title: Thermal modeling, analysis, and management in VLSI circuits: principles and methods

abstract: The growing packing density and power consumption of very large scale integration (VLSI) circuits have made thermal effects one of the most important concerns of VLSI designers. The increasing variability of key process parameters in nanometer CMOS technologies has resulted in larger impact of the substrate and metal line temperatures on the reliability and performance of the devices and interconnections. Recent data shows that more than 50% of all integrated circuit failures are related to thermal issues. This paper presents a brief discussion of key sources of power dissipation and their temperature relation in CMOS VLSI circuits, and techniques for full-chip temperature calculation with special attention to its implications on the design of high-performance, low-power VLSI circuits. The paper is concluded with an overview of techniques to improve the full-chip thermal integrity by means of off-chip versus on-chip and static versus adaptive methods


title: Hot & tight: exploring thermo and squeeze cues recognition on wrist wearables

abstract: Wrist worn wearable computing devices are ideally suited for presenting notifications through haptic stimuli as they are always in direct contact with the user's skin. While prior work has explored the feasibility of haptic notifications, we highlight a lack of empirical studies on thermal and pressure feedback in the context of wearable devices. This paper introduces prototypes for thermal and pressure (squeeze) feedback on the wrist. It then presents a study characterizing recognition performance with thermal and pressure cues against baseline performance with vibrations.


An impression of home: player nostalgia and the impulse to explore game worlds


Coordination of capital income taxes in the economic and monetary union: what needs to be done?


title: PhishNet: Predictive Blacklisting to Detect Phishing Attacks

abstract: Phishing has been easy and effective way for trickery and deception on the Internet. While solutions such as URL blacklisting have been effective to some degree, their reliance on exact match with the blacklisted entries makes it easy for attackers to evade. We start with the observation that attackers often employ simple modifications (e.g., changing top level domain) to URLs. Our system, PhishNet, exploits this observation using two components. In the first component, we propose five heuristics to enumerate simple combinations of known phishing sites to discover new phishing URLs. The second component consists of an approximate matching algorithm that dissects a URL into multiple components that are matched individually against entries in the blacklist. In our evaluation with real-time blacklist feeds, we discovered around 18,000 new phishing URLs from a set of 6,000 new blacklist entries. We also show that our approximate matching algorithm leads to very few false positives (3%) and negatives (5%).


PHYSICAL ACTIVITY AND SPORT PREFERENCES OF 10 TO 14-YEAR-OLD CHILDREN: A 5-YEAR PROSPECTIVE STUDY


title: Sampling based scene-space video processing

abstract: Many compelling video processing effects can be achieved if per-pixel depth information and 3D camera calibrations are known. However, the success of such methods is highly dependent on the accuracy of this "scene-space" information. We present a novel, sampling-based framework for processing video that enables high-quality scene-space video effects in the presence of inevitable errors in depth and camera pose estimation. Instead of trying to improve the explicit 3D scene representation, the key idea of our method is to exploit the high redundancy of approximate scene information that arises due to most scene points being visible multiple times across many frames of video. Based on this observation, we propose a novel pixel gathering and filtering approach. The gathering step is general and collects pixel samples in scene-space, while the filtering step is application-specific and computes a desired output video from the gathered sample sets. Our approach is easily parallelizable and has been implemented on GPU, allowing us to take full advantage of large volumes of video data and facilitating practical runtimes on HD video using a standard desktop computer. Our generic scene-space formulation is able to comprehensively describe a multitude of video processing applications such as denoising, deblurring, super resolution, object removal, computational shutter functions, and other scene-space camera effects. We present results for various casually captured, hand-held, moving, compressed, monocular videos depicting challenging scenes recorded in uncontrolled environments.


title: Using Coloured Petri Nets in Penetration Testing

abstract: Network penetration testing is a well-known approach used for security testing. Penetrating testing can be a laborious task which relies much on human knowledge and expertise, with various techniques employed, and an extensive amount of tools used in the process. A methodical approach to penetration testing is therefore recommended. The flaw hypothesis methodology, used in this thesis, represent one of the most used models for penetration testing and have great similarities in other penetration testing methodologies and standards used today. Petri nets represent a graph based mathematically sound modelling technique for concurrent systems, and provide a graphically intuitive approach for modelling, simulation and execution. A coloured Petri net is a high level Petri net that provides a significant increase in the expressiveness and compactness of Petri net models. The flaw hypothesis methodology used together with coloured Petri net attack models is presented in the thesis. The use of coloured Petri nets is described and analysed through case studies elucidating several properties of Petri net variants and their suitability to modelling attacks in penetration testing. Advantages of modelling attacks with coloured Petri nets have been explored and are described. Coloured Petri nets have been found to have many usefull mechanisms for modelling, analysing, and automatically executing penetration attempts, e.g. through their ability to model states, transitions, concurrency, and timing. The overall topic of the thesis is the technical aspects of penetration testing, what it is, and a methodical approach to it.


title: Multi-Image Deblurring Using Complementary Sets of Fluttering Patterns

abstract: We present a novel coded exposure video technique for multi-image motion deblurring. The key idea of this paper is to capture video frames with a set of complementary fluttering patterns, which enables us to preserve all spectrum bands of a latent image and recover a sharp latent image. To achieve this, we introduce an algorithm for generating a complementary set of binary sequences based on the modern communication theory and implement the coded exposure video system with an off-the-shelf machine vision camera. To demonstrate the effectiveness of our method, we provide in-depth analyses of the theoretical bounds and the spectral gains of our method and other state-of-the-art computational imaging approaches. We further show deblurring results on various challenging examples with quantitative and qualitative comparisons to other computational image capturing methods used for image deblurring, and show how our method can be applied for protecting privacy in videos.


title: Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

abstract: Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. To address these issues, here we show that the long-searched-for missing link is the convolution framelets for representing a signal by convolving local and non-local bases. The convolution framelets was originally developed to generalize the theory of low-rank Hankel matrix approaches for inverse problems, and this paper further extends the idea so that we can obtain a deep neural network using multilayer convolution framelets with perfect reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our analysis also shows that the popular deep network components such as residual block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help to achieve the PR, while the pooling and unpooling layers should be augmented with high-pass branches to meet the PR condition. Moreover, by changing the number of filter channels and bias, we can control the shrinkage behaviors of the neural network. This discovery leads us to propose a novel theory for deep convolutional framelets neural network. Using numerical experiments with various inverse problems, we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures.This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.


title: Effects of high-frequency current therapy on abdominal obesity in young women: a randomized controlled trial

abstract: [Purpose] The aim of this study was to determine the effects of high-frequency current therapy on the abdominal obesity levels of young women. [Subjects] Twenty-two women with abdominal obesity were randomly allocated to either an experimental group (n1 = 10) or a control group (n2 = 12). [Methods] The experimental group subjects received high-frequency current therapy for the abdominal region 3 times per week for 6 weeks (a total of 18 sessions). Outcome measures were waist circumference, body mass index, and body composition data (abdominal obesity rate, subcutaneous fat mass, and body fat percentage). [Results] Significant main effects of time in the waist circumference, abdominal obesity rate, subcutaneous fat mass, and body fat percentage were found. Significant time-by-group interactions were found for waist circumference, abdominal obesity rate, subcutaneous fat mass, and body fat percentage. [Conclusion] The use of the high-frequency current therapy may be beneficial for reducing the levels of abdominal obesity in young women.


title: Content-Aware Collaborative Music Recommendation Using Pre-trained Neural Networks

abstract: Although content is fundamental to our music listening preferences, the leading performance in music recommendation is achieved by collaborative-filtering-based methods which exploit the similarity patterns in user’s listening history rather than the audio content of songs. Meanwhile, collaborative filtering has the well-known “cold-start” problem, i.e., it is unable to work with new songs that no one has listened to. Efforts on incorporating content information into collaborative filtering methods have shown success in many non-musical applications, such as scientific article recommendation. Inspired by the related work, we train a neural network on semantic tagging information as a content model and use it as a prior in a collaborative filtering model. Such a system still allows the user listening data to “speak for itself”. The proposed system is evaluated on the Million Song Dataset and shows comparably better result than the collaborative filtering approaches, in addition to the favorable performance in the cold-start case.


