title: When flexible routines meet flexible technologies: affordance, constraint, and the imbrication of human and material agencies

abstract: Employees in many contemporary organizations work with flexible routines and flexible technologies. When those employees find that they are unable to achieve their goals in the current environment, how do they decide whether they should change the composition of their routines or the materiality of the technologies with which they work? The perspective advanced in this paper suggests that the answer to this question depends on how human and material agencies-the basic building blocks common to both routines and technologies-are imbricated. Imbrication of human and material agencies creates infrastructure in the form of routines and technologies that people use to carry out their work. Routine or technological infrastructure used at any given moment is the result of previous imbrications of human and material agencies. People draw on this infrastructure to construct a perception that a technology either constrains their ability to achieve their goals, or that the technology affords the possibility of achieving new goals. The case of a computer simulation technology for automotive design used to illustrate this framework suggests that perceptions of constraint lead people to change their technologies while perceptions of affordance lead people to change their routines. This imbrication metaphor is used to suggest how a human agency approach to technology can usefully incorporate notions of material agency into its explanations of organizational change.


title: Prescience: Probabilistic Guidance on the Retraining Conundrum for Malware Detection

abstract: Malware evolves perpetually and relies on increasingly so- phisticated attacks to supersede defense strategies. Data-driven approaches to malware detection run the risk of becoming rapidly antiquated. Keeping pace with malware requires models that are periodically enriched with fresh knowledge, commonly known as retraining. In this work, we propose the use of Venn-Abers predictors for assessing the quality of binary classification tasks as a first step towards identifying antiquated models. One of the key benefits behind the use of Venn-Abers predictors is that they are automatically well calibrated and offer probabilistic guidance on the identification of nonstationary populations of malware. Our framework is agnostic to the underlying classification algorithm and can then be used for building better retraining strategies in the presence of concept drift. Results obtained over a timeline-based evaluation with about 90K samples show that our framework can identify when models tend to become obsolete.


title: Capacitive sensing of droplets for microfluidic devices based on thermocapillary actuation

abstract: The design and performance of a miniaturized coplanar capacitive sensor is presented whose electrode arrays can also function as resistive microheaters for thermocapillary actuation of liquid films and droplets. Optimal compromise between large capacitive signal and high spatial resolution is obtained for electrode widths comparable to the liquid film thickness measured, in agreement with supporting numerical simulations which include mutual capacitance effects. An interdigitated, variable width design, allowing for wider central electrodes, increases the capacitive signal for liquid structures with non-uniform height profiles. The capacitive resolution and time response of the current design is approximately 0.03 pF and 10 ms, respectively, which makes possible a number of sensing functions for nanoliter droplets. These include detection of droplet position, size, composition or percentage water uptake for hygroscopic liquids. Its rapid response time allows measurements of the rate of mass loss in evaporating droplets.


title: Mitigation of stress: new treatment alternatives

abstract: Complaints of stress are common in modern life. Psychological stress is a major cause of lifestyle-related issues, contributing to poor quality of life. Chronic stress impedes brain function, causing impairment of many executive functions, including working memory, decision making and attentional control. The current study sought to describe newly developed stress mitigation techniques, and their influence on autonomic and endocrine functions. The literature search revealed that the most frequently studied technique for stress mitigation was biofeedback (BFB). However, evidence suggests that neurofeedback (NFB) and noninvasive brain stimulation (NIBS) could potentially provide appropriate approaches. We found that recent studies of BFB methods have typically used measures of heart rate variability, respiration and skin conductance. In contrast, studies of NFB methods have typically utilized neurocomputation techniques employing electroencephalography, functional magnetic resonance imaging and near infrared spectroscopy. NIBS studies have typically utilized transcranial direct current stimulation methods. Mitigation of stress is a challenging but important research target for improving quality of life.


title: Efficient 3D aerial base station placement considering users mobility by reinforcement learning

abstract: This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial-BS assisted one, we keep the total number of BSs identical in both networks. Obtaining the best performance in such networks highly depends on the optimal placement of the aerial-BS. To this end, an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences is required. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.


title: CTOD‐R curves of the metal‐clad interface of API X52 pipes cladded with an Inconel 625 alloy by welding overlay

abstract: Crack-tip opening displacement resistance curves (CTOD-R) of the substrate/cladding interface of an API 5 L X52 steel pipe internally coated with Inconel 625 applied by TIG (GTAW) welding were experimentally evaluated. A small pipe section with 168 mm of outer diameter and 22.5 mm of thickness was internally coated with a 15 mm thick layer of Inconel 625 corrosion resistant alloy. Tension test specimens were obtained from both substrate and cladding, as well as compact tension test specimens (C(T)) for the evaluation of CTOD-R curves. The fracture testing specimens were notched at the interface in RL orientation. In addition to fracture and tensile testing, microstructural characterization was conducted at the interface using optical microscopy (OM) and scanning electron microscopy (SEM). Qualitative chemical composition scanning and microhardness determination were also performed. The results indicated high fracture toughness for the substrate/cladding interface and the absence of low toughness regions at the interface of the tested samples.


title: An Analysis of Visual Question Answering Algorithms

abstract: In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way algorithms are evaluated on them. As a result, evaluation scores are inflated and predominantly determined by answering easier questions, making it difficult to compare different methods. In this paper, we analyze existing VQA algorithms using a new dataset called the Task Driven Image Understanding Challenge (TDIUC), which has over 1.6 million questions organized into 12 different categories. We also introduce questions that are meaningless for a given image to force a VQA system to reason about image content. We propose new evaluation schemes that compensate for over-represented question-types and make it easier to study the strengths and weaknesses of algorithms. We analyze the performance of both baseline and state-of-the-art VQA models, including multi-modal compact bilinear pooling (MCB), neural module networks, and recurrent answering units. Our experiments establish how attention helps certain categories more than others, determine which models work better than others, and explain how simple models (e.g. MLP) can surpass more complex models (MCB) by simply learning to answer large, easy question categories.


title: Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System

abstract: This paper proposes a Markov Decision Process and reinforcement learning based approach for domain selection in a multidomain Spoken Dialogue System built on a distributed architecture. In the proposed framework, the domain selection problem is treated as sequential planning instead of classification, such that confirmation and clarification interaction mechanisms are supported. In addition, it is shown that by using a model parameter tying trick, the extensibility of the system can be preserved, where dialogue components in new domains can be easily plugged in, without re-training the domain selection policy. The experimental results based on human subjects suggest that the proposed model marginally outperforms a non-trivial baseline.


title: Social Glass: A Platform for Urban Analytics and Decision-making Through Heterogeneous Social Data

abstract: This demo presents Social Glass, a novel web-based platform that supports the analysis, valorisation, integration, and visualisation of large-scale and heterogeneous urban data in the domains of city planning and decision-making. The platform systematically combines publicly available social datasets from municipalities together with social media streams (e.g. Twitter, Instagram and Foursquare) and resources from knowledge repositories. It further enables the mapping of demographic information, human movement patterns, place popularity, traffic conditions, as well as citizens' and visitors' opinions and preferences with regard to specific venues in the city. Social Glass will be demonstrated through several real-world case studies, that exemplify the framework's conceptual properties, and its potential value as a solution for urban analytics and city-scale event monitoring and assessment.


title: Abstract A Data Distributed Parallel Algorithm for Nonrigid Image Registration ⋆

abstract: Image registration is a technique for defining a geometric relationship between each point in images. This paper presents a data distributed parallel algorithm that is capable of aligning large-scale three-dimensional (3-D) images of deformable objects. The novelty of our algorithm is to overcome the limitations on the memory space as well as the execution time. In order to enable this, our algorithm incorporates data distribution, data-parallel processing, and load balancing techniques into Schnabel's registration algorithm that realizes robust and efficient alignment based on information theory and adaptive mesh refinement. We also present some experimental results obtained on a 128-CPU cluster of PCs interconnected by Myrinet and Fast Ethernet switches. The results show that our algorithm requires less amount of memory resources, so that aligns datasets up to 1024x1024x590 voxel images with reducing the execution time from hours to minutes, a clinically compatible time.


