title: A 137 dB Dynamic Range and 0.32 V Self-Powered CMOS Imager With Energy Harvesting Pixels

abstract: This work presents an 0.32 V self-powered high dynamic range (DR) CMOS imager in standard $0.18~\mu \text {m}$ CMOS technology with dual-mode operation: imaging (IMG) and optical energy harvesting (OEH) modes. In IMG mode, a dual-exposure extended-counting (DEEC) scheme is proposed and implemented with a 5-bit programmable current-controlled threshold (PCCT) generator. By combining the DR of a short-exposure (88 dB) and an extended long-exposure (49 dB) conversions, the DEEC achieves a high DR of 137 dB. The chip consumes $10.6~\mu \text {W}$ at 6.5 fps with 0.32 V operation and $32.1~\mu \text {W}$ at 16.5 fps with 0.4 V operation, which results in an iFoM of 8.1 f and 9.8 fJ/pixel $\cdot $ code. In OEH mode, the sensing pixels turns into energy harvesting pixels with an additional global micro solar cell and corresponding mode control circuit, which generates 455 mV and $14~\mu \text {W}$ at 60 klux (sunny day) and supports a self-powered imaging operation at 4.1 fps.


title: Wideband Planar Array With Integrated Feed and Matching Network for Wide-Angle Scanning

abstract: It is traditionally known that wideband apertures lose bandwidth when placed over a ground plane. To overcome this issue, this paper introduces a new non-symmetric tightly coupled dipole element for wideband phased arrays. The proposed array antenna incorporates additional degrees of freedom to control capacitance and cancel the ground plane inductance. Specifically, each arm on the dipole is different than the other (or non-symmetric). The arms are identical near the center feed section but dissimilar towards the ends, forming a ball-and-cup. It is demonstrated that the non-symmetric qualities achieve wideband performance. Concurrently, a design example for planar installation with balun and matching network is presented to cover X-band. The balun avoids extraneous radiation, maintains the array's low-profile height and is printed on top of the ground plane connecting to the array aperture with 180° out of phase vertical twin-wire transmission lines. To demonstrate the concept, a 64-element array with integrated feed and matching network is designed, fabricated and verified experimentally. The array aperture is placed λ/7 (at 8 GHz) above the ground plane and shown to maintain a active VSWR less than 2 from 8-12.5 GHz while scanning up to 70° and 60° in E- and H-plane, respectively. The array's simulated diagonal plane cross-polarization is approximately 10 dB below the co-polarized component during 60° diagonal scan and follows the theoretical limit for an infinite current sheet.


title: Texture-aware ASCII art synthesis with proportional fonts

abstract: We present a fast structure-based ASCII art generation method that accepts arbitrary images (real photograph or hand-drawing) as input. Our method supports not only fixed width fonts, but also the visually more pleasant and computationally more challenging proportional fonts, which allows us to represent challenging images with a variety of structures by characters. We take human perception into account and develop a novel feature extraction scheme based on a multi-orientation phase congruency model. Different from most existing contour detection methods, our scheme does not attempt to remove textures as much as possible. Instead, it aims at faithfully capturing visually sensitive features, including both main contours and textural structures, while suppressing visually insensitive features, such as minor texture elements and noise. Together with a deformation-tolerant image similarity metric, we can generate lively and meaningful ASCII art, even when the choices of character shapes and placement are very limited. A dynamic programming based optimization is proposed to simultaneously determine the optimal proportional-font characters for matching and their optimal placement. Experimental results show that our results outperform state-of-the-art methods in term of visual quality.


title: Use of Multimedia as a New Educational Technology Tool-A Study

abstract: In the current scenario of educational institutions, multimedia has dig up its own kind of space in some or the other way as a tool of educational technology. Multimedia has overcome the barriers of time and space and provides evidence to be accepted as an anytime and anywhere tool for educating multi-disciplinary masses. The process of knowledge acquisition becomes more efficient when the learners experience an event through a multimedia simulation. Multimedia technology empowers the educational process by means of increased interaction between teachers and the students. Apart from the fact that multimedia can provide educators and students with endless possibilities of quality teaching and learning, taking vital considerations of the pedagogical strengths and limitations of Multimedia, it can be used to its fullest potency, and reach the eminence of 'New Educational Technology tool'. This paper presents a comprehensive study of selected papers that are pertinent to the use of Multimedia in Education, as well as lists down the various proposed multi-disciplinary educational frameworks and tools for the same. In this paper, a study of most commonly used methods and issues related to the use of Multimedia as a new education technology tool has been carried out and reported. It also presents a categorized listing of such papers, accompanied by annotations that describe the content of the papers and their relevance to the use of Multimedia in Education.


title: How to Fit when No One Size Fits

abstract: no one size fits all" is a sound philosophy for system de- signers to follow, it poses multiple challenges for application de- velopers and system administrators. It can be hard for an applica- tion developer to pick one system when the needs of her applica- tion match the features of multiple "one size" systems. The choice becomes considerably harder when different components of an ap- plication fit the features of different "one size" systems. Consider- able manual effort goes into creating and tuning such multi-system applications. An application's data and workload properties may change over time, often in unpredictable and bursty ways. Conse- quently, the "one size" system that is best for an application can change over time. Adapting to change can be hard when applica- tion development is coupled tightly with any individual "one size" system. In this paper, we make the case for developing a new breed of Database Management Systems that we term DBMS + . A DBMS + contains multiple "one size" systems internally. An application specifies its execution requirements on aspects like performance,


title: Vision-Based Mobile Robot Localization with Simple Artificial Landmarks

abstract: Abstract This article presents a vision-based method for mobile robot localization in partially known environments. Simple, unobtrusive artificial landmarks are used as external localization aids. The landmarks are labelled with unique code patterns, what enables unambiguous global localization. The analysis is presented which enables prediction of the uncertainty of the position and orientation of the robot relative to the landmark. Experimental evidence is provided, demonstrating that the proposed method can be reliably used over a wide range of relative positions between the camera and the landmark.


title: Beliefs and biases in web search

abstract: People's beliefs, and unconscious biases that arise from those beliefs, influence their judgment, decision making, and actions, as is commonly accepted among psychologists. Biases can be observed in information retrieval in situations where searchers seek or are presented with information that significantly deviates from the truth. There is little understanding of the impact of such biases in search. In this paper we study search-related biases via multiple probes: an exploratory retrospective survey, human labeling of the captions and results returned by a Web search engine, and a large-scale log analysis of search behavior on that engine. Targeting yes-no questions in the critical domain of health search, we show that Web searchers exhibit their own biases and are also subject to bias from the search engine. We clearly observe searchers favoring positive information over negative and more than expected given base rates based on consensus answers from physicians. We also show that search engines strongly favor a particular, usually positive, perspective, irrespective of the truth. Importantly, we show that these biases can be counterproductive and affect search outcomes; in our study, around half of the answers that searchers settled on were actually incorrect. Our findings have implications for search engine design, including the development of ranking algorithms that con-sider the desire to satisfy searchers (by validating their beliefs) and providing accurate answers and properly considering base rates. Incorporating likelihood information into search is particularly important for consequential tasks, such as those with a medical focus.


title: Much Ado About Grit: A Meta-Analytic Synthesis of the Grit Literature

abstract: Grit has been presented as a higher order personality trait that is highly predictive of both success and performance and distinct from other traits such as conscientiousness. This paper provides a meta-analytic review of the grit literature with a particular focus on the structure of grit and the relation between grit and performance, retention, conscientiousness, cognitive ability, and demographic variables. Our results based on 584 effect sizes from 88 independent samples representing 66,807 individuals indicate that the higher order structure of grit is not confirmed, that grit is only moderately correlated with performance and retention, and that grit is very strongly correlated with conscientiousness. We also find that the perseverance of effort facet has significantly stronger criterion validities than the consistency of interest facet and that perseverance of effort explains variance in academic performance even after controlling for conscientiousness. In aggregate our results suggest that interventions designed to enhance grit may only have weak effects on performance and success, that the construct validity of grit is in question, and that the primary utility of the grit construct may lie in the perseverance facet. (PsycINFO Database Record (c) 2017 APA, all rights reserved)


title: PySEAL: A Python wrapper implementation of the SEAL homomorphic encryption library

abstract: Motivation: The ability to perform operations on encrypted data has a growing number of applications in bioinformatics, with implications for data privacy in health care and biosecurity. The SEAL library is a popular implementation of fully homomorphic encryption developed in C++ by Microsoft Research. Despite the advantages of C++, Python is a flexible and dominant programming language that enables rapid prototyping of bioinformatics pipelines. ::: Results: In an effort to make homomorphic encryption accessible to a broader range of bioinformatics scientists and applications, we present a Python binding implementation of the popular homomorphic encryption library, SEAL, using pybind11. The software contains a Docker image to facilitate easy installation and execution of the SEAL build process. ::: Availability: All code is publicly available at this https URL Contact: lab41@iqt.org Supplementary information: Supplementary information is available on the Lab41 GitHub.


