title: Tactile-based active object discrimination and target object search in an unknown workspace

abstract: The tasks of exploring unknown workspaces and recognizing objects based on their physical properties are challenging for autonomous robots. In this paper, we present strategies solely based on tactile information to enable robots to accomplish such tasks. (1) An active exploration approach for the robot to explore unknown workspaces; (2) an active touch objects learning method that enables the robot to learn efficiently about unknown objects via their physical properties (stiffness, surface texture, and center of mass); and (3) an active object recognition strategy, based on the knowledge the robot has acquired. Furthermore, we propose a tactile-based approach for estimating the center of mass of rigid objects. Following the active touch for workspace exploration, the robotic system with the sense of touch in fingertips reduces the uncertainty of the workspace up to 65 and 70% compared respectively to uniform and random strategies, for a fixed number of samples. By means of the active touch learning method, the robot achieved 20 and 15% higher learning accuracy for the same number of training samples compared to uniform strategy and random strategy, respectively. Taking advantage of the prior knowledge obtained during the active touch learning, the robot took up to 15% fewer decision steps compared to the random method to achieve the same discrimination accuracy in active object discrimination task.


title: Folding Flat Crease Patterns with Thick Materials

abstract: Modeling folding surfaces with nonzero thickness is of practical interest for mechanical engineering. There are many existing approaches that account for material thickness in folding applications. We propose a new systematic and broadly applicable algorithm to transform certain flat-foldable crease patterns into new crease patterns with similar folded structure but with a facet-separated folded state. We provide conditions on input crease patterns for the algorithm to produce a thickened crease pattern avoiding local self intersection, and provide bounds for the maximum thickness that the algorithm can produce for a given input. We demonstrate these results in parameterized numerical simulations and physical models.


title: Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution

abstract: Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.


title: Qthreads: An API for programming with millions of lightweight threads

abstract: Large scale hardware-supported multithreading, an attractive means of increasing computational power, benefits significantly from low per-thread costs. Hardware support for lightweight threads is a developing area of research. Each architecture with such support provides a unique interface, hindering development for them and comparisons between them. A portable abstraction that provides basic lightweight thread control and synchronization primitives is needed. Such an abstraction would assist in exploring both the architectural needs of large scale threading and the semantic power of existing languages. Managing thread resources is a problem that must be addressed if massive parallelism is to be popularized. The qthread abstraction enables development of large-scale multithreading applications on commodity architectures. This paper introduces the qthread API and its Unix implementation, discusses resource management, and presents performance results from the HPCCG benchmark.


Concealing Danger: How the Regulation of Cosmetics in the United States Puts Consumers at Risk


title: Wernicke's encephalopathy in non-alcoholics. An autopsy study.

abstract: In a 5-year autopsy material constituting 6,964 autopsies, there were 52 cases of Wernicke's encephalopathy of which 12 (23%) occurred in non-alcoholics. Among 18 cases with active (acute) disease, 7 cases (39%) were found in non-alcoholics. Only 4 cases of active Wernicke's disease were diagnosed clinically, all of them in alcoholics. The predominant clinical symptoms were disorientation and depressed levels of consciousness, whereas eye symptoms were recorded in only 3 cases. None of the non-alcoholics were given specific thiamine therapy, whereas some alcoholics received large doses of the vitamin as a routine procedure. However, the thiamine therapy was often instituted too late. It is concluded that active Wernicke's encephalopathy should be considered in all patients with prolonged malnutrition and that disorientation and depressed levels of consciousness may be the predominating symptoms of the disease. Even the slightest suspicion of Wernicke's encephalopathy should prompt immediate administration of large doses of thiamine parenterally.


title: Now I Remember! Episodic Memory For Reinforcement Learning

abstract: Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car. Endowing reinforcement learning agents with episodic memory is a key step on the path toward replicating human-like general intelligence. We analyze why standard RL agents lack episodic memory today, and why existing RL tasks don't require it. We design a new form of external memory called Masked Experience Memory, or MEM, modeled after key features of human episodic memory. To evaluate episodic memory we define an RL task based on the common children's game of Concentration. We find that a MEM RL agent leverages episodic memory effectively to master Concentration, unlike the baseline agents we tested.


title: With the Future Behind Them: Convergent Evidence From Aymara Language and Gesture in the Crosslinguistic Comparison of Spatial Construals of Time

abstract: Cognitive research on metaphoric concepts of time has focused on differences between moving Ego and moving time models, but even more basic is the contrast between Ego- and temporal-reference-point models. Dynamic models appear to be quasi-universal cross-culturally, as does the generalization that in Ego-reference-point models, FUTURE IS IN FRONT OF EGO and PAST IS IN BACK OF EGO. The Aymara language instead has a major static model of time wherein FUTURE IS BEHIND EGO and PAST IS IN FRONT OF EGO; linguistic and gestural data give strong confirmation of this unusual culture-specific cognitive pattern. Gestural data provide crucial information unavailable to purely linguistic analysis, suggesting that when investigating conceptual systems both forms of expression should be analyzed complementarily. Important issues in embodied cognition are raised: how fully shared are bodily grounded motivations for universal cognitive patterns, what makes a rare pattern emerge, and what are the cultural entailments of such patterns?


title: A simulation model to improve warehouse operations

abstract: Warehouse or distribution centre managers have to decide how to collect the products to fulfill customers requests but also where to locate the products (SKUs) and how much space to allocate to each of them. Moreover, they have to deploy replenishment strategies to guarantee the reliability of their own stocks. These are challenging decisions because of their level of complexity and their high impact on the centre performance in terms of both its throughput and the operation costs. In particular, the goal of this work is to evaluate whether specific strategies to share the storage space could lead to reduce the operation costs while keeping the service level as high as possible. To this end, this paper develops a discrete event simulation model of the logistic operations at a real high throughput warehouse which handles more than 12 millions of cases annually. Preliminary results show that potential economies may be achieved by reducing the number of stock-outs at the picking area where customer orders are collected.


title: Left luggage detection using bayesian inference

abstract: This paper presents a system that incorporates low-level object tracking and high-level event inference to solve the video event recognition problem. First, the tracking module combines the results of block tracking and human tracking to provide the trajectory as well as the basic type (human or non-human)of each detected object. The trajectories are then mapped to 3D world coordinates, given the camera model. Useful features such as speed, direction and distance between objects are computed and used as evidence. Events are represented as hypotheses and recognized in a Bayesian inference framework. The proposed system has been successfully applied to many event recognition tasks in the real world environment. In particular, we show results of detecting the left-luggage event on the PETS 2006 dataset.


title: Automatic complex schema matching across Web query interfaces: A correlation mining approach

abstract: To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings are common, because of their far more complex search space, most existing techniques focus on simple 1:1 matchings. To tackle this challenge, this article takes a conceptually novel approach by viewing schema matching as correlation mining, for our task of matching Web query interfaces to integrate the myriad databases on the Internet. On this “deep Web ” query interfaces generally form complex matchings between attribute groups (e.g., {author} corresponds to {first name, last name} in the Books domain). We observe that the co-occurrences patterns across query interfaces often reveal such complex semantic relationships: grouping attributes (e.g., {first name, last name}) tend to be co-present in query interfaces and thus positively correlated. In contrast, synonym attributes are negatively correlated because they rarely co-occur. This insight enables us to discover complex matchings by a correlation mining approach. In particular, we develop the DCM framework, which consists of data preprocessing, dual mining of positive and negative correlations, and finally matching construction. We evaluate the DCM framework on manually extracted interfaces and the results show good accuracy for discovering complex matchings. Further, to automate the entire matching process, we incorporate automatic techniques for interface extraction. Executing the DCM framework on automatically extracted interfaces, we find that the inevitable errors in automatic interface extraction may significantly affect the matching result. To make the DCM framework robust against such “noisy” schemas, we integrate it with a novel “ensemble” approach, which creates an ensemble of DCM matchers, by randomizing the schema data into many trials and aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the robustness of the ensemble approach. Empirically, our experiments show that the “ensemblization” indeed significantly boosts the matching accuracy, over automatically extracted and thus noisy schema data. By employing the DCM framework with the ensemble approach, we thus complete an automatic process of matchings Web query interfaces.


