title: Two-axis tilt angle detection based on dielectric liquid capacitive sensor

abstract: This paper presents the design, fabrication and characterization of a two-axis tilt angle sensor based on dielectric liquid capacitive sensing structure. The sensor consists of five electrodes, including one electrode serves as the exciting electrode and two pairs of sensing electrodes arranged at identified positions surrounding a plastic tube, which is partly filled with dielectric liquid. With this unique arrangement, the proposed sensor can detect two components of tilt angle in x-axis and x-axis, simultaneously. A computational multiphysic simulation and experimental measurements study the performance of the sensor. The numerical simulation is carried out with a finite element analysis using COMSOL Multiphysics. A prototype of the sensor was fabricated and its performance was evaluated. The tilt angle sensor is employed on a PCB with conditioning circuit, which consists of a 170 kHz sine wave generator, pre-amplifiers, rectifiers and low pass filters. Experiment results confirmed the performance of the sensor to detect tilt angle in both x-axis and y-axis. Tilt angle sensitivities to x-axis and y-axis are 16.5 mV/degree and 57 mV/degree, respectively, with cross-axis sensitivity less than 5.5% in the linear range.


title: Correcting Length Bias in Neural Machine Translation

abstract: We study two problems in neural machine translation (NMT). First, in beam search, whereas a wider beam should in principle help translation, it often hurts NMT. Second, NMT has a tendency to produce translations that are too short. Here, we argue that these problems are closely related and both rooted in label bias. We show that correcting the brevity problem almost eliminates the beam problem; we compare some commonly-used methods for doing this, finding that a simple per-word reward works well; and we introduce a simple and quick way to tune this reward using the perceptron algorithm.


title: Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators

abstract: This paper presents a review of self-organizing feature maps (SOFMs), in particular, those based on the Kohonen algorithm, applied to adaptive modeling and control of robotic manipulators. Through a number of references we show how SOFMs can learn nonlinear input–output mappings needed to control robotic manipulators, thereby coping with important robotic issues such as the excess degrees of freedom, computation of inverse kinematics and dynamics, hand–eye coordination, path-planning, obstacle avoidance, and compliant motion. We conclude the paper arguing that SOFMs can be a much simpler, feasible alternative to MLP and RBF networks for function approximation and for the design of neurocontrollers. Comparison with other supervised/unsupervised approaches and directions for further work on the field are also provided.


title: Neural Network Memory Architectures for Autonomous Robot Navigation

abstract: This paper highlights the significance of including memory structures in neural networks when the latter are used to learn perception-action loops for autonomous robot navigation. Traditional navigation approaches rely on global maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet, maintaining an accurate global map may be challenging in real-world settings. A possible way to mitigate this limitation is to use learning techniques that forgo hand-engineered map representations and infer appropriate control responses directly from sensed information. An important but unexplored aspect of such approaches is the effect of memory on their performance. This work is a first thorough study of memory structures for deep-neural-network-based robot navigation, and offers novel tools to train such networks from supervision and quantify their ability to generalize to unseen scenarios. We analyze the separation and generalization abilities of feedforward, long short-term memory, and differentiable neural computer networks. We introduce a new method to evaluate the generalization ability by estimating the VC-dimension of networks with a final linear readout layer. We validate that the VC estimates are good predictors of actual test performance. The reported method can be applied to deep learning problems beyond robotics.


title: Functions of nominal classification

abstract: Abstract Nominal classification systems are generally categorized on the basis of morphosyntactic criteria. However, the functional motivations for these phenomena do not coincide directly with their morphosyntactic properties: some functions are shared by diverse systems, and each morphosyntactic type may serve diverse communicative functions. We provide a functional typology for nominal classification, including both noun class and classifier systems. We focus on two types of functions: semantic, i.e., the use of classification markers to expand the referential power of the lexicon, and discourse/pragmatic, i.e., the use of classification markers to establish and manipulate the status of discourse referents. We identify functions that are shared by formally diverse systems as well as functions that depend on means of expression. We also review psycholinguistic evidence for the role of nominal classification in language comprehension and production.


title: Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction

abstract: Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the potential of deep learning methods in traffic forecasting has not yet fully been exploited in terms of the depth of the model architecture, the spatial scale of the prediction area, and the predictive power of spatial-temporal data. In this paper, a deep stacked bidirectional and unidirectional LSTM (SBU- LSTM) neural network architecture is proposed, which considers both forward and backward dependencies in time series data, to predict network-wide traffic speed. A bidirectional LSTM (BDLSM) layer is exploited to capture spatial features and bidirectional temporal dependencies from historical data. To the best of our knowledge, this is the first time that BDLSTMs have been applied as building blocks for a deep architecture model to measure the backward dependency of traffic data for prediction. The proposed model can handle missing values in input data by using a masking mechanism. Further, this scalable model can predict traffic speed for both freeway and complex urban traffic networks. Comparisons with other classical and state-of-the-art models indicate that the proposed SBU-LSTM neural network achieves superior prediction performance for the whole traffic network in both accuracy and robustness.


title: Movie success prediction using data mining

abstract: In this project, we developed a mathematical model to predict the success and failure of the upcoming movies based on several attributes. Some of the criteria in calculating movie success included budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience. The success prediction of a movie plays a vital role in movie industry because it involves huge investments. However, success cannot be predicted based on a particular attribute. So, we have built a model based on interesting relation between attributes. The movie industry can use this model to modify the movie criteria for obtaining likelihood of blockbusters. Also, this model can be used by movie watchers in determining a blockbuster before purchasing a ticket. Each of the criteria involved was given a weight and then the prediction was made based on these. For example, if a movies budget was below 5 million, the budget was given a lower weight. Depending on the number of actors, directors and producers past successful movies, each of these categories was given a weight. If the movie was to be released on a weekend, it was given higher weight because the chances of success were greater. If with the release of a movie, there was another high success movie released, a lower weight was given to the release time indicating that the chances of movie success were low due to the competition. The criteria were not limited just to the ones mentioned. There were be additional factors discussed in this work. We have conducted our work with simulation data.


title: Learning for serving deadline-constrained traffic in multi-channel wireless networks

abstract: We study the problem of serving randomly arriving and delay-sensitive traffic over a multi-channel communication system with time-varying channel states and unknown statistics. This problem deviates from the classical exploration-exploitation setting in that the design and analysis must accommodate the dynamics of packet availability and urgency as well as the cost of each channel use at the time of decision. To that end, we have developed and investigated two policies, one index-based (UCB-Deadline) and the other Bayesian (TS-Deadline), both of which perform dynamic channel allocation decisions that incorporate these traffic requirements and costs. Under symmetric channel conditions, we have proved that the UCB-Deadline policy can achieve bounded regret in the likely case where the cost of using a channel is not too high to prevent all transmissions, and logarithmic regret otherwise. In our numerical studies, we also show that TS-Deadline achieves superior performance over its UCB counterpart, making it a potentially useful alternative when fast convergence to optimal is important.


title: Exploiting Context for Rumour Detection in Social Media

abstract: Tools that are able to detect unverified information posted on social media during a news event can help to avoid the spread of rumours that turn out to be false. In this paper we compare a novel approach using Conditional Random Fields that learns from the sequential dynamics of social media posts with the current state-of-the-art rumour detection system, as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying the stance of a post to deem it a rumour but, instead, exploits context learned during the event. Our classifier has improved precision and recall over the state-of-the-art classifier that relies on querying tweets, as well as outperforming our best baseline. Moreover, the results provide evidence for the generalisability of our classifier.


title: The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge

abstract: This article describes the final solution of team monkeytyping, who finished in second place in the YouTube-8M video understanding challenge. The dataset used in this challenge is a large-scale benchmark for multi-label video classification. We extend the work in [1] and propose several improvements for frame sequence modeling. We propose a network structure called Chaining that can better capture the interactions between labels. Also, we report our approaches in dealing with multi-scale information and attention pooling. In addition, We find that using the output of model ensemble as a side target in training can boost single model performance. We report our experiments in bagging, boosting, cascade, and stacking, and propose a stacking algorithm called attention weighted stacking. Our final submission is an ensemble that consists of 74 sub models, all of which are listed in the appendix.


