title: Gender, Entrepreneurial Self-Efficacy, and Entrepreneurial Career Intentions: Implications for Entrepreneurship Education

abstract: The relationships between gender, entrepreneurial self-efficacy, and entrepreneurial intentions were examined for two sample groups: adolescents and adult master of business administration (MBA) students. Similar gender effects on entrepreneurial self-efficacy are shown for both groups and support earlier research on the relationship between self-efficacy and career intentions. Additionally, the effects of entrepreneurship education in MBA programs on entrepreneurial self-efficacy proved stronger for women than for men. Implications for educators and policy makers were discussed, and areas for future research outlined.


title: Reverse Engineering of Network Signatures

abstract: Network-based intrusion detection systems analyze network traffic looking for evidence of attacks. The analysis is usually performed using signatures, which are rules that describe what traffic should be considered as malicious. If the signatures are known, it is possible to either craft an attack to avoid detection or to send synthetic traffic that will match the signature to over-stimulate the network sensor causing a denial of service attack. To prevent these attacks, commercial systems usually do not publish their signature sets and their analysis algorithms. This paper describes a reverse engineering process and a reverse engineering tool that are used to analyze the way signatures are matched by network-based intrusion detection systems. The results of the analysis are used to either generate variations of attacks that evade detection or produce non-malicious traffic that over-stimulates the sensor. This shows that security through obscurity does not work. That is, keeping the signatures secret does not necessarily increase the resistance of a system to evasion and over-stimulation attacks.


title: "The Myths of Coalition" from Black Power:T he Politics of Liberation in America

abstract: I I“1 â– here is a strongly held view in this society that the â– bestÂ—indeed, perhaps the onlyÂ—way for black peoJL pie to win their political and economic rights is by forming coalitions with liberal, labor, church and other kinds of sympathetic organizations or forces, including the "liberal left" wing of the Democratic Party. With such allies, they could influence national legislation and national social patterns; racism could thus be ended. This school sees the "Black Power Move-


title: Conversation with and through computers

abstract: People design what they say specifically for their conversational partners, and they adapt to their partners over the course of a conversation. A comparison of keyboard conversations involving a simulated computer partner (as in a natural language interface) with those involving a human partner (as in teleconferencing) yielded striking differences and some equally striking similarities. For instance, there were significantly fewer acknowledgments in human/computer dialogue than in human/human. However, regardless of the conversational partner, people expected connectedness across conversational turns. In addition, the style of a partner's response shaped what people subsequently typed. These results suggest some issues that need to be addressed before a natural language computer interface will be able to hold up its end of a conversation.


Operating System Security and Host Vulnerability Evaluation


title: KeeLoq and Side-Channel Analysis-Evolution of an Attack

abstract: Last year we were able to break KeeLoq, which is a 64 bit block cipher that is popular for remote keyless entry (RKE) systems. KeeLoq RKEs are widely used for access control purposes such as garage openers or car door systems. Even though the attack seems almost straightforward in hindsight, there where many practical and theoretical problems to overcome. In this talk I want to describe the evolution of the attack over about two years. Also, some possible future improvements using fault-injection will be mentioned. During the first phase of breaking KeeLoq, a surprisingly long time was spent on analyzing the target hardware, taking measurements and wondering why we did not succeed. In the second phase, we were able to use differential power analysis attacks successfully on numerous commercially available products employing KeeLoq code hopping. Our techniques allow for efficiently revealing both the secret key of a remote transmitter and the manufacturer key stored in a receiver. As a result, a remote control can be cloned from only ten power traces, allowing for a practical key recovery in a few minutes. With similar techniques but with considerably more measurements (typically on the order of 10,000) we can extract the manufacturer key which is stored in every receiver device, e.g., a garage door opener unit. In the third phase, and most recent phase, we were able to come up with several improvements. Most notably, we found that an SPA (simple power analysis) attack allows to recover the manufacturer key with one measurement. In the talk, we will also speculate about extensions to fault-injection and timing attacks. It is important to note that most of our findings are not specific to KeeLoq but are - in principle - applicable to any symmetric cipher with an implementation that is not sidechannel resistant.


title: Relating Cascaded Random Forests to Deep Convolutional Neural Networks for Semantic Segmentation

abstract: We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). The main contribution of this work is to explore the relationship between two special forms of these techniques: stacked RFs and deep Convolutional Neural Networks (CNN). We show that there exists a mapping from stacked RF to deep CNN, and an approximate mapping back. This insight gives two major practical benefits: Firstly, deep CNNs can be intelligently constructed and initialized, which is crucial when dealing with a limited amount of training data. Secondly, it can be utilized to create a new stacked RF with improved performance. Furthermore, this mapping yields a new CNN architecture, that is well suited for pixel-wise semantic labeling. We experimentally verify these practical benefits for two different application scenarios in computer vision and biology, where the layout of parts is important: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.


title: Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion

abstract: Graphical abstractDisplay Omitted HighlightsA new global optimization meta-heuristic inspired by galactic motion is proposed.The proposed algorithm employs alternating phases of exploration and exploitation.Performance on rotated and shifted versions of benchmark problems is also considered.The proposed GSO algorithm outperforms 8 state-of-the-art PSO algorithms. This paper proposes a new global optimization metaheuristic called Galactic Swarm Optimization (GSO) inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. GSO employs multiple cycles of exploration and exploitation phases to strike an optimal trade-off between exploration of new solutions and exploitation of existing solutions. In the explorative phase different subpopulations independently explore the search space and in the exploitative phase the best solutions of different subpopulations are considered as a superswarm and moved towards the best solutions found by the superswarm. In this paper subpopulations as well as the superswarm are updated using the PSO algorithm. However, the GSO approach is quite general and any population based optimization algorithm can be used instead of the PSO algorithm. Statistical test results indicate that the GSO algorithm proposed in this paper significantly outperforms 4 state-of-the-art PSO algorithms and 4 multiswarm PSO algorithms on an overwhelming majority of 15 benchmark optimization problems over 50 independent trials and up to 50 dimensions. Extensive simulation results show that the GSO algorithm proposed in this paper converges faster to a significantly more accurate solution on a wide variety of high dimensional and multimodal benchmark optimization problems.


title: Species-specific wiring for direction selectivity in the mammalian retina

abstract: Directionally tuned signalling in starburst amacrine cell (SAC) dendrites lies at the heart of the circuit that detects the direction of moving stimuli in the mammalian retina. The relative contributions of intrinsic cellular properties and network connectivity to SAC direction selectivity remain unclear. Here we present a detailed connectomic reconstruction of SAC circuitry in mouse retina and describe two previously unknown features of synapse distributions along SAC dendrites: input and output synapses are segregated, with inputs restricted to proximal dendrites; and the distribution of inhibitory inputs is fundamentally different from that observed in rabbit retina. An anatomically constrained SAC network model suggests that SAC–SAC wiring differences between mouse and rabbit retina underlie distinct contributions of synaptic inhibition to velocity and contrast tuning and receptive field structure. In particular, the model indicates that mouse connectivity enables SACs to encode lower linear velocities that account for smaller eye diameter, thereby conserving angular velocity tuning. These predictions are confirmed with calcium imaging of mouse SAC dendrites responding to directional stimuli.


title: An experimental investigation of malware attacks on SCADA systems

abstract: Abstract Modern critical infrastructures are continually exposed to new threats due to the vulnerabilities and architectural weaknesses introduced by the extensive use of information and communications technologies (ICT). Of particular significance are the vulnerabilities in the communication protocols used in supervisory control and data acquisition (SCADA) systems that are commonly employed to control industrial processes. This paper presents the results of our research on the impact of traditional ICT malware on SCADA systems. In addition, it discusses the potential damaging effects of computer malware created for SCADA systems.


title: Multi-Robot nonlinear model predictive formation control: the obstacle avoidance problem

abstract: This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.


title: Laser ranging at 1550 nm with 1-GHz sine-wave gated InGaAs/InP APD single-photon detector

abstract: We demonstrated a laser ranging system with single photon detection at 1550 nm. The single-photon detector was a 1-GHz sine-wave gated InGaAs/InP avalanche photodiode. In daylight, 8-cm depth resolution was achieved directly by using a time-of-flight approach based on time-correlated single photon counting measurement. This system presented a potential for low energy level and eye-safe laser ranging system in long-range measurement.


