title: Mining interesting locations and travel sequences from GPS trajectories

abstract: The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.


title: Stakeholder Engagement: Achieving Sustainability in the Construction Sector

abstract: Achieving sustainability-related targets in construction projects is increasingly becoming a key performance driver. Yet sustainability is a complex concept in projects and there are many diverse stakeholders. Some stakeholders are generally recognized as important, i.e., the client and main contractor, yet there are others not always perceived as such and whose absence from the decision-making processes may result in a failure to address sustainability issues. Hence there is a need for a systematic approach to engage with stakeholders with high salience in relation to sustainability. This paper reports the results of an exploratory study involving interviews with construction project practitioners that are involved in sustainability in some way. Data were collected from the practitioners in terms of the processes for engaging with stakeholders to deliver sustainability. The data suggests six steps to a stakeholder engagement process: (i) identification; (ii) relating stakeholders to different sustainability-related targets; (iii) prioritization; (iv) managing; (v) measuring performance; and (vi) putting targets into action. The results suggest that understanding the different sustainability agendas of stakeholders and measuring their performance using key performance indicators are important stages to be emphasized in any stakeholder engagement process to achieve sustainability-related goals.


title: Image Classification using Support Vector Machine and Artificial Neural Network

abstract: Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. Firstly, we separate the image into many sub-images based on the features of images. Each sub-image is classified into the responsive class by an ANN. Finally, SVM has been compiled all the classify result of ANN. Our proposal classification model has brought together many ANN and one SVM. Let it denote ANN_SVM. ANN_SVM has been applied for Roman numerals recognition application and the precision rate is 86%. The experimental results show the feasibility of our proposal model.


title: Assessing the Impact of Service Workers on the Energy Efficiency of Progressive Web Apps

abstract: Context. Mobile web apps represent a large share of the Internet today. However, they still lag behind native apps in terms of user experience. Progressive Web Apps (PWAs) are a new technology introduced by Google that aims at bridging this gap, with a set of APIs known as service workers at its core. Goal. In this paper, we present an empirical study that evaluates the impact of service workers on the energy efficiency of PWAs, when operating in different network conditions on two different generations of mobile devices. Method. We designed an empirical experiment with two main factors: the use of service workers and the type of network available (2G or WiFi). We performed the experiment by running a total of 7 PWAs on two devices (an LG G2 and a Nexus 6P) that we evaluated as blocking factor. Our response variable is the energy consumption of the devices. Results. Our results show that service workers do not have a significant impact over the energy consumption of the two devices, regardless of the network conditions. Also, no interaction was detected between the two factors. However, some patterns in the data show different behaviors among PWAs. Conclusions. This paper represents a first empirical investigation on PWAs. Our results show that the PWA and service workers technology is promising in terms of energy efficiency.


title: Millimeter-Wave Electronically Steerable Integrated Lens Antennas for WLAN/WPAN Applications

abstract: This paper presents design and experimental verification of electronically steerable integrated lens antennas (ILAs) for WLAN/WPAN communication systems operating in the 60-GHz frequency band. The antenna is comprised of a quartz extended hemispherical lens, four switched aperture coupled microstrip antenna (ACMA) elements, and a distribution circuit based on SPDT MMIC switches. The designed ILAs are capable of electronic steering between four different antenna main beam directions in one plane. Fixed beam and electronically steerable ILA prototypes are fabricated and tested. The results are given for two quartz dielectric lenses with the radii of 7.5 and 12.5 mm in order to meet a wide range of WLAN/WPAN requirements. The measured maximum gains of the designed ILAs are 18.4 and 23.2 dBi. The experimental results of the fabricated electronically steerable quartz ILA prototypes prove the simulation results and show $\pm {\hbox{35}}^{\circ}$ and $\pm {\hbox{22}}^{\circ}$ angle sector coverage for the lenses with the 7.5 and 12.5 mm radii, respectively. The bandwidth of the ILAs exceeds the frequency band of 57–66 GHz allocated for WLAN/WPAN applications. The designed ILAs meet all the requirements for steerable directional antennas of 60-GHz WLAN/WPAN systems.


Soil erosion and Conservation. Third edition


title: An IDMS approach towards privacy and new requirements in IoT

abstract: Identities are known as the most sensitive information. With the increasing number of connected objects and identities (a connected object may have one or many identities), the computing and communication capabilities improved to manage these connected devices and meet the needs of this progress. Therefore, new IoT Identity Management System (IDMS) requirements have been introduced. In this work, we suggest an IDMS approach to protect private information and ensures domain change in IoT for mobile clients using a personal authentication device. Firstly, we present basic concepts, existing requirements and limits of related works. We also propose new requirements and show our motivations. Next, we describe our proposal. Finally, we give our security approach validation, perspectives, and some concluding remarks.


title: ViST: a dynamic index method for querying XML data by tree structures

abstract: With the growing importance of XML in data exchange, much research has been done in providing flexible query facilities to extract data from structured XML documents. In this paper, we propose ViST, a novel index structure for searching XML documents. By representing both XML documents and XML queries in structure-encoded sequences, we show that querying XML data is equivalent to finding subsequence matches. Unlike index methods that disassemble a query into multiple sub-queries, and then join the results of these sub-queries to provide the final answers, ViST uses tree structures as the basic unit of query to avoid expensive join operations. Furthermore, ViST provides a unified index on both content and structure of the XML documents, hence it has a performance advantage over methods indexing either just content or structure. ViST supports dynamic index update, and it relies solely on B+ Trees without using any specialized data structures that are not well supported by DBMSs. Our experiments show that ViST is effective, scalable, and efficient in supporting structural queries.


title: LOPES: a lower extremity powered exoskeleton

abstract: Due to aging of the population, growing numbers of people are affected by impairments of their motor system, caused by disorders like stroke. Treatment of stroke patients is very intensive in the amount of training per patient per day. The treatment duration per patient combined with the total number of people suffering from a stroke makes rehabilitation therapy extremely costly. This makes devices that could reduce the costs of therapy attractive to rehabilitation centres. Currently there are robotic rehabilitation devices under development for both the upper and lower extremities. Our group focuses mainly on the recovery of gait after stroke. The LOPES robot is designed for use in training on a treadmill to accommodate for limited space of rehabilitation centres and easy access for the therapist to the patient. As a 'robotic therapist' it is meant to make rehabilitation more effective for patients and less demanding for therapists.


title: The World's Health Care Crisis: From the Laboratory Bench to the Patient's Bedside

abstract: At present, human society is facing a health care crisis that is affecting patients worldwide. In the United States, it is generally believed that the major problem is lack of affordable access to health care (i.e. health insurance). This book takes an unprecedented approach to address this issue by proposing that the major problem is not lack of affordable access to health care per se, but lack of access to better, safer, and more affordable medicines. The latter problem is present not only in the United States and the developing world but also in countries with socialized health care systems, such as Europe and the rest of the industrialized world. This book provides a comparative analysis of the health care systems throughout the world and also examines the biotechnology and pharmaceutical industries. It examines the health care structure of the United States, Europe, and the third world, both separately and comparatively. It offers primary source insight through in-depth interviews with pharmaceutical and health care industry leaders from around the world. It carefully explains, in clear terms, the intricacies of the health care and pharmaceutical system and how these intricacies have led to the current crisis. It also offers concrete, comprehensive solutions to the health care crisis.


title: On the Feasibility of Malware Authorship Attribution

abstract: There are many occasions in which the security community is interested to discover the authorship of malware binaries, either for digital forensics analysis of malware corpora or for thwarting live threats of malware invasion. Such a discovery of authorship might be possible due to stylistic features inherent to software codes written by human programmers. Existing studies of authorship attribution of general purpose software mainly focus on source code, which is typically based on the style of programs and environment. However, those features critically depend on the availability of the program source code, which is usually not the case when dealing with malware binaries. Such program binaries often do not retain many semantic or stylistic features due to the compilation process. Therefore, authorship attribution in the domain of malware binaries based on features and styles that will survive the compilation process is challenging. This paper provides the state of the art in this literature. Further, we analyze the features involved in those techniques. By using a case study, we identify features that can survive the compilation process. Finally, we analyze existing works on binary authorship attribution and study their applicability to real malware binaries.