Advantages and challenges associated with augmented reality for education : A systematic review of the literature


Riemann manifold Langevin and Hamiltonian Monte Carlo methods


title: Buck/boost bidirectional converter TCM control without zero-crossing detection

abstract: Bidirectional buck/boost converter is widely used in new energy storage systems. By utilizing the traditional triangular current mode (TCM) control strategy, soft-switching can be achieved for all switches. However, traditional TCM scheme needs current zero-crossing detection to regulate conduction interval and a feed-forward regulator is needed due to detection delay, which make the control more complicated and the multi-modules interleaving more difficult. To solve these problems, a new method based on variable frequency to achieve TCM scheme is proposed in this paper. Firstly, variable frequency scheme is based on soft-switching condition and analysis of frequency characteristics, achieving soft-switching by building regulators of frequency and duty cycle to control inductor current. The scheme not only simplify the control scheme, but also achieve cost reduction. Secondly, a master-slave control method with same frequency is proposed to makes multi-modules easier without phase shift regulator. Finally, the correctness of the new program has been verified by experiment.


title: Techlens: a researcher's desktop

abstract: Rapid and continuous growth of digital libraries, coupled with brisk advancements in technology, has driven users to seek tools and services that are not only customized to their specific needs, but are also helpful in keeping them stay abreast with the latest developments in their field. TechLens is a recommender system that learns about its users through implicit feedback, builds correlations among them, and uses that information to generate recommendations that match the user's profile. It gives users control over which parts of their profile of known citations are used in forming recommendations for new articles. This demonstration is a prototype that showcases some of the tools and services that TechLens offers to the users of digital libraries.


title: Unsupervised Personal Name Disambiguation

abstract: This paper presents a set of algorithms for distinguishing personal names with multiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a language-independent bootstrapping process. The induced clustering of named entities are then partitioned and linked to their real referents via the automatically extracted biographic data. Performance is evaluated based on both a test set of handlabeled multi-referent personal names and via automatically generated pseudonames.


title: Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

abstract: Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it has been shown that these algorithms are vulnerable to attacks. Data poisoning is one of the most relevant security threats against machine learning systems, where attackers can subvert the learning process by injecting malicious samples in the training data. Recent work in adversarial machine learning has shown that the so-called optimal attack strategies can successfully poison linear classifiers, degrading the performance of the system dramatically after compromising a small fraction of the training dataset. In this paper we propose a defence mechanism to mitigate the effect of these optimal poisoning attacks based on outlier detection. We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack. Hence, they can be detected with an appropriate pre-filtering of the training dataset.


title: App Store 2.0: From Crowd Information to Actionable Feedback in Mobile Ecosystems

abstract: Given the increasing competition in mobile-app ecosystems, improving the user experience has become a major goal for app vendors. App Store 2.0 will exploit crowdsourced information about apps, devices, and users to increase the overall quality of the delivered mobile apps. App Store 2.0 generates different kinds of actionable feedback from the crowd information. This feedback helps developers deal with potential errors that could affect their apps before publication or even when the apps are in the users' hands. The App Store 2.0 vision has been transformed into a concrete implementation for Android devices. This article is part of a special issue on Crowdsourcing for Software Engineering.


title: Gene-dropping vs. empirical variance estimation for allele-sharing linkage statistics.

abstract: In this study, we compare the statistical properties of a number of methods for estimating P-values for allele-sharing statistics in non-parametric linkage analysis. Some of the methods are based on the normality assumption, using different variance estimation methods, and others use simulation (gene-dropping) to find empirical distributions of the test statistics. For variance estimation methods, we consider the perfect variance approximation and two empirical variance estimates. The simulation-based methods are gene-dropping with and without conditioning on the observed founder alleles. We also consider the Kong and Cox linear and exponential models and a Monte Carlo method modified from a method for finding genome-wide significance levels. We discuss the analytical properties of these various P-value estimation methods and then present simulation results comparing them. Assuming that the sample sizes are large enough to justify a normality assumption for the linkage statistic, the best P-value estimation method depends to some extent on the (unknown) genetic model and on the types of pedigrees in the sample. If the sample sizes are not large enough to justify a normality assumption, then gene-dropping is the best choice. We discuss the differences between conditional and unconditional gene-dropping.


title: Stereolithography-Based Antennas for Satellite Communications in Ka-Band

abstract: This paper illustrates the role of additive manufacturing (AM) as enabling technology to realize high-performance low-cost antennas for Ka-band applications. In addition to the inherent electromagnetic challenges implicit in the conception of such complex devices, this paper also points out the stringent limitations that appear when opting for classical fabrication techniques, based on assembled split-block models. AM emerges in this context as a change of paradigm, allowing monolithic fabrication and design freedom, which result in substantial improvements in terms of compactness, mass, simplicity, cost, and production time. Two different antennas for Ka-band satellite communications are presented here, namely a wideband horn and a dual-band circular cavity. Both prototypes are fabricated using a stereolithographic (SLA) AM process followed by metal coating. This fabrication approach is especially well suited to the implementation of these designs, since they have internal shapes that are inaccessible to conventional machining tools. The experimental results are not only in very good agreement with the theoretical predictions but also demonstrate improvements over the performances achieved by traditional milling and assembly fabrication approaches, thereby confirming the validity and great potential of SLA for Ka-band satellite communications.


title: Incremental Information Extraction Using Relational Databases

abstract: Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this paper, we describe a novel approach for information extraction in which extraction needs are expressed in the form of database queries, which are evaluated and optimized by database systems. Using database queries for information extraction enables generic extraction and minimizes reprocessing of data by performing incremental extraction to identify which part of the data is affected by the change of components or goals. Furthermore, our approach provides automated query generation components so that casual users do not have to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, we performed experiments to highlight two important aspects of an information extraction system: efficiency and quality of extraction results. Our experiments show that in the event of deployment of a new module, our incremental extraction approach reduces the processing time by 89.64 percent as compared to a traditional pipeline approach. By applying our methods to a corpus of 17 million biomedical abstracts, our experiments show that the query performance is efficient for real-time applications. Our experiments also revealed that our approach achieves high quality extraction results.


title: Compact Multiband Planar Antenna for 2.4/3.5/5.2/5.8-GHz Wireless Applications

abstract: A low-profile planar monopole antenna is proposed to operate within WLAN and WiMAX frequency bands. The antenna is composed of three radiating elements together with an additional strip to control the antenna performance. An electromagnetic (EM) model of the proposed antenna is developed in CST Microwave Studio for numerical analysis and optimization. The principle of operation and parametric study on the antenna performance are provided. Two dual-band and triple-band antennas are fabricated, and experimental results are presented.


title: Connected Car: Technologies, Issues, Future Trends

abstract: The connected car—a vehicle capable of accessing to the Internet, of communicating with smart devices as well as other cars and road infrastructures, and of collecting real-time data from multiple sources—is likely to play a fundamental role in the foreseeable Internet Of Things. In a context ruled by very strong competitive forces, a significant amount of car manufacturers and software and hardware developers have already embraced the challenge of providing innovative solutions for new-generation vehicles. Today’s cars are asked to relieve drivers from the most stressful operations needed for driving, providing them with interesting and updated entertainment functions. In the meantime, they have to comply with the increasingly stringent standards about safety and reliability. The aim of this article is to provide an overview of the possibilities offered by connected functionalities on cars and the associated technological issues and problems, as well as to enumerate the currently available hardware and software solutions and their main features.


title: The design of mobile apps: what and how to teach?