title: Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model

abstract: Automatic skin lesion segmentation on dermoscopic images is an essential component in computer-aided diagnosis of melanoma. Recently, many fully supervised deep learning based methods have been proposed for automatic skin lesion segmentation. However, these approaches require massive pixel-wise annotation from experienced dermatologists, which is very costly and time-consuming. In this paper, we present a novel semi-supervised method for skin lesion segmentation by leveraging both labeled and unlabeled data. The network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. In this paper, we present a novel semi-supervised method for skin lesion segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. Our method encourages a consistent prediction for unlabeled images using the outputs of the network-in-training under different regularizations, so that it can utilize the unlabeled data. To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations. Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model. With only 300 labeled training samples, our method sets a new record on the benchmark of the International Skin Imaging Collaboration (ISIC) 2017 skin lesion segmentation challenge. Such a result clearly surpasses fully-supervised state-of-the-arts that are trained with 2000 labeled data.


title: Comparison of the solution and X-ray structures of barley serine proteinase inhibitor 2

abstract: A comparison of the solution n.m.r. structures of barley serine protease inhibitor 2 (BSPI-2) with the X-ray structures of both subtilLsin complexed and native BSPI-2 is presented. It is shown that the n.m.r. and X-ray structures are very similar in terms of overall shape, size, polypeptide fold and secondary structure. The average atomic rms difference between the 11 restrained dynamics structures on the one hand and the two X-ray structures on the other is 1.9 ± 0.2 A for the backbone atoms and 3.0 ± 0.3 A for all atoms. The corresponding values for the restrained energy minimized mean dynamics structure are 1.5 and 2.4 A, respectively


title: A new standardized method for objectively measuring video quality

abstract: The National Telecommunications and Information Administration (NTIA) General Model for estimating video quality and its associated calibration techniques were independently evaluated by the Video Quality Experts Group (VQEG) in their Phase II Full Reference Television (FR-TV) test. The NTIA General Model was the only video quality estimator that was in the top performing group for both the 525-line and 625-line video tests. As a result, the American National Standards Institute (ANSI) adopted the NTIA General Model and its associated calibration techniques as a North American Standard in 2003. The International Telecommunication Union (ITU) has also included the NTIA General Model as a normative method in two Draft Recommendations. This paper presents a description of the NTIA General Model and its associated calibration techniques. The independent test results from the VQEG FR-TV Phase II tests are summarized, as well as results from eleven other subjective data sets that were used to develop the method.


Exploring the Role of Online Social Network Dependency in Habit Formation


title: POMDP-based control of workflows for crowdsourcing

abstract: Crowdsourcing, outsourcing of tasks to a crowd of unknown people ("workers") in an open call, is rapidly rising in popularity. It is already being heavily used by numerous employers ("requesters") for solving a wide variety of tasks, such as audio transcription, content screening, and labeling training data for machine learning. However, quality control of such tasks continues to be a key challenge because of the high variability in worker quality. In this paper we show the value of decision-theoretic techniques for the problem of optimizing workflows used in crowdsourcing. In particular, we design AI agents that use Bayesian network learning and inference in combination with Partially-Observable Markov Decision Processes (POMDPs) for obtaining excellent cost-quality tradeoffs. ::: ::: We use these techniques for three distinct crowdsourcing scenarios: (1) control of voting to answer a binary-choice question, (2) control of an iterative improvement workflow, and (3) control of switching between alternate workflows for a task. In each scenario, we design a Bayes net model that relates worker competency, task difficulty and worker response quality. We also design a POMDP for each task, whose solution provides the dynamic control policy. We demonstrate the usefulness of our models and agents in live experiments on Amazon Mechanical Turk. We consistently achieve superior quality results than non-adaptive controllers, while incurring equal or less cost.


title: Emerging Roles of the Brain’s Default Network

abstract: During periods of inactivity, global metabolism does not decrease in the brain, and small but consistent increases in activity occur in a specific set of regions called the “default network”. Although much is known about the topological and connectional properties of the default network, its functions remain a matter of debate. Functional neuroimaging and electrophysiological studies have suggested two apparently contrasting functions for this network: spontaneous cognition and monitoring the environment. Spontaneous cognition, however, is by default situated in a given external context, an external milieu to which we align ourselves and which must be monitored. This review integrates recent literature suggesting that the two proposed functions of the default network functions need not to be mutually exclusive, and that spontaneous cognition and monitoring of the environment represent complementary instances of conscious experiences occurring during idle moments of daily life.


title: Three-dimensional analysis of the change in the curvature of the smiling line following orthodontic treatment in incisor class II division 1 malocclusion

abstract: OBJECTIVES ::: To investigate the different effects of changes in the occlusal plane, incisors inclination, and maxillary intercanine width on the curvature of the smiling line. ::: ::: ::: MATERIALS ::: Records of 46 subjects (28 females and 18 males, mean age 16.6 ± 4.2 years) with incisor class II division 1 malocclusions were selected. All subjects had four premolar extractions and were treated with preadjusted edgewise appliances. ::: ::: ::: METHODS ::: Pre- and post-treatment maxillary dental digital models were virtually aligned via corresponding landmarks to the respective lateral cephalograms. Subsequent two-dimensional superimposition of the aligned cephalograms facilitated the three-dimensional superimposition of the pre- and post-treatment models. This process allowed the quantification of the curvature from a frontal perspective of the models. The change in curvature was then correlated with changes in the cephalometric inclination of the anterior occlusal plane (AOP), functional occlusal plane (FOP), maxillary central incisor (U1), and the intercanine width. ::: ::: ::: RESULTS ::: Orthodontic correction in this sample resulted in the clockwise rotation of the anterior occlusal plane (5.84 degrees), reduction in proclination of the incisors (-14.39 degrees), increase in intercanine width (2.48mm), and a corresponding increase in the curvature of the smiling line (6.83 degrees). ::: ::: ::: CONCLUSIONS ::: The change in curvature of the smiling line in these subjects was found to be related more significantly to the magnitude of difference in the inclination between the pre-treatment AOP and FOP than to the change in the inclination of the maxillary incisors. With orthodontic treatment, the smiling line can be correlated with cephalometric data to improve or maintain the curvature.


title: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks

abstract: We present weight normalization: a reparameterization of the weight vectors in a neural network that decouples the length of those weight vectors from their direction. By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time. We demonstrate the usefulness of our method on applications in supervised image recognition, generative modelling, and deep reinforcement learning.


title: Discriminative Mixture-of-Templates for Viewpoint Classification

abstract: Object viewpoint classification aims at predicting an approximate 3D pose of objects in a scene and is receiving increasing attention. State-of-the-art approaches to viewpoint classification use generative models to capture relations between object parts. In this work we propose to use a mixture of holistic templates (e.g. HOG) and discriminative learning for joint viewpoint classification and category detection. Inspired by the work of Felzenszwalb et al 2009, we discriminatively train multiple components simultaneously for each object category. A large number of components are learned in the mixture and they are associated with canonical viewpoints of the object through different levels of supervision, being fully supervised, semi-supervised, or unsupervised. We show that discriminative learning is capable of producing mixture components that directly provide robust viewpoint classification, significantly outperforming the state of the art: we improve the viewpoint accuracy on the Savarese et al 3D Object database from 57% to 74%, and that on the VOC 2006 car database from 73% to 86%. In addition, the mixture-of-templates approach to object viewpoint/pose has a natural extension to the continuous case by discriminatively learning a linear appearance model locally at each discrete view. We evaluate continuous viewpoint estimation on a dataset of everyday objects collected using IMUs for groundtruth annotation: our mixture model shows great promise comparing to a number of baselines including discrete nearest neighbor and linear regression.


title: Distributed Control Techniques in Microgrids

abstract: The objective of this paper is to provide a review of distributed control and management strategies for the next generation power system in the context of microgrids. This paper also identifies future research directions. The next generation power system, also referred to as the smart grid, is distinct from the existing power system due to its extensive use of integrated communication, advanced components such as power electronics, sensing, and measurement, and advanced control technologies. At the same time, the need for increased number of small distributed and renewable energy resources can exceed the capabilities of an available computational resource. Therefore, the recent literature has seen a significant research effort on dividing the control task among different units, which gives rise to the development of several distributed techniques. This paper discusses features and characteristics of these techniques, and identifies challenges and opportunities ahead. The paper also discusses the relationship between distributed control and hierarchical control.


