bone and/or immune systems. As for the mechanisms of the bone destruction found in RA, accumulating evidence lends support to the theory that interleukin (IL)-17-producing helper T (Th17) cells induce the expression of receptor activator of nuclear factor-κB ligand (RANKL) in synovial cells, which in turn stimulates the differentiation and activation of osteoclasts together with inflammatory cytokines. Thus, inhibition of Th17 is potentially beneficial for the amelioration of the bone damage which occurs in RA. A recent study revealed that IκBζ is essential to the development of Th17 cells. These findings comprise an important advance in our understanding of the pathogenesis of RA and potentially effective therapeutic strategies.


title: Deep Feature Compression for Collaborative Object Detection

abstract: Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compression of feature data, which has not been examined to date. In this work we focus on collaborative object detection and study the impact of both near-lossless and lossy compression of feature data on its accuracy. We also propose a strategy for improving the accuracy under lossy feature compression. Experiments indicate that using this strategy, the communication overhead can be reduced by up to 70% without sacrificing accuracy.


title: Simultaneous Camera Pose and Correspondence Estimation with Motion Coherence

abstract: Traditionally, the camera pose recovery problem has been formulated as one of estimating the optimal camera pose given a set of point correspondences. This critically depends on the accuracy of the point correspondences and would have problems in dealing with ambiguous features such as edge contours and high visual clutter. Joint estimation of camera pose and correspondence attempts to improve performance by explicitly acknowledging the chicken and egg nature of the pose and correspondence problem. However, such joint approaches for the two-view problem are still few and even then, they face problems when scenes contain largely edge cues with few corners, due to the fact that epipolar geometry only provides a "soft" point to line constraint. Viewed from the perspective of point set registration, the point matching process can be regarded as the registration of points while preserving their relative positions (i.e. preserving scene coherence). By demanding that the point set should be transformed coherently across views, this framework leverages on higher level perceptual information such as the shape of the contour. While thus potentially allowing registration of non-unique edge points, the registration framework in its traditional form is subject to substantial point localization error and is thus not suitable for estimating camera pose. In this paper, we introduce an algorithm which jointly estimates camera pose and correspondence within a point set registration framework based on motion coherence, with the camera pose helping to localize the edge registration, while the "ambiguous" edge information helps to guide camera pose computation. The algorithm can compute camera pose over large displacements and by utilizing the non-unique edge points can recover camera pose from what were previously regarded as feature-impoverished SfM scenes. Our algorithm is also sufficiently flexible to incorporate high dimensional feature descriptors and works well on traditional SfM scenes with adequate numbers of unique corners.


title: A novel connected PIFA array with MIMO configuration for 5G mobile applications

abstract: A novel connected printed inverted-F antenna (PIFA) array with multiple-input multiple-output (MIMO) configuration is presented. The array is designed to operate at the 28 GHz band for 5G mobile applications. It consists of 4-element MIMO antenna system where each element includes 8 PIFA antennas forming a connected antenna array. A 1×8 power divider/combiner in the form of tapered T-junction is used to excite that array. The array operates at 28 GHz with 10 dB bandwidth of 1 GHz from 27.5 to 28.5 GHz. The proposed design is modeled on RO-4350B with the height of 0.76 mm and dielectric constant of 3.48. The dimensions of the substrate are 130×68×0.76 mm3, which matches the dimensions of modern smart phones. The proposed design is simple in structure, low profile and has a compact size that is suitable for mobile handsets.


title: Visible forceps manipulator with novel linkage bending mechanism for neurosurgery

abstract: In minimally invasive surgery (MIS), especially the neurosurgery, surgeons often suffer from occlusion region problem. Common surgical instruments, like endoscope and corresponding operating tools, are hard to solve it due to their size and rigid mechanical structure. In this paper, we present a visible forceps manipulator with novel linkage bending mechanism, which realizes the flexible bending capability and high output force, as well as the integrated endoscopic function. We present the simplified experiment to evaluate the results of mechanical performance and brain phantom test to evaluate feasibility and usefulness in neurosurgery. Preliminary results show that phantom experiments using the brain phantom verify the feasibility of the novel manipulator.


