title: Generation of locomotion trajectories for series elastic and viscoelastic bipedal robots

abstract: Series-elastic and viscoelastic robots can provide performance gains in applications with high dynamics. Harnessing these, requires an understanding of the dynamics of the system, which can be gained using optimization-based methods. The result are motions which make optimal use of the intrinsic behavior, possibly exceeding the performance of an equivalent rigid-body robot. We present a collocation framework which enables both automatic computation of contact-switching patterns and allows the full utilization of the dynamics of the compliant system. The formulation also addresses the problem of redundant torque generation in viscoelastic actuators. The effectiveness of this method was demonstrated in simulations as well as experiments with a compliant bipedal robot. The approach is capable of providing gait primitives, longer gait sequences containing multiple steps as well as generating extremely dynamic motions, e.g. somersaults.


title: Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective

abstract: Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc. Accordingly, hardware architects have designed customized hardware for machine learning algorithms, especially neural networks, to improve compute efficiency. However, machine learning is typically just one processing stage in complex end-to-end applications, involving multiple components in a mobile Systems-on-a-chip (SoC). Focusing only on ML accelerators loses bigger optimization opportunity at the system (SoC) level. This paper argues that hardware architects should expand the optimization scope to the entire SoC. We demonstrate one particular case-study in the domain of continuous computer vision where camera sensor, image signal processor (ISP), memory, and NN accelerator are synergistically co-designed to achieve optimal system-level efficiency.


title: Facilitating relational governance through service level agreements in IT outsourcing: An application of the commitment-trust theory

abstract: Firms increasingly rely on outsourcing for strategic IT decisions, and the many sophisticated forms of outsourcing require significant management attention to ensure their success. Two forms of interorganizational governance-formal control and relational-have been used to examine the management of IT outsourcing relationships. Contrary to the conventional substitution view, recent studies have found that these two governance modes are complementary; however, the dynamics of their interactions remain unexplored. Based on the commitment-trust theory, this paper focuses on how the formal control mechanism can influence the relational governance in an outsourcing engagement. Using service level agreements (SLAs) as a proxy for formal control, this study finds that eleven contractual elements, characterized as foundation, governance, and change management variables in an SLA, are positively related to the trust and relationship commitment among the parties. Trust and commitment, in turn, positively influence relational outcomes that we theorize would contribute to outsourcing success. Both research and practical implications of the results are discussed.


title: [Diagnostic spectrum of synovitis].

abstract: This review will suggest an algorithm for standardised histopathological diagnosis of synovial biopsies and synovectomy specimens. In principal, changes of the synovial membrane can be inflammatory or non-inflammatory. To the latter group belong some benign tumors, such as tenosynovial giant cell tumor, lipoma or synovial chondromatosis. Rare non-inflammatory changes are the group of storage diseases. Inflammatory synovial diseases can be differentiated into crystal-induced arthropathy, such as gout and pseudogout, granulomatous diseases, such as tuberculosis, sarcoidosis and foreign body reactions and into the large group of non-granulomatous synovitis. This last group is by far the most common and often causes difficulties in assigning the histopathological findings to a definite diagnosis. Therefore, the synovitis score should be applied in these cases as a diagnostic means, leading to the diagnosis of low-grade synovitis (which is associated with degenerative and posttraumatic arthropathies) or high-grade synovitis (associated with rheumatic diseases), the sensitivity and specificity being 60.5% and 95.5%, respectively. In detritus synovitis the synovitis score is not applicable.


title: Constructing Reliable Gradient Exploration for Online Learning to Rank

abstract: With the rapid development of information retrieval (IR) systems, online learning to rank (OLR) approaches, which allow retrieval systems to automatically learn best parameters from user interactions, have attracted great research interests in recent years. In OLR, the algorithms usually need to explore some uncertain retrieval results for updating current parameters meanwhile guaranteeing to produce quality retrieval results by exploiting what have already been learned, and the final retrieval results is an interleaved list from both exploratory and exploitative results. However, existing OLR algorithms perform exploration based on either only one stochastic direction or multiple randomly selected stochastic directions, which always involve large variance and uncertainty into the exploration, and may further harm the retrieval quality. Moreover, little historical exploration knowledge is considered when conducting current exploration. In this paper, we propose two OLR algorithms that improve the reliability of the exploration by constructing robust exploratory directions. First, we describe a Dual-Point Dueling Bandit Gradient Descent (DP-DBGD) approach with a Contextual Interleaving (CI) method. In particular, the exploration of DP-DBGD is carefully conducted via two opposite stochastic directions and the proposed CI method constructs a qualified interleaved retrieval result list by taking historical explorations into account. Second, we introduce a Multi-Point Deterministic Gradient Descent (MP-DGD) method that constructs a set of deterministic standard unit basis vectors for exploration. In MP-DGD, each basis direction will be explored and the parameter updating is performed by walking along the combination of exploratory winners from the basis vectors. We conduct experiments on several datasets and show that both DP-DBGD and MP-DGD improve the online learning to rank performance over 10% compared with baseline methods.


title: DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

abstract: Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomously. Falling in between the two extremes of mediated perception and behavior reflex, we argue that our direct perception representation provides the right level of abstraction. To demonstrate this, we train a deep Convolutional Neural Network using recording from 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments. We also train a model for car distance estimation on the KITTI dataset. Results show that our direct perception approach can generalize well to real driving images. Source code and data are available on our project website.


title: Feature Generation for Text Categorization Using World Knowledge

abstract: We enhance machine learning algorithms for text categorization with generated features based on domain-specific and common-sense knowledge. This knowledge is represented using publicly available ontologies that contain hundreds of thousands of concepts, such as the Open Directory; these ontologies are further enriched by several orders of magnitude through controlled Web crawling. Prior to text categorization, a feature generator analyzes the documents and maps them onto appropriate ontology concepts, which in turn induce a set of generated features that augment the standard bag of words. Feature generation is accomplished through contextual analysis of document text, implicitly performing word sense disambiguation. Coupled with the ability to generalize concepts using the ontology, this approach addresses the two main problems of natural language processing--synonymy and polysemy. Categorizing documents with the aid of knowledge-based features leverages information that cannot be deduced from the documents alone. Experimental results confirm improved performance, breaking through the plateau previously reached in the field.


title: Shopping Behavior and Consumer Preference for Store Price Format: Why "Large Basket" Shoppers Prefer EDLP

abstract: In recent years, the supermarket industry has become increasingly competitive. One outcome has been the proliferation of a variety of pricing formats, and considerable debate among academics and practitioners about how these formats affect consumers' store choice behavior. This paper advances the idea that consumer shopping behavior as defined by average size of the shopping basket and the frequency of store visits is an important determinant of the store choice decision when stores offer different price formats. A recent Wall Street Journal article that summarized the result of Bruno's management switching the chain from EDLP to HILO illustrates the importance of this issue: "The company's price-conscious customers, used to shopping for a fixed basket of goods, stayed away in droves." Thus, the audience for this paper includes practitioners and academics who wish to understand store choices or predict how a change in price format might affect store profitability and the mix of clientele that shop there. ::: ::: This paper attempts to understand the relationship between grocery shopping behavior, retail price format, and store choice by posing and answering the following questions. First, after controlling for other factors e.g., distance to the store, prior experience in the store, advertised specials, do consumer expectations about prices for a basket of grocery products "expected basket attractiveness" influence the store choice decision? This is a fairly straightforward test of the effect of price expectations on store choice. Second, are different pricing formats EDLP or HILO more or less attractive to different types of shoppers? To adequately answer the second question, we must link consumers' category purchase decisions, which collectively define the market basket, and the store choice decision. ::: ::: We study the research questions using two complementary approaches. First, we develop a stylized theory of consumer shopping behavior under price uncertainty. The principal features and results from the stylized model can be summarized as follows. Shoppers are defined in a relative sense as either large or small basket shoppers. Thus, we abstract from the vicissitudes of individual shopping trips and focus on meaningful differences across shoppers in terms of the expected basket size per trip. The shoppers make category purchase incidence decisions and can choose to shop in either an EDLP or a HILO store. Large basket shoppers are shoppers who have a relatively high probability of purchase for any given category, and as such they are more captive to prices across many different categories. The first two propositions summarize the price responsiveness of shoppers. In particular, the large basket shoppers are less responsive to price in their individual category purchase incidence decisions; this makes them more responsive to the expected basket price in their store choice decisions. This key structural implication of the model highlights an asymmetry between response at the category level and response at the store level. The result is quite intuitive; a large basket shopper with less ability to respond to prices in individual product categories will be more sensitive to the expected cost of the overall portfolio the market basket when choosing a store. The final proposition derives the price at which a given shopper will be indifferent between an EDLP and a HILO store. The key insight is that as a shopper increases his or her tendency to become a large basket shopper, the EDLP store can increase its constant price closer and closer to the average price in the HILO store. Conversely, as the shopper becomes more of small basket shopper, the EDLP store must lower its price closer to the deal price in the HILO store. Thus, we have the interesting result that small basket shoppers prefer HILO stores, even at higher average prices. ::: ::: The empirical testing mirrors the development of the consumer theory. We test the implications of the propositions using a market basket scanner panel database. The database includes two years of shopping data for 1,042 households in two separate market areas. We first use household-level grocery expenditures to model the probability that a household is a large or small basket shopper. Subsequently, we estimate purchase incidence and store choice models. We find that after controlling for important factors such as household distance to the store, previous experience in the store, and advertised specials, price expectations for the basket influence store choice. Furthermore, EDLP stores get a greater than expected share of business from large basket shoppers; HILO stores get a greater than expected share from small basket shoppers. Consistent with the implications of the propositions, large basket shoppers are relatively price inelastic in their category purchase incidence decisions and price elastic in their store choice decisions.