title: Defect prediction for Cascading Style Sheets

abstract: Graphical abstractDisplay Omitted Testing is a crucial activity in software development. However exhaustive testing of a given software is impossible in practice because projects have serious time and budget limitations. Therefore, software testing teams need guidance about which modules they should focus on. Defect prediction techniques are useful for this situation because they let testers to identify and focus on defect prone parts of software. These techniques are essential for software teams, because they help teams to efficiently allocate their precious resources in testing phase. Software defect prediction has been an active research area in recent years. Researchers in this field have been using different types of metrics in their prediction models. However, value of extracting static code metrics for style sheet languages has been ignored until now. User experience is a very important part of web applications and its mostly provided using Cascading Style Sheets (CSS). In this research, our aim is to improve defect prediction performance for web applications by utilizing metrics generated from CSS code. We generated datasets from four open source web applications to conduct our experiments. Defect prediction is then performed using three different well-known machine learning algorithms. The results revealed that static code metrics based defect prediction techniques can be performed effectively to improve quality of CSS code in web applications. Therefore we recommend utilizing domain-specific characteristics of applications in defect prediction as they result in significantly high prediction performance with low costs.


title: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

abstract: Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO-K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO-SA), combination of ACO and SA (ACO-SA), combination of PSO and ACO (PSO-ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.


title: Comparative Document Analysis for Large Text Corpora

abstract: This paper presents a novel research problem, Comparative Document Analysis (CDA), that is, joint discovery of commonalities and differences between two individual documents (or two sets of documents) in a large text corpus. Given any pair of documents from a (background) document collection, CDA aims to automatically identify sets of quality phrases to summarize the commonalities of both documents and highlight the distinctions of each with respect to the other informatively and concisely. Our solution uses a general graph-based framework to derive novel measures on phrase semantic commonality and pairwise distinction, where the background corpus is used for computing phrase-document semantic relevance. We use the measures to guide the selection of sets of phrases by solving two joint optimization problems. A scalable iterative algorithm is developed to integrate the maximization of phrase commonality or distinction measure with the learning of phrase-document semantic relevance. Experiments on large text corpora from two different domains---scientific papers and news---demonstrate the effectiveness and robustness of the proposed framework on comparing documents. Analysis on a 10GB+ text corpus demonstrates the scalability of our method, whose computation time grows linearly as the corpus size increases. Our case study on comparing news articles published at different dates shows the power of the proposed method on comparing sets of documents.


title: Annual Research Review: Sleep problems in childhood psychiatric disorders – a review of the latest science

abstract: Background ::: Hippocrates flagged the value of sleep for good health. Nonetheless, historically, researchers with an interest in developmental psychopathology have largely ignored a possible role for atypical sleep. Recently, however, there has been a surge of interest in this area, perhaps reflecting increased evidence that disturbed or insufficient sleep can result in poor functioning in numerous domains. This review outlines what is known about sleep in the psychiatric diagnoses most relevant to children and for which associations with sleep are beginning to be understood. While based on a comprehensive survey of the literature, the focus of the current review is on the latest science (largely from 2010). There is a description of both concurrent and longitudinal links as well as possible mechanisms underlying associations. Preliminary treatment research is also considered which suggests that treating sleep difficulties may result in improvements in behavioural areas beyond sleep quality. ::: ::: Findings ::: To maximise progress in this field, there now needs to be: (a) greater attention to the assessment of sleep in children; (b) sleep research on a wider range of psychiatric disorders; (c) a greater focus on and examination of mechanisms underlying associations; (d) a clearer consideration ofdevelopmental questions and (e) large-scale well-designed treatment studies. ::: ::: Conclusions ::: While sleep problems may sometimes be missed by parents and healthcare providers; hence constituting a hidden risk for other psychopathologies – knowing about these difficulties creates unique opportunities. The current excitement in this field from experts in diverse areas including developmental psychology, clinical psychology, genetics and neuropsychology should make these opportunities a reality.


title: Model Predictive Control of Quasi-Z-Source Four-Leg Inverter

abstract: This paper presents a model predictive control (MPC) scheme for quasi-Z-source (qZS) three-phase four-leg inverter. In order to cope with the drawbacks of traditional voltage source inverters (VSIs), a qZS three-phase four-leg inverter topology is proposed. This topology features a wide range of voltage gain which is suitable for applications in renewable energy-based power systems, where the output of the renewable energy sources varies widely with operating conditions such as wind speed, temperature, and solar irradiation. To improve the capability of the controller, an MPC scheme is used which implements a discrete-time model of the system. The controller handles each phase current independently, which adds flexibility to the system. Simulation and experimental studies verify the performances of the proposed control strategy under balanced and unbalanced load conditions as well as single-phase open-circuit fault condition.


title: On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting Recognition

abstract: In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are powerful tools to extract general purpose features that even work well for unknown classes. We evaluate our system on a Chinese handwritten text database and provide a GPU-based implementation that can be used to reproduce the experiments. All experiments were conducted with RWTH OCR, an open-source system developed at our institute.


title: Separate visual pathways for perception and action

abstract: Abstract Accumulating neuropsychological, electrophysiological and behavioural evidence suggests that the neural substrates of visual perception may be quite distinct from those underlying the visual control of actions. In other words, the set of object descriptions that permit identification and recognition may be computed independently of the set of descriptions that allow an observer to shape the hand appropriately to pick up an object. We propose that the ventral stream of projections from the striate cortex to the inferotemporal cortex plays the major role in the perceptual identification of objects, while the dorsal stream projecting from the striate cortex to the posterior parietal region mediates the required sensorimotor transformations for visually guided actions directed at such objects.


title: Adoption of internet services in the acceding and candidate countries, lessons from the internet banking case

abstract: Little is known about the dynamics of adoption of Internet services in general in the Acceding and Candidate Countries (ACCs). Retail Internet banking, however, is one of the most developed e-services and, the drivers and barriers for the adoption of Internet banking are likely to be relevant to other sectors' e-services. They may also shed light on the underlying supply/demand dynamics. The paper addresses the following questions: • What is the current Internet banking level of supply in the current European Union countries (EU15) and in ACCs? What are the drivers and barriers for development? • What is the current level of adoption by consumers? What are the drivers and barriers for adoption? • Are there supply/demand issues that are specific to ACCs? • What are the potential private/public policy options for addressing these issues? This paper has been based on extensive data analysis, with contributions from Internet banking experts in the EU15 and in the ACCs. Results from this research have been used to develop and validate a model of adoption factors which includes access technology and infrastructure, and sector specific retail banking aspects. This model provides a more detailed understanding of the particular challenges in the ACCs for the development of the Information Society in general, and the adoption of Internet banking in particular. Policy options to address these are also suggested.


title: A Document Skew Detection Method Using the Hough Transform

abstract: Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other publidhed methods form O'Gorman, Hinds, Le, Baird, Posel and Akuyama. Finally, we discuss the theory of skew detection and the different apporaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to be extremenly fast, with run times averaging under 0.25 CPU seconds to calculate the angle on the DEC 5000/20 workstation.


title: A Functional Taxonomy of Music Generation Systems

abstract: Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.


title: How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation

abstract: Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems.


title: Magnetic Field Analysis of 3-DOF Permanent Magnetic Spherical Motor Using Magnetic Equivalent Circuit Method

abstract: The paper studies the magnetic field distribution of three degree-of-freedom (3-DOF) permanent magnet (PM) spherical motor adopting the 3-D magnetic equivalent circuits (MEC) method. Compared with the analytical method and finite element analysis (FEA) method, the MEC has such desirable attributes as moderate computational effort and reasonable accuracy. The model of PM spherical motor is evenly meshed into elements in spherical coordinates, and the expressions of reluctances and magnetomotive force (MMF) sources in the element have been deduced. Given that magnetic field of the PM spherical motor distributes in the whole space, to reduce the number of elements and increase the solving speed, an approximate equipotential surface is adopted based on the simplified magnetic circuit, and the different magnetic boundary conditions are represented by the corresponding states of nodes and branches of the elements. Finally, the MEC network, which represents half of the magnetic pole and the corresponding air region of the motor, is built. Flux density distribution is calculated by means of the proposed model and verified by the FEA method.


