title: ElasticFusion: Real-time dense SLAM and light source estimation

abstract: We present a novel approach to real-time dense visual simultaneous localisation and mapping. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments and beyond explored using an RGB-D camera in an incremental online fashion, without pose graph optimization or any post-processing steps. This is accomplished by using dense frame-to-model camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimizations as often as possible to stay close to the mode of the map distribution, while utilizing global loop closure to recover from arbitrary drift and maintain global consistency. In the spirit of improving map quality as well as tracking accuracy and robustness, we furthermore explore a novel approach to real-time discrete light source detection. This technique is capable of detecting numerous light sources in indoor environments in real-time as a user handheld camera explores the scene. Absolutely no prior information about the scene or number of light sources is required. By making a small set of simple assumptions about the appearance properties of the scene our method can incrementally estimate both the quantity and location of multiple light sources in the environment in an online fashion. Our results demonstrate that our technique functions well in many different environments and lighting configurations. We show that this enables (a) more realistic augmented reality rendering; (b) a richer understanding of the scene beyond pure geometry and; (c) more accurate and robust photometric tracking.


title: Design considerations and results for an overlapped subarray radar antenna

abstract: Overlapped subarray networks produce flat-topped sector patterns with low sidelobes that suppress grating lobes outside of the main beam of the subarray pattern. They are typically used in limited scan applications, where it is desired to minimize the number of controls required to steer the beam. However, the architecture of an overlapped subarray antenna includes many signal crossovers and a wide variation in splitting/combining ratios, which make it difficult to maintain required error tolerances. This paper presents the design considerations and results for an overlapped subarray radar antenna, including a custom subarray weighting function and the corresponding circuit design and fabrication. Measured pattern results will be shown for a prototype design compared with desired patterns.


title: Supporting Students Structurally: Engaging Architectural Students in Structurally Oriented Haptic Learning Exercises

abstract: Beginning architecture students have traditionally been taught structural design using an engineering-based educational model. Often, information is presented in formula-rich lectures filled with abstract representations of architectural space. In other words, when structural design is presented as a series of calculations instead of a series of design explorations, educators miss a great opportunity to develop a better integration between structural information and other architectural coursework—and integration that would enhance design development by balancing technical resolution and exploration. A new educational model for teaching structural design to architects is needed. Architecture students should be given a series of exercises that help to develop their understanding about the relationship between structural form and forces, structural behavior, and the array of potentially responsive architectural forms. This paper will demonstrate how a curriculum based on experiential exercises, haptic learning methodologies and project-based design exercises in a laboratory setting can provide a more effective way forward in educating architects about building structures. Because initial exposure to complex topics can often make a significant difference in long-term learning efficacy, this paper will primarily discuss the very first lab project in the sequence, an ergonomic lab in which the students use their bodies to explore basic structural principles related to the relationship between form and forces.


Contrast Enhancement and Visibility Restoration of Underwater Optical Images Using Fusion


title: Offline Delay-Optimal Transmission for Energy Harvesting Nodes

abstract: This paper investigates the offline packet-delay-minimization problem for an energy harvesting transmitter. To overcome the non-convexity of the problem, we propose a C2-diffeomorphic transformation and provide the necessary and sufficient condition for the transformed problem to a standard convex optimization problem. Based on this condition, a simple choice of the transformation is determined which allows an analytically tractable solution of the original non-convex problem to be easily obtained once the transformed convex problem is solved. We further study the structure of the optimal transmission policy in a special case and find it to follow a weighted-directional-water-filling structure. In particular, the optimal policy tends to allocate more power in earlier time slots and less power in later time slots. Our analytical insight is verified by simulation results.


title: CVSSearch: searching through source code using CVS comments

abstract: CVSSearch is a tool that searches for fragments of source code by using CVS comments. CVS is a version control system that is widely used in the open source community. Our search tool takes advantage of the fact that a CVS comment typically describes the lines of code involved in the commit and this description will typically hold for many future versions. In other words, CVSSearch allows one to better search the most recent version of the code by looking at previous versions to better understand the current version. In this paper we describe our algorithm for mapping CVS comments to the corresponding source code, present a search tool based on this technique, and discuss preliminary feedback.


title: Mobicare: E-Health and Emergency Wireless Monitoring System

abstract: With the advancement of electronic health (e- health) technologies in the past few years, a number of wearable technologies have been developed to improve the process of remotely monitoring patients. These technologies range from invasive to non-invasive, passive to active, offer a range of different applications, and are most commonly implemented in the form of a Wireless Body Area Network, allowing the patient to move freely while logging and storing their vital signals. This paper will discuss different implementations of e-health systems, and will propose Mobicare; a new ZigBee based implementation of a WBAN that is non- invasive, active, cost and power efficient. The paper will discuss the system architecture of the complete prototype, including the analysis of the patient's Electrocardiogram signal using MATLAB, as well as additions to the system in the form of a smartphone application and an automated pharmacy.


Concentrating Correctly on Cybercrime Concentration


title: CHALLENGES FOR ADOPTING CLOUD-BASED SOFTWARE AS A SERVICE (SAAS) IN THE PUBLIC SECTOR

abstract: Technological advances such as the creation of bandwidth, modular applications and cloud computing have enabled the creation of a distributed collaborative sourcing model named Software as a Service (SaaS). SaaS presents the embodiment of a new service delivery model in which a service provider is delivering its electronic services over the web to many users on a pay per use or period basis. SaaS is still unexplored in the public sector context and its use has been limited. Based on interviews and a group session the main challenges for adopting SaaS are explored from the government perspective. It was found that SaaS could provide many benefits which are related to the outsourcing of the local control, installation and development of software which could result in potential cost-savings and better cost control. Disadvantages and risks are related to the more difficult control of the IT function. Several items were mentioned as both a benefit and disadvantage, as this will be dependent on the organizational arrangements. There are also many challenges that need to be addressed including ensuring the quality, privacy, security and business continuity which require the implementation of organizational changes and governance mechanisms for public sector organizations that are considering SaaS.


title: Patterning Technique for Generating Arbitrary Anisotropic Impedance Surfaces

abstract: Anisotropic impedance surfaces have been demonstrated to be useful for a variety of applications ranging from antennas, to surface wave guiding, to control of scattering. To increase their anisotropy requires elongated unit cells which have reduced symmetry and thus are not easily arranged into arbitrary patterns. We discuss the limitations of existing patterning techniques, and explore options for generating anisotropic impedance surfaces with arbitrary spatial variation. We present an approach that allows a wide range of anisotropic impedance profiles, based on a point-shifting method combined with a Voronoi cell generation technique. This approach can be used to produce patterns which include highly elongated cells with varying orientation, and cells which can smoothly transition between square, rectangular, hexagonal, and other shapes with a wide range of aspect ratios. We demonstrate a practical implementation of this technique which allows us to define gaps between the cells to generate impedance surfaces, and we use it to implement a simple example of a structure which requires smoothly varying impedance, in the form of a planar Luneberg lens. Simulations of the lens are verified by measurements, validating our pattern generation technique.


title: Policy-regularized model predictive control to stabilize diverse quadrupedal gaits for the MIT cheetah

abstract: This paper introduces a new policy-regularized model-predictive control (PR-MPC) approach to automatically generate and stabilize a diverse set of quadrupedal gaits. Model-predictive methods offer great promise to address balance in dynamic robots, yet require the solution of challenging nonlinear optimization problems when applied to legged systems. The new proposed PR-MPC approach aims to improve the conditioning of these problems by adding regularization based on heuristic reference policies. With this approach, a unified MPC formulation is shown to generate and stabilize trotting, bounding, and galloping without retuning any cost-function parameters. Intuitively, the added regularization biases the solution of the MPC towards common heuristics from the literature that are based on simple physics. Simulation results show that PR-MPC improves the computation time and closed-loop outcomes of applying MPC to stabilize quadrupedal gaits.


title: Report on psychoactive drug use among adolescents using ayahuasca within a religious context.