title: Cloud service placement via subgraph matching

abstract: Fast service placement, finding a set of nodes with enough free capacity of computation, storage, and network connectivity, is a routine task in daily cloud administration. In this work, we formulate this as a subgraph matching problem. Different from the traditional setting, including approximate and probabilistic graphs, subgraph matching on data-center networks has two unique properties. (1) Node/edge labels representing vacant CPU cycles and network bandwidth change rapidly, while the network topology varies little. (2) There is a partial order on node/edge labels. Basically, one needs to place service in nodes with enough free capacity. Existing graph indexing techniques have not considered very frequent label updates, and none of them supports partial order on numeric labels. Therefore, we resort to a new graph index framework, Gradin, to address both challenges. Gradin encodes subgraphs into multi-dimensional vectors and organizes them with indices such that it can efficiently search the matches of a query's subgraphs and combine them to form a full match. In particular, we analyze how the index parameters affect update and search performance with theoretical results. Moreover, a revised pruning algorithm is introduced to reduce unnecessary search during the combination of partial matches. Using both real and synthetic datasets, we demonstrate that Gradin outperforms the baseline approaches up to 10 times.


title: Improved Bilinear Pooling with CNNs

abstract: Bilinear pooling of Convolutional Neural Network (CNN) features [22, 23], and their compact variants [10], have been shown to be effective at fine-grained recognition, scene categorization, texture recognition, and visual question-answering tasks among others. The resulting representation captures second-order statistics of convolutional features in a translationally invariant manner. In this paper we investigate various ways of normalizing these statistics to improve their representation power. In particular we find that the matrix square-root normalization offers significant improvements and outperforms alternative schemes such as the matrix logarithm normalization when combined with elementwise square-root and l2 normalization. This improves the accuracy by 2-3% on a range of fine-grained recognition datasets leading to a new state of the art. We also investigate how the accuracy of matrix function computations effect network training and evaluation. In particular we compare against a technique for estimating matrix square-root gradients via solving a Lyapunov equation that is more numerically accurate than computing gradients via a Singular Value Decomposition (SVD). We find that while SVD gradients are numerically inaccurate the overall effect on the final accuracy is negligible once boundary cases are handled carefully. We present an alternative scheme for computing gradients that is faster and yet it offers improvements over the baseline model. Finally we show that the matrix square-root computed approximately using a few Newton iterations is just as accurate for the classification task but allows an order-of-magnitude faster GPU implementation compared to SVD decomposition.


title: A Critical Review of Recent Progress in Mid-Range Wireless Power Transfer

abstract: Starting from Tesla's principles of wireless power transfer a century ago, this critical review outlines recent magneto-inductive research activities on wireless power transfer with the transmission distance greater than the transmitter coil dimension. It summarizes the operating principles of a range of wireless power research into 1) the maximum power transfer and 2) the maximum energy efficiency principles. The differences and the implications of these two approaches are explained in terms of their energy efficiency and transmission distance capabilities. The differences between the system energy efficiency and the transmission efficiency are also highlighted. The review covers the two-coil systems, the four-coil systems, the systems with relay resonators and the wireless domino-resonator systems. Related issues including human exposure issues and reduction of winding resistance are also addressed. The review suggests that the use of the maximum energy efficiency principle in the two-coil systems is suitable for short-range rather than mid-range applications, the use of the maximum power transfer principle in the four-coil systems is good for maximizing the transmission distance, but is under a restricted system energy efficiency (<;50%); the use of the maximum energy efficiency principle in relay or domino systems may offer a good compromise for good system energy efficiency and transmission distance on the condition that relay resonators can be placed between the power source and the load.


title: On integrating event definition and event detection

abstract: We develop, in this paper, a representation of time and events that supports a range of reasoning tasks such as monitoring and detection of event patterns which may facilitate the explanation of root cause(s) of faults. We shall compare two approaches to event definition: the active database approach in which events are defined in terms of the conditions for their detection at an instant, and the knowledge representation approach in which events are defined in terms of the conditions for their occurrence over an interval. We shall show the shortcomings of the former definition and employ a three-valued temporal first order nonmonotonic logic, extended with events, in order to integrate both definitions.


title: Run-time efficient probabilistic model checking

abstract: Unpredictable changes continuously affect software systems and may have a severe impact on their quality of service, potentially jeopardizing the system's ability to meet the desired requirements. Changes may occur in critical components of the system, clients' operational profiles, requirements, or deployment environments. The adoption of software models and model checking techniques at run time may support automatic reasoning about such changes, detect harmful configurations, and potentially enable appropriate (self-)reactions. However, traditional model checking techniques and tools may not be simply applied as they are at run time, since they hardly meet the constraints imposed by on-the-fly analysis, in terms of execution time and memory occupation. This paper precisely addresses this issue and focuses on reliability models, given in terms of Discrete Time Markov Chains, and probabilistic model checking. It develops a mathematical framework for run-time probabilistic model checking that, given a reliability model and a set of requirements, statically generates a set of expressions, which can be efficiently used at run-time to verify system requirements. An experimental comparison of our approach with existing probabilistic model checkers shows its practical applicability in run-time verification.


title: On the Optimal Convergence Probability of Univariate Estimation of Distribution Algorithms

abstract: In this paper we obtain bounds on the probability of convergence to the optimal solution for the compact genetic algorithm (cGA) and the population based incremental learning (PBIL). Moreover, we give a sufficient condition for convergence of these algorithms to the optimal solution and compute a range of possible values for algorithm parameters at which there is convergence to the optimal solution with a predefined confidence level.


title: A Hybrid Model Combining Convolutional Neural Network with XGBoost for Predicting Social Media Popularity

abstract: A hybrid model for social media popularity prediction is proposed by combining Convolutional Neural Network (CNN) with XGBoost. The CNN model is exploited to learn high-level representations from the social cues of the data. These high-level representations are used in XGBoost to predict the popularity of the social posts. We evaluate our approach on a real-world Social Media Prediction (SMP) dataset, which consists of 432K Flickr images. The experimental results show that the proposed approach is effective, achieving the following performance: Spearman's Rho: 0.7406, MSE: 2.7293, MAE: 1.2475.


title: Multi-Input Inverter for Grid-Connected Hybrid PV/Wind Power System

abstract: The objective of this paper is to propose a novel multi-input inverter for the grid-connected hybrid photovoltaic (PV)/wind power system in order to simplify the power system and reduce the cost. The proposed multi-input inverter consists of a buck/buck-boost fused multi-input dc-dc converter and a full-bridge dc-ac inverter. The output power characteristics of the PV array and the wind turbine are introduced. The perturbation and observation method is used to accomplish the maximum power point tracking algorithm for input sources. The operational principle of the proposed multi-input inverter is explained. The control circuit is realized by using a digital signal processor and auxiliary analog circuits. For practical applications, functions of soft-start and circuit protection are implemented. Experimental results have shown the performance of the proposed multi-input inverter with desired features


title: Active Memory Cube: A processing-in-memory architecture for exascale systems

abstract: Many studies point to the difficulty of scaling existing computer architectures to meet the needs of an exascale system (i.e., capable of executing $10^{18} $ floating-point operations per second), consuming no more than 20 MW in power, by around the year 2020. This paper outlines a new architecture, the Active Memory Cube, which reduces the energy of computation significantly by performing computation in the memory module, rather than moving data through large memory hierarchies to the processor core. The architecture leverages a commercially demonstrated 3D memory stack called the Hybrid Memory Cube, placing sophisticated computational elements on the logic layer below its stack of dynamic random-access memory (DRAM) dies. The paper also describes an Active Memory Cube tuned to the requirements of a scientific exascale system. The computational elements have a vector architecture and are capable of performing a comprehensive set of floating-point and integer instructions, predicated operations, and gather-scatter accesses across memory in the Cube. The paper outlines the software infrastructure used to develop applications and to evaluate the architecture, and describes results of experiments on application kernels, along with performance and power projections.


