title: Toward Naturalistic 2D-to-3D Conversion

abstract: Natural scene statistics (NSSs) models have been developed that make it possible to impose useful perceptually relevant priors on the luminance, colors, and depth maps of natural scenes. We show that these models can be used to develop 3D content creation algorithms that can convert monocular 2D videos into statistically natural 3D-viewable videos. First, accurate depth information on key frames is obtained via human annotation. Then, both forward and backward motion vectors are estimated and compared to decide the initial depth values, and a compensation process is applied to further improve the depth initialization. Then, the luminance/chrominance and initial depth map are decomposed by a Gabor filter bank. Each subband of depth is modeled to produce a NSS prior term. The statistical color-depth priors are combined with the spatial smoothness constraint in the depth propagation target function as a prior regularizing term. The final depth map associated with each frame of the input 2D video is optimized by minimizing the target function over all subbands. In the end, stereoscopic frames are rendered from the color frames and their associated depth maps. We evaluated the quality of the generated 3D videos using both subjective and objective quality assessment methods. The experimental results obtained on various sequences show that the presented method outperforms several state-of-the-art 2D-to-3D conversion methods.


title: Estimating the number of communities in a network

abstract: Community detection, the division of a network into dense subnetworks with only sparse connections between them, has been a topic of vigorous study in recent years. However, while there exist a range of effective methods for dividing a network into a specified number of communities, it is an open question how to determine exactly how many communities one should use. Here we describe a mathematically principled approach for finding the number of communities in a network by maximizing the integrated likelihood of the observed network structure under an appropriate generative model. We demonstrate the approach on a range of benchmark networks, both real and computer generated.


title: Artificial evolution using neuroevolution of augmenting topologies (NEAT) for kinetics study in diverse viscous mediums

abstract: This paper proposes a novel approach for the evolution of artificial creatures which moves in a 3D virtual environment based on the neuroevolution of augmenting topologies (NEAT) algorithm. The NEAT algorithm is used to evolve neural networks that observe the virtual environment and respond to it, by controlling the muscle force of the creature. The genetic algorithm is used to emerge the architecture of creature based on the distance metrics for fitness evaluation. The damaged morphologies of creature are elaborated, and a crossover algorithm is used to control it. Creatures with similar morphological traits are grouped into the same species to limit the complexity of the search space. The motion of virtual creature having 2–3 limbs is recorded at three different angles to check their performance in different types of viscous mediums. The qualitative demonstration of motion of virtual creature represents that improved swimming of virtual creatures is achieved in simulating mediums with viscous drag 1–10 arbitrary unit.


title: Qualitative Versus Quantitative Research Strategies: Contrasting Epistemological And Ontological Assumptions

abstract: This paper attempts to discuss how qualitative (intensive) and quantitative (extensive) research strategies differ by contrasting epistemological and ontological aspects and how these beliefs and views fit with their different intellectual goals. Firstly, this paper discusses the importance of understanding philosophy in social science research and its relation to qualitative (intensive) and quantitative (extensive) research strategies. Then it develops by contrasting these two types of research strategies in relation to the principle orientation to the role of theory, epistemological and ontological assumptions. Epistemological assumptions consist of interpretivism for qualitative (intensive) research strategies and positivism for quantitative (extensive) research strategies. Whereas ontological assumptions constitute subjectivism/constructivism for qualitative (intensive) research and objectivism for quantitative (extensive) research strategies. Further it will explain how these two types of research strategies fit the different intellectual goals and finally concludes by discussing an alternative research strategi namely mixed method that may be employed in social science research.


title: Decision Jungles : Compact and Rich Models for Classification Supplementary Material

abstract: Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision. However, they face a fundamental limitation: given enough data, the number of nodes in decision trees will grow exponentially with depth. For certain applications, for example on mobile or embedded processors, memory is a limited resource, and so the exponential growth of trees limits their depth, and thus their potential accuracy. This paper proposes decision jungles, revisiting the idea of ensembles of rooted decision directed acyclic graphs (DAGs), and shows these to be compact and powerful discriminative models for classification. Unlike conventional decision trees that only allow one path to every node, a DAG in a decision jungle allows multiple paths from the root to each leaf. We present and compare two new node merging algorithms that jointly optimize both the features and the structure of the DAGs efficiently. During training, node splitting and node merging are driven by the minimization of exactly the same objective function, here the weighted sum of entropies at the leaves. Results on varied datasets show that, compared to decision forests and several other baselines, decision jungles require dramatically less memory while considerably improving generalization.


title: A Hybrid Indexing Method for Approximate String Matching

abstract: We present a new indexing method for the approximate string matching problem. The method is based on a suffix array combined with a partitioning of the pattern. We analyze the resulting algorithm and show that the average retrieval time is O(n log n), for some > 0 that depends on the error fraction tolerated and the alphabet size . It is shown that < 1 for approximately < 1 e=p , where e = 2:718:::. The space required is four times the text size, which is quite moderate for this problem. We experimentally show that this index can outperform by far all the existing alternatives for indexed approximate searching. These are also the first experiments that compare the different existing schemes.


title: Automatically prioritizing pull requests

abstract: In previous work, we observed that in the pull-based development model integrators face challenges with regard to prioritizing work in the face of multiple concurrent pull requests. We present the design and initial implementation of a prototype pull request prioritisation tool called pr ioritizer. pr ioritizer works like a priority inbox for pull requests, recommending the top pull requests the project owner should focus on. A preliminary user study showed that pr ioritizer provides functionality that GitHub is currently lacking, even though users need more insight into how the priority ranking is established to make pr ioritizer really useful.


title: Deep Unsupervised Learning of Visual Similarities

abstract: Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.


title: An IoT based reference architecture for smart water management processes

abstract: Water is a vital resource for life, and its management is a key issue nowadays. Information and communications technology systems for water control are currently facing interoperability problems due to the lack of support of standardization in monitory and control equipment. This problem affects various processes in water management, such as water consumption, distribution, system identification and equipment maintenance. OPC UA (Object Linking and Embedding for Process Control Unified Architecture) is a platform independent service-oriented architecture for the control of processes in the logistic and manufacturing sectors. Based on this standard we propose a smart water management model combining Internet of Things technologies with business processes coordination and decision support systems. We provide an architecture for sub-system interaction and a detailed description of the physical scenario in which we will test our implementation, allowing specific vendor equipment to be manageable and interoperable in the specific context of water management processes.


title: Ray casting for modeling solids

abstract: Abstract This paper presents ray casting as the methodological basis for a CAD/CAM solid modeling system. Solid objects are modeled by combining primitive solids, such as blocks and cylinders, using the set operators union, intersection, and difference. To visualize and analyze the composite solids modeled, virtual light rays are cast as probes. By virtue of its simplicity, ray casting is reliable and extensible. The most difficult mathematical problem is finding line-surface intersection points. So surfaces such as planes, quadrics, tori, and probably even parametric surface patches may bound the primitive solids. The adequacy and efficiency of ray casting are issues addressed here. A fast picture generation capability for interactive modeling is the biggest challenge. New methods are presented, accompanied by sample pictures and CPU times, to meet the challenge.


title: Multiresolution splatting for indirect illumination

abstract: Global illumination provides a visual richness not achievable with the direct illumination models used by most interactive applications. To generate global effects, numerous approximations attempt to reduce global illumination costs to levels feasible in interactive contexts. One such approximation, reflective shadow maps, samples a shadow map to identify secondary light sources whose contributions are splatted into eye-space. This splatting introduces significant overdraw that is usually reduced by artificially shrinking each splat's radius of influence. This paper introduces a new, multi-resolution approach for interactively splatting indirect illumination. Instead of reducing GPU fill rate by reducing splat size, we reduce fill rate by rendering splats into a multi-resolution buffer. This takes advantage of the low-frequency nature of diffuse and glossy indirect lighting, allowing rendering of indirect contributions at low resolution where lighting changes slowly and at high resolution near discontinuities. Because this multi-resolution rendering occurs on a per-splat basis, we can significantly reduce fill rate without arbitrarily clipping splat contributions below a given threshold---those regions simply are rendered at a coarse resolution.