title: Ball wheel drive mechanism for holonomic omnidirectional vehicle

abstract: In this paper, we propose Ball Wheel Drive Mechanism for holonomic omnidirectional vehicle. Holonomici omnidirectional vehicles have high flexibility and high motion performance. Widely used omniwheels have complicated structure, because many free rollers are arranged on wheel circumference. Omniwheels cause poor motion stability and poor step overcomning ability. We solve these practical problems by using the ball wheel drive mechanism for holonomic omnidirectional vehicle. Because the ball wheel drive mechanism has only three ball wheels and three actuators, it is very simple. This mechanism do not cause over constraint, because the number of actuators agrees with the numbers of degrees of freedom. This mechanism realizes stable motions, because the rotation directions of ball wheels agree with moving directions of vehicle. Moreover, the step overcoming ability of ball wheels are higher than omniwheels. The ball wheel drive mechanism can be used under various circumstances. In this paper, we propose a ball wheel drive mechanism and evaluated its motion properties.


QUANTUM GRAVITY AS A DISSIPATIVE DETERMINISTIC


title: Generalized Impulse Response Analysis in Linear Multivariate Models

abstract: Building on Koop, Pesaran and Potter (1996), the authors propose the `generalised' impulse response analysis for unrestricted vector autoregressive (VAR) and cointegrated VAR models. Unlike the traditional impulse response analysis, this approach does not require orthogonalisation of shocks and is invariant to the ordering of the variables in the VAR. In particular, it is shown that in general both generalised and orthogonalised impulse responses are equivalent only when the effects of the shock for the first equation in the VAR is examined. An empirical illustration is also provided showing that the two impulse responses could differ substantially.


The reference intervals of zinc, copper, selenium and zinc/copper ratio of healthy adult in Licang


title: DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition

abstract: Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data. Collecting such datasets is however a tedious, often impossible task; hence a surge in approaches relying solely on synthetic data for their training. For depth images however, discrepancies with real scans still noticeably affect the end performance. We thus propose an end-to-end framework which simulates the whole mechanism of these devices, generating realistic depth data from 3D models by comprehensively modeling vital factors e.g. sensor noise, material reflectance, surface geometry. Not only does our solution cover a wider range of sensors and achieve more realistic results than previous methods, assessed through extended evaluation, but we go further by measuring the impact on the training of neural networks for various recognition tasks; demonstrating how our pipeline seamlessly integrates such architectures and consistently enhances their performance.


title: Accurate anchor-free node localization in wireless sensor networks

abstract: There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in order to correlate the gathered data to the origin of the sensed phenomena and assure the relevance of the reported information. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins.


title: On computing task-oriented grasps

abstract: This paper addresses the problem of optimal grasping of an object with a multi-fingered robotic hand for accomplishing a given task. The task is first demonstrated by a human operator and its force/torque requirements are captured through the usage of a sensorized tool. The grasp quality is computed through a task compatibility criterion. Grasp synthesis is then formulated as a single constrained optimization problem, generating grasps that are feasible for the hand's kinematics by maximizing the corresponding task-oriented quality criterion and ensuring grasp stability. The method was validated on a human hand model and is shown to be easily adapted to different hand kinematic models.


title: We're in it together: interpersonal management of disclosure in social network services

abstract: The workload needed for managing privacy and publicness in current social network services (SNSs) is placed on individuals, yet people have few means to control what others disclose about them. This paper considers SNS-users' concerns in relation to online disclosure and the ways in which they cope with these both individually and collaboratively. While previous work has focused mainly on individual coping strategies, our findings from a qualitative study with 27 participants suggest that collaborative strategies in boundary regulation are of additional importance. We present a framework of strategies for boundary regulation that informs both theoretical work and design practice related to management of disclosure in SNSs. The framework considers disclosure as an interpersonal process of boundary regulation, in which people are dependent on what others choose to disclose about them. The paper concludes by proposing design solutions supportive of collaborative and preventive strategies in boundary regulation that facilitate the management of disclosure online.


title: Sending linear functions of correlated gaussian sources over a MAC in the presence of known interference at the transmitter

abstract: In this work, we consider the problem of communicating linear functions of correlated Gaussian sources over a two-user additive Gaussian noise multiple access channel (MAC) in the presence of known interference at the transmitter. The goal is to estimate a linear combination of two sources at the receiver with minimum average mean squared error (MSE) distortion. Here, a lattice-based coding scheme is presented and its achievable distortion is analyzed. In addition, two lower bounds on the achievable distortion are derived. The proposed lattice coding scheme can cancel channel interference completely.


title: Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost

abstract: Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Auto-scaling (of resources without human intervention) has been investigated in academia and industry. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto- scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts QoS and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier. We also demonstrate using realistic traffic load traces and optical backbone network that our ML method improves QoS and saves significant cost for network owners as well as leasers.


title: Unsupervised Video Object Segmentation for Deep Reinforcement Learning

abstract: We present a new technique for deep reinforcement learning that automatically detects moving objects and uses the relevant information for action selection. The detection of moving objects is done in an unsupervised way by exploiting structure from motion. Instead of directly learning a policy from raw images, the agent first learns to detect and segment moving objects by exploiting flow information in video sequences. The learned representation is then used to focus the policy of the agent on the moving objects. Over time, the agent identifies which objects are critical for decision making and gradually builds a policy based on relevant moving objects. This approach, which we call Motion-Oriented REinforcement Learning (MOREL), is demonstrated on a suite of Atari games where the ability to detect moving objects reduces the amount of interaction needed with the environment to obtain a good policy. Furthermore, the resulting policy is more interpretable than policies that directly map images to actions or values with a black box neural network. We can gain insight into the policy by inspecting the segmentation and motion of each object detected by the agent. This allows practitioners to confirm whether a policy is making decisions based on sensible information.


title: Community preserving network embedding

abstract: Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.


title: “Resistance is futile”: reading science fiction alongside ubiquitous computing

abstract: Design-oriented research is an act of collective imagining--a way in which we work together to bring about a future that lies slightly out of our grasp. In this paper, we examine the collective imagining of ubiquitous computing by bringing it into alignment with a related phenomenon, science fiction, in particular as imagined by a series of television shows that form part of the cultural backdrop for many members of the research community. A comparative reading of these fictional narratives highlights a series of themes that are also implicit in the research literature. We argue both that these themes are important considerations in the shaping of technological design and that an attention to the tropes of popular culture holds methodological value for ubiquitous computing.


A Brief Overview of 5 G Research Activities


title: Sanctum: Minimal Hardware Extensions for Strong Software Isolation

abstract: Sanctum offers the same promise as Intel’s Software Guard Extensions (SGX), namely strong provable isolation of software modules running concurrently and sharing resources, but protects against an important class of additional software attacks that infer private information from a program’s memory access patterns. Sanctum shuns unnecessary complexity, leading to a simpler security analysis. We follow a principled approach to eliminating entire attack surfaces through isolation, rather than plugging attack-specific privacy leaks. Most of Sanctum’s logic is implemented in trusted software, which does not perform cryptographic operations using keys, and is easier to analyze than SGX’s opaque microcode, which does. Our prototype targets a Rocket RISC-V core, an open implementation that allows any researcher to reason about its security properties. Sanctum’s extensions can be adapted to other processor cores, because we do not change any major CPU building block. Instead, we add hardware at the interfaces between generic building blocks, without impacting cycle time. Sanctum demonstrates that strong software isolation is achievable with a surprisingly small set of minimally invasive hardware changes, and a very reasonable overhead.