title: An Information-Extraction System for Urdu---A Resource-Poor Language

abstract: There has been an increase in the amount of multilingual text on the Internet due to the proliferation of news sources and blogs. The Urdu language, in particular, has experienced explosive growth on the Web. Text mining for information discovery, which includes tasks such as identifying topics, relationships and events, and sentiment analysis, requires sophisticated natural language processing (NLP). NLP systems begin with modules such as word segmentation, part-of-speech tagging, and morphological analysis and progress to modules such as shallow parsing and named entity tagging. While there have been considerable advances in developing such comprehensive NLP systems for English, the work for Urdu is still in its infancy. The tasks of interest in Urdu NLP includes analyzing data sources such as blogs and comments to news articles to provide insight into social and human behavior. All of this requires a robust NLP system. The objective of this work is to develop an NLP infrastructure for Urdu that is customizable and capable of providing basic analysis on which more advanced information extraction tools can be built. This system assimilates resources from various online sources to facilitate improved named entity tagging and Urdu-to-English transliteration. The annotated data required to train the learning models used here is acquired by standardizing the currently limited resources available for Urdu. Techniques such as bootstrap learning and resource sharing from a syntactically similar language, Hindi, are explored to augment the available annotated Urdu data. Each of the new Urdu text processing modules has been integrated into a general text-mining platform. The evaluations performed demonstrate that the accuracies have either met or exceeded the state of the art.


title: Personality and patterns of Facebook usage

abstract: We show how users' activity on Facebook relates to their personality, as measured by the standard Five Factor Model. Our dataset consists of the personality profiles and Facebook profile data of 180,000 users. We examine correlations between users' personality and the properties of their Facebook profiles such as the size and density of their friendship network, number uploaded photos, number of events attended, number of group memberships, and number of times user has been tagged in photos. Our results show significant relationships between personality traits and various features of Facebook profiles. We then show how multivariate regression allows prediction of the personality traits of an individual user given their Facebook profile. The best accuracy of such predictions is achieved for Extraversion and Neuroticism, the lowest accuracy is obtained for Agreeableness, with Openness and Conscientiousness lying in the middle.


title: A Context-Dependent Attention System for a Social Robot

abstract: This paper presents part of an on-going project to integrate perception, attention, drives, emotions, behavior arbitration, and expressive acts for a robot designed to interact socially with humans. We present the design of a visual attention system based on a model of human visual search behavior from Wolfe (1994). The attention system integrates perceptions (motion detection, color saliency, and face popouts) with habituation effects and influences from the robot's motivational and behavioral state to create a context-dependent attention activation map. This activation map is used to direct eye movements and to satiate the drives of the motivational system.


title: K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes

abstract: In computer vision there has been increasing interest in learning hashing codes whose Hamming distance approximates the data similarity. The hashing functions play roles in both quantizing the vector space and generating similarity-preserving codes. Most existing hashing methods use hyper-planes (or kernelized hyper-planes) to quantize and encode. In this paper, we present a hashing method adopting the k-means quantization. We propose a novel Affinity-Preserving K-means algorithm which simultaneously performs k-means clustering and learns the binary indices of the quantized cells. The distance between the cells is approximated by the Hamming distance of the cell indices. We further generalize our algorithm to a product space for learning longer codes. Experiments show our method, named as K-means Hashing (KMH), outperforms various state-of-the-art hashing encoding methods.


title: Exploiting Open-Endedness to Solve Problems Through the Search for Novelty

abstract: This paper establishes a link between the challenge of solving highly ambitious problems in machine learning and the goal of reproducing the dynamics of open-ended evolution in artificial life. A major problem with the objective function in machine learning is that through deception it may actually prevent the objective from being reached. In a similar way, selection in evolution may sometimes act to discourage increasing complexity. This paper proposes a single idea that both overcomes the obstacle of deception and suggests a simple new approach to open-ended evolution: Instead of either explicitly seeking an objective or modeling a domain to capture the open-endedness of natural evolution, the idea is to simply search for novelty. Even in an objective-based problem, such novelty search ignores the objective and searches for behavioral novelty. Yet because many points in the search space collapse to the same point in behavior space, it turns out that the search for novelty is computationally feasible. Furthermore, because there are only so many simple behaviors, the search for novelty leads to increasing complexity. In fact, on the way up the ladder of complexity, the search is likely to encounter at least one solution. In this way, by decoupling the idea of open-ended search from only artificial life worlds, the raw search for novelty can be applied to real world problems. Counterintuitively, in the deceptive maze navigation task in this paper, novelty search significantly outperforms objective-based search, suggesting a surprising new approach to machine learning.


title: On the optimization of the daily operation of a wind-hydro power plant

abstract: This paper proposes the utilization of water storage ability to improve wind park operational economic gains and to attenuate the active power output variations due to the intermittence of the wind-energy resource. An hourly-discretized optimization algorithm is proposed to identify the optimum daily operational strategy to be followed by the wind turbines and the hydro generation pumping equipments, provided that a wind-power forecasting is available. The stochastic characteristics of the wind power are exploited in the approach developed in order to identify an envelope of recommended operational conditions. Three operational conditions were analyzed and the obtained results are presented and discussed.


title: Take-up and profile of individuals accessing a web-based bipolar self-test screening measure

abstract: Abstract Background Early detection of bipolar disorder is associated with improved long term outcome. In this study, we profile individuals accessing a web-based self-report screening tool for bipolar disorder. Method Participants accessing the self-screening tool for bipolar disorder (the Mood Swings Questionnaire; MSQ) via the Black Dog Institute website were asked to indicate whether they were willing to participate in a research study evaluating the tool. Demographic details, mood disorder history, previous treatments and the impact of mood problems were assessed, followed by completion of the MSQ to determine the likelihood of a bipolar disorder diagnosis. Results During the first year of the study, the bipolar self-screening test web-page was viewed 278,279 times. A total of 5803 were eligible to provide baseline data, and 4829 completed the MSQ. Of these, 65% were assigned a preliminary bipolar disorder diagnosis based on MSQ cut-off scores, with the majority (89%) expecting such a result, feeling reassured (64%) by the result, and viewing the self-test as helpful (94%). Encouragingly, over 70% indicated they would follow-up this suggested diagnosis with a health professional. Personal, social and relationship ‘costs’ were rated as more severe than financial costs in relation to the bipolar condition, highlighting the burden of illness across differing domains. Limitations Results may not be generalizable to a community sample of individuals diagnosed with bipolar disorder. Conclusion This web-based (E-health) screening strategy has proven to be extremely successful. Firstly, participants viewed the self-screening tool as useful, supporting the need for easily accessible mental health screening tools to assist with early detection and diagnosis. Secondly, this method has provided quantitative data on help-seeking behaviors, strategies employed to assist with mood problems, response to diagnosis, and the impact of a bipolar condition, which has previously been explored predominantly through qualitative means. The success of this strategy will next be assessed encouraging participants to have any bipolar disorder confirmed and more condition-specific management implemented to determine impact in illness course.


Classifying Diabetic Retinopathy using Deep Learning Architecture


title: Saliency Detection on Light Field: A Multi-Cue Approach

abstract: Saliency detection has recently received increasing research interest on using high-dimensional datasets beyond two-dimensional images. Despite the many available capturing devices and algorithms, there still exists a wide spectrum of challenges that need to be addressed to achieve accurate saliency detection. Inspired by the success of the light-field technique, in this article, we propose a new computational scheme to detect salient regions by integrating multiple visual cues from light-field images. First, saliency prior maps are generated from several light-field features based on superpixel-level intra-cue distinctiveness, such as color, depth, and flow inherited from different focal planes and multiple viewpoints. Then, we introduce the location prior to enhance the saliency maps. These maps will finally be merged into a single map using a random-search-based weighting strategy. Besides, we refine the object details by employing a two-stage saliency refinement to obtain the final saliency map. In addition, we present a more challenging benchmark dataset for light-field saliency analysis, named HFUT-Lytro, which consists of 255 light fields with a range from 53 to 64 images generated from each light-field image, therein spanning multiple occurrences of saliency detection challenges such as occlusions, cluttered background, and appearance changes. Experimental results show that our approach can achieve 0.6--6.7% relative improvements over state-of-the-art methods in terms of the F-measure and Precision metrics, which demonstrates the effectiveness of the proposed approach.


title: Supporting program comprehension using semantic and structural information

abstract: Focuses on investigating the combined use of semantic and structural information of programs to support the comprehension tasks involved in the maintenance and reengineering of software systems. "Semantic information" refers to the domain-specific issues (both the problem and the development domains) of a software system. The other dimension, structural information, refers to issues such as the actual syntactic structure of the program, along with the control and data flow that it represents. An advanced information retrieval method, latent semantic indexing, is used to define a semantic similarity measure between software components. Components within a software system are then clustered together using this similarity measure. Simple structural information (i.e. the file organization) of the software system is then used to assess the semantic cohesion of the clusters and files with respect to each other. The measures are formally defined for general application. A set of experiments is presented which demonstrates how these measures can assist in the understanding of a nontrivial software system, namely a version of NCSA Mosaic.