title: Image thresholding for optical character recognition and other applications requiring character image extraction

abstract: Two new, cost-effective thresholding algorithms for use in extracting binary images of characters from machine- or hand-printed documents are described. The creation of a binary representation from an analog image requires such algorithms to determine whether a point is converted into a binary one because it falls within a character stroke or a binary zero because it does not. This thresholding is a critical step in Optical Character Recognition (OCR). It is also essential for other Character Image Extraction (CIE) applications, such as the processing of machine-printed or handwritten characters from carbon copy forms or bank checks, where smudges and scenic backgrounds, for example, may have to be suppressed. The first algorithm, a nonlinear, adaptive procedure, is implemented with a minimum of hardware and is intended for many CIE applications. The second is a more aggressive approach directed toward specialized, high-volume applications which justify extra complexity.


title: Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

abstract: In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities, which we refer to as cell variables, may be associated with disease risks. Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for predictive models. With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.


title: Change impact analysis for object-oriented programs

abstract: Small changes can have major and nonlocal effects in object-oriented languages, due to the use of subtyping and dynamic dispatch. This complicates life for maintenance programmers, who need to fix bugs or add enhancements to systems originally written by others. Change impact analysis provides feedback on the semantic impact of a set of program changes. This analysis can be used to determine the regression test drivers that are affected by a set of changes. Moreover, if a test fails, a subset of changes responsible for the failure can be identified, as well as a subset of changes that can be incorporated safely without affecting any test driver.


title: Optimal shape design as a material distribution problem

abstract: Shape optimization in a general setting requires the determination of the optimal spatial material distribution for given loads and boundary conditions. Every point in space is thus a material point or a void and the optimization problem is a discrete variable one. This paper describes various ways of removing this discrete nature of the problem by the introduction of a density function that is a continuous design variable. Domains of high density then define the shape of the mechanical element. For intermediate densities, material parameters given by an artificial material law can be used. Alternatively, the density can arise naturally through the introduction of periodically distributed, microscopic voids, so that effective material parameters for intermediate density values can be computed through homogenization. Several examples in two-dimensional elasticity illustrate that these methods allow a determination of the topology of a mechanical element, as required for a boundary variations shape optimization technique.


A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients


Tezos : A Self-Amending Crypto-Ledger Position Paper


title: Tensor fusion of social structural and functional analytics over Neo4j

abstract: For large directed graphs ranking vertices is an algorithmic as well as a computational challenge. Also it is a cornerstone problem in fields so diverse such as online social media, combinatorial optimization, deep learning, econometrics, and computational neuroscience. Gell-point centrality metric is a common case of structural vertex ranking which can be efficiently computed through power method. This paper proposes harmonic centrality, also a structural ranking which can be computed equally efficiently. When the graph represents a network, both methods are oblivious to its functionality. To address that issue, a tensor based methodology is proposed for combining functional characteristics with these structural metrics. As a concrete demonstration, the tensor fusion methodology was implemented in Java over Neo4j and Tensor toolbox and was applied to a Twitter subgraph. The functional features were directly related to affective computing, while the hashtags in this subgraph were relared to a current controversial political topic, namely #brexit.


title: Nash Equilibrium Seeking in Noncooperative Games

abstract: We introduce a non-model based approach for locally stable convergence to Nash equilibria in static, noncooperative games with N players. In classical game theory algorithms, each player employs the knowledge of the functional form of his payoff and the knowledge of the other players' actions, whereas in the proposed algorithm, the players need to measure only their own payoff values. This strategy is based on the extremum seeking approach, which has previously been developed for standard optimization problems and employs sinusoidal perturbations to estimate the gradient. We consider static games with quadratic payoff functions before generalizing our results to games with non-quadratic payoff functions that are the output of a dynamic system. Specifically, we consider general nonlinear differential equations with N inputs and N outputs, where in the steady state, the output signals represent the payoff functions of a noncooperative game played by the steady-state values of the input signals. We employ the standard local averaging theory and obtain local convergence results for both quadratic payoffs, where the actual convergence is semi-global, and non-quadratic payoffs, where the potential existence of multiple Nash equilibria precludes semi-global convergence. Our convergence conditions coincide with conditions that arise in model-based Nash equilibrium seeking. However, in our framework the user is not meant to check these conditions because the payoff functions are presumed to be unknown. For non-quadratic payoffs, convergence to a Nash equilibrium is not perfect, but is biased in proportion to the perturbation amplitudes and the higher derivatives of the payoff functions. We quantify the size of these residual biases and confirm their existence numerically in an example noncooperative game. In this example, we present the first application of extremum seeking with projection to ensure that the players' actions remain in a given closed and bounded action set.


title: Topology-Aware and Dependence-Aware Scheduling and Memory Allocation for Task-Parallel Languages

abstract: We present a joint scheduling and memory allocation algorithm for efficient execution of task-parallel programs on non-uniform memory architecture (NUMA) systems. Task and data placement decisions are based on a static description of the memory hierarchy and on runtime information about intertask communication. Existing locality-aware scheduling strategies for fine-grained tasks have strong limitations: they are specific to some class of machines or applications, they do not handle task dependences, they require manual program annotations, or they rely on fragile profiling schemes. By contrast, our solution makes no assumption on the structure of programs or on the layout of data in memory. Experimental results, based on the OpenStream language, show that locality of accesses to main memory of scientific applications can be increased significantly on a 64-core machine, resulting in a speedup of up to 1.63× compared to a state-of-the-art work-stealing scheduler.


title: Multi-scale image harmonization

abstract: Traditional image compositing techniques, such as alpha matting and gradient domain compositing, are used to create composites that have plausible boundaries. But when applied to images taken from different sources or shot under different conditions, these techniques can produce unrealistic results. In this work, we present a framework that explicitly matches the visual appearance of images through a process we call image harmonization, before blending them. At the heart of this framework is a multi-scale technique that allows us to transfer the appearance of one image to another. We show that by carefully manipulating the scales of a pyramid decomposition of an image, we can match contrast, texture, noise, and blur, while avoiding image artifacts. The output composite can then be reconstructed from the modified pyramid coefficients while enforcing both alpha-based and seamless boundary constraints. We show how the proposed framework can be used to produce realistic composites with minimal user interaction in a number of different scenarios.


title: Modified P&O MPPT technique for photovoltaic systems

abstract: Maximum power point tracking (MPPT) techniques are widely applied in photovoltaic system to produce the maximum power of the PV generator. In this paper, we presented a modified variable step size P&O MPPT algorithm. The proposed method can greatly improve the response time and precision MPPT steady state simultaneously. The proposed approach has been tested in MATLAB / Simulink environment, and the results show that the new algorithm is more accurate and faster than the traditional P&O method.


title: MIMO Spectral Efficiency over Energy Consumption Requirements: Application to WSNs

abstract: This paper presents the evaluation of the “capacity to the total energy consumption per bit ratio” of multiple antennas systems with distributed fashion. We propose an adequate geometric channel modeling for the wireless communication system which operates in indoor propagation environment with scatterers. The channel model is derived in function of both the line of sight (LOS) and the non line of sight (NLOS) components. The aim of this paper is to study the limits in the gain concerning the capacity to the total energy consumption ratio when additional antennas are implemented in the communication system. To do so, we have evaluated by simulations both the capacity and the total energy consumption per bit. Then, we have determined the capacity to the total energy consumption ratio. Finally, the computational capacity to the total energy ratio is obtained for different system configurations. We have shown that the gain in capacity increases with the number of antennas but it stills be limited by the total energy consumption. The limits for increasing the number of transmit antennas are determined in function of the separation distances between the transmitter and the receiver sides of the communication system. Optimal power allocation strategy via water-filling algorithm has been carried out for evaluating the capacity to energy ratio. We find by simulation that optimal power allocation brings a gain in the addressed metric reaching a level of about 1.7 at transmit signal to noise ratio of 8 dB if comparing to the case when transmit energy is equally split among transmit antennas.


title: Correction to “Feedback Analysis and Design of RF Power Links for Low-Power Bionic Systems”

abstract: We report on corrections to two equations in a paper authored by two of us (M.W. Baker and R. Sarpeshkar, see ibid., vol. 1, no. 1, pp. 28?38, Mar. 2007). These corrections were discovered and graciously pointed out by two authors of this manuscript (M. Gasulla and J. Albesa). These corrections generate a more accurate set of equations that do not make approximations made in the original paper. The approximations to the equations do not impact fits to experimental data presented in the original paper, which continue to remain excellent: The experimental data in the original paper were obtained for typical near-field RF power links used in bionic implants where the accurate and approximate equations yield nearly identical results. However, in other experimental systems, the more accurate expressions derived by M. Gasulla and J. Albesa should be used.


title: TaskGenies: Automatically Providing Action Plans Helps People Complete Tasks

abstract: People complete tasks more quickly when they have concrete plans, especially for open-ended, creative tasks. However, people often fail to create such action plans. (How) can systems provide people with these concrete steps automatically? To scalably provide personalized action plans, this paper introduces and evaluates crowdsourcing and peer approaches for creating plans, and NLP techniques for reusing them. We evaluated the effects of action plans on different types of tasks. A between-subjects experiment found that people who received crowd-created plans completed more tasks than people asked to self-create plans and than a control group without action plans. We found that crowd-created action plans are especially effective for lingering and high-level tasks. A second experiment found that peer-provided plans led to more completed tasks than no plans. A third experiment found that participants who received reused action plans also completed more tasks than a control group without action plans. We have incorporated these principles into TaskGenies: a crowd-powered task management system.