title: Adaptive background mixture models for real-time tracking

abstract: A common method for real-time segmentation of moving regions in image sequences involves "background subtraction", or thresholding the error between an estimate of the image without moving objects and the current image. The numerous approaches to this problem differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model. The Gaussian, distributions of the adaptive mixture model are then evaluated to determine which are most likely to result from a background process. Each pixel is classified based on whether the Gaussian distribution which represents it most effectively is considered part of the background model. This results in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. This system has been run almost continuously for 16 months, 24 hours a day, through rain and snow.


title: GCMix: An Efficient Data Protection Scheme against the Paired Page Interference

abstract: In multi-level cell (MLC) NAND flash memory, two logical pages are overlapped on a single physical page. Even after a logical page is programmed, the data can be corrupted if the programming of the coexisting logical page is interrupted. This phenomenon is called paired page interference. This article proposes a novel software technique to deal with the paired page interference without any additional hardware or extra page write. The proposed technique utilizes valid pages in the victim block during garbage collection (GC) as the backup against the interference, and pairs them with incoming pages written by the host. This approach eliminates undesirable page copy to backup pages against the interference. However, such a strategy has an adverse effect on the hot/cold separation policy, which is essential to improve the efficiency of GC. To limit the downside, we devise a metric to estimate the benefit of GCMix on-the-fly so that GCMix can be adaptively utilized only when the benefit outweighs the overhead. Evaluations using synthetic and real workloads show GCMix can effectively deal with the paired page interference, reducing the write amplification factor by up to 17.5%compared to the traditional technique, while providing comparable I/O performance.


An Intellect Learning on E-mail Security and Fraud, Spam and Phishing


title: Scientific documents ontologies for semantic representation of digital libraries

abstract: We present a system of services for the automatic processing of collections of scientific documents that are part of digital libraries. These services are based on ontologies for scientific documents representation, as well as on methods for semantic analysis of mathematical documents. The developed tools automatically check validity of documents for compliance with manuscript guidelines, convert these documents into required formats and generate their metadata.


title: A framework for analysis of 2D platformer levels

abstract: Levels are the space where a player explores the rules and mechanics of a game; as such, good level design is critical to the game design process. While there are many broad design principles, level design is inherently genre-specific due to the wide variety of rules and types of challenge found between genres. Determining genre-specific design principles requires an in-depth analysis of games within the genre. We present such an analysis for the 2D platformer genre, examining level components and structure with a view to better understanding their level design. We then use this analysis to present a model for platformer levels, specifically focusing on areas of challenge. Our framework provides a common vocabulary for these items and provides level designers with a method for thinking about elements of platformers and how to compose them to create interesting and challenging levels.


title: Sleep duration and school performance in Korean adolescents

abstract: SUMMARY Background This study investigated the relationship between sleep duration and school performance in Korean adolescents. Methods In 2012, 63 688 adolescents participated in the 8th Korea Youth Risk Behavior Web-Based Survey (KYRBWS-VIII) project. The relationship between sleep duration and school performance was evaluated by multivariate logistic regression analysis after adjustment for covariate variables, including body mass index, age, parental education, economic status, mental stress, smoking, alcohol consumption, breakfast patterns, and three measures of physical activity. Results For boys, but not for girls, 5–6 hours of sleep/night were related to average or better academic performance (OR: 1.094, 95% CI [1.011–1.182], p=0.025) compared to ≤4 hours/night. For both boys and girls, nine or more hours/night were negatively related to academic performance (boys: 0.657, [0.554–0.779; p<0.001]; girls: ≥9 hours/night, 0.664 [0.572–0.771], p<0.001) compared to ≤4 hours/night. Conclusion Whilst it may appear that five to six hours of sleep are necessary to maintain average or better than average school performance for boys, nine or more hours are detrimental to school performance for both groups. This study was limited by the following factors: data were collected only for weekdays and it is. Other sleep variables, such as quality of sleep and sleepiness, should be investigated for a further understanding of these results.


title: Series Compensator Based on Cascaded Transformers Coupled With Three-Phase Bridge Converters

abstract: This paper proposes a multilevel series compensator (MSC) to deal with voltage sags/swells, harmonic compensation, or reactive power compensation. Such a device can be considered as a dynamic voltage restorer or a series active power filter (Series-APF). The MSC can improve the power quality of loads located in stiff systems. The configuration is based on three-phase bridge (TPB) converters connected by means of cascaded single-phase transformers. This arrangement permits the use of a single dc-link. A generalization for $K$ -stages in which $K$ -transformers are coupled with $K$ -TPB converters is presented. The topology permits generating a high number of levels in the voltage waveforms with a low number of power switches in comparison with a classic topology. The multilevel waveforms are generated by the converters through a suitable pulsewidth modulation (PWM) strategy that takes into consideration the transformer turns ratios. Modularity and simple maintenance make the proposed MSC an attractive solution compared with some conventional configurations. Model, PWM strategy, and overall control are discussed in this paper. Simulation and experimental results are presented as well.


title: Mediated disclosure on Twitter: The roles of gender and identity in boundary impermeability, valence, disclosure, and stage

abstract: Social media such as microblogs (Twitter(TM)) allow more people to disclose more personal and private information more frequently to more others than ever before. But what is the nature of, and what factors influence, those disclosures? Applying concepts from research and theory on self-disclosure research and microblogging, this study analyses 3751 tweets, with nearly half including disclosures, over a three-day period. At the user level, user-controlled boundary impermeability varied by user gender, feed identity (parenting, social media professional), and their interaction. At the tweet level, tweet valence, presence of disclosure, and front- or back-stage disclosure were variously influenced by user gender, Twitter feed identity, interactions between them, and boundary impermeability. Social construction of gender roles and social identities, as well as individual tendencies, and possibly communication contexts, are reflected in the valence, presence, and stage of disclosures in microblog content.


title: Prioritized Buffer Control in Two-tier 360 Video Streaming

abstract: 360 degree video compression and streaming is one of the key components of Virtual Reality (VR) applications. In 360 video streaming, a user may freely navigate through the captured 3D environment by changing her desired viewing direction. Only a small portion of the entire 360 degree video is watched at any time. Streaming the entire 360 degree raw video is therefore unnecessary and bandwidth-consuming. One the other hand, only streaming the video in the predicted user's view direction will introduce streaming discontinuity whenever the the prediction is wrong. In this work, a two-tier 360 video streaming framework with prioritized buffer control is proposed to effectively accommodate the dynamics in both network bandwidth and viewing direction. Through simulations driven by real network bandwidth and viewing direction traces, we demonstrate that the proposed framework can significantly outperform the conventional 360 video streaming solutions.


title: WiFi-Opp: ad-hoc-less opportunistic networking

abstract: Opportunistic networking offers many appealing application perspectives from local social-networking applications to supporting communications in remote areas or in disaster and emergency situations. Yet, despite the increasing penetration of smartphones, opportunistic networking is not feasible with most popular mobile devices. There is still no support for WiFi Ad-Hoc and protocols such as Bluetooth have severe limitations (short range, pairing). We believe that WiFi Ad-Hoc communication will not be supported by most popular mobile OSes (i.e., iOS and Android) and that WiFi Direct will not bring the desired features. Instead, we propose WiFi-Opp, a realistic opportunistic setup relying on (i) open stationary APs and (ii) spontaneous mobile APs (i.e., smartphones in AP or tethering mode), a feature used to share Internet access, which we use to enable opportunistic communications. We compare WiFi-Opp to WiFi Ad-Hoc by replaying real-world contact traces and evaluate their performance in terms of capacity for content dissemination as well as energy consumption. While achieving comparable throughput, WiFi-Opp is up to 10 times more energy efficient than its Ad-Hoc counterpart. Eventually, a proof of concept demonstrates the feasibility of WiFi-Opp, which opens new perspectives for opportunistic networking.


title: HoloSketch: a virtual reality sketching/animation tool

abstract: This article describes HoloSketch, a virtual reality-based 3D geometry creation and manipulation tool. HoloSketch is aimed at providing nonprogrammers with an easy-to-use 3D “What-You-See-Is-What-You-Get” environment. Using head-tracked stereo shutter glasses and a desktop CRT display configuration, virtual objects can be created with a 3D wand manipulator directly in front of the user, at very high accuracy and much more rapidly than with traditional 3D drawing systems. HoloSketch also supports simple animation and audio control for virtual objects. This article describes the functions of the HoloSketch system, as well as our experience so far with more-general issues of head-tracked stereo 3D user interface design.