title: Learning Causal Structures Using Regression Invariance

abstract: We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary. We introduce the idea of using the invariance of the functional relations of the variables to their causes across a set of environments. We define a notion of completeness for a causal inference algorithm in this setting and prove the existence of such algorithm by proposing the baseline algorithm. Additionally, we present an alternate algorithm that has significantly improved computational and sample complexity compared to the baseline algorithm. The experiment results show that the proposed algorithm outperforms the other existing algorithms.


title: attacks using RBF networks and statistical features

abstract: In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using the Radial-basis-function neural networks (RBF-NN). The proposed method is evaluated in a simulated public network and showed detection rate better than 98% of DDoS attacks using only three statistical features estimated from one window of data packets of 6 s length. The same type of experiments were carried out on a real network giving significantly better results: a 100% DDoS detection rate is achieved followed by a 0% of false alarm rate using different statistical descriptors and training conditions for the RBF-NN.


title: Using the SPEM 2.0 kind-based extension mechanism to define the SPEM4MDE metamodel

abstract: The objective 1 of the OMG's standard SPEM is to propose shared concepts for describing software and even systems processes. The SPEM 2.0 metamodel proposes concepts that are quite generic to describe model-driven development processes. Indeed, the artifacts of those processes are essentially models and relationships between them are numerous (e.g. impact, matching, overlap and so on). We notice that is difficult to have a process modeling language that is suitable to define any kind of process including MDE ones. To overcome this lack, we propose in this paper an extension of SPEM4MDE based on the SPEM 2.0 kind-based extension mechanism. It allows process designer to refine SPEM concepts in order to define the model-driven processes. To illustrate our approach, the MDE-based VUML process for models composition has been used.


An Overview of the Singularity Project 1


Ratings Lead You To The Product , Reviews Help You Clinch It : The Dynamics and Impact of Online Review Sentiments on Products Sales


title: On The Convergence Of The Lagged Diffusivity Fixed Point Method In Total Variation Image Restoration

abstract: In this paper we show that the lagged diffusivity fixed point algorithm introduced by Vogel and Oman in [ SIAM J. Sci. Comput., 17 (1996), pp. 227--238] to solve the problem of total variation denoising, proposed by Rudin, Osher, and Fatemi in [ Phys. D, 60 (1992), pp. 259--268], is a particular instance of a class of algorithms introduced by Voss and Eckhardt in [ Computing, 25 (1980), pp. 243--251], whose origins can be traced back to Weiszfeld's original work for minimizing a sum of Euclidean lengths [ Tohoku Math. J., 43 (1937), pp. 355--386]. There have recently appeared several proofs for the convergence of this algorithm [G. Aubert et al., Technical report 94-01, Informatique, Signaux et Systemes de Sophia Antipolis, 1994], [A. Chambolle and P.-L. Lions, Technical report 9509, CEREMADE, 1995], and [D. C. Dobson and C. R. Vogel, SIAM J. Numer. Anal., 34 (1997), pp. 1779--1791]. Here we present a proof of the global and linear convergence using the framework introduced in [H. Voss and U. Eckhart, Computing, 25 (1980), pp. 243--251] and give a bound for the convergence rate of the fixed point iteration that agrees with our experimental results. These results are also valid for suitable generalizations of the fixed point algorithm.


title: Directional guided seismic attributes and their use in assisting structural, stratigraphic and lithological interpretation

abstract: Summary Seismic attributes are one quantitative measure inherent in the phase, frequency and amplitude content of reflection and refraction data. Detection of structural and stratigraphic information based on seismic attributes is one of the fundamental workflows when it comes to interpreting subsurface features and seeking reservoir engineering information from seismic data. Not only do we need to identify the correct location of structures and edges like faults, it is of critical importance to understand throw profile behavior along faults and the true location of fault tips for volumetric and spill point analysis. Proper attribute analysis gives an interpreter a tool to help characterize the static and dynamic subsurface characteristics such as lithology terminations, possible juxtapositioning of lithologies and cross fault sealing capabilities to fluid flow. As we move into more complex environments, attributes based on structurally oriented filtering have been used to improve S/N ratios while preserving the discontinuities found in the data thereby enhancing stratigraphic boundaries and lithology terminations. We present an Euler-based method for directional filtering and apply this method to different structurally complex environments. We obtain improved S/N ratios and a more consistent and continuous edge mapping in comparison to conventional isotropic methods. Results for delineating salt in the Gulf of Mexico (GOM) and detecting fault extents offshore Norway indicate that methods based on structurally oriented filtering can provide increased clarity in the identification and evaluation of subsurface features.


title: Distributional Smoothing with Virtual Adversarial Training

abstract: We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model distribution. We named the LDS based regularization as virtual adversarial training (VAT). The LDS of a model at an input datapoint is defined as the KL-divergence based robustness of the model distribution against local perturbation around the datapoint. VAT resembles adversarial training, but distinguishes itself in that it determines the adversarial direction from the model distribution alone without using the label information, making it applicable to semi-supervised learning. The computational cost for VAT is relatively low. For neural network, the approximated gradient of the LDS can be computed with no more than three pairs of forward and back propagations. When we applied our technique to supervised and semi-supervised learning for the MNIST dataset, it outperformed all the training methods other than the current state of the art method, which is based on a highly advanced generative model. We also applied our method to SVHN and NORB, and confirmed our method's superior performance over the current state of the art semi-supervised method applied to these datasets.


title: New determinants of ease of use and perceived usefulness for mobile banking adoption

abstract: This research centres the variables affecting the intention of individuals to continue using mobile banking in Pakistan through using a model of technology acceptance model (TAM). Relevant information was collected through a structured instrument while the sample size included 300 users of mobile banking. Furthermore, statistical tools applied as a part of a study were reliability analysis and partial least square-SEM = Structural Equation Modelling in order to check the effect of those factors with the intention of the users. Outcomes suggest that resistance is significantly and negatively associated with perceived ease of use while it is significantly and positively associated with perceived usefulness. Also, perceived risk and compatibility have positive significant relationships with both perceived ease of use and perceived usefulness. However, awareness is positively and significantly connected with perceived ease of use and an insignificant relationship with perceived usefulness. Perceived ease of use has a significant positive relationship with both perceived usefulness and attitude, while perceived usefulness has a positive significant relationship with attitude and intention towards adopting mobile banking. Finally, attitude is also positively and significantly linked with the intention of using mobile banking. The results of this study provide useful information about the users' pattern of using the technology, which will be helpful for the financial institutions.


title: A Genetic-Algorithm-Optimized Fractal Model to Predict the Constriction Resistance From Surface Roughness Measurements

abstract: The electrical contact resistance greatly influences the thermal behavior of substation connectors and other electrical equipment. During the design stage of such electrical devices, it is essential to accurately predict the contact resistance to achieve an optimal thermal behavior, thus ensuring contact stability and extended service life. This paper develops a genetic algorithm (GA) approach to determine the optimal values of the parameters of a fractal model of rough surfaces to accurately predict the measured value of the surface roughness. This GA-optimized fractal model provides an accurate prediction of the contact resistance when the electrical and mechanical properties of the contacting materials, surface roughness, contact pressure, and apparent area of contact are known. Experimental results corroborate the usefulness and accuracy of the proposed approach. Although the proposed model has been validated for substation connectors, it can also be applied in the design stage of many other electrical equipments.


title: Single-sided AC magnetic fields for induction heating

abstract: We present simulations and demonstrate experimentally a new concept in winding a planar induction heater. The winding results in minimal ac magnetic field below the plane of the heater, while concentrating the flux above. Ferrites and other types of magnetic shielding are typically not required. The concept of a one-sided ac field can generalized to other geometries as well.