Planar Wireless Charging Technology for Portable Electronic Products and Qi


title: SemanticFusion: Dense 3D semantic mapping with convolutional neural networks

abstract: Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need extend beyond geometry and appearence - they need to contain semantics. We address this challenge by combining Convolutional Neural Networks (CNNs) and a state of the art dense Simultaneous Localisation and Mapping (SLAM) system, ElasticFusion, which provides long-term dense correspondence between frames of indoor RGB-D video even during loopy scanning trajectories. These correspondences allow the CNN's semantic predictions from multiple view points to be probabilistically fused into a map. This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions. We also show that for a smaller reconstruction dataset with larger variation in prediction viewpoint, the improvement over single frame segmentation increases. Our system is efficient enough to allow real-time interactive use at frame-rates of approximately 25Hz.


title: The character, functions, and styles of instant messaging in the workplace

abstract: Current perceptions of Instant Messaging (IM) use are based primarily on self-report studies. We logged thousands of (mostly) workplace IM conversations and evaluated their conversational characteristics and functions. Contrary to prior research, we found that the primary use of workplace IM was for complex work discussions. Only 28% of conversations were simple, single-purpose interactions and only 31% were about scheduling or coordination. Moreover, people rarely switched from IM to another medium when the conversation got complex. We found evidence of two distinct styles of use. Heavy IM users and frequent IM partners mainly used it to work together: to discuss a broad range of topics via many fast-paced interactions per day, each with many short turns and much threading and multitasking. Light users and infrequent pairs mainly used IM to coordinate: for scheduling, via fewer conversations per day that were shorter, slower-paced with less threading and multitasking.


title: Adaptation and coaching of periodic motion primitives through physical and visual interaction

abstract: In this paper we propose and evaluate a control system to (1) learn and (2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodic motion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contact with a given surface, maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period of motion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive. By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach.The complete system thus allows not only a transfer of motion, but a transfer of motion with matching correspondences, i.e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot.Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching. An intuitive and user friendly system for transferring of skills from a person to a robot.It allows online learning and adaptation of motion trajectories.It allows adaptation of trajectories through human coaching, from either force or visual feedback.It is based on the dynamic motion primitives framework.Surface wiping use-case through non-rigid contact is demonstrated and evaluated.


title: Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

abstract: Evolutionary artificial neural networks (EANNS) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. We propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1/sup st/ and 2/sup nd/ order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity.


title: Entity Recommendations Using Hierarchical Knowledge Bases

abstract: Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may offer better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may be utilized for recommendation systems. We propose a content-based recommendation approaches that adapts a spreading activation algorithm over the DBpedia category structure to identify entities of interest to the user. Evaluation of the algorithm over the Movielens dataset demonstrates that our method yields more accurate recommendations compared to a previously proposed taxonomy driven approach for recommendations.


title: Neural Machine Translation by Jointly Learning to Align and Translate

abstract: Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.


The Ethics of Artificial Intelligence


title: Dynamic pickup and delivery problems

abstract: In the last decade, there has been an increasing body of research in dynamic vehicle routing problems. This article surveys the subclass of those problems called dynamic pickup and delivery problems, in which objects or people have to be collected and delivered in real-time. It discusses some general issues as well as solution strategies.


title: Playing by the Book: Towards Agent-based Narrative Understanding through Role-playing and Simulation.

abstract: Understanding procedural text requires tracking entities, actions and effects as the narrative unfolds (often implicitly). We focus on the challenging real-world problem of structured narrative extraction in the materials science domain, where language is highly specialized and suitable annotated data is not publicly available. We propose an approach, Text2Quest, where procedural text is interpreted as instructions for an interactive game. A reinforcement-learning agent completes the game by understanding and executing the procedure correctly, in a text-based simulated lab environment. The framework is intended to be more broadly applicable to other domain-specific and data-scarce settings. We conclude with a discussion of challenges and interesting potential extensions enabled by the agent-based perspective.


title: A Millimeter Wave Beam Tracking in Vehicular Scenario via Particle Filter

abstract: Mobility tracking has been highlighted as one of the key issues in millimeter wave communication. Although several beam tracking algorithms have been proposed to improve mobility tracking, these algorithms suffer high complexity. In this paper, we propose a low complexity beam tracking protocol via particle filter. In the proposed algorithm, a single beam pair is trained to track a path by exploiting the location information of the mobile station. We investigate several effects on the performance of the proposed tracking algorithm, such as the signal-to-noise ratio (SNR) and the array size of the base station antennas. It is observed that as the signal-to-noise ratio and the array size increases, the beam tracking performance improves.


title: Speech Recognition Using Artificial Neural Network

abstract: This paper propose two approaches for speech recognition via supervised and unsupervised learning. Speech signals are non-stationary signals. Treating speech in computing domain falls under sequential learning task i.e. if we want to make a sense of current statement, we may need to go through the context in which it was spoken. Recurrent Neural Networks (RNN) is been used in speech recognition problems because of its powerful sequence modeling capacity. In this paper we have proposed Bi-directional Recurrent Neural Network with Long Short Term Memory model (LSTM), so that speech signal reconstruction can be done in a proper way without performance loss. For unsupervised learning, model is designed on the basis of Restricted Boltzmann Machine (RBM) which generates a reconstruction based output and helps in conversion of voice into text, each letter by letter.


title: Detect or Track: Towards Cost-Effective Video Object Detection/Tracking

abstract: State-of-the-art object detectors and trackers are developing fast. Trackers are in general more efficient than detectors but bear the risk of drifting. A question is hence raised – how to improve the accuracy of video object detection/tracking by utilizing the existing detectors and trackers within a given time budget? A baseline is frame skipping – detecting every N-th frames and tracking for the frames in between. This baseline, however, is suboptimal since the detection frequency should depend on the tracking quality. To this end, we propose a scheduler network, which determines to detect or track at a certain frame, as a generalization of Siamese trackers. Although being light-weight and simple in structure, the scheduler network is more effective than the frame skipping baselines and flow-based approaches, as validated on ImageNet VID dataset in video object detection/tracking.


title: OES Task 10 WEC heaving sphere performance modelling verification

abstract: The partners are grateful for the support offered by OES to host workshops. The in-kind contribution and support offered by the participating institutions are acknowledged, and the significant work done by NREL in compiling and analyzing the results is made possible via support from the U.S. Department of Energy. The Danish partners acknowledge the support from the Danish Energy Agency. ::: This work was authored, in part, by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, Water Power Technologies Office.


