title: Reinforcement Learning in Robotics: Applications and Real-World Challenges †

abstract: In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to learn, improve, adapt and reproduce tasks with dynamically changing constraints based on exploration and autonomous learning. We give a summary of the state-of-the-art of reinforcement learning in the context of robotics, in terms of both algorithms and policy representations. Numerous challenges faced by the policy representation in robotics are identified. Three recent examples for the application of reinforcement learning to real-world robots are described: a pancake flipping task, a bipedal walking energy minimization task and an archery-based aiming task. In all examples, a state-of-the-art expectation-maximization-based reinforcement learning is used, and different policy representations are proposed and evaluated for each task. The proposed policy representations offer viable solutions to six rarely-addressed challenges in policy representations: correlations, adaptability, multi-resolution, globality, multi-dimensionality and convergence. Both the successes and the practical difficulties encountered in these examples are discussed. Based on insights from these particular cases, conclusions are drawn about the state-of-the-art and the future perspective directions for reinforcement learning in robotics.


Internet of Things and Fog Computing


title: Consistent Jumpy Predictions for Videos and Scenes

abstract: Stochastic video prediction models take in a sequence of image frames, and generate a sequence of consecutive future image frames. These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive. We introduce a model that overcomes these drawbacks by generating a latent representation from an arbitrary set of frames that can then be used to simultaneously and efficiently sample temporally consistent frames at arbitrary time-points. For example, our model can"jump"and directly sample frames at the end of the video, without sampling intermediate frames. Synthetic video evaluations confirm substantial gains in speed and functionality without loss in fidelity. We also apply our framework to a 3D scene reconstruction dataset. Here, our model is conditioned on camera location and can sample consistent sets of images for what an occluded region of a 3D scene might look like, even if there are multiple possibilities for what that region might contain. Reconstructions and videos are available at https://bit.ly/2O4Pc4R.


title: SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition

abstract: Deep learning models are widely used for various purposes such as face recognition and speech recognition. However, researchers have shown that these models are vulnerable to adversarial attacks. These attacks compute perturbations to generate images that decrease the performance of deep learning models. In this research, we have developed a toolbox, termed as SmartBox, for benchmarking the performance of adversarial attack detection and mitigation algorithms against face recognition. SmartBox is a python based toolbox which provides an open source implementation of adversarial detection and mitigation algorithms. In this research, Extended Yale Face Database B has been used for generating adversarial examples using various attack algorithms such as DeepFool, Gradient methods, Elastic-Net, and $L_{2}$ attack. SmartBox provides a platform to evaluate newer attacks, detection models, and mitigation approaches on a common face recognition benchmark. To assist the research community, the code of SmartBox is made available11http://iab-rubric.org/resources/SmartBox.html.


title: Study of the intra- and interlaboratory reproducibility of partial single base C-sequencing of the 16S rRNA gene and its applicability for the identification of members of the genus Streptococcus.

abstract: The use of Single Base C-Sequencing of the first 500 bases of the 16S rRNA-gene (SBCS) combined with capillary electrophoresis was evaluated for the identification of reference strains of 30 different species within the genus Streptococcus. For SBCS, only dd-CTP's are used in the sequencing reactions instead of the four dideoxy bases and the primer is fluorescently labeled. The reproducibility, interlaboratory exchangeability and discriminative power of this method were studied by comparing the patterns obtained in three laboratories under highly standardized conditions. The interlaboratory reproducibility proved to be high, enabling the construction of a common database for the identification of strains belonging to the streptococcal species studied. Most of the examined species generated distinguishable profiles. SBCS did not differentiate between the closely related species S. constellatus and S. intermedius. Also S. thermophilus and S. vestibularis as well as S. mitis and S. pneumoniae showed highly resembling profiles. The previously reported heterogeneity within the species S. equinus was reflected by SBCS. For all other species, strains belonging to the same species generated indistinguishable patterns. In conclusion, Single Base C-sequencing of the first 500 bases of the 16S rRNA-gene could be a useful and widely applicable method for the identification of bacteria at the species level, with the added advantage of being more rapid and easier to automatize than full sequence determination.


