title: A situated computing framework for mobile and ubiquitous multimedia access using small screen and composite devices

abstract: In recent years, small screen devices, such as cellular phones or Personal Digital Assistants (PDAs), enjoy phenomenal popularity. PDAs can be used to complement traditional computing systems to access personal multimedia information beyond the usage as digital organizers. However, due to the physical limitations accessing rich multimedia contents and diverse services using a single PDA is more difficult. Hence, the Situated Computing Framework (SCF) research project at Siemens Corporate Research (SCR) aims to develop a ubiquitous computing infrastructure that facilitates nomadic users to access rich multimedia contents using small screen devices. This paper describes a new distributed computing concept, the Small Screen/Composite Device (SS/CD) framework, which offers mobile users new classes of ubiquitous and mobile multimedia services without to limit the diversity and the richness of the provided services.


title: Control and Circuit Techniques to Mitigate Partial Shading Effects in Photovoltaic Arrays

abstract: Partial shading in photovoltaic (PV) arrays renders conventional maximum power point tracking (MPPT) techniques ineffective. The reduced efficiency of shaded PV arrays is a significant obstacle in the rapid growth of the solar power systems. Thus, addressing the output power mismatch and partial shading effects is of paramount value. Extracting the maximum power of partially shaded PV arrays has been widely investigated in the literature. The proposed solutions can be categorized into four main groups. The first group includes modified MPPT techniques that properly detect the global MPP. They include power curve slope, load-line MPPT, dividing rectangles techniques, the power increment technique, instantaneous operating power optimization, Fibonacci search, neural networks, and particle swarm optimization. The second category includes different array configurations for interconnecting PV modules, namely series-parallel, total-cross-tie, and bridge-link configurations. The third category includes different PV system architectures, namely centralized architecture, series-connected microconverters, parallel-connected microconverters, and microinverters. The fourth category includes different converter topologies, namely multilevel converters, voltage injection circuits, generation control circuits, module-integrated converters, and multiple-input converters. This paper surveys the proposed approaches in each category and provides a brief discussion of their characteristics.


title: Spectral Efficiency Analysis for Downlink NOMA Aided Spatial Modulation With Finite Alphabet Inputs

abstract: As the conventional spatial modulation (SM) system lacks an efficient downlink multiple access scheme, this paper proposes a novel downlink nonorthogonal multiple access aided multiple-input multiple-output SM system with finite alphabet inputs. Mutual information (MI) is used to characterize the system's achievable spectral efficiency (SE). Because the MI lacks a closed-form formulation, in this paper we propose a computational-efficient lower bound to provide an approximation to the simulated MI. Besides, preprocessing is also designed at the receiver to mitigate the detrimental effect of channel correlation, which leads to striking a higher SE performance. Simulation results not only confirm the accuracy of our SE analysis, but also substantiate the superior SE performance achieved by the proposed preprocessing.


title: Internet of things (IoT): a survey on architecture, enabling technologies, applications and challenges

abstract: Convergence of the two technologies- Internet and sensing networks creates a paradigm, Internet of Things (IoT) which allows direct machine-to-machine(M2M) communication. The IoT has transformed human-to-human and human-to-machine communication to machine-to-machine communication. It encompasses huge number of heterogeneous smart devices that collaborates each other to achieve different smart application goals like smart cities, connected cars, etc. Though Radio Frequency Identification (RFID), wireless, mobile and sensor technologies make IoT feasible, it suffers from many challenges like scalability, security, and interoperability. This article presents a comprehensive overview of IoT and survey of existing architectures, enabling technologies, applications and research challenges for IoT.


title: On the possibility of Biosensors Based on Split Ring Resonators

abstract: An investigation on the feasibility of a biosensor based on split ring resonator (SRR) is reported in this paper. A two-pair SRR array is used for the biosensor device. The compact SRR composite is excited by a microstrip transmission line which can generate the quasi-TEM mode. The device showed sensitivity to the biosensing parameters of S21 resonant frequency and loaded Q values, due to the coupling of nano-size biomaterials. The feasibility of and the applications for a biosensor based on based on SRRs are also briefly discussed.


Acute alcohol effects on facial expressions of emotions in social drinkers: a systematic review


Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records


title: Structural Brain MRI Segmentation Using Machine Learning Technique

abstract: Segmenting brain MR scans could be highly benecial for diagnosing, treating and evaluating the progress of specic diseases. Up to this point, manual segmentation,performed by experts, is the conven ...


title: Splitting interfaces: making trust between applications and operating systems configurable

abstract: In current commodity systems, applications have no way of limiting their trust in the underlying operating system (OS), leaving them at the complete mercy of an attacker who gains control over the OS. In this work, we describe the design and implementation of Proxos, a system that allows applications to configure their trust in the OS by partitioning the system call interface into trusted and untrusted components. System call routing rules that indicate which system calls are to be handled by the un-trusted commodity OS, and which are to be handled by a trusted private OS, are specified by the application developer. We find that rather than defining a new system call interface, routing system calls of an existing interface allows applications currently targeted towards commodity operating systems to isolate their most sensitive components from the commodity OS with only minor source code modifications. We have built a prototype of our system on top of the Xen Virtual Machine Monitor with Linux as the commodity OS. In practice, we find that the system call routing rules are short and simple -- on the order of 10's of lines of code. In addition, applications in Proxos incur only modest performance overhead, with most of the cost resulting from inter-VM context switches.


title: A 3D Imaging Framework Based on High-Resolution Photometric-Stereo and Low-Resolution Depth

abstract: This paper introduces a 3D imaging framework that combines high-resolution photometric stereo and low-resolution depth. Our approach targets imaging scenarios based on either macro-lens photography combined with focal stacking or a large-format camera that are able to image objects with more than 600 samples per mm $$^2$$ . These imaging techniques allow photometric stereo algorithms to obtain surface normals at resolutions that far surpass corresponding depth values obtained with traditional approaches such as structured-light, passive stereo, or depth-from-focus. Our work offers two contributions for 3D imaging based on these scenarios. The first is a multi-resolution, patched-based surface reconstruction scheme that can robustly handle the significant resolution difference between our surface normals and depth samples. The second is a method to improve the initial normal estimation by using all the available focal information for images obtained using a focal stacking technique.


title: BranchConnect: Large-Scale Visual Recognition with Learned Branch Connections

abstract: We introduce an architecture for image categorization that enables the end-to-end learning of separate visual features for the different classes to distinguish. The proposed model consists of a deep CNN shaped like a tree. The stem of the tree includes a sequence of convolutional layers common to all classes. The stem then splits into multiple branches implementing parallel feature extractors, which are ultimately connected to the final classification layer via learned gated connections. These learned gates determine for each individual class the subset of features to use. Such a scheme naturally encourages the learning of a heterogeneous set of specialized features through the separate branches and it allows each class to use the subset of features that are optimal for its recognition. We show the generality of our proposed method by reshaping several popular CNNs from the literature into our proposed architecture. Our experiments on the CIFAR100, CIFAR10, ImageNet, and Synth datasets show that in each case our resulting model yields a substantial improvement in accuracy over the original CNN. Our empirical analysis also suggests that our scheme acts as a form of beneficial regularization improving generalization performance.


title: OSNAP! Introducing the open semantic network analysis platform

abstract: Graph visualization continues to be a major challenge in the field of information visualization, meanwhile gaining im- portance due to the power of graph-based formulations across a wide variety of domains from knowledge representation to network flow, bioinformatics, and software optimization. We present the Open Semantic Network Analysis Platform (OSNAP), an open-source visualization framework designed for the flexible composition of 2D and 3D graph layouts. Analysts can filter and map a graph's attributes and structural properties to a variety of geometric forms including shape, color, and 3D position. Using the Provider Model software engineering pattern, developers can extend the framework with additional mappings and layout algorithms. We demonstrate the framework's flexibility by applying it to two separate domain ontologies and finally outline a research agenda to improve the value of semantic network visualization for human insight and analysis.


title: A Composite Kernel To Extract Relations Between Entities With Both Flat And Structured Features

abstract: This paper proposes a novel composite kernel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution parse tree kernel that models syntactic information of relation examples. The motivation of our method is to fully utilize the nice properties of kernel methods to explore diverse knowledge for relation extraction. Our study illustrates that the composite kernel can effectively capture both flat and structured features without the need for extensive feature engineering, and can also easily scale to include more features. Evaluation on the ACE corpus shows that our method outperforms the previous best-reported methods and significantly out-performs previous two dependency tree kernels for relation extraction.


Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension


title: Design of low voltage bandgap reference circuit using subthreshold MOSFET

abstract: In this paper, we present the design of a low voltage bandgap reference (LVBGR) circuit for supply voltage of 1.2V which can generate an output reference voltage of 0.363V. Traditional BJT based bandgap reference circuits give very precise output reference but power and area consumed by these BJT devices is larger so for low supply bandgap reference we chose MOSFETs operating in subthreshold region based reference circuits. LVBGR circuits with less sensitivity to supply voltage and temperature is used in both analog and digital circuits like high precise comparators used in data converter, phase-locked loop, ring oscillator, memory systems, implantable biomedical product etc. In the proposed circuit subthreshold MOSFETs temperature characteristics are used to achieve temperature compensation of output voltage reference and it can work under very low supply voltage. A PMOS structure 2stage opamp which will be operating in subthreshold region is designed for the proposed LVBGR circuit whose gain is 89.6dB and phase margin is 74 °. Finally a LVBGR circuit is designed which generates output voltage reference of 0.364V given with supply voltage of 1.2 V with 10 % variation and temperature coefficient of 240ppm/ °C is obtained for output reference voltage variation with respect to temperature over a range of 0 to 100°C. The output reference voltage exhibits a variation of 230μV with a supply range of 1.08V to 1.32V at typical process corner. The proposed LVBGR circuit for 1.2V supply is designed with the Mentor Graphics Pyxis tool using 130nm technology with EldoSpice simulator. Overall current consumed by the circuit is 900nA and also the power consumed by the entire LVBGR circuit is 0.9μW and the PSRR of the LVBGR circuit is −70dB.


LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network


Multi-View Intrinsic Images of Outdoors Scenes with an Application to Relighting SYLVAIN DUCHÊNE and CLEMENT RIANT and GAURAV CHAURASIA and


title: A systematic analysis of textual variability modeling languages

abstract: Industrial variability models tend to grow in size and complexity due to ever-increasing functionality and complexity of software systems. Some authors report on variability models specifying several thousands of variabilities. However, traditional variability modeling approaches do not seem to scale adequately to cope with size and complexity of such models. Recently, textual variability modeling languages have been advocated as one scalable solution. In this paper, we provide a systematic analysis of the capabilities of current textual variability modeling languages, in particular regarding variability management in the large. Towards this aim, we define a classification schema consisting of five dimensions, classify ten different textual variability modeling languages using the classification schema and provide an analysis. In summary, some textual variability modeling languages go beyond textual representations of traditional variability modeling approaches and provide sophisticated modeling concepts and constraint languages. Three textual variability modeling approaches already support mechanisms for large-scale variability modeling such as model composition, modularization, or evolution support.


title: Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical magnetic resonance spectroscopy signals

abstract: This article describes a Java-based graphical user interface for the magnetic resonance user interface (MRUI) quantitation package. This package allows MR spectroscopists to easily perform time-domain analysis of in vivo/medical MR spectroscopy data. We have found that the Java programming language is very well suited for developing highly interactive graphical software applications such as the MRUI system. We also have established that MR quantitation algorithms, programmed in the past in other languages, can easily be embedded into the Java-based MRUI by using the Java native interface (JNI).


title: The dynamic placement of virtual network functions

abstract: This paper addresses the problem of managing highly dynamic network and service environments, where virtual nodes and virtual links are created and destroyed depending on traffic volumes, service requests, or high-level goals such as reduction in energy consumption. This problem will be one of the main technical challenges to be faced in the evolution towards Future Networks (FN). Emerging paradigms such as Software Defined Networks (SDN) and Network Function Virtualization (NfV) are concrete steps towards infrastructures where network functions and services will be executed as applications in ensembles of virtual machines (VMs) hosted in pervasive standard hardware resources located across a network. The paper argues that in order to manage these virtual infrastructures there is a need to introduce high-level systems orchestration. The paper describes an architecture based on an orchestrater that ensures the automatic placement of the virtual nodes and the allocation of network services on them, supported by a monitoring system that collects and reports on the behaviour of the resources. The orchestrater manages the creation and removal of the virtual nodes, as well as configuring, monitoring, running and stopping software on them. As a proof of these concepts, a distributed orchestrater prototype has been designed, implemented and tested with the results of different placement algorithms presented.


title: Applying wearable solutions in dependent environments

abstract: This paper proposes a multiagent system (MAS) that uses smart wearable devices and mobile technology for the care of patients in a geriatric home care facility. The system is based on an advanced ZigBee wireless sensor network (WSN) and includes location and identification microchips installed in patient clothing and caregiver uniforms. The use of radio-frequency identification and near-field communication technologies allows remote monitoring of patients, and makes it possible for them to receive treatment according to preventive medical protocol. The proposed MAS manage the infrastructure of services within the environment both efficiently and securely by reasoning, task-planning, and synchronizing the data obtained from the sensors. Additionally, this paper presents the design and implementation of the reasoning agent in the MAS. A system prototype was installed in a real environment and the results obtained are presented in this paper.


title: Traffic flow characteristic analysis at intersections from multi-layer spectral clustering of motion patterns using raw vehicle trajectory

abstract: Road intersections are important components of urban road system. It is the traffic flow characteristics representing the current traffic situation that provide a basis for the planning, designing and management of intersections. In this paper, we constructed an automatic processing framework on traffic flow characteristics analysis and understanding the traffic state at the urban road intersections, based on the collected raw vehicle motion trajectories. Our proposed method is basically attributed to identifying distinct vehicle motion patterns at intersections hierarchically using raw trajectory. Firstly, the fundamental assumption in traditional approaches that the trajectory set of high quality are readily available after manual rectification is not taken for granted any more. And by fully analyzing the local characteristics of trajectories, we figure out and explain various patterns behind traffic flow as well as yielded higher accuracy in motion trajectory clustering under the multi-layer spectral clustering method. At last, coupling the analyzing results with the surrounding characteristics of the intersection, the examples computing traffic flow features and predicting vehicle activity illustrates the potential of applying vehicle trajectories to traffic study, which are all suggested by experimental results.


title: Local causal and Markov blanket induction for causal discovery and feature selection for classification. Part II: Analysis and extensions

abstract: In part I of this work we introduced and evaluated the Generalized Local Learning (GLL) framework for producing local causal and Markov blanket induction algorithms. In the present second part we analyze the behavior of GLL algorithms and provide extensions to the core methods. Specifically, we investigate the empirical convergence of GLL to the true local neighborhood as a function of sample size. Moreover, we study how predictivity improves with increasing sample size. Then we investigate how sensitive are the algorithms to multiple statistical testing, especially in the presence of many irrelevant features. Next we discuss the role of the algorithm parameters and also show that Markov blanket and causal graph concepts can be used to understand deviations from optimality of state-of-the-art non-causal algorithms. The present paper also introduces the following extensions to the core GLL framework: parallel and distributed versions of GLL algorithms, versions with false discovery rate control, strategies for constructing novel heuristics for specific domains, and divide-and-conquer local-to-global learning (LGL) strategies. We test the generality of the LGL approach by deriving a novel LGL-based algorithm that compares favorably to the state-of-the-art global learning algorithms. In addition, we investigate the use of non-causal feature selection methods to facilitate global learning. Open problems and future research paths related to local and local-to-global causal learning are discussed.


title: Automated software transplantation

abstract: Automated transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system. This paper introduces a theory, an algorithm, and a tool that achieve this. Leveraging lightweight annotation, program analysis identifies an organ (interesting behavior to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. While we do not claim automated transplantation is now a solved problem, our results are encouraging: we report that in 12 of 15 experiments, involving 5 donors and 3 hosts (all popular real-world systems), we successfully autotransplanted new functionality and passed all regression tests. Autotransplantation is also already useful: in 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player; compare this to upgrading x264 within VLC, a task that we estimate, from VLC's version history, took human programmers an average of 20 days of elapsed, as opposed to dedicated, time.


title: Children and Adults Both Learn Motor Sequences Quickly, But Do So Differently

abstract: Both children and adults can learn motor sequences quickly in one learning session, yet little is known about potential age-related processes that underlie this fast sequence acquisition. Here, we examined the progressive performance changes in a one-session modified serial reaction time task in 6- and 10-year-old children and adults. We found that rapid sequence learning, as reflected by reaction time (RT), was comparable between groups. The learning was expressed through two behavioral processes: online progressive changes in RT while the task was performed in a continuous manner and offline changes in RT that emerged following a short rest. These offline and online RT changes were age-related; learning in 6-year-olds was primarily reflected through the offline process. In contrast, learning in adults was reflected through the online process; and both online and offline processes occurred concurrently in 10-year-olds. Our results suggest that early rapid sequence learning has a developmental profile. Although the unifying mechanism underlying these two age-related processes is unclear, we discuss possible explanations that need to be systematically elucidated in future studies.


title: Automated face recognition of rhesus macaques

abstract: Abstract Background Rhesus macaques are widely used in biomedical research. Automated behavior monitoring can be useful in various fields (including neuroscience), as well as having applications to animal welfare but current technology lags behind that developed for other species. One difficulty facing developers is the reliable identification of individual macaques within a group especially as pair- and group-housing of macaques becomes standard. Current published methods require either implantation or wearing of a tracking device. New method I present face recognition, in combination with face detection, as a method to non-invasively identify individual rhesus macaques in videos. The face recognition method utilizes local-binary patterns in combination with a local discriminant classification algorithm. Results A classification accuracy of between 90 and 96% was achieved for four different groups. Group size, number of training images and challenging image conditions such as high contrast all had an impact on classification accuracy. I demonstrate that these methods can be applied in real time using standard affordable hardware and a potential application to studies of social structure. Comparison with existing method(s) Face recognition methods have been reported for humans and other primate species such as chimpanzees but not rhesus macaques. The classification accuracy with this method is comparable to that for chimpanzees. Face recognition has the advantage over other methods for identifying rhesus macaques such as tags and collars of being non-invasive. Conclusions This is the first reported method for face recognition of rhesus macaques, has high classification accuracy and can be implemented in real time.