title: Heterogeneity of IT employees: an analysis of a model of perceived organizational support by job type

abstract: The retention of existing IT employees is crucial due to the expected shortage of the IT labor force in the U.S., Canada, and European countries. While much of the extant IT turnover literature implicitly assumes that IT employees are homogeneous, we contend that they are a diverse group and that exploring the group in depth would reveal further insights into why employees turnover. We examined a sample of employees by IT job type in a turnover model of the antecedents and impacts of perceived organizational support (POS), which is another infrequently studied concept in the literature but is a potentially important predictor of turnover. A survey of 302 IT employees at a large U.S.-based company showed that these employees are in fact diverse. The relationships between role ambiguity and POS and work schedule flexibility and POS were found to be significant for managerial employees, but not for technically-oriented employees. The relationship between career accommodations and POS, however, was found to be significant for technically-oriented employees, but not managerial employees. As a whole, this study suggests that by combining all IT employees together in our analyses, we may forego some of the unique insights about these employees that we can otherwise cultivate to strengthen the bond between the organization and its employees and to enhance our existing IT turnover literature. The results of this study provide implications for organizations on how they can better balance the tactics they use to retain their valued IT employees. IT managers can be in a better position to focus on building relationships with their employees based on what is generally important to those employees.


title: Extension of Soft-Switching Region of Dual-Active-Bridge Converter by a Tunable Resonant Tank

abstract: Hard-switching-induced switching loss can contribute significantly to the power loss of an isolated bidirectional dual-active-bridge (DAB) dc–dc converter operating at high frequency. An $LC$ -type series resonant DAB converter based on a switch-controlled-inductor (SCI) is proposed to mitigate the loss arising from hard switching under wide-range variations in output voltage and current. Zero-voltage switching is achieved at the primary side (high voltage), while at the secondary side (low voltage), zero-current switching is preferred to reduce excessive ringing due to circulating current and switching loss. In order to achieve reduced conduction loss, a nominal operating point is chosen where the root-mean-square resonant tank current is the minimum. To validate the proposed topology and modulation scheme, an $LC$ -type series resonant DAB converter based on SCI operating at 100 kHz is designed to interface a 400-V dc bus to a supercapacitor-based energy storage. Simulation and experimental results validate the effectiveness of the proposed topology for charging/discharging a supercapacitor with an output voltage variation of between 10 and 48 V and a maximum rated power of 480 W. A maximum efficiency of 94.6% is achieved using the proposed topology and modulation scheme.


title: Detection of molluscum contagiosum virus (MCV) DNA in the plasma of an immunocompromised patient and possible reduction of MCV DNA with CMX-001.

abstract: Molluscum contagiosum virus (MCV) is a poxvirus that causes localized papules in healthy persons. We evaluated a woman with severe immunodeficiency and disseminated MCV. During treatment with CMX-001, an antiviral with activity against other poxviruses, MCV DNA was detected in 20% of plasma samples. When the patient was not receiving CMX-001, MCV DNA was detected in 50% of samples. We also noted improvement in warts on her fingers during CMX-001 therapy. Although MCV is caused by direct inoculation of virus into skin in healthy persons, in a severely immunocompromised person MCV DNA was present in blood and may spread by viremia.


title: Automatic Radial Distortion Estimation from a Single Image

abstract: Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with off-the-shelf cameras is usually significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. We propose a new method for automatic radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon's division model, robust estimation of circular arcs, and robust estimation of distortion parameters. We perform an extensive empirical study of the method on synthetic images. We include a comparative statistical analysis of how different circle fitting methods contribute to accurate distortion parameter estimation. We finally provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method's ability to accurately identify distortion parameters and remove distortion from images.


title: A Review of More-Electric Aircraft

abstract: The More-Electric Aircraft (MEA) underlines the utilization of the electrical power to power the non-propulsive aircraft systems. Adopting the MEA achieves numerous advantages such as optimizing the aircraft performance and decreasing operating and maintenance costs. Moreover, the MEA reduces the emission of the air pollutant gases from the aircraft, which can contribute in solving the problem of climate change. However, the MEA put some challenge on the aircraft electrical system either in the amount of the required power or the processing and management of this power. This paper introduces a review for the MEA. The review includes the different options of generation and power system architectures.


title: Contactless Sleep Apnea Detection on Smartphones

abstract: We present a contactless solution for detecting sleep apnea events on smartphones. To achieve this, we introduce a novel system that monitors the minute chest and abdomen movements caused by breathing on smartphones. Our system works with the phone away from the subject and can simultaneously identify and track the fine-grained breathing movements from multiple subjects. We do this by transforming the phone into an active sonar system that emits frequency-modulated sound signals and listens to their reflections; our design monitors the minute changes to these reflections to extract the chest movements. Results from a home bedroom environment shows that our design operates efficiently at distances of up to a meter and works even with the subject under a blanket. Building on the above system, we develop algorithms that identify various sleep apnea events including obstructive apnea, central apnea, and hypopnea from the sonar reflections. We deploy our system at the UW Medicine Sleep Center at Harborview and perform a clinical study with 37 patients for a total of 296 hours. Our study demonstrates that the number of respiratory events identified by our system is highly correlated with the ground truth and has a correlation coefficient of 0.9957, 0.9860, and 0.9533 for central apnea, obstructive apnea and hypopnea respectively. Furthermore, the average error in computing of rate of apnea and hypopnea events is as low as 1.9 events/hr.


title: [Acquired neuromuscular syndrome in critically ill patients].

abstract: AIMS ::: To analyse the causes and signs of the muscle weakness that critically ill patients develop during their stay in the ICU and to review the literature. ::: ::: ::: DEVELOPMENT ::: In the early 80s, Bolton reported a mixed axonal polyneuropathy that he called 'polyneuropathy in critically ill patients'. Clinically it was characterised by muscle weakness that varied in intensity and caused quadriplegia and/or the need for assisted ventilation in its severest forms. It is currently considered to be the most common neurological disorder among critically ill patients. Many patients made a complete recovery after a few months or even a year. Running almost parallel to this, he also described 'acute myopathy in intensive care' among asthmatic patients who were hospitalised in the ICU due to exacerbation of their illness. It was later observed how other types of patients also had this myopathy, for instance, those who had undergone a transplant, sepsis or burns. Some authors consider acute myopathy in intensive care to be the primary cause of muscle weakness in the ICU. ::: ::: ::: CONCLUSIONS ::: On many occasions 'polyneuropathy of critically ill patients' and 'acute myopathy in intensive care' can be differentiated and to do so neurophysiological and, if necessary, muscle biopsy studies are a valuable aid. Although some authors prefer to group these conditions under the name 'polyneuromyopathy', we propose the general term 'acquired neuromuscular syndrome in critically ill patients' -a more descriptive expression that does not presuppose a particular mechanism or a single aetiology.


Fast Forward Through Opportunistic Incremental Meaning Representation Construction


title: A soft-sensor for estimating copper quality by image analysis technology

abstract: A quasi-line estimation method for copper content based on the color image information of copper alloys is proposed to solve the problem of the time lag and other shortcomings of the off-line chemical analysis method for copper content estimation. First, a 3-CCD color camera was used to obtain the color images of the samples in the standard D65 light source. Then two regression models were developed to estimate the copper contents using the least squares regression (LSR) method. For the first model, the mean values of the R, G and B color channels were chosen as the input variables, while the principal component scores extracted by the principal component analysis (PCA) were used for the second model. Finally, the models were tested by the testing samples for predicting the copper contents. The both mean square errors of the testing samples for the two methods were about 1.5%, which can meet the precision requirements in practice. Experiment results showed that the proposed methods were feasible to quantitatively analyze the copper content in the copper alloy.


title: Ideology and discourse analysis

abstract: Contrary to most traditional approaches, ideologies are defined here within a multidisciplinary framework that combines a social, cognitive and discursive component. As ‘systems of ideas’, ideologies are sociocognitively defined as shared representations of social groups, and more specifically as the ‘axiomatic’ principles of such representations. As the basis of a social group's self-image, ideologies organize its identity, actions, aims, norms and values, and resources as well as its relations to other social groups. Ideologies are distinct from the sociocognitive basis of broader cultural communities, within which different ideological groups share fundamental beliefs such as their cultural knowledge. Ideologies are expressed and generally reproduced in the social practices of their members, and more particularly acquired, confirmed, changed and perpetuated through discourse. Although general properties of language and discourse are not, as such, ideologically marked, systematic discourse analysis offe...


