title: A software component quality framework

abstract: One of the major problems with Component-Based Software Engineering (CBSE) is the quality of the components used in a system. The reliability of a component-based software system depends on the reliability of the components that is made of. In CBSE, the proper search, selection and evaluation process of components is considered the cornerstone for the development of any effective component-based system. So far the software industry was concentrated on the functional aspects of components, leaving aside the difficult task of assessing their quality. In this way, we propose a software component quality framework to evaluate the quality of software components in an efficient way. Moreover, an experimental study was accomplished in order to evaluate the viability of the proposed framework.


title: Dynamic Programming for Partially Observable Stochastic Games

abstract: We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable Markov decision processes (POMDPs) and iterated elimination or dominated strategies in normal form games. We prove that when applied to finite-horizon POSGs, the algorithm iteratively eliminates very weakly dominated strategies without first forming a normal form representation of the game. For the special case in which agents share the same payoffs, the algorithm can be used to find an optimal solution. We present preliminary empirical results and discuss ways to further exploit POMDP theory in solving POSGs.


title: Choosy: max-min fair sharing for datacenter jobs with constraints

abstract: Max-Min Fairness is a flexible resource allocation mechanism used in most datacenter schedulers. However, an increasing number of jobs have hard placement constraints, restricting the machines they can run on due to special hardware or software requirements. It is unclear how to define, and achieve, max-min fairness in the presence of such constraints. We propose Constrained Max-Min Fairness (CMMF), an extension to max-min fairness that supports placement constraints, and show that it is the only policy satisfying an important property that incentivizes users to pool resources. Optimally computing CMMF is challenging, but we show that a remarkably simple online scheduler, called Choosy, approximates the optimal scheduler well. Through experiments, analysis, and simulations, we show that Choosy on average differs 2% from the optimal CMMF allocation, and lets jobs achieve their fair share quickly.


title: Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining

abstract: Dealing with memory and time constraints are current challenges when learning from data streams with a massive amount of data. Many algorithms have been proposed to handle these difficulties, among them, the Very Fast Decision Tree (VFDT) algorithm. Although the VFDT has been widely used in data stream mining, in the last years, several authors have suggested modifications to increase its performance, putting aside memory concerns by proposing memory-costly solutions. Besides, most data stream mining solutions have been centred around ensembles, which combine the memory costs of their weak learners, usually VFDTs. To reduce the memory cost, keeping the predictive performance, this study proposes the Strict VFDT (SVFDT), a novel algorithm based on the VFDT. The SVFDT algorithm minimises unnecessary tree growth, substantially reducing memory usage and keeping competitive predictive performance. Moreover, since it creates much more shallow trees than VFDT, SVFDT can achieve a shorter processing time. Experiments were carried out comparing the SVFDT with the VFDT in 11 benchmark data stream datasets. This comparison assessed the trade-off between accuracy, memory, and processing time. Statistical analysis showed that the proposed algorithm obtained similar predictive performance and significantly reduced processing time and memory use. Thus, SVFDT is a suitable option for data stream mining with memory and time limitations, recommended as a weak learner in ensemble-based solutions.


Universal Dependencies Version 2 for Japanese.


title: The DARPA COSMOS program: The convergence of InP and Silicon CMOS technologies for high-performance mixed-signal

abstract: The COmpound Semiconductor Materials On Silicon (COSMOS) program of the U.S. Defense Advanced Research Projects Agency (DARPA) focuses on developing transistor-scale heterogeneous integration processes to intimately combine advanced compound semiconductor (CS) devices with high-density silicon circuits. The technical approaches being explored in this program include high-density micro assembly, monolithic epitaxial growth, and epitaxial layer printing processes. In Phase I of the program, performers successfully demonstrated world-record differential amplifiers through heterogeneous integration of InP HBTs with commercially fabricated CMOS circuits. In the current Phase II, complex wideband, large dynamic range, high-speed digital-to-analog convertors (DACs) are under development based on the above heterogeneous integration approaches. These DAC designs will utilize InP HBTs in the critical high-speed, high-voltage swing circuit blocks and will employ sophisticated in situ digital correction techniques enabled by CMOS transistors. This paper will also discuss the Phase III program plan as well as future directions for heterogeneous integration technology that will benefit mixed signal circuit applications.


title: Separation-Individuation Theory and Attachment Theory

abstract: Separation-individuation and attachment theories are compared and assessed in the context of psychoanalytic developmental theory and their application to clinical work. As introduced by Margaret Mahler and John Bowlby, respectively, both theories were initially regarded as diverging from traditional views. Separation-individuation theory, though it has had to be corrected in important respects, and attachment theory, despite certain limitations, have nonetheless enriched psychoanalytic thought. Without attachment an infant would die, and with severely insecure attachment is at greater risk for serious disorders. Development depends on continued attachment to a responsive and responsible caregiver. Continued attachment to the primary object was regarded by Mahler as as intrinsic to the process of separation-individuation. Attachment theory does not account for the essential development of separateness, and separation-individuation is important for the promotion of autonomy, independence, and identity. Salient historical and theoretical issues are addressed, including the renewed interest in attachment theory and the related decline of interest in separation-individuation theory.


title: Multiple-antenna techniques for wireless communications - a comprehensive literature survey

abstract: The use of multiple antennas for wireless communication systems has gained overwhelming interest during the last decade - both in academia and industry. Multiple antennas can be utilized in order to accomplish a multiplexing gain, a diversity gain, or an antenna gain, thus enhancing the bit rate, the error performance, or the signal-to-noise-plus-interference ratio of wireless systems, respectively. With an enormous amount of yearly publications, the field of multiple-antenna systems, often called multiple-input multiple-output (MIMO) systems, has evolved rapidly. To date, there are numerous papers on the performance limits of MIMO systems, and an abundance of transmitter and receiver concepts has been proposed. The objective of this literature survey is to provide non-specialists working in the general area of digital communications with a comprehensive overview of this exciting research field. To this end, the last ten years of research efforts are recapitulated, with focus on spatial multiplexing and spatial diversity techniques. In particular, topics such as transmitter and receiver structures, channel coding, MIMO techniques for frequency-selective fading channels, diversity reception and space-time coding techniques, differential and non-coherent schemes, beamforming techniques and closed-loop MIMO techniques, cooperative diversity schemes, as well as practical aspects influencing the performance of multiple-antenna systems are addressed. Although the list of references is certainly not intended to be exhaustive, the publications cited will serve as a good starting point for further reading.


title: WiFi Direct and LTE D2D in action

abstract: With the evolution of high-performance multi-radio smartphones, Device-to-Device (D2D) communications became an attractive solution for enhancing the performance of cellular networks. Although D2D communications have been widely studied within past few years, the majority of the literature is confined to new theoretical proposals and did not consider implementation challenges. In fact, the implementation feasibility of D2D communications and its challenges are still a relevant research question. In this paper, we introduce a protocol that focuses on D2D communications using LTE and WiFi Direct technologies. We also show that currently available WiFi Direct features permits to deploy the D2D paradigm on top of the LTE cellular infrastructure, without requiring any fundamental change in LTE protocols.


title: Balancing exploration and exploitation in learning to rank online

abstract: As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need to both explore new solutions to obtain feedback for effective learning, and exploit what has already been learned to produce results that are acceptable to users. We formulate this challenge as an exploration-exploitation dilemma and present the first online learning to rank algorithm that works with implicit feedback and balances exploration and exploitation. We leverage existing learning to rank data sets and recently developed click models to evaluate the proposed algorithm. Our results show that finding a balance between exploration and exploitation can substantially improve online retrieval performance, bringing us one step closer to making online learning to rank work in practice.


title: Accurate and Fast Proximity Queries Between Polyhedra Using Convex Surface Decomposition

abstract: The need to perform fast and accurate proximity queries arises frequently in physically-based modeling, simulation, animation, real-time interaction within a virtual environment, and game dynamics. The set of proximity queries include intersection detection, tolerance verification, exact and approximate minimum distance computation, and (disjoint) contact determination. Specialized data structures and algorithms have often been designed to perform each type of query separately. We present a unified approach to perform any of these queries seamlessly for general, rigid polyhedral objects with boundary representations which are orientable 2-manifolds. The proposed method involves a hierarchical data structure built upon a surface decomposition of the models. Furthermore, the incremental query algorithm takes advantage of coherence between successive frames. It has been applied to complex benchmarks and compares very favorably with earlier algorithms and systems.