OLAP, relational, and multidimensional database systems


title: A Review and Meta-Analysis of Multimodal Affect Detection Systems

abstract: Affect detection is an important pattern recognition problem that has inspired researchers from several areas. The field is in need of a systematic review due to the recent influx of Multimodal (MM) affect detection systems that differ in several respects and sometimes yield incompatible results. This article provides such a survey via a quantitative review and meta-analysis of 90 peer-reviewed MM systems. The review indicated that the state of the art mainly consists of person-dependent models (62.2p of systems) that fuse audio and visual (55.6p) information to detect acted (52.2p) expressions of basic emotions and simple dimensions of arousal and valence (64.5p) with feature- (38.9p) and decision-level (35.6p) fusion techniques. However, there were also person-independent systems that considered additional modalities to detect nonbasic emotions and complex dimensions using model-level fusion techniques. The meta-analysis revealed that MM systems were consistently (85p of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83p (median of 6.60p). However, improvements were three times lower when systems were trained on natural (4.59p) versus acted data (12.7p). Importantly, MM accuracy could be accurately predicted (cross-validated R2 of 0.803) from unimodal accuracies and two system-level factors. Theoretical and applied implications and recommendations are discussed.


title: WWHow! Freeing Data Storage from Cages

abstract: E cient data storage is a key component of data manag- ing systems to achieve good performance. However, currently data storage is either heavily constrained by static decisions (e.g. fixed data stores in DBMSs) or left to be tuned and configured by users (e.g. manual data backup in File Systems). In this paper, we take a holistic view of data storage and envision a virtual storage layer. Our virtual storage layer provides a unified storage framework for several use-cases including personal, enterprise, and cloud storage.


title: Botulinum neurotoxin alters the discharge characteristics of abducens motoneurons in the alert cat.

abstract: 1. The effects of botulinum neurotoxin (BoTx) injected into the lateral rectus muscle were examined in alert cats by recording the extracellular activity of abducens motoneurons during spontaneous ...


title: Automatically generating user interfaces adapted to users' motor and vision capabilities

abstract: Most of today's GUIs are designed for the typical, able-bodied user; atypical users are, for the most part, left to adapt as best they can, perhaps using specialized assistive technologies as an aid. In this paper, we present an alternative approach: SUPPLE++ automatically generates interfaces which are tailored to an individual's motor capabilities and can be easily adjusted to accommodate varying vision capabilities. SUPPLE++ models users. motor capabilities based on a onetime motor performance test and uses this model in an optimization process, generating a personalized interface. A preliminary study indicates that while there is still room for improvement, SUPPLE++ allowed one user to complete tasks that she could not perform using a standard interface, while for the remaining users it resulted in an average time savings of 20%, ranging from an slowdown of 3% to a speedup of 43%.


title: Capturing, indexing, clustering, and retrieving system history

abstract: We present a method for automatically extracting from a running system an indexable signature that distills the essential characteristic from a system state and that can be subjected to automated clustering and similarity-based retrieval to identify when an observed system state is similar to a previously-observed state. This allows operators to identify and quantify the frequency of recurrent problems, to leverage previous diagnostic efforts, and to establish whether problems seen at different installations of the same site are similar or distinct. We show that the naive approach to constructing these signatures based on simply recording the actual ``raw'' values of collected measurements is ineffective, leading us to a more sophisticated approach based on statistical modeling and inference. Our method requires only that the system's metric of merit (such as average transaction response time) as well as a collection of lower-level operational metrics be collected, as is done by existing commercial monitoring tools. Even if the traces have no annotations of prior diagnoses of observed incidents (as is typical), our technique successfully clusters system states corresponding to similar problems, allowing diagnosticians to identify recurring problems and to characterize the ``syndrome'' of a group of problems. We validate our approach on both synthetic traces and several weeks of production traces from a customer-facing geoplexed 24 x 7 system; in the latter case, our approach identified a recurring problem that had required extensive manual diagnosis, and also aided the operators in correcting a previous misdiagnosis of a different problem.


title: Shadow volume reconstruction from depth maps

abstract: Current graphics hardware can be used to generate shadows using either the shadow volume or shadow map techniques. However, the shadow volume technique requires access to a representation of the scence as a polygonal model, and handling the near plane clip correctly and efficiently is difficult; conversely, accurate shadow maps require high-precision texture map data representations, but these are not widely supported. We present a hybird of the shadow map and shadow volume approaches which does not have these difficulties and leverages high-performance polygon rendering. The scene is rendered from the point of view of the light source and a sampled depth map is recovered. Edge detection and a template-based reconstruction technique are used to generate a global shadow volume boundary surface, after which the pixels in shadow can be marked using only a one-bit stencil buffer and a single-pass rendering of the shadow volume boundary polygons. The simple form of our template-based reconstruction scheme simplifies capping the shadow volume after the near plane clip.


title: The impact of YouTube recommendation system on video views

abstract: Hosting a collection of millions of videos, YouTube offers several features to help users discover the videos of their interest. For example, YouTube provides video search, related video recommendation and front page highlight. The understanding of how these features drive video views is useful for creating a strategy to drive video popularity. In this paper, we perform a measurement study on data sets crawled from YouTube and find that the related video recommendation, which recommends the videos that are related to the video a user is watching, is one of the most important view sources of videos. Despite the fact that the YouTube video search is the number one source of views in aggregation, the related video recommendation is the main source of views for the majority of the videos on YouTube. Furthermore, our results reveal that there is a strong correlation between the view count of a video and the average view count of its top referrer videos. This implies that a video has a higher chance to become popular when it is placed on the related video recommendation lists of popular videos. We also find that the click through rate from a video to its related videos is high and the position of a video in a related video list plays a critical role in the click through rate. Finally, our evaluation of the impact of the related video recommendation system on the diversity of video views indicates that the current recommendation system helps to increase the diversity of video views in aggregation.


title: Output Range Analysis for Deep Feedforward Neural Networks

abstract: Given a neural network (NN) and a set of possible inputs to the network described by polyhedral constraints, we aim to compute a safe over-approximation of the set of possible output values. This operation is a fundamental primitive enabling the formal analysis of neural networks that are extensively used in a variety of machine learning tasks such as perception and control of autonomous systems. Increasingly, they are deployed in high-assurance applications, leading to a compelling use case for formal verification approaches. In this paper, we present an efficient range estimation algorithm that iterates between an expensive global combinatorial search using mixed-integer linear programming problems, and a relatively inexpensive local optimization that repeatedly seeks a local optimum of the function represented by the NN. We implement our approach and compare it with Reluplex, a recently proposed solver for deep neural networks. We demonstrate applications of our approach to computing flowpipes for neural network-based feedback controllers. We show that the use of local search in conjunction with mixed-integer linear programming solvers effectively reduces the combinatorial search over possible combinations of active neurons in the network by pruning away suboptimal nodes.


title: Stuck and frustrated or in flow and happy: sensing developers' emotions and progress

abstract: Software developers working on change tasks commonly experience a broad range of emotions, ranging from happiness all the way to frustration and anger. Research, primarily in psychology, has shown that for certain kinds of tasks, emotions correlate with progress and that biometric measures, such as electro-dermal activity and electroencephalography data, might be used to distinguish between emotions. In our research, we are building on this work and investigate developers' emotions, progress and the use of biometric measures to classify them in the context of software change tasks. We conducted a lab study with 17 participants working on two change tasks each. Participants were wearing three biometric sensors and had to periodically assess their emotions and progress. The results show that the wide range of emotions experienced by developers is correlated with their perceived progress on the change tasks. Our analysis also shows that we can build a classifier to distinguish between positive and negative emotions in 71.36% and between low and high progress in 67.70% of all cases. These results open up opportunities for improving a developer's productivity. For instance, one could use such a classifier for providing recommendations at opportune moments when a developer is stuck and making no progress.