title: Ankle-Dorsiflexion Range of Motion and Landing Biomechanics

abstract: CONTEXT ::: A smaller amount of ankle-dorsiflexion displacement during landing is associated with less knee-flexion displacement and greater ground reaction forces, and greater ground reaction forces are associated with greater knee-valgus displacement. Additionally, restricted dorsiflexion range of motion (ROM) is associated with greater knee-valgus displacement during landing and squatting tasks. Because large ground reaction forces and valgus displacement and limited knee-flexion displacement during landing are anterior cruciate ligament (ACL) injury risk factors, dorsiflexion ROM restrictions may be associated with a greater risk of ACL injury. However, it is unclear whether clinical measures of dorsiflexion ROM are associated with landing biomechanics. ::: ::: ::: OBJECTIVE ::: To evaluate relationships between dorsiflexion ROM and landing biomechanics. ::: ::: ::: DESIGN ::: Descriptive laboratory study. ::: ::: ::: SETTING ::: Research laboratory. ::: ::: ::: PATIENTS OR OTHER PARTICIPANTS ::: Thirty-five healthy, physically active volunteers. ::: ::: ::: INTERVENTION(S) ::: Passive dorsiflexion ROM was assessed under extended-knee and flexed-knee conditions. Landing biomechanics were assessed via an optical motion-capture system interfaced with a force plate. ::: ::: ::: MAIN OUTCOME MEASURE(S) ::: Dorsiflexion ROM was measured in degrees using goniometry. Knee-flexion and knee-valgus displacements and vertical and posterior ground reaction forces were calculated during the landing task. Simple correlations were used to evaluate relationships between dorsiflexion ROM and each biomechanical variable. ::: ::: ::: RESULTS ::: Significant correlations were noted between extended-knee dorsiflexion ROM and knee-flexion displacement (r = 0.464, P = .029) and vertical (r = -0.411, P = .014) and posterior (r = -0.412, P = .014) ground reaction forces. All correlations for flexed-knee dorsiflexion ROM and knee-valgus displacement were nonsignificant. ::: ::: ::: CONCLUSIONS ::: Greater dorsiflexion ROM was associated with greater knee-flexion displacement and smaller ground reaction forces during landing, thus inducing a landing posture consistent with reduced ACL injury risk and limiting the forces the lower extremity must absorb. These findings suggest that clinical techniques to increase plantar-flexor extensibility and dorsiflexion ROM may be important additions to ACL injury-prevention programs.


title: Optimizing VM placement for HPC in the cloud

abstract: Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.


ClassyFire: automated chemical classification with a comprehensive, computable taxonomy


title: Artificial intelligence: think again

abstract: Social and cultural conventions are an often-neglected aspect of intelligent-machine development.


title: Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization

abstract: In this paper, we study simultaneous matrix diagonalization-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis (ICA). This includes a generalization to underdetermined mixtures of the well-known SOBI algorithm. The problem is reformulated in terms of the parallel factor decomposition (PARAFAC) of a higher-order tensor. We present conditions under which the mixing matrix is unique and discuss several algorithms for its computation.


title: Boundary Objects in Design: An Ecological View of Design Artifacts

abstract: Traditionally, Systems Analysis and Design (SAD) research has focused on ways of working and ways of modeling. Design ecology – the task, organizational and political context surrounding design – is less well understood. In particular, relationships between design routines and products within ecologies have not received sufficient attention. In this paper, we theorize about design product and ecology relationships and deliberate on how design products – viewed as boundary objects – bridge functional knowledge and stakeholder power gaps across different social worlds. We identify four essential features of design boundary objects: capability to promote shared representation, capability to transform design knowledge, capability to mobilize for action, and capability to legitimize design knowledge. We show how these features help align, integrate, and transform heterogeneous technical and domain knowledge across social worlds as well as mobilize, coordinate, and align stakeholder power. We illustrate through an ethnography of a large aerospace laboratory how two design artifacts – early proto-architectures and project plans – shared these four features to coalesce design processes and propel successful movement of designs across social worlds. These artifacts resolved uncertainty associated with functional requirements and garnered political momentum to choose among design solutions. Altogether, the study highlights the importance of design boundary objects in multi-stakeholder designs and stresses the need to formulate sociology-based design theories on how knowledge is produced and consumed in complex SAD tasks.