title: Video retargeting: automating pan and scan

abstract: When a video is displayed on a smaller display than originally intended, some of the information in the video is necessarily lost. In this paper, we introduce Video Retargeting that adapts video to better suit the target display, minimizing the important information lost. We define a framework that measures the preservation of the source material, and methods for estimating the important information in the video. Video retargeting crops each frame and scales it to fit the target display. An optimization process minimizes information loss by balancing the loss of detail due to scaling with the loss of content and composition due to cropping. The cropping window can be moved during a shot to introduce virtual pans and cuts, subject to constraints that ensure cinematic plausibility. We demonstrate results of adapting a variety of source videos to small display sizes.


title: Much Ado About Something: Violent Video Game Effects and a School of Red Herring: Reply to Ferguson and Kilburn (2010)

abstract: In this article we reply to C. J. Ferguson and J. Kilburn’s (2010) critique of our meta-analysis on violent video game effects (C. A. Anderson et al., 2010). We rely on well-established methodological and statistical theory and on empirical data to show that claims of bias and misinterpretation on our part are simply wrong. One should not systematically exclude unpublished studies from meta-analytic reviews. There is no evidence of publication or selection bias in our data. We did not purposely exclude certain studies; we included all studies that met our inclusion criteria. Although C. J. Ferguson and J. Kilburn believe that the effects we obtained are trivial in size, they are larger than many effects that are deemed sufficiently large to warrant action in medical and violence domains. The claim that we (and other media violence scholars) are attempting to create a false crisis is a red herring.


title: Flink: Semantic Web Technology for the Extraction and Analysis of Social Networks

abstract: We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a web-based presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community.


title: Consumer Credit Risk Models Via Machine-Learning Algorithms

abstract: We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank's customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2's of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.


title: Deep Reinforcement Learning for Dialogue Generation

abstract: Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity (non-repetitive turns), coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues.


title: Workspace characterization for concentric tube continuum robots

abstract: Concentric tube robots exhibit complex workspaces due to the way their component tubes bend and twist as they interact with one another. This paper explores ways to compute and characterize their workspaces. We use Monte Carlo random samples of the robot’s joint space and a discrete volumetric workspace representation, which can describe both reachability and redundancy. Experiments on two physical prototypes are provided to illustrate the proposed approach.


title: DCT/DST-Based Transform Coding for Intra Prediction in Image/Video Coding

abstract: In this paper, we present a DCT/DST based transform scheme that applies either the conventional DCT or type-7 DST for all the video-coding intra-prediction modes: vertical, horizontal, and oblique. Our approach is applicable to any block-based intra prediction scheme in a codec that employs transforms along the horizontal and vertical direction separably. Previously, Han, Saxena, and Rose showed that for the intra-predicted residuals of horizontal and vertical modes, the DST is the optimal transform with performance close to the KLT. Here, we prove that this is indeed the case for the other oblique modes. The optimal choice of using DCT or DST is based on intra-prediction modes and requires no additional signaling information or rate-distortion search. The DCT/DST scheme presented in this paper was adopted in the HEVC standardization in March 2011. Further simplifications, especially to reduce implementation complexity, which remove the mode-dependency between DCT and DST, and simply always use DST for the 4 × 4 intra luma blocks, were adopted in the HEVC standard in July 2012. Simulation results conducted for the DCT/DST algorithm are shown in the reference software for the ongoing HEVC standardization. Our results show that the DCT/DST scheme provides significant BD-rate improvement over the conventional DCT based scheme for intra prediction in video sequences.


title: Exploitation of Dual-Polarization Diversity for 5G Millimeter-Wave MIMO Beamforming Systems

abstract: For the first time, to the best of our knowledge, this paper reports a complete millimeter wave (mmWave) wireless system, which coherently utilizes polarization diversity, multi-input multioutput (MIMO), and beamforming technologies applicable for upcoming fifth generation wireless mobile terminals. Using an interdisciplinary approach across the entire layers constituting of antennas, RF architecture, and digital modem, the polarization of the presented mmWave beamforming antenna system dynamically adapts to the channel environments and MIMO transmission modes. Empirical investigations, which are taken under real-life environments, ascertain that implementing switchable dual-polarized array antennas can alleviate the complexity of the beam searching procedure and MIMO channelization. In addition, experimental results acquired from intensive system level tests indicate more than 22 dB averaged cross-polarization discrimination under polarized MIMO channel conditions at the 60 GHz band. The beam selection algorithm that can be easily implemented based on power metric is confirmed to feature a 99.5% accuracy in comparison with the optimal selection algorithm derived from the theoretical maximal capacity.


title: Connotation Frames: A Data-Driven Investigation

abstract: Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how people use language and give rise to the connotative meanings. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.


title: Analyzing Regulatory Conformance in Medical Research Systems Using Multi-Paradigm Modeling

abstract: Many of today’s partially or fully automated medical research systems are rule, or constraint-driven systems that must be analyzed with respect to their rule compliance. We propose a new method called Multi Modeling Constraint-Driven System Analysis to explore and analyze rule conformance in these systems. Specifically, our method takes advantage of existing system documentation in the form of operational flow charts, and refines and transforms the information in them to into more formal system representations that allow automated analysis and reasoning, including reasoning about rule conformance. The method uses UML, Alloy, and protocol state machine models, and includes trace linking between informal and formal diagram and model elements, to allow analysis reporting on more informal diagrams and also ‘what if’ exploration to address atypical settings or problems found during analysis. Sometimes models are large and not amenable to automated analysis so we demonstrate how slicing techniques can be used to extract subset models relevant to particular analyses. We demonstrate our method using an existing medical research system.


title: Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems

abstract: Recently, methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. Although some of these methods have been studied independently, no prior research compared the performance of these methods to determine which of them is better than the others. This paper focuses on comparing the pre-filtering and the post-filtering approaches and identifying which method dominates the other and under which circumstances. Since there are no clear winners in this comparison, we propose an alternative more effective method of selecting the winners in the pre- vs. the post-filtering comparison. This strategy provides analysts and companies with a practical suggestion on how to pick a good pre- or post-filtering approach in an effective manner to improve performance of a context-aware recommender system.


title: An Attention-Based Deep Net for Learning to Rank

abstract: In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query. Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism. This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion. The embeddings are trained with convolutional neural networks or the word2vec model. We demonstrate the performance of this model with image retrieval and text querying data sets.


A Cautionary Note Regarding Evaluation of AES Candidates on Smart-Cards


title: Primary osteoarthritis of the hip joint in Japan.

abstract: Diagnostic criteria for primary osteoarthritis of the hip joint in the Japanese population were determined by the roentgenographic measurements of the center-edge (CE) angle, the Sharp angle, and the acetabular roof obliquity in 254 normal hips. Primary osteoarthritis must meet the following conditions: (1) absence of femoral head deformities; (2) a CE angle of Wiberg greater than 19 degrees; (3) a Sharp angle less than 45 degrees; and (4) acetabular roof obliquity less than 15 degrees. These measurements are taken from roentgenograms during the early stage of the disease. Under these criteria, primary osteoarthritis accounted for only 0.65% of 2000 consecutive cases of osteoarthritis. Observation of the natural course of primary osteoarthritis revealed two subtypes. The superolateral type developed from the subset of normal hips with a relatively greater degree of acetabular roof obliquity.


title: Measuring student engagement among elementary students: Pilot of the Student Engagement Instrument—Elementary Version.

abstract: Early school withdrawal, commonly referred to as dropout, is associated with a plethora of negative outcomes for students, schools, and society. Student engagement, however, presents as a promising theoretical model and cornerstone of school completion interventions. The purpose of the present study was to validate the Student Engagement Instrument-Elementary Version (SEI-E). The psychometric properties of this measure were assessed based on the responses of an ethnically diverse sample of 1,943 students from an urban locale. Exploratory and confirmatory factor analyses indicated that the 4-factor model of student engagement provided the best fit for the current data, which is divergent from previous SEI studies suggesting 5- and 6-factor models. Discussion and implications of these findings are presented in the context of student engagement and dropout prevention.