title: Unsupervised Multilingual Word Embeddings

abstract: Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities for low-resource languages. Prior art for learning UMWEs, however, merely relies on a number of independently trained Unsupervised Bilingual Word Embeddings (UBWEs) to obtain multilingual embeddings. These methods fail to leverage the interdependencies that exist among many languages. To address this shortcoming, we propose a fully unsupervised framework for learning MWEs that directly exploits the relations between all language pairs. Our model substantially outperforms previous approaches in the experiments on multilingual word translation and cross-lingual word similarity. In addition, our model even beats supervised approaches trained with cross-lingual resources.


title: Improved techniques for result caching in web search engines

abstract: Query processing is a major cost factor in operating large web search engines. In this paper, we study query result caching, one of the main techniques used to optimize query processing performance. Our first contribution is a study of result caching as a weighted caching problem. Most previous work has focused on optimizing cache hit ratios, but given that processing costs of queries can vary very significantly we argue that total cost savings also need to be considered. We describe and evaluate several algorithms for weighted result caching, and study the impact of Zipf-based query distributions on result caching. Our second and main contribution is a new set of feature-based cache eviction policies that achieve significant improvements over all previous methods, substantially narrowing the existing performance gap to the theoretically optimal (clairvoyant) method. Finally, using the same approach, we also obtain performance gains for the related problem of inverted list caching.


title: Design and Optimization of SIW Center-Fed Series Rectangular Dielectric Resonator Antenna Array With 45° Linear Polarization

abstract: The modeling, design, and optimization of a new substrate integrated waveguide middle-fed series rectangular dielectric resonator antenna array with 45° linear polarization are presented. The implicit space mapping technique is applied for the optimization of the antenna array. A tunable circuit model is built, which serves as the coarse/surrogate mode, and a full-wave solver is used as the fine model. High design and optimization efficiency is demonstrated using an eight-element array. The reflection coefficient of the array antenna is optimized with only two iterations. The experimental data for the eight elements antenna array, operating at the millimeter-wave band (34–36 GHz), are used for the validation of both performance of the design and modeling techniques. The measured radiation pattern demonstrates a broadside beam with a maximum radiated gain of 13.55 dB over an operating impedance bandwidth of 2.2 GHz.


title: Chapter 2 – Classifiers Based on Bayes Decision Theory

abstract: Publisher Summary ::: This chapter explores classifiers based on Bayes Decision Theory. The chapter primarily focuses on Bayesian classification and techniques for estimating unknown probability density functions based on the available experimental evidence. The chapter also deals with the design of the classifier in a pattern recognition system. While discussing the concept of minimizing the classification error probability, it is shown that the Bayesian classifier is optimal with respect to minimizing the classification error probability. Special focus is put on the Bayesian classification, the minimum distance (Euclidean and Mahalanobis), the nearest neighbor classifiers, and the naive Bayes classifier. The chapter approaches the classification problem via Bayesian probabilistic arguments with a goal to minimize the classification error probability or the risk. Examples are presented to show that not all problems are well suited to such approaches, and in those cases, it may be preferable to compute decision surfaces directly by means of alternative costs. The chapter also focuses on a particular family of decision surfaces associated with the Bayesian classification for the specific case of Gaussian density functions.


title: Formal Specifications and Test: Correctness and Oracle

abstract: This article presents a new formal approach to testing. In the field of dynamic testing, as soon as a program fails for a test set, it is flagged incorrect. The remaining question is: how far can a successful program be considered as correct? We give a definition of program correctness with respect to a specification which is adequate to dynamic testing. Similarly to the field of abstract implementation, the idea is that in order to declare a program as correct, it suffices that its behavior fulfills the specification requirements. An intermediate semantic level between the program and the specification, called the oracle framework, is introduced in order to interpret observable results obtained from dynamic experiments on the program. This allows to give algebraic semantics (i.e. a set of models) to the program, compatible with the program behavior. Program correctness is then defined by some adequacy criterion between the specification semantics and the program semantics. We point out that while for some specifications, there exist exhaustive test sets (the success of which means program correctness), for some other specifications, there only exist “complete” (but not exhaustive) test sets. Of course, all the programs rejected by a complete test set are incorrect but unfortunately, there still exist successful incorrect programs. We also explain how the test set selection can be formalized within our approach.


title: Dynamic FAUST: Registering Human Bodies in Motion

abstract: While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data, that is 3D scans of moving non-rigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http://dfaust.is.tue.mpg.de.


title: Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks

abstract: We compute upper and lower bounds on the VC dimension and pseudo-dimension of feedforward neural networks composed of piecewise polynomial activation functions. We show that if the number of layers is fixed, then the VC dimension and pseudo-dimension grow as WlogW, where W is the number of parameters in the network. This result stands in opposition to the case where the number of layers is unbounded, in which case the VC dimension and pseudo-dimension grow as W2. We combine our results with recently established approximation error rates and determine error bounds for the problem of regression estimation by piecewise polynomial networks with unbounded weights.


title: Central Pattern Generators for Gait Generation in Bipedal Robots

abstract: An obvious problem confronting humanoid robotics is the generation of stable and efficient gaits. Whereas wheeled robots normally are statically balanced and remain upright regardless of the torques applied to the wheels, a bipedal robot must be actively balanced, particularly if it is to execute a human-like, dynamic gait. The success of gait generation methods based on classical control theory, such as the zero-moment point (ZMP) method (Takanishi et al., 1985), relies on the calculation of reference trajectories for the robot to follow. In the ZMP method, control torques are generated in order to keep the zero-moment point within the convex hull of the support area defined by the feet. When the robot is moving in a well-known environment, the ZMP method certainly works well. However, when the robot finds itself in a dynamically changing real-world environment, it will encounter unexpected situations that cannot be accounted for in advance. Hence, reference trajectories can rarely be specified under such circumstances. In order to address this problem, alternative, biologically inspired control methods have been proposed, which do not require the specification of reference trajectories. The aim of this chapter is to describe one such method, based on central pattern generators (CPGs), for control of bipedal robots. Clearly, walking is a rhythmic phenomenon, and many biological organisms are indeed equipped with CPGs, i.e. neural circuits capable of producing oscillatory output given tonic (non-oscillating) activation (Grillner, 1996). There exists biological evidence for the presence of central pattern generators in both lower and higher animals. The lamprey, which is one of the earliest and simplest vertebrate animals, swims by propagating an undulation along its body. The wave-like motion is produced by an alternating activation of motor neurons on the left and right sides of the segments along the body. The lamprey has a brain stem and spinal cord with all basic vertebrate features, but with orders of magnitude fewer nerve cells of each type than higher vertebrates. Therefore, it has served as a prototype organism for the detailed analysis of the nervous system, including CPGs, in neurophysiological studies (Grillner, 1991; Grillner, 1995). In some early experiments by Brown (Brown, 1911, Brown, 1912), it was shown that cats with transected spinal cord and with cut dorsal roots still showed rhythmic alternating contractions in ankle flexors and extensors. This was the basis of the concept of a spinal locomotor center, which Brown termed the half-center model (Brown, 1914). Further biological support for the existence of a spinal CPG structure in vertebrates is presented in (Duysens & Van de Crommert, 1998).


title: The impact of phase noise parameters on target signal detection in FMCW-radar system simulations for automotive applications

abstract: Accurate system simulations have proven to be a valuable aid during the development process and help to decrease the time-to-market and the amount of design iterations, thus keeping development costs at a moderate level. Therefore a frequency-modulated continuous-wave (FMCW) radar system simulation environment has been built up, enabling for performance analysis of millimeter-wave radar frontends for automotive applications. A key parameter especially for radar systems applying the FMCW principle thereby is the linearity of the frequency ramp as it directly affects the quality of the target signal and thus the performance of the entire radar sensor. Hence in this scientific paper we investigate the influence of a phase noise afflicted VCO on the target signal detection, and the range resolution respectively. Leeson's equation is applied to model oscillator phase noise and the results show which parameters are crucial for the radar system. Eventually a two-target scenario is calculated to substantiate the statements taken.