title: Mobile-Edge Computing Versus Centralized Cloud Computing Over a Converged FiWi Access Network

abstract: The advent of Internet of Things and 5G applications renders the need for integration of both centralized cloud computing and emerging mobile-edge computing (MEC) with existing network infrastructures to enhance storage, processing, and caching capabilities in not only centralized but also distributed fashions for supporting both delay-tolerant and mission-critical applications. This paper investigates performance gains of centralized cloud and MEC enabled integrated fiber-wireless (FiWi) access networks. A novel unified resource management scheme incorporating both centralized cloud and MEC computation offloading activities into the underlying FiWi dynamic bandwidth allocation process is proposed. Both MEC and cloud traffic are scheduled outside the transmission slot of FiWi traffic by leveraging time division multiple access. An analytical framework is developed to model packet delay, response time efficiency, gain-offload overhead ratio, and communication-to-computation ratio for both cloud and broadband access traffic. In addition, given the importance of reliability in optical backhaul and MEC, this paper develops a probabilistic survivability analysis model to assess the impact of both fiber cuts and MEC server failures. The obtained results demonstrate the feasibility of implementing conventional cloud and MEC in FiWi access networks, without affecting network performance of broadband access traffic.


title: Simple SMS spam filtering on independent mobile phone

abstract: The amount of Short Message Service (SMS) spam is increasing. Various solutions to filter SMS spam on mobile phones have been proposed. Most of these use Text Classification techniques that consist of training, filtering, and updating processes. However, they require a computer or a large amount of SMS data in advance to filter SMS spam, especially for the training. This increases hardware maintenance and communication costs. Thus, we propose to filter SMS spam on independent mobile phones using Text Classification techniques. The training, filtering, and updating processes are performed on an independent mobile phone. The mobile phone has storage, memory and CPU limitations compared with a computer. As such, we apply a probabilistic Naive Bayes classifier using word occurrences for screening because of its simplicity and fast performance. Our experiment on an Android mobile phone shows that it can filter SMS spam with reasonable accuracy, minimum storage consumption, and acceptable processing time without support from a computer or using a large amount of SMS data for training. Thus, we conclude that filtering SMS spam can be performed on independent mobile phones. We can reduce the number of word attributes by almost 50% without reducing accuracy significantly, using our usability-based approach. Copyright © 2012 John Wiley & Sons, Ltd.


title: Binary Cyclic Codes that are Locally Repairable

abstract: Codes for storage systems aim to minimize the repair locality, which is the number of disks (or nodes) that participate in the repair of a single failed disk. Simultaneously, the code must sustain a high rate, operate on a small finite field to be practically significant and be tolerant to a large number of erasures. To this end, we construct new families of binary linear codes that have an optimal dimension (rate) for a given minimum distance and locality. Specifically, we construct cyclic codes that are locally repairable for locality 2 and distances 2, 6 and 10. In doing so, we discover new upper bounds on the code dimension, and prove the optimality of enabling local repair by provisioning disjoint groups of disks. Finally, we extend our construction to build codes that have multiple repair sets for each disk.


title: A Intuitive Visualization of Pareto Frontier for Multi- Objective Optimization in n-Dimensional Performance Space

abstract: A visualization methodology is presented in which a Pareto Frontier can be visualized in an intuitive and straightforward manner for an n-dimensional performance space. Based on this visualization, it is possible to quickly identify 'good' regions of the performance and optimal design spaces for a multi-objective optimization application, regardless of space complexity. Visualizing Pareto solutions for more than three objectives has long been a significant challenge to the multi-objective optimization community. The Hyper-space Diagonal Counting (HSDC) method described here enables the lossless visualization to be implemented. The proposed method requires no dimension fixing. In this paper, we demonstrate the usefulness of visualizing n-f space (i.e. for more than three objective functions in a multiobjective optimization problem). The visualization is shown to aid in the final decision of what potential optimal design point should be chosen amongst all possible Pareto solutions.


title: Mobile Location Prediction in Spatio-Temporal Context

abstract: The increasing use of mobile devices and popular mobile services has led to massive availability of mobile data. Location prediction is a specific topic in mobile data mining, with its potential application in traffic planning, location-base advertisement, and user oriented coupon dispersion. Traditional location prediction methods often separately consider spatial or temporal approach. Although there have been some attempts to integrate both spatial and temporal information for location prediction, most of them suffer from the overfitting problem due to the large number of spatio-temporal trajectory patterns. Therefore, smoothing techniques are indispensable for proper training of spatio-temporal models. In this paper, we propose a novel location prediction model to capture the spatio-temporal context of user visits. It considers not only the spatial historical trajectories, but also the temporal periodic patterns. By applying smoothing techniques on both patterns, our model obtains significant improvement compared to the state-of-the-art approaches.