abstract: Ritual use of ayahuasca within the context of the Brazilian ayahuasca churches often starts during late childhood or early adolescence. Premature access to psychoactive drugs may represent a risk factor for drug misuse. Conversely, religious affiliation seems to play a protective role in terms of substance abuse. The objective of this study was to describe patterns of drug use in a sample of adolescents using ayahuasca within a religious setting. Forty-one adolescents from a Brazilian ayahuasca sect were compared with 43 adolescents who never drank ayahuasca. No significant differences were identified in terms of lifetime substance consumption. Throughout the previous year period, ayahuasca adolescents used less alcohol (46.31%) than the comparison group (74.4%). Recent use of alcohol was also more frequent among the latter group (65.1%) than among ayahuasca drinkers (32.5%). Although not statistically significant, slight differences in terms of patterns of drug use were definitely observed among groups. Despite their early exposure to a hallucinogenic substance, adolescents using ayahuasca in a controlled setting were mostly comparable to controls except for a considerably smaller proportion of alcohol users. Religious affiliation may have played a central role as a possible protective factor for alcohol use. Thus, ayahuasca seems to be a relatively safe substance as far as drug misuse is concerned.


title: MFS-LDA: a multi-feature space tag recommendation model for cold start problem

abstract: Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem.,MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem).,Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods.,The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.


title: Modeling MongoDB with Relational Model

abstract: Relational databases have been prevailing for the last two decades, with features of clear semantics and ease of use with SQL supported by the underlying theory, relational algebra. Relational databases provide good support for structural data management. However, recent development in IT brings forward big data, featuring extreme large volume and variety in data type and structures, and relational databases are difficult to handle such big data due to the strict constraints on data structure and data relations, and so on. On the other hand, NoSQL databases, including HBase, MongoDB, Cassandra, etc., are receiving their popularity for their capability in dealing with large amount of complex data in various structures. Currently there is no way to know the feasibility of the migration of traditional relational databases to NoSQL databases. The migration requires evaluating the feasibility of the migration and potential performance of the new systems. This paper investigates these issues by modelling one of the NoSQL database, MongoDB, with relational algebra. The modelling will allow the comparison of semantic expression powers between relational databases and MongoDB, analysing the feasibility of the migration of a relational database to MongoDB and its evaluation. The model is expected to facilitate the optimization of the new NoSQL database.


title: Don’t Look at My Teeth When I Smile: Teeth Visibility in Smiling Faces Affects Emotionality Ratings and Gaze Patterns

abstract: Research on facial emotion processing has offered inconclusive results on whether certain emotional expressions, like happiness, are detected faster over others. A source of discrepancy among studies could stem from differences in physically salient features (e.g., teeth visibility), which are often left uncontrolled in this field of research. In Study 1, happy faces from the Karolinska Database Emotional Databse with visible, normal teeth unexpectedly obtained lower scores on intensity and prototypicality than the same faces with covered teeth. In Study 2, an eye-tracking methodology revealed that although faces with normal teeth drew participants’ initial attention, participants spent more time looking at the eye region in faces with covered teeth, a region that previous research had found to be more informative of emotion than the mouth region. Overall, these results suggest that advantages often associated with certain emotional faces might be partially due to artifacts that should be systematically controlled for in future studies.


title: Design of dual-polarized L-probe patch antenna arrays with high isolation

abstract: An experimental study of a dual-polarized L-probe patch antenna is presented. The antenna is designed to operate at around 1.8 GHz. A "dual-feed" technique is introduced to achieve high isolation between two input ports. The proposed antenna has an impedance bandwidth of 23.8% (SWR/spl les/2), 15% (SWR/spl les/1.5) and an isolation over 30 dB. In array designs, techniques for improving the isolation between two adjacent elements of an antenna array are also investigated. A two-element array with more than 30 dB isolation is designed and tested.


title: Multi-Agent Reinforcement Learning Based Cognitive Anti-Jamming

abstract: This paper proposes a reinforcement learning based approach to anti-jamming communications with wideband autonomous cognitive radios (WACRs) in a multi-agent environment. Assumed system model allows multiple WACRs to simultaneously operate over the same (wide) spectrum band. Each radio attempts to evade the transmissions of other WACRs as well as avoiding a jammer signal that sweeps across the whole spectrum band of interest. The WACR makes use of its spectrum knowledge acquisition ability to detect and identify the location (in frequency) of this sweeping jammer and the signals of other WACRs. This information and reinforcement learning is used to successfully learn a sub-band selection policy to avoid both the jammer signal as well as interference from other radios. It is shown, through simulations, that the proposed learning-based sub-band selection policy has low computational complexity and significantly outperforms the random sub-band selection policy.


title: Indoor navigation system for visually impaired

abstract: One of the most important limitations for people with visual impairment is the inability of unassisted navigation and orientation in unfamiliar buildings. An low-cost indoor navigation system, which is based on mobile terminals, supporting technology Near Field Communication (NFC), and Java program access to Radio Frequency Identification (RFID) tags, is developed. The proposed navigation system enables users to imagine the map of the rooms (dimensions, relative position of points of interest). This information is stored in RFID tags in WAP Binary eXtensible Markup Language (WBXML) format. The system allows leaving audio messages that are recorded in RFID tags in Adaptive Multi Rate (AMR) format. Voice enabled navigation, that is familiar to users with visual disabilities, is used.


title: Object Detection and Pose Tracking In Augmented Reality

abstract: These papers introduce technique for object detection and pose tracking with a monocular camera for augmented reality applications. To visually merge a virtual object onto a real scene with geometrical consistency, a camera pose with respect to the scene needs to be computed. AR Toolkit is software that provides the facility to create AR systems. This paper is study a fiducially marker based Augmented Reality system, the algorithms it uses and how it can be implemented.


title: Surface Reconstruction by Alpha Geometric Shape Based on Triangulation

abstract: Surface reconstruction by alpha geometric shape based on triangulation is a new idea and method in computer graphics and reverse engineering. In the process of Topology reconstruction, the points are reduced and projected to a middle slice plane. The adjacency among slices is established by section line subdivision, and triangle mesh is stored and called by the form of STL. The simplexes are classified and computed in order to obtain α interval by the external-ball radius and center of every simplex, and the data structure is designed to save them. Through the man-machine interactive selection modes, α-shape can be reconstructed by different α parameters. The surface reconstruction is completed.


title: Static and dynamic properties of the pneumatic actuator with artificial muscles

abstract: The paper contains information about the function and basic properties of the actuator based on pneumatic artificial muscles. It describes the design method of control structure of such actuator and shows the nonlinear static and dynamic characteristics of this actuator. The step responses and the non-linear static and dynamic characteristics were measured by authors on the real pneumatic actuator with artificial muscles Festo MAS 20–250.


title: 3-D Modeling of Tomato Canopies Using a High-Resolution Portable Scanning Lidar for Extracting Structural Information

abstract: In the present study, an attempt was made to produce a precise 3D image of a tomato canopy using a portable high-resolution scanning lidar. The tomato canopy was scanned by the lidar from three positions surrounding it. Through the scanning, the point cloud data of the canopy were obtained and they were co-registered. Then, points corresponding to leaves were extracted and converted into polygon images. From the polygon images, leaf areas were accurately estimated with a mean absolute percent error of 4.6%. Vertical profile of leaf area density (LAD) and leaf area index (LAI) could be also estimated by summing up each leaf area derived from the polygon images. Leaf inclination angle could be also estimated from the 3-D polygon image. It was shown that leaf inclination angles had different values at each part of a leaf.


title: Bad Is Stronger Than Good

abstract: The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interpersonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad information is processed more thoroughly than good. The self is more motivated to avoid bad self-definitions than to pursue good ones. Bad impressions and bad stereotypes are quicker to form and more resistant to disconfirmation than good ones. Various explanations such as diagnosticity and salience help explain some findings, but the greater power of bad events is still found when such variables are controlled. Hardly any exceptions (indicating greater power of good) can be found. Taken together, these findings suggest that bad is stronger than good, as a general principle across a broad range of psychological phenomena.


title: Demand forecasting with user-generated online information

abstract: Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.


title: Compressed Sensing System Considerations for ECG and EMG Wireless Biosensors

abstract: Compressed sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist processing of sparse signals such as electrocardiogram (ECG) and electromyogram (EMG) biosignals. Consequently, it can be applied to biosignal acquisition systems to reduce the data rate to realize ultra-low-power performance. CS is compared to conventional and adaptive sampling techniques and several system-level design considerations are presented for CS acquisition systems including sparsity and compression limits, thresholding techniques, encoder bit-precision requirements, and signal recovery algorithms. Simulation studies show that compression factors greater than 16X are achievable for ECG and EMG signals with signal-to-quantization noise ratios greater than 60 dB.