title: Nonparametric and sparse signal representations in array processing via iterative adaptive approaches

abstract: This paper addresses the problem of source localization and waveform estimation in array processing applications. We present two nonparametric user parameter free algorithms, namely the iterative adaptive approach (IAA) and the maximum likelihood based IAA (IAA-ML). Both IAA and IAA-ML can work with arbitrary array geometries and uncorrelated as well as coherent sources. We extend IAA and IAA-ML to yield sparse results by using the Bayesian information criterion (BIC). Further improvements in performance can be achieved by employing the last step of the parametric RELAX algorithm initialized by the estimates of IAA and IAA-ML with BIC. Our simulations demonstrate that IAA and IAA-ML show better performance than the most competitive sparse signal representation approaches in the literature: M-FOCUSS, M-SBL and lscr1-SVD.


title: City-scale traffic estimation from a roving sensor network

abstract: Traffic congestion, volumes, origins, destinations, routes, and other road-network performance metrics are typically collected through survey data or via static sensors such as traffic cameras and loop detectors. This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above. In this paper we conduct a case study that demonstrates that it is possible to accurately infer traffic volume through data collected from a roving sensor network of taxi probes that log their locations and speeds at regular intervals. Our model and inference procedures can be used to analyze traffic patterns and conditions from historical data, as well as to infer current patterns and conditions from data collected in real-time. As such, our techniques provide a powerful new sensor network approach for traffic visualization, analysis, and urban planning.


title: Characteristics and nutritional and cardiovascular-health properties of seaweeds.

abstract: While marine algae have traditionally formed part of the Oriental diet, their major use in Western countries has been in the phytocolloid industry. Only a few coastal communities outside Asia have customarily used seaweeds as components of special dishes. Of late, however, seaweeds have gained importance as foodstuffs in Western countries and most recently as components of functional foods because of their high dietary fiber, mineral, vitamin, and phytochemical content, low energy levels, and high concentrations of certain polyunsaturated fatty acids. The present paper reviews the available data for some of the components of the major edible algae and studies several factors that can affect their physiochemical properties (e.g., hydration, water and oil-holding capacity, fermentability, binding capacity, etc.) and, in turn, their nutritional importance. The effects of marine alga consumption on growth and body weight, mineral availability, lipid metabolism, blood pressure, and antioxidant properties are reviewed, together with preliminary data on the effects of some functional foods containing seaweeds on lipid metabolism and gene expression of enzymes engaged in antioxidant protection. This review concludes with some remarks regarding the danger of the improper use of seaweeds in herbal medications. In addition, as the properties of algae are highly dependent on their individual composition, any generalization regarding these properties may be considered misleading and scientifically inappropriate.


title: Learning-Based Shadow Recognition and Removal From Monochromatic Natural Images

abstract: This paper addresses the problem of recognizing and removing shadows from monochromatic natural images from a learning-based perspective. Without chromatic information, shadow recognition and removal are extremely challenging in this paper, mainly due to the missing of invariant color cues. Natural scenes make this problem even harder due to the complex illumination condition and ambiguity from many near-black objects. In this paper, a learning-based shadow recognition and removal scheme is proposed to tackle the challenges above-mentioned. First, we propose to use both shadow-variant and invariant cues from illumination, texture, and odd order derivative characteristics to recognize shadows. Such features are used to train a classifier via boosting a decision tree and integrated into a conditional random field, which can enforce local consistency over pixel labels. Second, a Gaussian model is introduced to remove the recognized shadows from monochromatic natural scenes. The proposed scheme is evaluated using both qualitative and quantitative results based on a novel database of hand-labeled shadows, with comparisons to the existing state-of-the-art schemes. We show that the shadowed areas of a monochromatic image can be accurately identified using the proposed scheme, and high-quality shadow-free images can be precisely recovered after shadow removal.


title: Unemployment Alters the Set Point for Life Satisfaction

abstract: According to set-point theories of subjective well-being, people react to events but then return to baseline levels of happiness and satisfaction over time. We tested this idea by examining reaction and adaptation to unemployment in a 15-year longitudinal study of more than 24,000 individuals living in Germany. In accordance with set-point theories, individuals reacted strongly to unemployment and then shifted back toward their baseline levels of life satisfaction. However, on average, individuals did not completely return to their former levels of satisfaction, even after they became reemployed. Furthermore, contrary to expectations from adaptation theories, people who had experienced unemployment in the past did not react any less negatively to a new bout of unemployment than did people who had not been previously unemployed. These results suggest that although life satisfaction is moderately stable over time, life events can have a strong influence on long-term levels of subjective well-being.


title: Mobility performance of LTE co-channel deployment of macro and pico cells

abstract: This paper aims at analyzing the mobility performance in heterogeneous 3GPP (3rd Generation Partnership Project) Long Term Evolution (LTE) networks. The main objective is to analyze the behavior of LTE macro/pico co-channel networks with different mobility parameters, such as the “Time-To-Trigger” (TTT). For this purpose, a system simulator has been utilized to assess the performance. The system must have different settings depending on the user velocity and on which cell-layer user is being serviced. Additionally, we have considered scenarios with a mixture of users moving freely or constrained to a hotspot, and different pico cell deployments.


MLP yes! ILP no


title: Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

abstract: This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.


title: Platform-based design and software design methodology for embedded systems

abstract: Embedded products have become so complex and must be developed so quickly that current design methodologies are no longer adequate. The authors' vision for the future of embedded-system design involves two essential components: a rigorous methodology for embedded software development and platform-based design.


title: Internal Permanent Magnet Motor Design for Electric Vehicle Drive

abstract: Power compaction and high efficiency are two key advantages of permanent magnet motors. This paper proposes an enhanced internal permanent magnet motor that delivers high torque, power compaction, and exceptionally high efficiency in the same operation area. The advantage of the proposed scheme is the magnetic flux accumulation in the air gap, which allows much higher values of magnetic flux density, compared to a surface permanent magnet motor of the same size. The original contribution of this paper resides on the adopted motor configuration, enabling to efficiently utilize the energy stored in the permanent magnet and to provide total loss minimization at the most frequently used speed range.


Convergence and Hybrid Information Technology


title: Study of battery modeling using mathematical and circuit oriented approaches

abstract: Energy storage improves the efficiency and reliability of the electric utility system. The most common device used for storing electrical energy is batteries. To investigate power converter-based charge and discharge control of a battery storage device, effective battery models are critically needed. This paper presents a comparison study of mathematical and circuit-oriented battery models with a focus on lead-acid batteries that are normally used for large power storage applications. The paper shows how mathematical and circuit-oriented battery models are developed to reflect typical battery electrochemical properties. The relation between mathematical and circuit-oriented battery models is analyzed in the paper. Comparison study is made to investigate the difference and complexity in parameter extraction using the two different modeling approaches. The paper shows that the fundamental battery electromechanical relationship is usually built into the battery mathematical model but is not directly available in the circuit-oriented model. In terms of the computational complexity, the circuit-oriented battery model requires much expensive computing resources. Performance study is conducted to evaluate various factors that may affect the behavior of the mathematical battery models.


title: Protecting posted genes: social networking and the limits of GINA.

abstract: The combination of decreased genotyping costs and prolific social media use is fueling a personal genetic testing industry in which consumers purchase and interact with genetic risk information online. Consumers and their genetic risk profiles are protected in some respects by the 2008 federal Genetic Information Nondiscrimination Act (GINA), which forbids the discriminatory use of genetic information by employers and health insurers; however, practical and technical limitations undermine its enforceability, given the everyday practices of online social networking and its impact on the workplace. In the Web 2.0 era, employers in most states can legally search about job candidates and employees online, probing social networking sites for personal information that might bear on hiring and employment decisions. We examine GINA's protections for online sharing of genetic information as well as its limitations, and propose policy recommendations to address current gaps that leave employees' genetic information vulnerable in a Web-based world.


title: SPHINX: Detecting Security Attacks in Software-Defined Networks.