title: DR. Swap: energy-efficient paging for smartphones

abstract: Smartphones are becoming increasingly energy-hungry to support feature-rich applications, posing a lot of pressure on battery lifetime and making energy consumption a non-negligible issue. In particular, DRAM is among the most demanding components in energy consumption. In this paper, we propose DR. Swap, an energy-efficient paging design to reduce energy consumption in smartphones. We adopt emerging energy-efficient non-volatile memory (NVM) and use it as the swap area. Utilizing NVM's byte-addressability, we propose direct read which guarantees zero-copy for read-only pages in the swap area. Experimental results based on the Google Nexus 5 smartphone show that our technique can effectively reduce energy consumption.


title: Less is More: Zero-Shot Learning from Online Textual Documents with Noise Suppression

abstract: Classifying a visual concept merely from its associated online textual source, such as a Wikipedia article, is an attractive research topic in zero-shot learning because it alleviates the burden of manually collecting semantic attributes. Several recent works have pursued this approach by exploring various ways of connecting the visual and text domains. This paper revisits this idea by stepping further to consider one important factor: the textual representation is usually too noisy for the zero-shot learning application. This consideration motivates us to design a simple-but-effective zero-shot learning method capable of suppressing noise in the text. ::: More specifically, we propose an $l_{2,1}$-norm based objective function which can simultaneously suppress the noisy signal in the text and learn a function to match the text document and visual features. We also develop an optimization algorithm to efficiently solve the resulting problem. By conducting experiments on two large datasets, we demonstrate that the proposed method significantly outperforms the competing methods which rely on online information sources but without explicit noise suppression. We further make an in-depth analysis of the proposed method and provide insight as to what kind of information in documents is useful for zero-shot learning.


title: Changes in Lung Volumes and Poor Perception of Bronchoconstriction-Induced Respiratory Symptoms

abstract: Background Changes in lung volumes may contribute to the perception of bronchoconstriction-induced breathlessness. Aim To verify whether histamine-induced increases in lung volumes are more marked in asthmatic subjects with normal compared with low symptom perception scores and whether changes in lung volumes are detected similarly in both groups of subjects. Methods A group of 16 asthmatic subjects with low (≤1) breathlessness score (scale: 0 to 10) at 20% fall in FEV 1 (PS 20 B) on at least one of 2 histamine inhalation tests (HIT) performed on separate days (hypoperceivers) were studied. They were compared with a group of 12 asthmatic patients with a PS 20 B >1 on the 2 tests (normoperceivers). Lung volumes were measured before and after the second HIT and changes correlated with PS 20 B and PS 20 for the sensation of chest tightness (PS 20 CT) and with the slopes of these perception scores/% fall in FEV 1 (PS B or CT/deltaFEV 1 ). Results Hypoperceivers had a mean (±sem) PS 20 B of 1.03 ± 0.20 on HIT1 and 0.66 ± 0.12 on HIT2 (P > .05) and normoperceivers 2.59 ± 0.14 on HIT1 and 2.38 ± 0.18 on HIT2 (P > .05). Subjects' age, gender, duration of asthma, medication needs, and baseline and post-HIT pulmonary function (FEV 1 , FVC, FRC) were not significantly different between the two groups. Contrary to PS 20 B (P 20 for chest tightness (PS 20 CT) was similar in the two groups (HIT 1/2; hypoperceivers: 1.41/1.07, normoperceivers: 1.57/1.99, both P > .05). Perception of bronchoconstriction-induced breathlessness was not correlated with the degree of reduction in FVC at 20% fall in FEV 1 . In normoperceivers, (PS B/deltaFEV 1 ) was significantly correlated with the postchallenge increase in FRC (rs = 0.63, P = .036) although there was no significant correlation in hypoperceivers (rs = −0.041, P > .05). Conclusion Changes in lung volumes were similar in hypoperceivers and hyperperceivers of bronchoconstriction. Perception of breathlessness, however, was positively correlated with changes in lung volumes in normoperceivers but not in hypoperceivers suggesting that normoperceivers have an ability to detect these changes, presumably through proprioception, ability that is lacking or lost in hypoperceivers. More subjects should be studied to confirm this finding.


title: Adaptive Overload Control for Busy Internet Servers

abstract: As Internet services become more popular and pervasive, a critical problem that arises is managing the performance of services under extreme overload. This paper presents a set of techniques for managing overload in complex, dynamic Internet services. These techniques are based on an adaptive admission control mechanism that attempts to bound the 90th-percentile response time of requests flowing through the service. This is accomplished by internally monitoring the performance of the service, which is decomposed into a set of event-driven stages connected with request queues. By controlling the rate at which each stage admits requests, the service can perform focused overload management, for example, by filtering only those requests that lead to resource bottlenecks. We present two extensions of this basic controller that provide class-based service differentiation as well as application-specific service degradation. We evaluate these mechanisms using a complex Web-based e-mail service that is subjected to a realistic user load, as well as a simpler Web server benchmark.


title: On Clustering fMRI Time Series

abstract: Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do not indicate whether sets of voxels are activated in a similar way or in different ways. Typically, delays between two activated signals are not identified. In this article, we use clustering methods to detect similarities in activation between voxels. We employ a novel metric that measures the similarity between the activation stimulus and the fMRI signal. We present two different clustering algorithms and use them to identify regions of similar activations in an fMRI experiment involving a visual stimulus. r 1999 Academic Press


title: Phase Noise and Jitter in CMOS Ring Oscillators

abstract: A simple, physically based analysis illustrate the noise processes in CMOS inverter-based and differential ring oscillators. A time-domain jitter calculation method is used to analyze the effects of white noise, while random VCO modulation most straightforwardly accounts for nicker (1/f) noise. Analysis shows that in differential ring oscillators, white noise in the differential pairs dominates the jitter and phase noise, whereas the phase noise due to flicker noise arises mainly from the tail current control circuit. This is validated by simulation and measurement. Straightforward expressions for period jitter and phase noise enable manual design of a ring oscillator to specifications, and guide the choice between ring and LC oscillator.


title: Substrate Integrated Waveguide Cavity Resonators for Complex Permittivity Characterization of Materials

abstract: A novel planar substrate integrated waveguide cavity resonator technique for measurement of complex permittivity is described, which has applications for dielectric measurement systems in the pharmaceutical industry. The high-Q resonant structure is a modernization of well-known measurement cells where the dielectric constant is deduced by cavity perturbation from the shift in resonant frequency and the change in the Q factor. The method uses extremely small amounts of a broad range of materials for accurate characterization. The ease of fabrication, low cost, and potential for integration with many other components on the same substrate allows it to be used in a disposable manner.


title: Using REST based protocol to enable ABAC within IoT systems

abstract: with the development of the Internet of Things (IoT) and the usage of low-powered devices (sensors and effectors), a large number of people are using IoT systems in their homes and businesses to have more control over their technology. However, a key challenge of IoT systems is data protection in case the IoT device is lost, stolen, or used by one of the owner's friends or family members. The problem studied here is how to protect the access to data of an IoT system. To solve the problem, an attribute-based access control (ABAC) mechanism is applied to give the system the ability to apply policies to detect any unauthorized entry. Finally, a prototype was built to test the proposed solution. The evaluation plan was applied on the proposed solution to test the performance of the system.


title: An Introduction to Boosting and Leveraging

abstract: We provide an introduction to theoretical and practical aspects of Boosting and Ensemble learning, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research. We begin with a short background concerning the necessary learning theoretical foundations of weak learners and their linear combinations. We then point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broad spectrum of problems. In order to increase the relevance of the paper to practitioners, we have added remarks, pseudo code, "tricks of the trade", and algorithmic considerations where appropriate. Finally, we illustrate the usefulness of Boosting algorithms by giving an overview of some existing applications. The main ideas are illustrated on the problem of binary classification, although several extensions are discussed.


title: Filling Knowledge Base Gaps for Distant Supervision of Relation Extraction

abstract: Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather employs existing knowledge bases to heuristically label a training corpus. However, previous work has failed to address the problem of false negative training examples mislabeled due to the incompleteness of knowledge bases. To tackle this problem, we propose a simple yet novel framework that combines a passage retrieval model using coarse features into a state-of-the-art relation extractor using multi-instance learning with fine features. We adapt the information retrieval technique of pseudorelevance feedback to expand knowledge bases, assuming entity pairs in top-ranked passages are more likely to express a relation. Our proposed technique significantly improves the quality of distantly supervised relation extraction, boosting recall from 47.7% to 61.2% with a consistently high level of precision of around 93% in the experiments.