title: Neuroimaging of nonaccidental head trauma: pitfalls and controversies

abstract: Although certain neuroimaging appearances are highly suggestive of abuse, radiological findings are often nonspecific. The objective of this review is to discuss pitfalls, controversies, and mimics occurring in neuroimaging of nonaccidental head trauma in order to allow the reader to establish an increased level of comfort in distinguishing between nonaccidental and accidental head trauma. Specific topics discussed include risk factors, general biomechanics and imaging strategies in nonaccidental head trauma, followed by the characteristics of skull fractures, normal prominent tentorium and falx versus subdural hematoma, birth trauma versus nonaccidental head trauma, hyperacute versus acute on chronic subdural hematomas, expanded subarachnoid space versus subdural hemorrhage, controversy regarding subdural hematomas associated with benign enlarged subarachnoid spaces, controversy regarding hypoxia as a cause of subdural hematoma and/or retinal hemorrhages without trauma, controversy regarding the significance of retinal hemorrhages related to nonaccidental head trauma, controversy regarding the significance of subdural hematomas in general, and pitfalls of glutaric aciduria type 1 and hemophagocytic lymphohistiocytosis mimicking nonaccidental head trauma.


Context-aware Argumentative Relation Mining


SURVEY OF BIOMETRIC RECOGNITION SYSTEMS AND THEIR


IllustStyleMap: visualization of illustrations based on similarity of drawing style of authors


title: Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network

abstract: A decision map contains complete and clear information about the image to be fused, and detecting the decision map is crucial to various image fusion issues, especially multi-focus image fusion. Nevertheless, in an attempt to obtain an approving image fusion effect, it is necessary and always difficult to obtain a decision map. In this paper, we address this problem with a novel image segmentation-based multi-focus image fusion algorithm, in which the task of detecting the decision map is treated as image segmentation between the focused and defocused regions in the source images. The proposed method achieves segmentation through a multi-scale convolutional neural network, which performs a multi-scale analysis on each input image to derive the respective feature maps on the region boundaries between the focused and defocused regions. The feature maps are then inter-fused to produce a fused feature map. Afterward, the fused map is post-processed using initial segmentation, morphological operation, and watershed to obtain the segmentation map/decision map. We illustrate that the decision map gained from the multi-scale convolutional neural network is trustworthy and that it can lead to high-quality fusion results. Experimental results evidently validate that the proposed algorithm can achieve an optimum fusion performance in light of both qualitative and quantitative evaluations.


title: Landing system for AR.Drone 2.0 using onboard camera and ROS

abstract: As the application of unmanned aerial vehicles(UAVs) becoming more widely, a demand for precise autonomous landing is rising quickly. AR.Drone 2.0 is a high-tech low-cost flying toy that is able to take role as UAV. In this paper, we present a system for the drone to implement autonomous landing. The image flow from the camera of the drone is obtained and rectified by camera calibration. Then the image frames are processed by a marker recognition package to detect the AR marker which is used as a landing pad, and calculate the position and orientation relation between the drone and the landing pad. Next a PID controller is used to control the drone's velocity to stably hovering on the center of the landing pad. A laptop computer running Ubuntu and ROS is used to communicate with the drone via Wi-Fi, throughout the landing process. Several experiments are made to verify the feasibility of the whole system.


title: Non-invasive glucose monitoring technology in diabetes management: A review

abstract: The frequent monitoring of glucose is an essential part of diabetes management. Despite the fact that almost all the commercially successful blood glucose monitoring devices are invasive, there is an immense need to develop non-invasive glucose monitoring (NGM) devices that will alleviate the pain and suffering of diabetics associated with the frequent pricking of skin for taking the blood sample for glucose testing. There have been numerous developments in the field of NGM during the last decade, which stress the need for a critical review. This manuscript aims to review the various NGM techniques and devices. The challenges and future trends in NGM are also discussed.


title: In vivo cancer targeting and imaging with semiconductor quantum dots

abstract: We describe the development of multifunctional nanoparticle probes based on semiconductor quantum dots (QDs) for cancer targeting and imaging in living animals. The structural design involves encapsulating luminescent QDs with an ABC triblock copolymer and linking this amphiphilic polymer to tumor-targeting ligands and drug-delivery functionalities. In vivo targeting studies of human prostate cancer growing in nude mice indicate that the QD probes accumulate at tumors both by the enhanced permeability and retention of tumor sites and by antibody binding to cancer-specific cell surface biomarkers. Using both subcutaneous injection of QD-tagged cancer cells and systemic injection of multifunctional QD probes, we have achieved sensitive and multicolor fluorescence imaging of cancer cells under in vivo conditions. We have also integrated a whole-body macro-illumination system with wavelength-resolved spectral imaging for efficient background removal and precise delineation of weak spectral signatures. These results raise new possibilities for ultrasensitive and multiplexed imaging of molecular targets in vivo.


title: Algorithmic motion planning in robotics

abstract: A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning. >


title: Recognition of Image-Orientation-Based Iris Spoofing

abstract: This paper presents a solution to automatically recognize the correct left/right and upright/upside-down orientation of iris images. This solution can be used to counter spoofing attacks directed to generate fake identities by rotating an iris image or the iris sensor during the acquisition. Two approaches are compared on the same data, using the same evaluation protocol: 1) feature engineering, using hand-crafted features classified by a support vector machine (SVM) and 2) feature learning, using data-driven features learned and classified by a convolutional neural network (CNN). A data set of 20 750 iris images, acquired for 103 subjects using four sensors, was used for development. An additional subject-disjoint data set of 1,939 images, from 32 additional subjects, was used for testing purposes. Both same-sensor and cross-sensor tests were carried out to investigate how the classification approaches generalize to unknown hardware. The SVM-based approach achieved an average correct classification rate above 95% (89%) for recognition of left/right (upright/upside-down) orientation when tested on subject-disjoint data and camera-disjoint data, and 99% (97%) if the images were acquired by the same sensor. The CNN-based approach performed better for same-sensor experiments, and presented slightly worse generalization capabilities to unknown sensors when compared with the SVM. We are not aware of any other papers on the automatic recognition of upright/upside-down orientation of iris images, or studying both hand-crafted and data-driven features in same-sensor and cross-sensor subject-disjoint experiments. The data sets used in this paper, along with random splits of the data used in cross-validation, are being made available.


title: How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface

abstract: The rising prevalence of algorithmic interfaces, such as curated feeds in online news, raises new questions for designers, scholars, and critics of media. This work focuses on how transparent design of algorithmic interfaces can promote awareness and foster trust. A two-stage process of how transparency affects trust was hypothesized drawing on theories of information processing and procedural justice. In an online field experiment, three levels of system transparency were tested in the high-stakes context of peer assessment. Individuals whose expectations were violated (by receiving a lower grade than expected) trusted the system less, unless the grading algorithm was made more transparent through explanation. However, providing too much information eroded this trust. Attitudes of individuals whose expectations were met did not vary with transparency. Results are discussed in terms of a dual process model of attitude change and the depth of justification of perceived inconsistency. Designing for trust requires balanced interface transparency - not too little and not too much.


title: Examining the Influences of Senior Discount Usage in the Hospitality Industry

abstract: ABSTRACT The main purpose of this study was to examine older adult attitudes and behaviors of discount usage in the hospitality industry. A secondary purpose was to understand the relationship between usage category (nonusers, light users and heavy users), age and influences of discount usage in the hospitality industry. Results suggested that (1) the profile of the older adult discount user in the hospitality industry reveals that the average discount user is married'partnered, educated, with a variety of income levels, basically the “average” older adult and (2) generally, the age of the senior does not have a significant impact on discount usage, but rather previous discount behavior and institutional barriers played a significant role. Employees in the hospitality industry need to be trained to recognize and manage for “ageism.” Seniors will be less inclined to use discounts if employees in the hospitality industry treat older adults poorly. If use of hospitality discounts is related to general discount then specials or cents-off coupons should be placed in the newspapers along side of general coupons.