title: A novel lane detection based on geometrical model and Gabor filter

abstract: Many people die each year in the world in single vehicle roadway departure crashes caused by driver inattention, especially on the freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, in which, the lane detection is a key issue. In this paper, after a brief overview of existing methods, we present a robust lane detection algorithm based on geometrical model and Gabor filter. This algorithm is based on two assumptions: the road in front of vehicle is approximately planar and marked which are often correct on the highway and freeway where most lane departure accidents happen [1]. The lane geometrical model we build in this paper contains four parameters which are starting position, lane original orientation, lane width and lane curvature. The algorithm is composed of three stages: the first stage is called off-line calibration which just runs once after the camera is mounted and fixed in the vehicle. The parameters of camera used for lane detection is accurately estimated by the 2D calibration method [2]; The second stage is called lane model parameters estimation and lane model candidates construction, the first three parameters, starting position, lane original orientation and lane width will be estimated using dominant orientation estimation [3] and local Hough transform. Then the construction of lane model candidates is implemented for the final lane model matching; the third stage is model matching. The proposed lane module matching algorithm is implemented to match the best fitted lane model. The combination of these modules can overcome the universal lane detection problems due to inaccuracies in edge detection such as shadow of tree and passengers on the road. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.


title: On Learning Meaningful Code Changes Via Neural Machine Translation

abstract: Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such as writing commit messages, generating comments and detecting vulnerabilities among others. One of the long lasting dreams of applying DL to source code is the possibility to automate non-trivial coding activities. While some steps in this direction have been taken (e.g., learning how to fix bugs), there is still a glaring lack of empirical evidence on the types of code changes that can be learned and automatically applied by DL. Our goal is to make this first important step by quantitatively and qualitatively investigating the ability of a Neural Machine Translation (NMT) model to learn how to automatically apply code changes implemented by developers during pull requests. We train and experiment with the NMT model on a set of 236k pairs of code components before and after the implementation of the changes provided in the pull requests. We show that, when applied in a narrow enough context (i.e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases. Moreover, our qualitative analysis shows that the model is capable of learning and replicating a wide variety of meaningful code changes, especially refactorings and bug-fixing activities. Our results pave the way for novel research in the area of DL on code, such as the automatic learning and applications of refactoring.


title: Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs

abstract: We tackle Attitude Detection, which we define as the task of extracting the replier's attitude, i.e., a target-polarity pair, from a given one-round conversation. While previous studies considered Target Extraction and Polarity Classification separately, we regard them as subtasks of Attitude Detection. Our experimental results show that treating the two subtasks independently is not the optimal solution for Attitude Detection, as achieving high performance in each subtask is not sufficient for obtaining correct target-polarity pairs. Our jointly trained model AD-NET substantially outperforms the separately trained models by alleviating the target-polarity mismatch problem. Moreover, we proposed a method utilising the attitude detection model to improve retrieval-based chatbots by re-ranking the response candidates with attitude features. Human evaluation indicates that with attitude detection integrated, the new responses to the sampled queries from are statistically significantly more consistent, coherent, engaging and informative than the original ones obtained from a commercial chatbot.


Natural Language Processing for Intelligent Access to Scientific Information


title: Learning Multi-level Deep Representations for Image Emotion Classification

abstract: In this paper, we propose a new deep network that learns multi-level deep representations for image emotion classification (MldrNet). Image emotion can be recognized through image semantics, image aesthetics and low-level visual features from both global and local views. Existing image emotion classification works using hand-crafted features or deep features mainly focus on either low-level visual features or semantic-level image representations without taking all factors into consideration. The proposed MldrNet combines deep representations of different levels, i.e. image semantics, image aesthetics and low-level visual features to effectively classify the emotion types of different kinds of images, such as abstract paintings and web images. Extensive experiments on both Internet images and abstract paintings demonstrate the proposed method outperforms the state-of-the-art methods using deep features or hand-crafted features. The proposed approach also outperforms the state-of-the-art methods with at least 6% performance improvement in terms of overall classification accuracy.