title: Medical diagnosis using neural network

abstract: This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. This paper describes a modified feedforward neural network constructive algorithm (MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm with backpropagation; offer an approach for the incremental construction of near-minimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. The MFNNCA was tested on several benchmarking classification problems including the cancer, heart disease and diabetes. Experimental results show that the MFNNCA can produce optimal neural network architecture with good generalization ability.


title: Detecting repackaged smartphone applications in third-party android marketplaces

abstract: Recent years have witnessed incredible popularity and adoption of smartphones and mobile devices, which is accompanied by large amount and wide variety of feature-rich smartphone applications. These smartphone applications (or apps), typically organized in different application marketplaces, can be conveniently browsed by mobile users and then simply clicked to install on a variety of mobile devices. In practice, besides the official marketplaces from platform vendors (e.g., Google and Apple), a number of third-party alternative marketplaces have also been created to host thousands of apps (e.g., to meet regional or localization needs). To maintain and foster a hygienic smartphone app ecosystem, there is a need for each third-party marketplace to offer quality apps to mobile users. In this paper, we perform a systematic study on six popular Android-based third-party marketplaces. Among them, we find a common "in-the-wild" practice of repackaging legitimate apps (from the official Android Market) and distributing repackaged ones via third-party marketplaces. To better understand the extent of such practice, we implement an app similarity measurement system called DroidMOSS that applies a fuzzy hashing technique to effectively localize and detect the changes from app-repackaging behavior. The experiments with DroidMOSS show a worrisome fact that 5% to 13% of apps hosted on these studied marketplaces are repackaged. Further manual investigation indicates that these repackaged apps are mainly used to replace existing in-app advertisements or embed new ones to "steal" or re-route ad revenues. We also identify a few cases with planted backdoors or malicious payloads among repackaged apps. The results call for the need of a rigorous vetting process for better regulation of third-party smartphone application marketplaces.


title: DeepDefense: Identifying DDoS Attack via Deep Learning

abstract: Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate a better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.


title: APPLYING MACHINE LEARNING METHODS FOR TIME SERIES FORECASTING

abstract: This paper describes a strategy on learning from time series data and on using learned model for forecasting. Time series forecasting, which analyzes and predicts a variable changing over time, has received much attention due to its use for forecasting stock prices, but it can also be used for pattern recognition and data mining. Our method for learning from time series data consists of detecting patterns within the data, describing the detected patterns, clustering the patterns, and creating a model to describe the data. It uses a change-point detection method to partition a time series into segments, each of the segments is then described by an autoregressive model. Then, it partitions all the segments into clusters, each of the clusters is considered as a state for a Markov model. It then creates the transitions between states in the Markov model based on the transitions between segments as the time series progressing. Our method for using the learned model for forecasting consists of indentifying current state, forecasting trends, and adapting to changes. It uses a moving window to monitor real-time data and creates an autoregressive model for the recently observed data, which is then matched to a state of the learned Markov model. Following the transitions of the model, it forecasts future trends. It also continues to monitor real-time data and makes corrections if necessary for adapting to changes. We implemented and successfully tested the methods for an application of load balancing on a parallel computing system.


title: Efficient elastic burst detection in data streams

abstract: Burst detection is the activity of finding abnormal aggregates in data streams. Such aggregates are based on sliding windows over data streams. In some applications, we want to monitor many sliding window sizes simultaneously and to report those windows with aggregates significantly different from other periods. We will present a general data structure for detecting interesting aggregates over such elastic windows in near linear time. We present applications of the algorithm for detecting Gamma Ray Bursts in large-scale astrophysical data. Detection of periods with high volumes of trading activities and high stock price volatility is also demonstrated using real time Trade and Quote (TAQ) data from the New York Stock Exchange (NYSE). Our algorithm beats the direct computation approach by several orders of magnitude.


title: Mobile robot kinematic reconfigurability for rough terrain

abstract: Future planetary exploration missions will use rovers to perform tasks in rough terrain. Working in such conditions, a rover could become trapped due to loss of wheel traction, or even tip over. The Jet Propulsion Laboratory has developed a new rover with the ability to reconfigure its structure to improve tipover stability and ground traction. This paper presents a method to control this reconfigurability to enhance system tipover stability. A stability metric is defined and optimized online using a quasi-static model. Simulation and experimental results for the Sample Return Rover (SSR) are presented. The method is shown to be practical and yields significantly improved stability in rough terrain.


title: Plan in 2D, execute in 3D: An augmented reality solution for cup placement in total hip arthroplasty

abstract: Abstract. Reproducibly achieving proper implant alignment is a critical step in total hip arthroplasty procedures that has been shown to substantially affect patient outcome. In current practice, correct alignment of the acetabular cup is verified in C-arm x-ray images that are acquired in an anterior–posterior (AP) view. Favorable surgical outcome is, therefore, heavily dependent on the surgeon’s experience in understanding the 3-D orientation of a hemispheric implant from 2-D AP projection images. This work proposes an easy to use intraoperative component planning system based on two C-arm x-ray images that are combined with 3-D augmented reality (AR) visualization that simplifies impactor and cup placement according to the planning by providing a real-time RGBD data overlay. We evaluate the feasibility of our system in a user study comprising four orthopedic surgeons at the Johns Hopkins Hospital and report errors in translation, anteversion, and abduction as low as 1.98 mm, 1.10 deg, and 0.53 deg, respectively. The promising performance of this AR solution shows that deploying this system could eliminate the need for excessive radiation, simplify the intervention, and enable reproducibly accurate placement of acetabular implants.


title: A state space model for online polyphonic audio-score alignment

abstract: We present a novel online audio-score alignment approach for multi-instrument polyphonic music. This approach uses a 2-dimensional state vector to model the underlying score position and tempo of each time frame of the audio performance. The process model is defined by dynamic equations to transition between states. Two representations of the observed audio frame are proposed, resulting in two observation models: a multi-pitch-based and a chroma-based. Particle filtering is used to infer the hidden states from observations. Experiments on 150 music pieces with polyphony from one to four show the proposed approach outperforms an existing offline global string alignment-based score alignment approach. Results also show that the multi-pitch-based observation model works better than the chroma-based one.


Combined Genetic and High-Throughput Strategies for Molecular Diagnosis of Inherited Retinal Dystrophies


title: A new measure of tipover stability margin for mobile manipulators

abstract: Mobile manipulators operating in field environments will be required to perform tasks on uneven terrain which may cause the system to approach, or achieve, a dangerous tipover instability. To avoid tipover in an automatic system, or to provide a human operator with an indication of proximity to tipover, it is necessary to define a measure of stability margin. This work presents a new tipover stability measure (the force-angle stability measure) which is easily computed and sensitive to topheaviness. The proposed metric is applicable to systems subject to inertial and external forces, operating over even or uneven terrains. Performance of the measure is demonstrated using a forestry vehicle simulation.


title: A comparative study of classifier ensembles for bankruptcy prediction

abstract: This paper examines the construction issues of classifier ensembles for bankruptcy prediction.The first issue focuses on the classification techniques, which are based on MLP, SVM, and DT.The second issue is the combination method, which is based on bagging and boosting.The third issue is based on examining different numbers of combined classifiers.We show that DT ensembles composed of 80-100 classifiers using the boosting method perform best. The aim of bankruptcy prediction in the areas of data mining and machine learning is to develop an effective model which can provide the higher prediction accuracy. In the prior literature, various classification techniques have been developed and studied, in/with which classifier ensembles by combining multiple classifiers approach have shown their outperformance over many single classifiers. However, in terms of constructing classifier ensembles, there are three critical issues which can affect their performance. The first one is the classification technique actually used/adopted, and the other two are the combination method to combine multiple classifiers and the number of classifiers to be combined, respectively. Since there are limited, relevant studies examining these aforementioned disuses, this paper conducts a comprehensive study of comparing classifier ensembles by three widely used classification techniques including multilayer perceptron (MLP) neural networks, support vector machines (SVM), and decision trees (DT) based on two well-known combination methods including bagging and boosting and different numbers of combined classifiers. Our experimental results by three public datasets show that DT ensembles composed of 80-100 classifiers using the boosting method perform best. The Wilcoxon signed ranked test also demonstrates that DT ensembles by boosting perform significantly different from the other classifier ensembles. Moreover, a further study over a real-world case by a Taiwan bankruptcy dataset was conducted, which also demonstrates the superiority of DT ensembles by boosting over the others.