REINFORCEMENT LEARNING FRAMEWORK FOR MEDICAL IMAGE SEGMENTATION USING MEDICAL IMAGES


title: Evolving BioAssay Ontology (BAO): modularization, integration and applications

abstract: The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.


title: Pseudomonas aeruginosa exhibits sliding motility in the absence of type IV pili and flagella.

abstract: Pseudomonas aeruginosa exhibits swarming motility on 0.5 to 1% agar plates in the presence of specific carbon and nitrogen sources. We have found that PAO1 double mutants expressing neither flagella nor type IV pili (fliC pilA) display sliding motility under the same conditions. Sliding motility was inhibited when type IV pilus expression was restored; like swarming motility, it also decreased in the absence of rhamnolipid surfactant production. Transposon insertions in gacA and gacS increased sliding motility and restored tendril formation to spreading colonies, while transposon insertions in retS abolished motility. These changes in motility were not accompanied by detectable changes in rhamnolipid surfactant production or by the appearance of bacterial surface structures that might power sliding motility. We propose that P. aeruginosa requires flagella during swarming to overcome adhesive interactions mediated by type IV pili. The apparent dependence of sliding motility on environmental cues and regulatory pathways that also affect swarming motility suggests that both forms of motility are influenced by similar cohesive factors that restrict translocation, as well as by dispersive factors that facilitate spreading. Studies of sliding motility may be particularly well-suited for identifying factors other than pili and flagella that affect community behaviors of P. aeruginosa.


Virtual realities


title: Compact CPW-Fed microstrip octagonal patch antenna with H slot for WLAN and WIMAX applications

abstract: In this paper, a Coplanar Wave Guide (CPW)-Fed microstrip octagonal patch antenna for WLAN and WIMAX Applications is proposed. The studied structure is suitable for 2.3/2.5/3.3/3.5/5/5.5GHz WiMAX and for 3.6/2.4–2.5/4.9–5.9GHz WLAN applications. The octagonal shape is obtained by making a cut in the four angles of the rectangular microstrip patch antenna; in addition the using of CPW-Fed allows obtaining UWB characteristics. The miniaturization in the antenna size for lower band is achieved by introducing an H slot in the radiating element. The proposed antenna is designed on a single and a small substrate board of dimensions 46×40×1.6 mm3. Moreover the miniaturized antenna has a good impedance matching and a reasonable gain. All the simulations were performed in CADFEKO, a Method of Moment (MoM) based solver.


title: Symbolic model checking for real-time systems

abstract: Finite-state programs over real-numbered time in a guarded-command language with real-valued clocks are described. Model checking answers the question of which states of a real-time program satisfy a branching-time specification. An algorithm that computes this set of states symbolically as a fixpoint of a functional on state predicates, without constructing the state space, is given. >


title: Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

abstract: We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. As an application of our method, we propose an amortized MLE algorithm for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realistic-looking images competitive with the state-of-the-art results.


title: SUS: A Retrospective

abstract: Rather more than 25 years ago, as part of a usability engineering program, I developed a questionnaire---the System Usability Scale (SUS)---that could be used to take a quick measurement of how people perceived the usability of computer systems on which they were working. This proved to be an extremely simple and reliable tool for use when doing usability evaluations, and I decided, with the blessing of engineering management at Digital Equipment Co. Ltd (DEC; where I developed SUS), that it was probably something that could be used by other organizations (the benefit for us being that if they did use it, we potentially had something we could use to compare their systems against ours). So, in 1986, I made SUS freely available to a number of colleagues, with permission to pass it on to anybody else who might find it useful, and over the next few years occasionally heard of evaluations of systems where researchers and usability engineers had used it with some success.


title: Psychosocial work load and stress in the geriatric care

abstract: BackgroundDue to the decrease in informal care by family members and the demographic development, the importance of professional geriatric care will rise considerably. Aim of this study was to investigate the psychosocial workplace situation for employees in this profession.MethodsThe German version of the COPSOQ (Copenhagen Psychosocial Questionnaire) was used for the assessment of psychosocial factors at work. The instrument includes 22 scales and 3 single items concerning demands, control, stress, support, and strain.Results between two study groups of geriatric care were compared to each other as well as to employees in general hospital care and a general population mean (COPSOQ database).Statistical analysis included t-tests, ANOVA and multiple comparisons of means. Statistical significance (p < 0.01, two-tailed) and a difference of at least 5 points in mean values were defined as the relevant threshold.ResultsIn total 889 respondents from 36 institutions took part in the study. 412 worked in Home Care (HC), 313 in Geriatric Nursing Homes (GNH), 164 in other professions (e.g. administration).Comparison between HC and GNH showed more favourable values for the first group for the most scales, e.g. lower quantitative and emotional demands and less work-privacy conflict, better possibilities for development etc. Compared to external values from the German COPSOQ database for general hospital care (N = 1.195) and the total mean across all professions, COPSOQ-total (N = 11.168), the results are again positive for HC workers on most of the scales concerning demands and social support. The only negative finding is the very low amount of social relations at work due to the obligation to work alone most of the time. Employees in GNH rate predictability, quality of leadership and feedback higher when compared to general hospital care and show some further favourable mean values compared to the COPSOQ mean value for all professions. A disadvantage for GNH is the high rating for job insecurity.A supplementary subgroup analysis showed that the degree of negative evaluation of psychosocial factors concerning demands was related to the amount of working hours per week and the number of on-call duties.ConclusionsCompared to employees in general hospital care and the COPSOQ overall mean value across all professions, geriatric care employees and especially home care workers evaluate their psychosocial working situation more positive for most aspects. However, this seems partly due to the very high proportion of part-time workers. Critical results for the two study groups are the relatively high job insecurity in nursing homes and the lack of social relations for the HCrs.


title: Resource-aware Mining of Data Streams

abstract: Mining data streams has raised a number of research challenges for the data mining community. These challenges include the limitations of computational resources, especially because mining streams of data most likely be done on a mobile device with limited resources. Also due to the continuality of data streams, the algorithm should have only one pass or less over the incoming data records. In this article, our Algorithm Output Granularity (AOG) approach in mining data streams is discussed. AOG is a novel adaptable approach that can cope with the challenging inherent features of data streams. We also show the results for AOG based clustering in a resource constrained environment.


title: Generative Adversarial Networks for Noise Reduction in Low-Dose CT

abstract: Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained to distinguish the output of the generator from routine-dose CT images. The performance of this discriminator was used as an adversarial loss for the generator. Experiments were performed using CT images of an anthropomorphic phantom containing calcium inserts, as well as patient non-contrast-enhanced cardiac CT images. The phantom and patients were scanned at 20% and 100% routine clinical dose. Three training strategies were compared: the first used only voxelwise loss, the second combined voxelwise loss and adversarial loss, and the third used only adversarial loss. The results showed that training with only voxelwise loss resulted in the highest peak signal-to-noise ratio with respect to reference routine-dose images. However, CNNs trained with adversarial loss captured image statistics of routine-dose images better. Noise reduction improved quantification of low-density calcified inserts in phantom CT images and allowed coronary calcium scoring in low-dose patient CT images with high noise levels. Testing took less than 10 s per CT volume. CNN-based low-dose CT noise reduction in the image domain is feasible. Training with an adversarial network improves the CNNs ability to generate images with an appearance similar to that of reference routine-dose CT images.


title: The relative success of alternative approaches to strategic information systems planning: an empirical analysis

abstract: Abstract Strategic information systems planning (SISP) is an exercise or ongoing activity that enables organisations to develop priorities for information systems development. It has been suggested that the ‘SISP approach’, a combination of method, process and implementation, is the most complete way of describing SISP activity. Based upon questionnaire responses from 267 IT Directors, four distinct approaches to SISP have been derived using cluster analysis. A comparison of these four approaches with five approaches of Earl, M.J., 1993. Experiences in SISP, MIS Quarterly, (March), 1–24, indicates that three bear strong similarities to the ‘organisational’, ‘business-led’, and ‘administrative’ approaches, whilst the fourth cluster is related to both Earl's ‘method-driven’ and ‘technological’ approaches. An analysis of the relationship between SISP approach and SISP success demonstrates that the ‘organisational approach’ is significantly more successful than the other three approaches.