title: Sigmoid based PID controller implementation for rotor control

abstract: This paper presents a sigmoid based variable coefficient PID (SBVC-PID) controller design for Twin Rotor MIMO System (TRMS). The proposed SBVC-PID controller dynamically changes controller coefficients according to a modified sigmoid function of the error signal. The modified sigmoid function is used to limit variability of PID controller coefficients in a predefined range. In the proposed method, each parameters of PID, namely p k , k t and k d , alter between predefined upper and lower bounds. A modified sigmoid function adjusted by a transition coefficient is used to alter each of the PID parameters between these bound limits. The variable coefficients of SBVC-PID maintain a hypercube in k p , k t and k d parameter space satisfying robust stability of the system. Well-known Kharitonov polynomials are used to ensure that the SBVC-PID coefficient alteration takes place in the robust stability intervals. Due to dynamically change of PID coefficients depending on magnitude of error signal, the control performance can be improved compared to conventional PID control. Performance of SBVC-PID controller is demonstrated via theoretical examples and TRMS rotor control simulations.


title: Can Your Eyes Tell Me How You Think? A Gaze Directed Estimation of the Mental Activity

abstract: We investigate the possibility of estimating the cognitive process used by a person when addressing a mental challenge by following the Eye Accessing Cue (EAC) model from the Neuro-Linguistic Programming (NLP) theory [1]. This model, showed in figure 1, describes the eyemovements that are not used for visual tasks (non visual movements) and suggests that the direction of gaze, in such a case, can be an indicator for the internal representational system used by a person facing a given query. The actual EAC is thought to be identified by distinguishing between the relative position of the iris and the eye socket (lid edge). Our approach is to determine the four limits of the eye socket: the inner and outer corners, the upper and lower lids and the iris center and to subsequently analyze the identified region. The entire method flowchart is presented in figure 2. The schematics of the method used for independently looking for the position of each eye landmark is described in figure 3. Given the face square position by Viola-Jones algorithm [4] and the eye centers given by the method from [3], we fuse information related to position, normalized luminance, template matching and shape constraining. For position and luminance, we construct priors over the training database, while for template matching we describe a patch by concatenation of integral and edge projections on horizontal and vertical directions. The score of how likely is a patch to be centered on the true landmark position is given by a Multi Layer Perceptron. For the shape constrain, inspired by the CLM [2], we construct the probability density function in the eigenspace of the shapes in the training set. By ordering the landmarks according to a prior confidence (e.g. eye outer corners are more reliable than upper and lower eye boundaries) and by keeping all points fixed with the exception of the current least reliable, we build the likelihood of various current landmark positions. This information is fused with previous stages and we iteratively improve the landmark position. The final landmark position is taken as the weighted center of mass of the convex combination between initial stages and shape likelihood. To study the specific of the gaze direction we introduce Eye-Chimera database, which comprises 1172 frontal face images, grouped according to the 7 gaze directions, with a set of 5 points marked for each eye: the iris center and 4 points delimiting the bounding box. Recognizing individual EACs. The recognition of the EAC case (gaze direction) is done by identifying the position of the iris center inside the eye socket complemented by the information of the interior of the eye delimited shape. The interior of the eye quadrilateral shape is described by the integral projections normalized to 32 samples. For the actual recognition we have trained a random forrest to take as input the EAC feature (landmarks positions and integral features). We consider two types of recognition situations: three cases (looking


title: Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms

abstract: The paper reports on mobile robot motion estimation based on matching points from successive two-dimensional (2D) laser scans. This ego-motion approach is well suited to unstructured and dynamic environments because it directly uses raw laser points rather than extracted features. We have analyzed the application of two methods that are very different in essence: (i) A 2D version of iterative closest point (ICP), which is widely used for surface registration; (ii) a genetic algorithm (GA), which is a novel approach for this kind of problem. Their performance in terms of real-time applicability and accuracy has been compared in outdoor experiments with nonstop motion under diverse realistic navigation conditions. Based on this analysis, we propose a hybrid GA-ICP algorithm that combines the best characteristics of these pure methods. The experiments have been carried out with the tracked mobile robot Auriga-α and an on-board 2D laser scanner. © 2006 Wiley Periodicals, Inc.


title: A Survey on Web Service Discovery Approaches

abstract: Web services are playing an important role in e-business and e-commerce applications. As web service applications are interoperable and can work on any platform, large scale distributed systems can be developed easily using web services. Finding most suitable web service from vast collection of web services is very crucial for successful execution of applications. Traditional web service discovery approach is a keyword based search using UDDI. Various other approaches for discovering web services are also available. Some of the discovery approaches are syntax based while other are semantic based. Having system for service discovery which can work automatically is also the concern of service discovery approaches. As these approaches are different, one solution may be better than another depending on requirements. Selecting a specific service discovery system is a hard task. In this paper, we give an overview of different approaches for web service discovery described in literature. We present a survey of how these approaches differ from each other.