title: Design of digital beamforming subarrays for a multifunction radar

abstract: Digital beamforming (DBF) in phased array radars is an enabling architecture for advanced signal processing algorithms such as adaptive beamforming and high resolution angle estimation. At the current level of technology, DBF can only be practically implemented at a subarray level for large arrays operating at high frequencies. The choice of subarray layout has a significant impact on the performance of array signal processing algorithms. In this paper, an approach to optimizing the subarray layout with respect to select performance metrics is presented.


title: Fused-Silica Micro Birdbath Resonator Gyroscope ( $\mu$-BRG)

abstract: We present a 3-D fused-silica micro-scale shell gyroscope, called the birdbath resonator gyroscope (BRG). The BRGs axisymmetric geometry leads to a good frequency and Q symmetry. The birdbath resonator can be fabricated with a good structural symmetry because its anchor is self-aligned to the rest of the structure. The BRG has n=2 wine-glass modes at 10.5 kHz and has a large frequency separation between the n=2 wine-glass modes and the closest parasitic mode (|fparasitic-fn=2|/fn=2=0.3), which will potentially lead to a low vibration sensitivity. The equations of motion for 3-D shell gyroscopes are derived and the effective mass and angular gain of the BRG is estimated using finite element method (FEM). The BRG is fabricated using a 3-D micrometer-blow-torching process and assembly on an electrode substrate made with the silicon-on-glass process. The BRG is operated in the force-rebalance mode at vacuum at room temperature and has a scale factor of 27.9 mV/(deg/s), a full-scale range , an angle random walk of 0.106 deg/√h, and a bias stability of 1 deg/h. A large angular gain (0.317) is measured, which is close to the estimated value of 0.25 obtained via FEM.


title: A New Class of Depth-Size Optimal Parallel Prefix Circuits

abstract: Given n values x1, x2, ... ,xn and an associative binary operation o, the prefix problem is to compute x1 o x2 o ··· o xi, 1≤i≤n. Many combinational circuits for solving the prefix problem, called prefix circuits, have been designed. It has been proved that the size s(D(n)) and the depth d(D(n)) of an n-input prefix circuit D(n) satisfy the inequality d(D(n))+s(D(n))≥2n−2; thus, a prefix circuit is depth-size optimal if d(D(n))+s(D(n))=2n−2. In this paper, we construct a new depth-size optimal prefix circuit SL(n). In addition, we can build depth-size optimal prefix circuits whose depth can be any integer between d(SL(n)) and n−1. SL(n) has the same maximum fan-out ⌈lg n⌉+1 as Snir's SN(n), but the depth of SL(n) is smaller; thus, SL(n) is faster. Compared with another optimal prefix circuit LYD(n), d(LYD(n))+2≥d(SL(n))≥d(LYD(n)). However, LYD(n) may have a fan-out of at most 2 ⌈lg n⌉−2, and the fan-out of LYD(n) is greater than that of SL(n) for almost all n≥12. Because an operation node with greater fan-out occupies more chip area and is slower in VLSI implementation, in most cases, SL(n) needs less area and may be faster than LYD(n). Moreover, it is much easier to design SL(n) than LYD(n).


Hate Speech Detection Using Natural Language Processing Techniques


title: The terminalization of supply chains: reassessing the role of terminals in port/hinterland logistical relationships

abstract: The paper discusses how logistics service providers are using terminals in their supply chains. It argues that an increasing ‘terminalization’ of supply chains is unfolding, whereby seaport and inland terminals are taking up a more active role in supply chains by increasingly confronting market players with operational considerations such as imposing berthing windows, dwell time charges, truck slots, all this to increase throughput, optimize terminal capacity and make the best use of available land. With the development of inland terminals, a new dimension is being added: logistics players are now making best use of the free time available in seaports terminals and inland terminals, thereby optimizing the terminal buffer function. As a result, transport terminals are achieving an additional level of integration within supply chains that goes beyond their conventional transshipment role. Given increasing levels of vertical integration in the market and an increasing pressure on port capacity, a further terminalization of supply chains is likely to occur, which will strengthen the active role of terminals in logistics.


title: Cognitive Control

abstract: This paper is inspired by how cognitive control manifests itself in the human brain and does so in a remarkable way. It addresses the many facets involved in the control of directed information flow in a dynamic system, culminating in the notion of information gap, defined as the difference between relevant information (useful part of what is extracted from the incoming measurements) and sufficient information representing the information needed for achieving minimal risk. The notion of information gap leads naturally to how cognitive control can itself be defined. Then, another important idea is described, namely the two-state model, in which one is the system's state and the other is the entropic state that provides an essential metric for quantifying the information gap. The entropic state is computed in the perceptual part (i.e., perceptor) of the dynamic system and sent to the controller directly as feedback information. This feedback information provides the cognitive controller the information needed about the environment and the system to bring reinforcement leaning into play; reinforcement learning (RL), incorporating planning as an integral part, is at the very heart of cognitive control. The stage is now set for a computational experiment, involving cognitive radar wherein the cognitive controller is enabled to control the receiver via the environment. The experiment demonstrates how RL provides the mechanism for improved utilization of computational resources, and yet is able to deliver good performance through the use of planning. The paper finishes with concluding remarks.


title: Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

abstract: Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.


title: Metallized solid rocket propellants based on AN/AP and PSAN/AP for access to space

abstract: Solidrocketpropellantsbasedondualmixesofinorganiccrystallineoxidizers(ammoniumnitrate(AN)andammoniumperchlorate(AP))with binderandamixtureofmicrometric-nanometricaluminumwereinvestigated. Ammoniumnitrateisalow-costoxidizer,producingenvironment friendly combustion products but with lower speci¦c impulse compared to AP. The better performance obtained with AP and the low quantity of toxic emissions obtained by using AN have suggested an interesting compromise based on a dual mixture of the two oxidizers. To improve


title: Comparison of Heuristic Algorithms for the N-Queen Problem

abstract: This paper addresses the way in which heuristic algorithms can be used to solve the n-queen problem. Metaheuristics for algorithm simulated annealing, tabu search and genetic algorithm are shown, test results are demonstrated and upper bound complexity is determined. The efficiencies of algorithms are compared and their achievements are measured. Due to the reduction of the fitness function complexity to O(1) problem instances with large dimensions are solved.


title: PassMap: a map based graphical-password authentication system

abstract: Text passwords have been used in authentication systems for many decades. Users must recall the textual strings selected during registration to pass authentication. However, there are some serious problems with text passwords---recollection and security. Hence, various graphical-password authentication systems have been proposed to solve the problems of text passwords. Previous studies indicate that humans are better at recognizing and recalling images than texts. In 2005, Wiedenbeck et al. proposed PassPoints in which a password consists of a sequence of click-points (5 to 8) that a user chooses on an image. In the paper, we proposed an alternative system in which users can memorize fewer points while providing more security than PassPoints. Based on the idea of using an extremely large image as the password space, we propose a novel world map based graphical-password authentication system called PassMap in which a password consists of a sequence of 2 click-points that a user selects on an large world map. We also conducted a user study for evaluation. The result shows that the passwords of PassMap are easy to memorize for humans and PassMap is friendly to use in practice. Furthermore, PassMap provides higher entropy than PassPoints and also increases the cost of attacks.


title: Faceted search and retrieval based on semantically annotated product family ontology

abstract: With the advent of various services and applications of Semantic Web, semantic annotation had emerged as an important research area. The use of semantically annotated ontology had been evident in numerous information processing and retrieval tasks. One of such tasks is utilizing the semantically annotated ontology in product design which is able to suggest many important applications that are critical to aid various design related tasks. However, ontology development in design engineering remains a time consuming and tedious task that demands tremendous human efforts. In the context of product family design, management of different product information that features efficient indexing, update, navigation, search and retrieval across product families is both desirable and challenging. This paper attempts to address this issue by proposing an information management and retrieval framework based on the semantically annotated product family ontology. Particularly, we propose a document profile (DP) model to suggest semantic tags for annotation purpose. Using a case study of digital camera families, we illustrate how the faceted search and retrieval of product information can be accomplished based on the semantically annotated camera family ontology. Lastly, we briefly discuss some further research and application in design decision support, e.g. commonality and variety, based on the semantically annotated product family ontology.


