title: Uncertainty-Aware Reinforcement Learning for Collision Avoidance

abstract: Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot. In this paper, we consider the specific case of a mobile robot learning to navigate an a priori unknown environment while avoiding collisions. In order to learn collision avoidance, the robot must experience collisions at training time. However, high-speed collisions, even at training time, could damage the robot. A successful learning method must therefore proceed cautiously, experiencing only low-speed collisions until it gains confidence. To this end, we present an uncertainty-aware model-based learning algorithm that estimates the probability of collision together with a statistical estimate of uncertainty. By formulating an uncertainty-dependent cost function, we show that the algorithm naturally chooses to proceed cautiously in unfamiliar environments, and increases the velocity of the robot in settings where it has high confidence. Our predictive model is based on bootstrapped neural networks using dropout, allowing it to process raw sensory inputs from high-bandwidth sensors such as cameras. Our experimental evaluation demonstrates that our method effectively minimizes dangerous collisions at training time in an obstacle avoidance task for a simulated and real-world quadrotor, and a real-world RC car. Videos of the experiments can be found at https://sites.google.com/site/probcoll.


title: Automated Identification of Diabetic Retinopathy Using Deep Learning

abstract: Purpose Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses. Design We developed and evaluated a data-driven deep learning algorithm as a novel diagnostic tool for automated DR detection. The algorithm processed color fundus images and classified them as healthy (no retinopathy) or having DR, identifying relevant cases for medical referral. Methods A total of 75 137 publicly available fundus images from diabetic patients were used to train and test an artificial intelligence model to differentiate healthy fundi from those with DR. A panel of retinal specialists determined the ground truth for our data set before experimentation. We also tested our model using the public MESSIDOR 2 and E-Ophtha databases for external validation. Information learned in our automated method was visualized readily through an automatically generated abnormality heatmap, highlighting subregions within each input fundus image for further clinical review. Main Outcome Measures We used area under the receiver operating characteristic curve (AUC) as a metric to measure the precision–recall trade-off of our algorithm, reporting associated sensitivity and specificity metrics on the receiver operating characteristic curve. Results Our model achieved a 0.97 AUC with a 94% and 98% sensitivity and specificity, respectively, on 5-fold cross-validation using our local data set. Testing against the independent MESSIDOR 2 and E-Ophtha databases achieved a 0.94 and 0.95 AUC score, respectively. Conclusions A fully data-driven artificial intelligence–based grading algorithm can be used to screen fundus photographs obtained from diabetic patients and to identify, with high reliability, which cases should be referred to an ophthalmologist for further evaluation and treatment. The implementation of such an algorithm on a global basis could reduce drastically the rate of vision loss attributed to DR.


title: BEADS: Automated Attack Discovery in OpenFlow-Based SDN Systems

abstract: We create BEADS, a framework to automatically generate test scenarios and find attacks in SDN systems. The scenarios capture attacks caused by malicious switches that do not obey the OpenFlow protocol and malicious hosts that do not obey the ARP protocol. We generated and tested almost 19,000 scenarios that consist of sending malformed messages or not properly delivering them, and found 831 unique bugs across four well-known SDN controllers: Ryu, POX, Floodlight, and ONOS. We classify these bugs into 28 categories based on their impact; 10 of these categories are new, not previously reported. We demonstrate how an attacker can leverage several of these bugs by manually creating 4 representative attacks that impact high-level network goals such as availability and network topology.


title: IoT-based continuous glucose monitoring system: A feasibility study

abstract: Abstract: Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device.


title: *Metaphor and the Dynamics of Knowledge in Organization Theory: A Case Study of the Organizational Identity Metaphor

abstract: Despite the increased salience of metaphor in organization theory, there is still very little conceptual machinery for capturing and explaining how metaphor creates and/or reorders knowledge within organization theory. Moreover, prior work on metaphor has insufficiently accounted for the context of interpreting a metaphor. Many metaphors in organization theory, including the 'organizational identity' metaphor, have often been treated in singular and monolithic terms; seen to offer a similar or largely synonymous interpretation to theorists and researchers working along the entire spectrum of disciplines (e.g. organizational behaviour, organizational psychology) in organization theory. We argue in this paper that contextual variation however exists in the interpretation of metaphors in organization theory. This argument is developed by proposing and elaborating on a so-called image-schematic model of metaphor, which suggests that the image-schemata (abstract imaginative structures) that are triggered by the metaphorical comparison of concepts may vary among individuals. Accordingly, once different schemata are triggered the completion and interpretation of a metaphor may equally vary among different individuals or, indeed, research communities. These points associated with the image-schematic model of metaphor are illustrated with a case study of the 'organizational identity' metaphor. The case study shows that this particular metaphor has spiralled out into different research communities and has been comprehended in very different ways as different communities work from very different conceptions, or image-schemata, of 'organization' and 'identity', and use different theoretical frameworks and constructs as a result. The implications of the image-schematic view of metaphor for knowledge development and theoretical progress in organization theory are discussed. Copyright Blackwell Publishing Ltd 2006.


title: Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission

abstract: Dengue, chikungunya, and Zika virus epidemics transmitted by Aedes aegypti mosquitoes have recently (re)emerged and spread throughout the Americas, Southeast Asia, the Pacific Islands, and elsewhere. Understanding how environmental conditions affect epidemic dynamics is critical for predicting and responding to the geographic and seasonal spread of disease. Specifically, we lack a mechanistic understanding of how seasonal variation in temperature affects epidemic magnitude and duration. Here, we develop a dynamic disease transmission model for dengue virus and Aedes aegypti mosquitoes that integrates mechanistic, empirically parameterized, and independently validated mosquito and virus trait thermal responses under seasonally varying temperatures. We examine the influence of seasonal temperature mean, variation, and temperature at the start of the epidemic on disease dynamics. We find that at both constant and seasonally varying temperatures, warmer temperatures at the start of epidemics promote more rapid epidemics due to faster burnout of the susceptible population. By contrast, intermediate temperatures (24-25°C) at epidemic onset produced the largest epidemics in both constant and seasonally varying temperature regimes. When seasonal temperature variation was low, 25-35°C annual average temperatures produced the largest epidemics, but this range shifted to cooler temperatures as seasonal temperature variation increased (analogous to previous results for diurnal temperature variation). Tropical and sub-tropical cities such as Rio de Janeiro, Fortaleza, and Salvador, Brazil; Cali, Cartagena, and Barranquilla, Colombia; Delhi, India; Guangzhou, China; and Manila, Philippines have mean annual temperatures and seasonal temperature ranges that produced the largest epidemics. However, more temperate cities like Shanghai, China had high epidemic suitability because large seasonal variation offset moderate annual average temperatures. By accounting for seasonal variation in temperature, the model provides a baseline for mechanistically understanding environmental suitability for virus transmission by Aedes aegypti. Overlaying the impact of human activities and socioeconomic factors onto this mechanistic temperature-dependent framework is critical for understanding likelihood and magnitude of outbreaks.


title: DeepTLR: A single deep convolutional network for detection and classification of traffic lights

abstract: Reliable real-time detection of traffic lights is a major concern for the task of autonomous driving. As deep convolutional networks have proven to be a powerful tool in visual object detection, we propose DeepTLR, a camera-based system for real-time detection and classification of traffic lights. Detection and state classification are realized using a single deep convolutional network. DeepTLR does not use any prior knowledge about traffic light locations. Also the detection is executed frame by frame without using temporal information. It is able to detect traffic lights on the whole camera image without any presegmentation. This is achieved by classifying each fine-grained pixel region of the input image and performing a bounding box regression on regions of each class. We show that our algorithm is able to run on frame-rates required for real-time applications while reaching notable results.


title: Better I/O through byte-addressable, persistent memory

abstract: Modern computer systems have been built around the assumption that persistent storage is accessed via a slow, block-based interface. However, new byte-addressable, persistent memory technologies such as phase change memory (PCM) offer fast, fine-grained access to persistent storage. In this paper, we present a file system and a hardware architecture that are designed around the properties of persistent, byteaddressable memory. Our file system, BPFS, uses a new technique called short-circuit shadow paging to provide atomic, fine-grained updates to persistent storage. As a result, BPFS provides strong reliability guarantees and offers better performance than traditional file systems, even when both are run on top of byte-addressable, persistent memory. Our hardware architecture enforces atomicity and ordering guarantees required by BPFS while still providing the performance benefits of the L1 and L2 caches. Since these memory technologies are not yet widely available, we evaluate BPFS on DRAM against NTFS on both a RAM disk and a traditional disk. Then, we use microarchitectural simulations to estimate the performance of BPFS on PCM. Despite providing strong safety and consistency guarantees, BPFS on DRAM is typically twice as fast as NTFS on a RAM disk and 4-10 times faster than NTFS on disk. We also show that BPFS on PCM should be significantly faster than a traditional disk-based file system.