The Complexity of Different Types of Attitudes in Initial and Continued ICT Use


title: Runtime Prediction of Failure Modes from System Error Logs

abstract: Predicting potential failure occurrences during runtime is important to achieve system resilience and avoid hazardous consequences of failures. Existing failure prediction techniques in software systems involve forecasting failure counts, effects, and occurrences. Most of these techniques predict failures that may occur in future runtime intervals and only few techniques predict them at runtime. However, they do not estimate the failure modes and they require extensive instrumentation of source code. In this paper, we provide an approach for predicting failure occurrences and modes during system runtime. Our methodology utilizes system error log records to craft runtime error-spread signature. Using system error log history, we determine a predictive function (estimator) for each failure mode based on these signatures. This estimator can be used to predict a failure mode eventuality measure (a probability of failure mode occurrence) from system error log during system runtime. An experimental evaluation using PostgreSQL opensource database is provided. Our results show high accuracy of failure occurrence and mode predictions.


title: Efficient k-anonymization using clustering techniques

abstract: k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. The key observation here is that data records that are naturally similar to each other should be part of the same equivalence class. We thus formulate a specific clustering problem, referred to as k-member clustering problem. We prove that this problem is NP-hard and present a greedy heuristic, the complexity of which is in O(n2). As part of our approach we develop a suitable metric to estimate the information loss introduced by generalizations, which works for both numeric and categorical data.


Biologic Width Dimensions in Diseased And Healthy Periodontium - A Clinico- Radiographic Study


title: Integrated circuit design with NEM relays

abstract: To overcome the energy-efficiency limitations imposed by finite sub-threshold slope in CMOS transistors, this paper explores the design of integrated circuits based on nano-electro-mechanical (NEM) relays. A dynamical Verilog-A model of the NEM relay is described and correlated to device measurements. Using this model we explore NEM relay design strategies for digital logic and I/O that can significantly improve the energy efficiency of the whole VLSI system. By exploiting the low effective threshold voltage and zero leakage achievable with these relays, we show that NEM relay-based adders can achieve an order of magnitude or more improvement in energy efficiency over CMOS adders with ns-range delays and with no area penalty. By applying parallelism, this improvement in energy-efficiency can be achieved at higher throughputs as well, at the cost of increased area. Similar improvements in high-speed I/O energy are also predicted by making use of the relays to implement highly energy-efficient digital-to-analog and analog-to-digital converters.


title: The Effects of Twitter Sentiment on Stock Price Returns

abstract: Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.


title: Well Begun Is Half Done: Generating High-Quality Seeds for Automatic Image Dataset Construction from Web

abstract: We present a fully automatic approach to construct a large-scale, high-precision dataset from noisy web images. Within the entire pipeline, we focus on generating high quality seed images for subsequent dataset growing. High quality seeds are essential as we revealed, but they have received relatively less attention in previous works with respect to how to automatically generate them. In this work, we propose a density score based on rank-order distance to identify positive seed images. The basic idea is images relevant to a concept typically are tightly clustered, while the outliers are widely scattered. Through adaptive thresholding, we guarantee the selected seeds as numerous and accurate as possible. Starting with the high quality seeds, we grow a high quality dataset by dividing seeds and conducting iterative negative and positive mining. Our system can automatically collect thousands of images for one concept/class, with a precision rate of 95% or more. Comparisons with recent state-of-the-arts also demonstrate our method’s superior performance.