title: Dissociative part-dependent biopsychosocial reactions to backward masked angry and neutral faces: An fMRI study of dissociative identity disorder

abstract: Objective ::: The Theory of Structural Dissociation of the Personality (TSDP) proposes that dissociative identity disorder (DID) patients are fixed in traumatic memories as “Emotional Parts” (EP), but mentally avoid these as “Apparently Normal Parts” of the personality (ANP). We tested the hypotheses that ANP and EP have different biopsychosocial reactions to subliminally presented angry and neutral faces, and that actors instructed and motivated to simulate ANP and EP react differently.


title: A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback

abstract: User profiles in collaborative filtering (CF) recommendation technique are built based on ratings given by users on a set of items. The most eminent shortcoming of the CF technique is the sparsity problem. This problem refers to the low ratio of rated items by users to the total number of available items; hence the quality of recommendation will be affected. Most researchers use implicit data as a solution for sparsity problem, to decrease the dependency of CF technique on the user's rating and this term is more common in this field. The aim of this research is to aggregate evidence on state of research and practice of CF and implicit data applying systematic literature review (SLR) which is a method for evidence-based software engineering (EBSE). EBSE has the potential value for synthesizing evidence and make this evidence available to practitioners and researchers with providing the best references and appropriate software engineering solutions for sparsity problem. We executed the standard systematic literature review method using a manual search in 5 prestigious databases and 38 studies were finally included for analyzing. This paper follows manifestation of Kitchenham's SLR guidelines and describes in a great detail the process of selecting and analyzing research papers. This paper is first academic systematic literature review of CF technique along with implicit data from user behaviors and activities to aggregate existing evidence as a synthesis of best quality scientific studies. The 38 research papers are categorized into eleven application fields (movie, shopping, books, Social systems, music and others) and six data mining techniques (dimensionality reduction, association rule, heuristic methods and other). According to the review results, neighborhood formation is a relevant aspect of CF and it can be improved with the use of user-item preference matrix as implicit feedback mechanism, the most common domains of CF are in e-commerce and movie software applications.


title: Emerging usage of plant-based coagulants for water and wastewater treatment.

abstract: A review of plant-based coagulant sources, processes, effectiveness and relevant coagulating mechanisms for treatment of water and wastewater is presented. These coagulants are, in general, used as point-of-use technology in less-developed communities since they are relatively cost-effective compared to chemical coagulants, can be easily processed in usable form and biodegradable. These natural coagulants, when used for treatment of waters with low-to-medium turbidity range (50–500 NTU), are comparable to their chemical counterparts in terms of treatment efficiency. Their application for industrial wastewater treatment is still at their infancy, though they are technically promising as coagulant for dyeing effluent as afforded by Yoshida intermolecular interactions. These natural coagulants function by means of adsorption mechanism followed by charge neutralization or polymeric bridging effect. Frequently studied plant-based coagulants include nirmali seeds (Strychnos potatorum), Moringa oleifera, tannin and cactus. Utilization of these coagulants represents important progress in sustainable environmental technology as they are renewable resources and their application is directly related to the improvement of quality of life for underdeveloped communities.


UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings.


title: Can blog communication dynamics be correlated with stock market activity?

abstract: In this paper, we develop a simple model to study and analyze communication dynamics in the blogosphere and use these dynamics to determine interesting correlations with stock market movement. This work can drive targeted advertising on the web as well as facilitate understanding community evolution in the blogosphere. We describe the communication dynamics by several simple contextual properties of communication, e.g. the number of posts, the number of comments, the length and response time of comments, strength of comments and the different information roles that can be acquired by people (early responders / late trailers, loyals / outliers). We study a "technology-savvy" community called Engadget (http://www.engadget.com). There are two key contributions in this paper: (a) we identify information roles and the contextual properties for four technology companies, and (b) we model them as a regression problem in a Support Vector Machine framework and train the model with stock movements of the companies. It is interestingly observed that the communication activity on the blogosphere has considerable correlations with stock market movement. These correlation measures are further cross-validated against two baseline methods. Our results are promising yielding about 78% accuracy in predicting the magnitude of movement and 87% for the direction of movement.


title: A FPGA based Steganographic System Implementing a Modern Steganalysis Resistant LSB Algorithm

abstract: Steganography differs from other data hiding techniques because it encodes secret message inside cover object in such a way that transmission of secret message also remains a secret. Widespread usage of digital images, lower computational complexity and better performance makes spatial domain steganographic algorithms well suited for hardware implementation, which are not very frequent. This work tries to implement a modern steganalysis resistant LSB algorithm on FPGA based hardware. The presented work also optimises various operations and elements from original one third probability algorithm with respect to hardware implementation. The target FPGA for the implementation is Xilinx SP605 board (Spartan 6 series XC6SLX45T FPGA). Stego images obtained by the implementation have been thoroughly examined for various qualitative and quantitative aspects, which are found to be at par with original algorithm.


title: A campus-level view of Netflix and Twitch: Characterization and performance implications

abstract: Video streaming is a major contributor to modern Internet traffic. In this paper, we use data collected from a campus edge network to characterize two popular video streaming services: Netflix and Twitch. While these video streaming services provide inherently different types of content, namely video-on-demand and live-streaming, they nonetheless exhibit many similarities in traffic patterns, protocol usage, content popularity, and growth. We identify seven similarities and differences, and discuss how these could be leveraged to improve streaming video content delivery on the Internet in the future.


title: A Systematic Approach For Kinematic Design Of Upper Limb Rehabilitation Exoskeletons

abstract: Kinematic structure of an exoskeleton is the most fundamental block of its design and is determinant of many functional capabilities of it. Although numerous upper limb rehabilitation devices have been designed in the recent years, there is not a framework that can systematically guide the kinematic design procedure. Additionally, diversity of currently available devices and the many minute details incorporated to address certain design requirements hinders pinpointing the core kinematics of the available devices to compare them against each other. This makes the review of literature for identifying drawbacks of the state of the art systems a challenging and puzzling task. In fact, lack of a unifying framework makes designing rehabilitation devices an intuitive process and prone to biases from currently available designs. This research work proposes a systematic approach for kinematic design of upper limb rehabilitation exoskeletons based on conceptual design techniques. Having defined a solution neutral problem statement based on the characteristics of an ideal device, the main functionality of the system is divided into smaller functional units via the Functional Decomposition Method. Various directions for concept generation are explored and finally, it has been shown that a vast majority of the current exoskeleton designs fit within the proposed design framework and the defined functionalities.


title: Anti-disassembly using Cryptographic Hash Functions

abstract: Computer viruses sometimes employ coding techniques intended to make analysis difficult for anti-virus researchers; techniques to obscure code to impair static code analysis are called anti-disassembly techniques. We present a new method of anti-disassembly based on cryptographic hash functions which is portable, hard to analyze, and can be used to target particular computers or users. Furthermore, the obscured code is not available in any analyzable form, even an encrypted form, until it successfully runs. The method’s viability has been empirically confirmed. We look at possible countermeasures for the basic anti-disassembly scheme, as well as variants scaled to use massive computational power.


title: Staged correction of an equinovarus deformity due to pyoderma gangrenosum using a Taylor spatial frame and tibiotalar calcaneal fusion with an intramedullary device

abstract: Pyoderma gangrenosum is a rare autoinflammatory syndrome manifested by skin lesions eventually creating ulcers. Surgical management can lead to scarring and contracture at the site of the lesion due to the pathergy phenomenon. A 43-year-old woman presented with a 5-year history of severe equinovarus deformity due to chronic pyoderma gangrenosum on her posteromedial ankle. She underwent a staged fusion. A gradual “closed” correction was performed in a Taylor spatial frame for 8 weeks in order to obviate the need for a surgical release in the area of the ulcer. She was ambulatory and full weight-bearing within 4 weeks of her frame removal. She maintained her correction with an accommodative foot orthosis until she had an uneventful tibiotalar calcaneal fusion with an intramedullary device. This case represents the success of using a Taylor spatial frame for staged fusion involving soft-tissue correction of severe, rigid equinovarus deformity due to pyoderma gangrenosum.