title: Defending against Adversarial Images using Basis Functions Transformations

abstract: We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images. Specifically, we experiment with low-pass filtering, PCA, JPEG compression, low resolution wavelet approximation, and soft-thresholding. We evaluate these defense techniques using three types of popular attacks in black, gray and white-box settings. Our results show JPEG compression tends to outperform the other tested defenses in most of the settings considered, in addition to soft-thresholding, which performs well in specific cases, and yields a more mild decrease in accuracy on benign examples. In addition, we also mathematically derive a novel white-box attack in which the adversarial perturbation is composed only of terms corresponding a to pre-determined subset of the basis functions, of which a "low frequency attack" is a special case.


title: Efficient Path Planning Algorithm for Additive Manufacturing Systems

abstract: Recent additive manufacturing technologies, such as 3-D printing and printed electronics, require constant speed motion for consistent material deposition. In this paper, a new path planning algorithm is developed for an \(XY\) -motion stage with an emphasis on aerosol printing. The continuous aerosol stream provided by the printing nozzle requires constant velocity in relative motion of a substrate to evenly deposit inks. During transitioning between print segments, a shutter prevents the aerosol from reaching the substrate, therefore wasting material. The proposed path planning algorithm can control motion of an \(XY\) stage for an arbitrary printing path and desired velocity while minimizing material waste. Linear segments with parabolic blends (LSPBs) trajectory planning is used during printing, and minimum time trajectory (MTT) planning is used during printer transition. Simulation results show that combining LSPB with MTT can minimize the printing time while following the desired path.


title: Probing the active site of trypsin with rose bengal: insights into the photodynamic inactivation of the enzyme.

abstract: In this work the active site of trypsin has been probed with the dye rose bengal. The dye binds competitively to the enzyme, and it can be used as a probe of the active site of the enzyme. On the basis of the emission wavelength, the binding site of trypsin is relatively polar and is similar to that of acetone in its polarity. The triplet state of rose bengal is quenched by trypsin. This quenching may be caused by the tryptophan and tyrosine residues that are in the near vicinity of the trypsin active site. This quenching can compete with the formation of singlet oxygen from the excited triplet state of rose bengal. We demonstrate that the singlet oxygen involved in the photoinactivation of trypsin is produced by the free rose bengal in solution and the bound dye is incapable of producing singlet oxygen. This explains the lack of correlation between photoinactivation efficiency and sensitizer binding capability previously reported by Wade and Spikes.


title: Considering service strategy in ITIL V3 as a framework for IT Governance

abstract: IT governance is the process by which decisions are made around IT investments. A well-matured IT governance framework is based on three major elements: structure, process and communication. In addition, there are four objectives that drives IT governance and must be covered in IT governance processes: IT value and alignment, accountability, performance measurement, and risk management. As we know Information Technology Infrastructure Library (ITIL) is a public framework that describes Best Practice in IT service management. Although some researchers have considered ITIL V2 as a part of their IT Governance framework, this paper claims that ITIL V3 (new publication of ITIL in 2007) and “Service Strategy” as the core of this publication, can provide a well-matured framework for the governance of IT by covering all requirement of IT Governance.


title: Network models in neuroscience

abstract: From interacting cellular components to networks of neurons and neural systems, interconnected units comprise a fundamental organizing principle of the nervous system. Understanding how their patterns of connections and interactions give rise to the many functions of the nervous system is a primary goal of neuroscience. Recently, this pursuit has begun to benefit from the development of new mathematical tools that can relate a system's architecture to its dynamics and function. These tools, which are known collectively as network science, have been used with increasing success to build models of neural systems across spatial scales and species. Here we discuss the nature of network models in neuroscience. We begin with a review of model theory from a philosophical perspective to inform our view of networks as models of complex systems in general, and of the brain in particular. We then summarize the types of models that are frequently studied in network neuroscience along three primary dimensions: from data representations to first-principles theory, from biophysical realism to functional phenomenology, and from elementary descriptions to coarse-grained approximations. We then consider ways to validate these models, focusing on approaches that perturb a system to probe its function. We close with a description of important frontiers in the construction of network models and their relevance for understanding increasingly complex functions of neural systems.


title: FPGA implementation of a coherent SOQPSK-TG demodulator

abstract: In this paper, we present a hardware implementation of a demodulator for shaped offset quadrature phase shift keying, telemetry group version (SOQPSK-TG). The demodulator is implemented based on maximum likelihood sequence detection of SOQPSK using the soft-output Viterbi algorithm (SOVA). Symbol timing and carrier phase recovery are fundamental operations required for the best symbol detection. We describe how to generate time and phase error signals from the SOVA which are fed in two separate discrete phase locked loops (PLLs). The PLLs work along with other blocks including a down converter, matched filters and the SOVA in order to provide optimal sequence detection of the received signal from analog to digital converter. Additionally, the SOVA provides output reliabilities for the detected bits which can be used by iterative error correction schemes such as low-density parity-check (LDPC) codes. Moreover, we propose a simplified implementation of the SOVA based on the two-traceback method which reduces the overall size of the demodulator by 25%. The simplification causes a slight degradation in the output reliabilities which is examined by LDPC decoding of its output. The demodulator is targeted for field programmable gate arrays (FPGAs) which provide us with flexibility and rapid prototyping. Finally, the FPGA performance and utilization results along with bit error rate performance of both demodulator and LDPC coded system are presented.


title: Robust and Fast Three-Phase PLL Tracking System

abstract: This paper presents a simple, robust, and fast synchronization method to detect the phase angle of the positive sequence of a three-phase ac system. The algorithm was derived from a standard phase-locked-loop (PLL) circuit based on the pq theory, but a feedforward action is implemented to guarantee high dynamic performance in conjunction with a modified feedback action to reduce the phase-angle estimation error to zero. The result is a fast tracking system that improves the start-up stage and rapid input variations. It guarantees accurate phase locking and is robust with respect to ac distortions such as harmonics, subharmonics, and voltage imbalance. The control model for the proposed PLL system was based on the standard qPLL structure. A linear analysis was performed to show the benefits of the proposed solution, and recommendations are given for tuning its parameters. The proposed technique was tested and compared with other algorithms by means of simulations. The experimental and simulation results are shown and compared.


title: A canonical form of a system of microdifferential equations with non-involutory characteristics and branching of singularities

abstract: We study a system ℳ of microdifferential (=pseudodifferential) equations. We assume that the characteristic varietyV of ℳ takes the formV=V1∪V2; hereV1andV2are regular involutory submanifolds and intersect normally, andV1∩V2is non-involutory and 1-codimensional both inV1and inV2. We also assume that ℳ has regular singularities alongV. Then we give a canonical form of ℳ in the complex domain. This enables us to investigate the branching of supports of microfunction solutions of ℳ when ℳ is hyperbolic.


title: Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

abstract: Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, an approach to audit such models without probing the black-box model API or pre-defining features to audit. To gain insight into black-box models, we treat them as teachers, training transparent student models to mimic the risk scores assigned by the black-box models. We compare the mimic model trained with distillation to a second, un-distilled transparent model trained on ground truth outcomes, and use differences between the two models to gain insight into the black-box model. We demonstrate the approach on four data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending Club. We also propose a statistical test to determine if a data set is missing key features used to train the black-box model. Our test finds that the ProPublica data is likely missing key feature(s) used in COMPAS.