abstract: Software-defined networks (SDNs) allow greater control over network entities by centralizing the control plane, but place great burden on the administrator to manually ensure security and correct functioning of the entire network. We list several attacks on SDN controllers that violate network topology and data plane forwarding, and can be mounted by compromised network entities, such as end hosts and soft switches. We further demonstrate their feasibility on four popular SDN controllers. We propose SPHINX to detect both known and potentially unknown attacks on network topology and data plane forwarding originating within an SDN. SPHINX leverages the novel abstraction of flow graphs, which closely approximate the actual network operations, to enable incremental validation of all network updates and constraints. SPHINX dynamically learns new network behavior and raises alerts when it detects suspicious changes to existing network control plane behavior. Our evaluation shows that SPHINX is capable of detecting attacks in SDNs in realtime with low performance overheads, and requires no changes to the controllers for deployment.


title: Detection of concealed information by P3 and frontal EEG asymmetry

abstract: Psychophysiological detection of deception has seen increased attention in both research and applied settings. In this field, the most scientifically validated paradigm is the Concealed Information Test (CIT). The CIT does not directly deal with whether a participant is lying, but examines whether a participant recognizes a critical relevant detail, inferred by differences in physiological responses between critical and non-critical items. Although event-related potential (ERP) approaches to the CIT have shown high accuracy, a combination of measures might improve the test's performance. We thus assessed whether a new CIT index, frontal EEG asymmetry that is supposed to reflect differences in approach/withdrawal motivation, would prove useful. Nineteen participants were asked to steal one item in a mock crime, and were then administered two CITs while concealing the stolen item. One CIT included the stolen item (i.e., guilty condition), whereas the other CIT did not (i.e., innocent condition). In the guilty condition, the concealed stolen item elicited greater relative left frontal alpha activity (indicative of relative right frontal cortical activity) as compared to the other items, suggesting that the recognition of the concealed item might have induced withdrawal motivation. Although the discrimination between guilty and innocent conditions by the asymmetry score alone was not as good as that by the ERP P3 index, combining the asymmetry score and P3 improved the detection performance significantly. The results suggest that the frontal EEG asymmetry can be used as a new measure in the CIT that provides additional information beyond that captured by the traditional ERP index.


title: Development of anthropomorphic robot hand with dual-mode twisting actuation and electromagnetic joint locking mechanism

abstract: In this paper, the anthropomorphic robot hand is newly proposed by adopting dual-mode twisting actuation and EM joint locking mechanism. The proposed robot hand consists of five finger modules. Each finger has four links and three joints, and Joint 2 and 3 are coupled by the four-bar linkage mechanism. The dual-mode twisting actuation allows that the robot finger can move fast (up to 356.7 deg/sec) in Mode I and generate a large grasping force(up to 36.5 N) in Mode II. In addition, the workspace of the robot finger module is enlarged by EM joint locking mechanism depending on the locking states. In order to verify the effectiveness of the mechanisms adopted in the robot hand, we theoretically and numerically analyze the performances of the robot finger module such as bending speed, fingertip force, and workspace. Finally, through the developed robot hand, we perform the grasping test for various objects and the grasping performance is experimentally demonstrated.


title: Weakly- and Semi-Supervised Panoptic Segmentation

abstract: We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks. In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances. Moreover, we are able to segment both"thing"and"stuff"classes, and thus explain all the pixels in the image."Thing"classes are weakly-supervised with bounding boxes, and"stuff"with image-level tags. We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fully-supervised performance). Furthermore, we present the first weakly-supervised results on Cityscapes for both semantic- and instance-segmentation. Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.


Terrain Adaptive Navigation for planetary rovers


title: FlexVec: auto-vectorization for irregular loops

abstract: Traditional vectorization techniques build a dependence graph with distance and direction information to determine whether a loop is vectorizable. Since vectorization reorders the execution of instructions across iterations, in general instructions involved in a strongly connected component (SCC) are deemed not vectorizable unless the SCC can be eliminated using techniques such as scalar expansion or privatization. Therefore, traditional vectorization techniques are limited in their ability to efficiently handle loops with dynamic cross-iteration dependencies or complex control flow interweaved within the dependence cycles. When potential dependencies do not occur very often, the end-result is under utilization of the SIMD hardware. In this paper, we propose FlexVec architecture that combines new vector instructions with novel code generation techniques to dynamically adjusts vector length for loop statements affected by cross-iteration dependencies that happen at runtime. We have designed and implemented FlexVec's new ISA as extensions to the recently released AVX-512 ISA. We have evaluated the performance improvements enabled by FlexVec vectorization for 11 C/C++ SPEC 2006 benchmarks and 7 real applications with AVX-512 vectorization as baseline. We show that FlexVec vectorization technique produces a Geomean speedup of 9% for SPEC 2006 and a Geomean speedup of 11% for 7 real applications.


title: Adversarial Classifier for Imbalanced Problems

abstract: Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper, we propose an adversarial framework for classifier training that can also handle imbalanced data. Indeed, a network is trained via an adversarial approach to give weights to samples of the majority class such that the obtained classification problem becomes more challenging for the discriminator and thus boosts its classification capability. In addition to the general imbalanced classification problems, the proposed method can also be used for problems such as graph representation learning in which it is desired to discriminate similar nodes from dissimilar nodes. Experimental results on imbalanced data classification and on the tasks like graph link prediction show the superiority of the proposed method compared to the state-of-the-art methods.


title: Connecting the Dots Between MLE and RL for Sequence Generation

abstract: Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms. For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem. Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency. A variety of other algorithms such as RAML, SPG, and data noising, have also been developed from different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters. The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency. Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning. Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.


title: Hidden Markov Models and other Finite State Automata for . . .

abstract: Keywords: learning ; speech Reference EPFL-CHAPTER-82756 URL: http://publications.idiap.ch/downloads/reports/2001/rr01-37.pdf Related documents: http://publications.idiap.ch/index.php/publications/showcite/bourlard:2001:idiap-01-37 Record created on 2006-03-10, modified on 2017-05-10


title: Stand-Allocation System (SAS): A Constraint-Based System Developed with Software Components

abstract: The stand-allocation system (SAS) is an AI application developed for the Hong Kong International Airport (HKIA) at Chek Lap Kok. sas uses constraint-programming techniques to assign parking stands to aircraft and schedules tow movements based on a set of business and operational constraints. The system provides planning, real-time operation, and problem-solving capabilities. sas generates a stand-allocation plan that finely balances the objectives of the airline-handling agents, the convenience of passengers, and the operational constraints of the airport. The system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. SAS is an example of how development methodologies used to construct modern AI applications have become fully inline with mainstream practices.


title: Manufacturing Industries Need Design of Experiments (DoE)

abstract: Although experimentation is a frequent activity of engineers, they usually use primitive strategies to carry on their experiments. A survey identifies the needs of using an efficient and practical technique for the experimentation. Although Six Sigma improvement initiative emphasized the use of Design of Experiments (DoE) for experimentation, engineers still consider it a difficult technique to apply and interpret. For this reason, this paper presents a methodology that tries to make easier the implementation of DoE, as an approach to bridge the existing gap between the technique and industries.


title: Computing Communities in Large Networks Using Random Walks

abstract: Dense subgraphs of sparse graphs (communities), which appear in most real-world complex networks, play an important role in many contexts. Computing them however is generally expensive. We propose here a measure of similarities between vertices based on random walks which has several important advantages: it captures well the community structure in a network, it can be computed efficiently, it works at various scales, and it can be used in an agglomerative algorithm to compute efficiently the community structure of a network. We propose such an algorithm which runs in time O(mn2) and space O(n2) in the worst case, and in time O(n2log n) and space O(n2) in most real-world cases (n and m are respectively the number of vertices and edges in the input graph).


title: 3D Bounding Boxes for Road Vehicles: A One-Stage, Localization Prioritized Approach Using Single Monocular Images

abstract: Understanding 3D semantics of the surrounding objects is critically important and a challenging requirement from the safety perspective of autonomous driving. We present a localization prioritized approach for effectively localizing the position of the object in the 3D world and fit a complete 3D box around it. Our method requires a single image and performs both 2D and 3D detection in an end to end fashion. Estimating depth of an object from a monocular image is not as generalizable as pose and dimensions. Hence, we approach this problem by effectively localizing the projection of the center of bottom face of 3D bounding box (CBF) to the image. Later in our post processing stage, we use a look up table based approach to reproject the CBF in the 3D world. This stage is a single time setup and simple enough to be deployed in fixed map communities where we can store complete knowledge about the ground plane. The object’s dimension and pose are predicted in multitask fashion using a shared set of features. Experiments show that our method is able to produce smooth tracks for surround objects and outperforms existing image based approaches in 3D localization.