title: Low power 4×4 bit multiplier design using dadda algorithm and optimized full adder

abstract: This paper presents the model of 4-bit multiplier having low power and high speed using Algorithm named Dadda and the basic building block used is optimized Full adder having low power dissipation and minimum propagation delay. Full and half adder blocks have been designed using pass-transistor logic and CMOS process technology to reduce the power dissipation and propagation delay. We have also applied Dadda algorithm to reduce the propagation delay. The model has been designed using Cadence Virtuoso in 90-nm technology. The proposed multiplier starts its operation at the frequency of 3.83 GHz and its average dynamic power is 184.3 μW at the supply of 1V.


title: Intel(R) RealSense(TM) Stereoscopic Depth Cameras

abstract: We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and RS400.


title: Copy move forgery detection using SIFT and GMM

abstract: Modifying or enhancing an image is ubiquitous but, when enhancement tends to change the interpretation of the image they are termed as an attempt of forgery on digital images. Copy move forgery (CMF) is a simple technique and has a number of well built tools in a number of image enhancement software. CMF detection techniques often tend to establish similarity between copied and pasted region on the same image as both are from same original image. Keypoint and block based techniques are used to determine the CMF. SIFT keypoints are combined with different techniques to accurately localize forgery. High dimensionality of feature vector acts as a bottle neck in SIFT based analysis. We propose a method to detect CMF using SIFT descriptors which are clustered using GMM and segment the obtained suspect region speeding up the analysis.


title: Gender, Representation and Online Participation: A Quantitative Study of StackOverflow

abstract: Online communities are flourishing as social meeting web spaces for users and peer community members. Different online communities require different levels of competence for participants to join, and scattered evidence suggests that females and minorities as participants can be under-represented. Additional anecdotal evidence suggests that women withdraw from unfriendly online communities. Owing to the limited amount of empirical evidence on the matter, this paper provides a quantitative study of the phenomenon, in order to assess the representation and social impact of gender in online communities. This study positions itself within recent and focused international initiatives, launched by the European Commission in order to encourage women in the field of science and technology. Focusing on technical support networks around web content management tools (e.g. Drupal and WordPress) and on questions & answers websites (e.g. StackOverflow), this paper unearths a spectrum of online communities, in which women participate to various degrees. Keywords: gender; computer supported collaborative work; empirical studies in collaborative and social computing; online communities


title: Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

abstract: Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text usingAbstract Meaning Representation (AMR)has been limited, due to the relatively limited amount of labeled data and the non-sequential nature of the AMR graphs. We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH, the current best score reported without significant use of external semantic resources. For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8. We present extensive ablative and qualitative analysis including strong evidence that sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.


title: Directly Fabricating Soft Robotic Actuators With an Open-Source 3-D Printer

abstract: 3-D printing silicone has been a long sought for goal by roboticists. Fused deposition manufacturing (FDM) is a readily accessible and simple 3-D printing scheme that could hold the key to printing silicone. This study details an approach to 3-D print silicone elastomer through use of a thickening additive and heat curing techniques. We fabricated an identical control actuator using molding and 3-D printing techniques for comparison. By comparing the free space elongation and fixed length force of both actuators, we were able to evaluate the quality of the print. We observed that the 3-D printed linear actuator was able to perform similarly to the molded actuator, with an average error of 5.08% in actuator response, establishing the feasibility of such a system. We envision that further development of this system would contribute to the way soft robotic systems are fabricated.


title: A new grouping genetic algorithm approach to the multiple traveling salesperson problem

abstract: The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.


title: Planar morphometry, shear and optimal quasi-conformal mappings

abstract: To characterize the diversity of planar shapes in such instances as insect wings and plant leaves, we present a method for the generation of a smooth morphometric mapping between two planar domains which matches a number of homologous points. Our approach tries to balance the competing requirements of a descriptive theory which may not reflect mechanism and a multi-parameter predictive theory that may not be well constrained by experimental data. Specifically, we focus on aspects of shape as characterized by local rotation and shear, quantified using quasi-conformal maps that are defined precisely in terms of these fields. To make our choice optimal, we impose the condition that the maps vary as slowly as possible across the domain, minimizing their integrated squared-gradient. We implement this algorithm numerically using a variational principle that optimizes the coefficients of the quasi-conformal map between the two regions and show results for the recreation of a sample historical grid deformation mapping of D’Arcy Thompson. We also deploy our method to compare a variety of Drosophila wing shapes and show that our approach allows us to recover aspects of phylogeny as marked by morphology.


title: CMOS Imager With 1024 SPADs and TDCs for Single-Photon Timing and 3-D Time-of-Flight

abstract: We present a CMOS imager consisting of 32×32 smart pixels, each one able to detect single photons in the 300-900 nm wavelength range and to perform both photon-counting and photon-timing operations on very fast optical events with faint intensities. In photon-counting mode, the imager provides photon-number (i.e, intensity) resolved movies of the scene under observation, up to 100 000 frames/s. In photon-timing, the imager provides photon arrival times with 312 ps resolution. The result are videos with either time-resolved (e.g., fluorescence) maps of a sample, or 3-D depth-resolved maps of a target scene. The imager is fabricated in a cost-effective 0.35-μm CMOS technology, automotive certified. Each pixel consists of a single-photon avalanche diode with 30 μm photoactive diameter, coupled to an in-pixel 10-bit time-to-digital converter with 320-ns full-scale range, an INL of 10% LSB and a DNL of 2% LSB. The chip operates in global shutter mode, with full frame times down to 10 μs and just 1-ns conversion time. The reconfigurable imager design enables a broad set of applications, like time-resolved spectroscopy, fluorescence lifetime imaging, diffusive optical tomography, molecular imaging, time-of-flight 3-D ranging and atmospheric layer sensing through LIDAR.


title: DDoS attacks and defense mechanisms: classification and state-of-the-art

abstract: Denial of Service (DoS) attacks constitute one of the major threats and among the hardest security problems in today's Internet. Of particular concern are Distributed Denial of Service (DDoS) attacks, whose impact can be proportionally severe. With little or no advance warning, a DDoS attack can easily exhaust the computing and communication resources of its victim within a short period of time. Because of the seriousness of the problem many defense mechanisms have been proposed to combat these attacks. This paper presents a structural approach to the DDoS problem by developing a classification of DDoS attacks and DDoS defense mechanisms. Furthermore, important features of each attack and defense system category are described and advantages and disadvantages of each proposed scheme are outlined. The goal of the paper is to place some order into the existing attack and defense mechanisms, so that a better understanding of DDoS attacks can be achieved and subsequently more efficient and effective algorithms, techniques and procedures to combat these attacks may be developed.


title: Forensic Face Recognition: A Survey

abstract: Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework.


title: An AST-based Code Plagiarism Detection Algorithm

abstract: In modern software engineering, software plagiarism is widespread and uncurbed, developing plagiarism detection methods is imperative. Popular technologies of software plagiarism detection are mostly based on text, token and syntax tree. Among these plagiarism detection technologies, tree-based plagiarism detection technology can effectively detect the code which cannot be detected by the other two kinds of technologies. In this paper, we propose a more effective plagiarism detection algorithm based on abstract syntax tree (AST) by computing the hash values of the syntax tree nodes, and comparing them. In order to implement the algorithm more effectively, special measurement is taken to reduce the error rate when calculating the hash values of operations, especially the arithmetic operations like subtraction and division. Results of the test showed that the measurement is reliable and necessary. It performs well in the code comparison field, and is helpful in the area of protecting source code's copyright.


title: Detecting Sarcasm in Multimodal Social Platforms

abstract: Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.