Combining Deep Learning and Survival Analysis for Asset Health Management


title: On Map-Matching Vehicle Tracking Data

abstract: Vehicle tracking data is an essential "raw" material for a broad range of applications such as traffic management and control, routing, and navigation. An important issue with this data is its accuracy. The method of sampling vehicular movement using GPS is affected by two error sources and consequently produces inaccurate trajectory data. To become useful, the data has to be related to the underlying road network by means of map matching algorithms. We present three such algorithms that consider especially the trajectory nature of the data rather than simply the current position as in the typical map-matching case. An incremental algorithm is proposed that matches consecutive portions of the trajectory to the road network, effectively trading accuracy for speed of computation. In contrast, the two global algorithms compare the entire trajectory to candidate paths in the road network. The algorithms are evaluated in terms of (i) their running time and (ii) the quality of their matching result. Two novel quality measures utilizing the Frechet distance are introduced and subsequently used in an experimental evaluation to assess the quality of matching real tracking data to a road network.


title: Thermally controlled dual-mode display media with red-green-blue coloration and fluorescence via energy transfer between emission materials and leuco dyes

abstract: Multifunctional materials such as those with thermo-switchable absorption or emission are widely applied to thermal recording media. We combined red-green-blue (RGB) thermochromic materials and luminescent materials in order to construct novel dual-mode display media that can control both coloration and emission. The composite films showed thermo-responsive control of RGB coloration with high reversibility through structural changes of nanoscale aggregates of the leuco dye and its developer. Moreover, RGB photoluminescence was also controlled by specific thermal treatment via intermolecular energy transfer from luminescent materials in the excited state to the colored leuco dyes.


title: Improving deep neural network ensembles using reconstruction error

abstract: Ensemble learning of neural network is a learning paradigm where ensembles of several neural networks show improved generalization capabilities that outperform those of single networks. For deep learning of multi-layer neural networks, ensemble learning is still applicable. In addition, characteristics of deep neural networks can provide potential opportunities to improve the performance of traditional neural network ensembles. In this paper, we propose an ensemble criterion of deep neural networks that is based on the reconstruction error and present two strategies to solve the most important issues in ensemble learning of neural networks: component dataset sampling and output averaging. Component training datasets are selected according to the reconstruction error instead of random bootstrap sampling or re-weighting. Moreover, for each testing instance, we can compute the reconstruction error yielded by the sub-model simultaneously with the output. The reconstruction error is used as the weights in output averaging. From the perspectives of prediction interval and confidence interval, we demonstrated that smaller reconstruction error could ensure smaller prediction interval. We also incorporate the famous structure ensemble approach “Dropout” into the proposed approach to achieve the best performance. We conduct experiments on classification and regression datasets to validate the effectiveness of our approach.


title: DSMR: A Parallel Algorithm for Single-Source Shortest Path Problem

abstract: The Single Source Shortest Path (SSSP) problem consists in finding the shortest paths from a vertex (the source vertex) to all other vertices in a graph. SSSP has numerous applications. For some algorithms and applications, it is useful to solve the SSSP problem in parallel. This is the case of Betweenness Centrality which solves the SSSP problem for multiple source vertices in large graphs. In this paper, we introduce the Dijkstra Strip Mined Relaxation (DSMR) algorithm, an efficient parallel SSSP algorithm for shared and distributed-memory systems. We also introduce a set of preprocessing optimization techniques that significantly reduce the communication overhead without increasing the total amount of work dramatically. Our results show that, DSMR is faster than the best previous algorithm, parallel Δ-Stepping, by up-to 7.38×.


title: Efficient Sparse Voxel Octrees

abstract: In this paper, we examine the possibilities of using voxel representations as a generic way for expressing complex and feature-rich geometry on current and future GPUs. We present in detail a compact data structure for storing voxels and an efficient algorithm for performing ray casts using this structure. We augment the voxel data with novel contour information that increases geometric resolution, allows more compact encoding of smooth surfaces, and accelerates ray casts. We also employ a novel normal compression format for storing high-precision object-space normals. Finally, we present a variable-radius postprocess filtering technique for smoothing out blockiness caused by discrete sampling of shading attributes. Based on benchmark results, we show that our voxel representation is competitive with triangle-based representations in terms of ray casting performance, while allowing tremendously greater geometric detail and unique shading information for every voxel. Our voxel codebase is open sourced and available at http://code.google.com/p/efficient-sparse-voxel-octrees/.


title: Guest Editorial Industry 4.0-Prerequisites and Visions

abstract: The papers in this special section focus on the concept of Industry 4.0. From its origin, Industry 4.0–derived from the German term Industrie 4.0–is used as a synonym for Cyber-Physical Production Systems (CPPS), i.e., Cyber-Physical Systems applied in the domain of manufacturing/production. To enable CPPS their automation systems need to be enabled to fulfill the requirements. The term Industrie 4.0 was first used in 2011 at the Hannover Fair and the topic has grown every year not only on the fair. There are still several definitions of Industrie 4.0 (I4.0).


title: Recruitment of lactating women into a randomized dietary intervention: successful strategies and factors promoting enrollment and retention.

abstract: INTRODUCTION ::: Recruitment and retention of lactating women require unique strategies to prevent high attrition. The purpose of this report is to identify successful recruitment strategies and evaluate demographic and lifestyle characteristics associated with study completion. ::: ::: ::: METHODS ::: A randomized, controlled trial was initiated to test the hypothesis that lactating women adhering to a Mediterranean diet will show a significant reduction in anthropometric measurements as compared to lactating women randomized to the USDA's MyPyramid diet for Pregnancy and Breastfeeding (control diet). Measurements were collected at baseline, 2 months, and 4 months. Recruitment methods and baseline characteristics of completers and non-completers are described. ::: ::: ::: RESULTS ::: The largest percentage of women, 24.8%, were recruited from a local parenting magazine, 20.9% from Craig's List, 20.2% from local hospitals, and 34.1% from various other sources. At baseline, women (n = 129) were mostly Non-Hispanic (75.2%), average age 29.7 years, BMI averaged 27.2 kg/m(2), waist:hip ratio 0.84 cm (SD: 0.07), and body fat averaged 30.8%. Approximately 72% were exclusively breastfeeding, a mean 17.5 weeks postpartum, and 69.0% had a college degree. Non-completers were more likely to have supplemented with formula at baseline as compared to completers (P<0.001). No other characteristics were significantly associated with attrition. ::: ::: ::: CONCLUSION ::: Researchers conducting studies with lactating women may consider "exclusive breastfeeding" as a study inclusion criterion to prevent high attrition rates or include additional breastfeeding support to study participants.


title: CoCoA: A General Framework for Communication-Efficient Distributed Optimization

abstract: The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets.


title: Vibration analysis of cutting force in titanium alloy milling

abstract: Machining processes such as milling, which are characterized by interrupted cutting, are often susceptible to problems involving vibration of the machine-tool-workpiece fixation device system because of the proximity between their natural frequency harmonics and the frequency of tool entry on the workpiece. This phenomenon is particularly important in the milling of titanium alloys, because these materials show a low Young modulus, and hence, an extended elastic behavior, which means tremendous variations in chip thickness and fluctuating cutting forces. Moreover, very low heat conductivity causes the formation of serrated chips, which further increase the fluctuation in cutting forces. The purpose of this work is to study the influence of the tool entering angle on the stability of the process and on tool life based on a time and frequency domain analysis of the cutting forces. The results show that lower entering angles may provide stabler cutting, as indicated by the regular tool wear instead of the microchipping resulting from the use of a higher value of this angle. Although cutting forces are larger at lower entering angles, the tool life is much longer, since most of this load is associated with low frequencies, at which the tool behaves like a rigid body.


title: Using RSSI value for distance estimation in wireless sensor networks based on ZigBee

abstract: In todaypsilas modern wireless ZigBee-based modules, there are two well-known values for link quality estimation: RSSI (received signal strength indicator) and LQI (link quality indicator). In respect to wireless channel models, received power should be a function of distance. From this aspect, we believed that RSSI can be used for evaluating distances between nodes. The experiment described in this paper indicates that RSSI is, in fact, a poor distance estimator when using wireless sensor networks in buildings. Reflection, scattering and other physical properties have an extreme impact on RSSI measurement and so we can conclude: RSSI is a bad distance estimator.


title: moocRP: An Open-source Analytics Platform

abstract: In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflows as well as a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing data models, all of which can be accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and interactive visualizations are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium institutions and beyond.


title: Development of an ultra wideband ground penetrating radar (UWB GPR) for nondestructive testing of underground objects

abstract: A compact ground penetrating radar (GPR) using an impulse waveform is developed. The UWB GPR system is composed of an impulse generator, ultra wideband transmitting and receiving antennas, high speed digitizer, and a laptop computer for data storage and image processing. By considering the maximum measuring depth and soil properties, a proper impulse generator is made and then a novel compact traveling wave monopole antenna is designed to transmit the impulse and receive the reflected signal. Also, image processing for visualization of buried targets is performed. For verification, a UWB GPR prototype is built and tested. It is shown that the system has very good depth and spatial resolution.