title: Working Memory : Theories , Models , and Controversies

abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.


title: Honor among thieves: A common's analysis of cybercrime economies

abstract: Underground forums enable technical innovation among criminals as well as allow for specialization, thereby making cybercrime economically efficient. The success of these forums is contingent on collective action twixt a variety of stakeholders. What distinguishes sustainable forums from those that fail? We begin to address these questions by examining underground forums under an economic framework that has been used to prescribe institutional choices in other domains, such as fisheries and forests. This framework examines the sustainability of cybercrime forums given a self governance model for a common-pool resource. We analyze five distinct forums: AntiChat (AC), BadHackerZ (BH), BlackhatWorld (BW), Carders (CC), and L33tCrew (LC). Our analyses indicate that successful/sustainable forums: 1) have easy/cheap community monitoring, 2) show moderate increase in new members, 3) do not witness reduced connectivity as the network size increases, 4) limit privileged access, and 5) enforce bans or fines on offending members. We define success as forums demonstrating small world effect.


title: On Space-Time Interest Points

abstract: Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for interpretation of spatio-temporal events. ::: ::: To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events, we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how a video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.


title: An Improved Algorithm for Harris Corner Detection

abstract: Corner points are formed from two or more edges and edges usually define the boundary between two different objects or parts of the same objects. In this novel we discuss the theory of the Harris corner detection and indicate its disadvantage. Then it proposes an improved algorithm of Harris detection algorithm based on the neighboring point eliminating method. It reduces the time of the detection, and makes the corners distributing more homogenous so that avoids too many corners stay together. Experimental results show that the algorithm can detect corner more equality distributing, and can be used in some fact applications such as image registration well. Key words-Corner point; Harris corner; neighboring


title: Subsampling Methods for Persistent Homology

abstract: Persistent homology is a multiscale method for analyzing the shape of sets and functions from point cloud data arising from an unknown distribution supported on those sets. When the size of the sample is large, direct computation of the persistent homology is prohibitive due to the combinatorial nature of the existing algorithms. We propose to compute the persistent homology of several subsamples of the data and then combine the resulting estimates. We study the risk of two estimators and we prove that the subsampling approach carries stable topological information while achieving a great reduction in computational complexity.


title: Geometry-shader-based real-time voxelization and applications

abstract: This work proposes a new voxelization algorithm based on newly available GPU functionalities and designs several real-time applications to render complex lighting effects with the voxelization result. The voxelization algorithm can efficiently transform a highly complex scene in a surface-boundary representation into a set of voxels in one GPU pass using the geometry shader. Newly available 3D textures are used to directly record the surficial and volumetric properties of objects such as opaqueness, refraction, and transmittance. In the first, the usage of 3D textures can remove those strenuous efforts required to modify the encoding and decoding scheme when adjusting the voxel resolution. Second, surficial and volumetric properties recorded in 3D textures can be used to interactively compute and render more realistic lighting effects including the shadow of objects with complex occlusion and the refraction and transmittance of transparent objects. The shadow can be rendered with an absorption coefficient which is computed according to the number of surfaces drawing in each voxel during voxelization and used to compute the amount of light passing through partially occluded complex objects. The surface normal, transmittance coefficient and refraction index recorded in each voxel can be used to simulate the refraction and transmittance lighting effects of transparent objects using our multiple-surfaced refraction algorithm. Finally, the results demonstrate that our algorithm can transform a dynamic scene into a set of voxels and render complex lighting effects in real time without any pre-processing.


title: Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios

abstract: The development and usage of Unmanned Aerial Vehicles (UAVs) quickly increased in the last decades, mainly for military purposes. This technology is also now of high interest in non-military contexts like logistics, environmental studies and different areas of civil protection. While the technology for operating a single UAV is rather mature, additional efforts are still necessary for using UAVs in fleets (or swarms). The Aid to SItuation Management based on MUltimodal, MUltiUAVs, MUltilevel acquisition Techniques (ASIMUT) project which is supported by the European Defence Agency (EDA) aims at investigating and demonstrating dedicated surveillance services based on fleets of UAVs. The aim is to enhance the situation awareness of an operator and to decrease his workload by providing support for the detection of threats based on multi-sensor multi-source data fusion. The operator is also supported by the combination of information delivered by the heterogeneous swarms of UAVs and by additional information extracted from intelligence databases. As a result, a distributed surveillance system increasing detection, high-level data fusion capabilities and UAV autonomy is proposed.


title: Effects of a Dynamic Warm-Up, Static Stretching or Static Stretching with Tendon Vibration on Vertical Jump Performance and EMG Responses

abstract: The purpose of this study was to investigate the short-term effects of static stretching, with vibration given directly over Achilles tendon, on electro-myographic (EMG) responses and vertical jump (VJ) performances. Fifteen male, college athletes voluntarily participated in this study (n=15; age: 22±4 years old; body height: 181±10 cm; body mass: 74±11 kg). All stages were completed within 90 minutes for each participant. Tendon vibration bouts lasted 30 seconds at 50 Hz for each volunteer. EMG analysis for peripheral silent period, H-reflex, H-reflex threshold, T-reflex and H/M ratio were completed for each experimental phases. EMG data were obtained from the soleus muscle in response to electro stimulation on the popliteal post tibial nerve. As expected, the dynamic warm-up (DW) increased VJ performances (p=0.004). Increased VJ performances after the DW were not statistically substantiated by the EMG findings. In addition, EMG results did not indicate that either static stretching (SS) or tendon vibration combined with static stretching (TVSS) had any detrimental or facilitation effect on vertical jump performances. In conclusion, using TVSS does not seem to facilitate warm-up effects before explosive performance.


title: The Kinematics Model of a Two-Wheeled Self-Balancing Autonomous Mobile Robot and Its Simulation

abstract: The researches on two-wheeled self-balancing robots have gained momentum in recent decade around the world. This paper describes the kinematics model of a two-wheeled self-balancing autonomous mobile robot. After mechatronics system design of the robot is completed, the analysis of the whole kinematics model can be divided into two wheels and a body. Then the velocity decompositions of robot wheel and body are analyzed respectively. After the left and right wheel kinematic model is established based on above velocity decomposition method, and the kinematic model of self-balancing robot’s body is also calculated by this method. The whole kinematics model of the two-wheeled self-balancing robot system is then established. The effectiveness of the kinematics model is testified by the simulation analysis on ADAMS and experimental validation.


title: Abusing social networks for automated user profiling

abstract: Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored on these sites calls for appropriate security precautions to protect this data. ::: ::: In this paper, we describe how we are able to take advantage of a common weakness, namely the fact that an attacker can query popular social networks for registered e-mail addresses on a large scale. Starting with a list of about 10.4 million email addresses, we were able to automatically identify more than 1.2 million user profiles associated with these addresses. By automatically crawling and correlating these profiles, we collect detailed personal information about each user, which we use for automated profiling (i.e., to enrich the information available from each user). Having access to such information would allow an attacker to launch sophisticated, targeted attacks, or to improve the efficiency of spam campaigns. We have contacted the most popular providers, who acknowledged the threat and are currently implementing our proposed countermeasures. Facebook and XING, in particular, have recently fixed the problem.


title: Delving deeper into convolutional neural networks for camera relocalization

abstract: Convolutional Neural Networks (CNNs) have been applied to camera relocalization, which is to infer the pose of the camera given a single monocular image. However, there are still many open problems for camera relocalization with CNNs. We delve into the CNNs for camera relocalization. First, a variant of Euler angles named Euler6 is proposed to represent orientation. Then a data augmentation method named pose synthesis is designed to reduce spsarsity of poses in the whole pose space to cope with overfitting in training. Third, a multi-task CNN named BranchNet is proposed to deal with the complex coupling of orientation and translation. The network consists of several shared convolutional layers and splits into two branches which predict orientation and translation, respectively. Experiments on the 7Scenes dataset show that incorporating these techniques one by one into an existing model PoseNet always leads to better results. Together these techniques reduce the orientation error by 15.9% and the translation error by 38.3% compared to the state-of-the-art model Bayesian PoseNet. We implement BranchNet on an Intel NUC mobile platform and reach a speed of 43 fps, which meets the real-time requirement of many robotic applications.


title: T-wave amplitude and the P-Q interval: relationships to noninvasive indices of myocardial performance.