title: How to Make a Digital Currency on a Blockchain Stable

abstract: Abstract Bitcoin and other similar digital currencies on blockchains are not ideal means for payment, because their prices tend to go up in the long term (thus people are incentivized to hoard those currencies), and to fluctuate widely in the short term (thus people would want to avoid risks of losing values). The reason why those blockchain currencies based on proof of work are unstable may be found in their designs that the supplies of currencies do not respond to their positive and negative demand shocks, as the authors have formulated in our past work. Continuing from our past work, this paper proposes minimal changes to the design of blockchain currencies so that their market prices are automatically stabilized, absorbing both positive and negative demand shocks of the currencies by autonomously controlling their supplies. Those changes are: 1) limiting re-adjustment of proof-of-work targets, 2) making mining rewards variable according to the observed over-threshold changes of block intervals, and 3) enforcing negative interests to remove old coins in circulation. We have made basic design checks and evaluations of these measures through simple simulations. In addition to stabilization of prices, the proposed measures may have effects of making those currencies preferred means for payment by disincentivizing hoarding, and improving sustainability of the currency systems by making rewards to miners perpetual.


Approximation by Fully Complex Multilayer Perceptrons


title: Design and simulation of a Comb-line fed microstrip antenna array with low side lobe level at 77GHz for automotive collision avoidance radar

abstract: This article presents design and simulation of Comb-line fed microstrip antenna array with low side lobe level at 77GHz which will be used in automotive collision avoidance radar. At the first, design of single antenna element will be presented. Then we propose the design of a row of microstrip antenna array with 32 elements and at final a to two-dimensional antenna array with 32 rows will be presented. The goal of design of this antenna is to acquire an appropriate radiation pattern with low side lobe level better than −21dB.


title: A co-word analysis of library and information science in China

abstract: This study aims to reveal the intellectual structure of Library and Information Science (LIS) in China during the period 2008---2012 utilizing co-word analysis. The status and trends of LIS in China are achieved by measuring the correlation coefficient of selected keywords extracted from relevant journals in the Chinese Journal Full-Text Database. In co-word analysis, multivariate statistical analysis and social network analysis are applied to obtain 13 clusters of keywords, a two-dimensional map, centrality and density of clusters, a strategic diagram and a relation network. Based on these results, the following conclusions can be drawn: (i) LIS in China has some established and well-developed research topics; (ii) a few emerging topics have a great potential for development; and (iii), the research topics in this LIS field are largely decentralized as a whole, where there are many marginal and immature topics.


title: Depth map coding with distortion estimation of rendered view

abstract: New data formats that include both video and the corresponding depth maps, such as multiview plus depth ::: (MVD), enable new video applications in which intermediate video views (virtual views) can be generated using ::: the transmitted/stored video views (reference views) and the corresponding depth maps as inputs. We propose a ::: depth map coding method based on a new distortion measurement by deriving relationships between distortions ::: in coded depth map and rendered view. In our experiments we use a codec based on H.264/AVC tools, where the ::: rate-distortion (RD) optimization for depth encoding makes use of the new distortion metric. Our experimental ::: results show the efficiency of the proposed method, with coding gains of up to 1.6 dB in interpolated frame ::: quality as compared to encoding the depth maps using the same coding tools but applying RD optimization ::: based on conventional distortion metrics.


Sound Analogies with Phoneme Embeddings


Mental Stress Assessment of ECG Signal Using Statistical Analysis of Bio-orthogonal Wavelet Coefficients : Part-2


title: Financial Time Series Forecasting Using Support Vector Machine

abstract: The traditional financial time series forecasting methods use accurate input data for prediction, and then make single-step or multi-step prediction based on the established regression model. So its prediction result is one or more specific values. But because of the complexity of financial markets, the traditional forecasting methods are less reliable. In this paper, we transform the financial time series into fuzzy grain particle sequences, and use support vector machine regression to regress the upper and lower bounds of the fuzzy particles, and then apply regression model single-step prediction on the upper and lower bounds, which will limit the predict results within a range. This is a new idea. The Shanghai Composite Index Week closed index for the experimental data, experimental results show the effectiveness of this approach.


Contextual and Structural Language Understanding in Dialogues


title: Molecular substrates of action control in cortico-striatal circuits

abstract: Abstract The purpose of this review is to describe the molecular mechanisms in the striatum that mediate reward-based learning and action control during instrumental conditioning. Experiments assessing the neural bases of instrumental conditioning have uncovered functional circuits in the striatum, including dorsal and ventral striatal sub-regions, involved in action–outcome learning, stimulus–response learning, and the motivational control of action by reward-associated cues. Integration of dopamine (DA) and glutamate neurotransmission within these striatal sub-regions is hypothesized to enable learning and action control through its role in shaping synaptic plasticity and cellular excitability. The extracellular signal regulated kinase (ERK) appears to be particularly important for reward-based learning and action control due to its sensitivity to combined DA and glutamate receptor activation and its involvement in a range of cellular functions. ERK activation in striatal neurons is proposed to have a dual role in both the learning and performance factors that contribute to instrumental conditioning through its regulation of plasticity-related transcription factors and its modulation of intrinsic cellular excitability. Furthermore, perturbation of ERK activation by drugs of abuse may give rise to behavioral disorders such as addiction.


title: Cyberbullying and its correlation to traditional bullying, gender and frequent and risky usage of internet mediated communication tools

abstract: This study examined the relationships between cyber and traditional bullying experiences regarding gender differences. Also, the contributions of frequent and risky usage of internet to cyberbullying experiences were examined. The participants were 276 adolescents (123 females, 151 males and 2 unknown) ranging in age from 14 to 18 years. The results revealed that 32 percent of the students were victims of both cyber and traditional bullying, while 26 percent of the students bullied others in both cyber and physical environments. Compared to female students, male students were more likely to be bullies and victims in both physical and cyber-environments.The multivariate statistical analysis indicated that cyber and traditional bullying were related for male students but not for female students. Moreover, the multiple regression analysis revealed that both frequent and risky usage of internet account for a significant variance of cyberbullying but their contributions differ based on genders.


title: Tversky loss function for image segmentation using 3D fully convolutional deep networks

abstract: Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks.


title: A Knowledge-Based Approach to Intrusion Detection Modeling

abstract: Current state of the art intrusion detection and prevention systems (IDPS) are signature-based systems that detect threats and vulnerabilities by cross-referencing the threat or vulnerability signatures in their databases. These systems are incapable of taking advantage of heterogeneous data sources for analysis of system activities for threat detection. This work presents a situation-aware intrusion detection model that integrates these heterogeneous data sources and build a semantically rich knowledge-base to detect cyber threats/vulnerabilities.


title: Smart Grid Privacy via Anonymization of Smart Metering Data

abstract: The security and privacy of future smart grid and smart metering networks is important to their rollout and eventual acceptance by the public: research in this area is ongoing and smart meter users will need to be reassured that their data is secure. This paper describes a method for securely anonymizing frequent (for example, every few minutes) electrical metering data sent by a smart meter. Although such frequent metering data may be required by a utility or electrical energy distribution network for operational reasons, this data may not necessarily need to be attributable to a specific smart meter or consumer. It does, however, need to be securely attributable to a specific location (e.g. a group of houses or apartments) within the electricity distribution network. The method described in this paper provides a 3rd party escrow mechanism for authenticated anonymous meter readings which are difficult to associate with a particular smart meter or customer. This method does not preclude the provision of attributable metering data that is required for other purposes such as billing, account management or marketing research purposes.


title: Infrastructuring and the formation of publics in participatory design

abstract: Of late, there has been a renewed and reinvigorated exchange of ideas across science and technology studies and participatory design, emerging from a shared interest in ‘publics’. In this article, we explore the role of participatory design in constituting publics, drawing together recent scholarship in both science and technology studies and participatory design. To frame our discussion, we present two case studies of community-based participatory design as empirical examples. From these examples and the literature, we discuss the ways in which the concepts of infrastructuring and attachments are central to the constitution of publics. Finally, through an analysis of our case studies, we consider the differences between the practices of enabling participation and infrastructuring, calling attention to the ways that constituting publics foregrounds an engagement with authority structures and unknown futures through the participatory design process.