abstract: Mobile applications (or mobile apps or apps for short) gain importance, and will, as is our expectation, find a place in the curricula of Computer Science and Software Engineering. In books, courses and tutorials, not much attention has been given to the design of mobile applications. In this paper, we describe the anatomy of mobile apps, using Android as an example. Based on this anatomy, we offer an inventarization of modeling techniques that can be applied to adequately design mobile apps. Some of these modeling techniques are already taught in most curricula, albeit in different courses. A modeling technique that is useful for several aspects of mobile apps is the Interaction Flow Modeling Language (IFML). This modeling technique would have to be introduced when one would like to teach students how to design apps. We also describe which strategies can be followed when introducing mobile apps in a curriculum: as a subject of a course, together with knowledge of the concepts and the necessary modeling techniques, or as examples in different courses. We discuss advantages and disadvantages of both approaches.


title: The effect of infliximab on bone metabolism markers in patients with rheumatoid arthritis

abstract: Objective. The aim of this study was to evaluate urinary excretion of N-telopeptide of type I collagen (NTX) and deoxypyridinoline (DPD), markers of bone resorption, and serum bone alkaline phosphatase (BAP) level, a marker of bone formation and an early marker of osteoblast differentiation, in patients with rheumatoid arthritis (RA) treated with infliximab. Methods. Seventeen male and female patients (age 60.7 ± 2.53 yr; mean disease duration 12.9 ± 3.01 yr; Steinbrocker's class II-IV) with RA, diagnosed according to the criteria of the American College of Rheumatology (ACR), took part in the study between March 2003 and January 2005. None of the patients had a history of oestrogen replacement therapy. All patients were treated with infliximab combined with methotrexate. Infliximab was infused intravenously at 3mg/kg at baseline, 2 and 6 weeks, then every 8 weeks. To evaluate disease activity, ESR, CRP, the numbers of swollen and tender joints, modified Stanford Health Assessment Questionnaire (mHAQ) score and ACR score were measured. Levels of NTX and DPD in urine and BAP in serum were measured in all patients. Results. ESR, CRP, the number of swollen joints and tender joints, and mHAQ score had decreased significantly 6 weeks after initial treatment and were still low 6 months after initial treatment. NTX levels had decreased significantly 6 weeks after the initial treatment and were still low 6 months after initial treatment. DPD levels had decreased 6 months after initial infusion. Mean serum BAP level did not differ significantly among the three time points. NTX levels were statistically corresponding with the number of swollen joints and mHAQ scores. DPD levels were statistically lower corresponding with ESR. Conclusion. Infliximab therapy may inhibit generalized bone loss in patients with RA. NTX is a more sensitive marker than DPD.


title: Deep Canonically Correlated LSTMs

abstract: We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform correlation analysis on the outputs of these neural networks to find a correlated subspace through which we get our final representation via projection. This work follows from previous work done on Deep Canonical Correlation (DCCA), in which deep feed-forward neural networks were used to learn nonlinear transformations of data while maximizing correlation.


title: Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models

abstract: Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and efficiency, several potential problems have yet to be addressed, particularly the potential for unintentional discrimination. We present an iterative procedure, based on orthogonal projection of input attributes, for enabling interpretability of black-box predictive models. Through our iterative procedure, one can quantify the relative dependence of a black-box model on its input attributes.The relative significance of the inputs to a predictive model can then be used to assess the fairness (or discriminatory extent) of such a model.


title: Security and Privacy Issues in Cloud Computing

abstract: Cloud computing transforms the way information technology (IT) is consumed and managed, promising improved cost efficiencies, accelerated innovation, faster time-to-market, and the ability to scale applications on demand (Leighton, 2009). According to Gartner, while the hype grew exponentially during 2008 and continued since, it is clear that there is a major shift towards the cloud computing model and that the benefits may be substantial (Gartner Hype-Cycle, 2012). However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges. In this chapter, we describe various service and deployment models of cloud computing and identify major challenges. In particular, we discuss three critical challenges: regulatory, security and privacy issues in cloud computing. Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment.


title: Broad Context Language Modeling as Reading Comprehension

abstract: Progress in text understanding has been driven by large datasets that test particular capabilities, like recent datasets for reading comprehension (Hermann et al., 2015). We focus here on the LAMBADA dataset (Paperno et al., 2016), a word prediction task requiring broader context than the immediate sentence. We view LAMBADA as a reading comprehension problem and apply comprehension models based on neural networks. Though these models are constrained to choose a word from the context, they improve the state of the art on LAMBADA from 7.3% to 49%. We analyze 100 instances, finding that neural network readers perform well in cases that involve selecting a name from the context based on dialogue or discourse cues but struggle when coreference resolution or external knowledge is needed.


title: Real-time 6D stereo Visual Odometry with non-overlapping fields of view

abstract: In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multi-camera system in challenging indoor environments. It operates in real-time and employs information from two cameras with non-overlapping fields of view. Monocular Visual Odometry supplying up-to-scale 6D motion information is carried out in each of the cameras, and the metric scale is recovered via a linear solution by imposing the known static transformation between both sensors. The redundancy in the motion estimates is finally exploited by a statistical fusion to an optimal 6D metric result. The proposed technique is robust to outliers and able to continuously deliver a reasonable measurement of the scale factor. The quality of the framework is demonstrated by a concise evaluation on indoor datasets, including a comparison to accurate ground truth data provided by an external motion tracking system.


title: DESIGN OPTIMIZATION OF TWO SYNCHRONOUS RE- LUCTANCE MACHINE STRUCTURES WITH MAXI- MIZED TORQUE AND POWER FACTOR

abstract: The subject of this article is to optimize the design of synchronous reluctance machines with massive rotor and multi-∞ux barrier rotor. The optimization procedure, which aims to improve simultaneously the machines' torque and the power factor, uses the cyclic coordinate method coupled with the magnetostatic flnite- element (FE) fleld solutions. The optimization results regarding these two types of machines, which provide the optimized rotor geometrical dimensions and the in∞uence of the current angle, are discussed.


title: A fix-and-optimize approach for efficient and large scale virtual network function placement and chaining

abstract: Network Function Virtualization (NFV) is a novel concept that is reshaping the middlebox arena, shifting network functions (e.g. firewall, gateways, proxies) from specialized hardware appliances to software images running on commodity hardware. This concept has potential to make network function provision and operation more flexible and cost-effective, paramount in a world where deployed middleboxes may easily reach the order of hundreds. In spite of recent research activity in the field, little has been done towards efficient and scalable placement & chaining of virtual network functions (VNFs) a key feature for the effective success of NFV. More specifically, existing strategies have either neglected the chaining aspect of NFV, focusing on efficient placement only, or failed to scale to hundreds of network functions. In this paper, we approach VNF placement and chaining as an optimization problem, and propose a fix-and-optimize-based heuristic algorithm for tackling it. Our algorithm incorporates a Variable Neighborhood Search (VNS) meta-heuristic, for efficiently exploring the placement and chaining solution space. The goal is to minimize required resource allocation, while meeting network flow requirements and constraints. We provide evidence that our algorithm is able to find feasible, high quality solutions efficiently, even in scenarios scaling to hundreds of VNFs.


title: Benefits of Network Coding for Unicast Application in Disruption-Tolerant Networks

abstract: In this paper, we investigate the benefits of applying a form of network coding known as random linear coding (RLC) to unicast applications in disruption-tolerant networks (DTNs). Under RLC, nodes store and forward random linear combinations of packets as they encounter each other. For the case of a single group of packets originating from the same source and destined for the same destination, we prove a lower bound on the probability that the RLC scheme achieves the minimum time to deliver the group of packets. Although RLC significantly reduces group delivery delays, it fares worse in terms of average packet delivery delay and network transmissions. When replication control is employed, RLC schemes reduce group delivery delays without increasing the number of transmissions. In general, the benefits achieved by RLC are more significant under stringent resource (bandwidth and buffer) constraints, limited signaling, highly dynamic networks, and when applied to packets in the same flow. For more practical settings with multiple continuous flows in the network, we show the importance of deploying RLC schemes with a carefully tuned replication control in order to achieve reduction in average delay, which is observed to be as large as 20% when buffer space is constrained.


title: Communities from seed sets

abstract: Expanding a seed set into a larger community is a common procedure in link-based analysis. We show how to adapt recent results from theoretical computer science to expand a seed set into a community with small conductance and a strong relationship to the seed, while examining only a small neighborhood of the entire graph. We extend existing results to give theoretical guarantees that apply to a variety of seed sets from specified communities. We also describe simple and flexible heuristics for applying these methods in practice, and present early experiments showing that these methods compare favorably with existing approaches.


title: Fuzzy spatial relation ontology for image interpretation

abstract: The semantic interpretation of images can benefit from representations of useful concepts and the links between them as ontologies. In this paper, we propose an ontology of spatial relations, in order to guide image interpretation and the recognition of the structures it contains using structural information on the spatial arrangement of these structures. As an original theoretical contribution, this ontology is then enriched by fuzzy representations of concepts, which define their semantics, and allow establishing the link between these concepts (which are often expressed in linguistic terms) and the information that can be extracted from images. This contributes to reducing the semantic gap and it constitutes a new methodological approach to guide semantic image interpretation. This methodological approach is illustrated on a medical example, dealing with knowledge-based recognition of brain structures in 3D magnetic resonance images using the proposed fuzzy spatial relation ontology.