title: Consistent inference of probabilities in layered networks: predictions and generalizations

abstract: The problem of learning a general input-output relation using a layered neural network is discussed in a statistical framework. By imposing the consistency condition that the error minimization be equivalent to a likelihood maximization for training the network, the authors arrive at a Gibbs distribution on a canonical ensemble of networks with the same architecture. This statistical description enables them to evaluate the probability of a correct prediction of an independent example, after training the network on a given training set. The prediction probability is highly correlated with the generalization ability of the network, as measured outside the training set. This suggests a general and practical criterion for training layered networks by minimizing prediction errors. The authors demonstrate the utility of this criterion for selecting the optimal architecture in the continuity problem. As a theoretical application of the statistical formalism, they discuss the question of learning curves and estimate the sufficient training size needed for correct generalization, in a simple example. >


title: Optimal Detection of Fetal Chromosomal Abnormalities by Massively Parallel DNA Sequencing of Cell-Free Fetal DNA from Maternal Blood

abstract: BACKGROUND: Massively parallel DNA sequencing of cell-free fetal DNA from maternal blood can detect fetal chromosomal abnormalities. Although existing algorithms focus on the detection of fetal trisomy 21 (T21), these same algorithms have difficulty detecting trisomy 18 (T18). METHODS: Blood samples were collected from 1014 patients at 13 US clinic locations before they underwent an invasive prenatal procedure. All samples were processed to plasma, and the DNA extracted from 119 samples underwent massively parallel DNA sequencing. Fifty-three sequenced samples came from women with an abnormal fetal karyotype. To minimize the intra- and interrun sequencing variation, we developed an optimized algorithm by using normalized chromosome values (NCVs) from the sequencing data on a training set of 71 samples with 26 abnormal karyotypes. The classification process was then evaluated on an independent test set of 48 samples with 27 abnormal karyotypes. RESULTS: Mapped sites for chromosomes of interest in the sequencing data from the training set were normalized individually by calculating the ratio of the number of sites on the specified chromosome to the number of sites observed on an optimized normalizing chromosome (or chromosome set). Threshold values for trisomy or sex chromosome classification were then established for all chromosomes of interest, and a classification schema was defined. Sequencing of the independent test set led to 100% correct classification of T21 (13 of 13) and T18 (8 of 8) samples. Other chromosomal abnormalities were also identified. CONCLUSION: Massively parallel sequencing is capable of detecting multiple fetal chromosomal abnormalities from maternal plasma when an optimized algorithm is used.


title: Modeling trend progression through an extension of the Polya Urn Process

abstract: Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.


title: The Cognitive Linguistics of Scalar Humor

abstract: Humor holds a place among the most intriguing and least understood of ourcognitive capacities. It has been the topic of occasional linguistic investiga-tion (e.g. Attardo 1994, Coulson 2000), but since humor falls outside thetraditional scope of core language, grammars rarely refer to humorous usesof language. And yet humor, like communicating and instructing, is one ofa small number of pervasive and universal language functions.Among the various approaches to language, those falling under the ru-bric of cognitive linguistics may be best equipped to bring humor underrigorous scrutiny. The reasons for this are many, and include the focus incognitive approaches on the form and functions of linguistic items, on thepragmatics and semantics of linguistic constructions, including idioms, andon domain-general cognitive mechanisms underlying language use.There are various types of humor conveyed through a linguistic modal-ity, including puns, formulaic jokes, insult humor, and non-sequitur humor.In this paper, however, we will focus on just one sort of linguistic humor.We dub this type of humor scalar humor, because at its core is themanipulation of a conceptual scale. Some examples of this type of humorfollow in (1).(1) (a) I was such an ugly kid that when I worked in a pet store, peoplekept asking how big I’d get.(b) I was such an ugly kid that when I was born the doctor slappedmy face.


Explanations in Proactive Recommender Systems in Automotive Scenarios


title: ITIL Adoption Model based on TAM

abstract: although Information technology infrastructure library (ITIL) is the most popular framework for IT service management all around the world, problems for organizations in the process of adoption would eliminate its benefits. In fact the ability of organization to adopt this framework is the basis for IT service improvement. Few studies have tried to find out success factor that influence ITIL adoption but no study was found investigating an adoption model for this framework. In this study, first seven success factors were identified through qualitative meta-Synthesis relying on previous studies; then, a research model based on the technology acceptance model (TAM) for representing the influence of these critical success factors on ITIL adoption is proposed. The success factor used are: (1) top management support, (2) communication and cooperation, (3) training and competence of involved stakeholder in ITIL project, (4) change management and organizational culture, (5) project management and governance, (6) ITIL process implementation and applied technology, (7) monitoring and evaluation.


title: Why-Question Answering using Intra- and Inter-Sentential Causal Relations

abstract: In this paper, we explore the utility of intra- and inter-sentential causal relations between terms or clauses as evidence for answering why-questions. To the best of our knowledge, this is the first work that uses both intra- and inter-sentential causal relations for why-QA. We also propose a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation proposed by Hashimoto et al. (2012). By applying these ideas to Japanese why-QA, we improved precision by 4.4% against all the questions in our test set over the current state-of-theart system for Japanese why-QA. In addition, unlike the state-of-the-art system, our system could achieve very high precision (83.2%) for 25% of all the questions in the test set by restricting its output to the confident answers only.


title: PREDICTING THE FINANCIAL SUCCESS OF HOLLYWOOD MOVIES USING AN INFORMATION FUSION APPROACH

abstract: Hollywood has often been called the land of hunches and wild guesses. The uncertainty associated with the predictability of product demand makes the movie business a risky endeavor. Therefore, predicting the box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, with a rather large and feature rich dataset, we explored the use of data mining methods (e.g., artificial neural networks, decision trees and support vector machines along with information fusion based ensembles) to predict the financial performance of a movie at the box-office before its theatrical release. In our prediction models, we have converted the forecasting problem into a classification problem—rather than forecasting the point estimate of box-office receipts; we classified a movie (based on its box-office receipts) into nine categories, ranging from a “flop” to a “blockbuster.” Herein we present our exciting prediction results where we compared individual models to those of the ensamples.


title: Dynamic non-parametric joint sentiment topic mixture model

abstract: The reviews in social media are produced continuously by a large and uncontrolled number of users. To capture the mixture of sentiment and topics simultaneously in reviews is still a challenging task. In this paper, we present a novel probabilistic model framework based on the non-parametric hierarchical Dirichlet process (HDP) topic model, called non-parametric joint sentiment topic mixture model (NJST), which adds a sentiment level to the HDP topic model and detects sentiment and topics simultaneously from reviews. Then considered the dynamic nature of social media data, we propose dynamic NJST (dNJST) which adds time decay dependencies of historical epochs to the current epochs. Compared with the existing sentiment topic mixture models which are based on latent Dirichlet allocation (LDA), the biggest difference of NJST and dNJST is that they can determine topic number automatically. We implement NJST and dNJST with online variational inference algorithms, and incorporate the sentiment priors of words into NJST and dNJST with HowNet lexicon. The experiment results in some Chinese social media dataset show that dNJST can effectively detect and track dynamic sentiment and topics.


title: Federated Learning of Deep Networks using Model Averaging

abstract: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data-center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. ::: We present a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise. This method allows high-quality models to be trained in relatively few rounds of communication, the principal constraint for federated learning. The key insight is that despite the non-convex loss functions we optimize, parameter averaging over updates from multiple clients produces surprisingly good results, for example decreasing the communication needed to train an LSTM language model by two orders of magnitude.


title: Ghostrail: Ad Hoc Control-Flow Integrity for Web Applications

abstract: Modern web applications frequently implement complex control flows, which require the users to perform actions in a given order. Users interact with a web application by sending HTTP requests with parameters and in response receive web pages with hyperlinks that indicate the expected next actions. If a web application takes for granted that the user sends only those expected requests and parameters, malicious users can exploit this assumption by crafting harming requests. We analyze recent attacks on web applications with respect to user-defined requests and identify their root cause in the missing enforcement of allowed next user requests. Based on this result, we provide our approach, named Ghostrail, a control-flow monitor that is applicable to legacy as well as newly developed web applications. It observes incoming requests and lets only those pass that were provided as next steps in the last web page. Ghostrail protects the web application against race condition exploits, the manipulation of HTTP parameters, unsolicited request sequences, and forceful browsing. We evaluate the approach and show that it neither needs a training phase nor a manual policy definition while it is suitable for a broad range of web technologies.