Comprehensive Utilization of Paigeite Ore Using Iron Nugget Making Process


title: People’s conditional probability judgments follow probability theory (plus noise)

abstract: Abstract A common view in current psychology is that people estimate probabilities using various ‘heuristics’ or rules of thumb that do not follow the normative rules of probability theory. We present a model where people estimate conditional probabilities such as P ( A | B ) (the probability of A given that B has occurred) by a process that follows standard frequentist probability theory but is subject to random noise. This model accounts for various results from previous studies of conditional probability judgment. This model predicts that people’s conditional probability judgments will agree with a series of fundamental identities in probability theory whose form cancels the effect of noise, while deviating from probability theory in other expressions whose form does not allow such cancellation. Two experiments strongly confirm these predictions, with people’s estimates on average agreeing with probability theory for the noise-cancelling identities, but deviating from probability theory (in just the way predicted by the model) for other identities. This new model subsumes an earlier model of unconditional or ‘direct’ probability judgment which explains a number of systematic biases seen in direct probability judgment (Costello & Watts, 2014). This model may thus provide a fully general account of the mechanisms by which people estimate probabilities.


title: Practical issues in temporal difference learning

abstract: This paper examines whether temporal difference methods for training connectionist networks, such as Sutton's TD(λ) algorithm, can be successfully applied to complex real-world problems. A number of important practical issues are identified and discussed from a general theoretical perspective. These practical issues are then examined in the context of a case study in which TD(λ) is applied to learning the game of backgammon from the outcome of self-play. This is apparently the first application of this algorithm to a complex non-trivial task. It is found that, with zero knowledge built in, the network is able to learn from scratch to play the entire game at a fairly strong intermediate level of performance, which is clearly better than conventional commercial programs, and which in fact surpasses comparable networks trained on a massive human expert data set. This indicates that TD learning may work better in practice than one would expect based on current theory, and it suggests that further analysis of TD methods, as well as applications in other complex domains, may be worth investigating.


title: Decoding reveals the contents of visual working memory in early visual areas

abstract: Although we can hold several different items in working visual memory, how we remember specific details and visual features of individual objects remains a mystery. The neurons in the higher-order areas responsible for working memory seem to exhibit no selectivity for visual detail, and the early visual areas of the cerebral cortex are uniquely able to process incoming visual signals from the eye but, it was thought, not to perform higher cognitive functions such as memory. Using a new technique for decoding data from functional magnetic resonance imaging (fMRI), Stephanie Harrison and Frank Tong have found that early visual areas can retain specific information about features held in working memory. Volunteers were shown two striped patterns at different orientations and asked to memorize one of the orientations whilst being scanned by fMRI. From analysis of the scans it was possible to predict which of the two orientation patterns a subject was being retained in over 80% of tests. This study shows that early visual areas can retain specific information about features held in working memory even when there is no physical stimulus present. Using functional magnetic resonance imaging decoding methods, visual features could be predicted from early visual area activity with a high degree of accuracy. Visual working memory provides an essential link between perception and higher cognitive functions, allowing for the active maintenance of information about stimuli no longer in view1,2. Research suggests that sustained activity in higher-order prefrontal, parietal, inferotemporal and lateral occipital areas supports visual maintenance3,4,5,6,7,8,9,10,11, and may account for the limited capacity of working memory to hold up to 3–4 items9,10,11. Because higher-order areas lack the visual selectivity of early sensory areas, it has remained unclear how observers can remember specific visual features, such as the precise orientation of a grating, with minimal decay in performance over delays of many seconds12. One proposal is that sensory areas serve to maintain fine-tuned feature information13, but early visual areas show little to no sustained activity over prolonged delays14,15,16. Here we show that orientations held in working memory can be decoded from activity patterns in the human visual cortex, even when overall levels of activity are low. Using functional magnetic resonance imaging and pattern classification methods, we found that activity patterns in visual areas V1–V4 could predict which of two oriented gratings was held in memory with mean accuracy levels upwards of 80%, even in participants whose activity fell to baseline levels after a prolonged delay. These orientation-selective activity patterns were sustained throughout the delay period, evident in individual visual areas, and similar to the responses evoked by unattended, task-irrelevant gratings. Our results demonstrate that early visual areas can retain specific information about visual features held in working memory, over periods of many seconds when no physical stimulus is present.


title: The impact of corporate reputation on brand attitude and purchase intention

abstract: The purpose of this study is to explore the influences of the negative corporate CEO and corporate social responsibility reputations on the changes in consumers’ brand attitudes and purchase intentions. To perform this study purpose, we proposed the relationships between brand awareness and perceived quality and initial brand attitude and purchase intention by relying on the hierarchy of effects model; then we assessed the changes in brand attitude and purchase intention after providing information on negative corporate reputation. Multiple regression analysis and paired samples T-tests were conducted to test the hypothesized relationships using a convenience sample of 212 respondents. The empirical results support significant effects of brand awareness and perceived quality on brand attitude and purchase intention. Furthermore, respondents’ brand attitudes and purchase intentions are reduced after they are being provided with the information on negative corporate reputation. This result confirms that negative corporate reputation significantly aggravates consumers’ attitudes and purchase intention. However, the type of reputation is not a decisive factor for consumers’ cognitive processes. The results imply that marketing managers need to manage a negative reputation carefully because negative corporate reputation aggravates consumers’ cognitive process However, consumers are not influenced by specific type of negative information, thus a company can mitigate the negative impact of their negative reputation by another type of positive reputation.


Recent advances in conservation and population genomics data analysis


title: Generalized Capsule Networks with Trainable Routing Procedure

abstract: CapsNet (Capsule Network) was first proposed by~\citet{capsule} and later another version of CapsNet was proposed by~\citet{emrouting}. CapsNet has been proved effective in modeling spatial features with much fewer parameters. However, the routing procedures in both papers are not well incorporated into the whole training process. The optimal number of routing procedure is misery which has to be found manually. To overcome this disadvantages of current routing procedures in CapsNet, we embed the routing procedure into the optimization procedure with all other parameters in neural networks, namely, make coupling coefficients in the routing procedure become completely trainable. We call it Generalized CapsNet (G-CapsNet). We implement both"full-connected"version of G-CapsNet and"convolutional"version of G-CapsNet. G-CapsNet achieves a similar performance in the dataset MNIST as in the original papers. We also test two capsule packing method (cross feature maps or with feature maps) from previous convolutional layers and see no evident difference. Besides, we also explored possibility of stacking multiple capsule layers. The code is shared on \hyperlink{https://github.com/chenzhenhua986/CAFFE-CapsNet}{CAFFE-CapsNet}.


title: Taking up the challenge of measuring prolonged pain in (premature) neonates the COMFORTneo scale seems promising

abstract: Objectives: Pain assessment is essential to tailor intensive care of neonates. The present focus is on acute procedural pain; assessment of pain of longer duration remains a challenge. We therefore tested a modified version of the COMFORT-behavior scale—named COMFORTneo—for its psychometric qualities in the Neonatal Intensive Care Unit setting. Methods: In a clinical observational study, nurses assessed patients with COMFORTneo and Numeric Rating Scales (NRS) for pain and distress, respectively. Interrater reliability, concurrent validity, and sensitivity to change were calculated as well as sensitivity and specificity for different cut-off scores for subsets of patients. Results: Interrater reliability was good: median linearly weighted Cohen k 0.79. Almost 3600 triple ratings were obtained for 286 neonates. Internal consistency was good (Cronbach a 0.84 and 0.88). Concurrent validity was demonstrated by adequate and good correlations, respectively, with NRS-pain and NRS-distress: r = 0.52 (95% confidence interval 0.44-0.59) and r = 0.70 (95% confidence interval 0.64-0.75). COMFORTneo cut-off scores of 14 or higher (score range is 6 to 30) had good sensitivity and specificity (0.81 and 0.90, respectively) using NRS-pain or NRS-distress scores of 4 or higher as criterion. Discussion: The COMFORTneo showed preliminary reliability. No major differences were found in cut-off values for low birth weight, small for gestational age, neurologic impairment risk levels, or sex. Multicenter studies should focus on establishing concurrent validity with other instruments in a patient group with a high probability of ongoing pain.


title: Techniques of male circumcision

abstract: Male circumcision is a controversial subject in surgical practice. There are, however, clear surgical indications of this procedure. The American Academy of Pediatrics (AAP) recommends newborn male circumcision for its preventive and public health benefits that has been shown to outweigh the risks of newborn male circumcision. Many surgical techniques have been reported. The present review discusses some of these techniques with their merits and drawbacks. This is an attempt to inform the reader on surgical aspects of male circumcision aiding in making appropriate choice of a technique to offer patients. Pubmed search was done with the keywords: Circumcision, technique, complications, and history. Relevant articles on techniques of circumcision were selected for the review. Various methods of circumcision including several devices are in use for male circumcision. These methods can be grouped into three: Shield and clamp, dorsal slit, and excision. The device methods appear favored in the pediatric circumcision while the risk of complications increases with increasing age of the patient at surgery. Key words: Complication, dorsal slit, device, excision, male circumcision, public health benefit, technique


title: Skin buttons: cheap, small, low-powered and clickable fixed-icon laser projectors

abstract: Smartwatches are a promising new interactive platform, but their small size makes even basic actions cumbersome. Hence, there is a great need for approaches that expand the interactive envelope around smartwatches, allowing human input to escape the small physical confines of the device. We propose using tiny projectors integrated into the smartwatch to render icons on the user's skin. These icons can be made touch sensitive, significantly expanding the interactive region without increasing device size. Through a series of experiments, we show that these 'skin buttons' can have high touch accuracy and recognizability, while being low cost and power-efficient.


title: On the impossibility of three-move blind signature schemes

abstract: We investigate the possibility to prove security of the well-known blind signature schemes by Chaum, and by Pointcheval and Stern in the standard model, i.e., without random oracles. We subsume these schemes under a more general class of blind signature schemes and show that finding security proofs for these schemes via black-box reductions in the standard model is hard. Technically, our result deploys meta-reduction techniques showing that black-box reductions for such schemes could be turned into efficient solvers for hard non-interactive cryptographic problems like RSA or discrete-log. Our approach yields significantly stronger impossibility results than previous meta-reductions in other settings by playing off the two security requirements of the blind signatures (unforgeability and blindness).