Beyond Toplines: Heterogeneous Treatment Effects in Randomized Experiments ∗


title: Anticipatory Driving for a Robot-Car Based on Supervised Learning

abstract: Prediction and Planning are essential elements of successful human driving, making them equally important for autonomously driving systems. Many approaches achieve planning based on built-in world-knowledge. However, we show how a learning-based system can be extended to planning, needing little a priori knowledge. A car-like robot is trained by a human driver by constructing a database, where look ahead sensory information is stored together with action sequences . From that we achieve a novel form of velocity control, based only on information in image coordinates. For steering we employ a two-level approach in which database information is combined with an additional reactive controller. The result is a trajectory planning robot running at real-time, issuing steering and velocity control commands in a human manner.


title: Process Discovery under Precedence Constraints

abstract: Process discovery has emerged as a powerful approach to support the analysis and the design of complex processes. It consists of analyzing a set of traces registering the sequence of tasks performed along several enactments of a transactional system, in order to build a process model that can explain all the episodes recorded over them. An approach to accomplish this task is presented that can benefit from the background knowledge that, in many cases, is available to the analysts taking care of the process (re-)design. The approach is based on encoding the information gathered from the log and the (possibly) given background knowledge in terms of precedence constraints, that is, of constraints over the topology of the resulting process models. Mining algorithms are eventually formulated in terms of reasoning problems over precedence constraints, and the computational complexity of such problems is thoroughly analyzed by tracing their tractability frontier. Solution algorithms are proposed and their properties analyzed. These algorithms have been implemented in a prototype system, and results of a thorough experimental activity are discussed.


title: Lessons from the Amazon Picking Challenge.

abstract: This paper summarizes lessons learned from the first Amazon Picking Challenge in which 26 international teams designed robotic systems that competed to retrieve items from warehouse shelves. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned.


title: Splitting approaches for context-aware recommendation: an empirical study

abstract: User and item splitting are well-known approaches to context-aware recommendation. To perform item splitting, multiple copies of an item are created based on the contexts in which it has been rated. User splitting performs a similar treatment with respect to users. The combination of user and item splitting: UI splitting, splits both users and items in the data set to boost context-aware recommendations. In this paper, we perform an empirical comparison of these three context-aware splitting approaches (CASA) on multiple data sets, and we also compare them with other popular context-aware collaborative filtering (CACF) algorithms. To evaluate those algorithms, we propose new evaluation metrics specific to contextual recommendation. The experiments reveal that CASA typically outperform other popular CACF algorithms, but there is no clear winner among the three splitting approaches. However, we do find some underlying patterns or clues for the application of CASA.


Towards the High-quality Anime Characters Generation with Generative Adversarial Networks


title: Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme.

abstract: Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions-only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents ( approximately 10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.


title: Perceptual expertise in forensic facial image comparison

abstract: Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These id...


title: Indoor scene recognition through object detection

abstract: Scene recognition is a highly valuable perceptual ability for an indoor mobile robot, however, current approaches for scene recognition present a significant drop in performance for the case of indoor scenes. We believe that this can be explained by the high appearance variability of indoor environments. This stresses the need to include high-level semantic information in the recognition process. In this work we propose a new approach for indoor scene recognition based on a generative probabilistic hierarchical model that uses common objects as an intermediate semantic representation. Under this model, we use object classifiers to associate low-level visual features to objects, and at the same time, we use contextual relations to associate objects to scenes. As a further contribution, we improve the performance of current state-of-the-art category-level object classifiers by including geometrical information obtained from a 3D range sensor that facilitates the implementation of a focus of attention mechanism within a Monte Carlo sampling scheme. We test our approach using real data, showing significant advantages with respect to previous state-of-the-art methods.


title: Russian hybrid warfare and extended deterrence in eastern Europe

abstract: Russia's use of force against Ukraine since early 2014 has prompted some observers to remark that it is engaging in ‘hybrid warfare’. This form of military statecraft has made other former Soviet republics, such as the Baltic countries, fear that Russia would use subversion rather than pursue a conventional military engagement against them. Despite this concern about Russian hybrid war, existing descriptions of this form of war suffer from conceptual weaknesses. In this article hybrid warfare is conceived as a strategy that marries conventional deterrence and insurgency tactics. That is, the belligerent uses insurgent tactics against its target while using its conventional military power to deter a strong military response. The article then outlines why some former Soviet republics are susceptible to Russian hybrid warfare, allowing it to postulate inductively the conditions under which hybrid warfare might be used in general. The analysis yields two policy implications. First, military solutions are not wholly appropriate against hybrid warfare since it exploits latent ethnic grievances and weak civil societies. Second, only under narrow circumstances would belligerents resort to hybrid warfare. Belligerents need to be revisionist and militarily stronger than their targets, but they also need to have ethnic or linguistic ties with the target society to leverage in waging hybrid warfare.


title: A Collaborative Approach for Monitoring Nodes Behavior during Spectrum Sensing to Mitigate Multiple Attacks in Cognitive Radio Networks

abstract: Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.


title: Linking Religion and Spirituality with Psychological Well-being: Examining Self-actualisation, Meaning in Life, and Personal Growth Initiative

abstract: Research largely shows that religion and spirituality have a positive correlation to psychological well-being. However, there has been a great deal of confusion and debate over their operational definitions. This study attempted to delineate the two constructs and categorise participants into different groups based on measured levels of religious involvement and spirituality. The groups were then scored against specific measures of well-being. A total of 205 participants from a wide range of religious affiliations and faith groups were recruited from various religious institutions and spiritual meetings. They were assigned to one of four groups with the following characteristics: (1) a high level of religious involvement and spirituality, (2) a low level of religious involvement with a high level of spirituality, (3) a high level of religious involvement with a low level of spirituality, and (4) a low level of religious involvement and spirituality. Multiple comparisons were made between the groups on three measures of psychological well-being: levels of self-actualisation, meaning in life, and personal growth initiative. As predicted, it was discovered that, aside from a few exceptions, groups (1) and (2) obtained higher scores on all three measures. As such, these results confirm the importance of spirituality on psychological well-being, regardless of whether it is experienced through religious participation.


title: University course timetabling using hybridized artificial bee colony with hill climbing optimizer

abstract: University course timetabling is concerned with assigning a set of courses to a set of rooms and timeslots according to a set of constraints. This problem has been tackled using metaheuristics techniques. Artificial bee colony (ABC) algorithm has been successfully used for tackling uncapaciated examination and course timetabling problems. In this paper, a novel hybrid ABC algorithm based on the integrated technique is proposed for tackling the university course timetabling problem. First of all, initial feasible solutions are generated using the combination of saturation degree (SD) and backtracking algorithm (BA). Secondly, a hill climbing optimizer is embedded within the employed bee operator to enhance the local exploitation ability of the original ABC algorithm while tackling the problem. Hill climbing iteratively navigates the search space of each population member in order to reach a local optima. The proposed hybrid ABC technique is evaluated using the dataset established by Socha including five small, five medium and one large problem instances. Empirical results on these problem instances validate the effectiveness and efficiency of the proposed algorithm. Our work also shows that a well-designed hybrid technique is a competitive alternative for addressing the university course timetabling problem.


title: An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

abstract: BACKGROUND ::: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false-alarm rates. We propose a deep learning-based early warning system that shows higher performance than the existing track-and-trigger systems. ::: ::: ::: METHODS AND RESULTS ::: This retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning-based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning-based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity. ::: ::: ::: CONCLUSIONS ::: An algorithm based on deep learning had high sensitivity and a low false-alarm rate for detection of patients with cardiac arrest in the multicenter study.