title: Shear failures in reinforced concrete members without transverse reinforcement: An analysis of the critical shear crack development on the basis of test results

abstract: Abstract Shear in concrete members without transverse reinforcement can be carried by various potential shear-transfer actions, whose activation depends much on the actual cracking pattern and kinematics at failure. Failures can occur in a progressive manner (at the end of a stable propagation of a critical shear crack) or in a sudden manner (by an unstable progression or development of a new crack). In addition, the development and shape of the failure crack may also be very different from case to case. These differences influence which shear-transfer actions may be governing for a given member and loading situation. Despite the large number of specimens tested in shear, almost no information on the actual crack development during the process of failure is yet available. This paper presents the results of an experimental programme consisting of thirteen beams. The tests were designed to investigate different structural systems and loading conditions commonly found in practice (cantilevers with concentrated and distributed loading, single span beams with distributed loading and continuous beams). The cracking patterns and their associated kinematics were tracked in detail by using photogrammetric techniques at high frequencies during testing and particularly during the process of failure, providing data on the actual crack development leading to shear failure. The observations show that very different cracking patterns may be found and that they might be also developed in different manners. The results are interpreted with reference to the measured crack kinematics and related to the various potential shear-transfer actions, with the aim of providing a useful material towards the development of rational approaches for shear design.


title: An Adaptive Algorithm for Harris Corner Detection

abstract: Corner points are interesting as they are formed from two or more edges and edges usually define the boundary between two different objects or parts of the same objects. In this paper we discuss the theory of the Harris corner detection and point out its disadvantage. Then it proposes an adaptive algorithm for Harris corner detection based on the neighboring point eliminating method. It reduces the amount of the calculation, and makes the corners distributing well-proportion so that avoids too many corners stay together. Experimental results show that the algorithm can detect corners more uniform distribution, and can be used in some fact applications such as image registration well. Key words-Harris corner; neighboring point;


title: Dual quaternion based IMU and vision fusion framework for mobile augmented reality

abstract: Mobile Augmented Reality (MAR) is an emerging field and its nascent applications are finding its ways into the current deployments of cyber physical system. Mobile devices can harness augmented reality technology in any unprepared environment. This introduces a challenge to achieve an accurate and robust registration and tracking of mobile device. For accurate tracking, much research is being carried out to fuse inertial and vision sensor data. The resultant tracking can be further made better by finding means to track coupled translational and rotational motions. This problem is tackled with a neat formalism in terms of dual quaternion. Unit dual quaternion can capture the coupling between translational and rotational motions. In this paper, the requisite machinery is pivoted around Extended Kalman Filter (EKF) and is derived based on dual quaternion. The derived EKF expression is verified through experimentation involving both simulated and realistic data, the latter being obtained from a prototype for MAR. The simulation results show the effectiveness of dual quaternion on position and orientation estimation. This novel fusion framework resulted in more accurate tracking as compared to that of the existing quaternion based algorithm.


title: Towards knowledge-defined networking using in-band network telemetry

abstract: Today's network is rapidly growing in terms of the number of connected devices and becomes more complex, making the complexity of network management increasing. Self-driving network concept is arising to solve those problems and a good candidate for the closed-loop network management solution. Knowledge-defined Networking (KDN), network telemetry and Software-defined Networking (SDN) are fundamental building blocks to realize self-driving network. Using the self-driving network, OPEX can be reduced and the performance of the network can be grown. In this paper, we propose an architecture for collecting network telemetry and combining KDN to work with SDN and P4 INT. We also suggest several use-cases for the self-driving network architecture.


title: Analysis of microstructural evolution in SLID-bonding used for hermetic encapsulation of MEMS devices

abstract: The need for reliable hermetic sealing of electronic components has arisen along the increasing popularity of silicon based Micro Electro Mechanical Systems (MEMS). In this paper we have analyzed the formation and evolution interconnections made with Solid Liquid Interdiffusion (SLID) bonding by utilizing thermodynamic-kinetic method. The analysis of the phase transformations and consequent formation of stresses in Au-Sn system during bonding as well as the remelting temperatures of the Au-In-Sn interconnections can be carried out with the help of thermodynamic equilibrium diagrams. In addition, by combining qualitative thermodynamic calculations with qualitative kinetic considerations the evolution of interfacial microstructures between Ni contact metallization and the Au-Sn bonding alloy was predicted.


title: Turner and Sahani Probabilistic amplitude and frequency demodulation NIPS 2011 Pre-conference version Probabilistic

abstract: A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time. Although signal processing provides algorithms for so-called amplitude- and frequency-demodulation (AFD), there are well known problems with all of the existing methods. Motivated by the fact that AFD is ill-posed, we approach the problem using probabilistic inference. The new approach, called probabilistic amplitude and frequency demodulation (PAFD), models instantaneous frequency using an auto-regressive generalization of the von Mises distribution, and the envelopes using Gaussian auto-regressive dynamics with a positivity constraint. A novel form of expectation propagation is used for inference. We demonstrate that although PAFD is computationally demanding, it outperforms previous approaches on synthetic and real signals in clean, noisy and missing data settings.


Amalgamation of VLSI & Signal processing Technologies for the Implementation and Enhancement in Wireless Communication


Serological Specificity of an Auto-Antibody in Atypical Pneumonia


title: Mutlimodal Learning with Deep Boltzmann Machine for Emotion Prediction in User Generated Videos

abstract: Detecting emotions from user-generated videos, such as "anger" and "sadness", has attracted widespread interest recently. The problem is challenging as effectively representing video data with multi-view information (e.g., audio, video or text) is not trivial. In contrast to the existing works that extract features from each modality (view) separately followed by early or late fusion, we propose to learn a joint density model over the space of multi-modal inputs (including visual, auditory and textual modalities) with Deep Boltzmann Machine (DBM). The model is trained directly on the user-generated Web videos without any labeling effort. More importantly, the deep architecture enlightens the possibility of discovering the highly non-linear relationships that exist between low-level features across different modalities. The experiment results show that the DBM model learns joint representation complementary to the hand-crafted visual and auditory features, leading to 7.7% performance improvement in classification accuracy on the recently released VideoEmotion dataset.


title: Biometric Sensor Interoperability: A Case Study in Fingerprints

abstract: The problem of biometric sensor interoperability has received limited attention in the literature. Most biometric systems operate under the assumption that the data (viz., images) to be compared are obtained using the same sensor and, hence, are restricted in their ability to match or compare biometric data originating from different sensors. Although progress has been made in the development of common data exchange formats to facilitate the exchange of feature sets between vendors, very little effort has been invested in the actual development of algorithms and techniques to match these feature sets. In the Fingerprint Verification Competition (FVC 2002), for example, the evaluation protocol only matched images originating from the same sensor although fingerprint data from 3 different commercial sensors was available. This is an indication of the difficulty in accommodating sensor interoperability in biometric systems. In this paper we discuss this problem and present a case study involving two different fingerprint sensors.


title: Optimum formulation of feed for rohu, Labeo rohita (Hamilton), with biofloc as a component

abstract: The present study was conducted to design an optimum feed mix for the growth of rohu (Labeo rohita) in light-limited indoor culture with biofloc as a component along with fish feed. Eighteen 700-L tanks were utilized for producing biofloc using aquaculture effluent from a nearby carp culture pond. Fifteen different feed mixes were prepared using fish feed and biofloc in dry (4 % moisture content) and wet (90 % moisture content) forms at different proportions and used in feeding trials (three replications) conducted in forty-five 50-L glass aquarium stocked with three rohu fingerlings with average individual weight of 20 ± 1.5 g for a period of 90 days. Fish survival was 100 % in all the treatments. Mixture design was used to obtain a solution of best combination of feed source to obtain the optimum growth parameters of rohu. Optimum growth parameters (net yield, specific growth rate, protein efficiency ratio and feed conversion ratio) of rohu were obtained at feed mix containing 50 % fish feed and 50 % wet floc. The nutritional quality of biofloc was found to be quite suitable for rohu. The images of 3-week-old biofloc captured in scanning electron microscope (SEM) indicated the presence of different types of bacteria, algae, protozoa, rotifers, etc. in different sizes ranging from 10 to 100 μm.


