title: A clutched parallel elastic actuator concept: Towards energy efficient powered legs in prosthetics and robotics

abstract: Parallel passive-elastic elements can reduce the energy consumption and torque requirements for motors in powered legged systems. However, the hardware design for such combined actuators is challenged by the need to engage and disengage the parallel elasticity depending on the gait phase. Although clutches in the drive train are often proposed, compact and low cost solutions of clutched parallel elastic actuators have so far not been established. Here we present the design and control of an initial prototype for a parallel elastic actuator. The actuator combines a DC motor with a parallel spring that is engaged and disengaged by a commercially available, compact and low-cost electric clutch. In experiments that mimic the torque and motion patterns of knee extensor muscles in human rebounding tasks we find that the parallel spring in the prototype reduces the energy consumption of the actuator by about 80% and the peak torque requirement for the DC motor by about 66%. In addition, we find that a simple trigger-based control can reliably engage and disengage the electric clutch during the motion, allowing the spring to support the motor in rebound, to remove stored energy from the system as necessary for stopping, and to virtually disappear at the actuator output level. On the other hand, the hardware experiments also reveal that our initial design limits the precision in the torque control, and we propose specific improvements to overcome these limitations.


title: Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities

abstract: This paper presents an introduction to time-frequency (T-F) methods in signal processing, and a novel approach for EEG abnormalities detection and classification based on a combination of signal related features and image related features. These features which characterize the non-stationary nature and the multi-component characteristic of EEG signals, are extracted from the T-F representation of the signals. The signal related features are derived from the T-F representation of EEG signals and include the instantaneous frequency, singular value decomposition, and energy based features. The image related features are extracted from the T-F representation considered as an image, using T-F image processing techniques. These combined signal and image features allow to extract more information from a signal. The results obtained on newborn and adult EEG data, show that the image related features improve the performance of the EEG seizure detection in classification systems based on multi-SVM classifier.


title: Person-Independent 3D Gaze Estimation Using Face Frontalization

abstract: Person-independent and pose-invariant estimation of eye-gaze is important for situation analysis and for automated video annotation. We propose a fast cascade regression based method that first estimates the location of a dense set of markers and their visibility, then reconstructs face shape by fitting a part-based 3D model. Next, the reconstructed 3D shape is used to estimate a canonical view of the eyes for 3D gaze estimation. The model operates in a feature space that naturally encodes local ordinal properties of pixel intensities leading to photometric invariant estimation of gaze. To evaluate the algorithm in comparison with alternative approaches, three publicly-available databases were used, Boston University Head Tracking, Multi-View Gaze and CAVE Gaze datasets. Precision for head pose and gaze averaged 4 degrees or less for pitch, yaw, and roll. The algorithm outperformed alternative methods in both datasets.


title: Silhouette and stereo fusion for 3D object modeling

abstract: We present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multistereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multigrid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the apparent contours of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.


title: The role of mitochondria and sarcoplasmic reticulum calcium handling upon reoxygenation of hypoxic myocardium.

abstract: The mechanism of twitch prolongation of hypoxic myocardium after reoxygenation was studied before and after interventions that affect cellular cyclic nucleotide levels, subcellular calcium handling, or oxygen-derived free radical production/survival. Right ventricular ferret papillary muscles were subjected to two 20-minute periods of hypoxia, each followed by 1 hour of reoxygenation. The first sequence of hypoxia/reoxygenation was done without intervention. Before the second sequence, the pharmacological agent under study was added to the superfusate or the superfusate calcium concentration was increased from 2.5 to 8 mM. Time from peak to 80% decline in twitch tension was measured in the presence and absence of each intervention immediately before each period of hypoxia and after reoxygenation at maximal twitch prolongation. Interventions that affect Ca2+ flux across the sarcolemma (verapamil and 8 mM [Ca2+]o) or agents that affect oxygen free radical production/survival (dimethyl sulfoxide and allopurinol) did not affect twitch prolongation. Pharmacological agents that increase cyclic AMP levels (forskolin and milrinone) or those that inhibit mitochondrial activity (oligomycin B and ruthenium red) attenuated twitch prolongation. Pharmacological agents that decrease cyclic AMP levels (carbachol) or inhibit function of the sarcoplasmic reticulum (ryanodine) augmented twitch prolongation. The effect of mitochondrial inhibitors on intracellular calcium handling during hypoxia and reoxygenation was examined using muscles loaded with the bioluminescent calcium indicator aequorin. Mitochondrial inhibitors abbreviated the calcium transient and maximal twitch prolongation after hypoxia. We conclude that alterations in sarcoplasmic reticulum and mitochondria calcium handling contribute to the prolonged relaxation seen upon reoxygenation of hypoxic myocardium.


title: Near-Field Experimental Verification of Separation of OAM Channels

abstract: The experimental proof that near-field radio communication channels based on orbital angular momentum (OAM) are naturally isolated is presented. In near-field zone, we show that two antennas, built for producing a beam with the same value of OAM, have a good throughput. On the other hand, antennas of different types, i.e., built for generating beams with different OAM values, including a standard $\ell = 0$ antenna, exhibit a good modal isolation .


title: SEEG assistant: a 3DSlicer extension to support epilepsy surgery

abstract: In the evaluation of Stereo-Electroencephalography (SEEG) signals, the physicist’s workflow involves several operations, including determining the position of individual electrode contacts in terms of both relationship to grey or white matter and location in specific brain regions. These operations are (i) generally carried out manually by experts with limited computer support, (ii) hugely time consuming, and (iii) often inaccurate, incomplete, and prone to errors. In this paper we present SEEG Assistant, a set of tools integrated in a single 3DSlicer extension, which aims to assist neurosurgeons in the analysis of post-implant structural data and hence aid the neurophysiologist in the interpretation of SEEG data. SEEG Assistant consists of (i) a module to localize the electrode contact positions using imaging data from a thresholded post-implant CT, (ii) a module to determine the most probable cerebral location of the recorded activity, and (iii) a module to compute the Grey Matter Proximity Index, i.e. the distance of each contact from the cerebral cortex, in order to discriminate between white and grey matter location of contacts. Finally, exploiting 3DSlicer capabilities, SEEG Assistant offers a Graphical User Interface that simplifies the interaction between the user and the tools. SEEG Assistant has been tested on 40 patients segmenting 555 electrodes, and it has been used to identify the neuroanatomical loci and to compute the distance to the nearest cerebral cortex for 9626 contacts. We also performed manual segmentation and compared the results between the proposed tool and gold-standard clinical practice. As a result, the use of SEEG Assistant decreases the post implant processing time by more than 2 orders of magnitude, improves the quality of results and decreases, if not eliminates, errors in post implant processing. The SEEG Assistant Framework for the first time supports physicists by providing a set of open-source tools for post-implant processing of SEEG data. Furthermore, SEEG Assistant has been integrated into 3D Slicer, a software platform for the analysis and visualization of medical images, overcoming limitations of command-line tools.


title: The eyes have it : the neuroethology , function and evolution of social gaze

abstract: Abstract Gaze is an important component of social interaction. The function, evolution and neurobiology of gaze processing are therefore of interest to a number of researchers. This review discusses the evolutionary role of social gaze in vertebrates (focusing on primates), and a hypothesis that this role has changed substantially for primates compared to other animals. This change may have been driven by morphological changes to the face and eyes of primates, limitations in the facial anatomy of other vertebrates, changes in the ecology of the environment in which primates live, and a necessity to communicate information about the environment, emotional and mental states. The eyes represent different levels of signal value depending on the status, disposition and emotional state of the sender and receiver of such signals. There are regions in the monkey and human brain which contain neurons that respond selectively to faces, bodies and eye gaze. The ability to follow another individual's gaze direction is affected in individuals with autism and other psychopathological disorders, and after particular localized brain lesions. The hypothesis that gaze following is “hard-wired” in the brain, and may be localized within a circuit linking the superior temporal sulcus, amygdala and orbitofrontal cortex is discussed.


title: A Low-Power Self-Forward-Body-Bias CMOS LNA for 3–6.5-GHz UWB Receivers

abstract: A low-power low-noise amplifier (LNA) implemented in 0.18 ?m CMOS technology utilizing a self-forward-body-bias (SFBB) technique is proposed for UWB low-frequency band system. By using the SFBB technique, it reduces supply voltage as well as saves additional bias circuits, which leads to low power consumption of 4.5 mW with low supply voltage of 1.06 V for two drain-to-source voltage drops. The complementary architecture and direct coupling technique between the first two stages also save bias circuits. The measurement result shows that the proposed LNA presents a maximum power gain of 16 dB with a good input impedance matching (S11 < -12 dB) and an average noise figure of 2.65 dB in the frequency range of 3-6.5 GHz.