Multilabel text classification for automated tag suggestion


title: Optimizing and Contrasting Recurrent Neural Network Architectures

abstract: Recurrent Neural Networks (RNNs) have long been recognized for their potential to model complex time series. However, it remains to be determined what optimization techniques and recurrent architectures can be used to best realize this potential. The experiments presented take a deep look into Hessian free optimization, a powerful second order optimization method that has shown promising results, but still does not enjoy widespread use. This algorithm was used to train to a number of RNN architectures including standard RNNs, long short-term memory, multiplicative RNNs, and stacked RNNs on the task of character prediction. The insights from these experiments led to the creation of a new multiplicative LSTM hybrid architecture that outperformed both LSTM and multiplicative RNNs. When tested on a larger scale, multiplicative LSTM achieved character level modelling results competitive with the state of the art for RNNs using very different methodology.


title: Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation

abstract: Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which to base the predictions remains a challenge, especially when the sample size is small. Incorporating expert knowledge offers a promising alternative to improve a prediction model, but collecting such knowledge is laborious to the expert if the number of candidate features is very large. We introduce a probabilistic model that can incorporate expert feedback about the impact of genomic measurements on the sensitivity of a cancer cell for a given drug. We also present two methods to intelligently collect this feedback from the expert, using experimental design and multi-armed bandit models. In a multiple myeloma blood cancer data set (n=51), expert knowledge decreased the prediction error by 8%. Furthermore, the intelligent approaches can be used to reduce the workload of feedback collection to less than 30% on average compared to a naive approach.


title: Learning from the Past: Answering New Questions with Past Answers ABSTRACT

abstract: Community-based Question Answering sites, such as Yahoo! Answers or Baidu Zhidao, allow users to get answers to complex, detailed and personal questions from other users. However, since answering a question depends on the ability and willingness of users to address the asker's needs, a significant fraction of the questions remain unanswered. We measured that in Yahoo! Answers, this fraction represents 15% of all incoming English questions. At the same time, we discovered that around 25% of questions in certain categories are recurrent, at least at the question-title level, over a period of one year. We attempt to reduce the rate of unanswered questions in Yahoo! Answers by reusing the large repository of past resolved questions, openly available on the site. More specifically, we estimate the probability whether certain new questions can be satisfactorily answered by a best answer from the past, using a statistical model specifically trained for this task. We leverage concepts and methods from query-performance prediction and natural language processing in order to extract a wide range of features for our model. The key challenge here is to achieve a level of quality similar to the one provided by the best human answerers. We evaluated our algorithm on offline data extracted from Yahoo! Answers, but more interestingly, also on online data by using three "live" answering robots that automatically provide past answers to new questions when a certain degree of confidence is reached. We report the success rate of these robots in three active Yahoo! Answers categories in terms of both accuracy, coverage and askers' satisfaction. This work presents a first attempt, to the best of our knowledge, of automatic question answering to questions of social nature, by reusing past answers of high quality.


title: Theory of operation, design procedure and simulation of a bidirectional LLC resonant converter for vehicular applications

abstract: Usually a LLC resonant converter is used in applications with high input voltage (300–400 V) and low output voltage (48–60 V). This type of converter includes a half bridge topology and is only designed for unidirectional power transfer. Since the benefits of a LLC resonant converter are high efficiency, high power density and low EMI, it is a very interesting topology for vehicular applications. For bidirectional power transfer, a full bridge topology is established. In this way bidirectional functionality can be combined with high performance. In the past LLC resonant converters have been supplying telecommunication or consumer electronic - usually as a buck converter. In the present paper the challenge is to make it fit for automotive requirements.


Preparing quality improvement, research, and evidence-based practice manuscripts.


title: A New Similarity Measure for Non-Local Means Filtering of MRI Images

abstract: The acquisition of MRI images offers a trade-off in terms of acquisition time, spatial/temporal resolution and signal-to-noise ratio (SNR). Thus, for instance, increasing the time efficiency of MRI often comes at the expense of reduced SNR. This, in turn, necessitates the use of post-processing tools for noise rejection, which makes image de-noising an indispensable component of computer assistance diagnosis. In the field of MRI, a multitude of image de-noising methods have been proposed hitherto. In this paper, the application of a particular class of de-noising algorithms - known as non-local mean (NLM) filters - is investigated. Such filters have been recently applied for MRI data enhancement and they have been shown to provide more accurate results as compared to many alternative de-noising algorithms. Unfortunately, virtually all existing methods for NLM filtering have been derived under the assumption of additive white Gaussian (AWG) noise contamination. Since this assumption is known to fail at low values of SNR, an alternative formulation of NLM filtering is required, which would take into consideration the correct Rician statistics of MRI noise. Accordingly, the contribution of the present paper is two-fold. First, it points out some principal disadvantages of the earlier methods of NLM filtering of MRI images and suggests means to rectify them. Second, the paper introduces a new similarity measure for NLM filtering of MRI Images, which is derived under bona fide statistical assumptions and results in more accurate reconstruction of MR scans as compared to alternative NLM approaches. Finally, the utility and viability of the proposed method is demonstrated through a series of numerical experiments using both in silico and in vivo MRI data.


Another Algorithm for Computing Longest Common Increasing Subsequence for Two Random Input Sequences


Enterprise Service Bus


title: Apache VXQuery: A Scalable XQuery Implementation

abstract: The wide use of XML for document management and data exchange has created the need to query large repositories of XML data. To efficiently query such large data collections and take advantage of parallelism, we have implemented Apache VXQuery, an open-source scalable XQuery processor. The system builds upon two other open-source frameworks -- Hyracks, a parallel execution engine, and Algebricks, a language agnostic compiler toolbox. Apache VXQuery extends these two frameworks and provides an implementation of the XQuery specifics (data model, data-model dependent functions and optimizations, and a parser). We describe the architecture of Apache VXQuery, its integration with Hyracks and Algebricks, and the XQuery optimization rules applied to the query plan to improve path expression efficiency and to enable query parallelism. An experimental evaluation using a real 500GB dataset with various selection, aggregation and join XML queries shows that Apache VXQuery performs well both in terms of scale-up and speed-up. Our experiments show that it is about 3x faster than Saxon (an open-source and commercial XQuery processor) on a 4-core, single node implementation, and around 2.5x faster than Apache MRQL (a MapReduce-based parallel query processor) on an eight (4-core) node cluster.


title: Multi-Agent Based Simulation: Where are the Agents?, Multi-Agent-Based Simulation

abstract: This paper is devoted to exploring the relationships between computational agents, as they can be found in multi-agent systems (MAS) or Distributed Artificial Intelligence (DAI), and the different techniques regrouped under the generic name "multi-agent based simulation" (MABS). Its main purpose is to show that MABS, despite its name, is in fact rarely based on computational agents. We base our demonstration on an innovative presentation of the methodological process used in the development of current MABS systems. This presentation relies on the definition of the different roles involved in the design process, and we are able to show that the notion of "agent", although shared at a conceptual level by the different participants, does not imply a systematic use of computational agents in the systems deployed. We then conclude by discussing what the use of computational agents, based on the most interesting research trends in DAI or MAS, might provide MABS with.


title: OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph

abstract: This paper reconstructs the Freebase data dumps to understand the underlying ontology behind Google's semantic search feature. The Freebase knowledge base was a major Semantic Web and linked data technology that was acquired by Google in 2010 to support the Google Knowledge Graph, the backend for Google search results that include structured answers to queries instead of a series of links to external resources. After its shutdown in 2016, Freebase is contained in a data dump of 1.9 billion Resource Description Format (RDF) triples. A recomposition of the Freebase ontology will be analyzed in relation to concepts and insights from the literature on classification by Bowker and Star. This paper will explore how the Freebase ontology is shaped by many of the forces that also shape classification systems through a deep dive into the ontology and a small correlational study. These findings will provide a glimpse into the proprietary blackbox Knowledge Graph and what is meant by Google's mission to"organize the world's information and make it universally accessible and useful".


title: Metformin may produce antidepressant effects through improvement of cognitive function among depressed patients with diabetes mellitus.

abstract: Diabetes mellitus and depressive disorders are both common chronic diseases that increase functional disability and social burden. Cognitive impairment is a potentially debilitating feature of depression. Previous evidence indicates that the antidiabetic drug metformin could be suitable for diabetic patients with cognitive impairment. However, there is no direct evidence from clinical studies that metformin treatment improves cognitive function in diabetic patients suffering from depression. In the present study, 58 participants diagnosed with depression and type 2 diabetes mellitus (T2DM) were recruited and divided into two groups, one treated with metformin and the other treated with placebo for 24 weeks. Cognitive function, depressive behaviour and diabetes improvement were evaluated. Chronic treatment with metformin for 24 weeks improved cognitive performance, as assessed by the Wechsler Memory Scale-Revised, in depressed patients with T2DM. In addition, metformin significantly improved depressive performance and changed the glucose metabolism in depressed patients with diabetes. Depressive symptoms were negatively correlated with cognitive performance in metformin-treated participants. Furthermore, associations were observed between the parameters of blood glucose metabolism and the depression phenotype. These findings suggest that chronic treatment with metformin has antidepressant behavioural effects and that improved cognitive function is involved in the therapeutic outcome of metformin. The results of the present study also raise the possibility that supplementary administration of antidiabetic medications may enhance the recovery of depression, comorbid with T2DM, through improvements in cognitive performance.