title: DDefender: Android application threat detection using static and dynamic analysis

abstract: Android is the most widespread mobile operating system in the world. Due to its popularity, malware has been increasing every year steadily, which causes lots of problems to users, such as using the device's resources and transmitting private information without user's awareness. As malware has increased, anti-malware solutions have as well. Current anti-malware solutions often have very serious limitations and malware is becoming more apt to take advantage of them. In this paper, we present DDefender, a user-friendly application that detects Android malicious applications on device. DDefender is a comprehensive solution that utilizes static and dynamic analysis techniques to extract features from the user's device, then applies deep learning algorithm to detect malicious applications. At first, we use dynamic analysis to extract system calls, system information, network traffics, and requested permissions of an inspected application. Then we use static analysis to extract significant features from the inspected application such as application's components. By utilizing neural network and a large feature set of 1007 features, we evaluated our system with 4208 applications (2104 benign applications and 2104 malicious applications) and we achieved up to 95% accuracy.


title: Real Time Sentiment Change Detection of Twitter Data Streams

abstract: In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime.


title: Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection

abstract: We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical interpretation. This is in response to the question: "what are the implicit statistical assumptions of feature selection criteria based on mutual information?". To answer this, we adopt a different strategy than is usual in the feature selection literature--instead of trying to define a criterion, we derive one, directly from a clearly specified objective function: the conditional likelihood of the training labels. While many hand-designed heuristic criteria try to optimize a definition of feature 'relevancy' and 'redundancy', our approach leads to a probabilistic framework which naturally incorporates these concepts. As a result we can unify the numerous criteria published over the last two decades, and show them to be low-order approximations to the exact (but intractable) optimisation problem. The primary contribution is to show that common heuristics for information based feature selection (including Markov Blanket algorithms as a special case) are approximate iterative maximisers of the conditional likelihood. A large empirical study provides strong evidence to favour certain classes of criteria, in particular those that balance the relative size of the relevancy/redundancy terms. Overall we conclude that the JMI criterion (Yang and Moody, 1999; Meyer et al., 2008) provides the best tradeoff in terms of accuracy, stability, and flexibility with small data samples.


title: A high voltage battery charger with smooth charge mode transition in BCD process

abstract: A 60 V battery charger implemented using TSMC 0.25 µm Bipolar-CMOS-DMOS (BCD) 60 V process is presented in this work. A novel transition method is proposed to ensure smooth transitions between constant current (CC) and constant voltage (CV) modes. Since the proposed approach would require only additional diodes, power consumption and chip area are conserved in comparison with other sophisticated methods proposed in prior works. The charger sources a current of 50 mA in CC mode and has an efficiency of 75% ∼ 80% throughout the charging sequence. The supply voltage is kept at 3 volts higher than the battery voltage throughout the charging sequence to maintain charging efficiency. A thermal protection circuit is included in this design to prevent the charger from operating at a temperature over the maximum allowed temperature.


Who's in Control of Your Control System? Device Fingerprinting for Cyber-Physical Systems


title: An Evaluation Of N-gram System Call Sequence In Mobile Malware Detection

abstract: The rapid growth of Android-based mobile devices technology in recent years has increased the proliferation of mobile devices throughout the community at large. The ability of Android mobile devices has become similar to its desktop environment; users can do more than just a phone call and short text messaging. These days, Android mobile devices are used for various applications such as web browsing, ubiquitous services, social networking, MMS and many more. However, the rapid growth of Android mobile devices technology has also triggered the malware author to start ::: exploiting the vulnerabilities of the devices. Based on this reason, this paper explores mobile malware detection through an n-gram system call sequence which uses a sequence of system call invoked by the mobile application as the feature in classifying a benign and malicious mobile application. Several n-gram values are evaluated with Linear-SVM classifier to determine the best n system call sequence that produces the highest detection accuracy and highest True Positive Rate (TPR) with low False Positive Rate (FPR).


title: Wideband balun and impedance transformers integrated in a four-layer laminate PCB

abstract: Transmission line balun and impedance transformers are described. They were realized as part of a four-layer laminate PCB using a planar, quasi-coaxial transmission line structure. Approximately a decade bandwidth was achieved, with operating frequencies in the lower and sub gigahertz range. Both transformers employ the same planar coil geometry, each covering an area of less then 0.6 cm/sup 2/. The transformers can be used in medium power applications as their power handling capability is merely limited by the planar transmission lines.


title: Cuffless Blood Pressure Estimation Algorithms for Continuous Health-Care Monitoring

abstract: Goal : Continuous blood pressure (BP) monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This paper presents an efficient algorithm, based on the pulse arrival time (PAT), for the continuous and cuffless estimation of the systolic BP, diastolic blood pressure (DBP), and mean arterial pressure (MAP) values. Methods : The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the regression algorithms are employed for the BP estimation. Although the proposed algorithm works reliably without any need for calibration, an optional calibration procedure is also suggested, which can improve the system's accuracy even further. Results : The proposed method is evaluated on about a thousand subjects using the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. The method complies with the AAMI standard in the estimation of DBP and MAP values. Regarding the BHS protocol, the results achieve grade A for the estimation of DBP and grade B for the estimation of MAP. Conclusion : We conclude that by using the PAT in combination with informative features from the vital signals, the BP can be accurately and reliably estimated in a noninvasive fashion. Significance : The results indicate that the proposed algorithm for the cuffless estimation of the BP can potentially enable mobile health-care gadgets to monitor the BP continuously.


title: Constraint Selection in Metric Learning

abstract: Abstract A number of machine learning and knowledge-based algorithms are using a metric, or a distance, in order to compare individuals. The Euclidean distance is usually employed, but it may be more efficient to learn a parametric distance such as Mahalanobis metric. Learning such a metric is a hot topic since more than ten years now, and a number of methods have been proposed to efficiently learn it. However, the nature of the problem makes it quite difficult for large scale data, as well as data for which classes overlap. This paper presents a simple way of improving accuracy and scalability of any iterative metric learning algorithm, where constraints are obtained prior to the algorithm. The proposed approach relies on a loss-dependent weighted selection of constraints that are used for learning the metric. Using the corresponding dedicated loss function, the method clearly allows to obtain better results than state-of-the-art methods, both in terms of accuracy and time complexity. Some experimental results on real world, and potentially large, datasets are demonstrating the effectiveness of our proposition.


title: Nonoperative treatment of brain abscesses in selected high-risk patients

abstract: ✓ In a series of eight high-risk patients, presumed intraparenchymal pyogenic brain abscesses resolved or decreased markedly in size with antibiotic therapy and without surgery. Cases were selected for antibiotic trial because of multiplicity or location of the abscesses and complicating medical conditions. Patients were alert or lethargic, with stable or improving early clinical courses. Diagnosis was made by computerized tomography (CT) brain scans and corroborative laboratory and diagnostic studies. Cases were closely monitored with serial CT scans. In six cases, bacteria were cultured from blood, cerebrospinal fluid, or fluid aspirated from one of multiple abscesses. Antibiotics were selected for specificity against the cultured organism, or consisted of high-dose penicillin plus chloramphenicol; therapy continued for a median of 7 weeks. Serial CT scans showed decreased abscess size by a mean of 2.4 weeks (range, 1 to 4 weeks) and resolution of contrast-enhancement and mass effects by a mean of 9.8 w...


title: AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge

abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) "Real-life depression, and affect" will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. This paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous).


Deep embodiment: grounding semantics in perceptual modalities


title: Re-ranking Person Re-identification with k-Reciprocal Encoding

abstract: When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.


title: Machine learning in a quantum world

abstract: Quantum Information Processing (QIP) performs wonders in a world that obeys the laws of quantum mechanics, whereas Machine Learning (ML) is generally assumed to be done in a classical world. We initiate an investigation of the encounter of ML with QIP by defining and studying novel learning tasks that correspond to Machine Learning in a world in which the information is fundamentally quantum mechanical. We shall see that this paradigm shift has a profound impact on the learning process and that our classical intuition is often challenged.


title: Augmented Reality Visualization for Laparoscopic Surgery

abstract: We present the design and a prototype implementation of a three-dimensional visualization system to assist with laparoscopic surgical procedures. The system uses 3D visualization, depth extraction from laparoscopic images, and six degree-of-freedom head and laparoscope tracking to display a merged real and synthetic image in the surgeon’s video-see-through head-mounted display. We also introduce a custom design for this display. A digital light projector, a camera, and a conventional laparoscope create a prototype 3D laparoscope that can extract depth and video imagery.


title: The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses

abstract: GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It automatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. VarElect's capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. © 2016 by John Wiley & Sons, Inc.