title: Who keeps children alive? A review of the effects of kin on child survival

abstract: Children pose a problem. The extended period of childhood dependency and short interbirth intervals mean that human mothers have to care for several dependent children simultaneously. Most evolutionary anthropologists now agree that this is too much of an energetic burden for mothers to manage alone and that they must enlist help from other relatives to share the costs of raising children. Which kin help is the subject of much debate. Here, we review the evidence for whether the presence of kin affects child survival rates, in order to infer whether mothers do receive help in raising offspring and who provides this help. These 45 studies come from a variety of (mostly) natural fertility populations, both historical and contemporary, across a wide geographical range. We find that in almost all studies, at least one relative (apart from the mother) does improve the survival rates of children but that relatives differ in whether they are consistently beneficial to children or not. Maternal grandmothers tend to improve child survival rates as do potential sibling helpers at the nest (though the latter observation is based on rather few studies). Paternal grandmothers show somewhat more variation in their effects on child survival. Fathers have surprisingly little effect on child survival, with only a third of studies showing any beneficial effects. Overall, this review suggests that whilst help from kin may be a universal feature of human child-rearing, who helps is dependent on ecological conditions.


title: Performance evaluation of 3D keypoint detectors for time-of-flight depth data

abstract: In object recognition techniques, specially feature-based methods, a fundamental step is to extract keypoints which are distinct and considerably interesting in the image. There are many different keypoint detectors already available, each with its own specific use and results vary enormously. It is widely agreed that evaluation of feature detectors is important. To our knowledge there is no comparative study for the performance of keypoint detectors for only depth data from Time-Of-Flight (ToF) camera. As ToF sensors are cheap and extensively used for robotic applications, especially sensors with low sensor noise like Swiss Ranger SR-4k which give only depth data, need arises for this comparative study. A meticulous acquisition of different household object's depth data has been achieved using a Cartesian robot. The pose information from the robot has been used for more accurate evaluation. Different keypoints valid for only depth data are extracted and their repeatability is evaluated. A Comparative study has also been done on standard RGB-D datasets using the new metrics we have defined, to test the correctness of our approach with state of the art approaches which have used RGB-D data.


title: The Effect of Internet Security Breach Announcements on Market Value of

abstract: Assessing the value of information technology (IT) security is challenging because of the difficulty of measuring the cost of security breaches. An event-study analysis, using market valuations, was used to assess the impact of security breaches on the market value of breached firms. The information-transfer effect of security breaches (i.e., their effect on the market value of firms that develop security technology) was also studied. The results show that announcing an Internet security breach is negatively associated with the market value of the announcing firm. The breached firms in the sample lost, on average, 2.1 percent of their market value within two days of the announcement--an average loss in market capitalization of $1.65 billion per breach. Firm type, firm size, and the year the breach occurred help explain the cross-sectional variations in abnormal returns produced by security breaches. The effects of security breaches are not restricted to the breached firms. The market value of security developers is positively associated with the disclosure of security breaches by other firms. The security developers in the sample realized an average abnormal return of 1.36 percent during the two-day period after the announcement--an average gain of $1.06 billion in two days. The study suggests that the cost of poor security is very high for investors. rity, information technology security management, Internet security, security breach an-


title: Generalized state-space model for the interleaved buck converter

abstract: This paper presents a complete AC small-signal general model for the interleaved buck converter (IBC) operating in continuous conduction mode. Average state-space technique is used and the most important parasitic elements are included. A ready-to-use generalized model for n-cells structure is developed by obtaining its DC model and AC small-signal model for a certain operation point. Besides, it is presented an n-cells inductor value analysis against duty-cycle and a second-order model is derived from that, which can replace the complete model with accuracy, simplifying the dynamics analysis. Models have been successfully verified experimentally, and compared via simulation in Matlab/Simulink.


title: Self-compassion and well-being among older adults

abstract: Two studies assessed the role of self-compassion as a moderator of the relationship between physical health and subjective well-being in the elderly. In Study 1, 132 participants, ranging in age from 67 to 90 years, completed a questionnaire that assessed their perceptions of their physical health, self-compassion, and subjective well-being. Participants who were in good physical health had high subjective well-being regardless of their level of self-compassion. However, for participants with poorer physical health, self-compassion was associated with greater subjective well-being. In Study 2, 71 participants between the ages of 63 and 97 completed a questionnaire assessing self-compassion, well-being, and their willingness to use assistance for walking, hearing, and memory. Self-compassionate participants reported being less bothered by the use of assistance than those low in self-compassion, although the relationship between self-compassion and willingness to use assistive devices was mixed. These findi...


title: A Content-aware Fridge based on RFID in smart home for home-healthcare

abstract: With the ever increasing popularity of Radio Frequency Identification, it is easier for us to lead a more convenient and healthier life. In this paper we present a Content-aware Fridge based on RFID to provide several content-aware services to adjust our Eating habits. Especially, for patients, the system is able to inform them to take certain food which is good for their health and print a shopping list intelligently when the stock of fridge is in short. With the food ontology, we analyze the nutrients between the different types of food. And make recommendations on recipe so as to ensure that each of the family members will receive a well-balanced diet.


title: Controlling Prominence Realisation in Parametric DNN-Based Speech Synthesis

abstract: This work aims to improve text-To-speech synthesis forWikipedia by advancing and implementing models of prosodic prominence. We propose a new system architecture with explicit prominence modeling a ...


title: An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol

abstract: Skype is a peer-to-peer VoIP client developed in 2003 by the organization that created Kazaa. Skype claims that it can work almost seamlessly across NATs and firewalls and has better voice quality than other VoIP clients. It encrypts calls end-to-end, and stores user information in a decentralized fashion. Skype also supports instant messaging and conferencing. This paper analyzes key Skype functions such as login, NAT and firewall traversal, call establishment, media transfer, codecs, and conferencing under three different network setups. Analysis is performed by careful study of the Skype network traffic and by intercepting the shared library and system calls of Skype. We draw a map of super nodes to which Skype establishes a TCP connection at login.


title: LIMBO: Scalable clustering of categorical data

abstract: Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. We introduce LIMBO, a scalable hierarchical categorical clustering algorithm that builds on the Information Bottleneck (IB) framework for quantifying the relevant information preserved when clustering. As a hierarchical algorithm, LIMBO has the advantage that it can produce clusterings of different sizes in a single execution. We use the IB framework to define a distance measure for categorical tuples and we also present a novel distance measure for categorical attribute values. We show how the LIMBO algorithm can be used to cluster both tuples and values. LIMBO handles large data sets by producing a memory bounded summary model for the data. We present an experimental evaluation of LIMBO, and we study how clustering quality compares to other categorical clustering algorithms. LIMBO supports a trade-off between efficiency (in terms of space and time) and quality. We quantify this trade-off and demonstrate that LIMBO allows for substantial improvements in efficiency with negligible decrease in quality.


title: An intelligent smartphone based approach using IoT for ensuring safe driving

abstract: Road accident is a widespread problem all over the world. The number of the vehicle is increasing rapidly. Therefore, the probability of an accident is also increasing. So in this paper, we propose an IoT-based system for providing safe driving. It will collect data using smartphone and show the driver about the condition of the road. We have developed an Android based application which will collect data from a vehicle and send it to its nearest IoT-Fog server for processing the data quickly. We also developed two algorithms using k-means clustering approach which find the location of road anomalies and accident prone area. The result will be shown in Google map of driver's smartphone. This information will be stored in cloud for further use. We have tested our proposed system through statistical as well as experimental evaluations. Simulation result shows that our approach can provide better performance compared to state-of-the-art methods.


title: An approach to abstractive text summarization

abstract: Abstractive summarization is the technique of generating a summary of a text from its main ideas, not by copying verbatim most salient sentences from text. This is an important and challenge task in natural language processing. In this paper, we propose an approach to abstractive text summarization based on discourse rules, syntactic constraints, and word graph. Discourse rules and syntactic constraints are used in the process of generating sentences from keywords. Word graph is used in the sentence combination process to represent word relations in the text and to combine several sentences into one. Experimental results show that our approach is promising in solving the abstractive summarization task.


title: Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images

abstract: Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection. Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset. Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot arm with small parallel gripper and RGB-D camera for grasping challenging small, novel objects. With accurate vision-robot coordinate calibration through our proposed learning-based, fully automatic approach, our proposed method yielded 90% success rate.


title: OriNet: A Fully Convolutional Network for 3D Human Pose Estimation

abstract: In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images. We use limb orientations as a new way to represent 3D poses and bind the orientation together with the bounding box of each limb region to better associate images and predictions. The 3D orientations are modeled jointly with 2D keypoint detections. Without additional constraints, this simple method can achieve good results on several large-scale benchmarks. Further experiments show that our method can generalize well to novel scenes and is robust to inaccurate bounding boxes.