title: Hadoop MapReduce for Mobile Clouds

abstract: The new generations of mobile devices have high processing power and storage, but they lag behind in terms of software systems for big data storage and processing. Hadoop is a scalable platform that provides distributed storage and computational capabilities on clusters of commodity hardware. Building Hadoop on a mobile network enables the devices to run data intensive computing applications without direct knowledge of underlying distributed systems complexities. However, these applications have severe energy and reliability constraints (e.g., caused by unexpected device failures or topology changes in a dynamic network). As mobile devices are more susceptible to unauthorized access, when compared to traditional servers, security is also a concern for sensitive data. Hence, it is paramount to consider reliability, energy efficiency and security for such applications. The MDFS (Mobile Distributed File System) [1] addresses these issues for big data processing in mobile clouds. We have developed the Hadoop MapReduce framework over MDFS and have studied its performance by varying input workloads in a real heterogeneous mobile cluster. Our evaluation shows that the implementation addresses all constraints in processing large amounts of data in mobile clouds. Thus, our system is a viable solution to meet the growing demands of data processing in a mobile environment.


title: Millimeter-Wave-Radar Sensor Based on a Transceiver Array for Automotive Applications

abstract: This paper describes a new radar sensor architecture comprising an array of transceiver modules. Target applications of the sensor are automotive driver assistance systems. In conjunction with a monolithic integration of each transceiver, the concept offers the possibility for a cost-effective realization of digital-beamforming radar sensors at millimeter-wave frequencies. A modulation sequence is investigated based on simultaneously transmitted frequency-modulated continuous-wave signals, which are separated by frequency multiplexing. Appropriate signal processing for the estimation of range, speed, and azimuth angle in multiple object situations is presented. Experimental results with an eight-channel radar sensor in the 76-77-GHz frequency band are presented, which demonstrate the feasibility of the proposed architecture and show the performance of the modulation sequence and signal processing.


title: A 250mV sub-threshold asynchronous 8051microcontroller with a novel 16T SRAM cell for improved reliability in 40nm CMOS

abstract: Asynchronous approach for digital systems is a way to resolve increased timing uncertainty with technology scaling since timing issue is eliminated in asynchronous systems. This paper presents a sub-threshold operating asynchronous 8051 microcontroller (A8051) with a novel 16T SRAM cell for improved reliability in asynchronous systems. This A8051, adopting a 4-phase dual-rail protocol, can operate up to 250 mV. A8051 has 67.53 μs as a critical path delay with 91.6 nW power consumption at 250 mV, which is equivalent to 12.88 kHz in synchronous systems. At 1.0 V, the delay of a critical path of A8051 microcontroller is 5.74 ns, which is equivalent to 151.55 MHz, with 8.98 mW power consumption. The proposed 16T SRAM cell is applied in memory blocks. The 16T SRAM structure eliminates charge contentions between devices during read and write operations so that SRAM can be operated fully in static mode, bringing about improved write margin (WM). The WM of this 16T SRAM cell is 1.81 times greater than the conventional 6T SRAM cell and 1.58 times better than 8T SRAM cell. At 250 mV, the SNM of SRAM cell is 12.5 mV under process and mismatch variations. Write delay of the asynchronous SRAM block is 4.02 μs (equivalent to 248.5 kHz) with 5.44 pJ energy dissipation, while read delay is 12.61 μs (equivalent to 79.3 kHz) with 9.08 pJ energy dissipation.


title: Fictional Realness: Towards a colloquial performance practice

abstract: This article develops the concept of a colloquial performance practice with the use of three examples: vernacular drag performance in Jennie Livingston's seminal documentary film on New York City ball culture Paris is Burning (1999), the “documentary fictions” of South African rap-rave group Die Antwoord, and the theoretical concept of a minor literature developed by Gilles Deleuze and Felix Guattari in their book on Kafka (1975).By investigating the colloquial term "realness", which meant to “pass” as a specific gender or social class other than ones own, and which was incorporated into drag ball performance in the 80s and 90s, this article explores how realness functions as a repetitious device that deterritorialises (Deleuze and Guattari) the major language (“the real”) and subverts it from within, calling forth a new language (“realness”), and a new scene (the ballroom).It argues that colloquial performances such as drag realness are a fictional inhabitation – an expansive force – rather than a reduct...


title: The impact of interpretive and reductive front-of-pack labels on food choice and willingness to pay

abstract: This study examined how front-of-pack labels and product healthfulness affect choice and willingness to pay across a range of foods. It was hypothesized that: (i) product choice and (ii) willingness to pay would be more aligned with product healthfulness when healthfulness was expressed through the Health Star Rating, followed by the Multiple Traffic Light, then the Daily Intake Guide, and (iii) the Nutrition Facts Panel would be viewed infrequently. Adults and children aged 10+ years (n = 2069) completed an online discrete choice task involving mock food packages. A 4 food type (cookies, corn flakes, pizza, yoghurt) × 2 front-of-pack label presence (present, absent) × 3 front-of-pack label type (Daily Intake Guide, Multiple Traffic Light, Health Star Rating) × 3 price (cheap, moderate, expensive) × 3 healthfulness (less healthy, moderately healthy, healthier) design was used. A 30 s time limit was imposed for each choice. Of the three front-of-pack labels tested, the Health Star Rating produced the largest differences in choices, with 40% (95% CIs: 38%-42%) of respondents selecting the healthier variant, 33% selecting the moderately healthy variant (95% CIs: 31%-35%), and 23% (95% CIs: 21%-24%) selecting the less healthy variant of the four products included in the study. The Multiple Traffic Light led to significant differences in choices between healthier (35%, 95% CIs: 33%-37%) and less healthy products (29%, 95% CIs: 27%-31%), but not moderately healthy products (32%, 95% CIs: 30%-34%). No significant differences in choices were observed by product healthfulness when the Daily Intake Guide was present. Only the Health Star Rating resulted in a significantly greater willingness to pay for healthier versus less healthy products. The Nutrition Facts Panel was viewed for only 7% of all mock packages. Front-of-pack labels that are more interpretive, such as the Health Star Rating, can be more effective at directing consumers towards healthier choices than reductive front-of-pack labels such as the Daily Intake Guide. The study results provide policy makers with clear guidance on the types of front-of-pack labels that are most likely to achieve positive health outcomes at a population level.


title: 1 € filter: a simple speed-based low-pass filter for noisy input in interactive systems

abstract: The 1 € filter ("one Euro filter") is a simple algorithm to filter noisy signals for high precision and responsiveness. It uses a first order low-pass filter with an adaptive cutoff frequency: at low speeds, a low cutoff stabilizes the signal by reducing jitter, but as speed increases, the cutoff is increased to reduce lag. The algorithm is easy to implement, uses very few resources, and with two easily understood parameters, it is easy to tune. In a comparison with other filters, the 1 € filter has less lag using a reference amount of jitter reduction.


title: No need to war-drive: unsupervised indoor localization

abstract: We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.


PERFORMANCE MEASUREMENT AND ORGANIZATIONAL EFFECTIVENESS : BRIDGING THE GAP


title: Solving Job Shop Scheduling Problem Using Genetic Algorithm with Penalty Function

abstract: This paper presents a genetic algorithm with a penalty function for the job shop scheduling problem. In the context of proposed algorithm, a clonal selection based hyper mutation and a life span extended strategy is designed. During the search process, an adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. Simulated experiments were conducted on 23 benchmark instances taken from the OR-library. The results show the effectiveness of the proposed algorithm.


title: Curiosity-driven Reinforcement Learning for Dialogue Management

abstract: In this paper we describe the use of curiosity rewards for dialogue policy learning of goal oriented dialogues via reinforcement learning. Using curiosity improves state-action space exploration and helps overcome reward sparsity. Additionally, for goal oriented dialogues it makes sense to perform inherently curious actions in order to gain knowledge about the user goal. We show that intrinsic curiosity rewards can replace random -greedy exploration and stabilize training. The best results are achieved when curiosity rewards are combined with -greedy exploration.


title: Software architecture: a roadmap

abstract: Over the past decade software architecture has received increasing attention as an important subfield of software engineering. During that time there has been considerable progress in developing the technological and methodological base for treating architectural design as an engineering discipline. However, much remains to be done to achieve that goal. Moreover, the changing face of technology raises a number of new challenges for software architecture. This paper examines some of the important trends of software architecture in research and practice, and speculates on the important emerging trends, challenges, and aspirations.