abstract: This study examines the interrelationship of five noninvasive measures of cardiac performance: T-wave amplitude (TWA), the P-Q interval of the EKG (PQI), Pulse Transit Time (PTT), Carotid dP/dt, and heart rate or interbeat interval (IBI). These measures were manually derived from high-speed recordings of EKG and carotid displacement pulse made as subjects performed a demanding mental arithmetic task. Within-subject correlations between the measures were determined from 18 cardiac cycles sampled from baseline and task periods. Five of the median correlations were significant in the overall sample (N=19), most notably that between IBI and PQI. When subjects were partitioned into high and low PTT reactors, a larger number of significant correlations were observed in the more reactive group. PTT reactivity significantly enhanced the magnitude of three correlations: PTT vs IBI, PTT vs PQI, and PQI vs TWA. ::: ::: ::: ::: Comparing the two electrocardiographic indices, PQI displayed larger correlations with both IBI and PTT than did TWA. The pattern of correlations suggests that PQI measurement provides little information beyond that provided by HR alone and that TWA, although strongly related to PTT in a few subjects, is not consistently related to any measure except PQI. The results are discussed in light of reports suggesting that these indices might be used to assess beta-adrenergic influences on the myocardium.


title: Almost perfect auto-correlation sequences for binary phase-modulated continuous wave radar

abstract: We propose the so-called “almost perfect auto-correlation sequences” as ideal spreading codes for binary phase-modulated continuous wave radar systems. After appropriate processing, these codes exhibit a perfect auto-correlation profile with zero sidelobes. This property can be exploited to build extremely high-dynamic range radar systems. Simulation examples show that a pedestrian with a radar cross section of -20 dBsm can be clearly distinguished from a bus of 30 dBsm even when they are only half a meter apart.


title: End-to-end learning for lane keeping of self-driving cars

abstract: Lane keeping is an important feature for self-driving cars. This paper presents an end-to-end learning approach to obtain the proper steering angle to maintain the car in the lane. The convolutional neural network (CNN) model takes raw image frames as input and outputs the steering angles accordingly. The model is trained and evaluated using the comma.ai dataset, which contains the front view image frames and the steering angle data captured when driving on the road. Unlike the traditional approach that manually decomposes the autonomous driving problem into technical components such as lane detection, path planning and steering control, the end-to-end model can directly steer the vehicle from the front view camera data after training. It learns how to keep in lane from human driving data. Further discussion of this end-to-end approach and its limitation are also provided.


title: Assistance: the work practices of human administrative assistants and their implications for it and organizations

abstract: Assistance - work carried out by one entity in support of another - is a concept of long-standing interest, both as a type of human work common in organizations and as a model of how computational systems might interact with humans. Surprisingly, the perhaps most paradigmatic form of assistance - the work of administrative assistants or secretaries - has received almost no attention. This paper reports on a study of assistants, and their principals and managers, laying out a model of their work, the skills and competencies they need to function effectively, and reflects on implications for the design of systems and organizations.


title: An explainable deep machine vision framework for plant stress phenotyping

abstract: Current approaches for accurate identification, classification, and quantification of biotic and abiotic stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework’s ability to identify and classify a diverse set of foliar stresses in soybean [ Glycine max (L.) Merr.] with remarkable accuracy. We also present an explanation mechanism, using the top-K high-resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of stress severity, allowing for identification (type of foliar stress), classification (low, medium, or high stress), and quantification (stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high-throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar stresses would have significant implications in scientific research, plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g., unmanned air vehicles and automated ground scouts) for rapid, large-scale scouting or as a mobile application for real-time detection of stress by farmers and researchers.


title: Reinforcement learning and dopamine in the striatum: A modeling perspective

abstract: Abstract The recent research evidences show that the dopamine (DA) system in the brain is involved in various functions like reward-related learning, exploration, preparation, and execution in goal directed behavior. It is suggested that dopaminergic neurons provide a prediction error akin to the error computed in the temporal difference learning (TDL) models of reinforcement learning (RL). Houk et al. (1995) [26] proposed a biochemical model in the spine head of neurons at the striatum in the basal ganglia which generates and uses neural signals to predict reinforcement. The model explains how the DA neurons are able to predict reinforcement and how the output from these neurons might then be used to reinforce the behaviors that lead to primary reinforcement. They proposed a scheme drawing that parallels between actor–critic architecture and dopamine activity in the basal ganglia. Houk et al. (1995) [26] also proposed a biochemical model of interactions between protein molecules which supports learning earlier predictions of reinforcement in the spine head of medium spiny neurons at the striatum. However, Houk׳s proposed cellular model fails to account for the time delay between the dopaminergic and glutamatergic activity required for reward-related learning and also fails to explain the ‘eligibility trace’ condition needed in delayed tasks of associative conditioning in which a memory trace of the antecedent signal is needed at the time of a succeeding reward. In this article, we review various models of RL with an emphasis on the cellular models of RL. In particular, we emphasize biochemical models of RL, and point out the future directions.


title: Network Intrusion Detection: Evasion, Traffic Normalization, and End-to-End Protocol Semantics

abstract: A fundamental problem for network intrusion detection systems is the ability of a skilled attacker to evade detection by exploiting ambiguities in the traffic stream as seen by the monitor. We discuss the viability of addressing this problem by introducing a new network forwarding element called a traffic normalizer. The normalizer sits directly in the path of traffic into a site and patches up the packet stream to eliminate potential ambiguities before the traffic is seen by the monitor, removing evasion opportunities. We examine a number of tradeoffs in designing a normalizer, emphasizing the important question of the degree to which normalizations undermine end-to-end protocol semantics. We discuss the key practical issues of "cold start" and attacks on the normalizer, and develop a methodology for systematically examining the ambiguities present in a protocol based on walking the protocol's header. We then present norm, a publicly available user-level implementation of a normalizer that can normalize a TCP traffic stream at 100,000 pkts/sec in memory-to-memory copies, suggesting that a kernel implementation using PC hardware could keep pace with a bidirectional 100 Mbps link with sufficient headroom to weather a high-speed flooding attack of small packets.


title: PCB multi-track coils for domestic induction heating applications

abstract: In this paper, an analysis of the resistance of multi-track coils in printed circuit board (PCB) implementations, where the conductors have rectangular cross-section, for spiral planar coils is carried out. For this purpose, different analytical losses models for the mentioned conductors have been reviewed. From this review, we conclude that for the range of frequencies, the coil dimensions and the planar configuration typically used in domestic induction heating, the application in which we focus, these analysis are unsatisfactory. Therefore, in this work the resistance of multi-track winding has been calculated by means of finite element analysis (FEA) tool. These simulations provide us some design guidelines that allow us to optimize the design of multi-track coils for domestic induction heating. Furthermore, several prototypes are used to verify the simulated results, both single-turn coils and multi-turn coils.


title: Living Together: Mind and Machine Intelligence

abstract: In this paper we consider the nature of the machine intelligences we have created in the context of our human intelligence. We suggest that the fundamental difference between human and machine intelligence comes down to \emph{embodiment factors}. We define embodiment factors as the ratio between an entity's ability to communicate information vs compute information. We speculate on the role of embodiment factors in driving our own intelligence and consciousness. We briefly review dual process models of cognition and cast machine intelligence within that framework, characterising it as a dominant System Zero, which can drive behaviour through interfacing with us subconsciously. Driven by concerns about the consequence of such a system we suggest prophylactic courses of action that could be considered. Our main conclusion is that it is \emph{not} sentient intelligence we should fear but \emph{non-sentient} intelligence.


title: Fuzzy Mining – Adaptive Process Simplification Based on Multi-Perspective Metrics

abstract: Process Mining is a technique for extracting process models from executionlogs. This is particularly useful in situations where people have an idealizedview of reality. Real-life processes turn out to be less structured than peopletend to believe. Unfortunately, traditional process mining approaches haveproblems dealing with unstructured processes. The discovered models are often"spaghetti-like", showing all details without distinguishing what is important andwhat is not. This paper proposes a new process mining approach to overcome thisproblem. The approach is configurable and allows for different faithfully simplifiedviews of a particular process. To do this, the concept of a roadmap is used asa metaphor. Just like different roadmaps provide suitable abstractions of reality,process models should provide meaningful abstractions of operational processesencountered in domains ranging from healthcare and logistics to web servicesand public administration.