title: Sex preferences among rural community: public health and social concern

abstract: Background: Sex preference is choice of selecting the sex of children by their parents or family members. The objective of the study was to study the existence of sex preference among rural community. Material and methods: A Cross-sectional study was carried out among 200 ever married women of reproductive age group. Random digits sampling method was used to select 10 villages in Rahata Tehsil of Ahmednagar, while systematic sampling was applied for selection of 20 samples in each village. Results: In the previous sex preference for male child was 37.3%, 58.75%, 88.5%, 100% and 100% from firstchild to fifth respectively, while female preference and either sexpreference was decreasing. In the current sex preference for male, female and either was 36.8%, 25% and 38.2% respectively. Future sex preference was 40.9% for male child, 22.7% for female child and 36.4% for either sex. The main reason for son preference was for old age care and support, to continue the family name and earning member in the family. Conclusion: Study confirms that son preference still existsin the rural community of Maharashtra. Attitude for son preference is mainly because of the economic earning, old age care and continuation of the family nameamong all groups.


INVENTORY MANAGEMENT, SERVICE LEVEL AND SAFETY STOCK


Extracting parallelism in OS kernels using type-safe languages


title: Truth Finding on the Deep Web: Is the Problem Solved?

abstract: The amount of useful information available on the Web has been growing at a dramatic pace in recent years and people rely more and more on the Web to fulfill their information needs. In this paper, we study truthfulness of Deep Web data in two domains where we believed data are fairly clean and data quality is important to people's lives: Stock and Flight. To our surprise, we observed a large amount of inconsistency on data from different sources and also some sources with quite low accuracy. We further applied on these two data sets state-of-the-art data fusion methods that aim at resolving conflicts and finding the truth, analyzed their strengths and limitations, and suggested promising research directions. We wish our study can increase awareness of the seriousness of conflicting data on the Web and in turn inspire more research in our community to tackle this problem.


title: Quantitative assessment of TV white space in India

abstract: Licensed but unutilized television (TV) band spectrum is called as TV white space in the literature. Ultra high frequency (UHF) TV band spectrum has very good wireless radio propagation characteristics. The amount of TV white space in the UHF TV band in India is of interest. Comprehensive quantitative assessment and estimates for the TV white space in the 470-590MHz band for four zones of India (all except north) are presented in this work. This is the first effort in India to estimate TV white spaces in a comprehensive manner. The average available TV white space per unit area in these four zones is calculated using two methods: (i) the primary (licensed) user and secondary (unlicensed) user point of view; and, (ii) the regulations of Federal Communications Commission in the United States. By both methods, the average available TV white space in the UHF TV band is shown to be more than 100MHz! A TV transmitter frequency-reassignment algorithm is also described. Based on spatial-reuse ideas, a TV channel allocation scheme is presented which results in insignicant interference to the TV receivers while using the least number of TV channels for transmission across the four zones. Based on this reassignment, it is found that four TV band channels (or 32MHz) are sufficient to provide the existing UHF TV band coverage in India.


title: Design and Analysis of a Multiport Circuit for Shaping Sub-Nanosecond Pulses

abstract: In this study, a coupled-line coupler was evaluated for its potential to shape sub-nanosecond ultra-wideband (UWB) pulses. The coupler was shown to exhibit a natural tendency to differentiate sub-nanosecond pulses, owing to its bandpass nature. Next, a Schottky detector diode was applied to confirm that the small parasitic reactance in the coupler gives rise to the differentiation behavior. The unconventional application of a coupler and Schottky diode towards signal differentiation culminated in the development of a novel UWB generator that simultaneously produces: a square waveform that has a 20%-80% rise time of 850 ps, a Gaussian waveform that has a pulse duration of 100 ps, and a monocycle waveform that has a 80 ps rise time between its peaks. These waveforms were obtained by taking advantage of the dc isolation of the coupled line structure and using step recovery diodes to compress the edges of a 14 MHz sinusoidal source. The resulting multiport circuit for simultaneous shaping of sub-nanosecond pulses (MCS3P) showed good agreement between measured and simulated results.


title: An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System

abstract: Summary The potential threats and attacks that can be caused by intrusions have been increased rapidly due to the dependence on network and internet connectivity. In order to prevent such attacks, Intrusion Detection Systems were designed. Different soft computing based methods have been proposed for the development of Intrusion Detection Systems. In this paper a multilayer perceptron is trained using an enhanced resilient backpropagation training algorithm for intrusion detection. In order to increase the convergence speed an optimal or ideal learning factor was added to the weight update equation. The performance and evaluations were performed using the NSLKDD anomaly intrusion detection dataset. The experiments results demonstrate that the system has promising results in terms of accuracy, storage and time; the designed system was capable to classify records with a detection rate about 94.7%.


title: Social media-induced technostress: Its impact on the job performance of it professionals and the moderating role of job characteristics

abstract: Abstract Using social media during work hours for non-work-related reasons is becoming commonplace. Organizations are therefore challenged with identifying and overcoming the consequences of such use. Social media-induced technostress has been identified as an important unintended consequence of using social media at work, as it could negatively impact job performance. This study draws on Person-Environment Fit to investigate the relationship between social media-induced technostress and job performance in IT professionals, and the moderating effect of job characteristics on this relationship. The results indicate that social media-induced technostress is negatively related to job performance and the negative impact of social media-induced technostress is intensified when the job characteristics are low. This work extends the literature on job-stress, social media, technostress, and job characteristics.


title: Establishing a Human Baseline for the Winograd Schema Challenge

abstract: The Winograd Schema Challenge (WSC) is a pronoun resolution task for which deep semantic knowledge is required to achieve high performance. Until now it has been assumed that human performance on the WSC is nearly at ceiling, but evidence for this has been mainly anecdotal. Here we present the results of a large online experiment that both establishes a baseline for human performance on the WSC and demonstrates the importance of human testing, not only as a means of validating a particular corpus, but more fundamentally as a guide in defining desirable characteristics for Winograd


title: Integrating Complementary Spectral Models In The Design Of A Musical Synthesizer

abstract: A new and distinct cultivar of Kalanchoe plant named La Cruz characterized by its relatively large lavender to pink flowers, compact and free branching habit, early flowering, floriferous habit, and by its adaptability to production in 10 to 15 cm pots.


title: Business model design: conceptualizing networked value co-creation

abstract: Purpose – A common thread in the modern marketing theories, such as service‐dominant logic and viable systems approach, is the notion value co‐creation: the locus of value creation is no longer perceived to reside within firm boundaries but value is considered to be co‐created among various actors within the networked market. The evolution of value creation, from value creation by the manufacturing firm to value co‐creation in a network, necessitates a corresponding change in the concepts used to depict value creation. The purpose of this paper is to investigate business models as a broader conceptualization of value co‐creation that captures this change.Design/methodology/approach – The topic is approached by a combination of literature review and interactive research, including interactions with managers from 12 international companies.Findings – Business models are defined as configurations of 12 interrelated elements, covering market, offering, operational, and management viewpoints. The effectiveness...


title: Direct voltage control of DC-DC boost converters using model predictive control based on enumeration

abstract: This paper presents a model predictive control (MPC) approach for the dc-dc boost converter. Based on a hybrid model of the converter suitable for both continuous and discontinuous conduction mode an objective function is formulated, which is to be minimized. The proposed MPC scheme, utilized as a voltage-mode controller, achieves regulation of the output voltage to its reference, without requiring a subsequent current control loop. Simulation and experimental results are provided to demonstrate the merits of the proposed control methodology, which include fast transient response and robustness.


title: Extending EMV Tokenised Payments to Offline-Environments

abstract: Tokenisation has been adopted by the payment industry as a method to prevent Personal Account Number (PAN) compromise in EMV (Europay MasterCard Visa) transactions. The current architecture specified in EMV tokenisation requires online connectivity during transactions. However, it is not always possible to have online connectivity. We identify three main scenarios where fully offline transaction capability is considered to be beneficial for both merchants and consumers. Scenarios include making purchases in locations without online connectivity, when a reliable connection is not guaranteed, and when it is cheaper to carry out offline transactions due to higher communication/payment processing costs involved in online approvals. In this study, an offline contactless mobile payment protocol based on EMV tokenisation is proposed. The aim of the protocol is to address the challenge of providing secure offline transaction capability when there is no online connectivity on either the mobile or the terminal. The solution also provides end-to-end encryption to provide additional security for transaction data other than the token. The protocol is analysed against protocol objectives and we discuss how the protocol can be extended to prevent token relay attacks. The proposed solution is subjected to mechanical formal analysis using Scyther. Finally, we implement the protocol and obtain performance measurements.