title: Market Orientation, Creativity, and New Product Performance in High-Technology Firms:

abstract: Abstract The ability to generate and market creative ideas in new products (NPs) and related marketing programs (MPs) in response to changing market needs is key to the success of a firm. This research examines the mediating role of NP and MP creativity between market orientation and NP success. The authors investigate (1) whether market orientation facilitates or inhibits creativity, (2) whether creativity influences NP performance, and (3) how to define and measure creativity in the NP development and launch contexts. They use a two-stage sampling frame to collect 312 sets of responses from managers and NP team leaders and thereby address the potential for common method bias in measures of creativity and NP performance. The findings indicate that NP and MP creativity mediates the relationship between market orientation and NP success. The authors also show that the meaningfulness dimension, rather than the novelty dimension, of creativity is of greater importance in explaining the link between market or...


title: Moderators of working memory deficits in children with attention-deficit/ hyperactivity disorder (ADHD): A meta-analytic review

abstract: Abstract Working memory has assumed a prominent role as a primary neurocognitive deficit or endophenotype in extant models of attention-deficit/hyperactivity disorder (ADHD). The current study updated previous reviews and employed meta-analytic techniques to examine a broad range of moderating variables of effect size heterogeneity across phonological and visuospatial working memory tasks. Collectively, results revealed large between-group effect sizes across both working memory domains. In addition, several sample (percent female) and task (number of experimental trials, recall vs. recognition tasks, and demands on the central executive) moderating variables explained significant effect size variability among phonological and visuospatial studies. These findings suggest that children with ADHD exhibit statistically significant, large magnitude working memory deficits relative to their typically developing peers.


title: Unsupervised Keyphrase Extraction with Multipartite Graphs

abstract: We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.


title: Text Summarization Extraction System (TSES) Using Extracted Keywords

abstract: A new technique to produce a summary of an original text investigated in this pape r. The system develops many approaches to solve this problem that gave a high quality result. The model consists of four stages. The preprocess stages convert the unstructured text into structured. In first stage, the system removes the stop words, pars th e text and assigning the POS (tag) for each word in the text and store the result in a table. The second stageis to extract the important keyphrases in the text by implementing a new algorithm through rankin g the candidate words. The system uses the extracted keywords/keyphrases to select the important sentence. Each sentence ranked depending on many features such as the existence of the keywords/keyphrase in it, the relation between the sentence and the title by using a similarity measurement and other many features. The Third stage of the proposed system is to extract the sentences with the highest rank. The Forth stage is the filtering stage. This stage reduced the amount of the candidate sentences in the summary in order to produce a qualitative summary using KFIDF measurement.


title: Predicting Outcome in Subarachnoid Hemorrhage (SAH) Utilizing the Full Outline of UnResponsiveness (FOUR) Score

abstract: Background ::: Existing scoring systems for aneurysmal subarachnoid hemorrhage (SAH) patients fail to accurately predict patient outcome. Our goal was to prospectively study the Full Outline of UnResponsiveness (FOUR) score as applied to newly admitted aneurysmal SAH patients.


title: Tissue culture and plant regeneration from sunflower (Helianthus annuus) and interspecific hybrids (H. tuberosus x H. annuus)

abstract: A method is described for the culture and regeneration of plants from callus of sunflower (Helianthus annuus) andH. annuus x H. tuberosus hybrids. Immature embryos proved to be the only explant which consistently gave regenerable cultures in all genotypes. The most responsive embryos were approximately 12 mm2 in area. Genotype had a significant effect on the capacity of cultures to regenerate. Some regeneration was also obtained from cultures of tuber tissue but only from one genotype,H. tuberosus x H. annuus cross 200. None of theH. annuus accessions gave regenerable callus from root tissue. Difficulties included the premature initiation of flowering of regenerating shoots and the frequent occurence of "vitreous" plantlets which could not be transplanted successfully to soil. Some amelioration of both these problems was achieved by replacing inorganic nitrogen partially with amino acids. More effective reduction of these difficulties was accomplished by the addition of 10, 30 and 100 μM phloridzin, esculin or naringin.


title: Design of low sidelobe slotted waveguide monopulse antenna array

abstract: In this paper, a low sidelobe slotted waveguide monopulse antenna array working at Ka-band is proposed. The design procedure of radiating element parameter extraction and the synthesis of planar array is introduced. A 28 × 28 circular slotted waveguide antenna array is presented. The simulated bandwidth (return loss less than -20 dB) of antenna array is 1.48% and the corresponding isolations between the sum and difference ports are better than 45 dB. The sum beam standard gain difference is about 1.69 dB while the sidelobe levels are -29.64 dB in E-plane and -28.86 dB in H-plane. The gain differences between the sum channel and difference channels are around 3.6 dB and the null depths are below -50 dB in both planes.


To appear in Phenomenology and the Cognitive Sciences, 2007 Participatory Sense-Making An Enactive Approach to Social Cognition


Applications of a 3-D microphone array


title: Abnormal Event Detection at 150 FPS in MATLAB

abstract: Speedy abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on inherent redundancy of video structures, we propose an efficient sparse combination learning framework. It achieves decent performance in the detection phase without compromising result quality. The short running time is guaranteed because the new method effectively turns the original complicated problem to one in which only a few costless small-scale least square optimization steps are involved. Our method reaches high detection rates on benchmark datasets at a speed of 140-150 frames per second on average when computing on an ordinary desktop PC using MATLAB.


title: An improved fully parallel 3D thinning algorithm

abstract: A 3D thinning algorithm erodes a 3D image layer by layer to extract the skeletons. This paper presents an improved fully parallel 3D thinning algorithm which extracts medial lines from a 3D image. This algorithm is based on Ma and Sonka’s thinning algorithm [16], which fails to preserve connectivity of 3D objects. We start with Ma and Sonka’s algorithm and examine its verification of connectivity preservation. Our analysis leads to a group of different deleting templates, which can preserve connectivity of 3D


title: Motion boundary based sampling and 3D co-occurrence descriptors for action recognition

abstract: Abstract Recent studies witness the success of Bag-of-Features (BoF) frameworks for video based human action recognition. The detection and description of local interest regions are two fundamental problems in BoF framework. In this paper, we propose a motion boundary based sampling strategy and spatial-temporal (3D) co-occurrence descriptors for action video representation and recognition. Our sampling strategy is partly inspired by the recent success of dense trajectory (DT) based features [Wang et al., 2013] for action recognition. Compared with DT, we densely sample spatial-temporal cuboids along a motion boundary which can greatly reduce the number of valid trajectories and preserve the discriminative power. Moreover, we develop a set of 3D co-occurrence descriptors which take account of the spatial-temporal context within local cuboids and deliver rich information for recognition. Furthermore, we decompose each 3D co-occurrence descriptor at pixel level and bin level and integrate the decomposed components with a multi-channel framework, which can improve the performance significantly. To evaluate the proposed methods, we conduct extensive experiments on three benchmarks including KTH, YouTube and HMDB51. The results show that our sampling strategy significantly reduces the computational cost of point tracking without degrading performance. Meanwhile, we achieve superior performance than the state-of-the-art methods. We report 95.6% on KTH, 87.6% on YouTube and 51.8% on HMDB51.


title: Summary-Based Context-Sensitive Data-Dependence Analysis in Presence of Callbacks

abstract: Building a summary for library code is a common approach to speeding up the analysis of client code. In presence of callbacks, some reachability relationships between library nodes cannot be obtained during library-code summarization. Thus, the library code may have to be analyzed again during the analysis of the client code with the library summary. In this paper, we propose to summarize library code with tree-adjoining-language (TAL) reachability. Compared with the summary built with context-free-language (CFL) reachability, the summary built with TAL reachability further contains conditional reachability relationships. The conditional reachability relationships can lead to much lighter analysis of the library code during the client code analysis with the TAL-reachability-based library summary. We also performed an experimental comparison of context-sensitive data-dependence analysis with the TAL-reachability-based library summary and context-sensitive data-dependence analysis with the CFL-reachability-based library summary using 15 benchmark subjects. Our experimental results demonstrate that the former has an 8X speed-up over the latter on average.


title: Security Evaluation of the Cyber Networks Under Advanced Persistent Threats

abstract: This paper is devoted to measuring the security of cyber networks under advanced persistent threats (APTs). First, an APT-based cyber attack-defense process is modeled as an individual-level dynamical system. Second, the dynamic model is shown to exhibit the global stability. On this basis, a new security metric of cyber networks, which is known as the limit security, is defined as the limit expected fraction of compromised nodes in the networks. Next, the influence of different factors on the limit security is illuminated through theoretical analysis and computer simulation. This work helps understand the security of cyber networks under APTs.