Crowd Density Estimation based on Improved Harris & OPTICS Algorithm


Second-Generation Wavelet Collocation Method for the Solution of Partial Differential Equations


title: Low-Complexity Iterative Detection for Large-Scale Multiuser MIMO-OFDM Systems Using Approximate Message Passing

abstract: One of the challenges in the design of large-scale multiuser MIMO-OFDM systems is developing low-complexity detection algorithms. To achieve this goal, we leverage message passing algorithms over the factor graph that represents the multiuser MIMO-OFDM systems and approximate the original discrete messages with continuous Gaussian messages through the use of the minimum Kullback-Leibler (KL) divergence criterion. Several signal processing techniques are then proposed to achieve near-optimal performance at low complexity. First, the principle of expectation propagation is employed to compute the approximate Gaussian messages, where the symbol belief is approximated by a Gaussian distribution and then the approximate message is calculated from the Gaussian approximate belief. In addition, the approximate symbol belief can be computed by the a posteriori probabilities fed back from channel decoders, which reduces the complexity dramatically. Second, the first-order approximation of the message is utilized to further simplify the message updating, leading to an algorithm that is equivalent to the AMP algorithm proposed by Donoho Finally, the message updating is further simplified using the central-limit theorem. Compared with the single tree search sphere decoder (STS-SD) and the iterative (turbo) minimum mean-square error based soft interference cancellation (MMSE-SIC) in the literature through extensive simulations, the proposed message passing algorithms can achieve a near-optimal performance while the complexity is decreased by tens of times for a 64 × 64 MIMO system. In addition, it is shown that the proposed message passing algorithms exhibit desirable tradeoffs between performance and complexity for a low-dimensional MIMO system.


Brief history of cortico‐hippocampal time with a special reference to the direct entorhinal input to CA1


title: Self-Affirmation Underlies Facebook Use

abstract: Social network sites, such as Facebook, have acquired an unprecedented following, yet it is unknown what makes them so attractive to users. Here we propose that these sites’ popularity can be understood through the fulfillment of ego needs. We use self-affirmation theory to hypothesize why and when people spend time on their online profiles. Study 1 shows that Facebook profiles are self-affirming in the sense of satisfying users’ need for self-worth and self-integrity. Study 2 shows that Facebook users gravitate toward their online profiles after receiving a blow to the ego, in an unconscious effort to repair their perceptions of self-worth. In addition to illuminating some of the psychological factors that underlie Facebook use, the results provide an important extension to self-affirmation theory by clarifying how self-affirmation operates in people’s everyday environments.


title: BOXES: AN EXPERIMENT IN ADAPTIVE CONTROL

abstract: BOXES is the name of a computer program. We shall also use the word boxes to refer to a particular approach to decision-taking under uncertainty which has been used as the basis of a number of computer programs. Fig. 1 shows a photograph of an assemblage of actual boxes—matchboxes to be exact. Although the construction of this Matchbox Educable Noughts and Crosses Engine (Michie 1961, 1963) was undertaken as a 'fun project', there was present a more serious intention to demonstrate the principle that it may be easier to learn to play many easy games than one difficult one. Consequently it may be advantageous to decompose a game into a number of mutually independent sub-games even if much relevant information is put out of reach in the process. The principle is related to the method of subgoals in problem-solving (see Newell et al. 1960) but differs in one fundamental respect: subgoals are linked in series, while sub-games are played in parallel, in a sense which will become apparent.


title: Analysis of TF-IDF Model and its Variant for Document Retrieval

abstract: An Information Retrieval System is a system that is capable of storage, retrieval, and maintenance of an Information. In this context Information can be composed of text (including numeric and date data), images, audio, video and other multi-media objects. The TF-IDF weight is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. There exist various models for weighting terms of corpus documents and query terms. This work is carried out to analyze and evaluate the retrieval effectiveness of vector -- space model while using the new data set of FIRE 2011. The experiments were performed with TF-IDF and its variants. For all experiments and evaluation the open search engine, Terrier 3.5 was used. Our result shows that TF-IDF model gives the highest precision values with the new corpus dataset.


title: Decoupling between physiological, self-reported, and expressed emotional responses in alexithymia

abstract: Abstract Alexithymia is characterized by difficulties identifying, describing, and expressing emotions and is associated with mental health problems involving emotion dysregulation. To understand the relationship between alexithymia and health, researchers have predominately examined group differences in physiological arousal and self-reported experience. The present study extends this research by examining differences in physiological arousal, self-reported experience, and observed expression. In addition to this between-subjects approach the present study examined within-person difference scores to better understand individual differences in decoupling between all three emotional response domains. Participants ( N = 106; M = 18.00 years), classified as alexithymic (males = 17, females = 34) or non-alexithymic (males = 23, females = 32), gave an impromptu 3-min speech while measures of heart rate and galvanic skin response were continuously recorded. Participants completed self-report measures of self-conscious affect and their behavior was later coded for self-conscious affect. Alexithymic participants: (1) had significantly higher heart rate during baseline however physiological responses were indistinguishable during arousal and recovery, (2) experienced greater self-consciousness collapsing across the tasks, and (3) expressed more self-consciousness as a result of the speech. In addition, findings support the decoupling hypothesis. Alexithymic males experienced and expressed greater self-consciousness compared to their physiological arousal. Results are discussed in terms of the underlying mechanisms of alexithymia.


title: Programming Heterogeneous Systems from an Image Processing DSL

abstract: Specialized image processing accelerators are necessary to deliver the performance and energy efficiency required by important applications in computer vision, computational photography, and augmented reality. But creating, "programming,"and integrating this hardware into a hardware/software system is difficult. We address this problem by extending the image processing language, Halide, so users can specify which portions of their applications should become hardware accelerators, and then we provide a compiler that uses this code to automatically create the accelerator along with the "glue" code needed for the user's application to access this hardware. Starting with Halide not only provides a very high-level functional description of the hardware, but also allows our compiler to generate the complete software program including the sequential part of the workload, which accesses the hardware for acceleration. Our system also provides high-level semantics to explore different mappings of applications to a heterogeneous system, with the added flexibility of being able to map at various throughput rates. ::: We demonstrate our approach by mapping applications to a Xilinx Zynq system. Using its FPGA with two low-power ARM cores, our design achieves up to 6x higher performance and 8x lower energy compared to the quad-core ARM CPU on an NVIDIA Tegra K1, and 3.5x higher performance with 12x lower energy compared to the K1's 192-core GPU.


Memoing for logic programs


title: Angular brushing of extended parallel coordinates

abstract: In this paper we present angular brushing for parallel coordinates (PC) as a new approach to highlighting rational data-properties, i.e., features which - in a non-separable way - depend on two data dimensions. We also demonstrate smooth brushing as an intuitive tool for specifying nonbinary degree-of-interest functions (for focus+context visualization). We also briefly describe our implementation as well as its application to the visualization of CFD data.


title: Range-IT: detection and multimodal presentation of indoor objects for visually impaired people

abstract: In the paper we present our Range-IT prototype, which is a 3D depth camera based electronic travel aid (ETA) to assist visually impaired people in finding out detailed information of surrounding objects. In addition to detecting indoor obstacles and identifying several objects of interest (e.g., walls, open doors and stairs) up to 7 meters, the Range-IT system employs a multimodal audio-vibrotactile user interface to present this spatial information.


title: A dynamic scheduling algorithm for bandwidth reservation requests in software-defined networks

abstract: With the rapid development of Internet technology and the emergence of more flexible applications, dynamic allocation of network resources is becoming more and more significant. Since bandwidth resource is limited, users' dynamic allocation requests cannot always be granted. Therefore, scheduling the dynamic requests to achieve optimal bandwidth utilization or service rate is of great importance. However, this is difficult to achieve in current networks due to the lack of programming interface of network devices for effective and centralized control and management. In recent years, the emergence of software-defined networks (SDN) provides us an opportunity to realize the goal. Centralized control and programmability of SDN let network managers configure, manage and optimize network resources very easily and quickly. In this paper, a dynamic scheduling algorithm based on an optimization model for scheduling reservation-based bandwidth allocation requests is proposed to achieve optimal resource utilization. Simulation result shows that the scheduling algorithm proposed in this paper could achieve higher bandwidth utilization compared to another two scheduling algorithms.


title: Spatio-Temporal Attention-Based LSTM Networks for 3D Action Recognition and Detection

abstract: Human action analytics has attracted a lot of attention for decades in computer vision. It is important to extract discriminative spatio-temporal features to model the spatial and temporal evolutions of different actions. In this paper, we propose a spatial and temporal attention model to explore the spatial and temporal discriminative features for human action recognition and detection from skeleton data. We build our networks based on the recurrent neural networks with long short-term memory units. The learned model is capable of selectively focusing on discriminative joints of skeletons within each input frame and paying different levels of attention to the outputs of different frames. To ensure effective training of the network for action recognition, we propose a regularized cross-entropy loss to drive the learning process and develop a joint training strategy accordingly. Moreover, based on temporal attention, we develop a method to generate the action temporal proposals for action detection. We evaluate the proposed method on the SBU Kinect Interaction data set, the NTU RGB + D data set, and the PKU-MMD data set, respectively. Experiment results demonstrate the effectiveness of our proposed model on both action recognition and action detection.