title: Using Scrum to guide the execution of software process improvement in small organizations

abstract: For software process improvement - SPI - there are few small organizations using models that guide the management and deployment of their improvement initiatives. This is largely because a lot of these models do not consider the special characteristics of small businesses, nor the appropriate strategies for deploying an SPI initiative in this type of organization. It should also be noted that the models which direct improvement implementation for small settings do not present an explicit process with which to organize and guide the internal work of the employees involved in the implementation of the improvement opportunities. In this paper we propose a lightweight process, which takes into account appropriate strategies for this type of organization. Our proposal, known as a ''Lightweight process to incorporate improvements'', uses the philosophy of the Scrum agile method, aiming to give detailed guidelines for supporting the management and performance of the incorporation of improvement opportunities within processes and their putting into practice in small companies. We have applied the proposed process in two small companies by means of the case study research method, and from the initial results, we have observed that it is indeed suitable for small businesses.


title: Selection of K in K-means clustering

abstract: AbstractThe K-means algorithm is a popular data-clustering algorithm. However, one of its drawbacks is the requirement for the number of clusters, K, to be specified before the algorithm is applied. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Factors that affect this selection are then discussed and a new measure to assist the selection is proposed. The paper concludes with an analysis of the results of using the proposed measure to determine the number of clusters for the K-means algorithm for different data sets.


title: Origins of Human Cooperation and Morality

abstract: From an evolutionary perspective, morality is a form of cooperation. Cooperation requires individuals either to suppress their own self-interest or to equate it with that of others. We review recent research on the origins of human morality, both phylogenetic (research with apes) and ontogenetic (research with children). For both time frames we propose a two-step sequence: first a second-personal morality in which individuals are sympathetic or fair to particular others, and second an agent-neutral morality in which individuals follow and enforce group-wide social norms. Human morality arose evolutionarily as a set of skills and motives for cooperating with others, and the ontogeny of these skills and motives unfolds in part naturally and in part as a result of sociocultural contexts and interactions.


title: Self-Selection and the Small Firm Worker-A Critique and an Alternative View

abstract: The thesis that some manual workers gradually develop a stable set of orientations which leads them to choose to work for small rather than large firms is tested against data from a study of small ...


title: Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents

abstract: Psychlab is a simulated psychology laboratory inside the first-person 3D game world of DeepMind Lab (Beattie et al. 2016). Psychlab enables implementations of classical laboratory psychological experiments so that they work with both human and artificial agents. Psychlab has a simple and flexible API that enables users to easily create their own tasks. As examples, we are releasing Psychlab implementations of several classical experimental paradigms including visual search, change detection, random dot motion discrimination, and multiple object tracking. We also contribute a study of the visual psychophysics of a specific state-of-the-art deep reinforcement learning agent: UNREAL (Jaderberg et al. 2016). This study leads to the surprising conclusion that UNREAL learns more quickly about larger target stimuli than it does about smaller stimuli. In turn, this insight motivates a specific improvement in the form of a simple model of foveal vision that turns out to significantly boost UNREAL's performance, both on Psychlab tasks, and on standard DeepMind Lab tasks. By open-sourcing Psychlab we hope to facilitate a range of future such studies that simultaneously advance deep reinforcement learning and improve its links with cognitive science.


title: Mining term association patterns from search logs for effective query reformulation

abstract: Search engine logs are an emerging new type of data that offers interesting opportunities for data mining. Existing work on mining such data has mostly attempted to discover knowledge at the level of queries (e.g., query clusters). In this paper, we propose to mine search engine logs for patterns at the level of terms through analyzing the relations of terms inside a query. We define two novel term association patterns (i.e., context-sensitive term substitutions and term additions) and propose new methods for mining such patterns from search engine logs. These two patterns can be used to address the mis-specification and under-specification problems of ineffective queries. Experiment results on real search engine logs show that the mined context-sensitive term substitutions can be used to effectively reword queries and improve their accuracy, while the mined context-sensitive term addition patterns can be used to support query refinement in a more effective way.