The evolution of agent-based simulation platforms: A review of NetLogo 5.0 and ReLogo


title: Feature Extraction and Representation for Face Recognition

abstract: Over the past two decades several attempts have been made to address the problem of face recognition and a voluminous literature has been produced. Current face recognition systems are able to perform very well in controlled environments e.g. frontal face recognition, where face images are acquired under frontal pose with strict constraints as defined in related face recognition standards. However, in unconstrained situations where a face may be captured in outdoor environments, under arbitrary illumination and large pose variations these systems fail to work. With the current focus of research to deal with these problems, much attention has been devoted in the facial feature extraction stage. Facial feature extraction is the most important step in face recognition. Several studies have been made to answer the questions like what features to use, how to describe them and several feature extraction techniques have been proposed. While many comprehensive literature reviews exist for face recognition a complete reference for different feature extraction techniques and their advantages/disadvantages with regards to a typical face recognition task in unconstrained scenarios is much needed. In this chapter we present a comprehensive review of the most relevant feature extraction techniques used in 2D face recognition and introduce a new feature extraction technique termed as Face-GLOH-signature to be used in face recognition for the first time (Sarfraz and Hellwich, 2008), which has a number of advantages over the commonly used feature descriptions in the context of unconstrained face recognition. The goal of feature extraction is to find a specific representation of the data that can highlight relevant information. This representation can be found by maximizing a criterion or can be a pre-defined representation. Usually, a face image is represented by a high dimensional vector containing pixel values (holistic representation) or a set of vectors where each vector summarizes the underlying content of a local region by using a high level 1


title: Extracting urban functional regions from points of interest and human activities on location‐based social networks

abstract: Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co-occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check-in activities on location-based social networks. Using a large corpus of about 100,000 Foursquare venues and user check-in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K-means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non-adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity-based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.


High-Speed Hardware Architectures of the Whirlpool Hash Function


title: AWSR: Active Web Service Recommendation Based on Usage History

abstract: Web services are very prevalent nowadays. Recommending Web services that users are interested in becomes an interesting and challenging research problem. In this paper, we present AWSR (Active Web Service Recommendation), an effective Web service recommendation system based on users' usage history to actively recommend Web services to users. AWSR extracts user's functional interests and QoS preferences from his/her usage history. Similarity between user's functional interests and a candidate Web service is calculated first. A hybrid new metric of similarity is developed to combine functional similarity measurement and nonfunctional similarity measurement based on comprehensive QoS of Web services. The AWSR ranks publicly available Web services based on values of the hybrid metric of similarity, so that a Top-K Web service recommendation list is created for a user. AWSR has been implemented and deployed on the Web. By conducting large-scale experiments based on a real-world Web services dataset, it is shown that our system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.


A future-adaptable password scheme


title: Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs

abstract: Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems employ a user-friendly "think like a vertex" programming model, and exhibit good scalability for tasks where the majority of graph vertices participate in computation. However, the design of these systems can seriously under-utilize the resources in a cluster for processing light-workload graph queries, where only a small fraction of vertices need to be accessed. In this work, we develop a new open-source system, called Quegel, for querying big graphs. Quegel treats queries as first-class citizens in its design: users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand, using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.


Introducing a Feasible-Infeasible Two-Population (FI-2Pop) Genetic Algorithm for Constrained Optimization: Distance Tracing and No Free Lunch


title: Distributed Circuit Plasticity: New Clues for the Cerebellar Mechanisms of Learning

abstract: The cerebellum is involved in learning and memory of sensory motor skills. However, the way this process takes place in local microcircuits is still unclear. The initial proposal, casted into the Motor Learning Theory, suggested that learning had to occur at the parallel fiber–Purkinje cell synapse under supervision of climbing fibers. However, the uniqueness of this mechanism has been questioned, and multiple forms of long-term plasticity have been revealed at various locations in the cerebellar circuit, including synapses and neurons in the granular layer, molecular layer and deep-cerebellar nuclei. At present, more than 15 forms of plasticity have been reported. There has been a long debate on which plasticity is more relevant to specific aspects of learning, but this question turned out to be hard to answer using physiological analysis alone. Recent experiments and models making use of closed-loop robotic simulations are revealing a radically new view: one single form of plasticity is insufficient, while altogether, the different forms of plasticity can explain the multiplicity of properties characterizing cerebellar learning. These include multi-rate acquisition and extinction, reversibility, self-scalability, and generalization. Moreover, when the circuit embeds multiple forms of plasticity, it can easily cope with multiple behaviors endowing therefore the cerebellum with the properties needed to operate as an effective generalized forward controller.


title: Integrated Modular Avionics (IMA) Partition Scheduling with Conflict-Free I/O for Multicore Avionics Systems

abstract: The trend in the semiconductor industry toward multicore processors poses a significant challenge to many suppliers of safety-critical real-time embedded software. Having certified their systems for use on single-core processors, these companies may be forced to migrate their installed base of software onto multicore processors as single-core processors become harder to obtain. These companies naturally want to minimize the potentially high costs of recertifying their software for multicore processors. In support of this goal, we propose an approach to solving a fundamental problem in migrating legacy software applications to multicore systems, namely that of preventing conflicts among I/O transactions from applications residing on different cores. We formalize the problem as a partition scheduling problem that serializes I/O partitions. Although this problem is strongly NP-complete, we formulate it as a Constraint Programming (CP) problem. Since the CP approach scales poorly, we propose a heuristic algorithm that outperforms the CP approach in scalability.


title: A 12-bit 1.74-mW 20-MS/s DAC with resistor-string and current-steering hybrid architecture

abstract: This paper presents a novel segmented hybrid digital-to-analog converter (DAC). It uses a resistor-string as the LSB part for low-power consumption, and uses a current-steering array as the MSB part for high-speed and small size. The LSB and MSB parts are combined by a slew-rate-enhanced class AB output amplifier for high speed. Compared to resistor string DACs, current steering DACs, or resistor-capacitor hybrid DACs, the proposed DAC shows a better tradeoff between power and speed at low power application demanding a sampling clock between 1 MHz and 100 MHz. The prototype is a 12-bit DAC implemented in 0.18-μm CMOS technology with the worst measured DNL/INL of 6.38 LSB / 7.55 LSB. The analogue part power consumption is 1.24 mW and the digital part 0.5mW at 1.35-V power supply at 20-MS/s sampling rate. Its output is single-end buffered voltage with a range of 500 mV. The core area is 0.16 mm2.


title: Abstract syntax and variable binding

abstract: We develop a theory of abstract syntax with variable binding. To every binding signature we associate a category of models consisting of variable sets endowed with compatible algebra and substitution structures. The syntax generated by the signature is the initial model. This gives a notion of initial algebra semantics encompassing the traditional one; besides compositionality, it automatically verifies the semantic substitution lemma.


title: DLOLIS-A: Description Logic based Text Ontology Learning

abstract: Ontology Learning has been the subject of intensive study for the past decade. Researchers in this field have been motivated by the possibility of automatically building a knowledge base on top of text documents so as to support reasoning based knowledge extraction. While most works in this field have been primarily statistical (known as light-weight Ontology Learning) not much attempt has been made in axiomatic Ontology Learning (called heavy-weight Ontology Learning) from Natural Language text documents. Heavy-weight Ontology Learning supports more precise formal logic-based reasoning when compared to statistical ontology learning. In this paper we have proposed a sound Ontology Learning tool DLOL_(IS-A) that maps English language IS-A sentences into their equivalent Description Logic (DL) expressions in order to automatically generate a consistent pair of T-box and A-box thereby forming both regular (definitional form) and generalized (axiomatic form) DL ontology. The current scope of the paper is strictly limited to IS-A sentences that exclude the possible structures of: (i) implicative IS-A sentences, and (ii) "Wh" IS-A questions. Other linguistic nuances that arise out of pragmatics and epistemic of IS-A sentences are beyond the scope of this present work. We have adopted Gold Standard based Ontology Learning evaluation on chosen IS-A rich Wikipedia documents.


title: Design and implementation of intelligent home control systems based on active sensor networks

abstract: The ubiquitous home network has gained widespread attentions due to its seamless integration into everyday life. This innovative system transparently unifies various home appliances, smart sensors/actuators and wireless communication technologies. The ubiquitous home network gradually forms a complex system to process various tasks. Developing this trend, we suggest a new intelligent home control system based on a wireless sensor/actuator network (we call it as an "Active sensor network"). The proposed intelligent home control system divides and assigns various home network tasks to appropriate components. It can integrate diversified physical sensing information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. We develop a new routing protocol LQIR (Link Quality Indicator based Routing) to improve the performance of our active sensor networks. This paper introduces the proposed home control system's design that provides intelligent services for users. We demonstrate its implementation using a real testbed.