Year : 2018 Data-Efficient Decentralized Visual SLAM


title: Multi-Agent Reinforcement Learning: A Survey

abstract: Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined


When to Book : Predicting Flight Pricing


title: PRE-CRASH SENSOR FOR PRE-CRASH SAFETY

abstract: Improvement of vehicle safety performance is one of the targets of ITS development. A pre-crash safety system has been developed that utilizes ITS technologies. The Pre-crash Safety system reduces collision injury by estimating TTC (time-to-collision) to pre-emptively activate safety devices, which consist of Pre-crash Seatbelt system and Pre-crash Brake Assist system. The key technology of these systems is a Pre-crash Sensor to detect obstacles and estimate TTC. The Pre-crash Sensor uses millimeter-wave radar to detect preceding vehicles, oncoming vehicles, roadside objects, etc. on the road ahead. Furthermore, by using a phased array system as a vehicle radar for the first time, a compact electronically scanned millimeter-wave radar with high recognition performance has been achieved. With respect to the obstacle determination algorithm, a crash determination algorithm has been newly developed, taking into account estimation of the direction of advance of the vehicle, in addition to the distance, relative speed and direction of the object. For the covering abstract see ITRD E825082.


title: Representing first-order logic using graphs

abstract: Abstract. We show how edge-labelled graphs can be used to represent first-order logic formulae. This gives rise to recursively nested structures, in which each level of nesting corresponds to the negation of a set of existentials. The model is a direct generalisation of the negative application conditions used in graph rewriting, which count a single level of nesting and are thereby shown to correspond to the fragment ∃¬∃ of first-order logic. Vice versa, this generalisation may be used to strengthen the notion of application conditions. We then proceed to show how these nested models may be flattened to (sets of) plain graphs, by allowing some structure on the labels. The resulting formulae-as-graphs may form the basis of a unification of the theories of graph transformation and predicate transformation.


The Distributed Simulation of Multi-Agent Systems


title: Predicting diverse subsets using structural SVMs

abstract: In many retrieval tasks, one important goal involves retrieving a diverse set of results (e.g., documents covering a wide range of topics for a search query). First of all, this reduces redundancy, effectively showing more information with the presented results. Secondly, queries are often ambiguous at some level. For example, the query "Jaguar" can refer to many different topics (such as the car or feline). A set of documents with high topic diversity ensures that fewer users abandon the query because no results are relevant to them. Unlike existing approaches to learning retrieval functions, we present a method that explicitly trains to diversify results. In particular, we formulate the learning problem of predicting diverse subsets and derive a training method based on structural SVMs.


title: Sequencing technologies — the next generation

abstract: Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.


title: Dermatological and environmental toxicological impact of the sunscreen ingredient oxybenzone/benzophenone‐3

abstract: Summary ::: Oxybenzone (Benzophenone-3) is an emerging human and environmental contaminant used in sunscreens and personal care products to help minimize the damaging effects of ultraviolet radiation. The Center for Disease Control fourth national report on human exposure to environmental chemicals demonstrated that approximately 97% of the people tested have oxybenzone present in their urine, and independent scientists have reported various concentrations in waterways and fish worldwide. Oxybenzone can also react with chlorine, producing hazardous by-products that can concentrate in swimming pools and wastewater treatment plants. Moreover, adverse reactions could very well be increased by the closed loop of ingesting fish contaminated with oxybenzone and/or washing the ingredient off our bodies and having it return in drinking water as treatment plants do not effectively remove the chemical as part of their processing protocols. In humans, oxybenzone has been reported to produce contact and photocontact allergy reactions, implemented as a possible endocrine disruptor and has been linked to Hirschsprung's disease. Environmentally, oxybenzone has been shown to produce a variety of toxic reactions in coral and fish ranging from reef bleaching to mortality. Lastly, with the rise in skin cancer rates and the availability of more effective sunscreen actives such as micronized zinc oxide and titanium dioxide, serious doubts about the relative prevention benefit of personal care products containing oxybenzone must be raised and compared with the potential negative health and environmental effects caused by the accumulation of this and other chemicals in the ecosystem.


A review of the wall pressure signature and other tunnel constraint correction methods for high angle-of-attack tests


title: Evidence-Based Practice, Response to Intervention, and the Prevention of Reading Difficulties

abstract: Purpose This article provides an evidence-based perspective on what school communities can do to lower the prevalence of reading difficulties among their pupils through preventive interventions. It also delineates the roles that speech-language pathologists (SLPs) might play in these interventions. Method This article is organized to first provide a broad overview of current directions in research, practice, and policy in educational interventions, with an emphasis on how the three are increasingly integrated to respond to evidence showing that American school children are underperforming in reading. Next, the concept of response to intervention (RTI) is described. RTI is an educational policy and practice that is grounded in the accumulated literature that focuses on how schools might better organize themselves to deliver multitiered reading interventions to reduce children’s risk for reading disability. Last, this article provides three organizational principles that school-based professionals, includin...


Digitized Locksmith Forensics: Design and Implementation of a Computer-Aided Forensic Analysis


title: QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks

abstract: Underwater sensor network (UWSN) has emerged in recent years as a promising networking technique for various aquatic applications. Due to specific characteristics of UWSNs, such as high latency, low bandwidth, and high energy consumption, it is challenging to build networking protocols for UWSNs. In this paper, we focus on addressing the routing issue in UWSNs. We propose an adaptive, energy-efficient, and lifetime-aware routing protocol based on reinforcement learning, QELAR. Our protocol assumes generic MAC protocols and aims at prolonging the lifetime of networks by making residual energy of sensor nodes more evenly distributed. The residual energy of each node as well as the energy distribution among a group of nodes is factored in throughout the routing process to calculate the reward function, which aids in selecting the adequate forwarders for packets. We have performed extensive simulations of the proposed protocol on the Aqua-sim platform and compared with one existing routing protocol (VBF) in terms of packet delivery rate, energy efficiency, latency, and lifetime. The results show that QELAR yields 20 percent longer lifetime on average than VBF.


title: Fingerprint Image Enhancement: Segmentation to Thinning

abstract: Fingerprint has remained a very vital index for human recognition. In the field of security, series of Automatic Fingerprint Identification Systems (AFIS) have been developed. One of the indices for evaluating the contributions of these systems to the enforcement of security is the degree with which they appropriately verify or identify input fingerprints. This degree is generally determined by the quality of the fingerprint images and the efficiency of the algorithm. In this paper, some of the sub-models of an existing mathematical algorithm for the fingerprint image enhancement were modified to obtain new and improved versions. The new versions consist of different mathematical models for fingerprint image segmentation, normalization, ridge orientation estimation, ridge frequency estimation, Gabor filtering, binarization and thinning. The implementation was carried out in an environment characterized by Window Vista Home Basic operating system as platform and Matrix Laboratory (MatLab) as frontend engine. Synthetic images as well as real fingerprints obtained from the FVC2004 fingerprint database DB3 set A were used to test the adequacy of the modified sub-models and the resulting algorithm. The results show that the modified sub-models perform well with significant improvement over the original versions. The results also show the necessity of each level of the enhancement. Keyword- AFIS; Pattern recognition; pattern matching; fingerprint; minutiae; image enhancement.


title: Naturalistic Recognition of Activities and Mood Using Wearable Electronics

abstract: Automatic recognition of user context is essential for a variety of emerging applications, such as context-dependent content delivery, telemonitoring of medical patients, or quantified life-logging. Although not explicitly observable as, e.g., activities, an important aspect towards understanding user context lies in the affective state of mood.While significant work has been done to assess mood, most approaches require the use of customized sensors and controlled laboratory settings.In this work, we engineer a recognition pipeline to recognize daily activities from commercially popularized wearable electronics. In turn, we use predicted activities to learn a regression model capable of assessing user mood.Using only commercially popularized wearable devices, we enable the potential for seamless deployment to the general public.Conducting a real-world study with a prototype system, we collect and evaluate data from 18 users, who provide over 93 user-days of labelled activity data.Regressing for mood based on predicted daily activities, we are able to infer mood angles with a mean absolute error of $0.24\pi$ radians on the Circumplex Model of Affect.Comparing with benchmark approaches, our approach outperforms with statistical significance and is validated for robustness against noise from activity misclassification.


title: Polygon‐based texture mapping for cyber city 3D building models

abstract: The three-dimensional building model is one of the most important components in a cyber city implementation. Currently, however, most building models do not have sufficient and accurate texture information. The lack of texture not only makes 3D building models less realistic in visualization, it may also fail to provide needed information in intricate applications. This study developed a polygon-based texture mapping system to produce near photo-realistic texture mappings for 3D building models. Textures of building exteriors were generated from mosaics of close-range photographs acquired with commodity digital cameras. The developed system integrated multiple digital photographs to create texture mosaics that were continuous in geometric outlines and smooth in colour shadings, and correctly mapped them onto corresponding building model facades. A test example demonstrated that the resultant building model had more complete and accurate texture features as well as a near-photo-realistic appearance.