title: Automatic free parking space detection by using motion stereo-based 3D reconstruction

abstract: This paper proposes a free parking space detection system by using motion stereo-based 3D reconstruction. An image sequence is acquired with a single rearview fisheye camera and the view behind the automobile is three-dimensionally reconstructed by using point correspondences. Metric information is recovered from the camera height ratio and free parking spaces are detected by estimating the positions of adjacent vehicles. Since adjacent vehicles are usually located near the epipole, their structures are seriously degraded. To solve this problem, we select point correspondences by using a de-rotation-based method and mosaic 3D structures by estimating a similarity transformation. Unlike in previous work, our system proposes an efficient way of locating free parking spaces in 3D point clouds. Odometry is not used because its accuracy depends largely on road conditions. In the experiments, the system was tested in 154 different parking situations and its success rate was 90% (139 successes in 154 cases). The detection accuracy was evaluated by using ground truth data that was acquired with a laser scanner.


title: X-ray Image Body Part Clustering using Deep Convolutional Neural Network: SNUMedinfo at ImageCLEF 2015 Medical Clustering Task

abstract: This paper describes our participation at the ImageCLEF 2015 Medical clustering task. The task is about clustering digital x-ray images into four groups with regard to the body parts. We experimented with deep convolutional neural network (GoogLeNet), finetuning pretrained models for ImageNet dataset. Ex- perimental results showed competitive performance with other top-performing runs.


WHO response to WTO member state challenges on tobacco, food and beverage policies


title: Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research

abstract: BACKGROUND ::: Internet sources are becoming increasingly important in seeking health information, such that they may have a significant effect on health care decisions and outcomes. Hence, given the wide range of different sources of Web-based health information (WHI) from different organizations and individuals, it is important to understand how information seekers evaluate and select the sources that they use, and more specifically, how they assess their credibility and trustworthiness. ::: ::: ::: OBJECTIVE ::: The aim of this study was to review empirical studies on trust and credibility in the use of WHI. The article seeks to present a profile of the research conducted on trust and credibility in WHI seeking, to identify the factors that impact judgments of trustworthiness and credibility, and to explore the role of demographic factors affecting trust formation. On this basis, it aimed to identify the gaps in current knowledge and to propose an agenda for future research. ::: ::: ::: METHODS ::: A systematic literature review was conducted. Searches were conducted using a variety of combinations of the terms WHI, trust, credibility, and their variants in four multi-disciplinary and four health-oriented databases. Articles selected were published in English from 2000 onwards; this process generated 3827 unique records. After the application of the exclusion criteria, 73 were analyzed fully. ::: ::: ::: RESULTS ::: Interest in this topic has persisted over the last 15 years, with articles being published in medicine, social science, and computer science and originating mostly from the United States and the United Kingdom. Documents in the final dataset fell into 3 categories: (1) those using trust or credibility as a dependent variable, (2) those using trust or credibility as an independent variable, and (3) studies of the demographic factors that influence the role of trust or credibility in WHI seeking. There is a consensus that website design, clear layout, interactive features, and the authority of the owner have a positive effect on trust or credibility, whereas advertising has a negative effect. With regard to content features, authority of the author, ease of use, and content have a positive effect on trust or credibility formation. Demographic factors influencing trust formation are age, gender, and perceived health status. ::: ::: ::: CONCLUSIONS ::: There is considerable scope for further research. This includes increased clarity of the interaction between the variables associated with health information seeking, increased consistency on the measurement of trust and credibility, a greater focus on specific WHI sources, and enhanced understanding of the impact of demographic variables on trust and credibility judgments.


title: Evaluation and Measurement of Software Process Improvement—A Systematic Literature Review

abstract: BACKGROUND-Software Process Improvement (SPI) is a systematic approach to increase the efficiency and effectiveness of a software development organization and to enhance software products. OBJECTIVE-This paper aims to identify and characterize evaluation strategies and measurements used to assess the impact of different SPI initiatives. METHOD-The systematic literature review includes 148 papers published between 1991 and 2008. The selected papers were classified according to SPI initiative, applied evaluation strategies, and measurement perspectives. Potential confounding factors interfering with the evaluation of the improvement effort were assessed. RESULTS-Seven distinct evaluation strategies were identified, wherein the most common one, “Pre-Post Comparison,” was applied in 49 percent of the inspected papers. Quality was the most measured attribute (62 percent), followed by Cost (41 percent), and Schedule (18 percent). Looking at measurement perspectives, “Project” represents the majority with 66 percent. CONCLUSION-The evaluation validity of SPI initiatives is challenged by the scarce consideration of potential confounding factors, particularly given that “Pre-Post Comparison” was identified as the most common evaluation strategy, and the inaccurate descriptions of the evaluation context. Measurements to assess the short and mid-term impact of SPI initiatives prevail, whereas long-term measurements in terms of customer satisfaction and return on investment tend to be less used.


title: State of the art of the maturity models to an evaluation of the enterprise architecture

abstract: The evaluation of enterprise Architecture EA allows companies to evaluate their current situation. More specifically, it optimizes the alignment, the business strategy, the organization, the resources, the processes and the technology. This optimized architecture provides a cost-efficient solution and also contributes to ensure the successful implementation of the purposes of the organization. However it is very difficult to find a complete method of evaluation in term of indicators of evaluation and which ensures the follow-up of all the stages of development of the architecture of company. This article presents the possibility of evaluation starting from the methods of maturity of existing enterprise architecture.


title: Overview of Power Management Strategies of Hybrid AC/DC Microgrid

abstract: Today, conventional power systems are evolving to modern smart grids, where interconnected microgrids may dominate the distribution system with high penetration of renewable energy and energy storage systems. The hybrid ac/dc systems with dc and ac sources/loads are considered to be the most possible future distribution or even transmission structures. For such hybrid ac/dc microgrids, power management strategies are one of the most critical operation aspects. This paper presents an overview of power management strategies for a hybrid ac/dc microgrid system, which includes different system structures (ac-coupled, dc-coupled, and ac–dc-coupled hybrid microgrids), different operation modes, a thorough study of various power management and control schemes in both steady state and transient conditions, and examples of power management and control strategies. Finally, discussion and recommendations of power management strategies for the further research are presented.


title: Computerized classification of intraductal breast lesions using histopathological images

abstract: In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250 000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus, improvement in the diagnostic reproducibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% is obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in improving diagnostic accuracy and reproducibility when used as a “second reader” in conjunction with the pathologists.


title: Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks

abstract: The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing, enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics, and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called online peer offloading (OPEN), by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared with the oracle solution that has the complete future information. In addition, this paper formulates a peer offloading game among SBSs and analyzes its equilibrium and efficiency loss in terms of the price of anarchy to thoroughly understand SBSs’ strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance.


title: Usage-based approaches to aphasia

abstract: ABSTRACTBackground: Effects of word frequency on language comprehension and production are pervasive in speakers with and without aphasia. Frequency effects at the sentence level are likewise pervasive in neurotypical speakers, but have received relatively little attention in the aphasia literature. When discussed, frequency-based explanations have typically been dismissed as explanations of sentence-level deficits. Usage-based approaches to understanding the accessibility of clause and phrase structures are not widely used in aphasiology, in spite of their psycholinguistic plausibility.Aims: One aim is to show that evidence in support of probabilistic models of aphasic sentence comprehension and production has been available for a long time, but has lacked visibility. Our second aim is to respond to one of the few papers [Bastiaanse, R. Bouma, G., & Post, W. (2009). Linguistic complexity and frequency in agrammatic speech production. Brain and Language, 109, 18–28.] that directly challenge frequency-base...


title: High speed gripping with a soft touch

abstract: This paper covers the development of a threefinger robot hand for fast and soft gripping of delicate and different shaped objects. The fingers are positioned with submillimetre accuracy at the tip within 0.3 seconds. The hand is also equipped with flexible tactile sensors featuring pressureand slip-measurement. Control algorithms for both position and force control are designed and implemented on a real-time target machine using MATLAB/Simulink. The robotic hand system is currently in its realization phase. This project is a joint project of the Mechatronics & Robotics lab (bachelor thesis student) and a Mechatronic Product Design specialization group of students working in their sixth semester of the Mechatronics Education program of Avans University of Applied Sciences.


title: A latent factor model for highly multi-relational data

abstract: Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In this paper, we propose a method for modeling large multi-relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.