title: Acting on Reflection: the Effect of Reflection on Students’ Clinical Performance on a Standardized Patient Examination

abstract: BackgroundLittle evidence exists to support the value of reflection in the clinical setting.ObjectiveTo determine whether reflecting and revisiting the “patient” during a standardized patient (SP) examination improves junior medical students’ performance and to analyze students’ perceptions of its value.DesignStudents completed a six-encounter clinical skills examination, writing a guided assessment after each encounter to trigger reflection. SPs evaluated the students with Medical Skills and Patient Satisfaction checklists. During the last three encounters, students could opt to revisit the SP and be reevaluated with identical checklists.ParticipantsOne hundred and forty-nine third year medical students.MeasurementsChanges in scores in the Medical Skills and Patient Satisfaction checklists between first visit and revisit were tested separately per case as well as across cases.ResultsOn the medical skills and patient satisfaction checklists, mean revisit scores across cases were significantly higher than mean first visit scores [12.6 vs 12.2 (pooled SD = 2.4), P = .0001; 31.2 vs 31.0 (pooled SD = 3.5), P = .0001)]. Sixty-five percent of the time, students rated “reflect–revisit” positively, 34% neutrally, and 0.4% negatively. Five themes were identified in the positive comments: enhancement of (1) medical decision making, (2) patient education/counseling, (3) student satisfaction/confidence, (4) patient satisfaction/confidence, and (5) clinical realism.ConclusionsOffering third year medical students the option to reflect and revisit an SP during a clinical skills examination produced a small but nontrivial increase in clinical performance. Students perceived the reflect–revisit experience as enhancing patient-centered practices (counseling, education) as well as their own medical decision making and clinical confidence.


title: Fuzzy-Rough Data Reduction with Ant Colony Optimization

abstract: Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding minimal reductions, the smallest sets of features possible. To alleviate this difficulty, a feature selection technique that employs a hybrid variant of rough sets, fuzzy-rough sets, has been developed recently and has been shown to be effective. However, this method is still not able to find the optimal subsets regularly. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat this. The method is then applied to the problem of finding optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring and experimentally compared with the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparisons with the use of a support vector classifier are also included.


title: A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms

abstract: This paper presents a differential synthetic aperture radar (SAR) interferometry (DIFSAR) approach for investigating deformation phenomena on full-resolution DIFSAR interferograms. In particular, our algorithm extends the capability of the small-baseline subset (SBAS) technique that relies on small-baseline DIFSAR interferograms only and is mainly focused on investigating large-scale deformations with spatial resolutions of about 100/spl times/100 m. The proposed technique is implemented by using two different sets of data generated at low (multilook data) and full (single-look data) spatial resolution, respectively. The former is used to identify and estimate, via the conventional SBAS technique, large spatial scale deformation patterns, topographic errors in the available digital elevation model, and possible atmospheric phase artifacts; the latter allows us to detect, on the full-resolution residual phase components, structures highly coherent over time (buildings, rocks, lava, structures, etc.), as well as their height and displacements. In particular, the estimation of the temporal evolution of these local deformations is easily implemented by applying the singular value decomposition technique. The proposed algorithm has been tested with data acquired by the European Remote Sensing satellites relative to the Campania area (Italy) and validated by using geodetic measurements.


title: How to Make a Multiprocessor Computer That Correctly Executes Multiprocess Programs

abstract: Many large sequential computers execute operations in a different order than is specified by the program. A correct execution is achieved if the results produced are the same as would be produced by executing the program steps in order. For a multiprocessor computer, such a correct execution by each processor does not guarantee the correct execution of the entire program. Additional conditions are given which do guarantee that a computer correctly executes multiprocess programs.


title: Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band

abstract: The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.


title: Exploitation of the genomic double-strand breaks to reduce the reproductive power of microorganisms

abstract: It is shown how to take advantage of the frequent occurrence of double-strand breaks in the genome of prokaryotic cells, in order to reduce their high efficient reproductive capability. The analysis examines the physical status of the free ends of each break and considers how this status can interfere with an external physical apparatus, with the aim of undermining the repair processes. We indicate the biological consequences of this interaction and we give an approximate evaluation of the topological and dynamical effects that arise on the genomic material involved. The overall result suggests a significant reduction of the dynamics of the repair.


title: A balanced scorecard for health services in Afghanistan

abstract: The Ministry of Public Health (MOPH) in Afghanistan has developed a balanced scorecard (BSC) to regularly monitor the progress of its strategy to deliver a basic package of health services. Although frequently used in other health-care settings, this represents the first time that the BSC has been employed in a developing country. The BSC was designed via a collaborative process focusing on translating the vision and mission of the MOPH into 29 core indicators and benchmarks representing six different domains of health services, together with two composite measures of performance. In the absence of a routine health information system, the 2004 BSC for Afghanistan was derived from a stratified random sample of 617 health facilities, 5719 observations of patient-provider interactions, and interviews with 5597 patients, 1553 health workers, and 13 843 households. Nationally, health services were found to be reaching more of the poor than the less-poor population, and providing for more women than men, both key concerns of the government. However, serious deficiencies were found in five domains, and particularly in counselling patients, providing delivery care during childbirth, monitoring tuberculosis treatment, placing staff and equipment, and establishing functional village health councils. The BSC also identified wide variations in performance across provinces; no province performed better than the others across all domains. The innovative adaptation of the BSC in Afghanistan has provided a useful tool to summarize the multidimensional nature of health-services performance, and is enabling managers to benchmark performance and identify strengths and weaknesses in the Afghan context.


title: Family pedigrees of children with suspected childhood apraxia of speech

abstract: Abstract Forty-two children (29 boys and 13 girls), ages 3–10 years, were referred from the caseloads of clinical speech-language pathologists for suspected childhood apraxia of speech (CAS). According to results from tests of speech and oral motor skills, 22 children met criteria for CAS, including a severely limited consonant and vowel repertoire, difficulty sequencing syllables, and inconsistent and unusual errors. Family pedigrees for these children were constructed through parent interviews and direct testing of nuclear family members. Familial aggregation for speech-sound and language disorders was demonstrated with 86% reporting at least one nuclear family member affected. Based on parent report, 13 of the 22 children (59%) had at least one affected parent. However, CAS was evident in only two siblings of probands with CAS and two probands with other speech-sound disorders. Based on testing, overall affection rates of speech-sound/language disorders were higher in families of children with CAS than in families of children with other speech-sound disorders. Mothers of children with CAS demonstrated a higher affection rate than mothers of children with other speech-sound disorders. A sex-related threshold model of transmission was also supported with brothers more often affected than sisters for male probands only. If our inclusionary criteria for CAS are valid, these findings support a general verbal trait deficit hypothesis. Learning outcomes (1) As a result of this activity, the participant will understand potential familial risk factors for CAS; (2) will differentiate aggregation for speech-sound and language disorders in families with CAS from families of children who have other speech-sound disorders; (3) will distinguish how familial aggregation differs in families of boys and girls with CAS; (4) will determine how children with CAS differ in severity from those with other speech-sound disorders.


title: Pano2CAD: Room Layout from a Single Panorama Image

abstract: This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image. Assuming Manhattan World geometry, we formulate the task as a Bayesian inference problem in which we estimate positions and orientations of walls and objects. The method combines surface normal estimation, 2D object detection and 3D object pose estimation. Quantitative results are presented on a dataset of synthetically generated 3D rooms containing objects, as well as on a subset of hand-labeled images from the public SUN360 dataset.