Folder vs . Tag Preference in Personal Information Management


title: Portable, high-performance containers for HPC

abstract: Building and deploying software on high-end computing systems is a challenging task. High performance applications have to reliably run across multiple platforms and environments, and make use of site-specific resources while resolving complicated software-stack dependencies. Containers are a type of lightweight virtualization technology that attempt to solve this problem by packaging applications and their environments into standard units of software that are: portable, easy to build and deploy, have a small footprint, and low runtime overhead. In this work we present an extension to the container runtime of Shifter that provides containerized applications with a mechanism to access GPU accelerators and specialized networking from the host system, effectively enabling performance portability of containers across HPC resources. The presented extension makes possible to rapidly deploy high-performance software on supercomputers from containerized applications that have been developed, built, and tested in non-HPC commodity hardware, e.g. the laptop or workstation of a researcher.


title: Powered ankle-foot prosthesis

abstract: The minimum level of series compliance that adequately protects the transmission from damage during foot collision fails to satisfy bandwidth requirements. As a resolution to this difficulty, parallel motor elasticity is used to lower the forces borne by the SEA, enhancing system force bandwidth. To minimize prosthesis cost of transport (COT) and motor or transmission size, we select a parallel stiffness that supplies the necessary ankle stiffness during early stance period dorsiflexion, eliminating the need for SEA during that gait phase. In future investigations, we hope to apply the ankle-foot design to robotic, orthotic, and exoskeletal applications. In the design of biomimetic ankle-foot systems, we feel both series and parallel motor elasticity are of paramount importance.


title: Privacy Analysis of Smart City Healthcare Services

abstract: A smart city is an interconnected entity of internet of things and intelligent systems to provide qualify services to its citizens in various sectors such as public safety, healthcare, transportation and energy. In order for users to get the provided services, they have to access these services, provide and exchange a lot of personal information in an open, unsafe and interconnected environment. Therefore, user information may be exposed to privacy violations, which is especially critical for healthcare related services. Privacy engineering challenges that are not considered in design and development of these services may lead to possible privacy holes and leakage points, which raise concerns among users. In this paper, a privacy engineering approach and safeguard framework are proposed for smart city healthcare services. The paper aims to understand the privacy holes, identify the privacy leakage points and study privacy requirements to propose a privacy safeguard design solution.


title: Polymorphism of waxy proteins in Spanish hulled wheats

abstract: Hulled wheats are neglected crops that have potential in plant breeding programmes of modern durum and common wheat. Among these wheats, three species were widely cultivated in Spain until the mid 20th century: Triticum monococcum ssp. monococcum (einkorn), Triticum turgidum ssp. dicoccum (emmer) and Triticum aestivum ssp. spelta (spelt). One important aspect of wheat grain quality is starch composition, which is related to the action of waxy proteins. A collection of 536 accessions of Spanish hulled wheats was analyzed for waxy protein composition using sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). Polymorphism was found for the Wx-A1, Wx-B1 and Wx-D1 proteins, including new and null alleles in the three species. An allelic variant with an electrophoretic mobility not previously described was found in einkorn wheat. In emmer and spelt, some alleles with different mobility were also found. A Wx-B1 null allele was detected in emmer wheat, and null alleles for Wx-A1, Wx-B1 and Wx-D1 were found in spelt wheat. The variations found could be used to enlarge the gene pool available to breeders, and to design new cultivars with different levels of amylose content.


title: 1.1 Definition Computer-Mediated Discourse

abstract: Computer-mediated discourse is the communication produced when human beings interact with one another by transmitting messages via networked computers. The study of computer-mediated discourse (henceforth CMD) is a specialization within the broader interdisciplinary study of computer-mediated communication (CMC), distinguished by its focus on language and language use in computer networked environments, and by its use of methods of discourse analysis to address that focus. Most CMC currently in use is text-based, that is, messages are typed on a computer keyboard and read as text on a computer screen, typically by a person or persons at a different location from the message sender. Text-based CMC takes a variety of forms (e.g., e-mail, discussion groups, real-time chat, virtual reality role-playing games) whose linguistic properties vary depending on the kind of messaging system used and the social and cultural context embedding particular instances of use. However, all such forms have in common that the activity that takes place through them is constituted primarily -in many cases, exclusively -by visually-presented language. These characteristics of the medium have important consequences for understanding the nature of computer-mediated language. They also provide a unique environment, free from competing influences from other channels of communication and from physical context, in which to study verbal interaction and the relationship between discourse and social practice.


title: Quality Improvement of Liver Ultrasound Images Using Fuzzy Techniques

abstract: BACKGROUND ::: Liver ultrasound images are so common and are applied so often to diagnose diffuse liver diseases like fatty liver. However, the low quality of such images makes it difficult to analyze them and diagnose diseases. The purpose of this study, therefore, is to improve the contrast and quality of liver ultrasound images. ::: ::: ::: METHODS ::: In this study, a number of image contrast enhancement algorithms which are based on fuzzy logic were applied to liver ultrasound images - in which the view of kidney is observable - using Matlab2013b to improve the image contrast and quality which has a fuzzy definition; just like image contrast improvement algorithms using a fuzzy intensification operator, contrast improvement algorithms applying fuzzy image histogram hyperbolization, and contrast improvement algorithms by fuzzy IF-THEN rules. ::: ::: ::: RESULTS ::: With the measurement of Mean Squared Error and Peak Signal to Noise Ratio obtained from different images, fuzzy methods provided better results, and their implementation - compared with histogram equalization method - led both to the improvement of contrast and visual quality of images and to the improvement of liver segmentation algorithms results in images. ::: ::: ::: CONCLUSION ::: Comparison of the four algorithms revealed the power of fuzzy logic in improving image contrast compared with traditional image processing algorithms. Moreover, contrast improvement algorithm based on a fuzzy intensification operator was selected as the strongest algorithm considering the measured indicators. This method can also be used in future studies on other ultrasound images for quality improvement and other image processing and analysis applications.


title: Early Stage Malware Prediction Using Recurrent Neural Networks

abstract: Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture - typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected. In this paper we investigate the possibility of predicting whether or not an executable is malicious based on a short snapshot of behavioural data. We find that an ensemble of recurrent neural networks are able to predict whether an executable is malicious or benign within the first 5 seconds of execution with 94% accuracy. This is the first time general types of malicious file have been predicted to be malicious during execution rather than using a complete activity log file post-execution, and enables cyber security endpoint protection to be advanced to use behavioural data for blocking malicious payloads rather than detecting them post-execution and having to repair the damage.


title: On-line detection of continuous changes in stochastic processes

abstract: This paper addresses the issue of detecting changes in stochastic processes. In conventional studies on change detection, it has been explored how to detect discrete changes for which the statistical models of data suddenly change. We are rather concerned with how to detect continuous changes which occurs incrementally over some successive periods. This paper gives a novel methodology for detecting continuous changes. We first define the information-theoretic measure of continuous change and prove that it is invariant with respect to the parametrization of statistical model. We then propose an efficient algorithm to detect continuous changes according to the proposed measure. We demonstrate the effectiveness of our method through the experiments using synthetic data and the applications to security and economic event detection.


title: Plot Induction and Evolutionary Search for Story Generation

abstract: In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children's stories show that our system outperforms previous data-driven approaches.