title: A 24-GHz SiGe phased-array receiver-LO phase-shifting approach

abstract: A local-oscillator phase-shifting approach is introduced to implement a fully integrated 24-GHz phased-array receiver using SiGe technology. Sixteen phases of the local oscillator are generated in one oscillator core, resulting in a raw beam-forming accuracy of 4 bits. These phases are distributed to all eight receiving paths of the array by a symmetric network. The appropriate phase for each path is selected using high-frequency analog multiplexers. The raw beam-steering resolution of the array is better than 10deg for a forward-looking angle, while the array spatial selectivity, without any amplitude correction, is better than 20 dB. The overall gain of the array is 61 dB, while the array improves the input signal-to-noise ratio by 9 dB


title: Scalable Kernel Methods via Doubly Stochastic Gradients

abstract: The general perception is that kernel methods are not scalable, so neural nets become the choice for large-scale nonlinear learning problems. Have we tried hard enough for kernel methods? In this paper, we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional gradients". Based on the fact that many kernel methods can be expressed as convex optimization problems, our approach solves the optimization problems by making two unbiased stochastic approximations to the functional gradient—one using random training points and another using random features associated with the kernel—and performing descent steps with this noisy functional gradient. Our algorithm is simple, need no commit to a preset number of random features, and allows the flexibility of the function class to grow as we see more incoming data in the streaming setting. We demonstrate that a function learned by this procedure after t iterations converges to the optimal function in the reproducing kernel Hilbert space in rate O(1/t), and achieves a generalization bound of O(1/√t). Our approach can readily scale kernel methods up to the regimes which are dominated by neural nets. We show competitive performances of our approach as compared to neural nets in datasets such as 2.3 million energy materials from MolecularSpace, 8 million handwritten digits from MNIST, and 1 million photos from ImageNet using convolution features.


title: Performance metrics in supply chain management

abstract: This survey paper starts with a critical analysis of various performance metrics for supply chain management (SCM), used by a specific manufacturing company. Then it summarizes how economic theory treats multiple performance metrics. Actually, the paper proposes to deal with multiple metrics in SCM via the balanced scorecard — which measures customers, internal processes, innovations, and finance. To forecast how the values of these metrics will change — once a supply chain is redesigned — simulation may be used. This paper distinguishes four simulation types for SCM: (i) spreadsheet simulation, (ii) system dynamics, (iii) discrete-event simulation, and (iv) business games. These simulation types may explain the bullwhip effect, predict fill rate values, and educate and train users. Validation of simulation models requires sensitivity analysis; a statistical methodology is proposed. The paper concludes with suggestions for a possible research agenda in SCM. A list with 50 references for further study is included.


title: DeepBox: Learning Objectness with Convolutional Networks

abstract: Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that "objectness" is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before and leads to a 4.5-point gain in detection mAP. Our implementation achieves this performance while running at 260 ms per image.


title: An Improved Three-Layer Low-Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks

abstract: Topology control in wireless sensor networks (WSNs) balances the communication load on sensor devices and increases the network lifetime and scalability. Hierarchical or cluster-based design is one of the approaches to conserve the energy of the sensor networks in which the nodes with the higher residual energy could be used to gather data and route the information. However, most of the previous work on clustering has adopted a two-layer hierarchy, and only few methods studied a three-layer scheme instead. Based on a three-layer hierarchy, this paper has proposed a semi-distributed clustering approach by considering a hybrid of centralized gridding for the upper level head selection and distributed clustering for the lower level head selection. The simulation results show that the proposed approach is more efficient than other distributed algorithms. Therefore, the technique presented in this paper could be further applied to large-scale WSNs.


title: A 4.5Tb/s 3.4Tb/s/W 64×64 switch fabric with self-updating least-recently-granted priority and quality-of-service arbitration in 45nm CMOS

abstract: High-speed and low-power routers form the basic building blocks of on-die interconnect fabrics that are critical to overall throughput and energy efficiency of high performance systems [1,2]. Conventional routers use distinct logic blocks for routing data and handling arbitration [3,4]. At higher radices, connections between these blocks become a bottleneck, limiting router scalability and degrading performance. Recently, two switch topologies [5,6] merged the data-routing fabric with arbitration control, avoiding this bottleneck. However, [6] relies on centralized control for channel allocation, limiting performance, while [5] is restricted to a small set of fixed priorities, rendering input ports prone to starvation. In addition, ever larger CMPs will require continued increases in bandwidth over previous designs. To address these issues, we present a 64×64 single-stage swizzle-switch network (SSN) with 128b data buses (8192 total input/output wires). The SSN can connect any input to any output, including multicast. It has a peak measured throughput of 4.5Tb/s at 1.1V in 45nm SOI CMOS at 25°C. The SSN's key features are: 1) a single-cycle least-recently-granted (LRG) priority arbitration technique that reuses the already present input and output data buses and their drivers and sense amps; 2) an additional 4-level message-based priority arbitration for quality of service (QoS) with 2% logic and 3% wiring overhead; 3) a bidirectional bitline repeater that allows the router to scale to >8000 wires. These features result in a compact fabric (4.06mm2) with throughput gain of 2.1× over [5] at 3.4Tb/s/W efficiency, which improves to 7.4Tb/s/W at 600mV.


title: Trust-based decision making for environmental health community of interest IoT systems

abstract: With the onset of the Internet of Things (IoT) era, the number of devices and sensors is increasing tremendously. This paper is concerned with Health IoT consisting of various devices carried by members of an environmental health community of interest (CoI). We propose trust based information sharing among the CoI users of these IoT devices, so that a collective knowledge base can be built to rate the environment at a particular location and at a given time. This rated knowledge of various environments would enable a user's mobile device to automatically decide whether or not the user should visit this place/environment. Our trust-based decision making framework considers risk classification, reliability trust, and loss of health probability as three design dimensions for decision making. Performance data are shown to demonstrate the feasibility of our approach.


title: On rank correlation and the distance between rankings

abstract: Rank correlation statistics are useful for determining whether a there is a correspondence between two measurements, particularly when the measures themselves are of less interest than their relative ordering. Kendall's - in particular has found use in Information Retrieval as a "meta-evaluation" measure: it has been used to compare evaluation measures, evaluate system rankings, and evaluate predicted performance. In the meta-evaluation domain, however, correlations between systems confound relationships between measurements, practically guaranteeing a positive and significant estimate of - regardless of any actual correlation between the measurements. We introduce an alternative measure of distance between rankings that corrects this by explicitly accounting for correlations between systems over a sample of topics, and moreover has a probabilistic interpretation for use in a test of statistical significance. We validate our measure with theory, simulated data, and experiment.


title: Capacitated Clustering Problem in Computational Biology: Combinatorial and Statistical Approach for Sibling Reconstruction

abstract: The capacitated clustering problem (CCP) has been studied in a wide range of applications. In this study, we investigate a challenging CCP in computational biology, namely, sibling reconstruction problem (SRP). The goal of SRP is to establish the sibling relationship (i.e., groups of siblings) of a population from genetic data. The SRP has gained more and more interests from computational biologists over the past decade as it is an important and necessary keystone for studies in genetic and population biology. We propose a large-scale mixed-integer formulation of the CCP for SRP that is based on both combinatorial and statistical genetic concepts. The objective is not only to find the minimum number of sibling groups, but also to maximize the degree of similarity of individuals in the same sibling groups while each sibling group is subject to genetic constraints derived from Mendel's laws. We develop a new randomized greedy optimization algorithm to effectively and efficiently solve this SRP. The algorithm consists of two key phases: construction and enhancement. In the construction phase, a greedy approach with randomized perturbation is applied to construct multiple sibling groups iteratively. In the enhancement phase, a two-stage local search with a memory function is used to improve the solution quality with respect to the similarity measure. We demonstrate the effectiveness of the proposed algorithm using real biological data sets and compare it with state-of-the-art approaches in the literature. We also test it on larger simulated data sets. The experimental results show that the proposed algorithm provide the best reconstruction solutions.


title: Design of LQR and PID controllers for the self balancing unicycle robot

abstract: This paper focus on the dynamic modeling and the LQR and PID controllers for the self balancing unicycle robot. The mechanism of the unicycle robot is designed. The pitching and rolling balance could be achieved by the driving of the motor on the wheel and the balance weight on the body of robot. The dynamic equations of the robot are presented based on the Routh equation. On this basis, the LQR and PID controllers of the unicycle robot are proposed. The experimentations of balance control are showed through the Simulink toolbox of Matlab. The simulation results show that the robot could achieve self balancing after a short period of time by the designed controllers. According to comparing the results, the errors of PID controller are relatively smaller than LQR. The response speed of LQR controller is faster than PID. At last a kind of LQR&PID controller is proposed. This controller has the advantages of both LQR and PID controllers.