title: Hypoceruloplasminemia: an unusual biochemical finding in a girl with Hashimoto's thyroiditis and severe hypothyroidism

abstract: Clinical picture of Hashimoto’s thyroiditis (HT) may significantly vary in pediatric age, ranging from euthyroidism to subclinical hypothyroidism or hyperthyroidism; only rarely HT presentation may be characterized by a severe hypothyroidism also in pediatric age. Here we describe a 3-year-old Caucasian girl who was admitted to our Clinic due to pericardial effusion, muscle weakness and weight gain. At clinical examination, she presented with bradycardia, pale and round face, pseudohypertrophy of calf muscles and no pitting edema of the limbs. Routine blood investigations showed high serum aspartate and alanine aminotransferase levels, low serum ceruloplasmin without clinical signs of Wilson’s disease, dyslipidemia. Thyroid function tests revealed a picture of severe hypothyroidism associated with HT. After the replacement treatment with L-T4, thyroid-stimulating hormone serum levels gradually decreased, with concomitant resolution of pericardial effusion and normalization of ceruloplasmin levels.


title: The Effect of GoF Design Patterns on Stability: A Case Study

abstract: Stability refers to a software system's resistance to the “ripple effect”, i.e., propagation of changes. In this paper, we investigate the stability of classes that participate in instances/occurrences of GoF design patterns. We examine whether the stability of such classes is affected by (a) the pattern type, (b) the role that the class plays in the pattern, (c) the number of pattern occurrences in which the class participates, and (d) the application domain. To this end, we conducted a case study on about 65.000 Java open-source classes, where we performed change impact analysis on classes that participate in zero, one (single pattern), or more than one (coupled) pattern occurrences. The results suggest that, the application of design patterns can provide the expected “shielding” of certain pattern-participating classes against changes, depending on their role in the pattern. Moreover, classes that participate in coupled pattern occurrences appear to be the least stable. The results can be used for assessing the benefits and liabilities of the use of patterns and for testing and refactoring prioritization, because less stable classes are expected to require more effort while testing, and urge for refactoring activities that would make them more resistant to change propagation.


title: Small Discrete Fourier Transforms on GPUs

abstract: Efficient implementations of the Discrete Fourier Transform (DFT) for GPUs provide good performance with large data sizes, but are not competitive with CPU code for small data sizes. On the other hand, several applications perform multiple DFTs on small data sizes. In fact, even algorithms for large data sizes use a divide-and-conquer approach, where eventually small DFTs need to be performed. We discuss our DFT implementation, which is efficient for multiple small DFTs. One feature of our implementation is the use of the asymptotically slow matrix multiplication approach for small data sizes, which improves performance on the GPU due to its regular memory access and computational patterns. We combine this algorithm with the mixed radix algorithm for 1-D, 2-D, and 3-D complex DFTs. We also demonstrate the effect of different optimization techniques. When GPUs are used to accelerate a component of an application running on the host, it is important that decisions taken to optimize the GPU performance not affect the performance of the rest of the application on the host. One feature of our implementation is that we use a data layout that is not optimal for the GPU so that the overall effect on the application is better. Our implementation performs up to two orders of magnitude faster than cuFFT on an NVIDIA GeForce 9800 GTX GPU and up to one to two orders of magnitude faster than FFTW on a CPU for multiple small DFTs. Furthermore, we show that our implementation can accelerate the performance of a Quantum Monte Carlo application for which cuFFT is not effective. The primary contributions of this work lie in demonstrating the utility of the matrix multiplication approach and also in providing an implementation that is efficient for small DFTs when a GPU is used to accelerate an application running on the host.


title: Oxytocin differentially modulates eye gaze to naturalistic social signals of happiness and anger

abstract: Summary A number of previous studies has shown that oxytocin (OT) promotes facial emotion recognition and enhances eye gaze to facial stimuli in humans. Other studies report valence-specific effects of OT, supporting the proposed prosocial role of OTin social interactions. In the present study, we tested the hypothesis whether OT might selectively enhance eye gaze to positive, approachrelated, but not to negative, threat-related social cues. In a placebo-controlled, double-blind, between-subject design, we assessed the effects of intranasal OT administration (24 IU) in 62 healthy male volunteers on eye gaze toward the eyes of neutral, positive (happy) and negative (angry) facial expressions compared with placebo. In order to capture the dynamics of facial expressions, we used video sequences showing neutral faces gradually displaying a specific emotion. In line with previous studies, OT increased eye gaze toward neutral facial expressions. Moreover, under OT treatment, eye gaze remained increased when the face showed a happy facial expression, but in contrast decreased when the face displayed an angry expression. These results support the notion that OT differentially modulates visual attention toward social signals of positive approach and threat and thereby contributes to the modulation of non-verbal interpersonal communication.


title: A V-Band Doherty Power Amplifier Based on Voltage Combination and Balance Compensation Marchand Balun

abstract: This paper presents a $V$ -band Doherty power amplifier (PA) which is implemented in a standard 65-nm CMOS technology. The voltage combination technique is used to realize the millimeter-wave Doherty PA without the $\lambda $ /4 transmission lines. A Marchand balun with balance compensation is designed to combine the output power with reduced power loss. Moreover, a nonlinear driver is used to drive the peaking amplifier to enhance the output power and the turn-on speed of it. The power-added efficiency (PAE) of this PA at the 6-dB power back-off point is 8.7% and the peak PAE reaches 16.8%. The small signal power gain is 18 dB, and the maximum output power is 14.9 dBm. The core circuit only costs 0.195 mm2 chip area as no $\lambda $ /4 transmission lines.


title: Improved Answer Selection with Pre-Trained Word Embeddings

abstract: This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant gains over traditional term frequency based approaches in both supervised and unsupervised settings. We show that combining these word embedding features with traditional learning-to-rank techniques can achieve similar performance to state-of-the-art neural networks trained for the answer selection task.


title: Benchmark for road marking detection: Dataset specification and performance baseline

abstract: Detection of drivable road area and other critical objects like obstacles and landmarks in traffic scenes is fundamental to advanced driver assistance systems (ADAS) and self-driving car. Although scene parsing is able to make segmentation of road area from other objects and background, it basically does not get involved in recognizing other on-road markings. In fact, detection and classification of road area are only one small step towards true autonomous driving, because there are many categories of informative markings embodied within road area, such as lane markings, arrows, guiding lines, pedestrian crosswalks, and no-vehicle signs. If system identifies those markings, more information for both ADAS and self-driving car system can be provided. For this purpose, we release a benchmark dataset named TRoM (Tsinghua Road Marking), which is served for detection of 19 road-marking categories in urban scenarios. TRoM was built by means of over one-month data covering a full spectrum of time, weather, and traffic-load. An annotation toolkit was also presented to facilitate enriching such dataset. By directly applying our state-of-the-art method called RPP (ResNet with Pyramid Pooling), a reasonably accurate baseline on TRoM benchmark is made public for further performance comparison and evaluation.


title: A new two-phase sampling based algorithm for discovering association rules

abstract: This paper introduces FAST, a novel two-phase sampling-based algorithm for discovering association rules in large databases. In Phase I a large initial sample of transactions is collected and used to quickly and accurately estimate the support of each individual item in the database. In Phase II these estimated supports are used to either trim "outlier" transactions or select "representative" transactions from the initial sample, thereby forming a small final sample that more accurately reflects the statistical characteristics (i.e., itemset supports) of the entire database. The expensive operation of discovering association rules is then performed on the final sample. In an empirical study, FAST was able to achieve 90--95% accuracy using a final sample having a size of only 15--33% of that of a comparable random sample. This efficiency gain resulted in a speedup by roughly a factor of 10 over previous algorithms that require expensive processing of the entire database --- even efficient algorithms that exploit sampling. Our new sampling technique can be used in conjunction with almost any standard association-rule algorithm, and can potentially render scalable other algorithms that mine "count" data.