title: Reasoning With Neural Tensor Networks for Knowledge Base Completion

abstract: Knowledge bases are an important resource for question answering and other tasks but often suffer from incompleteness and lack of ability to reason over their discrete entities and relationships. In this paper we introduce an expressive neural tensor network suitable for reasoning over relationships between two entities. Previous work represented entities as either discrete atomic units or with a single entity vector representation. We show that performance can be improved when entities are represented as an average of their constituting word vectors. This allows sharing of statistical strength between, for instance, facts involving the "Sumatran tiger" and "Bengal tiger." Lastly, we demonstrate that all models improve when these word vectors are initialized with vectors learned from unsupervised large corpora. We assess the model by considering the problem of predicting additional true relations between entities given a subset of the knowledge base. Our model outperforms previous models and can classify unseen relationships in WordNet and FreeBase with an accuracy of 86.2% and 90.0%, respectively.


title: PLLR features in language recognition system for RATS.

abstract: In this paper, we study the use of features based on frame-byframe phone posteriors (PLLRs) for language recognition. The results are reported on the datasets developed for the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We show that systems based on the PLLRs outperform the standard acoustic system based on PLP2 features. By experimenting with the system combinations, we also demonstrate that the PLLR-based systems contain complementary information with respect to the PLP2 system. Finally we make a comparison between the PLLR and phonotactic systems with the outcome favorable to the PLLR.


title: Modelling of intelligent intrusion detection system: making a case for snort

abstract: Intrusion Detection System (IDS) is a dynamic network security defense technology that can help to provide realtime detection of internal and external attacks on a computer network and alerting the administration for necessary action. However, the inconsistent nature of networks has resulted in a high number of false positives which makes many network administrators thought IDS to be unreliable for today’s network security system. Nowadays, hackers and attackers have created many new viruses and malware to invade one’s computer network system. Hence, this study proposes a method for early detection of an intrusion by using Snort software. The data collected was used to train the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm was simulated using MATLAB software. The performance of this classifier was evaluated based on three parameters: accuracy, sensitivity, and False Positive Rate (FPR). Preprocessing was done to classify the output data into normal and attack. Performance evaluation was done using confusion matrix on the data. The results showed that network-based intrusion detection system could be employed for early detection of intrusion due to the excellent performance recorded which were 94.92% of accuracy, 97.97% for sensitivity, and 0.69% for FPR.


title: Wearing your arm on your sleeve: Studying usage contexts for a wearable robotic forearm

abstract: This paper presents the design of a wearable robotic forearm that provides the user with an assistive third hand, along with a study of interaction scenarios for the design. Technical advances in sensors, actuators, and materials have made wearable robots feasible for personal use, but the interaction with such robots has not been sufficiently studied. We describe the development of a working prototype along with three usability studies. In an online survey we find that respondents presented with images and descriptions of the device see its use mainly as a functional tool in professional and military contexts. A subsequent contextual inquiry among building construction workers reveals three themes for user needs: extending a worker's reach, enhancing their safety and comfort through bracing and stabilization, and reducing their cognitive load in repetitive tasks. A subsequent laboratry study in which participants wear a working prototype of the robot finds that they prioritize lowered weight and enhanced dexterity, seek adjustable autonomy and transparency of the robot's intent, and prefer a robot that looks distinct from a human arm. These studies inform design implications for further development of wearable robotic arms.


Natural Resource Governance : Does Social Media Matter ? Oasis


title: Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects

abstract: Drug-drug interaction (DDI) is an important topic for public health and thus attracts attention from both academia and industry. Here we hypothesize that clinical side effects (SEs) provide a human phenotypic profile and can be translated into the development of computational models for predicting adverse DDIs. We propose an integrative label propagation framework to predict DDIs by integrating SEs extracted from package inserts of prescription drugs, SEs extracted from FDA Adverse Event Reporting System and chemical structures from PubChem. Experimental results based on hold-out validation demonstrated the effectiveness of the proposed algorithm. In addition, the new algorithm also ranked drug information sources based on their contributions to the prediction, thus not only confirming that SEs are important features for DDI prediction but also paving the way for building more reliable DDI prediction models by prioritizing multiple data sources. By applying the proposed algorithm to 1,626 small-molecule drugs which have one or more SE profiles, we obtained 145,068 predicted DDIs. The predicted DDIs will help clinicians to avoid hazardous drug interactions in their prescriptions and will aid pharmaceutical companies to design large-scale clinical trial by assessing potentially hazardous drug combinations. All data sets and predicted DDIs are available at http://astro.temple.edu/~tua87106/ddi.html ::: .


title: A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection

abstract: The popularity of Twitter attracts more and more spammers. Spammers send unwanted tweets to Twitter users to promote websites or services, which are harmful to normal users. In order to stop spammers, researchers have proposed a number of mechanisms. The focus of recent works is on the application of machine learning techniques into Twitter spam detection. However, tweets are retrieved in a streaming way, and Twitter provides the Streaming API for developers and researchers to access public tweets in real time. There lacks a performance evaluation of existing machine learning-based streaming spam detection methods. In this paper, we bridged the gap by carrying out a performance evaluation, which was from three different aspects of data, feature, and model. A big ground-truth of over 600 million public tweets was created by using a commercial URL-based security tool. For real-time spam detection, we further extracted 12 lightweight features for tweet representation. Spam detection was then transformed to a binary classification problem in the feature space and can be solved by conventional machine learning algorithms. We evaluated the impact of different factors to the spam detection performance, which included spam to nonspam ratio, feature discretization, training data size, data sampling, time-related data, and machine learning algorithms. The results show the streaming spam tweet detection is still a big challenge and a robust detection technique should take into account the three aspects of data, feature, and model.


title: Robust Lane Detection Based On Convolutional Neural Network and Random Sample Consensus

abstract: In this paper, we introduce a robust lane detection method based on the combined convolutional neural network (CNN) with random sample consensus (RANSAC) algorithm. At first, we calculate edges in an image using a hat shape kernel and then detect lanes using the CNN combined with the RANSAC. If the road scene is simple, we can easily detect the lane by using the RANSAC algorithm only. But if the road scene is complex and includes roadside trees, fence, or intersection etc., then it is hard to detect lanes robustly because of noisy edges. To alleviate that problem, we use CNN in the lane detection before and after applying the RANSAC algorithm. In training process of CNN, input data consist of edge images in a region of interest (ROI) and target data become the images that have only drawn real white color lane in black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) including 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged and their arrangement represents a deep structure in deep learning. As a result, proposed lane detection algorithm successfully eliminates noise lines and the performance is found to be better than other formal line detection algorithms such as RANSAC and hough transform.


Electronic Musical Instruments


title: The Effects of Media Violence on Anxiety in Late Adolescence

abstract: Exposure to media violence is related to anxiety in youth, but the causality of the effect has not been established. This experimental study examined the effects of media violence on anxiety, blood pressure, and heart rate in late adolescents. We also examined whether these responses varied by previous exposure to media and real-life violence. College students (N = 209; M age = 18.74; 75 % female; 50 % Caucasian, 34 % African American, 9 % Asian, 3 % Hispanic, and 3 % other racial minorities) were randomized to view either violent or nonviolent high-action movie clips. Participants reported on their anxiety before and after watching the clips, as well as their previous exposure to violence. Measures of blood pressure and heart rate were taken at baseline and during movie viewing. Participants watching violent movie clips showed a greater anxiety increase than those watching nonviolent clips. Both groups experienced increased blood pressure and reduced heart rate during movie watching compared to baseline. Prior exposure to media violence was associated with diminished heart rate response. Additionally, students previously exposed to high levels of real-life violence showed lower blood pressure increases when watching violent clips compared to nonviolent clips. Thus, relatively brief exposure to violent movie clips increased anxiety among late adolescents. Prior exposure to media and real-life violence were associated with lower physiological reactivity to high-action and violent movies, respectively, possibly indicating desensitization. Future studies should investigate long-term anxiety and physiological consequences of regular exposure to media violence in adolescence.


title: Content-Based Publish/Subscribe Systems

abstract: The application and deployment of publish/subscribe systems have developed significantly over the past years. A publish/subscribe system is a powerful paradigm for information dissemination from publishers (data/event producers) to subscribers (data/event consumers) in large-scale distributed networks. Publish/subscribe systems have been used in a variety of applications ranging from personalized information dissemination to large-scale and critical monitoring. This chapter provides a survey on current content-based publish/subscribe systems. It first introduces the publish/subscribe systems, then presents a survey of current systems based on three classification criteria: subscription model, routing and topology. It details different publish/subscribe systems in the centralized category and distributed category including multicast-based systems and Distributed hash table (DHT)-based systems. Finally, it concludes the chapter with concluding remarks and open issues.