title: Emotion malleability beliefs, emotion regulation, and psychopathology: Integrating affective and clinical science.

abstract: Beliefs that individuals hold about whether emotions are malleable or fixed, also referred to as emotion malleability beliefs, may play a crucial role in individuals' emotional experiences and their engagement in changing their emotions. The current review integrates affective science and clinical science perspectives to provide a comprehensive review of how emotion malleability beliefs relate to emotionality, emotion regulation, and specific clinical disorders and treatment. Specifically, we discuss how holding more malleable views of emotion could be associated with more active emotion regulation efforts, greater motivation to engage in active regulatory efforts, more effort expended regulating emotions, and lower levels of pathological distress. In addition, we explain how extending emotion malleability beliefs into the clinical domain can complement and extend current conceptualizations of major depressive disorder, social anxiety disorder, and generalized anxiety disorder. This may prove important given the increasingly central role emotion dysregulation has been given in conceptualization and intervention for these psychiatric conditions. Additionally, discussion focuses on how emotion beliefs could be more explicitly addressed in existing cognitive therapies. Promising future directions for research are identified throughout the review.


title: Psychological flexibility, ACT, and organizational behavior

abstract: This paper offers organizational behavior management (OBM) a behavior analytically consistent way to expand its analysis of, and methods for changing, organizational behavior. It shows how Relational Frame Theory (RFT) suggests that common, problematic, psychological processes emerge from language itself, and they produce psychological inflexibility. Research suggests that an applied extension of RFT, Acceptance and Commitment Therapy, has led to new interventions that increase psychological flexibility and, thereby enhance, organizational behavior and health.


title: Effect of Physical Cleanliness and Cognitive Cleanliness on Moral Judgment

abstract: Objectives: The present study aimed at examining the individual effect of physical cleanliness and cognitive cleanliness on the severity of moral judgment under the condition of previously induced experimental feelings of disgust. Method: Seventy-five participants were shown 12 disgusting pictures. They were then randomly assigned to one of the three conditions: Condition 1: hand-washing with cognitive cleanliness priming; Condition 2: non-hand-washing with cognitive cleanliness priming; Condition 3: non-hand-washing without cognitive cleanliness priming. Participants were asked to rate six moral dilemmas and the emotion rating for the 12 pictures was collected. Demographic information was obtained and all participants were debriefed before they left. Results: With the experimentally induced feeling of disgust, the mixed effect of physical cleanliness and cognitive cleanliness significantly reduced the severity of moral judgment. When the individual effects were addressed, however, only cognitive cleanliness and not physical cleanliness significantly reduced the severity of moral judgment. Conclusions: Cognitive cleanliness alone reduced the severity of moral judgment by removing the feeling of disgust. Physical cleanliness reduced the severity of moral judgment only when it was considered together with cognitive cleanliness.


title: RETSIM : A shoe store agent-based simulation for fraud detection

abstract: RetSim is an agent-based simulator of a shoe store basedon the transactional data of one of the largest retail shoesellers in Sweden. The aim of RetSim is the generationof synthetic data that can b ...


title: A rule extraction study on a neural network trained by deep learning

abstract: Rule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. In this work the Discretized Multi Layer Perceptron (DIMLP) was trained by deep learning, then symbolic rules were extracted in an easier way with respect to standard MLPs. We compared the accuracy of deep trained DIMLPs and DIMLP ensembles on a subset of the MNIST dataset. The former networks were more accurate than the latter. Moreover, the complexity of the rules extracted from deep trained DIMLPs was similar to that obtained by boosted ensembles of DIMLPs. Finally, we examined the generated rules with respect to the centroids of the covered samples. Qualitatively, no clear difference in the strategy of classification emerged between deep trained DIMLPs and the ensembles.


title: Memory training impacts short-term changes in aging white matter: a longitudinal diffusion tensor imaging study.

abstract: A growing body of research indicates benefits of cognitive training in older adults, but the neuronal mechanisms underlying the effect of cognitive intervention remains largely unexplored. Neuroimaging methods are sensitive to subtle changes in brain structure and show potential for enhancing our understanding of both aging- and training-related neuronal plasticity. Specifically, studies using diffusion tensor imaging (DTI) suggest substantial changes in white matter (WM) in aging, but it is not known whether cognitive training might modulate these structural alterations. We used tract-based spatial statistics (TBSS) optimized for longitudinal analysis to delineate the effects of 8 weeks intensive memory training on WM microstructure. 41 participants (mean age 61 years) matched for age, sex and education were randomly assigned to an intervention or control group. All participants underwent MRI-scanning and neuropsychological assessments at the beginning and end of the study. Longitudinal analysis across groups revealed significant increase in frontal mean diffusivity (MD), indicating that DTI is sensitive to WM structural alterations over a 10-week interval. Further, group analysis demonstrated positive effects of training on the short-term changes. Participants in the training group showed a relative increase in fractional anisotropy (FA) compared with controls. Further, a significant relationship between memory improvement and change in FA was found, suggesting a possible functional significance of the reported changes. The training effect on FA seemed to be driven by a relative decrease in radial diffusivity, which might indicate a role for myelin-related processes in WM plasticity. Hum Brain Mapp 33:2390–2406, 2012. © 2011 Wiley Periodicals, Inc.


Online interval scheduling


title: Investigating deep side layers for skin lesion segmentation

abstract: Accurate skin lesion segmentation is an important yet challenging problem for medical image analysis. The skin lesion segmentation is subject to variety of challenges such as the significant pattern and colour diversity found within the lesions, presence of various artifacts, etc. In this paper, we present two fully convolutional networks with several side outputs to take advantage of discriminative capability of features learned at intermediate layers with varying resolutions and scales for the lesion segmentation. More specifically, we integrate fine and coarse prediction scores of the side-layers which allows our framework to not only output accurate probability map for the lesion, but also extract fine lesion boundary details such as the fuzzy border, which further improves the lesion segmentation. Quantitative evaluation is performed on the 2016 International Symposium on Biomedical Imaging (ISBI 2016) dataset, which shows our proposed approach compares favorably with state-of-the-art skin segmentation methods.


Handbook of Genetic Algorithms (L. D. Davis)


title: High Prevalence ofEscherichia coli-Producing CTX-M-15 Extended-Spectrum Beta-Lactamases in Poultry and Human Clinical Isolates in Romania

abstract: Use of antibiotics in food animals may contribute to development and spread of resistant organisms, particularly so in some countries. The aim of this study was two-fold; first, to establish the prevalence of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli in chicken production in a region within Romania. Second, to study the relatedness of ESBL-producing E. coli isolates recovered from broilers, abattoir workers where the chickens were slaughtered and from the human clinical specimens from two regional hospitals. The results indicated a very high (69%) rate of carriage of ESBL and AmpC-producing E. coli in chickens with 36% CTX-M producers. Sequencing showed that chickens in Romania have the highest worldwide prevalence (53%) of blaCTX-M-15 reported in poultry E. coli isolates. The majority (53%) of the extended-spectrum cephalosporin-resistant E. coli carried plasmid-mediated blaampC genes, mostly blaCMY-2 type, one of the highest prevalences reported in Europe. The predominant CTX-M type found in the human clinical E. coli isolates was blaCTX-M-15 and most isolates coharbored blaOXA-1, blaTEM, and aac(6')-ib-cr. The majority (60%) of the human clinical isolates belonged to the pandemic virulent clone B2-ST131. The clonal relationship between broiler and the human CTX-M-producing E. coli isolates was assessed by macrorestriction pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST), which indicated strain diversity with no common STs found between human and poultry isolates. Moreover, IncI1 was the most prevalent replicon found in broiler ESBL-producing E. coli isolates and also in transconjugants, indicating that plasmids and not clonal spread may play a role in the transfer of blaCTX-M genes. This study identifies a high prevalence of ESBL-producing E. coli from broiler chickens in Romania with a high occurrence incidence of blaCTX-M-15, which reflects the main ESBL type found in human E. coli infections in this country.