title: Modeling and control of a single axis tilting quadcopter

abstract: In this paper, the dynamic model of a tilting rotor quadcopter, i.e., a quad-rotor aerial vehicle with rotors that can tilt along one of its axes, is presented. The tilting rotor quadcopter provides the added advantage in terms of additional stable configurations, made possible by additional actuated controls, as compared to a traditional quadcopter without titling rotors. The tilting rotor quadcopter design is accomplished by using an additional motor for each rotor that enables the rotor to rotate along the axis of the quadcopter arm. This turns the traditional quadcopter into an over-actuated flying vehicle allowing us to have complete control over its position and the orientation. In this paper, a dynamic model of the tilting rotor quadcopter vehicle is derived for flying and hovering modes. The model includes the relationship between vehicle orientation angle and rotor tilt-angle. Furthermore, a PD controller is designed to achieve the hovering and navigation capability at any desired pitch or roll angle. The dynamic model and the control design is verified with the help of numerical studies.


title: Variational Mixture of Gaussian Process Experts

abstract: Mixture of Gaussian processes models extended a single Gaussian process with ability of modeling multi-modal data and reduction of training complexity. Previous inference algorithms for these models are mostly based on Gibbs sampling, which can be very slow, particularly for large-scale data sets. We present a new generative mixture of experts model. Each expert is still a Gaussian process but is reformulated by a linear model. This breaks the dependency among training outputs and enables us to use a much faster variational Bayesian algorithm for training. Our gating network is more flexible than previous generative approaches as inputs for each expert are modeled by a Gaussian mixture model. The number of experts and number of Gaussian components for an expert are inferred automatically. A variety of tests show the advantages of our method.


title: Teams That Finish Early Accelerate Faster: A Pattern Language for High Performing Scrum Teams

abstract: Recent surveys show that 42% of Agile projects are successful. While this is three times better than traditional projects, 49% of Agile projects are late or over budget and 9% are total failures [1]. There is a better way to help Agile teams to implement Scrum. At the 2013 Scrum PLoP Conference held in Tisvildeleje, Denmark thought leaders in the Agile community reviewed a set of Scrum Patterns that together generate a high performing Scrum team. During this editorial process it became apparent that a combination of nine Patterns in conjunction with the Scrum framework could help teams achieve Hyper-Productivity, more than a 400% increase in velocity over a team's initial velocity.


A Comparative Study of Classification Techniques in Data Mining Algorithms


title: A Low Q Electrically Small Magnetic (TE Mode) Dipole

abstract: Electrically small antennas can be made to exhibit omnidirectional, dipole-like radiation patterns when operating in the transverse magnetic (electric dipole) mode, the transverse electric (magnetic dipole) mode or some combination of transverse magnetic (TM) and transverse electric (TE) mode radiation. When designing electrically small antennas whose performance properties closely approach theoretical limits, engineers generally focus on the design of electric dipoles or TM mode radiators. These radiators can be designed to exhibit low voltage standing wave ratio (VSWR), high radiation efficiency, and a low quality factor. TM mode radiators also have the advantage of a straightforward transition from the dipole to the monopole configuration for use over metallic ground planes or surfaces. Following our previous work in the design of small electric dipoles, we present the design of a new, electrically small magnetic dipole or TE mode radiator. This new design is shown to exhibit low VSWR, high radiation efficiency, and a low quality factor. We show that the quality factor of the magnetic dipole radiator approaches the lower bound (the Chu-limit) to within a ratio consistent with recent limits presented by Thal.


title: Neuromechanical models for insect locomotion: Stability, maneuverability, and proprioceptive feedback

abstract: We describe a hierarchy of models for legged locomotion, emphasizing relationships among feedforward (preflexive) stability, maneuverability, and reflexive feedback. We focus on a hexapedal geometry representative of insect locomotion in the ground plane that includes a neural central pattern generator circuit, nonlinear muscles, and a representative proprioceptive sensory pathway. Although these components of the model are rather complex, neglect of leg mass yields a neuromechanical system with only three degrees of freedom, and numerical simulations coupled with a Poincare map analysis shows that the feedforward dynamics is strongly stable, apart from one relatively slow mode and a neutral mode in body yaw angle. These modes moderate high frequency perturbations, producing slow heading changes that can be corrected by a stride-to-stride steering strategy. We show that the model’s response to a lateral impulsive perturbation closely matches that of a cockroach subject to a similar impulse. We also descri...


title: Neuro-fuzzy methods for nonlinear system identification

abstract: Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but also in industrial applications. Neuro-fuzzy modeling can be regarded as a gray-box technique on the boundary between neural networks and qualitative fuzzy models. The tools for building neuro-fuzzy models are based on combinations of algorithms from the fields of neural networks, pattern recognition and regression analysis. In this paper, an overview of neuro-fuzzy modeling methods for nonlinear system identification is given, with an emphasis on the tradeoff between accuracy and interpretability. © 2003 Published by Elsevier Science Ltd.


title: On the vulnerability of face recognition systems towards morphed face attacks

abstract: Morphed face images are artificially generated images, which blend the facial images of two or more different data subjects into one. The resulting morphed image resembles the constituent faces, both in visual and feature representation. If a morphed image is enroled as a probe in a biometric system, the data subjects contributing to the morphed image will be verified against the enroled probe. As a result of this infiltration, which is referred to as morphed face attack, the unambiguous assignment of data subjects is not warranted, i.e. the unique link between subject and probe is annulled. In this work, we investigate the vulnerability of biometric systems to such morphed face attacks by evaluating the techniques proposed to detect morphed face images. We create two new databases by printing and scanning digitally morphed images using two different types of scanners, a flatbed scanner and a line scanner. Further, the newly created databases are employed to study the vulnerability of state-of-the-art face recognition systems with a comprehensive evaluation.


title: SVATS: A Sensor-Network-Based Vehicle Anti-Theft System

abstract: Today vehicle theft rate is very high, thus tracking/alarming systems are being deployed with an increasingly popularity. These systems however bear some limitations such as high cost, high false-alarm rate, and easy to be disabled. This paper describes the design, implementation and evaluation of a Sensor-network-based Vehicle Anti-Theft System (SVATS) to address these limitations. In this system, the sensors in the vehicles that are parked within the same parking area first form a sensor network, then monitor and identify possible vehicle thefts by detecting unauthorized vehicle movement. When an unauthorized movement is detected, an alert will be reported to a base station in the parking area, which sends warning messages to the security office. This paper focuses on the technical issues specific to the system such as topology management, theft detection, and intra-vehicle networking.


title: Factors that impact implementing an agile software development methodology

abstract: The Internet economy has altered the rules of software engineering. Traditional development methodologies are too cumbersome to meet the rapidly changing requirements and short product cycles demanded by business. To meet these changing requirements, software developers have developed agile software development methodologies (SDMs) utilizing iterative development, prototyping, and templates. This research project investigated agile SDM implementation using an online survey sent to practitioners worldwide. This survey data were used to identify factors related to agile SDM implementation. Factors that included training, management involvement, access to external resources, and corporate size were found to impact implementation of an agile software development methodology. Factors such as using models, having an implementation plan, collocating the development team, and developing software for Internet or intranet use did not impact the implementation of an agile software development methodology. Several of the factors that impact the implementation of an agile development methodology are under the control of management. Organizations considering the implementation of an agile methodology may wish to manipulate some of these factors to increase the opportunities for success of their methodology.


title: An Overview of Solid-State Integrated Circuit Amplifiers in the Submillimeter-Wave and THz Regime

abstract: We present an overview of solid-state integrated circuit amplifiers approaching terahertz frequencies based on the latest device technologies which have emerged in the past several years. Highlights include the best reported data from heterojunction bipolar transistor (HBT) circuits, high electron mobility transistor (HEMT) circuits, and metamorphic HEMT (mHEMT) amplifier circuits. We discuss packaging techniques for the various technologies in waveguide modules and describe the best reported noise figures measured in these technologies. A consequence of THz transistors, namely ultra-low-noise at cryogenic temperatures, will be explored and results presented. We also present a short review of power amplifier technologies for the THz regime. Finally, we discuss emerging materials for THz amplifiers into the next decade.


title: Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

abstract: Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an p-distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction error metrics.