Wire Bonding Challenges in Optoelectronics Packaging author ( s )


title: MONAS: Multi-Objective Neural Architecture Search using Reinforcement Learning

abstract: Recent studies on neural architecture search have shown that automatically designed neural networks perform as good as expert-crafted architectures. While most existing works aim at finding architectures that optimize the prediction accuracy, these architectures may have complexity and is therefore not suitable being deployed on certain computing environment (e.g., with limited power budgets). We propose MONAS, a framework for Multi-Objective Neural Architectural Search that employs reward functions considering both prediction accuracy and other important objectives (e.g., power consumption) when searching for neural network architectures. Experimental results showed that, compared to the state-ofthe-arts, models found by MONAS achieve comparable or better classification accuracy on computer vision applications, while satisfying the additional objectives such as peak power.


Classification of LIDAR Sensor Contaminations with Deep Neural Networks


title: Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks

abstract: In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness measures, LPOCSIN utilizes a simple yet effective one. Using this measure, we transform the cluster assignment problem into a series of alternating linear programs, and further propose a highly efficient procedure for solving those alternating problems. We evaluate LPOCSIN and other state-of-the-art methods by extensive experiments covering a wide range of synthetic and real networks. The experiments show that LPOCSIN significantly outperforms the other methods in recovering ground-truth clusters while being an order of magnitude faster than the most efficient state-of-the-art method.


title: Performance comparison of multi-label learning algorithms on clinical data for chronic diseases

abstract: We are motivated by the issue of classifying diseases of chronically ill patients to assist physicians in their everyday work. Our goal is to provide a performance comparison of state-of-the-art multi-label learning algorithms for the analysis of multivariate sequential clinical data from medical records of patients affected by chronic diseases. As a matter of fact, the multi-label learning approach appears to be a good candidate for modeling overlapped medical conditions, specific to chronically ill patients. With the availability of such comparison study, the evaluation of new algorithms should be enhanced.According to the method, we choose a summary statistics approach for the processing of the sequential clinical data, so that the extracted features maintain an interpretable link to their corresponding medical records. The publicly available MIMIC-II dataset, which contains more than 19,000 patients with chronic diseases, is used in this study. For the comparison we selected the following multi-label algorithms: ML-kNN, AdaBoostMH, binary relevance, classifier chains, HOMER and RAkEL.Regarding the results, binary relevance approaches, despite their elementary design and their independence assumption concerning the chronic illnesses, perform optimally in most scenarios, in particular for the detection of relevant diseases. In addition, binary relevance approaches scale up to large dataset and are easy to learn. However, the RAkEL algorithm, despite its scalability problems when it is confronted to large dataset, performs well in the scenario which consists of the ranking of the labels according to the dominant disease of the patient. Graphical abstractDisplay Omitted HighlightsWe evaluate multi-label learning algorithms for the analysis of clinical data.We focus on patients affected by multiple chronic diseases.We use a summary statistics approach to extract features on medical time series.


MDT ‘ Electrospinning ’ ELECTROSPINNING OF NANOFIBERS AND THEIR APPLICATIONS


title: Faktor -Faktor yang memengaruhi Peningkatan Daya Saing Klaster Mebel di Kabupaten Jepara

abstract: Indonesia is known as one of the nation's largest mebel exporter in the world. But, the development of Central Java mebel exports for four years fluctuated but tended to decline that was caused by economic crisis of the world. Industrial clusters are one of the strategic that was used to develop industry based on region. The purpose of this study was to analyze the factors affecting the competitiveness of the mebel industry in order to take policies that can be used to improve the competitiveness of Indonesian mebel industry of Central Java especially in overseas markets. This study use PLS (Partial Least Square) as tools that serve to test the diamond model porter plus social capital variables and analyze the factors that affect competitiveness, caused total population size used in this study is small. The results showed that competitiveness in the Mulyoharjo furniture cluster is influenced by condition factors, demand condition, related supporting industries. Whereas, Senenan furniture cluster is influenced by condition factors, the role of government, the opportunities, and social capital.


title: Morphological and Molecular Characterization of a Fungus, Hirsutella sp., Isolated from Planthoppers and Psocids in Argentina

abstract: Abstract A mycosed planthopper, Oliarus dimidiatus Berg (Hemiptera: Cixiidae), and two psocids, Heterocaecilius sp. (Psocodea: Pseudocaeciliidae) and Ectopsocus sp. (Ectopsocidae), were collected from Los Hornos and La Plata, Buenos Aires, Argentina between February and September 2007. Observations of mycelia growing on the host revealed that the putative fungal parasite had synnemata supporting monophialidic conidiogenous cells. Likewise, in vitro fungal cultures presented characteristics typical of the fungus Hirsutella citriformis Speare (Ascomycota: Hypocreales: Clavicipitaceae). The identity of the isolated fungi characterized based on morphological aspects was complemented by means of the internal transcribed spacer sequences. The sequences of both isolates were highly homologous to those of Cordyceps sp. (Fries) Link and Ophiocordyceps sinensis (Berkely) G.H. Sung, J.M. Sung, Hywel-Jones, and Spatafora (Ophiocordycipitaceae). We additionally confirmed that both isolates had the ability to infect and kill adults of Delphacodes kuscheli Fennah (Hemiptera: Delphacidae) after 10 days. Therefore, based on the morphology of the isolated fungi, their ribosomal internal transcribed spacer sequence, and their ability to parasite insects, we conclude that the fungi isolated belong to the genus Hirsutella and might have biotechnological potential.


title: Enhanced multisensory integration in older adults

abstract: Information from the different senses is seamlessly integrated by the brain in order to modify our behaviors and enrich our perceptions. It is only through the appropriate binding and integration of information from the different senses that a meaningful and accurate perceptual gestalt can be generated. Although a great deal is known about how such cross-modal interactions influence behavior and perception in the adult, there is little knowledge as to the impact of aging on these multisensory processes. In the current study, we examined the speed of discrimination responses of aged and young individuals to the presentation of visual, auditory or combined visual–auditory stimuli. Although the presentation of multisensory stimuli speeded response times in both groups, the performance gain was significantly greater in the aged. Most strikingly, multisensory stimuli restored response times in the aged to those seen in young subjects to the faster of the two unisensory stimuli (i.e., visual). The current results suggest that despite the decline in sensory processing that accompanies aging, the use of multiple sensory channels may represent an effective compensatory strategy to overcome these unisensory deficits.


title: Variational Policy Search via Trajectory Optimization

abstract: In order to learn effective control policies for dynamical systems, policy search methods must be able to discover successful executions of the desired task. While random exploration can work well in simple domains, complex and high-dimensional tasks present a serious challenge, particularly when combined with high-dimensional policies that make parameter-space exploration infeasible. We present a method that uses trajectory optimization as a powerful exploration strategy that guides the policy search. A variational decomposition of a maximum likelihood policy objective allows us to use standard trajectory optimization algorithms such as differential dynamic programming, interleaved with standard supervised learning for the policy itself. We demonstrate that the resulting algorithm can outperform prior methods on two challenging locomotion tasks.