title: Help to find the needle in a haystack: integrating recommender systems in an IT supported staff recruitment system

abstract: Despite the benefits of information technology for corporations in staff recruitment (reduced time and costs per hire) the increased use also led to glut of applications especially in major enterprises. Therefore the companies forced to find the best candidate in times of a "War for Talent" need help to find this needle in a haystack. This help could be provided by recommender systems predominately used in e-commerce to recommend products or services to customers purchasing specific products. Recommender systems could assist the recruiter to find the adequate candidate within the applicant's database. In order to support this search and selection process we conduct a design science approach to integrate recommender systems in a holistic e-recruiting architecture and therewith provide a complete and new solution for IT support in staff recruitment.


title: Multi-task rank learning for image quality assessment

abstract: In practice, multiple types of distortions are associated with an image quality degradation process. The existing machine learning (ML) based image quality assessment (IQA) approaches generally established a unified model for all distortion types, or each model is trained independently for each distortion type by using single-task learning, which lead to the poor generalization ability of the models as applied to practical image processing. There are often the underlying cross relatedness amongst these single-task learnings in IQA, which is ignored by the previous approaches. To solve this problem, we propose a multi-task learning framework to train IQA models simultaneously across individual tasks each of which concerns one distortion type. These relatedness can be therefore exploited to improve the generalization ability of IQA models from single-task learning. In addition, pairwise image quality rank instead of image quality rating is optimized in learning task. By mapping image quality rank to image quality rating, a novel no-reference (NR) IQA approach can be derived. The experimental results confirm that the proposed Multi-task Rank Learning based IQA (MRLIQ) approach is prominent among all state-of-the-art NR-IQA approaches.


title: The hippocampus and visual perception

abstract: In this review, we will discuss the idea that the hippocampus may be involved in both memory and perception, contrary to theories that posit functional and neuroanatomical segregation of these processes. This suggestion is based on a number of recent neuropsychological and functional neuroimaging studies that have demonstrated that the hippocampus is involved in the visual discrimination of complex spatial scene stimuli. We argue that these findings cannot be explained by long-term memory or working memory processing or, in the case of patient findings, dysfunction beyond the medial temporal lobe (MTL). Instead, these studies point toward a role for the hippocampus in higher-order spatial perception. We suggest that the hippocampus processes complex conjunctions of spatial features, and that it may be more appropriate to consider the representations for which this structure is critical, rather than the cognitive processes that it mediates.


title: PSP: An Advanced Surface-Potential-Based MOSFET Model for Circuit Simulation

abstract: This paper describes the latest and most advanced surface-potential-based model jointly developed by The Pennsylvania State University and Philips. Specific topics include model structure, mobility and velocity saturation description, further development and verification of symmetric linearization method, recent advances in the computational techniques for the surface potential, modeling of gate tunneling current, inclusion of the retrograde impurity profile, and noise sources. The emphasis of this paper is on incorporating the recent advances in MOS device physics and modeling within the compact modeling context


Adapting Pre-trained Word Embeddings For Use In Medical Coding


title: Human mobility, social ties, and link prediction

abstract: Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals' movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.


title: Shadow optimization from structured deep edge detection

abstract: Local structures of shadow boundaries as well as complex interactions of image regions remain largely unexploited by previous shadow detection approaches. In this paper, we present a novel learning-based framework for shadow region recovery from a single image. We exploit local structures of shadow edges by using a structured CNN learning framework. We show that using structured label information in classification can improve local consistency over pixel labels and avoid spurious labelling. We further propose and formulate shadow/bright measure to model complex interactions among image regions. The shadow and bright measures of each patch are computed from the shadow edges detected by the proposed CNN. Using the global interaction constraints on patches, we formulate a least-square optimization problem for shadow recovery that can be solved efficiently. Our shadow recovery method achieves state-of-the-art results on major shadow benchmark databases collected under various conditions.


title: Agile-SD: A Linux-based TCP Congestion Control Algorithm for Supporting High-speed and Short-distance Networks

abstract: Recently, high-speed and short-distance networks are widely deployed and their necessity is rapidly increasing everyday. This type of networks is used in several network applications; such as Local Area Networks (LAN) and Data Center Networks (DCN). In LANs and DCNs, high-speed and short-distance networks are commonly deployed to connect between computing and storage elements in order to provide rapid services. Indeed, the overall performance of such networks is significantly influenced by the Congestion Control Algorithm (CCA) which suffers from the problem of bandwidth under-utilization, especially if the applied buffer regime is very small. In this paper, a novel loss-based CCA tailored for high-speed and Short-Distance (SD) networks, namely Agile-SD, has been proposed. The main contribution of the proposed CCA is to implement the mechanism of agility factor. Further, intensive simulation experiments have been carried out to evaluate the performance of Agile-SD compared to Compound and Cubic which are the default CCAs of the most commonly used operating systems. The results of the simulation experiments show that the proposed CCA outperforms the compared CCAs in terms of average throughput, loss ratio and fairness, especially when a small buffer is applied. Moreover, Agile-SD shows lower sensitivity to the buffer size change and packet error rate variation which increases its efficiency.


title: Towards detecting fake user accounts in facebook

abstract: People are highly dependent on online social networks (OSNs) which have attracted the interest of cyber criminals for carrying out a number of malicious activities. An entire industry of black-market services has emerged which offers fake accounts based services for sale. We, therefore, in our work, focus on detecting fake accounts on a very popular (and difficult for data collection) online social network, Facebook. Key contributions of our work are as follows. The first contribution has been collection of data related to real and fake accounts on Facebook. Due to strict privacy settings and ever evolving API of Facebook with each version adding more restrictions, collecting user accounts data became a major challenge. Our second contribution is the use of user-feed information on Facebook to understand user profile activity and identifying an extensive set of 17 features which play a key role in discriminating fake users on Facebook with real users. Third contribution is the use these features and identifying the key machine learning based classifiers who perform well in detection task out of a total of 12 classifiers employed. Fourth contribution is the identifying which type of activities (like, comment, tagging, sharing, etc) contribute the most in fake user detection. Results exhibit classification accuracy of 79% among the best performing classifiers. In terms of activities, likes and comments contribute well towards detection task. Although the accuracy is not very high, however, our work forms a baseline for further improvement. Our results indicate that many fake users are classified as real suggesting clearly that fake accounts are mimicking real user behavior to evade detection mechanisms. Our work concludes by enlisting a number of future course of actions that can be undertaken.


title: Enterprise Architecture for Digital Transformation

abstract: Digital transformation requires an altogether new institutional logic and effective response at a requisite organizational level. Sensing and seizing fleeting market opportunities and reconfiguring the business in line with the shifting value proposition requires increasingly specialized resources, more dynamic capabilities, and in-built resilience in the face of change. While Enterprise Architecture (EA) has been suggested to facilitate enterprise transformation, the focus has traditionally been on process standardization and integration rather than on continuous adaptation to the changing business and technological landscape. For EA to have a desired impact, more adaptive conceptualizations of EA that address the requirements of the new digital environment are called for. In this conceptual paper, we explore the implications of digital transformation on enterprise architecture. In particular, we note that existing approaches to EA address integration and coherence within a single organization but typically fall short in the face of complexities pertaining to digital ecosystems. We suggest a vertical typology of organizational capabilities and postulate that today's digital environment increasingly requires adaptive capabilities that transcend the traditional notion of dynamic capabilities. We then investigate how EA can help build flexibility and resilience in the organization.


title: A Spectral Framework for Detecting Inconsistency across Multi-source Object Relationships

abstract: In this paper, we propose to conduct anomaly detection across multiple sources to identify objects that have inconsistent behavior across these sources. We assume that a set of objects can be described from various perspectives (multiple information sources). The underlying clustering structure of normal objects is usually shared by multiple sources. However, anomalous objects belong to different clusters when considering different aspects. For example, there exist movies that are expected to be liked by kids by genre, but are liked by grown-ups based on user viewing history. To identify such objects, we propose to compute the distance between different eigen decomposition results of the same object with respect to different sources as its anomalous score. We also give interpretations from the perspectives of constrained spectral clustering and random walks over graph. Experimental results on several UCI as well as DBLP and Movie Lens datasets demonstrate the effectiveness of the proposed approach.