title: Digital product innovation within four classes of innovation networks

abstract: The increased digitization of organizational processes and products poses new challenges for understanding product innovation. It also opens new horizons for information systems research. We analyse how ongoing pervasive digitization of product innovation reshapes knowledge creation and sharing in innovation networks. We argue that advances in digital technologies 1 increase innovation network connectivity by reducing communication costs and increasing its reach and scope and 2 increase the speed and scope of digital convergence, which increases network knowledge heterogeneity and need for integration. These developments, in turn, stretch existing innovation networks by redistributing control and increasing the demand for knowledge coordination across time and space presenting novel challenges for knowledge creation, assimilation and integration. Based on this foundation, we distinguish four types of emerging innovation networks supported by digitalization: 1 project innovation networks; 2 clan innovation networks; 3 federated innovation networks; and 4 anarchic innovation networks. Each network involves different cognitive and social translations - or ways of identifying, sharing and assimilating knowledge. We describe the role of five novel properties of digital infrastructures in supporting each type of innovation network: representational flexibility, semantic coherence, temporal and spatial traceability, knowledge brokering and linguistic calibration. We identify several implications for future innovation research. In particular, we focus on the emergence of anarchic network forms that follow full-fledged digital convergence founded on richer innovation ontologies and epistemologies calling to critically re-examine the nature and impact of modularization for innovation.


title: TILT: Transform Invariant Low-Rank Textures

abstract: In this paper, we show how to efficiently and effectively extract a class of"low-rank textures"in a 3D scene from 2D images despite significant corruptions and warping. The low-rank textures capture geometrically meaningful structures in an image, which encompass conventional local features such as edges and corners as well as all kinds of regular, symmetric patterns ubiquitous in urban environments and man-made objects. Our approach to finding these low-rank textures leverages the recent breakthroughs in convex optimization that enable robust recovery of a high-dimensional low-rank matrix despite gross sparse errors. In the case of planar regions with significant affine or projective deformation, our method can accurately recover both the intrinsic low-rank texture and the precise domain transformation, and hence the 3D geometry and appearance of the planar regions. Extensive experimental results demonstrate that this new technique works effectively for many regular and near-regular patterns or objects that are approximately low-rank, such as symmetrical patterns, building facades, printed texts, and human faces.


title: A high precision resolver-to-DC converter

abstract: A newly developed resolver converter, providing a pseudolinear voltage proportional to the shaft angle, is presented. This converter is based on a new concept involving the absolute values of the demodulated sine and cosine resolver signals together with a dedicated linearization technique. The converter enables instantaneous determination of the mechanical angle with a theoretical error of nonlinearity below 0.011/spl deg/ over the 360/spl deg/ range. The practical performance of this converter is compared to that of a 10/sup 5/ pulses per revolution optical encoder arrangement. The theory of operation, computer simulation, full circuit details, and experimental results are given.


title: Towards Unified Data and Lifecycle Management for Deep Learning

abstract: Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, e.g., learned parameters and training logs, and it comprises of several frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and largely left to the users. This paper describes our vision and implementation of a data and lifecycle management system for deep learning. First, we generalize model exploration and model enumeration queries from commonly conducted tasks by deep learning modelers, and propose a high-level domain specific language (DSL), inspired by SQL, to raise the abstraction level and thereby accelerate the modeling process. To manage the variety of data artifacts, especially the large amount of checkpointed float parameters, we design a novel model versioning system (dlv), and a read-optimized parameter archival storage system (PAS) that minimizes storage footprint and accelerates query workloads with minimal loss of accuracy. PAS archives versioned models using deltas in a multi-resolution fashion by separately storing the less significant bits, and features a novel progressive query (inference) evaluation algorithm. Third, we develop e cient algorithms for archiving versioned models using deltas under co-retrieval constraints. We conduct extensive experiments over several real datasets from computer vision domain to show the e ciency of the proposed techniques.


title: Spectral hashing

abstract: Semantic hashing[1] seeks compact binary codes of data-points so that the Hamming distance between codewords correlates with semantic similarity. In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard. By relaxing the original problem, we obtain a spectral method whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian. By utilizing recent results on convergence of graph Laplacian eigenvectors to the Laplace-Beltrami eigenfunctions of manifolds, we show how to efficiently calculate the code of a novel data-point. Taken together, both learning the code and applying it to a novel point are extremely simple. Our experiments show that our codes outperform the state-of-the art.


WALKING WALKing walking: Action Recognition from Action Echoes


title: Competition for consciousness among visual events: the Psychophysics of reentrant visual processes

abstract: Advances in neuroscience implicate reentrant signaling as the predominant form of communication between brain areas. This principle was used in a series of masking experiments that defy explanation by feed-forward theories. The masking occurs when a brief display of target plus mask is continued with the mask alone. Two masking processes were found: an early process affected by physical factors such as adapting luminance and a later process affected by attentional factors such as set size. This later process is called masking by object substitution, because it occurs whenever there is a mismatch between the reentrant visual representation and the ongoing lower level activity. Iterative reentrant processing was formalized in a computational model that provides an excellent fit to the data. The model provides a more comprehensive account of all forms of visual masking than do the long-held feed-forward views based on inhibitory contour interactions.


title: Distributed Semantic Web Data Management in HBase and MySQL Cluster

abstract: Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.


title: Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction

abstract: We propose geometric mean based boosting algorithm (GMBoost).We propose GMBoost to resolve data imbalance problem.GMBoost considers geometric mean of error rates of majority and minority classes.We experiment GMBoost, AdaBoost and cost-sensitive boosting on bankruptcy prediction.The comparative results shows GMBoost outperforms in imbalanced and balanced data. In classification or prediction tasks, data imbalance problem is frequently observed when most of instances belong to one majority class. Data imbalance problem has received considerable attention in machine learning community because it is one of the main causes that degrade the performance of classifiers or predictors. In this paper, we propose geometric mean based boosting algorithm (GMBoost) to resolve data imbalance problem. GMBoost enables learning with consideration of both majority and minority classes because it uses the geometric mean of both classes in error rate and accuracy calculation. To evaluate the performance of GMBoost, we have applied GMBoost to bankruptcy prediction task. The results and their comparative analysis with AdaBoost and cost-sensitive boosting indicate that GMBoost has the advantages of high prediction power and robust learning capability in imbalanced data as well as balanced data distribution.


title: [Studies on chemical constituents in herb from Artemisia rupestris].

abstract: OBJECTIVE ::: To study the chemical constituents of Artemisia rupestris. ::: ::: ::: METHOD ::: The chemical constituents were isolated by column chromatography on silical gel and sephadex LH - 20. Their structures were elucidated on the basis of spectral analysis. ::: ::: ::: RESULT ::: 8 compounds have isolated from this plant, and the structures of them have identified as rupestonic acid (1), chrysosplenetin B (2), artemetin (3), herniarin (4), isokaempferide (5), vanillic acid (6), kaempferol 3, 3', 4'-trimethyl ether (7) and ermanine (8). ::: ::: ::: CONCLUSION ::: Compounds 2-8 have been isolated from this plant for the first time.


title: An analysis of ICON aircraft log through sentiment analysis using SVM and Naive Bayes classification

abstract: A more of work has been made to check the content of the social websites, especially the posts or the views of the users; their sentiment analysis has to be done. The main aim is to analyze the sentiments and then to compare those views with the main topics and other platforms. There are enormous amount of the posts given by the users on the social websites. But some of the people are interested in knowing the influence and the relations among those posts. Many of the researchers find the influence of the post on these social websites. But, we cannot find the influence of these posts without knowing the sentiments of the posts. Here we have discussed various methodologies for the sentiment analysis. Online reviews are important for the developers to improve their products and can compete in the market properly. There are several dimensions which help us to know the sentiment of the reviews on the websites. These analyses are also used to find some patterns and draw the conclusions. Hence we can find the favorable and as well as unfavorable parts of the posts. These reviews are important to analyze but there are a very few techniques to determine the sentiment of the reviews [2]. For example, one has given the approach to know the multidimensional rating from various reviews. From the vast amount of reviews on the internet based on different areas, it is very important to know about the different sentiments of the people.


title: Modelling Reusable Security Requirements based on an Ontology Framework

abstract: In recent years, security in Information Systems (IS) has become an important issue, and needs to be taken into account in all stages of IS development, including the early phase of Requirements Engineering (RE). Reuse of requirements improves the productivity and quality of software process and products. This can be facilitated by Semantic Web technologies. We describe an ontology-based framework for representing and reusing security requirements based on risk analysis. A risk analysis ontology and a requirement ontology have been developed and combined to represent reusable security requirements formally and to improve security in IS by detecting incompleteness and inconsistency and achieving semantic processing in requirements analysis. This extensible framework is the basis on which to elaborate a “lightweight” method to elicit and specify security requirements, based on security standards.