On the Equivalence Between Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing


title: Analyzing attack strategies against rule-based intrusion detection systems

abstract: Intrusion Detection Systems (IDS) have been widely used to detect cyber attacks in Cyber-Physical Systems (CPS). However, attackers can often adapt their attacking strategies to evade detection. Many commercial IDS are rule-based systems. This paper analyzes the possible attacking strategies against a widely used rule-based IDS, Snort, using hyper graph model and clustering. We present initial results and discuss some techniques to prevent such attacks.


title: Worker productivity, and occupational health and safety issues in selected industries

abstract: The main objective of this research was to identify factors that affected worker productivity, occupational health and safety in selected industries in a developing country. Fifty production managers participated in the study. Fifty-four percent of the managers reported hot environmental conditions, 28% a noisy environment, and 26% a lack of resources and facilities. Managers received worker complaints of fatigue, back pain, upper-body pain, hand and wrist pain and headaches. Management (88%) acknowledged not having knowledge or access to ergonomics information. Ninety-four percent of the companies did not carry out ergonomic assessments. A significant correlation (p < 0.01) was found among productivity indicators and health and organizational attributes. Lack of skills in ergonomics and training, communication and resources are believed to be some of the factors contributing to the poor ergonomic conditions and consequent loss of worker productivity and reduced health and safety in these industries.


title: A novel explicit multi-focus image fusion method

abstract: We propose a multi-focus image fusion method. The energy of Laplacian of input images is obtained to decide which portions of the input images are in better focus, and the majority filter offers a means of spreading the focused regions to neighborhood. The edge-preserving guided image filter is applied to reduce the block effect. We conduct several experiments which demonstrate that it provided a better performance than the state of the art fusion methods in visual and quantitative evaluations. © 2015 ISSN.


title: Dyslexia in English as a second language

abstract: This study focused on English as L2 in a group of Norwegian dyslexic 12 year olds, compared to an age and gender matched control group. Norwegian school children learn English from the first grades on. The subjects were assessed with a test battery of verbal and written tasks. First, they were given a comprehension task; second, a model sentence task; third, two pragmatic tasks, and fourth, three tasks of literacy. The verbal tasks were scored according to comprehension, morphology, syntax and semantics, while the literacy tasks were scored by spelling, translation and reading skills. It was hypothesized that the results of the control group and the dyslexia group would differ on all tasks, but that subgrouping the dyslexia group by comprehension skills would show heterogeneity within the dyslexia group. The data analyses confirmed these hypotheses. Significant differences were seen between the dyslexia group and the control group. However, the subgrouping revealed minor differences between the control group and the subgroup with good comprehension skills, and major differences between the control group and the subgroup with poor comprehension skills. Especially morphology and spelling were difficult for the dyslexia group. The results were tentatively discussed within the framework of biological and cognitive models of how to interpret L2 performance in dyslexia, underlining the importance of further research in L2 acquisition in dyslexia. Copyright © 2004 John Wiley & Sons, Ltd.


title: Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment

abstract: Cars can nowadays record several thousands of signals through the controller area network (CAN) bus technology and potentially provide real-time information on the car, the driver, and the surrounding environment. This paper proposes a new methodology for near-real-time analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, longitudinal and lateral acceleration. Data have been collected in a completely uncontrolled experiment involving 54 people, where over 2000 trips have been recorded without any type of predetermined driving instruction on a wide variety of road scenarios. While only few works have analyzed the driving behavior of more than 50 drivers using CAN bus data, we propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings. The minimal amount of data needed to preserve robust driver clustering is also computed, showing that by properly choosing a subsampling strategy it is possible to reduce the size of the database as much as 99% without impairing the clustering performance.


title: Workload Classification for Efficient Auto-Scaling of Cloud Resources

abstract: Elasticity algorithms for cloud infrastructures dynamically change the amount of resources allocated to a running service according to the current and predicted future load. Since there is no perfe ...


title: Strengths and barriers behind the successful agile deployment—insights from the three software intensive companies in Finland

abstract: The number of success stories being reported concerning agile software development has led to an increase in interest among industries and research communities. The purpose of this paper is to identify strengths and barriers for `successful agile deployment' in the software companies. This knowledge can benefit software companies planning their current strategy for agile deployment. Analysis of 57 developers, architects, project managers, customers, quality managers, and line and product managers in three case companies identifies 71 strengths and 169 barriers of agile deployment. The analysis revealed the importance of management providing the necessary goals and support for agile development. It also indicated the significance of defining a tailored process model and giving developers the freedom to improve their own agile development process continuously during agile deployment. The identified barriers, strengths and recommendations can be used as a checklist for planning and/or monitoring the effectiveness of agile deployment in software companies. By identifying the barriers and strengths of agile deployment, the paper deepens understanding of this highly relevant but relatively under-researched phenomenon and contributes to the literature on agile deployment and software process improvement.


title: Envelope Tracking of an RF High Power Amplifier With an 8-Level Digitally Controlled GaN-on-Si Supply Modulator

abstract: This paper presents an envelope tracking (ET) transmitter architecture based on the combination of a novel 3-bit $(N = 3)$ supply modulator and digital predistortion (DPD). The proposed power converter is based on a direct digital-to-analog conversion architecture that implements the binary-coded sum of $N$ isolated dc voltages, allowing the synthesis of an output waveform with $L = 2^{N}$ voltage levels, with a binary distribution in the range $\Delta V = V_{M} - V_{O}$ (maximum voltage $V_{M}$ , offset voltage $Vo$ ). This solution provides a better voltage resolution $V_{S} = \Delta V/(2^{N}-1)$ with respect to typical multilevel switched-sources topologies $(V_{S} = \Delta V/N)$ . The improved voltage resolution enables the correction of the residual discretization error in the ET transmitter by means of DPD of the RF signal without the need of an auxiliary linear envelope amplifier. The proposed ET solution has been tested with an L-band 30-W lateral-diffused MOS RF high power amplifier (RF HPA) with 1.4- and 10-MHz long-term-evolution signals. In these conditions the converter demonstrated 92% and 83% efficiency, respectively, whereas the congregate efficiency of the transmitter are 38.3% and 23.9% at 5.5 and 1.9 W of average RF output power, respectively. These performances correspond to an improvement of 17.2 and 17.9 points for the power-added efficiency of the RF HPA and to 13.4 and 13 points of improvement for the efficiency of the entire transmitter with respect to fixed bias operation.


title: Learning Multiscale Features Directly From Waveforms

abstract: Deep learning has dramatically improved the performance of speech recognition systems through learning hierarchies of features optimized for the task at hand. However, true end-to-end learning, where features are learned directly from waveforms, has only recently reached the performance of hand-tailored representations based on the Fourier transform. In this paper, we detail an approach to use convolutional filters to push past the inherent tradeoff of temporal and frequency resolution that exists for spectral representations. At increased computational cost, we show that increasing temporal resolution via reduced stride and increasing frequency resolution via additional filters delivers significant performance improvements. Further, we find more efficient representations by simultaneously learning at multiple scales, leading to an overall decrease in word error rate on a difficult internal speech test set by 20.7% relative to networks with the same number of parameters trained on spectrograms.


title: Anomaly detection via 3D-HOF and fast double sparse representation

abstract: This paper presents a framework for anomaly detection in videos which considers both motion and appearance features. For motion cues, we propose a new feature called 3D-HOF, which effectively extracts both velocity and orientation from the optical flow map. At the same time, we introduce the concept of “depth of field” problem to make the detection more accurate when the velocity of an object may seem to be different according to its distance to the camera. For appearance cues, we use 3D gradients of spatio-temporal cuboids as features. Next in the detection part, we propose a fast double sparse representation method in order to make the process faster in some actual scenes. Finally, we integrate both the outcomes of using motion and appearance cues as the final outcomes. Results on discriminative datasets show efficiency and effectiveness compared to the state-of-the-art methods.


title: Mixing Domain Rules with Machine Learning for Radiology Text Classification

abstract: In this work we compare and contrast proposed techniques and experiments in the subdomain of radiology text classication. In the past, systems have relied on two main approaches: rule-based or natural language processing plus machine learning (NLP/ML). Simple rule-based approaches have demonstrated great ecacy, at the high cost of requiring medical professionals to build and maintain. Complex NLP pipeline and ML approaches have also demonstrated efcacy, but rely heavily on computing professionals to build and maintain, in addition to requiring the involvement of medical domain experts. We propose a hybrid classication mechanism for radiology text combining rules and NLP/ML that in our trials surpasses existing rule-based and NLP/ML techniques for the task of classifying incidental ndings. We evaluate our approach against three approaches from related work using our own gold standard data set of 661 records. Our hybrid approach achieves a 13% F-measure gain over our prior rulebased approach[16] and a 4% F-measure improvement vs. a manual classication process in a hospital setting.