title: An Effective Path Load Balancing Mechanism Based on SDN

abstract: Path load balancing is used for distributing workload across an array of paths to increase network reliability and optimize link utilization. However, it is not easy to realize the load balancing globally in traditional networks as the whole status of the network is difficult to obtain. To address this problem, we propose the Fuzzy Synthetic Evaluation Mechanism (FSEM), a path load balancing solution based on Software Defined Networking (SDN). In this mechanism, the network traffic is allocated to the paths operated by Open Flow switches, where the flow-handling rules are installed by the central SDN controller. The paths can be dynamically adjusted with the aid of FSEM according to the global view of the network. Experimental results verify that the proposed solution can effectively balance the traffic and avoid unexpected breakdown caused by link failure. The overall network performance is also improved as well.


title: The impact of business analytics on supply chain performance

abstract: The paper investigates the relationship between analytical capabilities in the plan, source, make and deliver area of the supply chain and its performance using information system support and business process orientation as moderators. Structural equation modeling employs a sample of 310 companies from different industries from the USA, Europe, Canada, Brazil and China. The findings suggest the existence of a statistically significant relationship between analytical capabilities and performance. The moderation effect of information systems support is considerably stronger than the effect of business process orientation. The results provide a better understanding of the areas where the impact of business analytics may be the strongest.


title: Scratch: A Way to Logo and Python

abstract: There is concern amongst teachers about how to support all pupils in making the transition from popular graphical languages like Scratch to text-based languages like Python. In a new subject, not taught widely before at both primary and secondary education in England, there is inevitably a lack of tuned-in pedagogical expertise. In this paper, the authors address the transition process by exploring established pedagogy in Computer Science, and other subjects including Mathematics, Science and Languages, and by sharing and testing their findings with pupils and teachers in the classroom. Teaching the fundamentals of programming is well served by applying sequential solutions in both graphical and text-based languages. This practitioner action research paper focuses on scaffolding support for pupils when making the transition from graphical to text-based languages. In an approach which uses graphical languages in conjunction with, not in place of, text-based programming languages, the authors discuss ways to tackle the difficulties presented to pupils by text-based languages, and propose a tested strategy for teachers to enable pupils to undertake the transition successfully.


title: A Systematic Underpinning and Framing of the Servicescape: Reflections on Future Challenges in Healthcare Services

abstract: Ever since Bitner defined the term “servicescape” as the physical environment in which the service is assembled, several scholars have attempted to better understand the impact of the built environment in the context of different service settings. While servicescape is a topic of increasing academic interest among scholars and practitioners, most studies in the area are dedicated to understanding the built environment of hedonic service. More studies are needed to examine utilitarian servicescape and in this paper, we have focused on the healthcare environment. This study aims to identify the gap in servicescape and healthscape studies by providing a theoretical structure of the current servicescape literature and comprehend the academic differences between hedonic servicescape and utilitarian healthscape studies. After reviewing 44 selected papers based on rigorous criteria, we: (1) framed the servicescape factors; (2) analyzed the servicescape literature from the perspectives of terminologies, research fields, methodologies, and frameworks; and (3) identified the current paths of healthscape research. Through this work, we highlight the significance of adopting different dimensions and factors to evaluate the distinguished service environment by the servicescape type and propose several research agendas for future studies on healthscapes. The research findings can contribute to a deep understanding of healthscapes and can introduce a new viewpoint for interpreting the servicescape in diversified service settings.


title: A Reactive Planner for a Model-based Executive

abstract: A new generation of reactive, model-based executives are emerging that make extensive use of componentbased declarative models to analyze anomalous situations and generate novel sequences for the internal control of complex autonomous systems. Burton, a generative, model-based planner offers a core element that bridges the gap between current and target states within the reactive loop. Burton is a sound, complete, reactive planner that generates a single control action of a valid plan in average case constant time, and compensates for anomalies at every step. Burton will not generate irreversible, potentially damaging sequences, except to effect repairs. We present model compilation, causal analysis, and online policy construction methods that are key to Burton's performance.


title: Multi-pedestrian detection in crowded scenes: A global view

abstract: Recent state-of-the-art algorithms have achieved good performance on normal pedestrian detection tasks. However, pedestrian detection in crowded scenes is still challenging due to the significant appearance variation caused by heavy occlusions and complex spatial interactions. In this paper we propose a unified probabilistic framework to globally describe multiple pedestrians in crowded scenes in terms of appearance and spatial interaction. We utilize a mixture model, where every pedestrian is assumed in a special subclass and described by the sub-model. Scores of pedestrian parts are used to represent appearance and quadratic kernel is used to represent relative spatial interaction. For efficient inference, multi-pedestrian detection is modeled as a MAP problem and we utilize greedy algorithm to get an approximation. For discriminative parameter learning, we formulate it as a learning to rank problem, and propose Latent Rank SVM for learning from weakly labeled data. Experiments on various databases validate the effectiveness of the proposed approach.


title: Dual-Band Omnidirectional Circularly Polarized Antenna

abstract: A dual-band omnidirectional circularly polarized antenna is proposed. The antenna comprises back-to-back microstrip patches fed by a coplanar waveguide. A very low frequency ratio of 1.182 has been achieved, which can be easily tuned by adjusting four lumped capacitors incorporated into the antenna. An analysis of the omnidirectional circular polarization mechanism as well the dual band operation is provided and confirmed by numerical and experimental data. Key parameters to tune the resonant frequencies and the axial ratio have been identified. The prototype antenna provides omnidirectional circular polarization in one plane with cross polar isolation better than 12 dB for both frequency bands.


title: Y chromosome STR haplotypes and the genetic structure of U.S. populations of African, European, and Hispanic ancestry.

abstract: To investigate geographic structure within U.S. ethnic populations, we analyzed 1705 haplotypes on the basis of 9 short tandem repeat (STR) loci on the Y-chromosome from 9-11 groups each of African-Americans, European-Americans, and Hispanics. There were no significant differences in the distribution of Y-STR haplotypes among African-American groups, whereas European-American and Hispanic groups did exhibit significant geographic heterogeneity. However, the significant heterogeneity resulted from one sample; removal of that sample in each case eliminated the significant heterogeneity. Multidimensional scaling analysis of R(ST) values indicated that African-American groups formed a distinct cluster, whereas there was some intermingling of European-American and Hispanic groups. MtDNA data exist for many of these same groups; estimates of the European-American genetic contribution to the African-American gene pool were 27.5%-33.6% for the Y-STR haplotypes and 9%-15.4% for the mtDNA types. The lack of significant geographic heterogeneity among Y-STR and mtDNA haplotypes in U.S ethnic groups means that forensic DNA databases do not need to be constructed for separate geographic regions of the U.S. Moreover, absence of significant geographic heterogeneity for these two loci means that regional variation in disease susceptibility within ethnic groups is more likely to reflect cultural/environmental factors, rather than any underlying genetic heterogeneity.


title: Clinical detection and characterization of bacterial pathogens in the genomics era

abstract: The availability of genome sequences obtained using next-generation sequencing (NGS) has revolutionized the field of infectious diseases. Indeed, more than 38,000 bacterial and 5,000 viral genomes have been sequenced to date, including representatives of all significant human pathogens. These tremendous amounts of data have not only enabled advances in fundamental biology, helping to understand the pathogenesis of microorganisms and their genomic evolution, but have also had implications for clinical microbiology. Here, we first review the current achievements of genomics in the development of improved diagnostic tools, including those that are now available in the clinic, such as the design of PCR assays for the detection of microbial pathogens, virulence factors or antibiotic-resistance determinants, or the design of optimized culture media for ‘unculturable’ pathogens. We then review the applications of genomics to the investigation of outbreaks, either through the design of genotyping assays or the direct sequencing of the causative strains. Finally, we discuss how genomics might change clinical microbiology in the future.


title: Real-Time Scalable Depth Sensing With Hybrid Structured Light Illumination

abstract: Time multiplexing (TM) and spatial neighborhood (SN) are two mainstream structured light techniques widely used for depth sensing. The former is well known for its high accuracy and the latter for its low delay. In this paper, we explore a new paradigm of scalable depth sensing to integrate the advantages of both the TM and SN methods. Our contribution is twofold. First, we design a set of hybrid structured light patterns composed of phase-shifted fringe and pseudo-random speckle. Under the illumination of the hybrid patterns, depth can be decently reconstructed either from a few consecutive frames with the TM principle for static scenes or from a single frame with the SN principle for dynamic scenes. Second, we propose a scene-adaptive depth sensing framework based on which a global or region-wise optimal depth map can be generated through motion detection. To validate the proposed scalable paradigm, we develop a real-time (20 fps) depth sensing system. Experimental results demonstrate that our method achieves an efficient balance between accuracy and speed during depth sensing that has rarely been exploited before.