title: An Analysis of the Energy Consumption of LPWA-based IoT Devices

abstract: The unique challenges posed by the breadth of Internet of Things applications have resulted in the development of a number of different Low Power Wide Area wireless solutions. These technologies enable scalable long range networks on cheap low power devices, facilitating the development of a ubiquitous Internet of Things. The energy efficiency of these wireless technologies have a significant impact on battery lifetime. In this paper we propose an approach to energy efficiency calculations suited to this new paradigm by focusing on daily throughput. We present a set of deployment cases, develop energy models to represent each of the technologies studied, and use these models to provide a thorough comparison in terms of predicted device lifetime for a range of daily throughputs. This quantitative analysis of network device efficiency vs. daily throughput enables identification of the changeover point between optimal solutions. Our contributions are the integration of different energy models that have not been previously compared into a common framework, and the identification of the energy-efficiency crossover points between these models. This enables the selection of the most efficient wireless solution for specific Internet of Things applications, which is a key factor in optimising device lifetime.


title: Evaluation of the correlation between blood pressure and pulse transit time

abstract: The arterial blood pressure is an essential physiological parameter for health monitoring. Most blood measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This method is uncomfortable to the user and may cause anxiety which in turns can affect the blood pressure (white coat syndrome). This paper investigates a cuff-less non-intrusive approach to measure arterial blood pressure that is suitable for continuous measurement. The approach is based on measuring the delay between the R-peak of the electrocardiogram (ECG) signal and the peaks of the finger photoplethysmograph (PPG) signal. The results of this paper show a high correlation between the blood pressure and the pulse transit time (PTT). Different polynomial regressions are applied for further estimation. The paper uses actual ECG, PPG and blood pressure measurements extracted from the MIMIC database that contains clinical signal data reflecting real measurements. The simulation results verify that the delay (PTT) between the R-peak of the ECG signal and the peaks of the finger PPG signal have a high correlation with arterial blood pressure and can be used as an indicator of the arterial blood pressure.


Interactive Form-Finding of Elastic Origami


title: Molecular Nutrition Research—The Modern Way Of Performing Nutritional Science

abstract: In spite of amazing progress in food supply and nutritional science, and a striking increase in life expectancy of approximately 2.5 months per year in many countries during the previous 150 years, modern nutritional research has a great potential of still contributing to improved health for future generations, granted that the revolutions in molecular and systems technologies are applied to nutritional questions. Descriptive and mechanistic studies using state of the art epidemiology, food intake registration, genomics with single nucleotide polymorphisms (SNPs) and epigenomics, transcriptomics, proteomics, metabolomics, advanced biostatistics, imaging, calorimetry, cell biology, challenge tests (meals, exercise, etc.), and integration of all data by systems biology, will provide insight on a much higher level than today in a field we may name molecular nutrition research. To take advantage of all the new technologies scientists should develop international collaboration and gather data in large open access databases like the suggested Nutritional Phenotype database (dbNP). This collaboration will promote standardization of procedures (SOP), and provide a possibility to use collected data in future research projects. The ultimate goals of future nutritional research are to understand the detailed mechanisms of action for how nutrients/foods interact with the body and thereby enhance health and treat diet-related diseases.


title: Path planning based on Constrained Delaunay Triangulation

abstract: This paper proposes a path planning algorithm for determining an optimal path with respect to the costs of a dual graph on the Constrained Delaunay Triangulation (CDT) of an environment. The advantages of using triangles for environment expression are: less data storage required, available mature triangulation methods and consistent with a potential motion planning framework. First we represent the polygon environment as a planar straight line graph (PSLG) described as a collection of vertices and segments, and then we adopt the CDT to partition the environment into triangles. Then on this CDT of the environment, a dual graph is constructed following the target attractive principle in order to avoid the nonoptimal paths caused by the different geometric size of the triangles. Correspondingly, a path planning algorithm via A* search algorithm finds an optimal path on the real-time building dual graph. In addition, completeness and optimization analysis of the algorithm is given. The simulation results demonstrate the effectiveness and optimization of the algorithm.