title: A survey of tools for variant analysis of next-generation genome sequencing data

abstract: Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.


title: Retrieval-Based Learning: A Perspective for Enhancing Meaningful Learning

abstract: Learning is often identified with the acquisition, encoding, or construction of new knowledge, while retrieval is often considered only a means of assessing knowledge, not a process that contributes to learning. Here, we make the case that retrieval is the key process for understanding and for promoting learning. We provide an overview of recent research showing that active retrieval enhances learning, and we highlight ways researchers have sought to extend research on active retrieval to meaningful learning—the learning of complex educational materials as assessed on measures of inference making and knowledge application. However, many students lack metacognitive awareness of the benefits of practicing active retrieval. We describe two approaches to addressing this problem: classroom quizzing and a computer-based learning program that guides students to practice retrieval. Retrieval processes must be considered in any analysis of learning, and incorporating retrieval into educational activities represents a powerful way to enhance learning.


title: Readability of Texts: State of the Art

abstract: In TEFL, it is often stated that communication presupposes comprehension. The main purpose of readability studies is thus to measure the comprehensibility of a piece of writing. In this regard, different readability measures were initially devised to help educators select passages suitable for both children and adults. However, readability formulas can certainly be extremely helpful in the realm of EFL reading. They were originally designed to assess the suitability of books for students at particular grade levels or ages. Nevertheless, they can be used as basic tools in determining certain crucial EFL text-characteristics instrumental in the skill of reading and its related issues. The aim of the present paper is to familiarize the readers with the most frequently used readability formulas as well as the pros and cons views toward the use of such formulas. Of course, this part mostly illustrates studies done on readability formulas with the results obtained. The main objective of this part is to help readers to become familiar with the background of the formulas, the theory on which they stand, what they are good for and what they are not with regard to a number of studies cited in this section.


title: Trust in Smart Contracts is a Process, As Well

abstract: Distributed ledger technologies are rising in popularity, mainly for the host of financial applications they potentially enable, through smart contracts. Several implementations of distributed ledgers have been proposed, and different languages for the development of smart contracts have been suggested. A great deal of attention is given to the practice of development, i.e. programming, of smart contracts. In this position paper, we argue that more attention should be given to the “traditional developers” of contracts, namely the lawyers, and we propose a list of requirements for a human and machine-readable contract authoring language, friendly to lawyers, serving as a common (and a specification) language, for programmers, and the parties to a contract.


title: Selective optimization of test for embedded flash memory

abstract: Testing of embedded flash can be performed cost effectively in a high volume commodity environment assuming a) the chip is designed to support DAT (direct access test) for flash; b) the design supports full structural scan and boundary scan test; c) the tester supports flash memory test and scan test; and d) the tester can run concurrent scan/boundary scan test and memory test to make use of long flash test time.


title: A D-Q Frame Controller for a Full-Bridge Single Phase Inverter Used in Small Distributed Power Generation Systems

abstract: This paper presents a Direct-Quadrature (DQ) rotating frame control method for single phase full-bridge inverters used in small hybrid power systems. A secondary orthogonal imaginary circuit is created to provide the second phase required for the transformation; thus a DQ model of the inverter is obtained and its controller designed emulating the controls of three-phase power converters. The proposed controller attains infinite loop gain in the rotating coordinate, thus providing zero steady-state error at the fundamental frequency of the converter. The proposed controller is designed and validated through simulations using a DQ-frame average model in Matlab and a detailed switching model in Saber, as well as experimental results obtained with a 2.5 kW single phase full-bridge inverter prototype using a DSP/FPGA based digital control system where the proposed DQ-frame controller is fully implemented.


title: Resonant Switches - A Unified Approach to Improve Performances of Switching Converters

abstract: In searching for a general "zero-current-Switching" technique for DC-DC converters, the concept of resonant switches is developed. As a combination of switching device and LC network, the resonant switch offers advantages of quasi-sinusoidal current waveforms, zero switching stresses, zero switching losses, self-commutation, and reduced EMI. Furthermore, application of the resonant switch concept to conventional converters leads to the discovery of a host of new converter circuits.


title: Multi-scale pyramid pooling for deep convolutional representation

abstract: Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image representation by combining the two approaches. To take advantages of both representations, we extract a fair amount of multi-scale dense local activations from a pre-trained CNN. We then aggregate the activations by Fisher kernel framework, which has been modified with a simple scale-wise normalization essential to make it suitable for CNN activations. Our representation demonstrates new state-of-the-art performances on three public datasets: 80.78% (Acc.) on MIT Indoor 67, 83.20% (mAP) on PASCAL VOC 2007 and 91.28% (Acc.) on Oxford 102 Flowers. The results suggest that our proposal can be used as a primary image representation for better performances in wide visual recognition tasks.


On stress and linguistic rhythm


title: Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition

abstract: Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols. To settle this problem, we utilize the attention based encoder-decoder model that recognizes mathematical expression images from two-dimensional layouts to one-dimensional LaTeX strings. We improve the encoder by employing densely connected convolutional networks as they can strengthen feature extraction and facilitate gradient propagation especially on a small training set. We also present a novel multi-scale attention model which is employed to deal with the recognition of math symbols in different scales and save the fine-grained details that will be dropped by pooling operations. Validated on the CROHME competition task, the proposed method significantly outperforms the state-of-the-art methods with an expression recognition accuracy of 52.8% on CROHME 2014 and 50.1% on CROHME 2016, by only using the official training dataset.


Pectic Signal Transduction in Carrot Cells: Membrane, Cytosolic and Nuclear Responses Induced by Oligogalacturonides


title: An Efficient Framework for Generating Storyline Visualizations from Streaming Data

abstract: This paper presents a novel framework for applying storyline visualizations to streaming data. The framework includes three components: a new data management scheme for processing and storing the incoming data, a layout construction algorithm specifically designed for incrementally generating storylines from streaming data, and a layout refinement algorithm for improving the legibility of the visualization. By dividing the layout computation to two separate components, one for constructing and another for refining, our framework effectively provides the users with the ability to follow and reason dynamic data. The evaluation studies of our storyline visualization framework demonstrate its efficacy to present streaming data as well as its superior performance over existing methods in terms of both computational efficiency and visual clarity.


title: Multi-objective evolutionary emergency response optimization for major accidents.

abstract: Emergency response planning in case of a major accident (hazardous material event, nuclear accident) is very important for the protection of the public and workers' safety and health. In this context, several protective actions can be performed, such as, evacuation of an area; protection of the population in buildings; and use of personal protective equipment. The best solution is not unique when multiple criteria are taken into consideration (e.g. health consequences, social disruption, economic cost). This paper presents a methodology for multi-objective optimization of emergency response planning in case of a major accident. The emergency policy with regards to protective actions to be implemented is optimized. An evolutionary algorithm has been used as the optimization tool. Case studies demonstrating the methodology and its application in emergency response decision-making in case of accidents related to hazardous materials installations are presented. However, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency response procedures in other cases (nuclear accidents, transportation of hazardous materials) or for land-use planning issues.


title: Automated malware classification based on network behavior

abstract: Over the past decade malware, i.e., malicious software, has become a major security threat on the Internet. Today anti-virus companies receive thousands of malicious samples every day. However the vast majority of these samples are variants of the existing malware. Due to the sheer number of malware variants it is important to accurately determine whether a sample belongs to a known malware family or exhibits a new behavior and thus requires further analysis and separate detection signature. Despite of the importance of network activity, the existing research on malware analysis does not fully leverage the malware network behavior for classification. In this paper, we propose an automated malware classification system that focuses on network behavior of malware samples. Our approach employs behavioral profiles that summarize the network behavior of malware samples. The proposed approach is applied to a real world malware corpus. Our experimental results show the effectiveness of the proposed approach in classifying malware samples only based on the network activity exhibited by the samples.


title: Indian Language Speech Database: A Review

abstract: is the most prominent and natural form of communication between humans. Human beings have long been motivated to create computer that can understand and talk like human. When the research tries to develop certain recognition system they require certain previously stored data i.e. database for respective recognition system. There are various speech databases available for European Language but very less for Indian Language. In this paper we discuss the various Speech Database developed in different Indian Languages for speech recognition system & Text to Speech System.


title: Towards Nearly Zero Energy Buildings in Europe: A Focus on Retrofit in Non-Residential Buildings

abstract: Buildings are the focus of European (EU) policies aimed at a sustainable and competitive low-carbon economy by 2020. Reducing energy consumption of existing buildings and achieving nearly zero energy buildings (NZEBs) are the core of the Energy Efficiency Directive (EED) and the recast of the Energy Performance of Building Directive (EPBD). To comply with these requirements, Member States have to adopt actions to exploit energy savings from the building sector. This paper describes the differences between deep, major and NZEB renovation and then it provides an overview of best practice policies and measures to target retrofit and investment related to non-residential buildings. Energy requirements defined by Member States for NZEB levels are reported comparing both new and existing residential and non-residential buildings. The paper shows how the attention given to refurbishment of NZEBs increased over the last decade, but the achievement of a comprehensive implementation of retrofit remains one of main challenges that Europe is facing.