Hypophysectomy and replacement therapy in relation to the growth and secretory activity of the mammary gland.


title: "Science Citation Index"- A New Dimension in Indexing

abstract: ing Services Recently Bennett (47) has reiterated my earlier recommendation that an index to the abstracts in specialty journals and to abstracts prepared by the smaller abstracting services be compiled (21). Frequently these are abstracts which include criticism. As such, they constitute original publications. Every author should know of such critical abstracts of his papers. In literature searches, abstracts may serve in lieu of the original articles, particularly when the original article is in a foreign language, or when it is not readily available. Citation indexes can be used to locate these abstracts quickly and to identify unabstracted articles (31). Author Citation Index A random-access computer memory does not require special ordering for data storage. In such memories the arbitrarily assigned data-addresses need not be known to the user. By contrast, a printed citation index must have a


title: AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

abstract: Apparent age estimation from face image has attracted more and more attentions as it is favorable in some real-world applications. In this work, we propose an end-to-end learning approach for robust apparent age estimation, named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models, i.e., real-value based regression models and Gaussian label distribution based classification models. For both kind of models, large-scale deep convolutional neural network is adopted to learn informative age representations. Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale web-collected face dataset with identity label, and then it is fine-tuned on a large-scale real age dataset with noisy age label. Finally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn 2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent age estimation.


A Survey on Various Applications of Swarm Intelligence in Wireless Sensor Networks


title: Application of Trust and Distrust in Recommender System: A Study

abstract: Recommender systems help customers to choose right product or service from large number of alternatives available on Internet. In recent time, trust becomes an important issue in designing effective recommender systems. In this paper we have studied the role of trust and distrust in designing recommender systems. General Terms E-Commerce, Information Retrieval, Web Mining.


Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images


title: A Semi-supervised Framework for Image Captioning

abstract: State-of-the-art approaches for image captioning require supervised training data consisting of captions with paired image data. These methods are typically unable to use unsupervised data such as textual data with no corresponding images, which is a much more abundant commodity. We here propose a novel way of using such textual data by artificially generating missing visual information. We evaluate this learning approach on a newly designed model that detects visual concepts present in an image and feed them to a reviewer-decoder architecture with an attention mechanism. Unlike previous approaches that encode visual concepts using word embeddings, we instead suggest using regional image features which capture more intrinsic information. The main benefit of this architecture is that it synthesizes meaningful thought vectors that capture salient image properties and then applies a soft attentive decoder to decode the thought vectors and generate image captions. We evaluate our model on both Microsoft COCO and Flickr30K datasets and demonstrate that this model combined with our semi-supervised learning method can largely improve performance and help the model to generate more accurate and diverse captions.


title: Real-Time Vehicle Detection and Tracking Using Haar-Like Features and Compressive Tracking

abstract: This paper presents a real-time vision framework that detects and tracks vehicles from stationary camera. It can be used to calculate statistical information such as average traffic speed and flow as well as in surveillance tasks. The framework consists of three main stages. Vehicles are first detected using Haar-like features. In the second phase, an adaptive appearance-based model is built to dynamically keep track of the detected vehicles. This model is also used in the third phase of data association to fuse the detection and tracking results. The use of detection results to update the tracker enhances the overall framework accuracy. The practical value of the proposed framework is demonstrated in real-life experiments where it is used to robustly compute vehicle counts within certain region of interest under variety of challenges.


title: The Internet of Things: New Interoperability, Management and Security Challenges

abstract: The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.


Co-design of Interdisciplinary Projects as a Mechanism for School Capacity and Teacher Professional Community Growth


title: Detecting Influencers in Multiple Online Genres

abstract: Social media has become very popular and mainstream, leading to an abundance of content. This wealth of content contains many interactions and conversations that can be analyzed for a variety of information. One such type of information is analyzing the roles people take in a conversation. Detecting influencers, one such role, can be useful for political campaigning, successful advertisement strategies, and detecting terrorist leaders. We explore influence in discussion forums, weblogs, and micro-blogs through the development of learned language analysis components to recognize known indicators of influence. Our components are author traits, agreement, claims, argumentation, persuasion, credibility, and certain dialog patterns. Each of these components is motivated by social science through Robert Cialdini’s “Weapons of Influence” [Cialdini 2007]. We classify influencers across five online genres and analyze which features are most indicative of influencers in each genre. First, we describe a rich suite of features that were generated using each of the system components. Then, we describe our experiments and results, including using domain adaptation to exploit the data from multiple online genres.


Situation entity types: automatic classification of clause-level aspect


title: When Good Becomes Evil: Keystroke Inference with Smartwatch

abstract: One rising trend in today's consumer electronics is the wearable devices, e.g., smartwatches. With tens of millions of smartwatches shipped, however, the security implications of such devices are not fully understood. Although previous studies have pointed out some privacy concerns about the data that can be collected, like personalized health information, the threat is considered low as the leaked data is not highly sensitive and there is no real attack implemented. In this paper we investigate a security problem coming from sensors in smartwatches, especially the accelerometer. The results show that the actual threat is much beyond people's awareness. Being worn on the wrist, the accelerometer built within a smartwatch can track user's hand movements, which makes inferring user inputs on keyboards possible in theory. But several challenges need to be addressed ahead in the real-world settings: e.g., small and irregular hand movements occur persistently during typing, which degrades the tracking accuracy and sometimes even overwhelms useful signals. In this paper, we present a new and practical side-channel attack to infer user inputs on keyboards by exploiting sensors in smartwatch. Novel keystroke inference models are developed to mitigate the negative impacts of tracking noises. We focus on two major categories of keyboards: one is numeric keypad that is generally used to input digits, and the other is QWERTY keyboard on which a user can type English text. Two prototypes have been built to infer users' banking PINs and English text when they type on POS terminal and QWERTY keyboard respectively. Our results show that for numeric keyboard, the probability of finding banking PINs in the top 3 candidates can reach 65%, while for QWERTY keyboard, a significant accuracy improvement is achieved compared to the previous works, especially of the success rate of finding the correct word in the top 10 candidates.


title: Is container-based technology a winner for high performance scientific applications?

abstract: High Performance Computing (HPC) applications require systems with environments for maximum use of limited resources to facilitate efficient computations. However, these systems are faced with a large trade-off between efficient resource allocation and minimum execution times for the applications executed on them. Also, deploying applications in newer environments is exacting. To alleviate this challenge, container-based systems are recently being deployed to reduce the trade-off. In this paper, we investigate container-based technology as an efficient virtualization technology for running high performance scientific applications. We select Docker as the container-based technology for our test bed. We execute autodock3, a molecular modeling simulation software mostly used for Protein-ligand docking, in Docker containers and VMs created using OpenStack. We compare the execution times of the docking process in both Docker containers and in VMs.


title: Rapid safety evaluation of hardware architectural designs compliant with ISO 26262

abstract: The international standard ISO 26262 “Road vehicles - Functional safety” claims qualitative and quantitative analysis of hardware designs at the appropriate level of abstraction. For large-scaled hardware designs, these evaluations have to be initiated early in development adequate to hardware architectural design and not delayed to hardware detailed design at the level of electronic schematics. Therefore, we describe a structural modeling and annotation of failure data for hardware architectural designs. Based on a top-down qualitative fault tree analysis, the classification of hardware failure modes in context of system behavior can be determined according to ISO 26262. Using these classifications and assumed failure rates, we facilitate a rapid quantitative safety analysis regarding evaluation of the hardware architectural metrics and evaluation of safety goal violations.