A Robust Distributed Big Data Clustering-based on Adaptive Density Partitioning using Apache Spark


title: A massively parallel keypoint detection and description (MP-KDD) algorithm for high-speed vision chip

abstract: This paper proposes a massively parallel keypoint detection and description (MP-KDD) algorithm for the vision chip with parallel array processors. The MP-KDD algorithm largely reduces the computational overhead by removing all floating-point and multiplication operations while preserving the currently popular SIFT and SURF algorithm essence. The MP-KDD algorithm can be directly and effectively mapped onto the pixel-parallel and row-parallel array processors of the vision chip. The vision chip architecture is also enhanced to realize direct memory access (DMA) and random access to array processors so that the MP-KDD algorithm can be executed more effectively. An FPGA-based vision chip prototype is implemented to test and evaluate our MP-KDD algorithm. Its image processing speed reaches 600–760 fps with high accuracy for complex vision applications, such as scene recognition.摘要本文提出了一种面向视觉芯片并行图像处理阵列的高效图像特征点提取和描述算法。 该算法基于SIFT特征点检测及SURF特征点描述, 但简化避免了浮点运算以及乘除法操作, 极大地节约了硬件开销和处理时间。 该算法可以直接、 高效地映射到视觉芯片的像素级并行和行并行阵列处理器。 本文实现了基于FPGA的视觉原型, 在其上成功测试了所提出的算法, 达到了600–700帧/秒的较高速度。


title: Neural Extractive Summarization with Side Information

abstract: Most extractive summarization methods focus on the main body of the document from which sentences need to be extracted. However, the gist of the document may lie in side information, such as the title and image captions which are often available for newswire articles. We propose to explore side information in the context of single-document extractive summarization. We develop a framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor with attention over side information. We evaluate our model on a large scale news dataset. We show that extractive summarization with side information consistently outperforms its counterpart that does not use any side information, in terms of both informativeness and fluency.


title: A Multi-View Embedding Space for Modeling Internet Images, Tags, and Their Semantics

abstract: This paper investigates the problem of modeling Internet images and associated text or tags for tasks such as image-to-image search, tag-to-image search, and image-to-tag search (image annotation). We start with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporate a third view capturing high-level image semantics, represented either by a single category or multiple non-mutually-exclusive concepts. We present two ways to train the three-view embedding: supervised, with the third view coming from ground-truth labels or search keywords; and unsupervised, with semantic themes automatically obtained by clustering the tags. To ensure high accuracy for retrieval tasks while keeping the learning process scalable, we combine multiple strong visual features and use explicit nonlinear kernel mappings to efficiently approximate kernel CCA. To perform retrieval, we use a specially designed similarity function in the embedded space, which substantially outperforms the Euclidean distance. The resulting system produces compelling qualitative results and outperforms a number of two-view baselines on retrieval tasks on three large-scale Internet image datasets.


Real-Time Lane and Obstacle Detection on the GOLD System


title: Promotional Chat on the Internet

abstract: Chat rooms, recommendation sites, and customer review sections allow consumers to overcome geographic boundaries and to communicate based on mutual interests. However, marketers also have incentives to supply promotional chat or reviews in order to influence the consumers' evaluation of their products. Moreover, firms can disguise their promotion as consumer recommendations due to the anonymity afforded by online communities. We explore this new setting where advertising and word of mouth become perfect substitutes because they appear indistinguishable to the consumer. Specifically, we investigate here whether word of mouth remains credible and whether firms choose to devote more resources promoting their inferior or superior products. ::: ::: We develop a game theoretic model in which two products are differentiated in their value to the consumer. Unlike the firms, the consumers are uncertain about the products' quality. The consumers read messages online that help them decide on the identity of the superior product. We find a unique equilibrium where online word of mouth is persuasive despite the promotional chat activity by competing firms. In this equilibrium, firms spend more resources promoting inferior products, in striking contrast to existing advertising literature. In addition, we discuss consumer welfare implications and how other marketing strategies might interact with promotional chat.


title: Automatic identification of bird species based on sinusoidal modeling of syllables

abstract: Syllables are elementary building blocks of bird song. In the sounds of many songbirds, a large class of syllables can be approximated as amplitude and frequency varying brief sinusoidal pulses. We test how well bird species can be recognized by comparing simple sinusoidal representations of isolated syllables. Results are encouraging and show that, with limited sets of bird species, a recognizer based on this signal model may already be sufficient.


title: Illusory Control: A Generative Force behind Power's Far-Reaching Effects

abstract: Three experiments demonstrated that the experience of power leads to an illusion of personal control. Regardless of whether power was experientially primed (Experiments 1 and 3) or manipulated through manager-subordinate roles (Experiment 2), it led to perceived control over outcomes that were beyond the reach of the powerholder. Furthermore, this illusory control mediated the influence of power on several self-enhancement and approach-related effects found in the power literature, including optimism (Experiment 2), self-esteem (Experiment 3), and action-orientation (Experiment 3), demonstrating its theoretical importance as a generative cause and driving force behind many of power's far-reaching effects. A fourth experiment ruled out an alternative explanation: that positive mood, rather than illusory control, is at the root of power's effects. The discussion considers implications for existing and future research on the psychology of power, perceived control, and positive illusions.


title: From dependency to causality: a machine learning approach

abstract: The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with n > 2 variables. The approach relies on the asymmetry of some conditional (in) dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for n gt; 2 variate distributions.


title: A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique

abstract: The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The Healthcare industry is generally "information rich", which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. Kidney disease is a complex task which requires much experience and knowledge. Kidney disease is a silent killer in developed countries and one of the main contributors to disease burden in developing countries. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The Data mining classification techniques, namely Decision trees, ANN, Naive Bayes are analyzed on Kidney disease data set.


title: Building contingency planning for closed-loop supply chains with product recovery

abstract: Abstract Contingency planning is the first stage in developing a formal set of production planning and control activities for the reuse of products obtained via return flows in a closed-loop supply chain. The paper takes a contingency approach to explore the factors that impact production planning and control for closed-loop supply chains that incorporate product recovery. A series of three cases are presented, and a framework developed that shows the common activities required for all remanufacturing operations. To build on the similarities and illustrate and integrate the differences in closed-loop supply chains, Hayes and Wheelwright’s product–process matrix is used as a foundation to examine the three cases representing Remanufacture-to-Stock (RMTS), Reassemble-to-Order (RATO), and Remanufacture-to-Order (RMTO). These three cases offer end-points and an intermediate point for closed-loop supply operations. Since they represent different positions on the matrix, characteristics such as returns volume, timing, quality, product complexity, test and evaluation complexity, and remanufacturing complexity are explored. With a contingency theory for closed-loop supply chains that incorporate product recovery in place, past cases can now be reexamined and the potential for generalizability of the approach to similar types of other problems and applications can be assessed and determined.


title: Trace Alignment in Process Mining: Opportunities for Process Diagnostics

abstract: Process mining techniques attempt to extract non-trivial knowledge and interesting insights from event logs. Process mining provides a welcome extension of the repertoire of business process analysis techniques and has been adopted in various commercial BPM systems (BPM|one, Futura Reflect, ARIS PPM, Fujitsu, etc.). Unfortunately, traditional process discovery algorithms have problems dealing with lessstructured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in a way that event logs can be explored easily. Trace alignment can be used in a preprocessing phase where the event log is investigated or filtered and in later phases where detailed questions need to be answered. Hence, it complements existing process mining techniques focusing on discovery and conformance checking.


title: Deep Predictive Learning: A Comprehensive Model of Three Visual Streams

abstract: How does the neocortex learn and develop the foundations of all our high-level cognitive abilities? We present a comprehensive framework spanning biological, computational, and cognitive levels, with a clear theoretical continuity between levels, providing a coherent answer directly supported by extensive data at each level. Learning is based on making predictions about what the senses will report at 100 msec (alpha frequency) intervals, and adapting synaptic weights to improve prediction accuracy. The pulvinar nucleus of the thalamus serves as a projection screen upon which predictions are generated, through deep-layer 6 corticothalamic inputs from multiple brain areas and levels of abstraction. The sparse driving inputs from layer 5 intrinsic bursting neurons provide the target signal, and the temporal difference between it and the prediction reverberates throughout the cortex, driving synaptic changes that approximate error backpropagation, using only local activation signals in equations derived directly from a detailed biophysical model. In vision, predictive learning requires a carefully-organized developmental progression and anatomical organization of three pathways (What, Where, and What * Where), according to two central principles: top-down input from compact, high-level, abstract representations is essential for accurate prediction of low-level sensory inputs; and the collective, low-level prediction error must be progressively and opportunistically partitioned to enable extraction of separable factors that drive the learning of further high-level abstractions. Our model self-organized systematic invariant object representations of 100 different objects from simple movies, accounts for a wide range of data, and makes many testable predictions.