Development of Rope Climbing Robot with Caterpillar


title: Design and Comparison of Digital Filters for Removal of Baseline Wandering from ECG Signal

abstract: Heart diseases, which are one of the death reasons, are among the several serious problems in this century and as per the latest survey, 60% of the patients die due to Heart problems. These diseases can be diagnosed by ECG (Electrocardiogram) signals. ECG measures electrical potentials on the body surface via contact electrodes thus it is very important signal in cardiology. Different artifacts affect the ECG signals which can thus cause problems in analyzing the ECG Thus signal processing schemes are applied to remove those interferences. The work proposed in this paper is removal of low frequency interference i.e. baseline wandering in ECG signal and digital filters are designed to remove it. The digital filters designed are FIR with different windowing methods as of Rectangular, Gaussian, Hamming, and Kaiser. The results obtained are at a low order of 56. The signals are taken from the MIT-BIH database which includes the normal and abnormal waveforms. The work has been done in MAT LAB environment where filters are designed in FDA Tool. The parameters are selected such that the noise is removed permanently. Also the best results are obtained at an order of 56 which makes hardware implementation easier. The result obtained for all FIR filters with different windows are compared by comparing the waveforms and power spectrums of the original and filtered ECG signals. The filters which gives the best results is the one using Kaiser Window.


title: Cortical substrates for exploratory decisions in humans

abstract: Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.


title: Conducted EMI noise mitigation in DC-DC converters using active filtering method

abstract: Electromagnetic interference (EMI) noise mitigation is an important issue that should be addressed and emphasized when designing DC/DC converters. These later, are known to be the primary culprit of the EMI noise generation in most of the electronic systems, mainly due to the switching action of the MOSFET circuitries. Passive input EMI LC filters have been the intuitive solution for EMI noise mitigation; hence they have been integrated in almost every DC/DC converters. However, their size, weight and cost can cause a significant constraint in some applications. To overcome these constraints, an input active EMI filter is proposed. The active filter is based on the noise current phase shift and the injection of this noise current back to the DC input bus. However, the combination of the input active and the passive filters shows a substantial attenuation of the conducted emissions as compared to the passive filter only, which in turn contributes to the reduction of the size and weight of the input passive EMI filter. The proposed combination provides a design solution for compliance engineers where the PCB real-estate is an issue. Experimental results to demonstrate the performance and the effectiveness of the input active EMI filter in DC/DC converters are presented.


title: Reliability and Practicality of the Core Score: Four Dynamic Core Stability Tests Performed in a Physician Office Setting

abstract: OBJECTIVE ::: Pilot study to determine the practicality and inter-rater reliability of the "Core Score," a composite measure of 4 clinical core stability tests. ::: ::: ::: DESIGN ::: Repeated measures. ::: ::: ::: SETTING ::: Academic hospital physician clinic. ::: ::: ::: PARTICIPANTS ::: 23 healthy volunteers with mean age of 32 years (12 females, 11 males). ::: ::: ::: ASSESSMENT OF VARIABLES ::: All subjects performed 4 core stability maneuvers under direct observation from 3 independent physicians in sequence. ::: ::: ::: MAIN OUTCOME MEASURES ::: Inter-rater reliability and time necessary to perform examination. ::: ::: ::: RESULTS ::: The Core Score scale is 0 to 12, with 12 reflecting the best core stability. The mean composite score of all 4 tests for all subjects was 9.54 (SD, 1.897; range, 4-12). The intraclass correlation coefficients (ICC 1,1) for inter-rater reliability for the composite Core Score and 4 individual tests were 0.68 (Core Score), 0.14 (single-leg squat), 0.40 (supine bridge), 0.69 (side bridge), and 0.46 (prone bridge). The time required for a single examiner to assess a given subject's core stability in all 4 maneuvers averaged 4 minutes (range, 2-6 minutes). ::: ::: ::: CONCLUSIONS ::: Even without specialized equipment, a clinically practical and moderately reliable measure of core stability may be possible. Further research is necessary to optimize this measure for clinical application. ::: ::: ::: CLINICAL RELEVANCE ::: Despite the known value of core stability to athletes and patients with low back pain, there is currently no reliable and practical means for rating core stability in a typical office-based practice. This pilot study provides a starting point for future reliability research on clinical core stability assessments.


title: Towards Enhancing the Security of OAuth Implementations in Smart Phones

abstract: With the roaring growth and wide adoption of smart mobile devices, users are continuously integrating with culture of the mobile applications (apps). These apps are not only gaining access to information on the smartphone but they are also able gain users' authorization to access remote servers on their behalf. The Open standard for Authorization (OAuth) is widely used in mobile apps for gaining access to user's resources on remote service providers. In this paper, we analyze the different OAuth implementations adopted by the SDKs of the popular resource providers on smartphones and demonstrate possible attacks on most OAuth implementations. By analyzing source code of more than 430 popular Android apps we summarized the trends followed by the service providers and by the OAuth development choices made by application developers. In addition, we propose an application-based OAuth Manager framework, that provides a secure OAuth flow in smartphones that is based on the concept of privilege separation and does not require high overhead.


Challenges in developing opinion mining tools for social media


title: AutoStyle: automatic style transfer from image collections to users' images

abstract: Stylizing photos, to give them an antique or artistic look, has become popular in recent years. The available stylization filters, however, are usually created manually by artists, resulting in a narrow set of choices. Moreover, it can be difficult for the user to select a desired filter, since the filters' names often do not convey their functions. We investigate an approach to photo filtering in which the user provides one or more keywords, and the desired style is defined by the set of images returned by searching the web for those keywords. Our method clusters the returned images, allows the user to select a cluster, then stylizes the user's photos by transferring vignetting, color, and local contrast from that cluster. This approach vastly expands the range of available styles, and gives each filter a meaningful name by default. We demonstrate that our method is able to robustly transfer a wide range of styles from image collections to users' photos.


title: BLACR: TTP-Free Blacklistable Anonymous Credentials with Reputation

abstract: Anonymous authentication can give users the license to misbehave since there is no fear of retribution. As a deterrent, or means to revocation, various schemes for accountable anonymity feature some kind of (possibly distributed) trusted third party (TTP) with the power to identify or link misbehaving users. Recently, schemes such as BLAC and PEREA showed how anonymous revocation can be achieved without such TTPs—anonymous users can be revoked if they misbehave, and yet nobody can identify or link such users cryptographically. Despite being the state of the art in anonymous revocation, these schemes allow only a basic form of revocation amounting to ‘revoke anybody with d or more misbehaviors’ or ‘revoke anybody whose combined misbehavior score is too high’ (where misbehaviors are assigned a ‘severity’ score). We present BLACR, which significantly advances anonymous revocation in three ways: 1) It constitutes a first attempt to generalize reputation-based anonymous revocation, where negative or positive scores can be assigned to anonymous sessions across multiple categories. Servers can block users based on policies, which specify a boolean combination of reputations in these categories; 2) We present a weighted extension, which allows the total severity score to ramp up for multiple misbehaviors by the same user; and, 3) We make a significant improvement in authentication times through a technique we call express lane authentication, which makes reputation-based anonymous revocation practical.