title: A 0.5-V bulk-input fully differential operational transconductance amplifier

abstract: We present a fully differential two-stage Miller op-amp operating from a 0.5 V power supply. The input signal is applied to the bulk nodes of the input devices. A prototype was designed in a standard 0.18 /spl mu/m CMOS process using standard 0.5 V V/sub T/ devices. It has a measured 52 dB DC gain, a 2.5 MHz gain-bandwidth and consumes 110 /spl mu/W.


title: The Performance of Greedy Geographic Forwarding in Unmanned Aeronautical Ad-Hoc Networks

abstract: The availability of accurate location information (for airborne purposes) in unmanned aeronautical ad-hoc networks (UAANETs) makes it possible to deploy geographic routing mechanisms as the main communication protocol in such networks. However, the performance of geographic routing is not obvious, especially for sparse networks (i.e., networks with low connectivity levels). In this paper, the main goal is to explore the effects of network sparsity on the performance of greedy geographic forwarding and to determine in different scenarios the percentage of source-destination pairs that can successfully establish communication based on greedy geographic forwarding only. To that end, Monte Carlo simulations have been performed to collect statistics in estimating the performance of UAANETs in sparse situations. Simulation results show that greedy geographic forwarding can be used for less critical applications of UAANETs. However, for applications with guaranteed delivery requirements, other alternatives should be combined with greedy geographic forwarding.


title: Effective Straggler Mitigation with Cross-Layer Interference-Aware Optimization

abstract: In-memory data processing frameworks (e.g., Spark) make big data analysis greatly simpler and efficient. However, stragglers that take much longer to finish than other tasks significantly degrade performance. There exist multiple factors that cause stragglers, either from the hardware resource layer or application layer, e.g. hardware heterogeneity, interference, data locality and data skew. While state-of-the-art straggler mitigation techniques have presented partial solutions on data skew and data locality, we find that the other factors can also result in serious problems. We present Clio, a cross-layer interference-aware optimization system that can effectively mitigate stragglers for data processing frameworks. Clio supports the scheduling of both map and reduce tasks. It heuristically dispatches intermediate data in proportion to the actual computing ability of each worker node, which is estimated considering various straggler factors, to balance the completion times of tasks in a much finer way. We implement Clio in Apache Spark, and evaluate its performance using both synthetic and real datasets. Experiment results show that, Clio can speed up the execution of applications by up to 67%, compared with the existing algorithms.


abstract: The Footbridge Dilemma Reflects More Utilitarian Thinking Than The Trolley Dilemma: Effect Of Number Of Victims In Moral Dilemmas Kuninori Nakamura (nakamura.kuninori@gmail.com) Graduate School of Decision Science & Technology, Tokyo Institute of Technology 2-12-1, Ohkayama, Meguro-Ku, Tokyo 152-8552, Japan Abstract Previous studies on moral judgment have assumed that the trolley and footbridge dilemmas (Thomson, 1985) reflect utilitarian and deontologist thinking, respectively. However, on the basis of the “intervention myopia” hypothesis (Waldmann & Dieterich, 2007) and recent findings in analyses of moral dilemmas (Nakamura, 2011), the current study led a somewhat paradoxical prediction: An effect of the manipulation of the number of victims, considered a utilitarian aspect of moral dilemmas, is larger in the footbridge dilemma than in the trolley dilemma. In order to test this prediction, two experimental studies were conducted in which the number of victims in the trolley and footbridge dilemmas were manipulated. Results of the two studies consistently showed an interaction between the dilemma type and the number of victims, thereby indicating that the manipulation of the utilitarian aspect of moral dilemmas has more effect on the footbridge dilemma, which is believed to reflect deontologist thinking. Keywords: trolley dilemma, footbridge dilemma, utilitarian, deontologist Introduction Is it permissible to sacrifice fewer lives to save more? This is a central question in the debate between utilitarianism and deontology. Utilitarians (e.g., Bentham, 1789; 1948) argue that it is indeed permissible because saving more lives results in greater utility for society than saving fewer ones, whereas deontologists (e.g., Kant, 1965) argue that it is not permissible because life is an ultimate right that should not be violated, irrespective of the number benefit yielded by its sacrifice. This debate has drawn the attention of various researchers who have proposed a number of solutions (see e.g., Singer, 1979; Thomson, 1986; Greene & Haidt, 2002; Mikhail, 2009). The philosophical debate between utilitarians and deontologists concerns the normative theory of moral judgment. However, psychologists are interested in the descriptive aspect of moral judgment: are people utilitarian or deontologist? The answer to this question is, surprisingly, “it depends on the context.” A discrepancy between the trolley and footbridge dilemmas (Thomson, 1985) clearly demonstrates the context dependency in moral judgment. The trolley dilemma can be described in the following manner: A runaway trolley is headed for five people who will be killed if it proceeds on its current course. The only way to save them is to hit a switch that will turn the trolley onto an alternate set of tracks where it will kill one person instead of five. Should one turn the trolley in order to save five people at the expense of one? Most people answer yes to this dilemma. Then, consider a similar problem, the footbridge dilemma. As before, a trolley threatens to kill five people. You are standing next to a large stranger on a footbridge that spans the tracks, in between the oncoming trolley and the five people. In this scenario, the only way to save the five people is to push this stranger off the bridge, onto the tracks below. He will die if you do this, but his body will stop the trolley from reaching the others. Should one save the five others by pushing this stranger to his death? To this question, most people answer no (with regard to precise data, see Greene & Haidt, 2002). With regard to the dominant responses in these dilemmas, people appear to be utilitarians when solving the trolley dilemma and deontologists when solving the footbridge dilemma. In the former dilemma, a person’s choice appears to depend on the number of workmen to be saved, whereas people make much of the right of the man on the bridge in the latter dilemma. Thus, it has been considered that the trolley dilemma reflects the utilitarian way of thinking, whereas the footbridge dilemma reflects the deontologist way of thinking (Greene, Sommerville, Nystrom, Darley, & Cohen, 2001; Waldmann & Dieterich, 2007). With regard to this discrepancy in the dominant responses between the trolley and footbridge dilemmas, various theoretical explanations have been proposed such as the dual process theory (Greene et al., 2001), moral grammar theory (Hauser, 2007; Mikhail, 2009), or causal decision theory (Waldmann & Dieterich, 2007). Although theorists of moral reasoning differ in terms of how to explain the discrepancy between the trolley and footbridge dilemmas, they consistently receive correspondence between the two dilemmas and philosophical way of thinking without any doubt. For example, Greene et al. (2001) said that the dominant response in the footbridge dilemma could be justified in a Kantian (deontologist) vein, but this justification has trouble when considering the trolley dilemma (Greene et al., 2001, p2106). Hauser (2006, p113–4) explained the philosophical implication of the trolley and footbridge dilemmas in terms of whether the utilitarian calculation can justify dominant responses in these dilemmas. Waldmann and Dieterich (2007) also argued that throwing the switch in the trolley dilemma is in line with the utilitarian view, whereas the footbridge dilemma is in line with the deontologist perspective (Waldmann & Dieterich, 2007, p247–8). Although there are differing theoretical explanations for the discrepancy between the trolley and footbridge dilemmas, the theorists in moral reasoning research have no doubt in


title: Traffic-Aware Placement of NFV Middleboxes

abstract: Network Function Virtualization (NFV) enables flex- ible deployment of middleboxes as Virtual Machines (VMs) running on general hardware. Different types of middleboxes have the potential to either increase or decrease the volume of processed traffic. In this paper, we investigate the traffic changing effects of middleboxes, and study efficient deployment of NFV middleboxes in Software-Defined Networks (SDNs). To begin with, we formulate the Traffic-Aware Middlebox Placement (TAMP) problem as a graph optimization problem, and show that it is NP-hard when there are multiple flows to consider. Next, by observing that in reality flows arrive one at a time, we leverage the SDN central control mechanism, and propose an optimal solution for the TAMP problem with a single flow. We develop the solution in two steps. First, when the flow path has been determined, we present the Least-First-Greatest- Last (LFGL) rule to place middleboxes. Second, we integrate the LFGL rule with widest-path routing to propose the LFGL based MinMax routing algorithm. Further, we have implemented the proposed algorithm as a module running on top of the Floodlight SDN controller, and conducted experiments in the Mininet emulation system. The experiment results fully demonstrate the superiority of our algorithm over other benchmark solutions.