title: A framework for robust discovery of entity synonyms

abstract: Entity synonyms are critical for many applications like information retrieval and named entity recognition in documents. The current trend is to automatically discover entity synonyms using statistical techniques on web data. Prior techniques suffer from several limitations like click log sparsity and inability to distinguish between entities of different concept classes. In this paper, we propose a general framework for robustly discovering entity synonym with two novel similarity functions that overcome the limitations of prior techniques. We develop efficient and scalable techniques leveraging the MapReduce framework to discover synonyms at large scale. To handle long entity names with extraneous tokens, we propose techniques to effectively map long entity names to short queries in query log. Our experiments on real data from different entity domains demonstrate the superior quality of our synonyms as well as the efficiency of our algorithms. The entity synonyms produced by our system is in production in Bing Shopping and Video search, with experiments showing the significance it brings in improving search experience.


title: Direct torque control, induction motor vector control without an encoder

abstract: The basic evolution of direct torque control from other drive types is explained. Qualitative comparisons with other drives are included. The basic concepts behind direct torque control are clarified. An explanation of direct self control and the field-orientation concepts implemented in the adaptive motor model block is presented. The reliance of the control method on fast processing techniques is stressed. The theoretical foundations for the control concept are provided in summary format. Information on the ancillary control blocks outside the basic direct torque control is given. The implementation of special functions directly related to the control approach is described. Finally, performance data from an actual system are presented.


title: Maps and Globes in Virtual Reality

abstract: This paper explores different ways to render world-wide geographic maps in virtual reality (VR). We compare: (a) a 3D exocentric globe, where the user's viewpoint is outside the globe; (b) a flat map (rendered to a plane in VR); (c) an egocentric 3D globe, with the viewpoint inside the globe; and (d) a curved map, created by projecting the map onto a section of a sphere which curves around the user. In all four visualisations the geographic centre can be smoothly adjusted with a standard handheld VR controller and the user, through a head-tracked headset, can physically move around the visualisation. For distance comparison, exocentric globe is more accurate than egocentric globe and flat map. For area comparison, more time is required with exocentric and egocentric globes than with flat and curved maps. For direction estimation, the exocentric globe is more accurate and faster than the other visual presentations. Our study participants had a weak preference for the exocentric globe. Generally, the curved map had benefits over the flat map. In almost all cases the egocentric globe was found to be the least effective visualisation. Overall, our results provide support for the use of exocentric globes for geographic visualisation in mixed-reality.


title: Discontinuous Displacement Mapping for Volume Graphics

abstract: Displacement mapping is commonly used for adding surface details to an object. In this paper, we outline a generalized notion of displacement mapping, which allows for unconventional features such as unorthogonal and discontinuous displacement. By lifting the restriction on the geometric properties of the displacement, we can generate many different special effects including peeling, cutting and deforming an object. These types of operations are useful for volumetric objects, where the interior of objects is represented. To address the technical difculties associated with this generalization, we employed inverse displacement maps in 3D vector space, and devised a collection of techniques, including sampling displaced objects through a proxy geometry, computing displaced surface normals, correcting lighting artifacts at breaking points in a discontinuous displacement map, and creating composite displacement maps from primitive maps on the y . Through a number of examples of displacement maps, we demonstrate the generality, interactivity and usability of this approach on a set of volumetric objects.


Issues in Distributed Artificial Intelligence


title: Human mimetic forearm mechanism towards bionic arm

abstract: The paper presents a mechanical forearm mechanism to extend the pronation and supination (P/S) movement of the trans-radial amputee when he/she is able to conduct the P/S motion partially with his/her remaining forearm. The P/S motions of the existing prostheses and robotic arms developed till now are completely different from those of the human. When we are to develop the robotic prostheses for the amputees, two important issues should be considered such as cosmetic issue to look like normal arm and motion harmony issue between robotic prosthesis and the amputated living arm. Most prostheses do not realize the human-like P/S motion, but the simple rotational motion at the wrist by using the electric motor. In this paper, the well-known spatial four-bar mechanism is adopted in order to mimic human P/S motion as if the person does, because it is one of the best candidates to describe actual human forearm movement. To obtain the design parameters of the mechanism, first, 3D data of the amputated arm as well as the opposite side non-amputated arm are captured by using MRI (Magnetic Resonance Imaging) and 3D scanner. The design parameters such as link lengths, joint locations, and volumetric shape are determined from the measured data. Second, mathematical analysis is performed to simulate one degree-of-freedom P/S motion realized from the spatial four-bar mechanism, and the simulation results are suggested to confirm whether or not it acts like the relative movement between human ulnar and radius bones. Finally the effectiveness of the proposed mechanism is verified through two ADL (activities of daily living) tests.


title: system for organizational semantic intranets

abstract: The semantic web vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We say that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog presents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string metric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. Moreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the particular community jargon used by end users.


title: Vocal masculinity is a robust dominance signal in men

abstract: Dominance assessment is important in mating competition across a variety of species, but little is known about how individuals’ own quality affects their assessment of potential rivals. We conducted two studies to test whether men’s own dominance affects their attentiveness to a putative dominance signal, vocal masculinity, when assessing competitors. Study I examined dominance ratings made by men in relation to their self-rated physical dominance. Study II examined dominance ratings made by men in relation to objective measures of their physical dominance, including size, strength, testosterone, and physical aggressiveness. Vocal masculinity strongly affected dominance ratings, but a man’s own dominance did not alter his attention to vocal masculinity when assessing dominance. However, men who rated themselves high on physical dominance rated the voices of other men lower on dominance and reported more sex partners (study I). Men with intermediate testosterone concentrations rated the voices of other men lower on dominance (study II). These results confirm the effect of vocal masculinity on dominance perceptions, provide further evidence that dominance is relevant to mating success, and shed new light on how men assess the dominance of rivals and potential allies. Our results suggest that attention to dominance signals may depend less on the observer’s own dominance in species with coalitional aggression, where individuals must assess others not only in relation to themselves but also in relation to each other. Among men, the effect of a deep, masculine voice on perceptions of dominance appears to be robust and unmediated by the formidability of the listener.


title: Automatic detection of motorcyclists without helmet

abstract: Motorcycle accidents have been rapidly growing throughout the years in many countries. Due to various social and economic factors, this type of vehicle is becoming increasingly popular. The helmet is the main safety equipment of motorcyclists, but many drivers do not use it. If an motorcyclist is without helmet an accident can be fatal. This paper aims to explain and illustrate an automatic method for motorcycles detection and classification on public roads and a system for automatic detection of motorcyclists without helmet. For this, a hybrid descriptor for features extraction is proposed based in Local Binary Pattern, Histograms of Oriented Gradients and the Hough Transform descriptors. Traffic images captured by cameras were used. The best result obtained from classification was an accuracy rate of 0.9767, and the best result obtained from helmet detection was an accuracy rate of 0.9423.


title: Performance Engineering for Microservices: Research Challenges and Directions

abstract: Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices. Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.


title: A discriminative spatio-temporal mapping of face for liveness detection

abstract: The proposed system aims to boost the performance of a face anti-spoofing system by fusing pulse based features with other spatial and temporal information that markedly define liveness. Most face recognition systems do not have an effective spoof detection module and hence are vulnerable to spoofing attacks. We address the above problem by developing a spatio-temporal mapping of face and then using a deep Convolutional Neural Network (CNN) to learn discriminative features for liveness detection. CNNs can act directly on the raw inputs, thus automating the process of feature construction. Instead of only relying on the deep CNN to learn features by skimming through all the frames of a sequence, a compact representation of face that captures only the selective features is given as an input. Features are extracted from both spatial and temporal dimensions through spectral analysis, thereby capturing the motion and physiological information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation is obtained by combining information from all channels. Our model differs from other models in this aspect. Our system is evaluated on two challenging databases, CASIA [27] and Replay-Attack [7], and the achieved results are presented in this paper. This work shows that the proposed model outperforms state-of-the-art methods on CASIA, achieves comparable result on REPLAY-ATTACK and reduces model complexity by exploiting few key features of liveness.