A Single Hemispiral Antenna for GNSS Interference Mitigation and Direction Estimation


title: Codebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems

abstract: In millimeter wave (mmWave) systems, antenna architecture limitations make it difficult to apply conventional fully digital precoding techniques but call for low cost analog radio-frequency (RF) and digital baseband hybrid precoding methods. This paper investigates joint RF-baseband hybrid precoding for the downlink of multiuser multi-antenna mmWave systems with a limited number of RF chains. Two performance measures, maximizing the spectral efficiency and the energy efficiency of the system, are considered. We propose a codebook based RF precoding design and obtain the channel state information via a beam sweep procedure. Via the codebook based design, the original system is transformed into a virtual multiuser downlink system with the RF chain constraint. Consequently, we are able to simplify the complicated hybrid precoding optimization problems to joint codeword selection and precoder design (JWSPD) problems. Then, we propose efficient methods to address the JWSPD problems and jointly optimize the RF and baseband precoders under the two performance measures. Finally, extensive numerical results are provided to validate the effectiveness of the proposed hybrid precoders.


title: Semantic Segmentation using Adversarial Networks

abstract: Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.


title: Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and Obesity

abstract: Suboptimal nutrition is a leading cause of poor health. Nutrition and policy science have advanced rapidly, creating confusion yet also providing powerful opportunities to reduce the adverse health and economic impacts of poor diets. This review considers the history, new evidence, controversies, and corresponding lessons for modern dietary and policy priorities for cardiovascular diseases, obesity, and diabetes mellitus. Major identified themes include the importance of evaluating the full diversity of diet-related risk pathways, not only blood lipids or obesity; focusing on foods and overall diet patterns, rather than single isolated nutrients; recognizing the complex influences of different foods on long-term weight regulation, rather than simply counting calories; and characterizing and implementing evidence-based strategies, including policy approaches, for lifestyle change. Evidence-informed dietary priorities include increased fruits, nonstarchy vegetables, nuts, legumes, fish, vegetable oils, yogurt, and minimally processed whole grains; and fewer red meats, processed (eg, sodium-preserved) meats, and foods rich in refined grains, starch, added sugars, salt, and trans fat. More investigation is needed on the cardiometabolic effects of phenolics, dairy fat, probiotics, fermentation, coffee, tea, cocoa, eggs, specific vegetable and tropical oils, vitamin D, individual fatty acids, and diet-microbiome interactions. Little evidence to date supports the cardiometabolic relevance of other popular priorities: eg, local, organic, grass-fed, farmed/wild, or non-genetically modified. Evidence-based personalized nutrition appears to depend more on nongenetic characteristics (eg, physical activity, abdominal adiposity, gender, socioeconomic status, culture) than genetic factors. Food choices must be strongly supported by clinical behavior change efforts, health systems reforms, novel technologies, and robust policy strategies targeting economic incentives, schools and workplaces, neighborhood environments, and the food system. Scientific advances provide crucial new insights on optimal targets and best practices to reduce the burdens of diet-related cardiometabolic diseases.


title: Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters

abstract: Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the inference algorithm used to find the most likely setting of each node in the MRF. Algorithms have been proposed which use graph cuts or belief propagation for inference. These stereo algorithms differ in both the inference algorithm used and the formulation of the MRF. It is unknown whether to attribute the responsibility for differences in performance to the MRF or the inference algorithm. We address this through controlled experiments by comparing the belief propagation algorithm and the graph cuts algorithm on the same MRF's, which have been created for calculating stereo disparities. We find that the labellings produced by the two algorithms are comparable. The solutions produced by graph cuts have a lower energy than those produced with belief propagation, but this does not necessarily lead to increased performance relative to the ground truth.


The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)


title: Text recognition for information retrieval in images of printed circuit boards

abstract: In order to achieve an efficient and environment-friendly recycling of printed circuit boards (PCBs), a comprehensive analysis of their material composition is essential. Besides sophisticated chemical and physical methods for a direct material analysis, an indirect method based on information retrieval provides a less costly and more efficient alternative. During the process of information retrieval, PCBs and their components need to be recognized based on their appearance and the corresponding text information. Their material composition is then available through a pre-established database. Therefore, a practical text recognition is necessary for a successful data analysis prior to PCB recycling. Our paper is focusing on two key aspects of text recognition: binarization and final recognition of text objects using optical character recognition (OCR) engines. For binarization of text contents, a novel local thresholding method using an adaptive window size along with background estimation is presented. Several state-of-the-art algorithms and the proposed method were evaluated for comparing their binarization performance on text objects in PCB images. With respect to a data set containing manually created references, our novel method provides superior results. Furthermore, in contrast to previous work on text recognition, an additional evaluation of available open source OCR engines was conducted to asses technical limitations of OCR applications. We show that the quality of text recognition can be significantly improved if the binarization approach accounts for these technical limitations of OCR software. The presented method and results are expected to provide improved OCR performance also in other applications.


title: How Has the Internet Reshaped Human Cognition?

abstract: Throughout our evolutionary history, our cognitive systems have been altered by the advent of technological inventions such as primitive tools, spoken language, writing, and arithmetic systems. Thirty years ago, the Internet surfaced as the latest technological invention poised to deeply reshape human cognition. With its multifaceted affordances, the Internet environment has profoundly transformed our thoughts and behaviors. Growing up with Internet technologies, “Digital Natives” gravitate toward “shallow” information processing behaviors characterized by rapid attention shifting and reduced deliberations. They engage in increased multitasking behaviors that are linked to increased distractibility and poor executive control abilities. Digital natives also exhibit higher prevalence of Internet-related addictive behaviors that reflect altered reward-processing and self-control mechanisms. Recent neuroimaging investigations have suggested associations between these Internet-related cognitive impacts and str...


title: Catch Me If You Can: A Cloud-Enabled DDoS Defense

abstract: We introduce a cloud-enabled defense mechanism for Internet services against network and computational Distributed Denial-of-Service (DDoS) attacks. Our approach performs selective server replication and intelligent client re-assignment, turning victim servers into moving targets for attack isolation. We introduce a novel system architecture that leverages a "shuffling" mechanism to compute the optimal re-assignment strategy for clients on attacked servers, effectively separating benign clients from even sophisticated adversaries that persistently follow the moving targets. We introduce a family of algorithms to optimize the runtime client-to-server re-assignment plans and minimize the number of shuffles to achieve attack mitigation. The proposed shuffling-based moving target mechanism enables effective attack containment using fewer resources than attack dilution strategies using pure server expansion. Our simulations and proof-of-concept prototype using Amazon EC2 [1] demonstrate that we can successfully mitigate large-scale DDoS attacks in a small number of shuffles, each of which incurs a few seconds of user-perceived latency.


title: Influence of Guide Imagery Technique to Reduce Pain Intensity in Rheumatoid Arthritis Patient (Case Study in Residence Werdha East Surabaya)

abstract: Introduction: Arthritis pain (joint) in elderly is a problem can influences activity daily living. In order to result from Research and Development Health Division (Balitbangkes) Health Departmnet, and Jakarta Health Department during 2006, show the prevalency of musculoskeletal pain which disturbes the activity, is disturbance often occured in the Daily activity most of responden. Methods: This study use the design ( One Group pretest-posttest ) is compare result with measure before and after guide imagery technique is did. The total of elderly population suffering of arthritis pain is 23 respondents. By using the Purposive Sampling Technique, was taken 17 respondents. After the data collected, it was Analitik test using Uji Wilcoxon Test . Results: Result of this study show that most of elderly before getting the treatment, kriteria of pain in moderate and after getting the treatment, criteria of pain be mild. And then, Three is guide imaginative technique to reduce pain intensity in rheumatoid arthritis p = 0,000, p < α = 0,005. Conclusion : This study shows that the guide imagery technique can reduces pain intensity in rheumatoid arthritis, and then perhaps by using the guide imaginative technique can applies in carring of elderly with pain, in the residence or in the community. Keywords : Guide Imagery, Pain Intensity, RhemathoidArthithis