title: Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions

abstract: It is important for a robot to be able to interpret natural language commands given by a human. In this paper, we consider performing a sequence of mobile manipulation tasks with instructions described in natural language. Given a new environment, even a simple task such as boiling water would be performed quite differently depending on the presence, location and state of the objects. We start by collecting a dataset of task descriptions in free-form natural language and the corresponding grounded task-logs of the tasks performed in an online robot simulator. We then build a library of verb-environment instructions that represents the possible instructions for each verb in that environment, these may or may not be valid for a different environment and task context. We present a model that takes into account the variations in natural language and ambiguities in grounding them to robotic instructions with appropriate environment context and task constraints. Our model also handles incomplete or noisy natural language instructions. It is based on an energy function that encodes such properties in a form isomorphic to a conditional random field. We evaluate our model on tasks given in a robotic simulator and show that it successfully outperforms the state of the art with 61.8% accuracy. We also demonstrate a grounded robotic instruction sequence on a PR2 robot using the Learning from Demonstration approach.


title: Understanding of Internal Clustering Validation Measures

abstract: Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a detailed study of 11 widely used internal clustering validation measures for crisp clustering. From five conventional aspects of clustering, we investigate their validation properties. Experiment results show that S\_Dbw is the only internal validation measure which performs well in all five aspects, while other measures have certain limitations in different application scenarios.


title: Dexterous anthropomorphic robot hand with distributed tactile sensor: Gifu hand II

abstract: Presents an anthropomorphic robot hand called Gifu hand II that is a revised version of the Gifu hand I. The Gifu hand II has five fingers in which all joints are driven by servomotors built in the fingers and the palm. The thumb has 4 joints with 4 degrees of freedom (DOF), the other fingers have 4 joints with 3 DOF respectively, and two axes of joints near the palm cross orthogonally at one point like the human finger. It can be equipped with a 6 axes force sensor at each fingertip and a developed distributed tactile sensor with 624 detecting points on its surface. The design concepts and the specifications of the Gifu hand II, the basic characteristics of the tactile sensor, and the pressure distributions at object grasping are shown. These show that the Gifu hand II has a high potential to perform dexterous object manipulation like a human hand.


title: Assessing the quality of textual features in social media

abstract: Social media is increasingly becoming a significant fraction of the content retrieved daily by Web users. However, the potential lack of quality of user generated content poses a challenge to information retrieval services, which rely mostly on textual features generated by users (particularly tags) commonly associated with the multimedia objects. This paper presents what, to the best of our knowledge, is currently the most comprehensive study of the relative quality of textual features in social media. We analyze four different features, namely, title, tags, description and comments posted by users, in four popular applications, namely, YouTube, Yahoo! Video, LastFM and CiteULike. Our study is based on an extensive characterization of data crawled from the four applications with respect to usage, amount and semantics of content, descriptive and discriminative power as well as content and information diversity across features. It also includes a series of object classification and tag recommendation experiments as case studies of two important information retrieval tasks, aiming at analyzing how these tasks are affected by the quality of the textual features. Classification and recommendation effectiveness is analyzed in light of our characterization results. Our findings provide valuable insights for future research and design of Web 2.0 applications and services.


title: Dynamic Data Center Power Management: Trends, Issues, and Solutions

abstract: A DC-DC converter with low power consumption and high power conversion efficiency is provided. The DC-DC converter includes a first transistor and a control circuit. The control circuit includes an operational amplifier generating a signal that controls switching of the first transistor, a bias circuit generating a bias potential supplied to the operational amplifier, and a holding circuit holding the bias potential. The holding circuit includes a second transistor and a capacitor to which the bias potential is supplied. The first transistor and the second transistor include a first oxide semiconductor film and a second oxide semiconductor film, respectively. The first oxide semiconductor film and the second oxide semiconductor film each contain In, M (M is Ga, Y, Zr, La, Ce, or Nd), and Zn. The atomic ratio of In to M in the first oxide semiconductor film is higher than that in the second oxide semiconductor film.


title: Flexible Near-Field Wireless Optoelectronics as Subdermal Implants for Broad Applications in Optogenetics

abstract: Summary In vivo optogenetics provides unique, powerful capabilities in the dissection of neural circuits implicated in neuropsychiatric disorders. Conventional hardware for such studies, however, physically tethers the experimental animal to an external light source, limiting the range of possible experiments. Emerging wireless options offer important capabilities that avoid some of these limitations, but the current size, bulk, weight, and wireless area of coverage is often disadvantageous. Here, we present a simple but powerful setup based on wireless, near-field power transfer and miniaturized, thin, flexible optoelectronic implants, for complete optical control in a variety of behavioral paradigms. The devices combine subdermal magnetic coil antennas connected to microscale, injectable light-emitting diodes (LEDs), with the ability to operate at wavelengths ranging from UV to blue, green-yellow, and red. An external loop antenna allows robust, straightforward application in a multitude of behavioral apparatuses. The result is a readily mass-producible, user-friendly technology with broad potential for optogenetics applications.


title: Self-compassion, affect, and health-promoting behaviors.

abstract: Objective: Emerging theory and research suggest that self-compassion promotes the practice of health behaviors, and implicates self-regulation as an explanatory factor. However, previous investigations focused only on behavior intentions or health risk behaviors, and did not investigate the role of emotions. This study expands on this research using a small-scale meta-analysis approach with our own data sets to examine the associations of self-compassion with a set of health-promoting behaviors, and test the roles of high positive affect and low negative affect as potential explanatory mechanisms. Method: Fifteen independent samples (N = 3,252) with correlations of self-compassion with the frequency of self-reported health-promoting behaviors (eating habits, exercise, sleep behaviors, and stress management) were meta-analyzed. Eight of these samples completed measures of positive and negative affect. Results: Self-compassion was positively associated with the practice of health-promoting behaviors across all 15 samples. The meta-analysis revealed a small effect size (average r = .25; p ≥ .001) of self-compassion and health behaviors, with low variability. Tests of the indirect effects of selfcompassion on health behaviors through positive and negative affect with multiple mediator analyses revealed small effects for each. Separate meta-analyses of the indirect effects (IE) were significant for positive (average IE = .08; p ≥ .001) and negative affect (average IE = .06; p ≥ .001), and their combined indirect effects (average IE = .15; p ≥ .0001). Conclusion: Self-compassion may be an important quality to cultivate for promoting positive health behaviors, due in part to its association with adaptive emotions.


title: LightGBM: a highly efficient gradient boosting decision tree

abstract: Gradient Boosting Decision Tree (GBDT) is a popular machine learning algorithm, and has quite a few effective implementations such as XGBoost and pGBRT. Although many engineering optimizations have been adopted in these implementations, the efficiency and scalability are still unsatisfactory when the feature dimension is high and data size is large. A major reason is that for each feature, they need to scan all the data instances to estimate the information gain of all possible split points, which is very time consuming. To tackle this problem, we propose two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). With GOSS, we exclude a significant proportion of data instances with small gradients, and only use the rest to estimate the information gain. We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size. With EFB, we bundle mutually exclusive features (i.e., they rarely take nonzero values simultaneously), to reduce the number of features. We prove that finding the optimal bundling of exclusive features is NP-hard, but a greedy algorithm can achieve quite good approximation ratio (and thus can effectively reduce the number of features without hurting the accuracy of split point determination by much). We call our new GBDT implementation with GOSS and EFB LightGBM. Our experiments on multiple public datasets show that, LightGBM speeds up the training process of conventional GBDT by up to over 20 times while achieving almost the same accuracy.


title: Refining prediction in treatment-resistant depression: Results of machine learning analyses in the TRD III sample

abstract: OBJECTIVE ::: The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome. ::: ::: ::: METHOD ::: 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation. ::: ::: ::: RESULTS ::: The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization. ::: ::: ::: CONCLUSIONS ::: With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD.


title: Do Relationships Matter? Evidence from Loan Officer Turnover

abstract: We show that the cost of employee turnover in firms that rely on decentralized knowledge and personal relationships depends on the firms' planning horizons and the departing employees' incentives to transfer information. Using exogenous shocks to the relationship between borrowers and loan officers, we document that borrowers whose loan officers are on leave are less likely to receive new loans from the bank, are more likely to apply for credit from other banks, and are more likely to miss payments or go into default. These costs are smaller when turnover is expected, as in the case of maternity leave, or when loan officers have incentives to transfer information, as in the case of voluntary resignations. ::: ::: This paper was accepted by Wei Jiang, finance.