Beyond the Last Touch: Attribution in Online Advertising


title: A compact 0.135-mW/channel LNA array for piezoelectric ultrasound transducers

abstract: This paper presents a power- and area-efficient 9-channel LNA array for piezoelectric ultrasound transducers to enable real-time 3D imaging with miniature endoscopic and catheter-based probes. In view of the relatively low impedance of piezoelectric transducers, the LNA is implemented as a capacitive feedback voltage amplifier, rather than a trans-impedance amplifier, to achieve a better noise-power trade-off. The use of a current-efficient inverter-based OTA with optimized bias scheme and dual-rail regulation further improves the power efficiency of the LNA while keeping the area compact: 0.01 mm2 per channel. Electrical and acoustic measurement results show that the proposed LNA achieves a 0.6 mPa/√Hz input-referred noise at 4 MHz while consuming only 0.135 mW, which represents a noise efficiency 2.5 × better than the state-of-the-art.


title: Phylogeny of Philippine slender skinks (Scincidae: Brachymeles) reveals underestimated species diversity, complex biogeographical relationships, and cryptic patterns of lineage diversification.

abstract: The spectacular, virtually endemic radiation of Philippine semi-fossorial skinks of the genus Brachymeles represent one of the few radiations of scincid lizards to possess both fully limbed and limbless species. And yet, nothing is known of the phylogenetic relationships of this exceptional group. Morphologically similar body plans have made it difficult to assess species-level diversity, and the genus has long been recognized as one of the more modest radiations of southeast Asian lizards. However, recent large-scale survey efforts have resulted in the discovery of numerous new species, and taxonomic studies indicate that the diversity within the genus Brachymeles is grossly underestimated. In this study we provide the first robust estimate of phylogenetic relationships within the genus Brachymeles using a multi-locus dataset and nearly complete taxonomic sampling. We provide statistical tests of monophyly for all polytypic species and two widespread limb-reduced species and our results indicate wholesale deviations from past summaries and taxonomic evaluations of the genus. With few exceptions, we are able to reject the monophyly of all polytypic and widespread species, thereby validating the need for large-scale taxonomic revisions. Our results reveal that the limbless, monotypic, genus Davewakeum is nested within Brachymeles. Mapping of body form on our preferred phylogenetic tree suggests that limb-reduction and digit loss has occurred on multiple occasions in the history of the genus. A Bayesian reconstruction of ancestral areas indicates strong statistical support for a minimum of five major dispersal events that have given rise to a major component of the observed species diversity on separate Pleistocene aggregate island platforms of the archipelago.


title: Distributed and overlapping representations of faces and objects in ventral temporal cortex

abstract: The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.


title: Real-time user-guided image colorization with learned deep priors

abstract: We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer.


title: A Beacon System for the Localization of Distributed Robotic Teams

abstract: This paper presents the design of a localization system for a team of centimeter-scale robots that collaborate to map and explore unknown environments. The localization system uses ultrasound to measure the distance from each moving robot to three stationary robots that serve as beacons. From these distance measurements the position of the robots is derived using a trilateration algorithm. The robot team can move over large distances by using a leap-frogging approach in which different robots serve as beacons at different times. The localization system is able to obtain position estimates more accurate than can be achieved through dead reckoning, and yet, does not require any landmarks or previously deployed beacons.


title: Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

abstract: Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation


title: The availability and affordability of orphan drugs for rare diseases in China

abstract: Orphan drugs are intended to treat, prevent or diagnose rare diseases. In recent years, China healthcare policy makers and patients have become increasingly concerned about orphan drug issues. However, very few studies have assessed the availability and affordability of orphan drugs for rare diseases in China. The aim of this study was to provide an overview of the availability and affordability of orphan drugs in China and to make suggestions to improve patient access to orphan drugs. Two components of the availability of orphan drugs were examined. Market availability was assessed by the extent to which orphan drugs were marketed in China with a comparison to orphan drugs in international markets, such as the U.S., EU and Japan. We conducted surveys and collected data from 24 tertiary public hospitals in China to measure hospital-level availability of orphan drugs. The affordability of orphan drugs was calculated using hospital dispensary prices and was expressed as days of average daily income required for the cost of a course of treatment. Affordability was also analyzed under the Chinese basic medical insurance system. Orphan drugs approved in the U.S., EU and Japan had 37.8 %, 24.6 % and 52.4 % market availability in China, respectively. Median availability of 31 orphan drugs surveyed at the 24 tertiary public hospitals was 20.8 % (very low). Within a periodic treatment course, the average treatment cost of 23 orphan drugs is approximately 4, 843. 5 USD, which equates to 505.6 days of per capita net income for an urban resident with a middle income (187.4 days for a high-income urban resident) or 1,582.8 days’s income for a rural resident with a middle income (657.2 days for a high-income rural resident). Except for homoharringtonine, 22 orphan drugs for 14 rare diseases were unaffordable for the most of residents in China. With 5 % out-of-pocket expenses, only three generics could be afforded by middle-income residents, whereas seven drugs for high-income urban residents. The Chinese government can take more responsibility for improving the availability and affordability of orphan drugs through setting up incentive policies and public platforms for sharing of orphan drug information. Control of the high price of orphan drugs, combined with a joint funding model from both government and private enterprise can efficiently reduce the economic burden of affected patients in China.


title: Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach

abstract: Social scientists are increasingly using the vast amount of text available on social media to measure variation in happiness and other psychological states. Such studies count words deemed to be indicators of happiness and track how the word frequencies change across locations or time. This word count approach is simple and scalable, yet often picks up false signals, as words can appear in different contexts and take on different meanings. We characterize the types of errors that occur using the word count approach, and find lexical ambiguity to be the most prevalent. We then show that one can reduce error with a simple refinement to such lexica by automatically eliminating highly ambiguous words. The resulting refined lexica improve precision as measured by human judgments of word occurrences in Facebook posts.


Arcing the edge


title: Design and analysis of metastable-hardened flip-flops in sub-threshold region

abstract: Flip-flop metastability is becoming an important consideration for designing reliable synchronous and asynchronous systems, especially in the sub-threshold region where it degrades exponentially with the reduction in supply voltage. In this paper, detailed analysis is given on the design of metastable-hardened flip-flops in the sub-threshold region. Proper transistor sizing using either transconductance or load variation along with implementing the inverter pair in the flip-flop master-stage with low-V th can result in significant reduction in the time-resolving constant τ. Extensive simulation results have shown that the optimum metastability-power-delay-product (MPDP) design allows the flip-flops to improve its metastability with a more balanced design tradeoff between performance and power consumption.


title: A real-time EMG-driven virtual arm

abstract: An EMG-driven virtual arm is being developed in our laboratories for the purposes of studying neuromuscular control of arm movements. The virtual arm incorporates the major muscles spanning the elbow joint and is used to estimate tension developed by individual muscles based on recorded electromyograms (EMGs). It is able to estimate joint moments and the corresponding virtual movements, which are displayed in real-time on a computer screen. In addition, the virtual arm offers artificial control over a variety of physiological and environmental conditions. The virtual arm can be used to examine how the neuromuscular system compensates for the partial or total loss of a muscle's ability to generate force as might result from trauma or pathology. The purpose of this paper is to describe the design objectives, fundamental components and implementation of our real-time, EMG-driven virtual arm.


title: Experience, Results and Lessons Learned from Automated Driving on Germany's Highways

abstract: The BMW Group Research and Technology has been testing automated vehicles on Germany's highways since Spring 2011. Since then, thousands of kilometers have been driven on the highways around Munich, Germany. Throughout this project, fundamental technologies, such as environment perception, localization, driving strategy and vehicle control, were developed in order to safely operate prototype automated vehicles in real traffic with speeds up to 130 km/h. The goal of this project was to learn what technologies are necessary for automated driving. This paper presents the architecture and algorithms developed during this project, results from real driving scenarios, the lessons learned throughout the project and a quick introduction into the latest developments for improving the system.