title: A supervised extreme learning committee for food recognition

abstract: A food recognition system exploiting a supervised committee of classifiers is proposed.The system automatically selects the optimal features for the task.The structured fusion approach is designed to achieve an optimal ranking.Evaluations have been conducted on 4 publicly available benchmark datasets.Superior results than existing methods are achieved. Food recognition is an emerging topic in computer vision. The problem is being addressed especially in health-oriented systems where it is used as a support for food diary applications. The goal is to improve current food diaries, where the users have to manually insert their daily food intake, with an automatic recognition of the food type, quantity and consequent calories intake estimation. In addition to the classical recognition challenges, the food recognition problem is characterized by the absence of a rigid structure of the food and by large intra-class variations. To tackle such challenges, a food recognition system based on a committee classification is proposed. The aim is to provide a system capable of automatically choosing the optimal features for food recognition out of the existing plethora of available ones (e.g., color, texture, etc.). Following this idea, each committee member, i.e., an Extreme Learning Machine, is trained to specialize on a single feature type. Then, a Structural Support Vector Machine is exploited to produce the final ranking of possible matches by filtering out the irrelevant features and thus merging only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on four publicly available benchmark datasets.


title: CNN Cascades for Segmenting Whole Slide Images of the Kidney

abstract: Due to the increasing availability of whole slide scanners facilitating digitization of histopathological tissue, there is a strong demand for the development of computer based image analysis systems. In this work, the focus is on the segmentation of the glomeruli constituting a highly relevant structure in renal histopathology, which has not been investigated before in combination with CNNs. We propose two different CNN cascades for segmentation applications with sparse objects. These approaches are applied to the problem of glomerulus segmentation and compared with conventional fully-convolutional networks. Overall, with the best performing cascade approach, single CNNs are outperformed and a pixel-level Dice similarity coefficient of 0.90 is obtained. Combined with qualitative and further object-level analyses the obtained results are assessed as excellent also compared to recent approaches. In conclusion, we can state that especially one of the proposed cascade networks proved to be a highly powerful tool for segmenting the renal glomeruli providing best segmentation accuracies and also keeping the computing time at a low level.


title: A non-local cost aggregation method for stereo matching

abstract: Matching cost aggregation is one of the oldest and still popular methods for stereo correspondence. While effective and efficient, cost aggregation methods typically aggregate the matching cost by summing/averaging over a user-specified, local support region. This is obviously only locally-optimal, and the computational complexity of the full-kernel implementation usually depends on the region size. In this paper, the cost aggregation problem is re-examined and a non-local solution is proposed. The matching cost values are aggregated adaptively based on pixel similarity on a tree structure derived from the stereo image pair to preserve depth edges. The nodes of this tree are all the image pixels, and the edges are all the edges between the nearest neighboring pixels. The similarity between any two pixels is decided by their shortest distance on the tree. The proposed method is non-local as every node receives supports from all other nodes on the tree. As can be expected, the proposed non-local solution outperforms all local cost aggregation methods on the standard (Middlebury) benchmark. Besides, it has great advantage in extremely low computational complexity: only a total of 2 addition/subtraction operations and 3 multiplication operations are required for each pixel at each disparity level. It is very close to the complexity of unnormalized box filtering using integral image which requires 6 addition/subtraction operations. Unnormalized box filter is the fastest local cost aggregation method but blurs across depth edges. The proposed method was tested on a MacBook Air laptop computer with a 1.8 GHz Intel Core i7 CPU and 4 GB memory. The average runtime on the Middlebury data sets is about 90 milliseconds, and is only about 1.25× slower than unnormalized box filter. A non-local disparity refinement method is also proposed based on the non-local cost aggregation method.


title: Efficient Linear Programming for Dense CRFs

abstract: The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent. We show that each block of variables can be efficiently optimized. Specifically, for one block, the problem decomposes into significantly smaller subproblems, each of which is defined over a single pixel. For the other block, the problem is optimized via conditional gradient descent. This has two advantages: 1) the conditional gradient can be computed in a time linear in the number of pixels and labels, and 2) the optimal step size can be computed analytically. Our experiments on standard datasets provide compelling evidence that our approach outperforms all existing baselines including the previous LP based approach for dense CRFs.


title: An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring

abstract: In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method ''Random Subspace'' outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of Levenberg-Marquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit.


title: Gratifications and social network service usage: The mediating role of online experience

abstract: This paper incorporates dual theories from communication research (uses and gratifications) and psychology research (online flow) to examine consumer behavior in the use of social network services. In particular, the study proposes that consumers' online experience of interaction and arousal serves as the mediator of the relationship between social motivations and use behaviors. The empirical results indicate that arousal fully mediates the relationship between social gratifications and problematic social network service use. Furthermore, both interaction and arousal are partial mediators of the relationship between social gratifications and the intention to revisit social networking websites.


title: Predicting Domain Generation Algorithms with Long Short-Term Memory Networks

abstract: Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properties (e.g., banjori) or by using a dynamic list of seeds (e.g., bedep). Another technique to stop malware from using DGAs is to intercept DNS queries on a network and predict whether domains are DGA generated. Such a technique will alert network administrators to the presence of malware on their networks. In addition, if the predictor can also accurately predict the family of DGAs, then network administrators can also be alerted to the type of malware that is on their networks. This paper presents a DGA classifier that leverages long short-term memory (LSTM) networks to predict DGAs and their respective families without the need for a priori feature extraction. Results are significantly better than state-of-the-art techniques, providing 0.9993 area under the receiver operating characteristic curve for binary classification and a micro-averaged F1 score of 0.9906. In other terms, the LSTM technique can provide a 90% detection rate with a 1:10000 false positive (FP) rate---a twenty times FP improvement over comparable methods. Experiments in this paper are run on open datasets and code snippets are provided to reproduce the results.


title: F.: Soknos using semantic technologies in disaster management software

abstract: Disaster management software deals with supporting staff in large catastrophic incidents such as earthquakes or floods, e.g., by providing relevant information, facilitating task and resource planning, and managing communication with all involved parties. In this paper, we introduce the SoKNOS support system, which is a functional prototype for such software using semantic technologies for various purposes. Ontologies are used for creating a mutual understanding between developers and end users from different organizations. Information sources and services are annotated with ontologies for improving the provision of the right information at the right time, for connecting existing systems and databases to the SoKNOS system, and for providing an ontology-based visualization. Furthermore, the users' actions are constantly supervised, and errors are avoided by employing ontology-based consistency checking. We show how the pervasive and holistic use of semantic technologies leads to a significant improvement of both the development and the usability of disaster management software, and present some key lessons learned from employing semantic technologies in a large-scale software project.


title: JACQUEMART LAYOUT_Layout 1 11/9/12 3:44 PM Page 2 Visual Analytics for BGP Monitoring and Prefix Hijacking Identification

abstract: The control plane of the Internet relies entirely on BGP as the interdomain routing protocol to maintain and exchange routing information between large network providers and their customers. However, an intrinsic vulnerability of the protocol is its inability to validate the integrity and correctness of routing information exchanged between peer routers. As a result, it is relatively easy for people with malicious intent to steal legitimate IP blocks through an attack known as prefix hijacking, which essentially consists of injecting bogus routing information into the system to redirect or subvert network traffic. In this article, we give a short survey of visualization methods that have been developed for BGP monitoring, in particular for the identification of prefix hijacks. Our goal is to illustrate how network visualization has the potential to assist an analyst in detecting abnormal routing patterns in massive amounts of BGP data. Finally, we present an analysis of a real validated case of prefix hijacking, which took place between April and August 2011. We use this hijack case study to illustrate the ongoing work carried out in VIS-SENSE, a European research project that leverages visual analytics to develop more effective tools for BGP monitoring and prefix hijack detection.


title: Harper-dorn creep in al, pb, and sn

abstract: High-temperature creep has been studied in lead and tin in the stress range of 10−6 to 10−4G over a range of temperatures near the melting point. A composite plot of dimensionless parameter•/γkT/DGb vsτ/G revealed a transition from a high stress region (with slope ∼4.9 for lead and ∼6.6 for tin) to a low stress region (with slope ∼1 for both metals), at stresses of ∼2 × 10-5G for lead and ∼10−5G for tin. The creep behavior in the low stress region is identical to that attributed to creep of the Harper-Dorn type. In addition, earlier work on aluminum in the low stress region is confirmed using polycrystalline as well as single crystal samples.


title: Unsupervised Trajectory Clustering via Adaptive Multi-kernel-Based Shrinkage

abstract: This paper proposes a shrinkage-based framework for unsupervised trajectory clustering. Facing to the challenges of trajectory clustering, e.g., large variations within a cluster and ambiguities across clusters, we first introduce an adaptive multi-kernel-based estimation process to estimate the 'shrunk' positions and speeds of trajectories' points. This kernel-based estimation effectively leverages both multiple structural information within a trajectory and the local motion patterns across multiple trajectories, such that the discrimination of the shrunk point can be properly increased. We further introduce a speed-regularized optimization process, which utilizes the estimated speeds to regularize the optimal shrunk points, so as to guarantee the smoothness and the discriminative pattern of the final shrunk trajectory. Using our approach, the variations among similar trajectories can be reduced while the boundaries between different clusters are enlarged. Experimental results demonstrate that our approach is superior to the state-of-art approaches on both clustering accuracy and robustness. Besides, additional experiments further reveal the effectiveness of our approach when applied to trajectory analysis applications such as anomaly detection and route analysis.