title: Monitoring Power Data: A first step towards a unified energy efficiency evaluation toolset for HPC data centers ☆

abstract: Abstract The energy consumption of High Performance Computing (HPC) systems, which are the key technology for many modern computation-intensive applications, is rapidly increasing in parallel with their performance improvements. This increase leads HPC data centers to focus on three major challenges: the reduction of overall environmental impacts, which is driven by policy makers; the reduction of operating costs, which are increasing due to rising system density and electrical energy costs; and the 20 MW power consumption boundary for Exascale computing systems, which represent the next thousandfold increase in computing capability beyond the currently existing petascale systems. Energy efficiency improvements will play a major part in addressing these challenges. This paper presents a toolset, called Power Data Aggregation Monitor (PowerDAM), which collects and evaluates data from all aspects of the HPC data center (e.g. environmental information, site infrastructure, information technology systems, resource management systems, and applications). The aim of PowerDAM is not to improve the HPC data center's energy efficiency, but is to collect energy relevant data for analysis without which energy efficiency improvements would be non-trivial and incomplete. Thus, PowerDAM represents a first step towards a truly unified energy efficiency evaluation toolset needed for improving the overall energy efficiency of HPC data centers.


title: Improving Adversarial Robustness by Data-Specific Discretization

abstract: A recent line of research proposed (either implicitly or explicitly) gradient-masking preprocessing techniques to improve adversarial robustness. However, as shown by Athaley-Carlini-Wagner, essentially all these defenses can be circumvented if an attacker leverages approximate gradient information with respect to the preprocessing. This thus raises a natural question of whether there is a useful preprocessing technique in the context of white-box attacks, even just for only mildly complex datasets such as MNIST. In this paper we provide an affirmative answer to this question. Our key observation is that for several popular datasets, one can approximately encode entire dataset using a small set of separable codewords derived from the training set, while retaining high accuracy on natural images. The separability of the codewords in turn prevents small perturbations as in $\ell_\infty$ attacks from changing feature encoding, leading to adversarial robustness. For example, for MNIST our code consists of only two codewords, $0$ and $1$, and the encoding of any pixel is simply $\mathbb{1}[x > 0.5]$ (i.e., whether a pixel $x$ is at least $0.5$). Applying this code to a naturally trained model already gives high adversarial robustness even under strong white-box attacks based on Backward Pass Differentiable Approximation (BPDA) method of Athaley-Carlini-Wagner that takes the codes into account. We give density-estimation based algorithms to construct such codes, and provide theoretical analysis and certificates of when our method can be effective. Systematic evaluation demonstrates that our method is effective in improving adversarial robustness on MNIST, CIFAR-10, and ImageNet, for either naturally or adversarially trained models.


title: Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs

abstract: Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks. In this paper, we present a unified learning-based technique that simultaneously uses both types of cues for depth inference. Specifically, we use a pair of focal stacks as input to emulate human perception. We first construct a comprehensive focal stack training dataset synthesized by depth-guided light field rendering. We then construct three individual networks: a FocusNet to extract depth from a single focal stack, a EDoFNet to obtain the extended depth of field (EDoF) image from the focal stack, and a StereoNet to conduct stereo matching. We then integrate them into a unified solution to obtain high quality depth maps. Comprehensive experiments show that our approach outperforms the state-of-the-art in both accuracy and speed and effectively emulates human vision systems.


title: Study of Data Mining algorithm in cloud computing using

abstract: Today’s Cloud computing technology has been emerged to manage large data sets efficiently and due to rapid growth of data, large scale data processing is becoming a major point of information technique. The Hadoop Distributed File System (HDFS) is designed for reliable storage of very large data sets and to stream those data sets at high bandwidth to user applications. In a large cluster, hundreds of servers both host directly attached storage and execute user application tasks. By distributing storage and computation across many servers, the resource can grow on demand while remaining economical at every size. MapReduce has been widely used for large-scale data analysis in the Cloud. Hadoop is an open source implementation of MapReduce which can achieve better performance with the allocation of more compute nodes from the cloud to speed up computation; however, this approach of “renting more nodes" isn’t cost effective in a pay-as-you-go environment.


title: Self-organisation in the human visual system—Visuo-motor processing with congenitally abnormal V1 input

abstract: Due to an abnormal projection of the temporal retina the albinotic primary visual cortex receives substantial input from the ipsilateral visual field. To test whether representation abnormalities are also evident in higher tier visual, and in motor and somatosensory cortices, brain activity was measured with fMRI in 14 subjects with albinism performing a visuo-motor task. During central fixation, a blue or red target embedded in a distractor array was presented for 250 ms in the left or right visual hemifield. After a delay, the subjects were prompted to indicate with left or right thumb button presses the target presence in the upper or lower hemifield. The fMRI responses were evaluated for different regions of interest concerned with visual, motor and somatosensory processing and compared to previously acquired data from 14 controls. The following results were obtained: (1) in albinism the hit rates in the visuo-motor task were indistinguishable from normal. (2) In area MT and the intraparietal sulcus there was an indication of abnormal lateralisation patterns. (3) Largely normal lateralisation patterns were evident in motor and somatosensory cortices. It is concluded that in human albinism, the abnormal visual field representation is made available for visuo-motor processing with a motor cortex that comprises an essentially normal lateralisation. Consequently, specific adaptations of the mechanisms mediating visuo-motor integration are required in albinism.


Inference for High-dimensional Exponential Family Graphical Models


title: Stick a fork in it: Analyzing the Ethereum network partition

abstract: As blockchain technologies and cryptocurrencies increase in popularity, their decentralization poses unique challenges in network partitions. In traditional distributed systems, network partitions are generally a result of bugs or connectivity failures; the typical goal of the system designer is to automatically recover from such issues as seamlessly as possible. Blockchain-based systems, however, rely on purposeful "forks" to roll out protocol changes in a decentralized manner. Not all users may agree with proposed changes, and thus forks can persist, leading to permanent network partitions. In this paper, we closely study the large-scale fork that occurred in Ethereum, a new blockchain technology that allows for both currency transactions and smart contracts. Ethereum is currently the second-most-valuable cryptocurrency, with a market capitalization of over $28B. We explore the consequences of this fork, showing the impact on the two networks and their mining pools, and how the fork lead to unintentional incentives and security vulnerabilities.


title: A case study for scientific I / O : improving the FLASH astrophysics code

abstract: The FLASH code is a computational science tool for simulating and studying thermonuclear reactions. The program periodically outputs large checkpoint files (to resume a calculation from a particular point in time) and smaller plot files (for visualization and analysis). Initial experiments on BlueGene/P spent excessive time in input/output (I/O), making it difficult to do actual science. Our investigation of time spent in I/O revealed several locations in the I/O software stack where we could make improvements. Fixing data corruption in the MPI-IO library allowed us to use collective I/O, yielding an order of magnitude improvement. Restructuring the data layout provided a more efficient I/O access pattern and yielded another doubling of performance, but broke format assumptions made by other tools in the application workflow. Using new nonblocking APIs in the Parallel-NetCDF library allowed us to keep high performance and maintain backward compatibility. The I/O research community has studied a host of optimizations and strategies. Sometimes the challenge for applications is knowing how to apply these new techniques to production codes. In this case study, we offer a demonstration of how computational scientists, with a detailed understanding of their application, and the I/O community, with a wide array of approaches from which to choose, can magnify each other's efforts and achieve tremendous application productivity gains.


Genetic polymorphism of the renin-angiotensin-aldosterone system (RAAS) and organ damage (TOD) in essential hypertension (EH)


title: Road junction detection from 3D point clouds

abstract: Detecting changing traffic conditions is of primal importance for the safety of autonomous cars navigating in urban environments. Among the traffic situations that require more attention and careful planning, road junctions are the most significant. This work presents an empirical study of the application of well known machine learning techniques to create a robust method for road junction detection. Features are extracted from 3D pointclouds corresponding to single frames of data collected by a laser rangefinder. Three well known classifiers-support vector machines, adaptive boosting and artificial neural networks-are used to classify them into “junctions” or “roads”. The best performing classifier is used in the next stage, where structured classifiers-hidden Markov models and conditional random fields-are used to incorporate contextual information, in an attempt to improve the performance of the method. We tested and compared these approaches on datasets from two different 3D laser scanners, and in two different countries, Germany and Brazil.


title: Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density

abstract: The self-organizing map (SOM) has been widely used in many industrial applications. Classical clustering methods based on the SOM often fail to deliver satisfactory results, specially when clusters have arbitrary shapes. In this paper, through some preprocessing techniques for filtering out noises and outliers, we propose a new two-level SOM-based clustering algorithm using a clustering validity index based on inter-cluster and intra-cluster density. Experimental results on synthetic and real data sets demonstrate that the proposed clustering algorithm is able to cluster data better than the classical clustering algorithms based on the SOM, and find an optimal number of clusters.