Private Education: Relevant or Redundant? Private Education, Decentralisation and National Provision in Indonesia.


title: Network Calculus for the Validation of Automotive Ethernet In-vehicle Network Configurations

abstract: Automotive functions require validation of the underlying network in advance. Especially driver assistance functions may require hard time bounds concerning their communication infrastructure. In several works analytical models have been introduced, enabling the calculation of worst case delays for realtime applications using Ethernet as communication technology. On the other hand considerable effort has been spend to evaluate Ethernet for the use in automotive in-vehicle networks using simulation tools and prototypes. Showing that Ethernet can be used in general for the automotive use, they did not make an assertion about what happens if the worst case occurs. In this work we show by means of an analytic model the worst case analysis of a sample automotive Ethernet network configuration. We show that this configuration satisfies a current set of automotive functions requirements -- even for the worst case.


title: Where shall we go today?: planning touristic tours with tripbuilder

abstract: In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.


title: Mechanisms of water-holding capacity of meat: The role of postmortem biochemical and structural changes.

abstract: Unacceptable water-holding capacity costs the meat industry millions of dollars annually. However, limited progress has been made toward understanding the mechanisms that underlie the development of drip or purge. It is clear that early postmortem events including rate and extent of pH decline, proteolysis and even protein oxidation are key in influencing the ability of meat to retain moisture. Much of the water in the muscle is entrapped in structures of the cell, including the intra- and extramyofibrillar spaces; therefore, key changes in the intracellular architecture of the cell influence the ability of muscle cells to retain water. As rigor progresses, the space for water to be held in the myofibrils is reduced and fluid can be forced into the extramyofibrillar spaces where it is more easily lost as drip. Lateral shrinkage of the myofibrils occurring during rigor can be transmitted to the entire cell if proteins that link myofibrils together and myofibrils to the cell membrane (such as desmin) are not degraded. Limited degradation of cytoskeletal proteins may result in increased shrinking of the overall muscle cell, which is ultimately translated into drip loss. Recent evidence suggests that degradation of key cytoskeletal proteins by calpain proteinases has a role to play in determining water-holding capacity. This review will focus on key events in muscle that influence structural changes that are associated with water-holding capacity.


title: Network function virtualization in 5G

abstract: 5G wireless technology is paving the way to revolutionize future ubiquitous and pervasive networking, wireless applications, and user quality of experience. To realize its potential, 5G must provide considerably higher network capacity, enable massive device connectivity with reduced latency and cost, and achieve considerable energy savings compared to existing wireless technologies. The main objective of this article is to explore the potential of NFV in enhancing 5G radio access networks' functional, architectural, and commercial viability, including increased automation, operational agility, and reduced capital expenditure. The ETSI NFV Industry Specification Group has recently published drafts focused on standardization and implementation of NFV. Harnessing the potential of 5G and network functions virtualization, we discuss how NFV can address critical 5G design challenges through service abstraction and virtualized computing, storage, and network resources. We describe NFV implementation with network overlay and SDN technologies. In our discussion, we cover the first steps in understanding the role of NFV in implementing CoMP, D2D communication, and ultra densified networks.


title: Usability of String Distance Metrics for Name Matching Tasks in Polish

abstract: This paper presents results of the numerous experiments on usability of well-established string distance metrics and some new variants thereof for various name matching tasks in Polish.


title: Facial Expression Detection Techniques: Based on Viola and Jones Algorithm and Principal Component Analysis

abstract: Facial expression is a prominent posture beneath the skin of the face. They are the way of communication in humans which convey many things non-verbally. During the past years face recognition has received significant attention as one of the most important applications of image understanding and analysis. Many algorithms have been implemented on different static and non-static conditions. Static conditions include static and uniform background, identical poses, similar illumination, neutral frontal face. Non static conditions include position, partial occlusion orientation, varying lightening conditions and facial hair which make recognition process a complex problem. All these factors influence face recognition process. The main stages for face recognition include face detection, feature representation and classifications. Researchers have described distinct approaches for face recognition. In this work we present a glimpse of face detection techniques, methods used, their performance a their limitations and proposed a new technique for Face Detection based on Viola and Jones algorithm and principal component analysis. At the end we have shown simulation results for the proposed technique and established that proposed technique is performing better than the existing one. The proposed system is implemented in MATLAB version 7.1.4.0.739 (R2012a).


title: AP: Artificial Programming

abstract: The ability to automatically discover a program consistent with a given user intent (specification) is the holy grail of Computer Science. While significant progress has been made on the so-called problem of Program Synthesis, a number of challenges remain; particularly for the case of synthesizing richer and larger programs. This is in large part due to the difficulty of search over the space of programs. In this paper, we argue that the above-mentioned challenge can be tackled by learning synthesizers automatically from a large amount of training data. We present a first step in this direction by describing our novel synthesis approach based on two neural architectures for tackling the two key challenges of Learning to understand partial input-output specifications and Learning to search programs. The first neural architecture called the Spec Encoder computes a continuous representation of the specification, whereas the second neural architecture called the Program Generator incrementally constructs programs in a hypothesis space that is conditioned by the specification vector. The key idea of the approach is to train these architectures using a large set of (spec,P) pairs, where P denotes a program sampled from the DSL L and spec denotes the corresponding specification satisfied by P. We demonstrate the effectiveness of our approach on two preliminary instantiations. The first instantiation, called Neural FlashFill, corresponds to the domain of string manipulation programs similar to that of FlashFill. The second domain considers string transformation programs consisting of composition of API functions. We show that a neural system is able to perform quite well in learning a large majority of programs from few input-output examples. We believe this new approach will not only dramatically expand the applicability and effectiveness of Program Synthesis, but also would lead to the coming together of the Program Synthesis and Machine Learning research disciplines.


title: An optical fiber communication system supporting multi-topology for Profibus

abstract: Optical fiber communication has advantages of high transmission rate, high channel capacity, long transmission distance, and strong anti-interference ability so that it has been widely used in control and automation communication systems. Meanwhile, various topological structures are required in the different applications of optical fiber communication system. According to these demands, we propose a communication system named Frame Forwarding (FF) for Profibus in this paper. This system supports three kinds of network topologies which are liner, star and redundant ring. Especially, it could recover from link failure in redundant ring topology rapidly. It can replace traditional electronic transmission medium such as cable and has been applied to the Profibus communication system successfully. This paper introduces the design method of the communication strategy, and analysis the strategy based on application scenarios. Through the experiment, the feasibility of the Frame Forwarding system for Profibus is proved.


title: Addition of Clopidogrel to Aspirin and Fibrinolytic Therapy for Myocardial Infarction with ST-Segment Elevation

abstract: BACKGROUND ::: A substantial proportion of patients receiving fibrinolytic therapy for myocardial infarction with ST-segment elevation have inadequate reperfusion or reocclusion of the infarct-related artery, leading to an increased risk of complications and death. ::: ::: ::: METHODS ::: We enrolled 3491 patients, 18 to 75 years of age, who presented within 12 hours after the onset of an ST-elevation myocardial infarction and randomly assigned them to receive clopidogrel (300-mg loading dose, followed by 75 mg once daily) or placebo. Patients received a fibrinolytic agent, aspirin, and when appropriate, heparin (dispensed according to body weight) and were scheduled to undergo angiography 48 to 192 hours after the start of study medication. The primary efficacy end point was a composite of an occluded infarct-related artery (defined by a Thrombolysis in Myocardial Infarction flow grade of 0 or 1) on angiography or death or recurrent myocardial infarction before angiography. ::: ::: ::: RESULTS ::: The rates of the primary efficacy end point were 21.7 percent in the placebo group and 15.0 percent in the clopidogrel group, representing an absolute reduction of 6.7 percentage points in the rate and a 36 percent reduction in the odds of the end point with clopidogrel therapy (95 percent confidence interval, 24 to 47 percent; P<0.001). By 30 days, clopidogrel therapy reduced the odds of the composite end point of death from cardiovascular causes, recurrent myocardial infarction, or recurrent ischemia leading to the need for urgent revascularization by 20 percent (from 14.1 to 11.6 percent, P=0.03). The rates of major bleeding and intracranial hemorrhage were similar in the two groups. ::: ::: ::: CONCLUSIONS ::: In patients 75 years of age or younger who have myocardial infarction with ST-segment elevation and who receive aspirin and a standard fibrinolytic regimen, the addition of clopidogrel improves the patency rate of the infarct-related artery and reduces ischemic complications.