title: Relative Expression of OCT4, SOX2 and NANOG in Oral Squamous Cell Carcinoma Versus Adjacent Non- Tumor Tissue

abstract: Objective: Oral squamous cell carcinoma (OSCC) accounts for over 90% of oral neoplasms. Finding molecularmarkers for predicting prognosis is a high priority. The core transcription factors, OCT4, SOX2, and NANOG thatregulate embryonic stem cell pluripotency have been implicated in progression of various malignancies. The predictivevalue of these markers and their role in the development of OSCC is still controversial. In this study, we thereforeevaluated their expression in OSCCs and adjacent non-tumor tissue. Methods: A total of 60 frozen tumor and adjacentnon-tumor tissue samples from 30 patients with OSCC were examined using quantitative reverse transcription polymerasechain reaction (qRT-PCR). Clinical and pathological data of patients including tumor stage, lymph node metastasisand tumor grade were also recorded. Results: Expression of SOX2 was significantly higher in adjacent non-tumor ascompared to tumor tissue (P=0.04). No statistically significant differences were found for expression of OCT4 (P=0.50)and NANOG (P=0.68). Also, there was no significant association between expression of OCT4, SOX2, and NANOGand clinical or pathological data (P>0.05), although slightly higher values were noted in patients without lymph nodemetastasis. Conclusion: Based on the present data, decreased expression of SOX2 is correlated with carcinogenesisin the oral cavity and development of OSCC.


title: Understanding and Visualizing the District of Columbia Capital Bikeshare System Using Data Analysis for Balancing Purposes

abstract: Bike sharing systems' popularity has consistently been rising during the past years. Managing and maintaining these emerging systems are indispensable parts of these systems. Visualizing the current operations can assist in getting a better grasp on the performance of the system. In this paper, a data mining approach is used to identify and visualize some important factors related to bike-share operations and management. To consolidate the data, we cluster stations that have a similar pickup and drop-off profiles during weekdays and weekends. We provide the temporal profile of the center of each cluster which can be used as a simple and practical approach for approximating the number of pickups and drop-offs of the stations. We also define two indices based on stations' shortages and surpluses that reflect the degree of balancing aid a station needs. These indices can help stakeholders improve the quality of the bike-share user experience in at-least two ways. It can act as a complement to balancing optimization efforts, and it can identify stations that need expansion. We mine the District of Columbia's regional bike-share data and discuss the findings of this data set. We examine the bike-share system during different quarters of the year and during both peak and non-peak hours. Findings reflect that on weekdays most of the pickups and drop-offs happen during the morning and evening peaks whereas on weekends pickups and drop-offs are spread out throughout the day. We also show that throughout the day, more than 40% of the stations are relatively self-balanced. Not worrying about these stations during ordinary days can allow the balancing efforts to focus on a fewer stations and therefore potentially improve the efficiency of the balancing optimization models.


title: A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation

abstract: Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.


Jewish Emancipation and Bourgeois Society


title: The spatial self: Location-based identity performance on social media

abstract: As a growing number of social media platforms now include location information from their users, researchers are confronted with new online representations of individuals, social networks, and the places they inhabit. To better understand these representations and their implications, we introduce the concept of the “spatial self”: a theoretical framework encapsulating the process of online self-presentation based on the display of offline physical activities. Building on previous studies in social science, humanities, and computer and information science, we analyze the ways offline experiences are harnessed and performed online. We first provide an encompassing interdisciplinary survey of research that investigates the relationships between location, information technology, and identity performance. Then, we identify and characterize the spatial self as well as examine its occurrences through three case studies of popular social media sites: Instagram, Facebook, and Foursquare. Finally, we offer possible...


title: Comparison of drought strategies of three co-existing woody plants by their hydraulic structures

abstract: This paper investigates the physiological characteristics for drought resistance among three main afforestation woody species(ie Robinia pseudoacacia L.,Quercus variabilis Blume,Platycladus orientalis(L.) Franco) in lithoid mountainous areas in north China.Variations in their gas exchanges and water relations were comparatively measured during withholding water and subsequent rewatering of potted seedlings.The results showed that:1) under well-watered conditions,black locust(R.pseudoacacia) had the strongest photosynthesis,the greatest stomatal conductance and water consumption among the three species.2) However,black locust was most sensitive to soil drying among the three species,its photosynthesis and stomatal conductance rapidly declined.3) On being rewatered,both gas exchanges and water relations were able to be rapidly recovered in all three species,among which black locust and cork oak(Q.variabilis) had faster recovery than arborvitae(P.orientalis).4) Stomatal conductance(gs) of black locust changed rapidly when percentage loss of hydraulic conductivity(PLC) varied.By contrast,gs of cork oak and arborvitae changed much slower in response to soil moisture and PLC.In summary,black locust is able to rapidly assimilate and accumulate carbohydrates in well-watered conditions,however,when drought came,it sharply shut down its stomata and even shed leaves to prevent excessive loss of water.To the contrast,the other two species could maintain fairly high photosynthesis in intermediate drought stress,especially arborvitae.Soil moisture condition in the lithoid mountainous areas could restrict the development of black locust forests.


title: GOSELO: Goal-Directed Obstacle and Self-Location Map for Robot Navigation Using Reactive Neural Networks

abstract: Robot navigation using deep neural networks has been drawing a great deal of attention. Although reactive neural networks easily learn expert behaviors and are computationally efficient, they suffer from generalization of policies learned in specific environments. As such, reinforcement learning and value iteration approaches for learning generalized policies have been proposed. However, these approaches are more costly. In this letter, we tackle the problem of learning reactive neural networks that are applicable to general environments. The key concept is to crop, rotate, and rescale an obstacle map according to the goal location and the agent's current location so that the map representation will be better correlated with self-movement in the general navigation task, rather than the layout of the environment. Furthermore, in addition to the obstacle map, we input a map of visited locations that contains the movement history of the agent, in order to avoid failures that the agent travels back and forth repeatedly over the same location. Experimental results reveal that the proposed network outperforms the state-of-the-art value iteration network in the grid-world navigation task. We also demonstrate that the proposed model can be well generalized to unseen obstacles and unknown terrain. Finally, we demonstrate that the proposed system enables a mobile robot to successfully navigate in a real dynamic environment.


title: Kernelized movement primitives

abstract: Imitation learning has been studied widely as a convenient way to transfer human skills to robots. This learning approach is aimed at extracting relevant motion patterns from human demonstrations and subsequently applying these patterns to different situations. Despite many advancements have been achieved, the solutions for coping with unpredicted situations (e.g., obstacles and external perturbations) and high-dimensional inputs are still largely open. In this paper, we propose a novel kernelized movement primitive (KMP), which allows the robot to adapt the learned motor skills and fulfill a variety of additional constraints arising over the course of a task. Specifically, KMP is capable of learning trajectories associated with high-dimensional inputs due to the kernel treatment, which in turn renders a model with fewer open parameters in contrast to methods that rely on basis functions. Moreover, we extend our approach by exploiting local trajectory representations in different coordinate systems that describe the task at hand, endowing KMP with reliable extrapolation capabilities in broader domains. We apply KMP to the learning of time-driven trajectories as a special case, where a compact parametric representation describing a trajectory and its first-order derivative is utilized. In order to verify the effectiveness of our method, several examples of trajectory modulations and extrapolations associated with time inputs, as well as trajectory adaptations with high-dimensional inputs are provided.


Virtual Hagia Sophia : Restitution , Visualization and Virtual Life Simulation


title: Large microplastic particles in sediments of tributaries of the River Thames, UK - Abundance, sources and methods for effective quantification.

abstract: Sewage effluent input and population were chosen as predictors of microplastic presence in sediments at four sites in the River Thames basin (UK). Large microplastic particles (1mm-4mm) were extracted using a stepwise approach to include visual extraction, flotation and identification using Raman spectroscopy. Microplastics were found at all four sites. One site had significantly higher numbers of microplastics than other sites, average 66 particles 100g-1, 91% of which were fragments. This site was downstream of a storm drain outfall receiving urban runoff; many of the fragments at this site were determined to be derived of thermoplastic road-surface marking paints. At the remaining three sites, fibres were the dominant particle type. The most common polymers identified included polypropylene, polyester and polyarylsulphone. This study describes two major new findings: presence of microplastic particles in a UK freshwater system and identification of road marking paints as a source of microplastics. ::: ::: ::: CAPSULE ::: This study is the first to quantify microplastics of any size in river sediments in the UK and links their presence to terrestrial sources including sewage and road marking paints.


title: Design and Control Considerations for High-Performance Series Elastic Actuators

abstract: This paper discusses design and control of a prismatic series elastic actuator with high mechanical power output in a small and lightweight form factor. A design is introduced that pushes the performance boundary of electric series elastic actuators by using high motor voltage coupled with an efficient drivetrain to enable large continuous actuator force while retaining speed. Compact size is achieved through the use of a novel piston-style ball screw support mechanism and a concentric compliant element. Generic models for two common series elastic actuator configurations are introduced and compared. These models are then used to develop controllers for force and position tracking based on combinations of PID, model-based, and disturbance observer control structures. Finally, our actuator's performance is demonstrated through a series of experiments designed to operate the actuator at the limits of its mechanical and control capability.