Feature Attribution As Feature Selection


title: Research in music and artificial intelligence

abstract: Although the boundaries of artificial intelligence (AI) remain elusive, computers can now perform musical tasks that were formerly associated exclusively with naturally intelligent musicians. After a historical note, this paper sermonizes on the need for AI techniques in four areas of musical research: composition, performance, music theory, and digital sound processing. The next part surveys recent work involving AI and music. The discussion concentrates on applications in the four areas of research just mentioned. The final part examines how AI techniques of planning and learning could be used to expand the knowledge base and enrich the behavior of musically intelligent systems.


title: Multiple paired forward and inverse models for motor control

abstract: Abstract Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models. Within each pair, the inverse and forward models are tightly coupled both during their acquisition, through motor learning, and use, during which the forward models determine the contribution of each inverse model's output to the final motor command. This architecture can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment. Finally, we describe specific predictions of the model, which can be tested experimentally.


title: Intermodulation: Improvisation and Collaborative Art Practice for HCI

abstract: This paper integrates theory, ethnography, and collaborative artwork to explore improvisational activity as both topic and tool of multidisciplinary HCI inquiry. Building on theories of improvisation drawn from art, music, HCI and social science, and two ethnographic studies based on interviews, participant observation and collaborative art practice, we seek to elucidate the improvisational nature of practice in both art and ordinary action, including human-computer interaction. We identify five key features of improvisational action -- reflexivity, transgression, tension, listening, and interdependence -- and show how these can deepen and extend both linear and open-ended methodologies in HCI and design. We conclude by highlighting collaborative engagement based on 'intermodulation' as a tool of multidisciplinary inquiry for HCI research and design.


title: Review of Intrinsic Motivation in Simulation-based Game Testing

abstract: This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.


title: An online algorithm for large scale image similarity learning

abstract: Learning a measure of similarity between pairs of objects is a fundamental problem in machine learning. It stands in the core of classification methods like kernel machines, and is particularly useful for applications like searching for images that are similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, current approaches for learning similarity do not scale to large datasets, especially when imposing metric constraints on the learned similarity. We describe OASIS, a method for learning pairwise similarity that is fast and scales linearly with the number of objects and the number of non-zero features. Scalability is achieved through online learning of a bilinear model over sparse representations using a large margin criterion and an efficient hinge loss cost. OASIS is accurate at a wide range of scales: on a standard benchmark with thousands of images, it is more precise than state-of-the-art methods, and faster by orders of magnitude. On 2.7 million images collected from the web, OASIS can be trained within 3 days on a single CPU. The non-metric similarities learned by OASIS can be transformed into metric similarities, achieving higher precisions than similarities that are learned as metrics in the first place. This suggests an approach for learning a metric from data that is larger by orders of magnitude than was handled before.


title: Digging deeper into cluster system logs for failure prediction and root cause diagnosis

abstract: As the sizes of supercomputers and data centers grow towards exascale, failures become normal. System logs play a critical role in the increasingly complex tasks of automatic failure prediction and diagnosis. Many methods for failure prediction are based on analyzing event logs for large scale systems, but there is still neither a widely used one to predict failures based on both non-fatal and fatal events, nor a precise one that uses fine-grained information (such as failure type, node location, related application, and time of occurrence). A deeper and more precise log analysis technique is needed. We propose a three-step approach to draw out event dependencies and to identify failure-event generating processes. First, we cluster frequent event sequences into event groups based on common events. Then we infer causal dependencies between events in each event group. Finally, we extract failure rules based on the observation that events of the same event types, on the same nodes or from the same applications have similar operational behaviors. We use this rich information to improve failure prediction. Our approach semi-automates diagnosing the root causes of failure events, making it a valuable tool for system administrators.


title: Understanding Traffic Density from Large-Scale Web Camera Data

abstract: Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective. To deeply understand traffic density, we explore both optimization based and deep learning based methods. To avoid individual vehicle detection or tracking, both methods map the dense image feature into vehicle density, one based on rank constrained regression and the other based on fully convolutional networks (FCN). The regression based method learns different weights for different blocks of the image to embed road geometry and significantly reduce the error induced by camera perspective. The FCN based method jointly estimates vehicle density and vehicle count with a residual learning framework to perform end-to-end dense prediction, allowing arbitrary image resolution, and adapting to different vehicle scales and perspectives. We analyze and compare both methods, and get insights from optimization based method to improve deep model. Since existing datasets do not cover all the challenges in our work, we collected and labelled a large-scale traffic video dataset, containing 60 million frames from 212 webcams. Both methods are extensively evaluated and compared on different counting tasks and datasets. FCN based method significantly reduces the mean absolute error (MAE) from 10.99 to 5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.


title: A Time-Aware Exploration of RecSys15 Challenge Dataset

abstract: E-commerce is currently one of the main applications of recommender systems, since it generates vast amounts of data that can be used to make predictions and analyse users's behaviour. In this paper we present an overview of the public dataset used for the RecSys Challenge 2015. We describe the basic statistical properties of this dataset and how events (clicks and purchases) are distributed over products (items) and users (sessions). We also present a time-aware analysis of the dataset, with the aim to better understand the change of user behaviour within time cycles, and how it affects the activity in user purchases. We further study the relation between categories, the solely type of metadata present in this completely anonymised dataset. We are interested both in how these categories are distributed and how users and items interact with them. Finally, along the paper we explain the implications that the results obtained from our analysis may have when building models for the challenge.


title: Improved Training of Generative Adversarial Networks Using Representative Features

abstract: Despite of the success of Generative Adversarial Networks (GANs) for image generation tasks, the trade-off between image diversity and visual quality are an well-known issue. Conventional techniques achieve either visual quality or image diversity; the improvement in one side is often the result of sacrificing the degradation in the other side. In this paper, we aim to achieve both simultaneously by improving the stability of training GANs. A key idea of the proposed approach is to implicitly regularizing the discriminator using a representative feature. For that, this representative feature is extracted from the data distribution, and then transferred to the discriminator for enforcing slow updates of the gradient. Consequently, the entire training process is stabilized because the learning curve of discriminator varies slowly. Based on extensive evaluation, we demonstrate that our approach improves the visual quality and diversity of state-of-the art GANs.


title: Single image dehazing motivated by Retinex theory

abstract: Haze removal from a degraded image which is attenuated by the turbid media in the atmosphere is an important task in image and video processing. From a computer vision perspective, this task is extremely challenging because the haze is dependent on the unknown depth information, and will be under-constrained if only one single haze image is input. If the depth distribution is known, the problem can be well solved. In this paper, we propose a new method to estimate the transmission from which depth distribution can be obtained. It is based on the assumption that the airlight which depends on the distance of objects to the observers, tends to be smooth, and is similar to the Retinex theory. Motivated by this, we use a simple Gaussian filter to convolve the image to obtain the airlight just like we did in Retinex. Experimental results verify the effectiveness of this method.