Relevance processes in multiple document comprehension


title: Event Processing across Edge and the Cloud for Internet of Things Applications

abstract: The rapid growth of sensing devices has opened up complex event processing (CEP) for real-time analytics in Internet of Things (IoT) Applications. While CEP has traditionally been centralized, the increasing capabilities of edge devices like smart phones, and the operational needs of low latency and privacy makes it desirable to use both edge and the Cloud for distributed CEP, the former often serving as event sources. This paper motivates the need for real-time analytics across edge and the Cloud, formalizes an optimization problem for bi-partitioning a CEP query pipeline based on IoT application needs, and proposes an initial solution.


title: 2D mapping of cluttered indoor environments by means of 3D perception

abstract: This paper presents a combination of a 3D laser sensor and a line-base SLAM algorithm which together produce 2D line maps of highly cluttered indoor environments. The key of the described method is the replacement of commonly used 2D laser range sensors by 3D perception. A straightforward algorithm extracts a virtual 2D scan that also contains partially occluded walls. These virtual scans are used as input for SLAM using line segments as features. The paper presents the used algorithms and experimental results that were made in a former industrial bakery. The focus lies on scenes that are known to be problematic for pure 2D systems. The results demonstrate that mapping indoor environments can be made robust with respect to both, poor odometry and clutter.


title: VisComplete: Automating Suggestions for Visualization Pipelines

abstract: Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus-automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.


title: Deobfuscation of Virtualization-Obfuscated Code Through Symbolic Execution and Compilation Optimization

abstract: Virtualization-obfuscation replaces native code in a binary with semantically equivalent and self-defined bytecode, which, upon execution, is interpreted by a custom virtual machine. It makes the code very difficult to analyze and is thus widely used in malware. How to deobfuscate such virtualization obfuscated code has been an important and challenging problem. We approach the problem from an innovative perspective by transforming it into a compilation optimization problem, and propose a novel technique that combines trace analysis, symbolic execution and compilation optimization to defeat virtualization obfuscation. We implement a prototype system and evaluate it against popular virtualization obfuscators; the results demonstrate that our method is effective in deobfuscation of virtualization-obfuscated code.


title: Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction

abstract: For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a "budget-sensitive" progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance.


title: Comparison of Different Scheduling Algorithm for LTE

abstract: In this paper we evaluate the performance of Packet Scheduling (PS) for different packet scheduling algorithms of 3GPP UTRAN Long Term Evolution (LTE) Downlink. Packet scheduling is importance in 3G LTE, because different types of traffic with different Quality of Service requirements are competing of the resources. In this paper, packet scheduler for LTE downlink is described. Comparison of three basic packet scheduling algorithm with their simulation result with different amount of fairness is explained. This paper shows that by dividing the packet scheduler into a time domain, frequency domain and also utilizing those using different algorithms, the throughput fairness between users can be effectively controlled.


title: AFEW-VA database for valence and arousal estimation in-the-wild

abstract: Continuous dimensional models of human affect, such as those based on valence and arousal, have been shown to be more accurate in describing a broad range of spontaneous, everyday emotions than the more traditional models of discrete stereotypical emotion categories (e.g. happiness, surprise). However, most prior work on estimating valence and arousal considered only laboratory settings and acted data. It is unclear whether the findings of these studies also hold when the methodologies proposed in these works are tested on data collected in-the-wild. In this paper we investigate this. We propose a new dataset of highly accurate per-frame annotations of valence and arousal for 600 challenging video clips extracted from feature films (also used in part for the AFEW dataset). For each video clip, we further provide per-frame annotations of 68 facial landmarks. We subsequently evaluate a number of common baseline and state-of-the-art methods on both a commonly used laboratory recording dataset (Semaine database) and the newly proposed recording set (AFEW-VA). Our results show that geometric features perform well independently of the settings. However, as expected, methods that perform well on constrained data do not necessarily generalise to uncontrolled data and vice-versa. AFEW-VA dataset for continuous valence and arousal estimation in-the-wildReview of existing work and databases for valence and arousal estimation from audio-video cuesBaseline results and comparison of the performance of various featuresComparison of state-of-the art methods on controlled and unconstrained environments


title: Age-based garbage collection

abstract: Modern generational garbage collectors look for garbage among the young objects, because they have high mortality; however, these objects include the very youngest objects, which clearly are still live. We introduce new garbage collection algorithms, called age-based , some of which postpone consideration of the youngest objects. Collecting less than the whole heap requires write barrier mechanisms to track pointers into the collected region. We describe here a new, efficient write barrier implementation that works for age-based and traditional generational collectors. To compare several collectors, their configurations, and program behavior, we use an accurate simulator that models all heap objects and the pointers among them, but does not model cache or other memory effects. For object-oriented languages, our results demonstrate that an older-first collector, which collects older objects before the youngest ones, copies on average much less data than generational collectors. Our results also show that an older-first collector does track more pointers, but the combined cost of copying and pointer tracking still favors an older-first over a generational collector in many cases. More importantly, we reopen for consideration the question where in the heap and with which policies copying collectors will achieve their best performance.


title: Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going

abstract: Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, however, and so methods for hardware-oriented approximation have become a hot topic. Research has shown that custom hardware-based neural network accelerators can surpass their general-purpose processor equivalents in terms of both throughput and energy efficiency. Application-tailored accelerators, when co-designed with approximation-based network training methods, transform large, dense, and computationally expensive networks into small, sparse, and hardware-efficient alternatives, increasing the feasibility of network deployment. In this article, we provide a comprehensive evaluation of approximation methods for high-performance network inference along with in-depth discussion of their effectiveness for custom hardware implementation. We also include proposals for future research based on a thorough analysis of current trends. This article represents the first survey providing detailed comparisons of custom hardware accelerators featuring approximation for both convolutional and recurrent neural networks, through which we hope to inspire exciting new developments in the field.


title: Decoupling mechanism of torque and levitation-force control for 12/4 dual-winding bearingless switched reluctance motor

abstract: In conventional bearingless switched reluctance motors (BSRMs), the current control algorithm is quite complex due to the coupling between torque and levitation forces. In this paper, a 12/4 dual-winding BSRM is proposed, which can realize decoupling control of torque and levitation forces. Because of the special inductance profile and two windings mounted on the stator pole, the torque and levitation forces can be controlled separately. A finite element method (FEM) is used to verify the validity of the mathematical model and the decoupling characteristics.


title: Exploring Timelines of Confirmed Suicide Incidents Through Social Media

abstract: Suicide is one of leading causes of death worldwide, yet little data is available about the lives of suicide victims because most people do not seek treatment. Research has shown that people express suicidal ideation in social media, which can potentially be tapped to improve our understanding of the thoughts and behaviors of people prior to suicide. In this work, we introduce a novel dataset of Chinese social media accounts of 130 people who committed suicide between 2011 and 2016. We describe the demographic and geographic composition of the users, then conduct a longitudinal text analysis of their post histories, showing observable changes in content leading up to the time of death. With encouraging exploratory findings, we discuss directions for future research.


title: Digital forensic investigation in cloud computing environment: Impact on privacy

abstract: Cloud computing as an extensive IT service delivery platform is one of the most promising technologies for rapid business development and operational efficiency improvement. Unfortunately, many of the attractive cloud computing attributes can be utilized for cybercrime purposes and illegal activities. Effective combat of these cybercrimes requires careful application of digital forensics practice in the cloud computing environment. This refers not just to the adoption of specific technical methodologies or tools for cloud forensic investigation, but also to serious consideration of the legal and privacy aspects of this investigation. Accordingly, through this paper we discuss the potential impact of the cloud forensics on privacy in the cloud computing environment, and offer several recommendations for adoption of privacy aware cloud forensic investigation process.


Subtraction using Local SVD Binary Pattern


title: Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

abstract: In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlationbased (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calciumrelease mechanisms known to be involved in STDP.


title: Mobile Phones as 3-DOF Controllers: A Comparative Study

abstract: Conventional input devices such as the mouse and keyboard lack in intuitiveness when it comes to 3D manipulation tasks. In this paper, we explore the use of accelerometer and digital compass equipped mobile phones as 3-DOF controllers in a 3D object rotation task. We put the mobile phone up against the established standards, a mouse and a touch pen and compare their performance in a rotation task. Our preliminary evaluation indicates that for this type of task, with only 5 minutes of practice the mobile phone is significantly faster than both the mouse and the touch pen.