title: DDCO: Discovery of Deep Continuous Options forRobot Learning from Demonstrations

abstract: An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover options to accelerate reinforcement learning in Atari games. This paper studies an extension to robot imitation learning, called Discovery of Deep Continuous Options (DDCO), where low-level continuous control skills parametrized by deep neural networks are learned from demonstrations. We extend DDO with: (1) a hybrid categorical-continuous distribution model to parametrize high-level policies that can invoke discrete options as well continuous control actions, and (2) a cross-validation method that relaxes DDO's requirement that users specify the number of options to be discovered. We evaluate DDCO in simulation of a 3-link robot in the vertical plane pushing a block with friction and gravity, and in two physical experiments on the da Vinci surgical robot, needle insertion where a needle is grasped and inserted into a silicone tissue phantom, and needle bin picking where needles and pins are grasped from a pile and categorized into bins. In the 3-link arm simulation, results suggest that DDCO can take 3x fewer demonstrations to achieve the same reward compared to a baseline imitation learning approach. In the needle insertion task, DDCO was successful 8/10 times compared to the next most accurate imitation learning baseline 6/10. In the surgical bin picking task, the learned policy successfully grasps a single object in 66 out of 99 attempted grasps, and in all but one case successfully recovered from failed grasps by retrying a second time.


title: Fast QR code detection

abstract: Quick Response (QR) code is introduced by the Denso Wave in 1994 [7]. It can encode alphanumeric characters, a rich set of information can be made available through encoded URL addresses [2]. Nowadays, with the rich development of Mobile Internet and Internet of Things, it is desired to have a quick and efficient QR code recognition method. However, QR code detection is not an easy job. In this paper, a set of novel approaches to detecting QR code in arbitrarily acquired images are proposed. The proposed method has been evaluated with arbitrarily acquired images. The experimental results show that the proposed methods are effective way to detect QR code position.


title: Topic Quality Metrics Based on Distributed Word Representations

abstract: Automated evaluation of topic quality remains an important unsolved problem in topic modeling and represents a major obstacle for development and evaluation of new topic models. Previous attempts at the problem have been formulated as variations on the coherence and/or mutual information of top words in a topic. In this work, we propose several new metrics for evaluating topic quality with the help of distributed word representations; our experiments suggest that the new metrics are a better match for human judgement, which is the gold standard in this case, than previously developed approaches.


title: Understanding Long-Term Outcomes Following Sepsis: Implications and Challenges

abstract: Sepsis is life-threating organ dysfunction due to infection. Incidence of sepsis is increasing and the short-term mortality is improving, generating more sepsis survivors. These sepsis survivors suffer from additional morbidities such as higher risk of readmissions, cardiovascular disease, cognitive impairment and of death, for years following index sepsis episode. In the first year following index sepsis episode, approximately 60 % of sepsis survivors have at least one rehospitalisation episode, which is most often due to infection and one in six sepsis survivors die. Sepsis survivors also have a higher risk of cognitive impairment and cardiovascular disease contributing to the reduced life expectancy seen in this population, when assessed with life table comparisons. For optimal design of interventional trials to reduce these bad outcomes in sepsis survivors, in-depth understanding of major risk factors for these morbid events, their modifiability and a causal relationship to the pathobiology of sepsis is essential. This review highlights the recent advances, clinical and methodological challenges in our understanding of these morbid events in sepsis survivors.


title: Live Coding Towards Computational Creativity

abstract: Live coding is a way of improvising music or video animation through live edits of source code, using dynamic language interpreters. It requires artists to represent their work within a formal computer language, using a higher level of abstraction than is the norm. Although the only creative agents in live coding performance are human, this abstraction makes the practice of interest to the field of computational creativity. In this paper live coders are surveyed for their thoughts on live coding and creativity, related to the aims of building creative agents.


BatmanLab in the ImageCLEF Tuberculosis task 2017


title: Uniformly convergent schemes for singularly perturbed differential equations based on collocation methods

abstract: It is well known that a polynomial-based approximation scheme applied to a singularly perturbed equation is not uniformly convergent over the geometric domain of study. Such scheme results in a numerical solution, say σ which suffers from severe inac- curacies particularly in the boundary layer. What we say in the current paper is this: when one uses a grid which is not "too coarse" the resulted solution, even being nonuniformly convergent may be used in an iterated scheme to get a "good" approximation solution that is uniformly convergent over the whole geometric domain of study. In this paper, we use the collocation method as model of polynomial approximation. We start from a precise localization of the boundary layer then we decompose the domain of study, say Ω into the boundary layer, say Ω� and its complementary Ω0. Next we go to the heart of our work which is to make a repeated use of the collocation method. We show that the second generation of polynomial approximation is convergent and it yields an improved error bound compared to those usually appearing in the literature.


title: Single Image Super-resolution with a Parameter Economic Residual-like Convolutional Neural Network

abstract: Recent years have witnessed great success of convolutional neural network (CNN) for various problems both in low and high level visions. Especially noteworthy is the residual network which was originally proposed to handle high-level vision problems and enjoys several merits. This paper aims to extend the merits of residual network, such as skip connection induced fast training, for a typical low-level vision problem, i.e., single image super-resolution. In general, the two main challenges of existing deep CNN for supper-resolution lie in the gradient exploding/vanishing problem and large amount of parameters or computational cost as CNN goes deeper. Correspondingly, the skip connections or identity mapping shortcuts are utilized to avoid gradient exploding/vanishing problem. To tackle with the second problem, a parameter economic CNN architecture which has carefully designed width, depth and skip connections was proposed. Experimental results have demonstrated that the proposed CNN model can not only achieve state-of-the-art PSNR and SSIM results for single image super-resolution but also produce visually pleasant results.


title: On Incremental High Utility Sequential Pattern Mining

abstract: High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner+ algorithm to mine HUSPs incrementally. Specifically, to avoid redundant re-computations, we propose a tighter upper bound of the utility of a sequence, called Tight Sequence Utility (TSU), and then we design a novel data structure, called the candidate pattern tree, to buffer the sequences whose TSU values are greater than or equal to the minimum utility threshold in the original database. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of concise utility information is designed to be stored in each tree node. To improve the mining efficiency, several strategies are proposed to reduce the amount of computation for utility update and the scopes of database scans. Moreover, several strategies are also proposed to properly adjust the candidate pattern tree for the support of multiple database updates. Experimental results on some real and synthetic datasets show that IncUSP-Miner+ is able to efficiently mine HUSPs incrementally.


title: Eureka: A Framework for Enabling Static Malware Analysis

abstract: We introduce Eureka, a framework for enabling static analysis on Internet malware binaries. Eureka incorporates a novel binary unpacking strategy based on statistical bigram analysis and coarse-grained execution tracing. The Eureka framework uniquely distinguishes itself from prior work by providing effective evaluation metrics and techniques to assess the quality of the produced unpacked code. Eureka provides several Windows API resolution techniques that identify system calls in the unpacked code by overcoming various existing control flow obfuscations. Eureka's unpacking and API resolution capabilities facilitate the structural analysis of the underlying malware logic by means of micro-ontology generation that labels groupings of identified API calls based on their functionality. They enable a visual means for understanding malware code through the automated construction of annotated control flow and call graphs.Our evaluation on multiple datasets reveals that Eureka can simplify analysis on a large fraction of contemporary Internet malware by successfully unpacking and deobfuscating API references.


title: A Hybrid Approach for Named Entity Recognition in Indian Languages

abstract: In this paper we describe a hybrid system that applies Maximum Entropy model (MaxEnt), language specific rules and gazetteers to the task of Named Entity Recognition (NER) in Indian languages designed for the IJCNLP NERSSEAL shared task. Starting with Named Entity (NE) annotated corpora and a set of features we first build a baseline NER system. Then some language specific rules are added to the system to recognize some specific NE classes. Also we have added some gazetteers and context patterns to the system to increase the performance. As identification of rules and context patterns requires language knowledge, we were able to prepare rules and identify context patterns for Hindi and Bengali only. For the other languages the system uses the MaxEnt model only. After preparing the one-level NER system, we have applied a set of rules to identify the nested entities. The system is able to recognize 12 classes of NEs with 65.13% f-value in Hindi, 65.96% f-value in Bengali and 44.65%, 18.74%, and 35.47% f-value in Oriya, Telugu and Urdu respectively.