title: Non-Fragile Exponential $H_\infty $ Control for a Class of Nonlinear Networked Control Systems With Short Time-Varying Delay via Output Feedback Controller

abstract: This paper investigates non-fragile exponential $ {H_\infty }$ control problems for a class of uncertain nonlinear networked control systems (NCSs) with randomly occurring information, such as the controller gain fluctuation and the uncertain nonlinearity, and short time-varying delay via output feedback controller. Using the nominal point technique, the NCS is converted into a novel time-varying discrete time model with norm-bounded uncertain parameters for reducing the conservativeness. Based on linear matrix inequality framework and output feedback control strategy, design methods for general and optimal non-fragile exponential $ {H_\infty }$ controllers are presented. Meanwhile, these control laws can still be applied to linear NCSs and general fragile control NCSs while introducing random variables. Finally, three examples verify the correctness of the presented scheme.


title: Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

abstract: This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVD-based point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.


title: I-media-cities, a searchable platform on moving images with automatic and manual annotations

abstract: 9 European audiovisual archives provide access to their AV works, describing cities in all aspects from the end of the 19th century onwards, through the I-Media-Cities platform. The platform, with its dynamic processing pipeline and automatic video analysis tools, enables the enrichment of the moving images metadata in an innovative way, improving the “search and discovery” capabilities made available to researchers and citizens.


title: The role of core stability in athletic function.

abstract: The importance of function of the central core of the body for stabilisation and force generation in all sports activities is being increasingly recognised. 'Core stability' is seen as being pivotal for efficient biomechanical function to maximise force generation and minimise joint loads in all types of activities ranging from running to throwing. However, there is less clarity about what exactly constitutes 'the core', either anatomically or physiologically, and physical evaluation of core function is also variable. 'Core stability' is defined as the ability to control the position and motion of the trunk over the pelvis to allow optimum production, transfer and control of force and motion to the terminal segment in integrated athletic activities. Core muscle activity is best understood as the pre-programmed integration of local, single-joint muscles and multi-joint muscles to provide stability and produce motion. This results in proximal stability for distal mobility, a proximal to distal patterning of generation of force, and the creation of interactive moments that move and protect distal joints. Evaluation of the core should be dynamic, and include evaluation of the specific functions (trunk control over the planted leg) and directions of motions (three-planar activity). Rehabilitation should include the restoring of the core itself, but also include the core as the base for extremity function.


title: Land-Use Classification Using Taxi GPS Traces

abstract: Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.


title: ETSI reconfigurable radio system — Standard architecture and radio application

abstract: This paper introduces the ETSI-standard architecture for a reconfigurable Mobile Device (MD) of which the configuration is determined by the downloaded radio application. First, this paper introduces key components of the standard architecture. Then, this paper shows how the radio application codes can be distributed from a public domain to target MDs. In the distribution of radio application codes, this paper considered 3 different cases, i.e., when the radio application codes are given in a form of executable code, source code, or intermediate representation, while both static and dynamic linking of functional blocks are also taken into considerations.


title: DeepConfig: Automating Data Center Network Topologies Management with Machine Learning

abstract: In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often designed using heuristics that leverage domain-specific properties of the workload or hardware. ::: In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals actually share design and architectural similarity. We present a design for developing general intermediate representations of network topologies using deep learning that is amenable to solving classes of data center problems. We develop a framework, DeepConfig, that simplifies the processing of configuring and training deep learning agents that use the intermediate representation to learns different tasks. To illustrate the strength of our approach, we configured, implemented, and evaluated a DeepConfig-Agent that tackles the data center topology augmentation problem. Our initial results are promising --- DeepConfig performs comparably to the optimal.


The lure of food: food as an attraction in destination marketing in Manitoba, Canada


title: Emergency situation awareness from twitter for crisis management

abstract: This paper describes ongoing work with the Australian Government to detect, assess, summarise, and report messages of interest for crisis coordination published by Twitter. The developed platform and client tools, collectively termed the Emergency Situation Awareness - Automated Web Text Mining (ESA-AWTM) system, demonstrate how relevant Twitter messages can be identified and utilised to inform the situation awareness of an emergency incident as it unfolds. A description of the ESA-AWTM platform is presented detailing how it may be used for real life emergency management scenarios. These scenarios are focused on general use cases to provide: evidence of pre-incident activity; near-real-time notification of an incident occurring; first-hand reports of incident impacts; and gauging the community response to an emergency warning. Our tools have recently been deployed in a trial for use by crisis coordinators.


title: Battery Pack Modeling, Simulation, and Deployment on a Multicore Real Time Target

abstract: Battery Management System (BMS) design is a complex task requiring sophisticated models that mimic the electrochemical behavior of the battery cell under a variety of operating conditions. Equivalent circuits are well-suited for this task because they offer a balance between fidelity and simulation speed, their parameters reflect direct experimental observations, and they are scalable. Scalability is particularly important at the real time simulation stage, where a model of the battery pack runs on a real-time simulator that is physically connected to the peripheral hardware in charge of monitoring and control. With modern battery systems comprising hundreds of cells, it is important to employ a modeling and simulation approach that is capable of handling numerous simultaneous instances of the basic unit cell while maintaining real time performance. In previous publications we presented a technique for the creation of a battery cell model that contains the electrochemical fingerprints of a battery cell based on equivalent circuit model fitting to experimental data. In this work we extend our previous model to represent a battery pack, featuring cell creation, placement, and connection using automation scripts, thus facilitating the design of packs of arbitrary size and electrical topology. In addition, we present an assessment of model partitioning schemes for real time execution on multicore targets to ensure efficient use of hardware resources, a balanced computational load, and a study of the potential impact of the calculation latencies inherent to distributed systems on solver accuracy. Prior to C code generation for real time execution, a model profiler assesses the model partitioning and helps determine the multicore configuration that results in the lowest average turnaround time, the time elapsed between task start and finish.


title: An Algorithm for Training Polynomial Networks

abstract: We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of an efficient layer-by-layer algorithm for training such networks, which we denote as the \emph{Basis Learner}. The algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.


title: Sexbots: the ethical ramifications of social robotics' dark side

abstract: Recently I sat down with an older friend. During our conversation, it came up that I was researching the sex robots ("sexbots") industry. His initial reaction was to laugh in disbelief. "Are you kidding me? You mean like the robots in sci-fi?" My friend's disbelief makes sense; the idea of an actual industry for sex robots seems like a Hollywood movie in the tone of Ex Machina. However, the "sexbot industry" is real, and it's growing in popularity. According to a recent study from Tufts University, people find it "relatively permissible" to have sex with a robot. In fact, over 50 percent of people surveyed said they were open to using one (Scheutz, 2016).


title: FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

abstract: A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale Almost Matching Exactly), learns a distance metric for matching using a hold-out training data set. In order to perform matching efficiently for large datasets, FLAME leverages techniques that are natural for query processing in the area of database management, and two implementations of FLAME are provided: the first uses SQL queries and the second uses bit-vector techniques. The algorithm starts by constructing matches of the highest quality (exact matches on all covariates), and successively eliminates variables in order to match exactly on as many variables as possible, while still maintaining interpretable high-quality matches and balance between treatment and control groups. We leverage these high quality matches to estimate conditional average treatment effects (CATEs). Our experiments show that FLAME scales to huge datasets with millions of observations where existing state-of-the-art methods fail, and that it achieves significantly better performance than other matching methods.