VARIATIONS IN THE MORPHOGENESIS OF SQUAMOUS CARCINOMA OF THE CERVIX.


title: Approaches and Tools for Automated End-to-End Web Testing

abstract: Abstract The importance of test automation in web engineering comes from the widespread use of web applications and the associated demand for code quality. Test automation is considered crucial for delivering the quality levels expected by users, since it can save a lot of time in testing and it helps developers to release web applications with fewer defects. The main advantage of test automation comes from fast, unattended execution of a set of tests after some changes have been made to a web application. Moreover, modern web applications adopt a multitier architecture where the implementation is scattered across different layers and run on different machines. For this reason, end-to-end testing techniques are required to test the overall behavior of web applications. In the last years, several approaches have been proposed for automated end-to-end web testing and the choice among them depends on a number of factors, including the tools used for web testing and the costs associated with their adoption. They can be classified using two main criteria: the first concerns how test cases are developed (ie, Capture-Replay and Programmable approaches), while, the second concerns how test cases localize the web elements to interact with (ie, Coordinates-based, DOM-based, and Visual approaches), that is what kind of locators are used for selecting the target GUI components. For developers and project managers it is not easy to select the most suitable automated end-to-end web testing approach for their needs among the existing ones. This chapter provides a comprehensive overview of the automated end-to-end web testing approaches and summarizes the findings of a long term research project aimed at empirically investigating their strengths and weaknesses.


title: Headlocks and Dropkicks: A Butt-Kicking Ride through the World of Professional Wrestling

abstract: Is it sport or is it entertainment? As presented by World Wrestling Entertainment, Inc., the most well-known promoter of professional wrestling, it is hard for the uninitiated to tell. A refuge for the very athletic, and often a breeding ground for the highly dysfunctional, professional wrestling is, in the truest sense, life on the fringes. * 15 illustrations


title: Leveraging single rate schemes in multiple rate multicast congestion control design

abstract: A significant impediment to deployment of multicast services is the daunting technical complexity of developing, testing and validating congestion control protocols fit for wide-area deployment. Protocols such as pragmatic general multicast congestion control (pgmcc) and TCP-friendly multicast congestion control (TFMCC) have recently made considerable progress on the single rate case, i.e., where one dynamic reception rate is maintained for all receivers in the session. However, these protocols have limited applicability, since scaling to session sizes beyond tens of participants with heterogeneous available bandwidth necessitates the use of multiple rate protocols. Unfortunately, while existing multiple rate protocols exhibit better scalability, they are both less mature than single rate protocols and suffer from high complexity. We propose a new approach to multiple rate congestion control that leverages proven single rate congestion control methods by orchestrating an ensemble of independently controlled single rate sessions. We describe a new multiple rate congestion control algorithm for layered multicast sessions that employs a single rate multicast congestion control as the primary underlying control mechanism for each layer. Our new scheme combines the benefits of single rate congestion control with the scalability and flexibility of multiple rates to provide a sound multiple rate multicast congestion control policy.


title: Identifying Syntactic Differences Between Two Programs

abstract: Programmers frequently face the need to identify the differences between two programs, usually two different versions of a program. Text-based tools such as the UNIXr utility diff often produce unsatisfactory comparisons because they cannot accurately pinpoint the differences and because they sometimes produce irrelevant differences. Since programs have a rigid syntactic structure as described by the grammar of the programming language in which they are written, we develop a comparison algorithm that exploits knowledge of the grammar. The algorithm, which is based on a dynamic programming scheme, can point out the differences between two programs more accurately than previous text comparison tools. Finally, the two programs are pretty-printed ‘synchronously’ with the differences highlighted so that the differences are easily identified.


title: Real-time and scalable incremental segmentation on dense SLAM

abstract: This work proposes a real-time segmentation method for 3D point clouds obtained via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework. Differently from all other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time, with a complexity that does not depend on the size of the global model. At the same time, it is also general, as it can be deployed with any frame-wise segmentation approach as well as any SLAM algorithm. We validate our proposal by a comparison with the state of the art in terms of computational efficiency and accuracy on a benchmark dataset, as well as by showing how our method can enable real-time segmentation from reconstructions of diverse real indoor environments.


title: Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks

abstract: Abstract Sentiment analysis is one of the new challenges appeared in automatic language processing with the advent of social networks. Taking advantage of the amount of information is now available, research and industry have sought ways to automatically analyze sentiments and user opinions expressed in social networks. In this paper, we place ourselves in a difficult context, on the sentiments that could thinking of suicide. In particular, we propose to address the lack of terminological resources related to suicide by a method of constructing a vocabulary associated with suicide. We then propose, for a better analysis, to investigate Weka as a tool of data mining based on machine learning algorithms that can extract useful information from Twitter data collected by Twitter4J. Therefore, an algorithm of computing semantic analysis between tweets in training set and tweets in data set based on WordNet is proposed. Experimental results demonstrate that our method based on machine learning algorithms and semantic sentiment analysis can extract predictions of suicidal ideation using Twitter Data. In addition, this work verify the effectiveness of performance in term of accuracy and precision on semantic sentiment analysis that could thinking of suicide.


title: clCaffe: OpenCL Accelerated Caffe for Convolutional Neural Networks

abstract: Recent advances in deep convolutional neural networks enable researchers and developers to apply machine learning to a much broader number of applications. With the proliferation of deep learning applications, widely used deep learning frameworks, such as Caffe, Theano and Torch, have been significantly improved with the support of powerful GPUs and GPU-accelerated libraries. However, lack of frameworks and libraries built on OpenCL could hinder exploration of more diverse compute devices (CPUs, GPUs, DSPs and FPGAs) in future deep learning domains. In this work, we present OpenCL acceleration of a well-known deep learning framework, Caffe, while focusing on the convolution layer which has been optimized with three different approaches, GEMM, spatial domain, and frequency domain. Our work, clCaffe, greatly enhances the ability to leverage deep learning use cases on all types of OpenCL devices, particularly on small form factor devices in which discrete GPUs are rare and integrated GPUs are much more common. Our benchmark shows 2.5x speedup on the Intel integrated-GPU, compared to CPU-only AlexNet on ImageNet dataset. As such, our work provides the deep learning community with the opportunity to embrace a broad range of devices through OpenCL.


title: Comparing twitter and traditional media using topic models

abstract: Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types. Our comparisons show interesting and useful findings for downstream IR or DM applications.


Author ' s personal copy Plug-in hybrid electric vehicles as regulating power providers : Case studies of Sweden and


title: Direct analytical methods for solving Poisson equations in computer vision problems

abstract: Direct analytical methods are discussed for solving Poisson equations of the general form Delta u=f on a rectangular domain. Some embedding techniques that may be useful when boundary conditions (obtained from stereo and occluding boundary) are defined on arbitrary contours are described. The suggested algorithms are computationally efficient owing to the use of fast orthogonal transforms. Applications to shape from shading, lightness and optical flow problems are also discussed. A proof for the existence and convergence of the flow estimates is given. Experiments using synthetic images indicate that results comparable to those using multigrid can be obtained in a very small number of iterations. >


title: Biosynthesis of Anthocyanins and Their Regulation in Colored Grapes

abstract: Anthocyanins, synthesized via the flavonoid pathway, are a class of crucial phenolic compounds which are fundamentally responsible for the red color of grapes and wines. As the most important natural colorants in grapes and their products, anthocyanins are also widely studied for their numerous beneficial effects on human health. In recent years, the biosynthetic pathway of anthocyanins in grapes has been thoroughly investigated. Their intracellular transportation and accumulation have also been further clarified. Additionally, the genetic mechanism regulating their biosynthesis and the phytohormone influences on them are better understood. Furthermore, due to their importance in the quality of wine grapes, the effects of the environmental factors and viticulture practices on anthocyanin accumulation are being investigated increasingly. The present paper summarizes both the basic information and the most recent advances in the study of the anthocyanin biosynthesis in red grapes, emphasizing their gene structure, the transcriptional factors and the diverse exterior regulation factors.


title: A Magneto-Electric Dipole Antenna With Low-Profile and Simple Structure

abstract: By combining a horizontal planar dipole and a vertically oriented folded shorted patch antenna, a new low-profile magneto-electric dipole antenna is presented. The antenna is simply excited by a coaxial feed that works as a balun. A prototype was fabricated and measured. Simulated and measured results agree well. An impedance bandwidth of 45.6% for ${\rm SWR}\leq 1.5$ from 1.86 to 2.96 GHz was achieved. Stable radiation pattern with low cross polarization, low back radiation, and an antenna gain of 8.1 $\pm$ 0.8 dBi was found over the operating frequencies. The height of the antenna is only $0.169\lambda$ (where $\lambda$ is the free-space wavelength at the center frequency). In addition, the antenna is dc grounded, which satisfies the requirement of many outdoor antennas.