Finding the K Shortest Loopless Paths in a Network


title: Using very deep autoencoders for content-based image retrieval

abstract: We show how to learn many layers of features on color images and we use these features to initialize deep autoencoders. We then use the autoencoders to map images to short binary codes. Using semantic hashing [6], 28-bit codes can be used to retrieve images that are similar to a query image in a time that is independent of the size of the database. This extremely fast retrieval makes it possible to search using multiple di erent transformations of the query image. 256-bit binary codes allow much more accurate matching and can be used to prune the set of images found using the 28-bit codes.


title: A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations

abstract: The softmax content-based attention mechanism has proven to be very beneficial in many applications of recurrent neural networks. Nevertheless it suffers from two major computational limitations. First, its computations for an attention lookup scale linearly in the size of the attended sequence. Second, it does not encode the sequence into a fixed-size representation but instead requires to memorize all the hidden states. These two limitations restrict the use of the softmax attention mechanism to relatively small-scale applications with short sequences and few lookups per sequence. In this work we introduce a family of linear attention mechanisms designed to overcome the two limitations listed above. We show that removing the softmax non-linearity from the traditional attention formulation yields constant-time attention lookups and fixed-size representations of the attended sequences. These properties make these linear attention mechanisms particularly suitable for large-scale applications with extreme query loads, real-time requirements and memory constraints. Early experiments on a question answering task show that these linear mechanisms yield significantly better accuracy results than no attention, but obviously worse than their softmax alternative.


title: A 40.9 µW high sensitivity wake-up radio for wireless sensor networks using uncertain-IF architecture

abstract: This paper presents an 868 MHz wake-up radio using uncertain-IF architecture for wireless sensor network. It is designed to receive data modulated with on-off-keying in UMC 65nm CMOS technology and it employs uncertain-IF architecture to reduce the oscillator design constraints and to decrease power consumption. The wake-up radio consist of a single-ended active mixer, a five stage IF-amplifier and a differential envelope detector biased in weak inversion. The wake-up radio has been designed with to provide a trade-off between sensitivity and power consumption. The obtained sensitivity and power consumption is −78 dBm and 40.9 µW, respectively, with 1.2 V power supply.


title: Wide-band dual sleeve antenna

abstract: This correspondence presents a top loaded dual sleeve antenna with a substantially small ground plane for broad band applications. The impedance and radiation properties of the monopole were investigated numerically and experimentally. The antenna features excellent radiation characteristics within a broad impedance bandwidth of 4.2:1, covering 0.5-2.1 GHz


title: A Markov Clustering Topic Model for mining behaviour in video

abstract: This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models (e.g. HMMs) and Bayesian topic models (e.g. Latent Dirichlet Allocation), and overcomes their drawbacks on accuracy, robustness and computational efficiency. Specifically, our model profiles complex dynamic scenes by robustly clustering visual events into activities and these activities into global behaviours, and correlates behaviours over time. A collapsed Gibbs sampler is derived for offline learning with unlabeled training data, and significantly, a new approximation to online Bayesian inference is formulated to enable dynamic scene understanding and behaviour mining in new video data online in real-time. The strength of this model is demonstrated by unsupervised learning of dynamic scene models, mining behaviours and detecting salient events in three complex and crowded public scenes.


title: Dynamics and control of a quadrotor with a cable suspended payload

abstract: The dynamics and control of a quadrotor with a cable suspended payload is studied in this paper. The payload whose tether point deviates from the center of mass of the quadrotor is called an off-centered suspended payload. By using the matrix form of Kane's method, the equations of motion are obtained. The system is decomposed into rotational and translational channels. A nonlinear controller is developed to enable stabilization of the quadrotor by using partial linearization of the system. Simulations of a set point regulation are presented to verify the effectiveness of the dynamics modeling control design.


title: Effect of the Polysaccharide Extract from the Edible Mushroom Pleurotus ostreatus against Infectious Bursal Disease Virus

abstract: The polysaccharide-containing extracellular fractions (EFs) of the edible mushroom Pleurotus ostreatus have immunomodulating effects. Being aware of these therapeutic effects of mushroom extracts, we have investigated the synergistic relations between these extracts and BIAVAC and BIAROMVAC vaccines. These vaccines target the stimulation of the immune system in commercial poultry, which are extremely vulnerable in the first days of their lives. By administrating EF with polysaccharides from P. ostreatus to unvaccinated broilers we have noticed slow stimulation of maternal antibodies against infectious bursal disease (IBD) starting from four weeks post hatching. For the broilers vaccinated with BIAVAC and BIAROMVAC vaccines a low to almost complete lack of IBD maternal antibodies has been recorded. By adding 5% and 15% EF in the water intake, as compared to the reaction of the immune system in the previous experiment, the level of IBD antibodies was increased. This has led us to believe that by using this combination of BIAVAC and BIAROMVAC vaccine and EF from P. ostreatus we can obtain good results in stimulating the production of IBD antibodies in the period of the chicken first days of life, which are critical to broilers’ survival. This can be rationalized by the newly proposed reactivity biological activity (ReBiAc) principles by examining the parabolic relationship between EF administration and recorded biological activity.


title: RED-UCATION: A Novel CNN Architecture Based on Denoising Nonlinearities

abstract: Image denoising is the most fundamental image enhancement task, and many algorithms have been proposed over the years for its solution. Interestingly, such an image denoising “engine” can be used to solve general inverse problems. Indeed, in our recent work we have presented the Regularization by Denoising (RED) framework: using a denoising engine in defining the regularization of any inverse problem. We have shown how this scheme leads to well-founded iterative algorithms in which the denoiser is applied in each iteration. In this work we describe how a learned version of RED defines a novel convolutional neural network architecture, where the commonly used point-wise nonlinearities are replaced by a denoising engine. We show how this network can be optimized end- to-end using a back - propagation that relies on guided denoising algorithms. As a case-study, we concentrate on the image deblurring problem and show the superiority of the trainable variant of RED over its analytic form.


title: CPW Fed Wideband Corner Bent Antenna for 5G Mobile Terminals

abstract: A co-planar waveguide fed slot antenna element with 34% impedance bandwidth and 2–3 dBi broadside gain is proposed. The corner bent topology of the element is investigated for reduced physical footprint and easier integration with the mobile terminals operating in the 28-GHz band. The front-to-back ratio of the corner bent antenna is 1 dB in the <inline-formula> <tex-math notation="LaTeX">$xy$ </tex-math></inline-formula> plane, which indicates a high specific absorption rate. A compact wideband reflector is proposed which has transmission of less than −25 dB and a linear phase ±90° in the 27–33-GHz bandwidth. The wideband reflector is integrated with the corner bent antenna at <inline-formula> <tex-math notation="LaTeX">$0.135\lambda $ </tex-math></inline-formula> offset from the radiating aperture. The corner bent antenna integrated with the reflector operates in the 27–33-GHz band, with a front-to-back ratio of more than 14 dB across the band and a forward gain of 6–7.2 dBi. The radiation patterns are stable for 20% bandwidth. The simulated and measured results are presented.


title: Hierarchical performance analysis for process mining

abstract: Process mining techniques use event data from operational and software processes to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. In recent years, the process mining field made huge advances in terms of scalability. In addition, recent work in process discovery supports advanced process model constructs such as subprocesses, recursive structures, cancellation, and various notions of concurrency. Hence, one has to realize that a simple, small, and flat model will not suffice anymore, especially when applied to analyzing software system processes. However, state of the art performance analysis is still typically performed either over the whole process model or at the level of individual activities. There is a lack of formal support for performance analysis on various submodel abstractions while taking into account the execution semantics. This paper presents 1) a framework for establishing precise relationships between events and submodels, taking into account execution semantics; and 2) a novel formalization of existing and novel performance metrics. Our approach enables advanced performance analysis at various submodel abstractions. An implementation is made available, and we demonstrate the advantages of our approach to various software system processes, showing the applicability and advantage with respect to existing techniques.


title: 3D-DRAM Performance for Different OpenMP Scheduling Techniques in Multicore Systems

abstract: Advances in memory technologies including 3DDRAM memories (such as High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) systems), wide I/O memory promise very large bandwidths at lower power consumption to address the needs of high-performance computing as well as emerging big data applications. However, in order to fully benefit from such bandwidths, it is necessary to understand how to optimally organize data across channels, ranks, banks or vaults of the memory structures, how to obtain large volumes of data with fewer accesses and how to schedule threads of multi threaded applications to benefit from these memory organizations. In this paper, we will examine different memory organizations that spread data across channels, ranks, and banks and identify application features that benefit from different organizations. Our study applies to generic DDR memory structures as well as 3DDRAMs. We will also evaluate scheduling of OpenMP threads (e.g., using static, dynamic and guided) but with emphasis on how different scheduling methods benefit from different memory organizations. Using the best scheduling for the application, proper memory organization, our experiments show, we can achieve up to 16 percent performance gains depending on workload.