title: Model-Driven Constraint Programming

abstract: Constraint programming can definitely be seen as a model-driven paradigm. The users write programs for modeling problems. These programs are mapped to executable models to calculate the solutions. This paper focuses on efficient model management (definition and transformation). From this point of view, we propose to revisit the design of constraint-programming systems. A model-driven architecture is introduced to map solving-independent constraint models to solving-dependent decision models. Several important questions are examined, such as the need for a visual highlevel modeling language, and the quality of metamodeling techniques to implement the transformations. A main result is the s-COMMA platform that efficiently implements the chain from modeling to solving constraint problems


title: Exploring universal patterns in human home-work commuting from mobile phone data

abstract: Home-work commuting has always attracted significant research attention because of its impact on human mobility. One of the key assumptions in this domain of study is the universal uniformity of commute times. However, a true comparison of commute patterns has often been hindered by the intrinsic differences in data collection methods, which make observation from different countries potentially biased and unreliable. In the present work, we approach this problem through the use of mobile phone call detail records (CDRs), which offers a consistent method for investigating mobility patterns in wholly different parts of the world. We apply our analysis to a broad range of datasets, at both the country (Portugal, Ivory Coast, and Saudi Arabia), and city (Boston) scale. Additionally, we compare these results with those obtained from vehicle GPS traces in Milan. While different regions have some unique commute time characteristics, we show that the home-work time distributions and average values within a single region are indeed largely independent of commute distance or country (Portugal, Ivory Coast, and Boston)–despite substantial spatial and infrastructural differences. Furthermore, our comparative analysis demonstrates that such distance-independence holds true only if we consider multimodal commute behaviors–as consistent with previous studies. In car-only (Milan GPS traces) and car-heavy (Saudi Arabia) commute datasets, we see that commute time is indeed influenced by commute distance. Finally, we put forth a testable hypothesis and suggest ways for future work to make more accurate and generalizable statements about human commute behaviors.


title: Design science in information systems research

abstract: Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology. Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research. In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact. Three recent exemplars in the research literature are used to demonstrate the application of these guidelines. We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community.


title: Directional Cooperative MAC Protocol Design and Performance Analysis for IEEE 802.11ad WLANs

abstract: In this paper, we consider the directional multigigabit (DMG) transmission problem in IEEE 802.11ad wireless local area networks (WLANs) and design a random-access-based medium access control (MAC) layer protocol incorporated with a directional antenna and cooperative communication techniques. A directional cooperative MAC protocol, namely, D-CoopMAC, is proposed to coordinate the uplink channel access among DMG stations (STAs) that operate in an IEEE 802.11ad WLAN. Using a 3-D Markov chain model with consideration of the directional hidden terminal problem, we develop a framework to analyze the performance of the D-CoopMAC protocol and derive a closed-form expression of saturated system throughput. Performance evaluations validate the accuracy of the theoretical analysis and show that the performance of D-CoopMAC varies with the number of DMG STAs or beam sectors. In addition, the D-CoopMAC protocol can significantly improve system performance, as compared with the traditional IEEE 802.11ad MAC protocol.


title: Beyond frequency: Habit as mental construct

abstract: Progress in habit theory can be made by distinguishing habit from frequency of occurrence, and using independent measures for these constructs. This proposition was investigated in three studies using a longitudinal, cross-sectional and experimental design on eating, mental habits and word processing, respectively. In Study 1, snacking habit and past snacking frequency independently predicted later snacking behaviour, while controlling for the theory of planned behaviour variables. Habit fully mediated the effect of past on later behaviour. In Study 2, habitual negative self-thinking and past frequency of negative self-thoughts independently predicted self-esteem and the presence of depressive and anxiety symptoms. In Study 3, habit varied as a function of experimentally manipulated task complexity, while behavioural frequency was held constant. Taken together, while repetition is necessary for habits to develop, these studies demonstrate that habit should not be equated with frequency of occurrence, but rather should be considered as a mental construct involving features of automaticity, such as lack of awareness, difficulty to control and mental efficiency.


title: Optimising Rule Order for a Packet Filtering Firewall

abstract: A heuristic approximation algorithm that can optimise the order of firewall rules to minimise packet matching is presented. It has been noted that firewall operators tend to make use of the fact that some firewall rules match most of the traffic, and conversely that others match little of the traffic. Consequently, ordering the rules such that the highest matched rules are as high in the table as possible reduces the processing load in the firewall. Due to dependencies between rules in the rule set this problem, optimising the cost of the packet matching process, has been shown to be NP-hard. This paper proposes an algorithm that is designed to give good performance in terms of minimising the packet matching cost of the firewall. The performance of the algorithm is related to complexity of the firewall rule set and is compared to an alternative algorithm demonstrating that the algorithm here has improved the packet matching cost in all cases.


Image-to-Word Transformation Based on Dividing and Vector Quantizing Images With Words


title: A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications

abstract: Crowdsourcing is a new emerging distributed computing and business model on the backdrop of Internet blossoming. With the development of crowdsourcing systems, the data size of crowdsourcers, contractors and tasks grows rapidly. The worker quality evaluation based on big data analysis technology has become a critical challenge. This paper first proposes a general worker quality evaluation algorithm that is applied to any critical tasks such as tagging, matching, filtering, categorization and many other emerging applications, without wasting resources. Second, we realize the evaluation algorithm in the Hadoop platform using the MapReduce parallel programming model. Finally, to effectively verify the accuracy and the effectiveness of the algorithm in a wide variety of big data scenarios, we conduct a series of experiments. The experimental results demonstrate that the proposed algorithm is accurate and effective. It has high computing performance and horizontal scalability. And it is suitable for large-scale worker quality evaluations in a big data environment.


title: Value Creation Process in the Fast Fashion Industry: Towards a Networking Approach

abstract: Purpose – Quick response, short product life cycles, customer-centric businesses, agile supply chains and reduction of lead times are considered to be the core business strategies in the sector of fast fashion. The work is an attempt to identify the most revealing management and organizational tools that support the final value creation and service delivery processes in the international industry of fast fashion.Design/Methodology/approach – In order to fulfill such a purpose, the paper detects the sector through the recent developments from Service-Dominant (S-D) Logic and Network Theory scientific proposals.Findings – Value is a co-created performance, requiring several actors participation. In the fast fashion, such actors are represented by textile businesses, providers, retailers, stores, customers. They all interact within a supply chain becoming a value chain in which stakeholders interact with each other contributing to the final value generation. At the light of this research, it seems to be restrictive to identify in the lead time reduction the success factor of the fast fashion businesses, but it should be pursued in politics of integration, interaction, co-creation and sharing throughout the chain.Fast fashion is an example of totally integrated global chain working and performing as a whole business in which every single component co-creates the final mutual benefit.Practical implications – Textile organizations taking part in the network represented by fast fashion are called to re-organize their activities in function of a highly developed integration level and so to move towards a networking approach, fostering the mutual exchange of resources in order to co-create a common value.Originality/value – The paper adds value to the study of the fast fashion industry, valorizing the importance of the co-creating perspective within the supply chain management.


title: Synchronous Binarization For Machine Translation

abstract: Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine translation output, but are often very computationally intensive. The complexity is exponential in the size of individual grammar rules due to arbitrary re-orderings between the two languages, and rules extracted from parallel corpora can be quite large. We devise a linear-time algorithm for factoring syntactic re-orderings by binarizing synchronous rules when possible and show that the resulting rule set significantly improves the speed and accuracy of a state-of-the-art syntax-based machine translation system.