title: Image Classification using Super-Vector Coding of Local Image Descriptors

abstract: This paper introduces a new framework for image classification using local visual descriptors. The pipeline first performs a non-linear feature transformation on descriptors, then aggregates the results together to form image-level representations, and finally applies a classification model. For all the three steps we suggest novel solutions which make our approach appealing in theory, more scalable in computation, and transparent in classification. Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL benchmarks.


title: Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo

abstract: We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to "differential privacy", a cryptographic approach to protect individual-level privacy while permitting database-level utility. Specifically, we show that under standard assumptions, getting one sample from a posterior distribution is differentially private "for free"; and this sample as a statistical estimator is often consistent, near optimal, and computationally tractable. Similarly but separately, we show that a recent line of work that use stochastic gradient for Hybrid Monte Carlo (HMC) sampling also preserve differentially privacy with minor or no modifications of the algorithmic procedure at all, these observations lead to an "anytime" algorithm for Bayesian learning under privacy constraint. We demonstrate that it performs much better than the state-of-the-art differential private methods on synthetic and real datasets.


title: Exploring Appropriation of Enterprise Wikis:

abstract: The purpose of this paper is to provide both application-oriented researchers and practitioners with detailed insights into conception, implementation, and utilization of intra-organizational wikis to support knowledge management and group work. Firstly, we report on three case studies and describe how wikis have been appropriated in the context of a concrete practice. Our study reveals that the wikis have been used as Knowledge Base, Encyclopedia and Support Base, respectively. We present the identified practices as a result of the wiki appropriation process and argue that due to their open and flexible nature these wikis have been appropriated according to the users' needs. Our contribution helps to understand how platforms support working practices that have not been supported by groupware before, or at least not in the same way. Secondly, three detailed implementation reports uncover many aspects of wiki projects, e.g., different viewpoints of managers and users, an investigation of other sources containing business-relevant information, and perceived obstacles to wiki projects. In this context, our study generates a series of lessons learned for people who intend to implement wikis in their own organizations, including the awareness of usage potential, the need for additional managerial support, and clear communication strategies to promote wiki usage.


title: Energy budgeting for CubeSats with an integrated FPGA

abstract: CubeSats are a simple, low-cost option for developing quickly-deployable satellites, however, the tradeoff for these benefits is a small physical size, which restricts the CubeSat's solar panels' size and thus the available power budget and stored energy reserves. These power/energy limitations restrict the CubeSat's functionality and data processing capabilities, which makes leveraging CubeSats for compute-intensive missions challenging. Additionally, increasing sensor capabilities due to technological advances further compounds this functionality limitation, enabling sensors to gather significantly more data than a satellite's limited downlink bandwidth can accommodate. The influx in sensed data, which is particularly high for image-processing applications, introduces a pressing need for high-performance on-board data processing, which preprocesses and/or compresses the data before transmission. FPGAs have been incorporated into state-of-the-art satellites to provide high-performance on-board data processing, while simultaneously reducing the satellites' data processing energy consumption. However, even though FPGAs can provide these capabilities in full-scale satellites, a CubeSat's limited power budget makes integration of FPGAs into CubeSats a challenging task. For example, the commonly used Virtex4QV Radiation Tolerant FPGA family's average power consumption ranges from 1.25 to 12.5 Watts, whereas the CubeSat's power budget ranges from 2 to 8 Watts, with the smallest, cheapest CubeSat systems at the lower end of this range. Therefore, in order to successfully integrate FPGAs into CubeSats, the components' power consumptions must be clearly budgeted with respect to the CubeSat's specific functionalities and orbital pattern, which dictates the available power and stored energy reserves. In this paper, we present two detailed energy reserve budgeting case studies for FPGA-based CubeSats with respect to stored energy reserves for image compression and processing using a Canny edge detector. CubeSat designers can leverage this energy reserve budget with the application-specific components' power consumptions for applications such as hyper-spectral imaging (HSI), ground motion target indication (GMTI), and star tracking to quickly determine maximum payload operational time with respect to specific orbital patterns and mission requirements.


title: Learning Social Networks from Web Documents Using Support Vector Classifiers

abstract: Automatic generation of a social network requires extracting pair-wise relations of the individuals. In this research, Learning social network from incomplete relationship data is proposed. It is assumed that only a small subset of relations between the individuals is known. With this assumption, the social network extraction is translated into a text classification problem. The relations between two individuals are modeled by merging their document vectors and the given relations are used as labels of training data. By this transformation, a text classifier such as SVM is used for learning the unknown relations. We show that there is a link between the intrinsic sparsity of social networks and class distribution imbalance of the training data. In order to re-balance the unbalanced training data, a minority class down-sampling strategy is employed. The proposed framework is applied to a true FOAF (Friend Of A Friend) database and evaluated by the macro-averaged F-measure.


On tight approximate inference of logistic-normal admixture model


title: Body Image and Personality in Aesthetic Plastic Surgery: A Case-Control Study

abstract: Introduction: The amount of research on the relationship between plastic surgery and psychological features, such as personality disorders and indexes of body image dissatisfaction, has increased significantly in the last years. Aim: The aim of the present study was to examine these psychological features among Italian patients who underwent aesthetic plastic surgery, testing the mediating role of the mass media influence on the relationship between them and the choice of aesthetic plastic surgery. The Personality Diagnostic Questionnaire 4+ (PDQ-4+) and the Body Uneasiness Test (BUT) were administered to patients who underwent aesthetic plastic surgery (N = 111) and participants who had no history of aesthetic plastic surgical procedures (N = 149). Results: Results showed that aesthetic patients reported higher indexes of body image disturbance than controls. No significant differences between aesthetic participants and controls were found in all three cluster B personality disorders. Moreover, the effect of body image dissatisfaction on the choice to undergo aesthetic plastic surgery was partially mediated by the influence of mass media. Conclusions: In conclusion, the present study confirmed the importance of body dissatisfaction as a predictor of the choice to undergo aesthetic surgery and highlighted the influence of media messages regarding physical appearance.


title: Autonomicity in Oracle Database Management System

abstract: Human world is becoming more and more dependent on computers and information technology (IT). The autonomic capabilities in computers and IT have become the need of the day. These capabilities in software and systems increase performance, accuracy, availability and reliability with less or no human intervention (HI). Database has become the integral part of information system in most of the organizations. Databases are growing w.r.t size, functionality, heterogeneity and due to this their manageability needs more attention. Autonomic capabilities in Database Management Systems (DBMSs) are also essential for ease of management, cost of maintenance and hide the low level complexities from end users. With autonomic capabilities administrators can perform higher-level tasks. The DBMS that has the ability to manage itself according to the environment and resources without any human intervention is known as Autonomic DBMS (ADBMS). The paper explores and analyzes the autonomic components of Oracle by considering autonomic characteristics. This analysis illustrates how different components of Oracle manage itself autonomically. The research is focused to find and earmark those areas in Oracle where the human intervention is required. We have performed the same type of research over Microsoft SQL Server and DB2 [1, 2]. A comparison of autonomic components of Oracle with SQL Server is provided to show their autonomic status.


title: Invar-36 Micro Hemispherical Shell Resonators

abstract: We report, for the first time, on the successful fabrication and operational characterization of electroplated Invar Micro-Hemispherical Shell Resonators (μHSR). The heat treatment of the samples and its effect on the quality factor (Q) of the resonators is studied. We show that thermal annealing shifts the coefficient of thermal expansion (CTE) of the alloy towards its minimum of ~2ppm/°C, as a result of which the Q of a 29kHz μHSR with diameter of 780 μm increases at least 3 times and reaches 7500 in vacuum.


title: A Novel Environmental Performance Evaluation of Thailand’s Food Industry Using Structural Equation Modeling and Fuzzy Analytic Hierarchy Techniques

abstract: Currently, the environment and sustainability are important topics for every industry. The food industry is particularly complicated in this regard because of the dynamic and complex character of food products and their production. This study uses structural equation modeling (SEM) and a fuzzy analytic hierarchy process (FAHP) to investigate which factors are suitable for evaluating the environmental performance of Thailand’s food industry. A first-stage questionnaire survey was conducted with 178 managers in the food industry that obtained a certificate from the Department of Industrial Work of Thailand to synthesize the performance measurement model and the significance of the relationship between the indicators. A second-stage questionnaire measured 18 experts’ priorities regarding the criteria and sub-factors involved in the different aspects and assessment items regarding environmental performance. SEM showed that quality management, market orientation, and innovation capability have a significantly positive effect on environmental performance. The FAHP showed that the experts were most concerned about quality management, followed by market orientation and innovation capability; the assessment items for quality policy, quality assurance, and customer orientation were of the most concern. The findings of this study can be referenced and support managerial decision making when monitoring environmental performance.