title: A Linear Algorithm for Edge-Coloring Series–Parallel Multigraphs

abstract: Many combinatorial problems can be efficiently solved for series?parallel multigraphs. However, the edge-coloring problem of finding the minimum number of colors required for edge-coloring given graphs is one of a few well-known combinatorial problems for which no efficient algorithms have been obtained for series?parallel multigraphs. This paper gives a linear algorithm for the problem on series?parallel multigraphs.


title: A Resistorless Low-Power Voltage Reference

abstract: A novel low-power temperature-stable voltage reference without resistors is presented in this brief, which is compatible with standard CMOS technology. In order to reduce the temperature nonlinearity in the proposed voltage reference, threshold voltage and a proportional-to-absolute-temperature voltage form the basic linear-temperature components, which are achieved by resistorless threshold voltage extractor and asymmetric differential difference amplifier. Moreover, a self-biased current source with feedback is used to provide stable bias currents for the whole voltage reference, which can improve the power-supply noise attenuation (PSNA) with reduced current mirror errors. Verification results of the proposed voltage reference implemented with 0.18- $\mu\text{m}$ CMOS technology demonstrate that the temperature coefficient of 14.1 ppm/°C with a temperature range of −20 °C to 80 °C is obtained at 1.35-V power supply, and a PSNA of 75.7 dB is achieved without any filtering capacitor while dissipating a maximum supply current of 880 nA. The active area is $115\ \mu\text{m}\times 130\ \mu\text{m}$ .


title: Measurement of Substrate Oxidation During Exercise by Means of Gas Exchange Measurements

abstract: Measures of substrate oxidation have traditionally been calculated from indirect calorimetry measurements using stoichiometric equations. Although this has proven to be a solid technique and it has become one of the standard techniques to measure whole body substrate metabolism, there are also several limitations that have to be considered. When indirect calorimetry is used during exercise most of the assumptions on which the method is based hold true although changes in the size of the bicarbonate pool at higher exercise intensities may invalidate the calculations of carbohydrate and fat oxidation. Most of the existing equations are based on stoichiometric equations of glucose oxidation and the oxidation of a triacylglycerol that is representative of human adipose tissue. However, in many exercise conditions, glycogen and not glucose is the predominant carbohydrate substrate. Therefore we propose slightly modified equations for the calculation of carbohydrate and fat oxidation for use during low to high intensity exercise. Studies that investigated fat oxidation over a wide range of intensities and that determined the exercise intensity at which fat oxidation is maximal have provided useful insights in the variation in fat oxidation between individuals and in the factors that affect fat oxidation. Fat oxidation during exercise can be influenced by exercise intensity and duration, diet, exercise training, exercise mode and gender. Although a number of important factors regulating fat oxidation have been identified, it is apparent that a considerable degree of inter-subject variability in substrate utilization persists and cannot be explained by the aforementioned factors. Future research should investigate the causes of the large inter-individual differences in fat metabolism between individuals and their links with various disease states.


title: PICO extraction by combining the robustness of machine-learning methods with the rule-based methods

abstract: Machine-learning methods (MLMs) are robust methods in the extraction of the information; they have been also used in the extraction of PICO elements in order to answer clinical questions; MLMs are only used at coarse-grained level in PICO extraction, because of lack of training corpora for PICO at the fine-grained level. Coarse-grained level cannot explore the semantics within the sentence for use as a means of relevance between different answers. We propose a hybrid approach combining the robustness of MLMs and the fine grained level of RBMs to enhance PICO extraction process and facilitate the validity and the pertinence of the answers to clinic questions formulated with the PICO framework.


title: A Visual Model-Based Perceptual Image Hash for Content Authentication

abstract: Perceptual image hash has been widely investigated in an attempt to solve the problems of image content authentication and content-based image retrieval. In this paper, we combine statistical analysis methods and visual perception theory to develop a real perceptual image hash method for content authentication. To achieve real perceptual robustness and perceptual sensitivity, the proposed method uses Watson’s visual model to extract visually sensitive features that play an important role in the process of humans perceiving image content. We then generate robust perceptual hash code by combining image-block-based features and key-point-based features. The proposed method achieves a tradeoff between perceptual robustness to tolerate content-preserving manipulations and a wide range of geometric distortions and perceptual sensitivity to detect malicious tampering. Furthermore, it has the functionality to detect compromised image regions. Compared with state-of-the-art schemes, the proposed method obtains a better comprehensive performance in content-based image tampering detection and localization.


title: Developing game AI agent behaving like human by mixing reinforcement learning and supervised learning

abstract: Artificial intelligence (AI) agent created with Deep Q-Networks (DQN) can defeat human agents in video games. Despite its high performance, DQN often exhibits odd behaviors, which could be immersion-breaking against the purpose of creating game AI. Moreover, DQN is capable of reacting to the game environment much faster than humans, making itself invincible (thus not fun to play with) in certain types of games. On the other hand, supervised learning framework trains an AI agent using historical play data of human agents as training data. Supervised learning agent exhibits a more human-like behavior than reinforcement learning agents because of imitating training data. However, its performance is often no better than human agents. The ultimate purpose of AI agents is to entertain human players. A good performance and a humanlike behavior are important factors of the AI agents, and both of them should be achieved simultaneously. This study proposes frameworks combining reinforcement learning and supervised learning and we call then separated network model and shared network model. We evaluated their performances by the game scores and behaviors by Turing test. The experimental results demonstrate that the proposed frameworks develop an AI agent of better performance than human agent and natural behavior than reinforcement learning agents.


title: Design of consequent pole, toroidal winding, outer rotor vernier permanent magnet machines

abstract: Vernier permanent magnet (VPM) machines are attracting more and more attentions for several advantages, such as high torque density, ultra low pulsation torque and so on. This paper proposes a consequent-pole, toroidal-winding, outer-rotor VPM machine, which owns much less magnet usage and shorter end-winding length than that of the regular VPM machine. It is also proved that this machine exhibits ~20% higher back-EMF and electromagnetic torque than that of regular VPM machines while its magnet usage is halved. In this paper, the influence of design parameters, such as pole arc, slot opening, magnet thickness etc., on the electromagnetic performance is analyzed based on the finite element analysis (FEA), and experimentally validated by a prototype machine.


title: Machine discovery of effective admissible heuristics

abstract: Admissible heuristics are an important class of heuristics worth discovering: they guarantee shortest path solutions in search algorithms such as A and they guarantee less expensively produced, but boundedly longer solutions in search algorithms such as dynamic weighting. Unfortunately, effective (accurate and cheap to compute) admissible heuristics can take years for people to discover. Several researchers have suggested that abstractions of a problem can be used to generate admissible heuristics. This paper describes and evaluates an implemented program (Absolver II) that uses a means-ends analysis search control strategy to discover abstracted problems that result in effective admissible heuristics. Absolver II discovered several well-known and novel admissible heuristics, including the first known effective one for Rubik's Cube, thus concretely demonstrating that effective admissible heuristics can be tractably discovered by a machine.


title: Smart Reply: Automated Response Suggestion for Email

abstract: In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggestions that can be used as complete email responses with just one tap on mobile. The system is currently used in Inbox by Gmail and is responsible for assisting with 10% of all mobile responses. It is designed to work at very high throughput and process hundreds of millions of messages daily. The system exploits state-of-the-art, large-scale deep learning. We describe the architecture of the system as well as the challenges that we faced while building it, like response diversity and scalability. We also introduce a new method for semantic clustering of user-generated content that requires only a modest amount of explicitly labeled data.


title: Quad Band Digitally Controlled Oscillator for WCDMA Transmitter in 90nm CMOS

abstract: We present the first published implementation and measurements of a fully integrated phase path for a 3G polar transmitter in deep sub micron CMOS. It includes a single quad band digitally controlled oscillator (DCO) providing modulation capability to handle the wide bandwidth of the WCDMA phase (frequency) data and a switched inverter divider. The complete chip, with integrated LDOs, consumes 20mA from a 1.4V supply while providing a PN of -157dBc/Hz at a 40MHz offset for a 2GHz output.


title: A theory for multiresolution signal decomposition: the wavelet representation

abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >


title: Survey of Probabilistic Graphical Models

abstract: Probabilistic graphical model (PGM) is a generic model that represents the probability-based relationships among random variables by a graph, and is a general method for knowledge representation and inference involving uncertainty. In recent years, PGM provides an important means for solving the uncertainty of intelligent information field, and becomes research focus in the fields of machine learning and artificial intelligence etc. In the paper, PGM and its three types of basic models are reviewed, including the learning and inference theory, research status, application and promotion.