title: Enhanced anomaly detection in wire ropes by combining structure and appearance

abstract: Automatic visual surface inspection is a challenging task, which has become important for quality assurance in the last years. Wire rope inspection is a special problem within this field. Usually, the huge and heavy ropes cannot be detached. Thus, an inspection of the ropes must be conducted, while the ropes are in use. The rope surface exhibits various appearance characteristics so that the existing, purely appearance-based approaches tend to fail. We explicitly integrate information about the object geometry, which we obtain by aligning a sequence of 2d rope images with a perfectly regular 3d model of the rope. The rendering equation is used to link object geometry to the observed rope appearance. Based on the connection between geometry and surface appearance we build a probabilistic appearance model which serves as representation for normal surface variations. A robust localization of rope surface defects is achieved by means of anomaly detection. The presented approach has no need for knowledge about the illumination setting or the reflectance properties of the material. An evaluation on real-world data from ropeways leads to an accuracy comparable to that of a human expert. With an accuracy of 95% and a false-alarm-rate of 1.5% the approach outperforms all other existing approaches.


title: A service-oriented Cloud-based management system for the Internet-of-Drones

abstract: Deploying drones over the Cloud is an emerging research area motivated by the emergence of Cloud Robotics and the Internet-of-Drones (IoD) paradigms. This paper contributes to IoD and to the deployment of drones over the cloud. It presents, Dronemap Planner, an innovative service-oriented cloud based drone management system that provides access to drones through web services (SOAP and REST), schedule missions and promotes collaboration between drones. A modular cloud proxy server was developed; it acts as a moderator between drones and users. Communication between drones, users and the Dronemap Planner cloud is provided through the MAVLink protocol, which is supported by commodity drones. To demonstrate the effectiveness of Dronemap Planner, we implemented and validated it using simulated and real MAVLink-enabled drones, and deployed it on a public cloud server. Experimental results show that Dronemap Planner is efficient in virtualizing the access to drones over the Internet, and provides developers with appropriate APIs to easily program drones' applications.


title: Implementation and Performance Analysis of a Video Streaming System using WEBRTC and Equal Cost Load Balancing

abstract: Real-time Internet based video communication applications are becoming widely adopted by internet users. However, the exponentially rising numbers of internet users is proving to be a real challenge to guarantee a good Quality of Service (QoS) for such applications. This paper investigates how video streaming quality is impacted by the increasing number of users using a video streaming application developed with Web Real Time Communications (WebRTC). The application considers both live video streaming from of an event captured by the initiator’s webcam and live streaming of a video file from the initiator’s device. Tests were performed using a Wi-Fi connection with different types of video sources. Moreover, equal cost load balancing was used to assess the impact on the video quality two servers are used at the initiator’s site. Videos were recorded for five minutes on both the initiator and client sides using the application’s record function and the Peak Signal to Noise Ratio (PSNR) was used to assess the quality of the videos. Results showed that with equal cost load balancing using two servers a maximum gain of up to 5.39 dB in PSNR could be obtained with live streaming from a webcam to ten clients.


title: A fast RRT algorithm for motion planning of autonomous road vehicles

abstract: The Rapidly-exploring Random Tree (RRT) is a classical algorithm of motion planning based on incremental sampling, which is widely used to solve the planning problem of mobile robots. But it, due to the meandering path, the inaccurate terminal state and the slow exploration, is often inefficient in many applications such as autonomous road vehicles. To address these issues and considering the realistic context of autonomous road vehicles, this paper proposes a fast RRT algorithm that introduces an off-line template set based on the traffic scenes and an aggressive extension strategy of search tree. Both improvements can lead to a faster and more accurate RRT towards the goal. Meanwhile, the authors approach combines the closed-loop prediction approach using the model of vehicle, which can smooth the portion of off-line template and the portion of on-line tree generated, while a trajectory and control sequence for the vehicle would be obtained. Experimental results illustrate that the authors method is fast and efficient in solving planning queries of autonomous road vehicle in urban environments.