Natural Language Supported Relation Matching for Question Answering with Knowledge Graphs


title: Dermal permeation of 2-hydroxypropyl acrylate, a model water-miscible compound: effects of concentration, thermodynamic activity and skin hydration.

abstract: UNLABELLED ::: The goal of these studies was to measure and interpret the skin permeability characteristics of 2-hydroxypropyl acrylate (HPA) as a model compound that is completely miscible with water. ::: ::: ::: METHODS ::: In vitro permeation from HPA-H2O binary mixtures through human epidermis and silicone membranes was measured. Thermodynamic activities of HPA and H2O in these mixtures were determined. Permeation was also measured through epidermis and silicone from donor solutions with constant HPA activity but different H2O activities. Water uptake into desiccated human stratum corneum (SC) equilibrated with HPA-H2O mixtures was determined. ::: ::: ::: RESULTS ::: Steady-state flux of HPA through silicone was a linear function of HPA activity but not HPA concentration. For epidermis on the other hand, flux increased with HPA activity only for HPA activities ≤ 0.35. At constant HPA activity, flux decreased 4.5-fold as water activity decreased from 1 to 0.8. Incubation of SC with HPA-H2O mixtures resulted in substantial changes in SC water content, dependent on the water activity of the mixture and consistent with measured SC water sorption data. ::: ::: ::: CONCLUSIONS ::: These experiments provide unequivocal evidence of a substantial increase in epidermal barrier function resulting from SC dehydration. Dehydration-related alterations in the SC appear responsible for the observed flux characteristics.


title: Self-adaptive systems: A survey of current approaches, research challenges and applications

abstract: Abstract Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.


title: Verbal fluency tests: Normative data for the Latin American Spanish speaking adult population

abstract: To generate normative data for the Verbal Fluency Tests across 11 countries in Latin America, with country-specific adjustments for gender, age, and education, where appropriate.The sample consisted of 3,977 healthy adults who were recruited from Argentina, Bolivia, Chile, Cuba, El Salvador, Guatemala, Honduras, Mexico, Paraguay, Peru, and, Puerto Rico. Each subject was administered the Verbal Fluency Test as part of a larger neuropsychological battery. A standardized five-step statistical procedure was used to generate the norms.The final multiple linear regression models for the letter F explained 8-30% of the variance, 7-32% for letter A, 8-32% for the letter S, and 16-43% for the animal category in Verbal Fluency Test scores. Although t-tests showed significant differences between men and women on the Verbal Fluency Test, they did not have an effect size larger than 0.3. As a result, gender-adjusted norms were not generated.This is the first normative multicenter study conducted in Latin America aiming to create norms for the Verbal Fluency Test; this study will have important outcomes for the future of neuropsychology in the region.


title: k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR Angiography

abstract: Time-resolved angiography with interleaved stochastic trajectories (TWIST) has been widely used for dynamic contrast enhanced MRI (DCE-MRI). To achieve highly accelerated acquisitions, TWIST combines the periphery of the k-space data from several adjacent frames to reconstruct one temporal frame. However, this view-sharing scheme limits the true temporal resolution of TWIST. Moreover, the k-space sampling patterns have been specially designed for a specific generalized autocalibrating partial parallel acquisition (GRAPPA) factor so that it is not possible to reduce the number of view-sharing once the k-data is acquired. To address these issues, this paper proposes a novel k-space deep learning approach for parallel MRI. In particular, we have designed our neural network so that accurate k-space interpolations are performed simultaneously for multiple coils by exploiting the redundancies along the coils and images. Reconstruction results using in vivo TWIST data set confirm that the proposed method can immediately generate high-quality reconstruction results with various choices of view- sharing, allowing us to exploit the trade-off between spatial and temporal resolution in time-resolved MR angiography.


title: Towards String-to-Tree Neural Machine Translation

abstract: We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees. An experiment on the WMT16 German-English news translation task resulted in an improved BLEU score when compared to a syntax-agnostic NMT baseline trained on the same dataset. An analysis of the translations from the syntax-aware system shows that it performs more reordering during translation in comparison to the baseline. A small-scale human evaluation also showed an advantage to the syntax-aware system.


title: Review A User-Centric View of Intelligent Environments: User Expectations, User Experience and User Role in Building Intelligent Environments

abstract: Our everyday environments are gradually becoming intelligent, facilitated both by technological development and user activities. Although large-scale intelligent environments are still rare in actual everyday use, they have been studied for quite a long time, and several user studies have been carried out. In this paper, we present a user-centric view of intelligent environments based on published research results and our own experiences from user studies with concepts and prototypes. We analyze user acceptance and users’ expectations that affect users’ willingness to start using intelligent environments and to continue using them. We discuss user experience of interacting with intelligent environments where physical and virtual elements are intertwined. Finally, we touch on the role of users in shaping their own intelligent environments instead of just using ready-made environments. People are not merely “using” the intelligent environments but they live in them, and they experience the environments via embedded services and new interaction tools as well as the physical and social environment. Intelligent environments should provide emotional as well as instrumental value to the people who live in them, and the environments should be trustworthy and controllable both by regular users and occasional visitors. Understanding user expectations and user experience in intelligent environments, and providing users with tools to influence the environments can help to shape the vision of intelligent environments into meaningful, acceptable and appealing service entities for all those who live and act in them.


title: Sparse Arrays and Sampling for Interference Mitigation and DOA Estimation in GNSS

abstract: This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and computational cost materializes when selecting a subset of array antennas without sacrificing jammer nulling or localization capabilities. In addition to the spatial array sparsity, antijam can utilize sparsity of jammers in the spatio–temporal frequency domains. By virtue of their finite number, jammers in the field of view are sparse in the azimuth and elevation directions. For the class of frequency modulated jammers, sparsity is also exhibited in the joint time-frequency signal representation. These spatial and signal characteristics have called for the development of sparsity-aware antijam techniques for the accurate estimation of jammer space-time-frequency signature, enabling its effective sensing and excision. Both theory and simulation examples demonstrate the utility of coarrays, sparse reconstructions, and antenna selection techniques for antijam GNSS.


title: Dynamic Analysis of a Nonholonomic Two-Wheeled Inverted Pendulum Robot

abstract: As a result of the increase in robots in various fields, the mechanical stability of specific robots has become an important subject of research. This study is concerned with the development of a two-wheeled inverted pendulum robot that can be applied to an intelligent, mobile home robot. This kind of robotic mechanism has an innately clumsy motion for stabilizing the robot's body posture. To analyze and execute this robotic mechanism, we investigated the exact dynamics of the mechanism with the aid of 3-DOF modeling. By using the governing equations of motion, we analyzed important issues in the dynamics of a situation with an inclined surface and also the effect of the turning motion on the stability of the robot. For the experiments, the mechanical robot was constructed with various sensors. Its application to a two-dimensional floor environment was confirmed by experiments on factors such as balancing, rectilinear motion, and spinning motion.


title: Social learning theory and developmental psychology: The legacies of Robert Sears and Albert Bandura.

abstract: Social learning theory began as an attempt by Robert Sears and others to meld psychoanalytic and stimulus-response learning theory into a comprehensive explanation of human behavior, drawing on the clinical richness of the former and the rigor of the latter. Albert Bandura abandoned the psychoanalytic and drive features of the approach, emphasizing instead cognitive and information-processing capacities that mediate social behavior. Both theories were intended as a general framework for the understanding of human behavior, and their developmental aspects remain to be worked out in detail


title: Probabilistic Frame Induction

abstract: In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formalized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for information extraction and natural language generation, and is usually done manually. Methods for inducing frames have been proposed recently, but they typically use ad hoc procedures and are difficult to diagnose or extend. In this paper, we propose the first probabilistic approach to frame induction, which incorporates frames, events, and participants as latent topics and learns those frame and event transitions that best explain the text. The number of frame components is inferred by a novel application of a split-merge method from syntactic parsing. In end-to-end evaluations from text to induced frames and extracted facts, our method produces state-of-the-art results while substantially reducing engineering effort.


title: A Risk Management Approach to the “ Insider Threat ”

abstract: Recent surveys indicate that the financial impact and operating losses due to insider intrusions are increasing. But these studies often disagree on what constitutes an insider; indeed, many define it only implicitly. In theory, appropriate selection of, and enforcement of, properly specified security policies should prevent legitimate users from abusing their access to computer systems, information, and other resources. However, even if policies could be expressed precisely, the natural mapping between the natural language expression of a security policy, and the expression of that policy in a form that can be implemented on a computer system or network, creates gaps in enforcement. This paper defines insider precisely, in terms of these gaps, and explores an access-based model for analyzing threats that include those usually termed insider threats. This model enables an organization to order its resources based on the business value for that resource and of the information it contains. By identifying those users with access to high-value resources, we obtain an ordered list of users who can cause the greatest amount of damage. Concurrently with this, we examine psychological indicators in order to determine which users are at the greatest risk of acting inappropriately. We conclude by examining how to merge this model with one of forensic logging and auditing.


title: Experiential Design of Shared Information Spaces

abstract: This chapter outlines an approach to designing information spaces that we call experiential design, and illustrates the approach with examples of our recent work. The main virtue of this approach is that it claims to draw on universal primitives in the way people understand things, events, relationships — and information. And because of this virtue, it naturally supports social navigation of information spaces. The basic idea of experiential design is that, because we are embodied beings, meaning ultimately resides in bodily experiences. We have evolved to act in the physical world, and how we are able to understand abstract information is derived from that capacity. If we design for embodiment, understanding comes free; this is the first major benefit of the approach.