title: Perceived Stress and Coffee and Energy Drink Consumption Predict Poor Sleep Quality in Podiatric Medical Students A Cross-sectional Study.

abstract: Background: A cross-sectional survey administered to first- and second-year podiatric medical students aimed to investigate the effect of coffee intake, energy drink consumption, and perceived stress on sleep quality in medical students during their preclinical studies. Methods: Ninety-eight of 183 students contacted (53.6%) completed a questionnaire comprising standard instruments measuring sleep quality (Pittsburgh Sleep Quality Index), daytime sleepiness (Epworth Sleepiness scale), and perceived stress (ten-item Perceived Stress Scale). Furthermore, we investigated coffee and energy drink consumption. Logistic regression was conducted to identify factors associated with poor sleep quality and the relation between sleep quality and academic performance (grade point average). Results: High prevalences of poor sleep quality, excessive daytime sleepiness, and perceived stress were reported. In addition, higher odds of developing poor sleep quality were associated with coffee and energy drink intake, percei...


title: Adaptive Planner Scheduling with Graph Neural Networks

abstract: Automated planning is one of the foundational areas of AI. Since a single planner unlikely works well for all tasks and domains, portfolio-based techniques become increasingly popular recently. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. ::: Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.


title: Trending topic prediction on social network

abstract: The fast information sharing on social network services generates more than thousands of topics every day. It is extremely important for business organizations and administrative decision makers to learn the popularity of these topics as quickly as possible. In this paper, we propose a prediction mode based on SVM with features of three subsets: quantity specific features, quality and user specific features which supplement each other. Furthermore, we divide topic data into time slices which is used as a unit of feature construction. Our findings suggest that the capability of our prediction model outperforms previous methods and also reveals that subsets of features play different role in the prediction of trending topics.


title: SemFacet: semantic faceted search over yago

abstract: In this paper we demonstrate a system SemFacet, that is a proof of concept prototype for our semantic faceted search approach. SemFacet is implemented on top of the Yago knowledge base, powered by the OWL 2 RL triple store RDFox, and the full text search engine Lucene. SemFacet has provided very encouraging results. Via logical reasoning SemFacet can automatically (i) extract facets, (ii) update the faceted query interface with facets relevant for the current stage of the users query construction's session. SemFacet supports faceted queries that are much more expressive than the ones of traditional faceted search applications; in particular SemFacet allows to (i) relate several collections of documents, and (ii) change the focus of queries (and, thus, SemFacet provides control over the documents in the query output to be displayed on the screen). Our approach is fully declarative: the same backend implementation can be used to power faceted search over any application, provided that metadata and knowledge are represented in RDF and OWL 2.


title: Social Network Extraction of Academic Researchers

abstract: This paper addresses the issue of extraction of an academic researcher social network. By researcher social network extraction, we are aimed at finding, extracting, and fusing the 'semantic '-based profiling information of a researcher from the Web. Previously, social network extraction was often undertaken separately in an ad-hoc fashion. This paper first gives a formalization of the entire problem. Specifically, it identifies the 'relevant documents' from the Web by a classifier. It then proposes a unified approach to perform the researcher profiling using conditional random fields (CRF). It integrates publications from the existing bibliography datasets. In the integration, it proposes a constraints-based probabilistic model to name disambiguation. Experimental results on an online system show that the unified approach to researcher profiling significantly outperforms the baseline methods of using rule learning or classification. Experimental results also indicate that our method to name disambiguation performs better than the baseline method using unsupervised learning. The methods have been applied to expert finding. Experiments show that the accuracy of expert finding can be significantly improved by using the proposed methods.


title: Characterization of doped hydrogenated nanocrystalline silicon films prepared by plasma enhanced chemical vapour deposition

abstract: The B- and P-doped hydrogenated nanocrystalline silicon films (nc-Si:H) are prepared by plasma-enhanced chemical vapour deposition (PECVD). The microstructures of doped nc-Si:H films are carefully and systematically characterized by using high resolution electron microscopy (HREM), Raman scattering, x-ray diffraction (XRD), Auger electron spectroscopy (AES), and resonant nucleus reaction (RNR). The results show that as the doping concentration of PH3 increases, the average grain size (d) tends to decrease and the crystalline volume percentage (Xc) increases simultaneously. For the B-doped samples, as the doping concentration of B2H6 increases, no obvious change in the value of d is observed, but the value of Xc is found to decrease. This is especially apparent in the case of heavy B2H6 doped samples, where the films change from nanocrystalline to amorphous.


title: Job performance and job satisfaction: an integrated survey

abstract: The empirical evidence from the econometrics of self-reported job satisfaction and from organisational psychology on job performance confronts economic theory with some puzzling results. Job performance is found to be positively correlated with job satisfaction, whereas effort is assumed to be a disutility in the theory. Economic incentives are not found to be the main motivations of job performance; in some cases, indeed, they are even counterproductive. Interest in the job is found to account better for job satisfaction. This paper proposes an integrated approach to these issues by (i) conducting an interdisciplinary critical survey, (ii) proposing a simple economic framework within which to explain the puzzles. The key idea behind this framework is that intrinsic motivations and self-esteem help explain both job satisfaction and job performance. The employer can thus adopt other, more friendly actions, besides using incentives and controls to enhance performance by employees.


title: Impact of Path Characteristics and Scheduling Policies on MPTCP Performance

abstract: With increasing deployment of Multipath TCP (MPTCP) in multihoming and data enter scenarios, there is a need to understand how its performance is affected in practice-both by traditional factors such as RTT measurements, and by new multipath-specific considerations such as sub flow selection. We carried out an initial but comprehensive study using an actual MPTCP implementation in an emulated network environment, to explore the impact of different factors on MPTCP throughput. We find that path selection and packet scheduling have a large effect on performance, and that merely trusting the congestion control mechanism to do the right thing is not enough. Moreover, we provide evidence that throughput can be improved by slight modifications to the send buffers and path selection components of the implementation. Important challenges in network design remain, if only to ensure that multiple suitable paths exist in a network.


title: Computer power and human reason

abstract: I had some strong reactions to Joe Weizenbaum's book, Computer Power and Human Reason. The book mentions some important concerns which are obscured by harsh and sometimes shrill accusations against the Artificial Intelligence research community. On the whole, it seems to me that the personal attacks distract and mislead the reader from more valuable abstract points. I strongly recommend Samuel Florman's article "In Praise of Technology" in the November, 1975, issue of Harper's Magazine to see a different opinion about the role of technology in modern society.


title: Improving transparency of powered exoskeletons using force/torque sensors on the supporting cuffs

abstract: Most of the control strategies embedded in recent robotic exoskeletons for rehabilitation and assistance are specific implementations of the well-known “assistance as needed” paradigm. A key point in the design of these systems is the requirement for the robot to exert negligible interaction forces to the wearer if he/she is performing well. Optimizing transparency of a device is a challenging task: various strategies have been proposed to achieve this goal, involving both the mechanical structure of the robot and the control algorithms. In this work, we propose a simple yet effective approach that requires minimal redesign efforts in the robotic structure and in the controller to be implemented on existing devices. We experimentally validate the method by comparing kinematic, kinetic and electromyo-graphic data collected from 3 healthy subjects as they walked in three different conditions: free treadmill walking, walking in a robotic trainer with a traditional zero-impedance configuration and walking in the same robot with the new zero-impedance configuration. Results show that the novel configuration was capable of effectively reducing the interaction forces and, as a consequence, it affected subjects' natural gait less than the traditional one did.


title: Ordering adverbs by their scaling effect on adjective intensity

abstract: In recent years, theoretical and computational linguistics has paid much attention to linguistic items that form scales. In NLP, much research has focused on ordering adjectives by intensity (tiny < small). Here, we address the task of automatically ordering English adverbs by their intensifying or diminishing effect on adjectives (e.g. extremely small < very small). We experiment with 4 different methods: 1) using the association strength between adverbs and adjectives; 2) exploiting scalar patterns (such as not only X but Y); 3) using the metadata of product reviews; 4) clustering. The method that performs best is based on the use of metadata and ranks adverbs by their scaling factor relative to unmodified adjectives.


title: Optimize MinMax algorithm to solve Blokus Duo game by HDL

abstract: In this paper, a solution for Blokus Duo game is presented using minmax algorithm. Then, Alpha-beta pruning method is implemented on the algorithm to reduce playing time in the manner that its running speed increases significantly. Moreover, Pentobi® software as a criterion is used as a competitor and the final results for that are reported. All codes are directly written on a hardware basis using VHDL language. After being synthesized by Quartus II®, the result is implemented on DE1-SOC board which uses cyclone V FPGA.