title: Attitudes towards assisted dying are influenced by question wording and order: a survey experiment

abstract: BackgroundSurveys on attitudes towards assisted dying play an important role in informing public debate, policy and legislation. Unfortunately, surveys are often designed with insufficient attention to framing effects; that is, effects on the respondents’ stated attitudes caused by question wording and context. The purpose of this study was to demonstrate and measure such framing effects.MethodsSurvey experiment in which an eight-question survey on attitudes towards assisted dying was distributed to Norwegian citizens through a web-based panel. Two variations of question wording as well as two variations of question order were employed. Respondents were randomized to receive one of four questionnaire versions.ResultsThree thousand and fifty responses were received. There were moderate to large question wording and question order effects. A majority of Norwegian citizens favour the legalization of assisted dying for patients with terminal or chronic disease.ConclusionsStakeholders in the assisted dying debate need to acknowledge potential framing effects, and accordingly should interpret survey results with caution. The same holds for researchers who conduct attitude surveys in the field of bioethics.


title: Ripple current cancellation circuit

abstract: A ripple current cancellation technique injects AC current into the output voltage bus of a converter that is equal and opposite to the normal converter ripple current. The output current ripple is ideally zero, leading to ultra-low noise converter output voltages. The circuit requires few additional components, no active circuits are required. Only an additional filter inductor winding, an auxiliary inductor, and small capacitor are required. The circuit utilizes leakage inductance of the modified filter inductor as all or part of the required auxiliary inductance. Ripple cancellation is independent of switching frequency, duty cycle, and other converter parameters. The circuit eliminates ripple current in both continuous conduction mode and discontinuous conduction mode. Experimental results provide better than an 80/spl times/ ripple current reduction.


title: A data clustering algorithm for mining patterns from event logs

abstract: Today, event logs contain vast amounts of data that can easily overwhelm a human. Therefore, mining patterns from event logs is an important system management task. The paper presents a novel clustering algorithm for log file data sets which helps one to detect frequent patterns from log files, to build log file profiles, and to identify anomalous log file lines.


title: Global ranking of knowledge management and intellectual capital academic journals: 2017 update

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this study is to update a global ranking of 27 knowledge management and intellectual capital (KM/IC) academic journals. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The ranking was developed based on a combination of results from a survey of 482 active KM/IC researchers and journal citation impact indices. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The ranking list includes 27 currently active KM/IC journals. The A+ journals are the Journal of Knowledge Management and the Journal of Intellectual Capital. The A journals are the Learning Organization, Knowledge Management Research & Practice, Knowledge and Process Management, VINE: The Journal of Information and Knowledge Management Systems and International Journal of Knowledge Management. A majority of recently launched journals did not fare well in the ranking. Whereas a journal’s longevity is important, it is not the only factor affecting its ranking position. Expert survey and citation impact measures are relatively consistent, but expert survey ranking scores change faster. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: KM/IC discipline stakeholders, including practitioners, editors, publishers, reviewers, researchers, students, administrators and librarians, may consult the developed ranking list for various purposes. Compared to 2008, more researchers indicated KM/IC as their primary area of concentration, which is a positive indicator of discipline development. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This is the most recent ranking list of KM/IC academic journals.


title: Cerebral Blood Flow Abnormalities in Cerebral Palsied Children with a Normal CT Scan

abstract: Cerebral palsy (CP) is often a consequence of a hypoxic-ischemic encephalopathy and/or intracerebral hemorrhage secondary to pre- and peri-natal asphyxia. Hypodense lesions on the CT-scan are found in about 70 to 80% of CP-patients. In the present study, regional cerebral blood flow (CBF) was measured in CP-patients having a normal CT-scan. The aim was to correlate the CBF changes with the clinical and the etiologic findings. CBF was measured by xenon-133 inhalation and single photon emission computer tomography. The mean CBF value in 20 CP-patients, age 6-19 years, was 67 +/- 11 (1 SD) ml/100g/min, the same value as found in the 9 normal children. However, 16 of the 20 CP-patients had focal hypoperfused areas on the tomographic flow map. In the preterm infants (n = 7) the hypoperfused areas were mainly located in the posterior watershed areas, often in one hemisphere only. In the term infants (n = 13) both asymmetrical and symmetrical hypoperfused areas were observed in the anterior and posterior watershed areas. In addition, several children had larger low flow areas in the frontal and fronto-parietal lobes. In both groups, a relatively poor concordance was observed between the clinical findings and the expected location of the low flow area.


title: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

abstract: Outlier detection research has been seeing many new algorithms every year that often appear to be only slightly different from existing methods along with some experiments that show them to "clearly outperform" the others. However, few approaches come along with a clear analysis of existing methods and a solid theoretical differentiation. Here, we provide a formalized method of analysis to allow for a theoretical comparison and generalization of many existing methods. Our unified view improves understanding of the shared properties and of the differences of outlier detection models. By abstracting the notion of locality from the classic distance-based notion, our framework facilitates the construction of abstract methods for many special data types that are usually handled with specialized algorithms. In particular, spatial neighborhood can be seen as a special case of locality. Here we therefore compare and generalize approaches to spatial outlier detection in a detailed manner. We also discuss temporal data like video streams, or graph data such as community networks. Since we reproduce results of specialized approaches with our general framework, and even improve upon them, our framework provides reasonable baselines to evaluate the true merits of specialized approaches. At the same time, seeing spatial outlier detection as a special case of local outlier detection, opens up new potentials for analysis and advancement of methods.


title: SegMatch: Segment based loop-closure for 3D point clouds

abstract: Place recognition in 3D data is a challenging task that has been commonly approached by adapting image-based solutions. Methods based on local features suffer from ambiguity and from robustness to environment changes while methods based on global features are viewpoint dependent. We propose SegMatch, a reliable place recognition algorithm based on the matching of 3D segments. Segments provide a good compromise between local and global descriptions, incorporating their strengths while reducing their individual drawbacks. SegMatch does not rely on assumptions of ‘perfect segmentation’, or on the existence of ‘objects’ in the environment, which allows for reliable execution on large scale, unstructured environments. We quantitatively demonstrate that SegMatch can achieve accurate localization at a frequency of 1Hz on the largest sequence of the KITTI odometry dataset. We furthermore show how this algorithm can reliably detect and close loops in real-time, during online operation. In addition, the source code for the SegMatch algorithm is made publicly available1.


title: Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods

abstract: In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global nonassymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.


title: Knowledge-based anti-money laundering: a software agent bank application

abstract: Purpose – Criminal elements in today’s technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many anti-money laundering (AML) solutions have been in place for some time within the financial community, they face the challenge to adapt to the ever-changing risk and methods in relation to money laundering (ML). This research seeks to focus on ML control and prevention, which aim to automate the monitoring and diagnosing of ML schemes in order to report suspicious activities to banks. Design/methodology/approach – The research adopted the technology of intelligent agents to provide a more adaptive, flexible, and knowledge-based solution for AML. Findings – Based on the analysis of monitoring, diagnosing, and reporting of ML activities occurring in electronic transactions, several types of intelligent agents are proposed and a multi-agent framework is presented for AML. Furthermore, business knowledge such as business rules and strategies are extracted from AML practice, and applied to the design of individual agents to make them act autonomously and collaboratively to fulfil the goal of ML detection. Practical implications – The proposed multi-agent framework is a stand-alone system, which can be integrated by banks to combat ML. Although it is a uni-bank framework at present, it can be extended to multi-bank application in the future. Originality/value – The research explores the approach of applying an intelligent agent for knowledge-based AML in an electronic transaction environment for banks. By separating business logic from the business model, such a business-rules approach can enhance the flexibility and adaptability of the agent-based AML system.


Integrated Learning of Dialog Strategies and Semantic Parsing


title: Depthwise Separable Convolutions for Neural Machine Translation

abstract: Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency. They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the Xception architecture) and considerably reducing the number of parameters required to perform at a given level (the MobileNets family of architectures). Recently, convolutional sequence-to-sequence networks have been applied to machine translation tasks with good results. In this work, we study how depthwise separable convolutions can be applied to neural machine translation. We introduce a new architecture inspired by Xception and ByteNet, called SliceNet, which enables a significant reduction of the parameter count and amount of computation needed to obtain results like ByteNet, and, with a similar parameter count, achieves new state-of-the-art results. In addition to showing that depthwise separable convolutions perform well for machine translation, we investigate the architectural changes that they enable: we observe that thanks to depthwise separability, we can increase the length of convolution windows, removing the need for filter dilation. We also introduce a new"super-separable"convolution operation that further reduces the number of parameters and computational cost for obtaining state-of-the-art results.