title: Sensorless control of doubly-fed induction generators in variable-speed wind turbine systems

abstract: This paper proposes a sensorless control strategy for doubly-fed induction generators (DFIGs) in variablespeed wind turbine systems (WTS). The proposed scheme uses an extended Kalman filter (EKF) for estimation of rotor speed and rotor position. Moreover, the EKF is used to estimate the mechanical torque of the generator to allow for maximum power point tracking control for wind speeds below the nominal wind speed. For EKF design, the nonlinear state space model of the DFIG is derived. The estimation and control performance of the proposed sensorless control method are illustrated by simulation results at low, high, and close to the synchronous speed. The designed EKF is robust to machine parameter variations within reasonable limits. Finally, the performances of the EKF and a model reference adaptive system (MRAS) observer are compared for time-varying wind speeds.


title: The Impacts of Ethical Philosophy on Corporate Hypocrisy Perception and Communication Intentions Toward CSR

abstract: This study investigates how perceptions of corporate hypocrisy from corporate social responsibility activities connect the public’s ethical philosophy to subsequent positive/negative opinion-sharin...


title: The neuronal basis and ontogeny of empathy and mind reading : Review of literature and implications for future research

abstract: Social neuro-science has recently started to investigate the neuronal mechanisms underlying our ability to understand the mental and emotional states of others. In this review, imaging research conducted on theory of mind (ToM or mentalizing) and empathy is selectively reviewed. It is proposed that even though these abilities are often used as synonyms in the literature these capacities represent different abilities that rely on different neuronal circuitry. ToM refers to our ability to understand mental states such as intentions, goals and beliefs, and relies on structures of the temporal lobe and the pre-frontal cortex. In contrast, empathy refers to our ability to share the feelings (emotions and sensations) of others and relies on sensorimotor cortices as well as limbic and para-limbic structures. It is further argued that the concept of empathy as used in lay terms refers to a multi-level construct extending from simple forms of emotion contagion to complex forms of cognitive perspective taking. Future research should investigate the relative contribution of empathizing and mentalizing abilities in the understanding of other people's states. Finally, it is suggested that the abilities to understand other people's thoughts and to share their affects display different ontogenetic trajectories reflecting the different developmental paths of their underlying neural structures. In particular, empathy develops much earlier than mentalizing abilities, because the former relys on limbic structures which develop early in ontogeny, whereas the latter rely on lateral temporal lobe and pre-frontal structures which are among the last to fully mature.


The Status of Research on Rough Sets for Knowledge Discovery in Databases


title: A database and lexicon of scripts for ThoughtTreasure

abstract: Since scripts were proposed in the 1970's as an inferencing mechanism for AI and natural language processing programs, there have been few attempts to build a database of scripts. This paper describes a database and lexicon of scripts that has been added to the ThoughtTreasure commonsense platform. The database provides the following information about scripts: sequence of events, roles, props, entry conditions, results, goals, emotions, places, duration, frequency, and cost. English and French words and phrases are linked to script concepts.


title: Adjustment in Middle School: An Exploratory Study

abstract: In an urban middle school, African American (n = 116), Latino (n = 118), and students from four other ethnic groups (labeled multiethnic, n = 172) completed nomination procedures that identified classmates who were perceived as aggressive or as victims of peer harassment. Peer acceptance and rejection also were measured by nomination procedures, and participants reported their self-perceived loneliness, social anxiety, and global self-esteem. Compared to Latino and multiethnic respondents, more African American students were nominated as aggressive, and fewer were nominated as victims of harassment. However, African American harassment victims reported more loneliness and lower self-esteem than did harassment victims in the other ethnic groups, and they were more rejected by peers. The data were interpreted as evidence that deviations from normative perceptions of a person’s group (i.e., being a victim of harassment when the perceived group norm is aggressiveness) are particularly detrimental to psycholog...


title: Binge eating as a consequence of unfulfilled basic needs : The moderating role of implicit achievement motivation

abstract: The present study hypothesized that unfulfilled basic needs for autonomy, competence and relatedness (SDT, Deci and Ryan, Psychol Inq 11:227–268, 2000) are associated with the impulse to eat and with binge eating. In addition, we assumed that individuals with a high achievement motive, who are characterized by high self-control competences, have the same impulse to eat when confronted with unfulfilled basic needs, but are better able to control the impulse to binge eat than individuals with a low achievement motive. In accordance with these hypotheses, unfulfilled basic needs significantly positively predicted the impulse to eat as well as binge eating behavior. As also expected, the achievement motive did not moderate the effect of unfulfilled needs on the impulse to eat, but did influence the effect of unfulfilled needs on binge eating. The results are discussed in terms of a broader debate about the interaction between basic needs and implicit motives.


title: Schism: a Workload-Driven Approach to Database Replication and Partitioning

abstract: We present Schism, a novel workload-aware approach for database partitioning and replication designed to improve scalability of shared-nothing distributed databases. Because distributed transactions are expensive in OLTP settings (a fact we demonstrate through a series of experiments), our partitioner attempts to minimize the number of distributed transactions, while producing balanced partitions. Schism consists of two phases: i) a workload-driven, graph-based replication/partitioning phase and ii) an explanation and validation phase. The first phase creates a graph with a node per tuple (or group of tuples) and edges between nodes accessed by the same transaction, and then uses a graph partitioner to split the graph into k balanced partitions that minimize the number of cross-partition transactions. The second phase exploits machine learning techniques to find a predicate-based explanation of the partitioning strategy (i.e., a set of range predicates that represent the same replication/partitioning scheme produced by the partitioner). ::: ::: The strengths of Schism are: i) independence from the schema layout, ii) effectiveness on n-to-n relations, typical in social network databases, iii) a unified and fine-grained approach to replication and partitioning. We implemented and tested a prototype of Schism on a wide spectrum of test cases, ranging from classical OLTP workloads (e.g., TPC-C and TPC-E), to more complex scenarios derived from social network websites (e.g., Epinions.com), whose schema contains multiple n-to-n relationships, which are known to be hard to partition. Schism consistently outperforms simple partitioning schemes, and in some cases proves superior to the best known manual partitioning, reducing the cost of distributed transactions up to 30%.


title: Diagnosing the Periphery: Using the Rey–Osterrieth Complex Figure Drawing Test to Characterize Peripheral Visual Function

abstract: Peripheral vision is strongly limited by crowding, the deleterious influence of neighboring stimuli on target perception. Many quantitative aspects of this phenomenon have been characterized, but the specific nature of the perceptual degradation remains elusive. We utilized a drawing technique to probe the phenomenology of peripheral vision, using the Rey–Osterrieth Complex Figure, a standard neuropsychological clinical instrument. The figure was presented at 12° or 6° in the right visual field, with eye tracking to ensure that the figure was only presented when observers maintained stable fixation. Participants were asked to draw the figure with free viewing, capturing its peripheral appearance. A foveal condition was used to measure copying performance in direct view. To assess the drawings, two raters used standard scoring systems that evaluated feature positions, spatial distortions, and omission errors. Feature scores tended to decrease with increasing eccentricity, both within and between conditions...


title: Tagging The Web: Building A Robust Web Tagger with Neural Network

abstract: In this paper, we address the problem of web-domain POS tagging using a twophase approach. The first phase learns representations that capture regularities underlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters of the neural network are trained using guided learning in the second phase. Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.


title: Analyzing process models using graph reduction techniques

abstract: The foundation of a process model lies in its structural specifications. Using a generic process modeling language for workflows, we show how a structural specification may contain deadlock and lack of synchronization conflicts that could compromise the correct execution of workflows. In general, identification of such conflicts is a computationally complex problem and requires development of effective algorithms specific for the target modeling language. We present a visual verification approach and algorithm that employs a set of graph reduction rules to identify structural conflicts in process models for the given workflow modeling language. We also provide insights into the correctness and complexity of the reduction process. Finally, we show how the reduction algorithm may be used to count possible instance subgraphs of a correct process model. The main contribution of the paper is a new technique for satisfying well-defined correctness criteria in process models. © 2000 Published by Elsevier Science Ltd. All rights reserved.