Automatic Summarization of Twitter Topics


title: Pechini-type in-situ polymerizable complex (IPC) method applied to the synthesis of Y2O3:Ln (Ln = Ce or Eu) nanocrystallites

abstract: A Pechini-type, in-situ polymerizable complex (IPC) method was demonstrated as applicable to the synthesis of nanocrystalline multicomponent metal oxides with reproducibility and reasonable yield. In oleic acid–octyl ether reaction media, nanocrystalline, phase-pure Y2O3:Ln (Ln = Ce or Eu) samples were prepared with size ranges from 6 to 14 nm at 40% yield. Sample sizes and morphology were characterized by TEM, XRD, and Raman spectroscopy. The amount of Ce and Eu dopants were identified quantitatively by ICP-AE analysis. Photoluminescence of both cerium- and europium-doped nanocrystalline samples were reported. The Y2O3:Ce (3 mol%) nanocrystallites demonstrated its activity as a scintillator in alpha particle detection.


title: Projections to the frontal cortex from the posterior parietal region in the rhesus monkey

abstract: The projections to the frontal cortex from the various subdivisions of the posterior parietal region in the rhesus monkey were studied by means of autoradiographic technique. The rostral superior parietal lobule (area PE) projects to the dorsal areas 4 and 6 on the lateral surface of the frontal lobe as well as to the supplementary motor area (MII) on its medial surface. The caudal area PE sends its connections to dorsal area 6 and MII. The projections from the medial parietal cortex (areas PEc and PGm) are similar to those of the superior parietal lobule but they tend to concentrate in the more rostral part of dorsal area 6, MII, and in the cingulate gyrus (area 24). The most caudal part of the medial parietal cortex also projects to area 8. The anteriormost part of the inferior parietal lobule (area PF) projects to the ventral area 6, including the caudal bank of the lower branch of the arcuate sulcus, to the ventral area 46 below the sulcus principalis, and to the frontal and pericentral opercular cortex. The middle inferior parietal lobule (areas PFG and PG) projects to the ventral part of area 46 and area 8, whilst the posteriormost inferior parietal lobule (caudal PG and area Opt) is connected with both dorsal and ventral area 46, dorsal area 8, as well as the anteriormost dorsal area 6, and the cingulate gyrus (area 24).


title: Hybrid Multi-Mode Narrow-Frame Antenna for WWAN/LTE Metal-Rimmed Smartphone Applications

abstract: A hybrid multi-mode narrow-frame antenna for WWAN/LTE metal-rimmed smartphone applications is proposed in this paper. The ground clearance is only 5 mm $\times45$ mm, which is promising for narrow-frame smartphones. The metal rim with a small gap is connected to the system ground by three grounded patches. This proposed antenna can excite three coupled-loop modes and one slot mode. By incorporating these four modes, the proposed antenna can provide coverage for GSM850/900, DCS/PCS/UMTS2100, and LTE2300/2500 operations. Detailed design considerations of the proposed antenna are described, and both experimental and simulated results are also presented.


title: Managing Non-Volatile Memory in Database Systems

abstract: Non-volatile memory (NVM) is a new storage technology that combines the performance and byte addressability of DRAM with the persistence of traditional storage devices like flash (SSD). While these properties make NVM highly promising, it is not yet clear how to best integrate NVM into the storage layer of modern database systems. Two system designs have been proposed. The first is to use NVM exclusively, i.e., to store all data and index structures on it. However, because NVM has a higher latency than DRAM, this design can be less efficient than main-memory database systems. For this reason, the second approach uses a page-based DRAM cache in front of NVM. This approach, however, does not utilize the byte addressability of NVM and, as a result, accessing an uncached tuple on NVM requires retrieving an entire page. In this work, we evaluate these two approaches and compare them with in-memory databases as well as more traditional buffer managers that use main memory as a cache in front of SSDs. This allows us to determine how much performance gain can be expected from NVM. We also propose a lightweight storage manager that simultaneously supports DRAM, NVM, and flash. Our design utilizes the byte addressability of NVM and uses it as an additional caching layer that improves performance without losing the benefits from the even faster DRAM and the large capacities of SSDs.


title: AUTOMATIC BLOOD VESSEL SEGMENTATION IN COLOR IMAGES OF RETINA

abstract: Automated image processing techniques have the ability to assist in the early detection of diabetic retinopathy disease which can be regarded as a manifestation of diabetes on the retina. Blood vessel segmentation is the basic foundation while developing retinal screening systems, since vessels serve as one of the main retinal landmark features. This paper proposes an automated method for identification of blood vessels in color images of the retina. For every image pixel, a feature vector is computed that utilizes properties of scale and orientation selective Gabor filters. The extracted features are then classified using generative Gaussian mixture model and discriminative support vector machines classifiers. Experimental results demonstrate that the area under the receiver operating characteristic (ROC) curve reached a value 0.974, which is highly comparable and, to some extent, higher than the previously reported ROCs that range from 0.787 to 0.961. Moreover, this method gives a sensitivity of 96.50% with a specificity of 97.10% for identification of blood vessels. Keywords– Retinal blood vessels, Gabor filters, support vector machines, vessel segmentation


title: Foot-and-mouth disease in Asiatic black bears (Ursus thibetanus)

abstract: Foot-and-mouth disease (FMD) is a highly contagious, debilitating, and globally significant viral disease typically affecting cloven-hoofed hosts. The diagnosis of FMD in bears in Vietnam is described. The current study describes a confirmed case of FMD in a bear species, and the clinical signs compatible with FMD in a Malayan sun bear. Thirteen Asiatic black bears (Ursus thibetanus) and 1 Malayan sun bear (Helarctos malayanus) were apparently affected. In August 2011, an adult bear became lethargic, and developed footpad vesicles. Over 15 days, 14 out of 17 bears developed similar signs; the remaining 3 co-housed bears and another 57 resident bears did not. All affected bears developed vesicles on all footpads, and most were lethargic for 24–48 hr. Nasal and oral lesions were noted in 6 and 3 cases, respectively. Within 1 month, all looked normal. Foot-and-mouth disease virus (FMDV) was detected by reverse transcription polymerase chain reaction, classified as serotype O, and isolated by virus isolation ...


title: A New Nine-Level Active NPC (ANPC) Converter for Grid Connection of Large Wind Turbines for Distributed Generation

abstract: Wind power is one of the most promising emerging renewable energy technologies for distributed generation (DG). In this paper, a new nine-level active neutral-point-clamped (9L ANPC) converter is proposed for the grid connection of large wind turbines (WTs) to improve the waveform quality of the converter output voltage and current. Therefore, the bulky passive grid filters can be reduced or even removed. The topology, operating principles, control schemes, and main features, as well as semiconductor device selection of the proposed converter are presented in detail. The floating capacitor voltage control based on redundant switching states and capacitor prioritization is detailed. A comparison between the new topology and other existing 9L topologies is presented to illustrate the characteristics and performance of the new converter. The proposed 9L ANPC converter is studied in the case of the grid connection of a 6-MW WT without using passive grid filters in DG systems. Simulation and experiment results are presented to validate the proposed converter topology and control schemes. The proper operation and the compliance to the harmonic limit standards of the filterless grid-connected WT system are verified by simulation results.


title: Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

abstract: Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more"legitimate,"interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural networks.


title: Overlapping mechanisms of attention and spatial working memory

abstract: Abstract Spatial selective attention and spatial working memory have largely been studied in isolation. Studies of spatial attention have provided clear evidence that observers can bias visual processing towards specific locations, enabling faster and better processing of information at those locations than at unattended locations. We present evidence supporting the view that this process of visual selection is a key component of rehearsal in spatial working memory. Thus, although working memory has sometimes been depicted as a storage system that emerges ‘downstream' of early sensory processing, current evidence suggests that spatial rehearsal recruits top-down processes that modulate the earliest stages of visual analysis.