title: Feeling the Future: The Emotional Oracle Effect

abstract: Eight studies reveal an intriguing phenomenon: individuals who have higher trust in their feelings can predict the outcomes of future events better than individuals with lower trust in their feelings. This emotional oracle effect was found across a variety of prediction domains, including (a) the 2008 US Democratic presidential nomination, (b) movie box-office success, (c) the winner of American Idol, (d) the stock market, (e) college football, and even (f) the weather. It is mostly high trust in feelings that improves prediction accuracy rather than low trust in feelings that impairs it. However, the effect occurs only among individuals who possess sufficient background knowledge about the prediction domain, and it dissipates when the prediction criterion becomes inherently unpredictable. The authors hypothesize that the effect arises because trusting one's feelings encourages access to a "privileged window" into the vast amount of predictive information that people learn, often unconsciously, about their environments.


title: Single-element and phased-array transceiver chipsets for 60-ghz Gb/s communications

abstract: This article summarizes the development of mature and highly integrated SiGe BiCMOS ICs for gigabit-per-second communications according to the requirements of the IEEE 802.15.3c and 802.11.ad-draft standards. A single-element transceiver chipset for point-to-point communications is described with emphasis on a feature-rich yet compact 60-GHz receiver. Next, a 16-element phased-array transceiver chipset for non-line-of-sight communications is described, with emphasis on a new power-efficient phased-array transmitter. Examples of gigabit-per-second line-of-sight and non-line-of-sight link experiments are provided, and system-level implementation trade-offs are discussed.


title: Internet of Things: Convenience vs. privacy and secrecy

abstract: In this article we introduce the Internet of Things to the broad managerial community and explore one of its central tensions: convenience vs. privacy and secrecy. We clarify the ways in which IoT differs from Web 2.0 and then highlight opportunities, challenges, and managerial guidance. In addition, we explore the prominent issue of privacy and secrecy. Due to substantial increases in amounts of consumer-related data and their accessibility as well as potential tradeoffs in benefits associated with IoT and in properties of humanness associated with the consumer experience, the managerial issue of privacy is elevated to a level never before realized—perhaps on par with, or worthy of inclusion as an element of, the classic marketing mix.


title: NeuralPower: Predict and Deploy Energy-Efficient Convolutional Neural Networks

abstract: "How much energy is consumed for an inference made by a convolutional neural network (CNN)?" With the increased popularity of CNNs deployed on the wide-spectrum of platforms (from mobile devices to workstations), the answer to this question has drawn significant attention. From lengthening battery life of mobile devices to reducing the energy bill of a datacenter, it is important to understand the energy efficiency of CNNs during serving for making an inference, before actually training the model. In this work, we propose NeuralPower: a layer-wise predictive framework based on sparse polynomial regression, for predicting the serving energy consumption of a CNN deployed on any GPU platform. Given the architecture of a CNN, NeuralPower provides an accurate prediction and breakdown for power and runtime across all layers in the whole network, helping machine learners quickly identify the power, runtime, or energy bottlenecks. We also propose the "energy-precision ratio" (EPR) metric to guide machine learners in selecting an energy-efficient CNN architecture that better trades off the energy consumption and prediction accuracy. The experimental results show that the prediction accuracy of the proposed NeuralPower outperforms the best published model to date, yielding an improvement in accuracy of up to 68.5%. We also assess the accuracy of predictions at the network level, by predicting the runtime, power, and energy of state-of-the-art CNN architectures, achieving an average accuracy of 88.24% in runtime, 88.34% in power, and 97.21% in energy. We comprehensively corroborate the effectiveness of NeuralPower as a powerful framework for machine learners by testing it on different GPU platforms and Deep Learning software tools.


title: Conceptualizing a nonnatural entity: Anthropomorphism in God concepts

abstract: Abstract We investigate the problem of how nonnatural entities are represented by examining university students’ concepts of God, both professed theological beliefs and concepts used in comprehension of narratives. In three story processing tasks, subjects often used an anthropomorphic God concept that is inconsistent with stated theological beliefs; and drastically distorted the narratives without any awareness of doing so. By heightening subjects’ awareness of their theological beliefs, we were able to manipulate the degree of anthropomorphization. This tendency to anthropomorphize may be generalizable to other agents. God (and possibly other agents) is unintentionally anthropomorphized in some contexts, perhaps as a means of representing poorly understood nonnatural entities.


title: Social media and emotions in organisational knowledge creation

abstract: Social media increases the connectivity of people inside and outside an organisation. It is not just the implementation of communication technology, but the transformation of working and organisational cultures. The paper presumes that social media provides new opportunities to the organisational knowledge creation process by amplifying knowledge created by individuals as well as crystallising and connecting it to an organisation's knowledge system. The process depends fundamentally on the individual's tacit knowledge and its conversion into organisational explicit knowledge. Knowledge conversion is not a linear and sequential process, but a process which is affected by the individual's emotions. This paper explores the interplay between knowledge and emotions in the organisational knowledge creation process in the context of social media. The paper concludes that knowledge and emotion shared in social media contribute to the social identity, which increases the odds of altruistic behaviour towards others in a way that benefits the organisation.


title: A Highly Efficient 200 000 RPM Permanent Magnet Motor System

abstract: This paper presents the development of an ultra-high-speed permanent magnet synchronous motor (PMSM) that produces output shaft power of 2000 W at 200 000 rpm with around 90% efficiency. Due to the guaranteed open-loop stability over the full operating speed range, the developed motor system is compact and low cost since it can avoid the design complexity of a closed-loop controller. This paper introduces the collaborative design approach of the motor system in order to ensure both performance requirements and stability over the full operating speed range. The actual implementation of the motor system is then discussed. Finally, computer simulation and experimental results are provided to validate the proposed design and its effectiveness


title: Preventing perioperative peripheral nerve injuries.

abstract: Peripheral nerve injuries are largely preventable injuries that can result from incorrect patient positioning during surgery. Patients who are diabetic, are extremely thin or obese, use tobacco, or undergo surgery lasting more than four hours are at increased risk for developing these injuries. When peripheral nerve injuries occur, patients may experience numbness, burning, or tingling and may have difficulty getting out of bed, walking, gripping objects, or raising their arms. These symptoms can interrupt activities of daily living and impede recovery. Signs and symptoms of peripheral nerve injury may appear within 24 to 48 hours of surgery or may take as long as a week to appear. Careful attention to body alignment and proper padding of bony prominences when positioning patients for surgery is necessary to prevent peripheral nerve injury. The use of a preoperative assessment tool to identify at-risk patients, collaboration between physical therapy and OR staff members regarding patient positioning, and neurophysiological monitoring can help prevent peripheral nerve injuries.


title: Graph-based malware detection using dynamic analysis

abstract: We introduce a novel malware detection algorithm based on the analysis of graphs constructed from dynamically collected instruction traces of the target executable. These graphs represent Markov chains, where the vertices are the instructions and the transition probabilities are estimated by the data contained in the trace. We use a combination of graph kernels to create a similarity matrix between the instruction trace graphs. The resulting graph kernel measures similarity between graphs on both local and global levels. Finally, the similarity matrix is sent to a support vector machine to perform classification. Our method is particularly appealing because we do not base our classifications on the raw n-gram data, but rather use our data representation to perform classification in graph space. We demonstrate the performance of our algorithm on two classification problems: benign software versus malware, and the Netbull virus with different packers versus other classes of viruses. Our results show a statistically significant improvement over signature-based and other machine learning-based detection methods.


title: The Inositol Trisphosphate/Calcium Signaling Pathway in Health and Disease

abstract: Many cellular functions are regulated by calcium (Ca2+) signals that are generated by different signaling pathways. One of these is the inositol 1,4,5-trisphosphate/calcium (InsP3/Ca2+) signaling pathway that operates through either primary or modulatory mechanisms. In its primary role, it generates the Ca2+ that acts directly to control processes such as metabolism, secretion, fertilization, proliferation, and smooth muscle contraction. Its modulatory role occurs in excitable cells where it modulates the primary Ca2+ signal generated by the entry of Ca2+ through voltage-operated channels that releases Ca2+ from ryanodine receptors (RYRs) on the internal stores. In carrying out this modulatory role, the InsP3/Ca2+ signaling pathway induces subtle changes in the generation and function of the voltage-dependent primary Ca2+ signal. Changes in the nature of both the primary and modulatory roles of InsP3/Ca2+ signaling are a contributory factor responsible for the onset of a large number human diseases.


A numerical three-dimensional viscous transonic wing-body analysis and design tool


Whole-exome sequencing identifies a novel somatic mutation in MMP8 associated with a t(1;22)-acute megakaryoblastic leukemia