ResNet and Model Fusion for Automatic Spoofing Detection.


title: A flexible and scalable SLAM system with full 3D motion estimation

abstract: For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multi-resolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded hand-held mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.


title: Fusing Multi-Stream Deep Networks for Video Classification

abstract: This paper studies deep network architectures to address the problem of video classification. A multi-stream framework is proposed to fully utilize the rich multimodal information in videos. Specifically, we first train three Convolutional Neural Networks to model spatial, short-term motion and audio clues respectively. Long Short Term Memory networks are then adopted to explore long-term temporal dynamics. With the outputs of the individual streams, we propose a simple and effective fusion method to generate the final predictions, where the optimal fusion weights are learned adaptively for each class, and the learning process is regularized by automatically estimated class relationships. Our contributions are two-fold. First, the proposed multi-stream framework is able to exploit multimodal features that are more comprehensive than those previously attempted. Second, we demonstrate that the adaptive fusion method using the class relationship as a regularizer outperforms traditional alternatives that estimate the weights in a "free" fashion. Our framework produces significantly better results than the state of the arts on two popular benchmarks, 92.2\% on UCF-101 (without using audio) and 84.9\% on Columbia Consumer Videos.


title: Path tracking of autonomous ground vehicle based on fractional order PID controller optimized by PSO

abstract: An optimal control technique is proposed to address the problem of path tracking of an autonomous ground vehicle. This technique utilizes a Fractional Order Proportional Integral Derivative (FOPID) controller to control a non-holonomic autonomous ground vehicle to track the behaviour of the predefined reference path. Two FOPID controllers are designed to control the vehicle's inputs. These inputs represent the torques that are used in order to manipulate the implemented model of the vehicle to obtain the actual path. The implemented model of the non-holonomic autonomous ground vehicle takes into consideration both of the kinematic and dynamic models. In additional, a Particle Swarm Optimization (PSO) algorithm is used to optimize the FOPID controllers' parameters. These optimal tuned parameters of FOPID controllers minimize the cost function used in the algorithm. The effectiveness and validation of the proposed method have been verified through different patterns of reference paths using MATLABSIMULINK software package. The newly obtained results of FOPID controller show the advantage and the performance of the technique in terms of minimizing path tracking error and the complement of the path following.


title: Relevance-Based Language Models

abstract: We explore the relation between classical probabilistic models of information retrieval and the emerging language modeling approaches. It has long been recognized that the primary obstacle to effective performance of classical models is the need to estimate a relevance model : probabilities of words in the relevant class. We propose a novel technique for estimating these probabilities using the query alone. We demonstrate that our technique can produce highly accurate relevance models, addressing important notions of synonymy and polysemy. Our experiments show relevance models outperforming baseline language modeling systems on TREC retrieval and TDT tracking tasks. The main contribution of this work is an effective formal method for estimating a relevance model with no training data.


title: A Simplified Model for Normal Mode Helical Antennas

abstract: Normal mode helical antennas are widely used for RFID and mobile communications applications due to their relatively small size and omni-directional radiation pattern. However, their highly curved geometry can make the design and analysis of helical antennas that are part of larger complex structures quite difficult. A simplified model is proposed that replaces the curved helix with straight wires and lumped elements. The simplified model can be used to reduce the complexity of full-wave models that include a helical antenna. It also can be used to estimate the performance of a helical antenna without full- wave modeling of the helical structure.


title: Extending Moore's Exhaustion Model: Including Further Dimensions of Burnout and Investigating Their Influence on Turnover Intention Among IT Professionals

abstract: This research focuses on burnout as a driver of turnover intention amongst IT professionals. We extend Moore's exhaustion model by including further dimensions of burnout into the model, namely depersonalization and reduced personal accomplishment. The effect of stressors on these dimensions and the original dimension of emotional exhaustion is investigated, as is their influence on turnover intention among IT professional. Results based on a data sample of 154 IT professionals show that not only emotional exhaustion but also depersonalization leads to turnover intention. This outcome cannot be neglected when trying to reduce turnover intention in an organization in order to maintain competitive advantages. Moreover, while the stressors suggested by Moore influence emotional exhaustion, they only slightly explain the dimensions of depersonalization and reduced personal accomplishment. This indicates that these two dimensions of burnout are caused by additional factors, which represents a research gap worth investigating in future research.


The extra element theorem


title: A New Resonant Gate-Drive Circuit With Efficient Energy Recovery and Low Conduction Loss

abstract: In this paper, a new resonant gate-drive circuit is proposed to recover a portion of the power-MOSFET-gate energy that is typically dissipated in high-frequency converters. The proposed circuit consists of four control switches and a small resonant inductance. The current through the resonant inductance is discontinuous in order to minimize circulating-current conduction loss that is present in other methods. The proposed circuit also achieves quick turn-on and turn-off transition times to reduce switching and conduction losses in power MOSFETs. An analysis, a design procedure, and experimental results are presented for the proposed circuit. Experimental results demonstrate that the proposed driver can recover 51% of the gate energy at 5-V gate-drive voltage.


title: Causes of neuropathy in patients referred as “idiopathic neuropathy”

abstract: INTRODUCTION ::: The etiology of neuropathy was idiopathic in 20%-30% of patients despite thorough investigation, based on results from the 1980s and 1990s. Since then, new etiologies have been recognized, and skin biopsy has been used to confirm small-fiber neuropathy. ::: ::: ::: METHODS ::: The authors reviewed the charts of 373 patients with idiopathic neuropathy who were referred to a neuropathy center between 2002 and 2012. ::: ::: ::: RESULTS ::: Among the 284 eligible patients, 93 (32.7%) remained idiopathic. The most common cause was impaired glucose metabolism (72 patients, 25.3%), including diabetes in 26 and prediabetes in 46. Other etiologies were chronic inflammatory demyelinating polyneuropathy (CIDP) in 57 (20%) and monoclonal gammopathy in 20 (7%), as well as toxic, Sjögren disease, celiac disease, other immune-mediated diseases, vitamin B12 deficiency, amyloidosis, vitamin B1 and B6 deficiency, vasculitis, hypothyroidism, hereditary, Lyme disease, and anti-sulfatide antibody. ::: ::: ::: CONCLUSIONS ::: The major causes of undiagnosed neuropathies were impaired glucose metabolism, CIDP, and monoclonal gammopathies. Despite thorough evaluation 32.7% remained idiopathic. Muscle Nerve 53: 856-861, 2016.


title: The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees

abstract: This article focuses on two under-researched areas of tourism management – the management of music festivals, and the influence of social media on customer relationships. In the new digital era of marketing communications little is known about how social media interactions with tourism brands affect how consumers think and feel about those brands, and consequently how those interactions affect desired marketing outcomes such as word mouth. Based on the literature, a conceptual model was developed and was tested using structural equation modeling. The results show that social media does indeed have a significant influence on emotions and attachments to festival brands, and that social media-based relationships lead to desired outcomes such as positive word of mouth.


Title : Towards a common framework for knowledge co-creation : opportunities for collaboration between Service Science and Sustainability Science Track : Viable Systems Approach


title: Multibase: integrating heterogeneous distributed database systems

abstract: Multibase is a software system for integrating access to preexisting, heterogeneous, distributed databases. The system suppresses differences of DBMS, language, and data models among the databases and provides users with a unified global schema and a single high-level query language. Autonomy for updating is retained with the local databases. The architecture of Multibase does not require any changes to local databases or DBMSs. There are three principal research goals of the project. The first goal is to develop appropriate language constructs for accessing and integrating heterogeneous databases. The second goal is to discover effective global and local optimization techniques. The final goal is to design methods for handling incompatible data representations and inconsistent data. Currently the project is in the first year of a planned three year effort. This paper describes the basic architecture of Multibase and identifies some of the avenues to be taken in subsequent research.