title: Satisfaction with Delivery Services Offered under the Free Maternal Healthcare Policy in Kenyan Public Health Facilities

abstract: Background ::: Patients' satisfaction is an individual's positive assessment regarding a distinct dimension of healthcare and the perception about the quality of services offered in that health facility. Patients who are not satisfied with healthcare services in a certain health facility will bypass the facility and are unlikely to seek treatment in that facility. ::: ::: ::: Objective ::: To determine satisfaction level of mothers with the free maternal services in selected Kenyan public health facilities after the implementation of the free maternal healthcare policy. ::: ::: ::: Methods ::: Data was collected through a quantitative exit survey questionnaire. The respondents were mothers who had delivered in the health facilities and were waiting to leave the health facilities after discharge. The sample included 2,216 mothers in 77 public health facilities across 14 counties in Kenya under tier 3 and tier 4 categories. The number of respondents to be interviewed was proportionately arrived at based on each health facility's bed capacity. ::: ::: ::: Results ::: The study established a satisfaction rate of 54.5% among the beneficiaries of the free maternal healthcare services in the country. Mothers benefiting from the free delivery services were satisfied with communication by the healthcare workers, staff availability in the delivery rooms, availability of staff in the wards, and drug and supplies availability (>56%) but unsatisfied with consultation time, cleanliness, and privacy in the wards (<56%). High education levels and lengthy stay in healthcare facilities were negatively associated with the satisfaction with the free delivery services (P < 0.05). ::: ::: ::: Conclusion ::: There is a high satisfaction with the free maternal healthcare services in Kenya. However, the implementation of the free maternal healthcare policy was associated with low privacy, poor hygiene, and low consultation time in the health facilities. Therefore there is need to address these service gaps so as to attract more mothers to deliver in public health facilities.


SHILLING ATTACK DETECTION IN RECOMMENDER SYSTEMS USING CLASSIFICATION TECHNIQUES


title: The costs and benefits of providing feedback during learning.

abstract: It seems uncontroversial that providing feedback after a test, in the form of the correct answer, enhances learning. In real-world educational situations, however, the time available for learning is often constrained— and feedback takes time. We report an experiment in which total time for learning was fixed, thereby creating a trade-off between spending time receiving feedback and spending time on other learning activities. Our results suggest that providing feedback is not universally beneficial. Indeed, under some circumstances, taking time to provide feedback can have a negative net effect on learning. We also found that learners appear to have some insight about the costs of feedback; when they were allowed to control feedback, they often skipped unnecessary feedback in favor of additional retrieval attempts, and they benefited from doing so. These results underscore the importance of considering the costs and benefits of interventions designed to enhance learning.


A Multi-task Approach to Predict Likability of Books


title: A Framework for Clustering Massive Text and Categorical Data Streams

abstract: Many applications such as news group filtering, text crawling, and document organization require real time clustering and segmentation of text data records. The categorical data stream clustering problem also has a number of applications to the problems of customer segmentation and real time trend analysis. We will present an online approach for clustering massive text and categorical data streams with the use of a statistical summarization methodology. We present results illustrating the effectiveness of the technique.


title: The Way They Move: Tracking Multiple Targets with Similar Appearance

abstract: We introduce a computationally efficient algorithm for multi-object tracking by detection that addresses four main challenges: appearance similarity among targets, missing data due to targets being out of the field of view or occluded behind other objects, crossing trajectories, and camera motion. The proposed method uses motion dynamics as a cue to distinguish targets with similar appearance, minimize target mis-identification and recover missing data. Computational efficiency is achieved by using a Generalized Linear Assignment (GLA) coupled with efficient procedures to recover missing data and estimate the complexity of the underlying dynamics. The proposed approach works with track lets of arbitrary length and does not assume a dynamical model a priori, yet it captures the overall motion dynamics of the targets. Experiments using challenging videos show that this framework can handle complex target motions, non-stationary cameras and long occlusions, on scenarios where appearance cues are not available or poor.


title: Oral Reading Fluency as an Indicator of Reading Competence: A Theoretical, Empirical, and Historical Analysis.

abstract: The purpose of this article is to consider oral reading fluency as an indicator of overall reading competence. We begin by examining theoretical arguments for supposing that oral reading fluency may reflect overall reading competence. We then summarize several studies substantiating this phenomenon. Next, we provide an historical analysis of the extent to which oral reading fluency has been incorporated into measurement approaches during the past century. We conclude with recommendations about the assessment of oral reading fluency for research and practice.


title: Stanislavky's System as a Game Design Method: A Case Study

abstract: The relationship between theatre and games has been repeatedly discussed (Laurel 1993; Murray 1997; Frasca 2004; El-Nasr 2007;Fernandez-Vara 2009), but its possibilities have not been explored in enough depth. This paper goes beyond a theoretical proposal, and describes how Stanislavski’s acting method (1959) served as the inspiration to design a game based on the Spanish classical theatre play, La Dama Boba (The Foolish Lady). The result was a point-and-click adventure game developed with the eAdventure platform, (Torrente, del Blanco, Marchiori, Moreno-Ger, Fernandez-Manjon 2010) a tool to create educational games. The paper provides an overview of the most and least successful aspects of this design method, and how it helped transform a narrative, dramatic in this case, into a digital game.


title: Platelet-Rich Plasma in Androgenic Alopecia: Indications, Technique, and Potential Benefits.

abstract: PURPOSE ::: The purpose of this study was to provide an overview of platelet-rich plasma (PRP) injected into the scalp for the management of androgenic alopecia. ::: ::: ::: MATERIALS AND METHODS ::: A literature review was performed to evaluate the benefits of PRP in androgenic alopecia. ::: ::: ::: RESULTS ::: Hair restoration has been increasing. PRP's main components of platelet-derived growth factor, transforming growth factor, and vascular endothelial growth factor have the potential to stimulate hard and soft tissue wound healing. In general, PRP showed a benefit on patients with androgenic alopecia, including increased hair density and quality. Currently, different PRP preparations are being used with no standard technique. ::: ::: ::: CONCLUSION ::: This review found beneficial effects of PRP on androgenic alopecia. However, more rigorous study designs, including larger samples, quantitative measurements of effect, and longer follow-up periods, are needed to solidify the utility of PRP for treating patients with androgenic alopecia.


title: Simulation of Interdigitated Electrodes (IDEs) Geometry Using COMSOL Multiphysics

abstract: Interdigitated Electrodes (IDEs) based sensor in comb-like electrodes structure is also an attractive candidate in gas sensor and biosensor applications, owing to its simple structural design. The sensitivity can be improved based on geometric parameters of the sensor. Hence, in this paper, we study the physical model of the IDEs-based sensor and concern with its geometrical parameters (gap sizes, number of fingers and width of fingers) based on the finite element simulations using COMSOL Multiphysics. The simulation results were used to show the electrical field that generated by the IDEs and compare to other to find the optimum design. Based on the results, the relationship between the geometric parameters and the IDEs performances are studied. The simulation results show that the higher electric fields are generated with the smaller gap size and smaller width of fingers. Besides that, the results show that the gap size and the width of fingers affect the IDEs performance significantly compared to the number of fingers. The IDE with the design 18 fingers, 0.1 mm gap size between fingers and 0.1 mm width of the finger is the optimum design which generates 30.6 kV/m average of electric field magnitude.


title: UAV Remote Sensing Image Mosaic and Its Application in Agriculture

abstract: UAV remote sensing image has the characteristics of higher spatial resolution, fine timeliness and high flexibility. It is widely used in many fields such as agriculture, forestry, soil resources and so on. Especially in agriculture, it plays an important role in information acquisition of agricultural production and agricultural condition monitoring. In the experiment, high-definition digital camera and multi-spectral camera are used to capture the visible light and near-infrared remote sensing image. Two pieces of 3m*3m calibration cloth is used for radiometric calibration and SIFT algorithm was used for image mosaic. This paper discusses the application of remote sensing image in agriculture, including rice lodging monitoring, diseases and pests monitoring, crop growth monitoring and crop nutrient diagnosis. The results show that UAV remote sensing provides an effective means for agricultural condition information acquisition, and has wide application prospect.


title: Security challenges in software defined network and their solutions

abstract: The main purpose of Software Defined Networking (SDN) is to allow network engineers to respond quickly to changing network industrial requirements. This network technology focuses on making network as adaptable and active as virtual server. SDN is physical separation of Control plane from Data plane and control plane is centralized to manage underlying infrastructure. Hence, the SDN permit network administrator to adjust wide traffic flow from centralized control console without having to touch Switches and Routers, and can provide the services to wherever they are needed in the network. As in SDN the control plane is disassociated from underlying forwarding plane, however, susceptible to many security challenges like Denial of Service (DoS) attack, Distributed DoS (DDoS) attack, Volumetric attack. In this paper, we highlight some security challenges and evaluate some security solutions.