title: A 17.5–22.5 GHz fractional — N wideband frequency synthesizer in 65 nm CMOS technology

abstract: This paper presents a 65 nm CMOS fractional — N frequency synthesizer operating at the 17.43–22.43 GHz range, suitable for E-band and unlicensed 60 GHz band applications. The VCO uses a varactor along with banks of capacitors to implement 16 overlapping bands giving the ability to cover both E-band systems. The overall system operates over a wide fractional bandwidth of 25%, achieving Phase Noise of −100.01 dBc/Hz at 1MHz offset. The fractional-N architecture guarantees channel spacing of 24.4 KHz with very low overall power consumption of 55mW.


title: Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

abstract: OBJECTIVE ::: Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. ::: ::: ::: MATERIALS AND METHODS ::: Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). ::: ::: ::: RESULTS ::: We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. ::: ::: ::: DISCUSSION ::: Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. ::: ::: ::: CONCLUSIONS ::: We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.


title: Left-Right Skip-DenseNets for Coarse-to-Fine Object Categorization

abstract: Inspired by the recent neuroscience studies on the left-right asymmetry of the brains in the low and high spatial frequency processing, we introduce a novel type of network -- the left-right skip-densenets for coarse-to-fine object categorization. This network can enable both coarse and fine-grained classification in a single framework. We also for the first time propose the layer-skipping mechanism which learns a gating network to predict whether skip some layers in the testing stage. This layer-skipping mechanism assigns more flexibility and capability to our network for the categorization tasks. Our network is evaluated on three widely used datasets; the results show that our network is more promising in solving the coarse-to-fine object categorization than the competitors.


title: Blockade of the ganglion impar (walther), using ultrasound and a loss of resistance technique.

abstract: The ganglion impar is an unpaired sympathetic structure located at the level of the sacrococcygeal joint. Blockade of this structure has been utilised to treat chronic perineal pain. Methods to achieve this block often involve the use of fluoroscopy which is associated with radiation exposure of staff involved in providing these procedures. We report a combined loss of resistance injection technique in association with ultrasound guidance to achieve the block. Ultrasound was used to identify the sacrococcygeal joint and a needle was shown to enter this region. Loss of resistance was then used to demonstrate that the needle tip lies in a presacral space. The implication being that any injectate would be located in an adequate position. The potential exception would be a neurodestructive procedure as radiographic control of needle tip in relation to the rectum should be performed and recorded. However when aiming for a diagnostic or local anaesthetic based treatment option we feel that this may become an accepted method.


title: GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy

abstract: GATE (Geant4 Application for Emission Tomography) is a Monte Carlo simulation platform developed by the OpenGATE collaboration since 2001 and first publicly released in 2004. Dedicated to the modelling of planar scintigraphy, single photon emission computed tomography (SPECT) and positron emission tomography (PET) acquisitions, this platform is widely used to assist PET and SPECT research. A recent extension of this platform, released by the OpenGATE collaboration as GATE V6, now also enables modelling of x-ray computed tomography and radiation therapy experiments. This paper presents an overview of the main additions and improvements implemented in GATE since the publication of the initial GATE paper (Jan et al 2004 Phys. Med. Biol. 49 4543–61). This includes new models available in GATE to simulate optical and hadronic processes, novelties in modelling tracer, organ or detector motion, new options for speeding up GATE simulations, examples illustrating the use of GATE V6 in radiotherapy applications and 0031-9155/11/040881+21$33.00 © 2011 Institute of Physics and Engineering in Medicine Printed in the UK 881


title: A design of inductively coupled wireless power transfer system with coupling coil optimization

abstract: For development of inductive coupled wireless power transfer (WPT) systems, it is important to consider the overall efficiency of the WPT systems, where the overall efficiency is expressed as the product of the transmission efficiency of coupling coils and the power-conversion efficiency of power converters. Additionally, there are mutual dependencies between coupling coil parameters and power-converter ones. Therefore, it is necessary to design the power converters and the coupling coils simultaneously for maximizing the overall efficiency. This paper presents a design of inductively coupled WPT systems with coupling coil optimization for maximizing the overall efficiency. By combining design equations of the WPT system with class-E switching converters and theoretical formulas of magnetic-component design, the WPT system achieving maximum overall efficiency under specified physical conditions, such as coils size, distance between coils and so on, can be designed. Circuit experiments showed the validity and usefulness of the proposed design method.


title: Helios: Web-based Open-Audit Voting

abstract: Voting with cryptographic auditing, sometimes called open-audit voting, has remained, for the most part, a theoretical endeavor. In spite of dozens of fascinating protocols and recent ground-breaking advances in the field, there exist only a handful of specialized implementations that few people have experienced directly. As a result, the benefits of cryptographically audited elections have remained elusive. ::: ::: We present Helios, the first web-based, open-audit voting system. Helios is publicly accessible today: anyone can create and run an election, and any willing observer can audit the entire process. Helios is ideal for on-line software communities, local clubs, student government, and other environments where trustworthy, secret-ballot elections are required but coercion is not a serious concern. With Helios, we hope to expose many to the power of open-audit elections.


title: Satellite Image Analysis for Disaster and Crisis-Management Support

abstract: This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis-management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called "Center for satellite-based Crisis Information" (ZKI), to facilitate the use of its Earth-observation capacities in the service of national and international response to major disaster situations, humanitarian relief efforts, and civil security issues. This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes. During the last years, various international coordination bodies were established, improving the disaster-response-related cooperation within the Earth-observation community worldwide. DLR/ZKI operates in this context, closely networking with public authorities (civil security), nongovernmental organizations (humanitarian relief organizations), satellite operators, and other space agencies. This paper reflects on several of these international activities, such as the International Charter Space and Major Disasters, describes mapping procedures, and reports on rapid-mapping experiences gained during various disaster-response applications. The example cases presented cover rapid impact assessment after the Indian Ocean Tsunami, forest fires mapping for Portugal, earthquake-damage assessment for Pakistan, and landslide extent mapping for the Philippines


title: Photoassisted water-gas shift reaction over platinized titanium dioxide catalysts

abstract: The reaction of gas-phase water with CO (water-gas shift reaction) over platinized, powdered TiO/sub 2/ is found to be catalytic under uv illumination. The reaction kinetics have been studied at temperatures from 0 to 60/sup 0/C. At 25/sup 0/C, the reaction is zero order both in CO and H/sub 2/O when p/sub CO/ greater than or equal to 0.3 torr and p/sub H/sub 2/O/ greater than or equal to 5 torr and the activation energy is 7.5 kcal/mol. The wavelength dependence of the reaction rate shows a cut-off near, but slightly below, the band gap of TiO/sub 2/. The quantum efficiency of the reaction is about 0.5% at 25/sup 0/C. A mechanism is proposed which involves the photodecomposition of H/sub 2/O over platinized TiO/sub 2/.


title: From music audio to chord tablature: Teaching deep convolutional networks toplay guitar

abstract: Automatic chord recognition is conventionally tackled as a general music audition task, where the desired output is a time-aligned sequence of discrete chord symbols, e.g. CMaj7, Esus2, etc. In practice, however, this presents two related challenges: one, the act of decoding a given chord sequence requires that the musician knows both the notes in the chord and how to play them on some instrument; and two, chord labeling systems do not degrade gracefully for users without significant musical training. Alternatively, we address both challenges by modeling the physical constraints of a guitar to produce human-readable representations of music audio, i.e guitar tablature via a deep convolutional network. Through training and evaluation as a standard chord recognition system, the model is able to yield representations that require minimal prior knowledge to interpret, while maintaining respectable performance compared to the state of the art.


title: Ontology-Alignment Techniques: Survey and Analysis

abstract: The ontology alignment consists in generating a set of correspondences between entities. These entities can be concepts, properties or instances. The ontology alignment is an important task because it allows the joint consideration of resources described by different ontologies. This paper aims at counting all works of the ontology alignment field and analyzing the approaches according to different techniques (terminological, structural, extensional and semantic). This can clear the way and help researchers to choose the appropriate solution to their issue. They can see the insufficiency, so that they can propose new approaches for stronger alignment. They can also adapt or reuse alignment techniques for specific research issues, such as semantic annotation, maintenance of links between entities, etc.