Iteratively unveiling new regions of interest in Deep Learning models


title: Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source

abstract: Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-Net. We call it Spectrogram-Channels U-Net, which means each channel of the output corresponds to the spectrogram of separated source itself. The proposed model can be used for not only singing voice separation but also multi-instrument separation by changing only the number of output channels. In addition, we propose a loss function that balances volumes between different sources. Finally, we yield performance that is state-of-the-art on both separation tasks.


title: Higher order contractive auto-encoder

abstract: We propose a novel regularizer when training an autoencoder for unsupervised feature extraction. We explicitly encourage the latent representation to contract the input space by regularizing the norm of the Jacobian (analytically) and the Hessian (stochastically) of the encoder's output with respect to its input, at the training points. While the penalty on the Jacobian's norm ensures robustness to tiny corruption of samples in the input space, constraining the norm of the Hessian extends this robustness when moving further away from the sample. From a manifold learning perspective, balancing this regularization with the auto-encoder's reconstruction objective yields a representation that varies most when moving along the data manifold in input space, and is most insensitive in directions orthogonal to the manifold. The second order regularization, using the Hessian, penalizes curvature, and thus favors smooth manifold. We show that our proposed technique, while remaining computationally efficient, yields representations that are significantly better suited for initializing deep architectures than previously proposed approaches, beating state-of-the-art performance on a number of datasets.


title: A critical review of the literature on spreadsheet errors ☆

abstract: Among those who study spreadsheet use, it is widely accepted that errors are prevalent in operational spreadsheets and that errors can lead to poor decisions and cost millions of dollars. However, relatively little is known about what types of errors actually occur, how they were created, how they can be detected, and how they can be avoided or minimized. This paper summarizes and critiques the research literature on spreadsheet errors from the viewpoint of a manager who wishes to improve operational spreadsheet quality. We also offer suggestions for future research directions that can improve the state of knowledge about spreadsheet errors and mitigate spreadsheet risks.


title: Using GOOSE messages in a main-tie-main scheme

abstract: Main-tie-main (MTM) transfer schemes increase reliability in a power system by switching a load bus to a secondary power source when a power interruption occurs on the primary source. Traditionally, the large number of physical I/O lines required makes main-tie-main schemes expensive to design and implement. Using Ethernet-based IEC 61850, these hardwired I/O lines can be removed and replaced with generic object-oriented substation event (GOOSE) messages. Adjusting the scheme for optimal performance is done via software which saves redesign time and rewiring time. Special attention is paid to change-of-state GOOSE only; no analog GOOSE messages are used, making the scheme fast and easy to configure, maintain, and troubleshoot. Applications such as fast motor-bus transfer are discussed with synchronization remaining at each breaker relay. Simulation test results recorded GOOSE message latencies on a system configured for a main-tie-main scheme. This paper presents details of open and closed, manual and automatic transfers.


title: A walking pattern generator for biped robots on uneven terrains

abstract: We present a new method to generate biped walking patterns for biped robots on uneven terrains. Our formulation uses a universal stability criterion that checks whether the resultant of the gravity wrench and the inertia wrench of a robot lies in the convex cone of the wrenches resulting from contacts between the robot and the environment. We present an algorithm to compute the feasible acceleration of the robot's CoM (center of mass) and use that algorithm to generate biped walking patterns. Our approach is more general and applicable to uneven terrains as compared with prior methods based on the ZMP (zero-moment point) criterion. We highlight its applications on some benchmarks.


title: Learning a discriminative dictionary for sparse coding via label consistent K-SVD

abstract: A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistent constraint called ‘discriminative sparse-code error’ and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single over-complete dictionary and an optimal linear classifier jointly. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse coding techniques for face and object category recognition under the same learning conditions.


An attributional theory of achievement motivation and emotion.


1 Flexible non-volatile optical memory TFT device with an unprecedented number of distinct levels based on an organic bi-component blend


title: Inverse reinforcement learning with leveraged Gaussian processes

abstract: In this paper, we propose a novel inverse reinforcement learning algorithm with leveraged Gaussian processes that can learn from both positive and negative demonstrations. While most existing inverse reinforcement learning (IRL) methods suffer from the lack of information near low reward regions, the proposed method alleviates this issue by incorporating (negative) demonstrations of what not to do. To mathematically formulate negative demonstrations, we introduce a novel generative model which can generate both positive and negative demonstrations using a parameter, called proficiency. Moreover, since we represent a reward function using a leveraged Gaussian process which can model a nonlinear function, the proposed method can effectively estimate the structure of a nonlinear reward function.


title: Saving Power in Video Playback on OLED Displays by Acceptable Changes to Perceived Brightness

abstract: Displays based on organic light-emitting diodes (OLEDs) are now widely used in mobile devices, in which they are major power consumers. The power drawn by an OLED display increases nonlinearly with sub-pixel intensities—thus reducing brightness saves appreciable power, but can displease users. This paper examines this tradeoff, and proposes a color blending scheme in which each frame is darkened in a way that reduces power consumption significantly while limiting the visual impact. The target lightness of frames is determined, in the LAB color space, from average intensities of the original frames; and these average values are in turn obtained by adaptive sampling, so as to reduce the computational overhead. An Android smartphone uses 12%–36% less power when running this scheme, compared to standard video playback, while a user evaluation suggested that reducing brightness to a certain extent can be largely unnoticed, or readily tolerated.


title: Automated reasoning for web page layout

abstract: Web pages define their appearance using Cascading Style Sheets, a modular language for layout of tree-structured documents. In principle, using CSS is easy: the developer specifies declarative constraints on the layout of an HTML document (such as the positioning of nodes in the HTML tree), and the browser solves the constraints to produce a box-based rendering of that document. In practice, however, the subtleties of CSS semantics make it difficult to develop stylesheets that produce the intended layout across different user preferences and browser settings. This paper presents the first mechanized formalization of a substantial fragment of the CSS semantics. This formalization is equipped with an efficient reduction to the theory of quantifier-free linear real arithmetic, enabling effective automated reasoning about CSS stylesheets and their behavior. We implement this reduction in Cassius, a solver-aided framework for building semantics-aware tools for CSS. To demonstrate the utility of Cassius, we prototype new tools for automated verification, debugging, and synthesis of CSS code. We show that these tools work on fragments of real-world websites, and that Cassius is a practical first step toward solver-aided programming for the web.


title: Feeling-based location privacy protection for location-based services

abstract: Anonymous location information may be correlated with restricted spaces such as home and office for subject re-identification. This makes it a great challenge to provide location privacy protection for users of location-based services. Existing work adopts traditional K-anonymity model and ensures that each location disclosed in service requests is a spatial region that has been visited by at least K users. This strategy requires a user to specify an appropriate value of K in order to achieve a desired level of privacy protection. This is problematic because privacy is about feeling, and it is awkward for one to scale her feeling using a number. In this paper, we propose a feeling-based privacy model. The model allows a user to express her privacy requirement by specifying a public region, which the user would feel comfortable if the region is reported as her location. The popularity of the public region, measured using entropy based on its visitors' footprints inside it, is then used as the user's desired level of privacy protection. With this model in place, we present a novel technique that allows a user's location information to be reported as accurate as possible while providing her sufficient location privacy protection. The new technique supports trajectory cloaking and can be used in application scenarios where a user needs to make frequent location updates along a trajectory that cannot be predicted. In addition to evaluating the effectiveness of the proposed technique under various conditions through simulation, we have also implemented an experimental system for location privacy-aware uses of location-based services.


title: Automatic placement of authorization hooks in the linux security modules framework

abstract: We present a technique for automatic placement of authorization hooks, and apply it to the Linux security modules (LSM) framework. LSM is a generic framework which allows diverse authorization policies to be enforced by the Linux kernel. It consists of a kernel module which encapsulates an authorization policy, and hooks into the kernel module placed at appropriate locations in the Linux kernel. The kernel enforces the authorization policy using hook calls. In current practice, hooks are placed manually in the kernel. This approach is tedious, and as prior work has shown, is prone to security holes.Our technique uses static analysis of the Linux kernel and the kernel module to automate hook placement. Given a non-hook-placed version of the Linux kernel, and a kernel module that implements an authorization policy, our technique infers the set of operations authorized by each hook, and the set of operations performed by each function in the kernel. It uses this information to infer the set of hooks that must guard each kernel function. We describe the design and implementation of a prototype tool called T AHOE (Tool for Authorization Hook Placement) that uses this technique. We demonstrate the effectiveness of T AHOE by using it with the LSM implementation of security-enhanced Linux (selinux). While our exposition in this paper focuses on hook placement for LSM, our technique can be used to place hooks in other LSM-like architectures as well.


title: Relation between size of skin excision, wound, and specimen.