title: A novel method for the extrinsic calibration of a 2-D laser-rangefinder & a camera

abstract: We present a novel method for extrinsically calibrating a camera and a 2-D Laser Rangefinder (LRF) whose beams are invisible from the camera image. We show that point-to-plane constraints from a single observation of a V-shaped calibration pattern composed of two non-coplanar triangles suffice to uniquely constrain the relative pose between two sensors. Next, we present an approach to obtain solutions using point-to-plane constraints from single or multiple observations. Along the way, we also show that previous solutions, in contrast to our method, have inherent ambiguities and therefore must rely on a good initial estimate. Real and synthetic experiments validate our method and show that it achieves better accuracy than previous methods.


title: Accurate Dynamic 3D Sensing With Fourier-Assisted Phase Shifting

abstract: Phase shifting profilometry (PSP) and Fourier transform profilometry (FTP) are two well-known fringe analysis methods for 3D sensing. PSP offers high accuracy but requires multiple images; FTP uses a single image but is limited in its accuracy. In this paper, we propose a novel Fourier-assisted phase shifting (FAPS) method for accurate dynamic 3D sensing. Our key observation is that the motion vulnerability of multi-shot PSP can be overcome through single-shot FTP, while the high accuracy of PSP is preserved. Moreover, to solve the phase ambiguity of complex scenes without additional images, we propose an efficient parallel spatial unwrapping strategy that embeds a sparse set of markers in the fringe patterns. Our dynamic 3D sensing system based on the above principles demonstrates superior performance over previous structured light techniques, including PSP, FTP, and Kinect.


title: Adaptive hydrostatic blood pressure calibration: Development of a wearable, autonomous pulse wave velocity blood pressure monitor

abstract: A technique for calibrating non-invasive peripheral arterial sensor signals to peripheral arterial blood pressure (BP) is proposed. The adaptive system identification method utilizes a measurable intra-arterial hydrostatic pressure change in the sensor outfitted appendage to identify the transduction dynamics relating the peripheral arterial blood pressure and the measured arterial sensor signal. The proposed algorithm allows identification of the calibration dynamics despite unknown physiologic fluctuations in arterial pressure during the calibration period under certain prescribed conditions. By employing unique wearable sensor architecture to estimate pulse wave velocity (PWV), this technique is used to calibrate peripheral pulse transit time measurements to arterial blood pressure. This sensor architecture is comprised of two inline photoplethysmograph sensors one in the form of a wristwatch measuring the pulse waveform in the ulnar artery and one in the form of a ring measuring the pulse waveform from the digital artery along the base of the little finger. Experimental results using the proposed algorithm to calibrate PTT to BP on human subjects will be presented.


title: Active learning for support vector regression in radiation shielding design

abstract: Recently a novel approach based on support vector regression technique has been proposed and tested for the estimation of multi layer buildup factors for gamma ray shielding calculations, while for neutron shielding calculations some initial analyses have been conducted. During the development of the model a number of questions regarding possible application of active learning measures have been raised. In this paper general applicability of the active learning measures on the problem, in particular data transfer method used in the investigation, and testing of the active procedure are discussed.


title: Diet shift induced rapid evolution of size and function in a predatory bird

abstract: A predator’s body size correlates with its prey size. Change in the diet may call for changes in the hunting mode and traits determining hunting success. We explored long-term trends in sternum size and shape in the northern goshawk by applying geometric morphometrics. Tetraonids, the primary prey of the goshawk, have decreased and been replaced by smaller birds in the diet. We expected that the size of the goshawk has decreased accordingly more in males than females based on earlier observations of outer morphology. We also expected changes in sternum shape as a function of changes in hunting mode. Size of both sexes has decreased during the preceding decades (1962−2008), seemingly reflecting a shift in prey size and hunting mode. Female goshawks hunting also mammalian prey tend to have a pronouncedly “Buteo-type” sternum compared to males preying upon birds. Interestingly, the shrinkage of body size resulted in an increasingly “Buteo-type” sternum in both sexes. In addition, the sternum shape in birds that died accidentally (i.e., fit individuals) was more Buteo-type than in starved ones, hinting that selection was towards a Buteo-type sternum shape. We conclude that these observed patterns are likely due to directional selection driven by changes in the diet towards smaller and more agile prey. On the other hand, global warming is predicted to also cause a decrease in size, thus these two scenarios are inseparable. Because of difficulties in studying fitness-related phenotypic changes of large raptors in the field, time series of museum exemplars collected over a wide geographical area may give answers to this conundrum.


title: Harmonic 3D shape matching

abstract: With the advent of the world wide web, the number of available 3D models has increased substantially and the challenge has changed from "How do we generate 3D models?" to "How do we find them?" In this sketch we describe a new 3D model matching and indexing algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46-245% better performance than related shape matching methods during precision-recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under half a second.


Reward Backpropagation Prioritized Experience Replay


title: A service-oriented mobile augmented reality architecture for personalized museum environments

abstract: This paper presents a mobile augmented reality platform that exploits service-orientation for the visualisation of cultural media objects in personalised interactive museum environments. The service orientated architecture is composed of a mobile client, web service framework and service providers utilized to perform augmented reality tasks as well as support for media contents acquisition and consumption via a mobile or wireless network. The mobile client performs augmented reality tasks including multiple object tracking of physical 3D objects concurrently and then visualizes associated media contents (e.g. 3D models, videos, images, text, social media data, etc.) that are associated with the tracked physical (or reference) objects and then overlaid in the correct perspective in the real scene in a personalised museum visualisation scenario. A unique feature includes media content acquisition from open service providers through a web service framework. Typical services include, a photogrammetry service that allows users to obtain virtual 3D models of preferred cultural objects through image-based reconstruction techniques. These acquired 3D reconstruction contents can then be integrated with existing associated contents in a personalized augmented reality museum environment that can be stored and saved for later viewing, such as in the home after visiting a particular augmented reality based museum interactive. This paper focuses on describing the service orientation architectural components that supports personalized mobile augmented reality museum environments.


title: Interpretability in Machine Learning – Principles and Practice

abstract: Theoretical advances in machine learning have been reflected in many research implementations including in safety-critical domains such as medicine. However this has not been reflected in a large number of practical applications used by domain experts. This bottleneck is in a significant part due to lack of interpretability of the non-linear models derived from data. This lecture will review five broad categories of interpretability in machine learning - nomograms, rule induction, fuzzy logic, graphical models & topographic mapping. Links between the different approaches will be made around the common theme of designing interpretability into the structure of machine learning models, then using the armoury of advanced analytical methods to achieve generic non-linear approximation capabilities.


title: Breaking the Target: An Analysis of Target Data Breach and Lessons Learned

abstract: This paper investigates and examines the events leading up to the second most devastating data breach in history: the attack on the Target Corporation. It includes a thorough step-by-step analysis of this attack and a comprehensive anatomy of the malware named BlackPOS. Also, this paper provides insight into the legal aspect of cybercrimes, along with a prosecution and sentence example of the well-known TJX case. Furthermore, we point out an urgent need for improving security mechanisms in existing systems of merchants and propose three security guidelines and defenses. Credit card security is discussed at the end of the paper with several best practices given to customers to hide their card information in purchase transactions.


title: Combining monoSLAM with object recognition for scene augmentation using a wearable camera

abstract: In wearable visual computing, maintaining a time-evolving representation of the 3D environment along with the pose of the camera provides the geometrical foundation on which person-centred processing can be built. In this paper, an established method for the recognition of feature clusters is used on live imagery to identify and locate planar objects around the wearer. Objects' locations are incorporated as additional 3D measurements into a monocular simultaneous localization and mapping process, which routinely uses 2D image measurements to acquire and maintain a map of the surroundings, irrespective of whether objects are present or not. Augmenting the 3D maps with automatically recognized objects enables useful annotations of the surroundings to be presented to the wearer. After demonstrating the geometrical integrity of the method, experiments show its use in two augmented reality applications.


title: An Integrated Laser Radar Receiver Channel Utilizing a Time-Domain Walk Error Compensation Scheme

abstract: An integrated receiver channel for a pulsed time-of-flight (TOF) laser rangefinder has been designed and fabricated in a 0.35-μm SiGe BiCMOS process. The receiver channel generates a timing mark for the TDC by means of a leading-edge timing discriminator that detects the crossover of the received pulse with respect to a set reference level. The walk error generated by the amplitude variation is compensated in the time domain on the basis of the measured dependence of the walk on the length of the received pulse. The measurement accuracy is ±15 ps with compensation within a dynamic range of 1:100000, and the single-shot precision and power consumption are 120 ps for a minimum detectable signal of ~1 μA and 115 mW, respectively.


title: Facial expression on robot SHFR-III based on head-neck coordination

abstract: As nearly 55% of information is transferred by facial expressions, facial expression robot take an important role in human-robot interaction. This paper introduces the development and experiments of facial expression robot SHFR-III. The robot's facial system is consist of eyebrow mechanism, eyeball mechanism, lid mechanism and jaw mechanism. Neck mechanism and its kinematic analysis is also done to make robot's expression more vivid. Experiment based on head-neck coordination and motion joining is also presented. Last, summary and further research plan are attached.