title: A COST-BENEFIT ANALYSIS (CBA) OF CONSTRUCTION HEALTH AND SAFETY MANAGEMENT: A THEORETICAL DISCUSSION

abstract: Research in construction health and safety has focussed on improving management strategies and policies that can effectively improve safety performance. The Health and Safety Executive (HSE) in 2005 developed on-line interactive tools for contractors to assess the cost of accidents to their organisations. Although providing good guidance, these tools developed do not provide sufficient detail to specifically assist contractors and designers in developing effective and efficient health and safety management systems. Therefore, it is considered important to develop a methodology to enable contractors to assess the true costs of accidents and the associated benefits of accident prevention as part of pre and post contract project evaluation. A framework is proposed based on an examination of economic viability of management of construction health and safety. The method developed incorporates accident cost elements, health and safety measures and benefit elements to improve decision making processes and guide contractors and designers in developing efficient and effective construction health and safety management practices.


title: Molecular generative model based on conditional variational autoencoder for de novo molecular design

abstract: We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.


title: Vehicle Make Recognition Based on Convolutional Neural Network

abstract: Vehicle analysis has been investigated for decades, which involves license plate recognition, intelligent traffic. Among these applications, vehicle make recognition is a challenging task due to the close appearance between car models. In this paper, we propose an architecture to recognize vehicle make based on convolutional neural network (CNN). The moving car is first localized by frame difference, the resultant binary image is used to detect the frontal view of a car by a symmetry filter. The detected frontal view is used to train and test the CNN. Experimental results show that our proposed framework achieves favorable recognition accuracy.


title: User Graph Regularized Pairwise Matrix Factorization for Item Recommendation

abstract: Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class examples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seamlessly integrate user information into pairwise matrix factorization procedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item recommendation task.


title: Two new species of Maritrema Nicoll, 1907 (Digenea: Microphallidae) from New Zealand: morphological and molecular characterisation

abstract: Two new species of the microphallid genus Maritrema (Maritrema) Nicoll, 1907 are described from freshwaters in the South Island of New Zealand. Maritrema deblocki n. sp. occurs as an adult in the mallard Anas platyrhynchos (L.); Maritrema poulini n. sp. is found as sporocysts/cercariae in Potamopyrgus antipodarum (Gray) and as metacercariae in two species of amphipod and two species of isopod. We use morphological and molecular characterisation to distinguish between the two species, and compare them to their four morphologically closest congeners. M. deblocki n. sp. and M. poulini n. sp. are distinguished from each other by the relative sucker size, the positions of the genital pore and ovary, the convergence of the vitelline ribbons, and overall size. With the aid of molecular data, we matched life cycle stages of M. poulini n. sp. and assessed its use of multiple second intermediate hosts. Phylogenetic analyses included sequences for the two new species and the available microphallid sequences for the large ribosomal subunit and the internal transcribed spacer 1 of the ribosomal RNA gene. Closer to each other than to any other species, the sister species together with Maritrema novaezealandense Martorelli, Fredensborg, Mouritsen & Poulin, 2004, Maritrema heardi (Kinsella & Deblock, 1994), Maritrema eroliae Yamaguti, 1939 and Maritrema oocysta (Lebour, 1907) formed a well-supported clade. In addition, we clarify the taxonomic identity of several unidentified Microphallus spp. in the recent ecological literature from New Zealand and propose corrected spellings for a number of Maritrema species epithets.


title: Collaborative Data Analytics with DataHub

abstract: While there have been many solutions proposed for storing and analyzing large volumes of data, all of these solutions have limited support for collaborative data analytics, especially given the many individuals and teams are simultaneously analyzing, modifying and exchanging datasets, employing a number of heterogeneous tools or languages for data analysis, and writing scripts to clean, preprocess, or query data. We demonstrate DataHub, a unified platform with the ability to load, store, query, collaboratively analyze, interactively visualize, interface with external applications, and share datasets. We will demonstrate the following aspects of the DataHub platform: (a) flexible data storage, sharing, and native versioning capabilities: multiple conference attendees can concurrently update the database and browse the different versions and inspect conflicts; (b) an app ecosystem that hosts apps for various data-processing activities: conference attendees will be able to effortlessly ingest, query, and visualize data using our existing apps; (c) thrift-based data serialization permits data analysis in any combination of 20+ languages, with DataHub as the common data store: conference attendees will be able to analyze datasets in R, Python, and Matlab, while the inputs and the results are still stored in DataHub. In particular, conference attendees will be able to use the DataHub notebook---an IPython-based notebook for analyzing data and storing the results of data analysis.


title: Impact of business analytics and enterprise systems on managerial accounting

abstract: The nature of management accountants' responsibility is evolving from merely reporting aggregated historical value to also including organizational performance measurement and providing management with decision related information. Corporate information systems such as enterprise resource planning (ERP) systems have provided management accountants with both expanded data storage power and enhanced computational power. With big data extracted from both internal and external data sources, management accountants now could utilize data analytics techniques to answer the questions including: what has happened (descriptive analytics), what will happen (predictive analytics), and what is the optimized solution (prescriptive analytics). However, research shows that the nature and scope of managerial accounting has barely changed and that management accountants employ mostly descriptive analytics, some predictive analytics, and a bare minimum of prescriptive analytics. This paper proposes a Managerial Accounting Data Analytics (MADA) framework based on the balanced scorecard theory in a business intelligence context. MADA provides management accountants the ability to utilize comprehensive business analytics to conduct performance measurement and provide decision related information. With MADA, three types of business analytics (descriptive, predictive, and prescriptive) are implemented into four corporate performance measurement perspectives (financial, customer, internal process, and learning and growth) in an enterprise system environment. Other related issues that affect the successful utilization of business analytics within a corporate-wide business intelligence (BI) system, such as data quality and data integrity, are also discussed. This paper contributes to the literature by discussing the impact of business analytics on managerial accounting from an enterprise systems and BI perspective and by providing the Managerial Accounting Data Analytics (MADA) framework that incorporates balanced scorecard methodology.


title: "GrabCut": interactive foreground extraction using iterated graph cuts

abstract: The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.


title: Empirical study of performance benefits of hardware assisted virtualization

abstract: An application's performance can suffer from significant computational overheads when it is moved from a native to a virtualized environment. Adoption of virtualization without understanding such overheads in detail can dramatically impact the overall performance of hosted applications. The rapid adoption of virtualization has fueled the development of new hardware technologies, which promise to optimize the performance and scalability of processor and network I/O virtualization. However, no comprehensive empirical study of the effectiveness of these hardware assistance technologies is publicly available. In this paper we focus on x86 architectures and study empirically the performance improvements introduced by Intel's VT and PCI-SIG's SR-IOV on a Xen-based hypervisor. Using a range of benchmark programs, we compare benchmark scores and resource utilization between native and virtual environments for two different testbeds, one with hardware assistance and one without. The results indicate that hardware assistance indeed eliminates most overheads, especially those relating to network I/O, but non-negligible CPU overheads still remain. Also, there is no hardware technology with specifically deals with disk I/O virtualization, and significant overheads do arise in workloads requiring intensive disk usage.


title: Toward the Human–Robot Co-Existence Society: On Safety Intelligence for Next Generation Robots

abstract: Technocrats from many developed countries, especially Japan and South Korea, are preparing for the human–robot co-existence society that they believe will emerge by 2030. Regulators are assuming that within the next two decades, robots will be capable of adapting to complex, unstructured environments and interacting with humans to assist with the performance of daily life tasks. Unlike heavily regulated industrial robots that toil in isolated settings, Next Generation Robots will have relative autonomy, which raises a number of safety issues that are the focus of this article. Our purpose is to describe a framework for a legal system focused on Next Generation Robots safety issues, including a Safety Intelligence concept that addresses robot Open-Texture Risk. We express doubt that a model based on Isaac Asimov’s Three Laws of Robotics can ever be a suitable foundation for creating an artificial moral agency ensuring robot safety. Finally, we make predictions about the most significant Next Generation Robots safety issues that will arise as the human–robot co-existence society emerges.