title: Provenance in Context of Hadoop as a Service (HaaS) - State of the Art and Research Directions

abstract: Hadoop as a service (HaaS), also known as Hadoop in the cloud, is a big data analytics framework that stores and analyzes data in the cloud using Hadoop/Spark. In this paper, we discuss the importance of providing provenance capabilities in context of Hadoop as a service (HaaS) framework. We first review the state of the art in provenance tracking in context of databases and work-flow processing, in context of cloud and in context of big data analytics frameworks like Hadoop and Spark. We next identify a number of provenance capabilities which have been developed in context of databases and workflow processing but the corresponding solutions have not been developed in context of Hadoop or Spark. We argue that developing these solutions is important so that a comprehensive provenance aware Hadoop as a Service (HaaS) can be provided on cloud. The paper ends by identifying some research challenges in developing these provenance capabilities.


title: Exploring trajectory-driven local geographic topics in foursquare

abstract: The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.


title: A Survey on the Communication Protocols and Security in Cognitive Radio Networks

abstract: A cognitive radio (CR) is a radio that can change its transmission parameters based on the perceived availability of the spectrum bands in its operating environment. CRs support dynamic spectrum access and can facilitate a secondary unlicensed user to efficiently utilize the available underutilized spectrum allocated to the primary licensed users. A cognitive radio network (CRN) is composed of both the secondary users with CR-enabled radios and the primary users whose radios need not be CR-enabled. Most of the active research conducted in the area of CRNs has been so far focused on spectrum sensing, allocation and sharing. There is no comprehensive review paper available on the strategies for medium access control (MAC), routing and transport layer protocols, and the appropriate representative solutions for CRNs. In this paper, we provide an exhaustive analysis of the various techniques/mechanisms that have been proposed in the literature for communication protocols (at the MAC, routing and transport layers), in the context of a CRN, as well as discuss in detail several security attacks that could be launched on CRNs and the countermeasure solutions that have been proposed to avoid or mitigate them. This paper would serve as a good comprehensive review and analysis of the strategies for MAC, routing and transport protocols and security issues for CRNs as well as would lay a strong foundation for someone to further delve onto any particular aspect in greater depth.


title: Multi-Channel Marketing with Budget Complementarities

abstract: Utility maximization under a budget constraint is a classical problem in economics and management science. It is commonly assumed that the utility is a "nice" known analytic function, for example, continuous and concave. In many domains, such as marketing, increased availability of computational resources and data has enabled the development of sophisticated simulations to evaluate the impact of allocating a fixed budget among alternatives (e.g., marketing channels) on outcomes, such as demand. While simulations enable high resolution evaluation of alternative budget allocation strategies, they significantly complicate the associated budget optimization problem. In particular, simulation runs are time consuming, significantly limiting the space of options that can be explored. An important second challenge is the common presence of budget complementarities, where non-negligible budget increments are required for an appreciable marginal impact from a channel. This introduces a combinatorial structure on the decision space. We propose to address these challenges by first converting the problem into a multi-choice knapsack optimization problem with unknown weights. We show that if weights (corresponding to marginal impact thresholds for each channel) are well approximated, we can achieve a solution within a factor of 2 of optimal, and this bound is tight. We then develop several parsimonious query algorithms for achieving this approximation in an online fashion. Experimental evaluation demonstrates the effectiveness of our approach.


title: A 8–40 GHz, 8 mW LNA with 27 dB peak gain and 5.2 dB NF for multiband applications

abstract: An extremely-wideband, 8-40 GHz, low power Low Noise Amplifier (LNA) is presented in this paper. The LNA utilizes a novel combination of design techniques, namely resistive feedback, emitter degeneration, and shunt-peaked split-inductor to provide wideband input and noise matching. Furthermore, the LNA is designed as 3-stage amplifier to provide better gain control through stagger-tuning method, to meet gain specification across the entire band. The proposed LNA was implemented in 0.18 μm SiGe BiCMOS process with 150 GHz fT. The design achieves a peak gain of 27 dB with minimum Noise Figure (NF) of 5.2 dB. The gain of the LNA is greater than 15.2 dB and NF is less than 7.2 dB from 8-40 GHz. The LNA consumes only 6.7 mA from a 1.2 V supply and occupies an area of only 0.26 mm2. Using the proposed architecture, this work demonstrates LNA with extremely wide bandwidth and 3.7X improvement in FoM over comparable state-of-the-art designs.


title: Intentiō: power distribution through a potentialized human body

abstract: We present Intentiō, which allows a potentialized human body to activate low-power electronic devices by touching them. A potentialized human body, i.e., one carrying our device, acts as a common power source and distributes power to electronic devices through their body. An electronic device generally requires its own power source that needs to be managed individually. The diversity of battery sources, which must also be frequently charged or exchanged, has introduced complications for users who manage them. Providing a common power source may help users more easily manage electric devices because the devices are passive, with no power source of their own. This paper presents Intentiō's design and implementation, followed by several proof-of-concept applications based on a potentialized human body. We also discuss the safety concerns of our method. Intentiō is a concept and framework for augmenting humans so that they may interact with and handle electronic devices with power distribution, where the electric devices can passively exist without complex wiring or power sources.


title: Annotating tense, mood and voice for English, French and German

abstract: We present the first open-source tool for annotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rul ...


title: ShadowReplica: efficient parallelization of dynamic data flow tracking

abstract: Dynamic data flow tracking (DFT) is a technique broadly used in a variety of security applications that, unfortunately, exhibits poor performance, preventing its adoption in production systems. We present ShadowReplica, a new and efficient approach for accelerating DFT and other shadow memory-based analyses, by decoupling analysis from execution and utilizing spare CPU cores to run them in parallel. Our approach enables us to run a heavyweight technique, like dynamic taint analysis (DTA), twice as fast, while concurrently consuming fewer CPU cycles than when applying it in-line. DFT is run in parallel by a second shadow thread that is spawned for each application thread, and the two communicate using a shared data structure. We avoid the problems suffered by previous approaches, by introducing an off-line application analysis phase that utilizes both static and dynamic analysis methodologies to generate optimized code for decoupling execution and implementing DFT, while it also minimizes the amount of information that needs to be communicated between the two threads. Furthermore, we use a lock-free ring buffer structure and an N-way buffering scheme to efficiently exchange data between threads and maintain high cache-hit rates on multi-core CPUs. Our evaluation shows that ShadowReplica is on average ~2.3× faster than in-line DFT (~2.75× slowdown over native execution) when running the SPEC CPU2006 benchmark, while similar speed ups were observed with command-line utilities and popular server software. Astoundingly, ShadowReplica also reduces the CPU cycles used up to 30%.


title: From Green Computing to Sustainable IT: Developing a Sustainable Service Orientation

abstract: The primary goal of the emerging discipline of IT sustainability is to enable firms to use computing resources more efficiently while maintaining or increasing overall performance. The first wave of these efforts is commonly identified as "green computing" where the emphasis has been primarily minimizing power usage for datacenters and technical equipments (such as desktops, projectors). The benefits of green computing in terms of reducing power consumption and corporate carbon footprints are direct and relatively rapid to achieve. However, to move beyond internally focused green-computing initiatives to the realm of competitive advantage and corporate sustainability, more attention needs to be directed to how a second wave of sustainable IT practices can align with and enable corporate sustainability strategy. Moreover, sustainable IT strategies need to accomplish this while delivering on core IT performance requirements to drive business productivity. Increasingly, sustainable IT will be impacted by the shift away from product strategies to embrace an integrated service-science and IT-service orientation that has the potential to redefine how customer value is created and how quality of life is improved with service. This paper will explore the dimensions of sustainable IT, discuss its development as a service, and provide criteria for improving its alignment with corporate sustainability strategy.


title: A generalized framework of exploring category information for question retrieval in community question answer archives

abstract: Community Question Answering (CQA) has emerged as a popular type of service where users ask and answer questions and access historical question-answer pairs. CQA archives contain very large volumes of questions organized into a hierarchy of categories. As an essential function of CQA services, question retrieval in a CQA archive aims to retrieve historical question-answer pairs that are relevant to a query question. In this paper, we present a new approach to exploiting category information of questions for improving the performance of question retrieval, and we apply the approach to existing question retrieval models, including a state-of-the-art question retrieval model. Experiments conducted on real CQA data demonstrate that the proposed techniques are capable of outperforming a variety of baseline methods significantly.