abstract: BACKGROUND ::: Skin wounds differ in shape and size compared with the planned excision, and skin shrinks after excision and fixation. ::: ::: ::: OBJECTIVE ::: This study was designed to quantify and to analyze the differences between the size and shape of the planned excision, wound, and specimen. ::: ::: ::: METHODS ::: Eighty-six patients with 93 benign or malignant skin tumors were prospectively studied. Length and width measurements were made of the lesion, planned surgical excision, postexcision wound, and prefixation and postfixation specimens. The results were analyzed to identify the effects of patient age and sex, and lesion type and site. ::: ::: ::: RESULTS ::: Wound size was larger than planned excision size in 90% of wounds, and this effect was greatest in young patients and at trunk and limb sites. Excision and fixation caused the specimens to shrink so that the postfixation area was on average 48% of the planned excision area; benign tumors shrank more than malignant tumors. ::: ::: ::: CONCLUSION ::: Significant differences among planned excision, wound, and specimen sizes are influenced by patient age and by lesion site and type. These results demonstrate that wound size is not equivalent to tumor size, a conclusion often made in Mohs surgery. Furthermore, assessment of tumor clearance margins from fixed tissue does not reflect in vivo clearance margins.


title: Circular/Spherical Robots for Crawling and Jumping

abstract: We describe circular/spherical robots for crawling and jumping. Locomotion over rough terrain has been achieved mainly by rigid body systems including crawlers and leg mechanisms. This paper presents an alternative method of moving over rough terrain, one that employs deformation. First, we describe the principle of crawling and jumping as performed through deformation of a robot body. Second, in a physical simulation, we investigate the feasibility of the approach. Next, we show experimentally that prototypes of a circular robot and a spherical robot can crawl and jump.


title: A maturity model for Business Intelligence System project in Small and Medium-sized Enterprises: an empirical investigation

abstract: In the recent years many studies on maturity model have been carried out. Some refer specifically to maturity models for Business Intelligence (BI). Starting from an analysis of the existing literature, the aim of this paper is to develop a maturity model for the business intelligence system project in small and medium-sized enterprises (SMEs) based on the concept of critical success factors (CSFs). This model will be validated by two approaches. The first is a pilot test of the model in a Moroccan medium-sized enterprise to demonstrate his capacity of assessing the maturity of BI System project and whether it can develop an improvement roadmap. The second is an empirical investigation in Moroccan SMEs by using a survey to depict whether it can evaluate the maturity of BI System project in different industries.


title: Subspace Outlier Detection in Linear Time with Randomized Hashing

abstract: Outlier detection algorithms are often computationally intensive because of their need to score each point in the data. Even simple distance-based algorithms have quadratic complexity. High-dimensional outlier detection algorithms such as subspace methods are often even more computationally intensive because of their need to explore different subspaces of the data. In this paper, we propose an exceedingly simple subspace outlier detection algorithm, which can be implemented in a few lines of code, and whose complexity is linear in the size of the data set and the space requirement is constant. We show that this outlier detection algorithm is much faster than both conventional and high-dimensional algorithms and also provides more accurate results. The approach uses randomized hashing to score data points and has a neat subspace interpretation. Furthermore, the approach can be easily generalized to data streams. We present experimental results showing the effectiveness of the approach over other state-of-the-art methods.


title: A functional analysis of the shift from gill to lung breathing during the evolution of land crabs crustacea decapoda

abstract: The evolution of air-breathing in land crabs is associated with a progressive shift in the primary site of respiratory gas exchange from the diffusion-limited gills used for water-breathing, via a simple ‘cutaneous’ lung surface to the perfusion-limited, invaginated lung described in the mountain crab, Pseudothelphusa garmani. The reduced diffusion limitation over the lungs facilitates oxygen transfer from air to the tissues at lower ventilation rates but is associated with accumulation of carbon dioxide. A potential respiratory acidosis is buffered by the respiratory pigment haemocyanin and by elevation of haemolymph bicarbonate levels. These changes parallel those described in vertebrates but air-breathing crustaceans maintain relatively low carbon dioxide levels in the haemolymph, either by retaining an aquatic route for its elimination over the reduced gills or by blowing it off across the lung. Maintenance of low carbon dioxide levels may be associated with a limited capacity to buffer against an acidosis due to low levels of circulating haemocyanin (i.e. crustaceans lack red blood cells). This may ultimately limit their survival in air as an acidosis will reduce oxygen transport due to a marked Bohr effect on haemocyanin. The primary role of an invaginated lung may be to reduce rates of water loss in air.


Mining linguistic tone patterns with symbolic representation


title: Surveillance and monitoring system using Raspberry Pi and SimpleCV

abstract: Surveillance and monitoring has become very important for security reasons these days. Residential areas, government organizations, commercial spaces, schools and hospitals, industries, banking and other challenging indoor and outdoor environments require high end surveillance systems, which are very expensive. This paper proposes the motion detection and tracking system for surveillance in this paper. The proposed system uses Raspberry Pi and computer vision using SimpleCV to detect moving objects in the surveillance area, switch on the lights to capture images and streams the camera feed online using MPJG Streamer, which can be viewed by any authorized person on the go.


title: Reliability-aware controller placement for Software-Defined Networks

abstract: The Software-Defined Network (SDN) approach decouples control and forwarding planes. Such separation introduces reliability design issues of the SDN control network, since disconnection between the control and forwarding planes may lead to severe packet loss and performance degradation. This paper addresses the problem of placing controllers in SDNs, so as to maximize the reliability of control networks. After presenting a metric to characterize the reliability of SDN control networks, several placement algorithms are developed. We evaluate these algorithms and further quantify the impact of controller number on the reliability of control networks using real topologies. Our approach can significantly improve the reliability of SDN control networks without introducing unacceptable latencies.


title: Dynamic neural architecture for social knowledge retrieval

abstract: Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often guide our decisions as we navigate complex social interactions. Even abstract traits associated with an individual, such as their political affiliation, can cue a rich cascade of person-specific knowledge. Here, we asked whether the anterior temporal lobe (ATL) serves as a hub for a distributed neural circuit that represents person knowledge. Fifty participants across two studies learned biographical information about fictitious people in a 2-d training paradigm. On day 3, they retrieved this biographical information while undergoing an fMRI scan. A series of multivariate and connectivity analyses suggest that the ATL stores abstract person identity representations. Moreover, this region coordinates interactions with a distributed network to support the flexible retrieval of person attributes. Together, our results suggest that the ATL is a central hub for representing and retrieving person knowledge.


title: A simple method to obtain visual attention data in head mounted virtual reality

abstract: Automatic prediction of salient regions in images is a well developed topic in the field of computer vision. Yet, virtual reality omnidirectional visual content brings new challenges to this topic, due to a different representation of visual information and additional degrees of freedom available to viewers. Having a model for visual attention is important to continue research in this direction. In this paper we develop such a model for head direction trajectories. The method consists of three basic steps: First, a computed head angular speed is used to exclude the parts of a trajectory where motion is too fast to fixate viewer's attention. Second, fixation locations of different subjects are fused together, optionally preceded by a re-sampling step to conform to the equal distribution of points on a sphere. Finally, a Gaussian based filtering is performed to produce continuous fixation maps. The developed model can be used to obtain ground truth experimental data when eye tracking is not available.


title: Fully Integrated Startup at 70 mV of Boost Converters for Thermoelectric Energy Harvesting

abstract: This paper presents an inductive DC-DC boost converter for energy harvesting using a thermoelectric generator with a minimum startup voltage of 70 mV and a regulated output voltage of 1.25 V. With a typical generator resistance of $40~\Omega $ , an output power of $17~ {\mu }\text{W}$ can be provided, which translates to an end-to-end efficiency of 58%. The converter employs Schmitt-trigger logic startup control circuitry and an ultra-low voltage charge pump using modified Schmitt-trigger driving circuits optimized for driving capacitive loads. Together with a novel ultra-low leakage power switch and the required control scheme, to the best of the authors’ knowledge, this enables the lowest minimum voltage with fully integrated startup.


title: Conceptual Annotations Preserve Structure Across Translations: A French-English Case Study

abstract: Divergence of syntactic structures between languages constitutes a major challenge in using linguistic structure in Machine Translation (MT) systems. Here, we examine the potential of semantic structures. While semantic annotation is appealing as a source of cross-linguistically stable structures, little has been accomplished in demonstrating this stability through a detailed corpus study. In this paper, we experiment with the UCCA conceptual-cognitive annotation scheme in an English-French case study. First, we show that UCCA can be used to annotate French, through a systematic type-level analysis of the major French grammatical phenomena. Second, we annotate a parallel English-French corpus with UCCA, and quantify the similarity of the structures on both sides. Results show a high degree of stability across translations, supporting the usage of semantic annotations over syntactic ones in structure-aware MT systems.