AxBench: A Benchmark Suite for Approximate Computing Across the System Stack


title: Low power sensor design for IoT and mobile healthcare applications

abstract: This paper reviews recent advances in radar sensor design for low-power healthcare, indoor real-time positioning and other applications of IoT. Various radar front-end architectures and digital processing methods are proposed to improve the detection performance including detection accuracy, detection range and power consumption. While many of the reported designs were prototypes for concept verification, several integrated radar systems have been demonstrated with reliable measured results with demo systems. A performance comparison of latest radar chip designs has been provided to show their features of different architectures. With great development of IoT, short-range low-power radar sensors for healthcare and indoor positioning applications will attract more and more research interests in the near future.


title: Making Sense by Building Sense: Kindergarten Children’s Construction and Understanding of Adaptive Robot Behaviors

abstract: This study explores young children’s ability to construct and explain adaptive behaviors of a behaving artifact, an autonomous mobile robot with sensors. A central component of the behavior construction environment is the RoboGan software that supports children’s construction of spatiotemporal events with an a-temporal rule structure. Six kindergarten children participated in the study, three girls and three boys. Activities and interviews were conducted individually along five sessions that included increasingly complex construction tasks. It was found that all of the children succeeded in constructing most such behaviors, debugging their constructions in a relatively small number of cycles. An adult’s assistance in noticing relevant features of the problem was necessary for the more complex tasks that involved four complementary rules. The spatial scaffolding afforded by the RoboGan interface was well used by the children, as they consistently used partial backtracking strategies to improve their constructions, and employed modular construction strategies in the more complex tasks. The children’s explanations following their construction usually capped at one rule, or two condition-action couples, one rule short of their final constructions. With respect to tasks that involved describing a demonstrated robot’s behavior, in describing their constructions, explanations tended to be more rule-based, complex and mechanistic. These results are discussed with respect to the importance of making such physical/computational environments available to young children, and support of young children’s learning about such intelligent systems and reasoning in developmentally-advanced forms.


title: Urticaria multiforme: A report of 5 cases and a review of the literature.

abstract: Urticaria multiforme is a cutaneous disorder that affects infants and small children. It is characterized by large, annular urticarial plaques with a violaceous center and is frequently accompanied by fever. Urticaria multiforme follows a benign, self-limiting course, but the striking appearance of the lesions can cause alarm and considerable anxiety among parents. We present 5 cases of urticaria multiforme seen in our hospital over a period of 18 months. All the cases were preceded by an infection of the upper airways or otitis, and they were all empirically with amoxicillin. It is important to be familiar with this condition to reassure parents and avoid unnecessary hospital admissions and tests.


title: Wine quality identification based on data mining research

abstract: For the quality of the wine big data identification technology, the introduction of data mining classification algorithm, effectively according to the content of several impact compounds in wine level identification;Are introduced including the Logistic regression and BP neural network and SVM classification algorithm, in view of the three algorithms identify the modeling analysis of wine quality. Data mining is closely related to big data, applying data mining to the wine in the quality detection of big data, can quickly to the quality of the wine.


title: Accelerated sampling for the Indian Buffet Process

abstract: We often seek to identify co-occurring hidden features in a set of observations. The Indian Buffet Process (IBP) provides a non-parametric prior on the features present in each observation, but current inference techniques for the IBP often scale poorly. The collapsed Gibbs sampler for the IBP has a running time cubic in the number of observations, and the uncollapsed Gibbs sampler, while linear, is often slow to mix. We present a new linear-time collapsed Gibbs sampler for conjugate likelihood models and demonstrate its efficacy on large real-world datasets.


title: Use and development of entertainment technologies in after school STEM program

abstract: This design research paper examines the implementation and curriculum changes of an after school computer science program that promotes computational thinking to middle school students. The program, Students in Programming, Robotics, and Computer Science (SPARCS), can adapt to different presentation environments, such as independent after school sessions or a semester-long apprenticeship program. We trace one implementation of the program through the initial deployment, the development of infrastructure, and a reorganization of content to address student interests. We found that student attrition dropped and the average session enjoyment increased when our sessions integrated consumer technologies such as mobile applications, video games, and the Minecraft computer game. In this paper, we provide readers a framework for running computing outreach activities around similar consumer technologies.


title: Designing a 1kW GaN PFC stage with over 99% efficiency and 155W/in3 power density

abstract: Stringent requirements on higher power density and efficiency for power electronics converters in applications such as server/telecom power systems and industrial motor drives necessitate transformative changes in power electronic architecture as well as replacement of Si based FETs with wide bandgap devices. For power-factor-correction (PFC) circuits, the path for greater power density necessitates the transition from Si based dual-boost to GaN based totem pole (TP) structure, which enables designers to reach over 99% efficiency at twice the power density of Si based converters. This paper compares GaN based TP PFC with conventional Si based topologies, and details design steps for above 99% efficiency and 155W/in3 power density in the switching network.


title: A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles

abstract: Abstract Motor vehicle powered by regular gasoline is one of major sources of pollutants for local and global environment. The current study developed and validated a new fuel-economy optimization system (FEOS), which receives input from vehicle variables and environment variables (e.g., headway spacing) as input, mathematically computes the optimal acceleration/deceleration value with Lagrange multipliers method, and sends the optimal values to drivers via a human-machine interface (HMI) or automatic control systems of autonomous vehicles. FEOS can be used in both free-flow and car-following traffic conditions. An experimental study was conducted to evaluate FEOS. It was found that without sacrificing driver safety, drivers with the aid of FEOS consumed significant less fuel than those without FEOS in all acceleration conditions (22–31% overall gas saving) and the majority of deceleration conditions (12–26% overall gas saving). Compared to relative expensive vehicle engineering system design and improvement, FEOS provides a feasible way to minimize fuel consumptions considering human factors. Applications of the optimal model in the design of both HMI for vehicles with human drivers and autonomous vehicles were discussed.


title: Classification of High Resolution Remote Sensing Images using Deep Learning Techniques

abstract: High Resolution Satellite Images are widely used in many applications. Since such images are useful to provide more useful information about the details about the every regions around the world. In this work, transfer learning is used efficiently for the feature extraction from a pretrained Convolutional Neural Network(CNN) model which is used for training in the classification task. Using transfer learning the classification yielded a better accurate results. The experiments are carried out on two high resolution remote sensing satellite images such as UC Merced LandUse and SceneSat Datasets. The pre-trained CNN used here is VGG-16 which is trained on millions of Image-Net Dataset. The proposed method yielded a classification accuracy of 93% in UC Merced LandUse Dataset and in SceneSat Dataset it is about 84%. This proposed method yielded a better precision of 0.93 and 0.86 in UC Merced LandUse Dataset and in SceneSat Dataset respectively.


title: Consumer Encounters: Improving Idea Development and Concept Optimization

abstract: Getting better products faster to market is critical to a company. Traditional approaches to ideation and concept development and optimization begin with ideas developed internally and then validated through consumer screening and concept testing. The concepts developed internally can be improved by first going to consumers and understanding the issues in their lives they are trying to improve, leading to a better acceptance of concepts in the screening process, more accurate consumer testing, and better volumetric and profit forecasting. At Rubbermaid, a Consumer Encounter Form was created to assist consumer investigation teams. This form was designed to facilitate a brief encounter, prioritize product categories and consumer needs based on the VOC, and lead to product ideas. This form flows directly into our Concept Testing Board for use in the consumer concept tests. This paper will introduce this systematic process and report on its successes.


A Newsletter of the IIVR dedicated to promoting the use of spontaneous personal imagery, a method of inner healing and self-transformation rooted in the Personal Totem Pole Process®.