title: Biofuels from microalgae--A review of technologies for production, processing, and extractions of biofuels and co-products

abstract: Sustainability is a key principle in natural resource management, and it involves operational efficiency, minimisation of environmental impact and socio-economic considerations; all of which are interdependent. It has become increasingly obvious that continued reliance on fossil fuel energy resources is unsustainable, owing to both depleting world reserves and the green house gas emissions associated with their use. Therefore, there are vigorous research initiatives aimed at developing alternative renewable and potentially carbon neutral solid, liquid and gaseous biofuels as alternative energy resources. However, alternate energy resources akin to first generation biofuels derived from terrestrial crops such as sugarcane, sugar beet, maize and rapeseed place an enormous strain on world food markets, contribute to water shortages and precipitate the destruction of the world's forests. Second generation biofuels derived from lignocellulosic agriculture and forest residues and from non-food crop feedstocks address some of the above problems; however there is concern over competing land use or required land use changes. Therefore, based on current knowledge and technology projections, third generation biofuels specifically derived from microalgae are considered to be a technically viable alternative energy resource that is devoid of the major drawbacks associated with first and second generation biofuels. Microalgae are photosynthetic microorganisms with simple growing requirements (light, sugars, CO2, N, P, and K) that can produce lipids, proteins and carbohydrates in large amounts over short periods of time. These products can be processed into both biofuels and valuable co-products. This study reviewed the technologies underpinning microalgae-to-biofuels systems, focusing on the biomass production, harvesting, conversion technologies, and the extraction of useful co-products. It also reviewed the synergistic coupling of microalgae propagation with carbon sequestration and wastewater treatment potential for mitigation of environmental impacts associated with energy conversion and utilisation. It was found that, whereas there are outstanding issues related to photosynthetic efficiencies and biomass output, microalgae-derived biofuels could progressively substitute a significant proportion of the fossil fuels required to meet the growing energy demand.


title: Efficacy of musical interventions in dementia: evidence from a randomized controlled trial.

abstract: Although musical interventions have recently gained popularity as a non-pharmacological treatment in dementia, there is still insufficient evidence of their effectiveness. To investigate this issue, a single-center randomized controlled trial was conducted with forty-eight patients with Alzheimer's disease or mixed dementia to compare the effects of music versus cooking interventions in the emotional, cognitive, and behavioral domain, as well as on professional caregiver distress. Each intervention lasted four weeks (two one-hour sessions a week). Multi-component evaluations (with blind assessors) were conducted before, during, and after the interventions to assess their short and long-term effects (up to four weeks post interventions). Analyses revealed that both music and cooking interventions led to positive changes in the patients' emotional state and decreased the severity of their behavioral disorders, as well as reduced caregiver distress. However, no benefit on the cognitive status of the patients was seen. While results did not demonstrate a specific benefit of music on any of the considered measures, the present study suggests the efficacy of two pleasant non-pharmacological treatments in patients with moderate to severe dementia. Our findings highlight the potential of such interventions in improving the well-being of patients living in residential care, as well as reducing caregiver distress.


title: Demonstration of vulnerabilities in GSM security with USRP B200 and open-source penetration tools

abstract: This paper showcases the vulnerabilities in the GSM security architecture through implementation of an active attack at the Um interface. The attack was carried out by taking advantage of lack of two-way authentication. A rogue GSM base transceiver system was established using Universal Software Radio Peripheral (USRP) B200 board and OpenBTS. B200 allows relaxation of an external 10 MHz reference signal as opposed to widely used USRP1 and N-series. After establishing rogue BTS, IMSI catch-attack and impersonation of a mobile subscriber to send malicious SMS are executed. Along with OpenBTS, standalone standard applications — Asterisk and smqueue are used for correct routing of messages. The attacks are observed on the TEST network and not the spoofed network so that no infringement is established on security and privacy of the existing GSM subscribers.


title: Sentiment analysis using Telugu SentiWordNet

abstract: In recent times, sentiment analysis in low resourced languages and regional languages has become emerging areas in natural language processing. Researchers have shown greater interest towards analyzing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, etc. In best of our knowledge, microscopic work has been reported till date towards Indian languages due to lack of annotated data set. In this paper, we proposed a two-phase sentiment analysis for Telugu news sentences using Telugu SentiWordNet. Initially, it identifies subjectivity classification where sentences are classified as subjective or objective. Objective sentences are treated as neutral sentiment as they don't carry any sentiment value. Next, Sentiment Classification has been done where the subjective sentences are further classified into positive and negative sentences. With the existing Telugu SentiWordNet, our proposed system attains an accuracy of 74% and 81% for subjectivity and sentiment classification respectively.


title: Effect of Preoperative Medical Status on Microsurgical Free Flap Reconstructions: A Matched Cohort Analysis of 969 Cases

abstract: Background Free tissue transfers have become standard for a wide variety of reconstructive purposes. In an era of increasing economic pressure, it is important to precisely define appropriate candidates and indications for a complex surgery to optimize efficiency and patient outcomes. This study evaluates the feasibility to perform microsurgical procedures in a medically compromised patient cohort at a major academic microsurgical center. Methods Within 7 years, 897 patients underwent 969 microvascular free flap reconstructions. The data were retrospectively screened for patients' demographics, perioperative details, flap survival, surgical complications, and outcomes. The cases were divided into two groups in “low-risk” (American Society of Anesthesiologists [ASA] I and II) or “high-risk” (ASA III and IV) as per the preoperative medical status classified according to the ASA score of physical status. Results Despite the significantly high prevalence of hypertension, peripheral artery disease, diabetes, obesity, and smoking status in the “high-risk” group ( p p > 0.05). Conclusion In this study, free flap reconstruction in medically compromised patients was not associated with higher rates of revision surgeries, overall complication rates, or surgery-related costs. Complex microsurgical free flap reconstruction can therefore also be advocated in patients with high preoperative risk constellation.


Constructing Social Media Knowledge Graphs with Social Scientists.


title: Robust Elevator Button Recognition in the Presence of Partial Occlusion and Clutter by Specular Reflections

abstract: This paper deals with vision-based elevator button recognition for a robot arm manipulating elevator buttons. The major difficulties in elevator button recognition are the presence of partial occlusion of the target objects and image clutter caused by specular reflection from mirrorlike walls inside the elevator. As a remedy for the elevator button recognition problem in highly complicated settings, we propose a robust button recognition algorithm, which is modularized into feature extraction, initial recognition, and postrefinement modules. In consideration of the diverse button shapes in the form of convex quadrilaterals with rounded button corners, a set of features is specially designed to describe each button contour subject to perspective distortion. A homography-based image transform is also employed for the template matching to achieve a more reliable matching performance. Like a grammar in linguistics, geometric button alignment, plays a key role in the elimination of false positives and the estimation of the missing buttons. The proposed algorithm is extensively tested in a robotic platform to verify effectiveness, robustness, and real-time performance.


title: A preference based smart parking system: KFUPM case study

abstract: This paper describes a proof of concept implementation of a smart parking system. At KFUPM there are several parking buildings available for student dorms and some of which can be completely full. A lot of time is wasted by students looking for the most suitable parking space. The developed parking system informs each user the closest vacant parking space to his preference through a display board and an SMS to his mobile phone. Every student will have his preference stored in the campus smartcard database that includes his mobile phone number. If the preferred parking space is occupied, the system finds the closest vacant parking space.


title: Multi-sensor Self-Quantification of Presentations

abstract: Presentations have been an effective means of delivering information to groups for ages. Over the past few decades, technological advancements have revolutionized the way humans deliver presentations. Despite that, the quality of presentations can be varied and affected by a variety of reasons. Conventional presentation evaluation usually requires painstaking manual analysis by experts. Although the expert feedback can definitely assist users in improving their presentation skills, manual evaluation suffers from high cost and is often not accessible to most people. In this work, we propose a novel multi-sensor self-quantification framework for presentations. Utilizing conventional ambient sensors (i.e., static cameras, Kinect sensor) and the emerging wearable egocentric sensors (i.e., Google Glass), we first analyze the efficacy of each type of sensor with various nonverbal assessment rubrics, which is followed by our proposed multi-sensor presentation analytics framework. The proposed framework is evaluated on a new presentation dataset, namely NUS Multi-Sensor Presentation (NUSMSP) dataset, which consists of 51 presentations covering a diverse set of topics. The dataset was recorded with ambient static cameras, Kinect sensor, and Google Glass. In addition to multi-sensor analytics, we have conducted a user study with the speakers to verify the effectiveness of our system generated analytics, which has received positive and promising feedback.