title: Fast Digital TRNG Based on Metastable Ring Oscillator

abstract: In this paper, a new true random number generator (TRNG), based entirely on digital components is proposed. The design has been implemented using a fast random number generation method, which is dependent on a new type of ring oscillator with the ability to be set in metastable mode. Earlier methods of random number generation involved employment of jitter, whereas the proposed method leverages the metastability phenomenon in digital circuits and applies it to a ring oscillator. The new entropy employment method allows an increase in the TRNG throughput by significantly reducing the required entropy accumulating time. Samples obtained from simulation of TRNG design have been evaluated using AIS.31 and FIPS 140-1/2 statistical tests. The results of these tests have proven the high quality of generated data. Corners analysis of the TRNG design was also performed to estimate the robustness to technology process and environment variations. Investigated in FPGA technology, phase distribution highlighted the advantages of the proposed method over traditional architectures.


title: OPTIMIZING GENERAL PURPOSE COMPUTATIONS USING KEPLER BASED GRAPHICS ACCELERATORS

abstract: The programming of GPUs (Graphics Processing Units) is ready for practical applications; the largest industry players (including research centres, financial and analyst corporations) have already announced that they use these new devices for high computing applications. There are several well-known areas, like image processing, simulations and obviously 3D graphics, where we can use these devices very efficiently. In this paper, we would like to show, that beyond these well-known topics, GPU programming is able to speed-up more general purpose applications. The key is the data parallel nature of the algorithm, and the minimization of data transfers between CPU and GPU.


title: Segment-aware energy-efficient management of heterogeneous memory system for ultra-low-power IoT devices

abstract: The emergence of IoT (Internet of Things) has brought various studies on low-power techniques back to embedded systems. In general, minimizing power consumed by program executions is the main consideration of system design. While running programs, executing data-dependent program code results in a large number of memory accesses which consume huge amount of power. Further, most embedded systems consist of multiple types of memory devices, i.e., heterogeneous memory system, to benefit the different characteristics of memory devices. In this paper, we conduct a research on low-power techniques to reduce the power consumption of heterogeneous memory to achieve ultra-low-power in the system level. This study proposes a segment-aware energy-efficient management to improve the power efficiency considering the characteristics and structures of the memory devices. In the proposed approach, the technique migrates program code from allocated memory device to another if the consumed power is considered to be less. We also analyze and evaluate the comprehensive effects on energy efficiency by applying the technique as well. Compared to the unmodified program code, our model reduces power consumption up to 12.98% by migrating functions.


title: Why do academics engage with industry? The entrepreneurial university and individual motivations

abstract: The debate on the entrepreneurial university has raised questions about what motivates academic scientists to engage with industry. This paper provides evidence based on survey data for a large sample of UK investigators in the physical and engineering sciences. The results suggest that most academics engage with industry to further their research rather than to commercialize their knowledge. However, there are differences in terms of the channels of engagement. Patenting and spin-off company formation are motivated exclusively by commercialization whilst joint research, contract research and consulting are strongly informed by research-related motives. We conclude that policy should refrain from overly focusing on monetary incentives for industry engagement and consider a broader range of incentives for promoting interaction between academia and industry.


title: Velodyne-based curb detection up to 50 meters away

abstract: Long range curb detection is crucial for an Autonomous Land Vehicle (ALV) navigation in urban environments. This paper presents a novel curb detection algorithm which can detect the curbs up to 50 meters away with Velodyne LIDAR. Instead of building a Digital Elevation Map (DEM) and utilizing geometric features (like normal direction) to extract candidate curb points, we take each scan line of Velodyne LIDAR as a processing unite directly. Some feature points, which are extracted from individual scan lines, are selected as the initial curb points by the distance criterion and Hough Transform (HT). Eventually, iterative Gaussian Process Regression (GPR), which utilizes the above initial curb points as the initial seeds, is exploited to represent both the curved and straight-line curb model. In order to verify the effectiveness of our algorithm quantitatively, 2934 Velodyne scans are collected in various urban scenes with our ALV, and 566 of them are labelled manually1. Our algorithm is also compared with two other curb detection techniques. The experimental results on the dataset show promising performance.