title: Microcontroller based control system for electric vehicle

abstract: This paper presents microcontroller based electric vehicle control system, especially for rickshaw. Switching pulses are generated by microcontroller for an inverter which drives the brushless DC motor in electric rickshaw. Pulses for controlling both motoring and regenerative braking actions are generated following the operating principles of brushless DC motor in these two modes. The proposed control system is very flexible and the results show that, the control system will be very effective if it is implemented in hardware.


title: Stochastic gradient descent on Riemannian manifolds

abstract: Stochastic gradient descent is a simple approach to find the local minima of a cost function whose evaluations are corrupted by noise. In this paper, we develop a procedure extending stochastic gradient descent algorithms to the case where the function is defined on a Riemannian manifold. We prove that, as in the Euclidian case, the gradient descent algorithm converges to a critical point of the cost function. The algorithm has numerous potential applications, and is illustrated here by four examples. In particular a novel gossip algorithm on the set of covariance matrices is derived and tested numerically.


title: Active compounds release from semisolid dosage forms.

abstract: The aim of this paper is to review all the aspects of the in vitro release testing (IVRT) from semisolid dosage forms. Although none of the official dissolution methods has been specified for use with semisolid dosage forms, their utility for assessing release rates of drugs from semisolid dosage forms has become a topic of considerable interest. One can expect to overcome such complexity in the future, when the official "Topical and Transdermal Drug Products-Product Performance Tests" will be published in an issue of the Pharmacopeial Forum. Many factors such as type of the dissolution medium, membrane, temperature, and speed have an influence on the mechanism and kinetics of the release testing from gels, creams, and ointments; therefore, those parameters have been widely discussed.


title: Comparison of SQL, NoSQL and NewSQL databases for internet of things

abstract: The Internet of Things (IoT) in all its essentiality is a collection of sensors bridged together tightly. The present day development in the technology industry has thus elevated the emphasis of large amounts of data. IoT is expected to generate and collect an enormous amount of data from varied locations very quickly. The concerns for features like storage and performance factors are coercing the databases to outperform themselves. Traditional relational databases have time and again proved to be efficient. NoSQL and more recently NewSQL databases having gained an impetus are asserted to perform better than the classic SQL counterpart. This paper compares the performance of three databases each from the said technologies; SQL (MySQL), NoSQL (MongoDB) and NewSQL (VoltDB) respectively for sensor readings. The sensor data handled ranges in vast (MB to GB) size and tested against: single write, single read, single delete and multi write operations.


title: Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

abstract: Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.


title: Fuzzy Sets as a Tool for Modeling.

abstract: A introduction to the basic concepts of fuzzy set theory is first provided. We next discuss some ideas from the theory of of approximate reasoning. As we shall see it is this theory, which uses fuzzy sets as its primary representational structure, that provides a formal mechanism for reasoning with uncertain information. Finally we discuss the technology of fuzzy systems modeling. This technology has provided the bases for most of the current generation of applications of fuzzy set theory.


title: AndroPIn: Correlating Android permissions and intents for malware detection

abstract: Smartphone» have become a prime target for cyber criminals. Android being the market leader is the major target of malicious attackers. Covert techniques used by the malware make them hard to detect with signature based methods. In this paper, we present AndroPIn — a novel Android based malware detection tool using Permissions and Intents. The proposed framework overcomes the limitation of stealthy techniques used by malware by exploiting the usage pattern of permissions and intents. These features, which play a major role in sharing user data and device resources cannot be obfuscated or altered. These vital features are well suited for resource constrained smartphones. Experimental evaluation on a corpus of real-world malware and benign apps demonstrate that the proposed algorithm can effectively detect malicious apps with a low runtime overheads and is resilient to common obfuscations methods.


title: Master data management and its organizational implementation: An ethnographical study within the public sector

abstract: Purpose ::: ::: ::: ::: ::: Master data management (MDM) aims to improve the value of an organization’s most important data, such as customer data, by bridging the silos between organizational units and information systems. However, incorporating data management practices into an organization is not a simple task. The purpose of this paper is to provide a new understanding of the challenges in establishing and developing the MDM function within an organization. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This papers report an ethnographic study within a municipality. The data were collected from two consecutive MDM development projects over the time period of 32 months by observing MDM-related activities and interviewing appropriate actors. Observations, interviews, and impressions were documented to a diary that was later qualitatively analyzed. Various project documentation were also used. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: In total 15 challenges were identified. Seven of these were not identified earlier in the literature. New challenges included legislation-driven challenges, mutual understanding of master data domains, and the level of granularity for those domains. Eight issues, such as data owner and data definitions, were MDM specific, others being more generic. All of the issues were identified as preconditions or as affecting factors for the others. Three of the issues were identified as pivotal. The issues emphasize strong alignment between the complex concept of MDM and the organization adopting it. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: This research was based on a single qualitative case study, and caution should be exercised with regard to generalizations. The findings increase understanding about the complex organizational phenomena. The study offers public sector and private sector practitioners insights of the organizational issues that establishing a MDM function can encounter. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The issues discovered in the research shed light on the strong alignment between the complex concept of MDM and the organization. The results of this study assist researchers in their endeavor to understand the organizational aspects of MDM, and to build theoretical models, frameworks, practices, and explanations.


title: Benign and malignant mammographic image classification based on Convolutional Neural Networks

abstract: Computerized breast cancer diagnosis system has played an import role in early cancer diagnosis. For this purpose, we apply deep learning by using convolutional neural networks (CNN) to classify abnormalities, benign or malignant, in mammographic images based on the mini Mammographic Image Analysis Society (mini-MIAS) database. Accuracy, sensitivity, and specific values are observed to evaluate the performance of the CNN. To improve the performance, we utilize image-preprocessing methods containing cropping, global contrast normalization, augmentation, local histogram equalization, and balancing preprocessing. We built four CNN models to study the impact of depth and hidden layer structure on model performance. The CNN-4d model performs best among four proposed CNN models consisting of four convolution layers with a dropout of 0.7. The CNN-4d model achieved a balance of high sensitivity (90.63%) and high specificity (87.67%), and an accuracy of 89.05%. The result of this study indicates that CNNs have promising potential in the field of intelligent medical image diagnosis.


The Neural Correlates of Humor Creativity


Presence-Related Influences of a Small Talking Facial Image on Psychophysiological Measures of Emotion and Attention


title: Big Data Initiatives by Governments -- Issues and Challenges: A Review

abstract: In this modern era, we are living in the age of big data. All overthe world recently, the organizations including the governmentis taking initiative to adapt and implement big data. Nowadays, government do not want to only collect data, but they want tounderstand the meaning and the significance of the data, aswell as to analyze the data and aid them in making strategicdecisions. This paper is aimed to review on the definition of bigdata and e-government specifically, also reviews the initiativesof big data on government in several countries, includinghighlights on issues and challenges peculiar to the big data initiatives.


title: On Markov Chain Monte Carlo Methods For Nonlinear And Non-Gaussian State-Space Models

abstract: In this paper, a nonlinear and/or non‐Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and transition equations are specified in any general formulation and the error terms in the state‐space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number generation, the Metropolis‐Hastings algorithm and the Gibbs sampling technique are utilized. The proposed procedure is very simple and easy for programming, compared with the existing nonlinear and non‐Gaussian smoothing techniques. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed estimator.


title: A 10-35-GHz six-channel microstrip multiplexer for wide-band communication systems

abstract: A six-channel multiplexer composed of parallel-coupled microstrip bandpass filters is developed. The passbands of the filters, i.e., channel frequencies, are 10, 12, 19, 21, 32, and 35 GHz. The operating frequency range of the multiplexer is so wide that a new method of harmonic suppression is devised for the filters designed at 10 and 12 GHz. The method is simple and does not require any change to the conventional parallel-coupled microstrip bandpass filter. A design procedure for the microstrip multiplexer is then introduced with a rule to set up initial conditions for better optimization. The designed multiplexer does not need any tuning elements, and the measurement results show insertion losses between 2.0-3.2 dB for all six channels. The multiplexer developed in this paper has been used in the wide-band transceiver system.