title: Ultrasound Indoor Positioning System Based on a Low-Power Wireless Sensor Network Providing Sub-Centimeter Accuracy

abstract: This paper describes the TELIAMADE system, a new indoor positioning system based on time-of-flight (TOF) of ultrasonic signal to estimate the distance between a receiver node and a transmitter node. TELIAMADE system consists of a set of wireless nodes equipped with a radio module for communication and a module for the transmission and reception of ultrasound. The access to the ultrasonic channel is managed by applying a synchronization algorithm based on a time-division multiplexing (TDMA) scheme. The ultrasonic signal is transmitted using a carrier frequency of 40 kHz and the TOF measurement is estimated by applying a quadrature detector to the signal obtained at the A/D converter output. Low sampling frequencies of 17.78 kHz or even 12.31 kHz are possible using quadrature sampling in order to optimize memory requirements and to reduce the computational cost in signal processing. The distance is calculated from the TOF taking into account the speed of sound. An excellent accuracy in the estimation of the TOF is achieved using parabolic interpolation to detect of maximum of the signal envelope at the matched filter output. The signal phase information is also used for enhancing the TOF measurement accuracy. Experimental results show a root mean square error (rmse) less than 2 mm and a standard deviation less than 0.3 mm for pseudorange measurements in the range of distances between 2 and 6 m. The system location accuracy is also evaluated by applying multilateration. A sub-centimeter location accuracy is achieved with an average rmse of 9.6 mm.


title: Customer Knowledge Management Framework in E-commerce

abstract: This paper aims to present a framework for describing Customer Knowledge Management in online purchase process using two models from literature including consumer online purchase process and E- CKM. Since CKM is a recent concept and little empirical research is available, we will first present the theories from which CKM derives. In the first stage we discuss about e-commerce trend and increasing importance of customer loyalty in today's business environment. Then some related concepts about Knowledge Management, Customer Relationship Management and CKM are presented, in order to provide the reader with a better understanding and clear picture regarding CKM. Finally, providing models representing e-CKM and online purchasing process, we propose a comprehensive procedure to manage customer data and knowledge in e-commerce.


title: Modeling bike availability in a bike-sharing system using machine learning

abstract: This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.


title: Control in Act-R and Soar

abstract: This paper compares the Act-R and Soar cognitive architectures, focusing on their theories of control. Act-R treats control (conflict resolution) as an automatic process, whereas Soar treats it as a potentially deliberate, knowledge-based process. The comparison reveals that Soar can model extremely flexible control, but has difficulty accounting for probabilistic operator selection and the independent effects of history and distance to goal on the likelihood of selecting an operator. In contrast, Act-R’s control is well supported by empirical data, but has difficulty modeling task-switching, multiple interleaved tasks, and dynamic abandoning of subgoals. The comparison also reveals that many of the justifications for each architecture’s control structure, such as some forms of flexible control and satisficing, are just as easily handled by both.


title: Breaking up is hard to do: security and functionality in a commodity hypervisor

abstract: Cloud computing uses virtualization to lease small slices of large-scale datacenter facilities to individual paying customers. These multi-tenant environments, on which numerous large and popular web-based applications run today, are founded on the belief that the virtualization platform is sufficiently secure to prevent breaches of isolation between different users who are co-located on the same host. Hypervisors are believed to be trustworthy in this role because of their small size and narrow interfaces. We observe that despite the modest footprint of the hypervisor itself, these platforms have a large aggregate trusted computing base (TCB) that includes a monolithic control VM with numerous interfaces exposed to VMs. We present Xoar, a modified version of Xen that retrofits the modularity and isolation principles used in micro-kernels onto a mature virtualization platform. Xoar breaks the control VM into single-purpose components called service VMs. We show that this componentized abstraction brings a number of benefits: sharing of service components by guests is configurable and auditable, making exposure to risk explicit, and access to the hypervisor is restricted to the least privilege required for each component. Microrebooting components at configurable frequencies reduces the temporal attack surface of individual components. Our approach incurs little performance overhead, and does not require functionality to be sacrificed or components to be rewritten from scratch.


title: Security and Protocol Exploit Analysis of the 5G Specifications

abstract: The third generation partnership project released its first 5G security specifications in March 2018. This paper reviews the proposed security architecture and its main requirements and procedures and evaluates them in the context of known and new protocol exploits. Although security has been improved from previous generations, our analysis identifies potentially unrealistic 5G system assumptions and protocol edge cases that can render 5G communication systems vulnerable to adversarial attacks. For example, null encryption and null authentication are still supported and can be used in valid system configurations. With no clear proposal to tackle pre-authentication message-based exploits, mobile devices continue to implicitly trust any serving network, which may or may not enforce a number of optional security features, or which may not be legitimate. Moreover, several critical security and key management functions are considered beyond the scope of the specifications. The comparison with known 4G long-term evolution protocol exploits reveals that the 5G security specifications, as of Release 15, Version 1.0.0, do not fully address the user privacy and network availability challenges.


title: What’s in Twitter, I know what parties are popular and who you are supporting now!

abstract: In modern politics, parties and individual candidates must have an online presence and usually have dedicated social media coordinators. In this context, we study the usefulness of analysing Twitter messages to identify both the characteristics of political parties and the political leaning of users. As a case study, we collected the main stream of Twitter related to the 2010 UK General Election during the associated period - gathering around 1, 150, 000 messages from about 220, 000 users. We examined the characteristics of the three main parties in the election and highlighted the main differences between parties. First, the retweet structure is highly clustered according to political parties. Second, users are more likely to refer to their preferred party and use more positive affect words for the party compared with other parties. Finally, the self-description of users and the List feature can reflect the political orien- tation of users. From these observations, we develop both an incremental and practical classification method which uses the number of Twitter messages re- ferring to a particular political party or retweets, and a classifier leveraging the valuable semantic content of the List feature to estimate the overall political leaning of users. The experimental results showed that the proposed incre- mental method achieved an accuracy of 86% for classifying the users' political leanings and outperforms other classification methods that require expensive costs for tuning classifier parameters and/or knowledge about network topol- ogy. This advantage allows this approach to be a good candidate for social media analytics application in real time for political institution. The proposed method using List feature, in turn, achieved an accuracy of 92%.


title: Pictorial Structures for Object Recognition

abstract: In this paper we present a computationally efficient framework for part-based modeling and recognition of objects. Our work is motivated by the pictorial structure models introduced by Fischler and Elschlager. The basic idea is to represent an object by a collection of parts arranged in a deformable configuration. The appearance of each part is modeled separately, and the deformable configuration is represented by spring-like connections between pairs of parts. These models allow for qualitative descriptions of visual appearance, and are suitable for generic recognition problems. We address the problem of using pictorial structure models to find instances of an object in an image as well as the problem of learning an object model from training examples, presenting efficient algorithms in both cases. We demonstrate the techniques by learning models that represent faces and human bodies and using the resulting models to locate the corresponding objects in novel images.


title: The form of the forgetting curve and the fate of memories

abstract: Abstract Psychologists have debated the form of the forgetting curve for over a century. We focus on resolving three problems that have blocked a clear answer on this issue. First, we analyzed data from a longitudinal experiment measuring cued recall and stem completion from 1 min to 28 days after study, with more observations per interval per participant than in previous studies. Second, we analyzed the data using hierarchical models, avoiding distortions due to averaging over participants. Third, we implemented the models in a Bayesian framework, enabling our analysis to account for the ability of candidate forgetting functions to imitate each other. An exponential function provided the best fit to individual participant data collected under both explicit and implicit retrieval instructions, but Bayesian model selection favored a power function. All analysis supported above chance asymptotic retention, suggesting that, despite quite brief study, storage of some memories was effectively permanent.


title: Performance comparison between Linux containers and virtual machines

abstract: With the advent of cloud computing and virtualization, modern distributed applications run on virtualized environments for hardware resource utilization and flexibility of operations in an infrastructure. However, when it comes to virtualization, resource overhead is involved. Linux containers can be an alternative to traditional virtualization technologies because of its high resource utilization and less overhead. This paper provides a comparison between Linux containers and virtual machines in terms of performance and scalability.


title: Machine Learning Techniques with Ontology for Subjective Answer Evaluation

abstract: Computerized Evaluation of English Essays is performed using Machine learning techniques like Latent Semantic Analysis (LSA), Generalized LSA, Bilingual Evaluation Understudy and Maximum Entropy. Ontology, a concept map of domain knowledge, can enhance the performance of these techniques. Use of Ontology makes the evaluation process holistic as presence of keywords, synonyms, the right word combination and coverage of concepts can be checked. In this paper, the above mentioned techniques are implemented both with and without Ontology and tested on common input data consisting of technical answers of Computer Science. Domain Ontology of Computer Graphics is designed and developed. The software used for implementation includes Java Programming Language and tools such as MATLAB, Prot\'eg\'e, etc. Ten questions from Computer Graphics with sixty answers for each question are used for testing. The results are analyzed and it is concluded that the results are more accurate with use of Ontology.