State-of-the-Art of Visualization for Eye Tracking Data


title: A cascaded convolutional neural network for age estimation of unconstrained faces

abstract: We propose a coarse-to-fine approach for estimating the apparent age from unconstrained face images using deep convolutional neural networks (DCNNs). The proposed method consists of three modules. The first one is a DCNN-based age group classifier which classifies a given face image into age groups. The second module is a collection of DCNN-based regressors which compute the fine-grained age estimate corresponding in each age class. Finally, any erroneous age prediction is corrected using an error-correcting mechanism. Experimental evaluations on three publicly available datasets for age estimation show that the proposed approach is able to reliably estimate the age; in addition, the coarse-to-fine strategy and the error correction module significantly improve the performance.


title: Noise in the nervous system

abstract: Noise — random disturbances of signals — poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.


title: Modelling and Simulation of photovoltaic module considering single-diode equivalent circuit model in MATLAB

abstract: This paper presents a photovoltaic (PV) cell to module simulation model using the single-diode five parameter models. The model was implemented in MATLAB software and the results have been compared with the data sheet values and characteristics of the PV module in Standard Test Conditions (STC). Parameters values were extracted using Newton Raphson's method from experimental Current (I)-Voltage (V) characteristics of Solarex MSX60 module. The results obtained are in good agreement with the experimental data provided by manufacturer. The approach can thus, be very useful for researchers or engineers to quickly and easily determine the performance of any photovoltaic module. Among these methods, in view of its quadratic convergence and enhanced accuracy, Newton Raphson's method remains attractive with the number of variables being limited to five parameters and their partial derivatives easily obtainable. In this work, we elaborate a MATLAB script file program, which uses Newton Raphson's method to compute the five parameters of the single diode model of illuminated solar cells. The results obtained by simulation show the consistency between the data and obtained the parameters given by the manufacturer, namely: short circuit current (ISC), open circuit voltage (VOC) and maximum power point (Pmpp). For instance, these parameters can be used for quality control during production or to provide insights into the operation of the devices, thereby leading to improvements in devices.


title: Uncertainties in quantitative time‐lapse seismic analysis

abstract: Most seismic time-lapse studies so far have been of a qualitative nature. Identification of areas with minor or no seismic changes has been used to plan new infill drilling targets. Increased accuracy in seismic acquisition methods, in both conventional streamer surveys and newer methods such as multicomponent sea-bed seismic and permanent sensors, opens possibilities for the next step: quantitative time-lapse analysis. Quantitative methods here mean the estimation of, for instance, a change in fluid saturation from 20% water to 90% water or the estimation of a pore pressure change of 5 MPa. Explicit expressions for the uncertainties associated with estimated changes in, for instance, reservoir pressure and fluid saturation are derived. These formulae can be used to compare relative uncertainties between estimated parameters as well as to identify the critical factors in various estimation techniques. The importance of accurate rock physics input, as well as that of highly repeatable time-lapse seismic data, is emphasized. Furthermore, uncertainty analysis can be used to find optimal weight factors when the same parameter (e.g. saturation change) is estimated by two or three different techniques.


title: The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model

abstract: Internet-based learning systems are being used in many universities and firms but their adoption requires a solid understanding of the user acceptance processes. Our effort used an extended version of the technology acceptance model (TAM), including cognitive absorption, in a formal empirical study to explain the acceptance of such systems. It was intended to provide insight for improving the assessment of on-line learning systems and for enhancing the underlying system itself. The work involved the examination of the proposed model variables for Internet-based learning systems acceptance. Using an on-line learning system as the target technology, assessment of the psychometric properties of the scales proved acceptable and confirmatory factor analysis supported the proposed model structure. A partial-least-squares structural modeling approach was used to evaluate the explanatory power and causal links of the model. Overall, the results provided support for the model as explaining acceptance of an on-line learning system and for cognitive absorption as a variable that influences TAM variables.


The multitasking generation.


title: Variability in measures of health and health behavior among emerging adults 1 year after high school according to college status.

abstract: OBJECTIVE ::: To examine changes in health behaviors among US emerging adults 1 year after high school. ::: ::: ::: PARTICIPANTS ::: The national sample of participants (N = 1,927), including those attending 4-year college/university (n = 884), 2-year colleges/technical schools (n = 588), and no college (n = 455), participated in annual spring surveys 2013-2014. ::: ::: ::: METHODS ::: Health behaviors were assessed the last year of high school and first year of college; differences by college status controlling for previous-year values were estimated using regression analyses. ::: ::: ::: RESULTS ::: Relative to 4-year college attendees, those attending technical school/community college were less likely to binge drink (odds ratio [OR] = 0.57, confidence interval [CI] = 0.38-0.86) but more likely to speed (OR = 1.26, CI = 1.0-2.84), consume sodas (OR = 1.57, CI = 1.0-2.47), and report lower family satisfaction (p < .01), with marginally more physical and depressive symptoms. College nonattendees reported more DWI (driving while intoxicated; OR = 1.60, CI = 1.05-2.47), soda drinking (OR = 2.51, CI = 1.76-3.59), oversleeping (OR = 4.78, CI = 3.65-8.63), and less family satisfaction (p < .04). ::: ::: ::: CONCLUSIONS ::: Health risk behaviors among emerging adults varied by college status.


title: The role of microbiome in central nervous system disorders

abstract: Abstract Mammals live in a co-evolutionary association with the plethora of microorganisms that reside at a variety of tissue microenvironments. The microbiome represents the collective genomes of these co-existing microorganisms, which is shaped by host factors such as genetics and nutrients but in turn is able to influence host biology in health and disease. Niche-specific microbiome, prominently the gut microbiome, has the capacity to effect both local and distal sites within the host. The gut microbiome has played a crucial role in the bidirectional gut–brain axis that integrates the gut and central nervous system (CNS) activities, and thus the concept of microbiome–gut–brain axis is emerging. Studies are revealing how diverse forms of neuro-immune and neuro-psychiatric disorders are correlated with or modulated by variations of microbiome, microbiota-derived products and exogenous antibiotics and probiotics. The microbiome poises the peripheral immune homeostasis and predisposes host susceptibility to CNS autoimmune diseases such as multiple sclerosis. Neural, endocrine and metabolic mechanisms are also critical mediators of the microbiome–CNS signaling, which are more involved in neuro-psychiatric disorders such as autism, depression, anxiety, stress. Research on the role of microbiome in CNS disorders deepens our academic knowledge about host-microbiome commensalism in central regulation and in practicality, holds conceivable promise for developing novel prognostic and therapeutic avenues for CNS disorders.


title: Dubowitz Syndrome: A Review and Implications for Cognitive, Behavioral, and Psychological Features

abstract: UNLABELLED ::: Dubowitz syndrome is a rare autosomal recessive disorder characterized by micorcephaly, short stature, abnormal faces, and mild to severe mental retardation. Growth retardation occurs both intrauterine and postnatal. Behavioral characteristics include hyperactivity, short attention span, and aggressiveness. Behavior problems include difficulty feeding, sleep disturbance, and bedwetting. Individuals with the disorder have displayed shyness, fear of crowds, and dislike of loud noises. A high-pitched or hoarse voice is common. Deficits have been found in speech and language skills, reasoning and memory skills, self-help skills, and psychomotor functioning. Ocular, dental, cutaneous, skeletal, cardiovascular, gastrointestinal, neurological, immunological, and hematological medical difficulties have been noted. Approximately 148 cases have been described in the literature. The cause of the disorder remains unknown, however, research suggests genetic origin. Past research emphasizes physical characteristics and medical complications. There is a lack of cognitive, behavioral, and psychological information available regarding the disorder. This article presents a review of the literature and provides assessment and treatment implications for the cognitive, behavioral, and psychological aspects of Dubowitz syndrome. ::: ::: ::: KEYWORDS ::: Dubowitz; Syndrome; Autosomal; Recessive.


title: Ultra-Compact Low-Loss Integrated Transformer-Based Ku-Band Quadrature Hybrid Coupler

abstract: In this paper, an ultra-compact transformer-based integrated quadrature hybrid coupler is presented. Its design, taking advantage of the transformer distributed parasitic capacitances to minimize lumped capacitors, achieves low insertion loss and high compactness. For demonstration purposes, a prototype, operating at Ku-band and based on the 130 nm BiCMOS technology from STMicroelectronics, has been fabricated. Theoretical, simulated and experimental results are reported. The demonstrated integrated coupler occupies an area as small as 0.0014 mm2. In the 17.3 to 20.2 GHz frequency range (15.5% relative bandwidth) used for SATCOM applications, an insertion loss and a phase imbalance of better than 0.4 dB and 2° are experimentally obtained, respectively, with an amplitude imbalance of less than 1 dB.