title: ESP: path-sensitive program verification in polynomial time

abstract: In this paper, we present a new algorithm for partial program verification that runs in polynomial time and space. We are interested in checking that a program satisfies a given temporal safety property. Our insight is that by accurately modeling only those branches in a program for which the property-related behavior differs along the arms of the branch, we can design an algorithm that is accurate enough to verify the program with respect to the given property, without paying the potentially exponential cost of full path-sensitive analysis.We have implemented this "property simulation" algorithm as part of a partial verification tool called ESP. We present the results of applying ESP to the problem of verifying the file I/O behavior of a version of the GNU C compiler (gcc, 140,000 LOC). We are able to prove that all of the 646 calls to .fprintf in the source code of gcc are guaranteed to print to valid, open files. Our results show that property simulation scales to large programs and is accurate enough to verify meaningful properties.


title: Coded MapReduce

abstract: MapReduce is a commonly used framework for executing data-intensive tasks on distributed server clusters. We present “Coded MapReduce”, a new framework that enables and exploits a particular form of coding to significantly reduce the inter-server communication load of MapReduce. In particular, Coded MapReduce exploits the repetitive mapping of data blocks at different servers to create coded multicasting opportunities in the shuffling phase, cutting down the total communication load by a multiplicative factor that grows linearly with the number of servers in the cluster. We also analyze the tradeoff between the “computation load” and the “communication load” of the Coded MapReduce.


title: Design Techniques for High-Speed, High-Resolution Comparators

abstract: Precision techniques for the design of comparators used in high-performance analog-to-digital converters employing parallel conversion stages are described. Following a review of conventional offset cancellation techniques, circuit designs achieving 12-b resolution in both BiCMOS and CMOS 5-V technologies are presented. The BiCMOS comparator consists of a preamplifier followed by two regenerative stages and achieves an offset of 200 mu V at a 10-MHz clock rate while dissipating 1.7 mW. In the CMOS comparator offset cancellation is used in both a single-stage preamplifier and a subsequent latch to achieve an offset of less than 300 mu V at comparison rates as high as 10 MHz, with a power dissipation of 1.8 mW. >


title: Real-Time Controllable and Multifunctional Metasurfaces Utilizing Indium Tin Oxide Materials: A Phased Array Perspective

abstract: In this paper, two electrically tunable dual-band reflectarray antennas are designed by the integration of a thin layer of indium tin oxide (ITO) into plasmonic multi-sized and multilayer unit cells. The presented geometries include two gold nanoribbons located next to each other with different widths and backed by a stack of alumina-ITO-metallic ground plane and two pairs of vertically stacked gold nanoribbon-alumina-ITO on a metallic ground plane. The double-resonance nature of the double metal-insulator-metal unit-cells is exploited to achieve two distinct operating frequencies in which the control over phase of the reflected beam is obtained via the tunable gate biasing of ITO. An in-depth phased array analysis is developed with emphasis on the accomplishment of a reconfigurable antenna with robust beam scanning and focusing characteristics. The proposed structures can be considered as dual-band bifunctional reflectarray antenna that can operate at two distinct frequencies in the near-infrared regime with two different functionalities as bending and focusing.


title: Extraction of Web News from Web Pages Using a Ternary Tree Approach

abstract: The spread of information available in the World Wide Web, it appears that the pursuit of quality data is effortless and simple but it has been a significant matter of concern. Various extractors, wrappers systems with advanced techniques have been studied that retrieves the desired data from a collection of web pages. In this paper we propose a method for extracting the news content from multiple news web sites considering the occurrence of similar pattern in their representation such as date, place and the content of the news that overcomes the cost and space constraint observed in previous studies which work on single web document at a time. The method is an unsupervised web extraction technique which builds a pattern representing the structure of the pages using the extraction rules learned from the web pages by creating a ternary tree which expands when a series of common tags are found in the web pages. The pattern can then be used to extract news from other new web pages. The analysis and the results on real time web sites validate the effectiveness of our approach.


title: Memory Retrieval in Mice and Men

abstract: Retrieval, the use of learned information, was until recently mostly terra incognita in the neurobiology of memory, owing to shortage of research methods with the spatiotemporal resolution required to identify and dissect fast reactivation or reconstruction of complex memories in the mammalian brain. The development of novel paradigms, model systems, and new tools in molecular genetics, electrophysiology, optogenetics, in situ microscopy, and functional imaging, have contributed markedly in recent years to our ability to investigate brain mechanisms of retrieval. We review selected developments in the study of explicit retrieval in the rodent and human brain. The picture that emerges is that retrieval involves coordinated fast interplay of sparse and distributed corticohippocampal and neocortical networks that may permit permutational binding of representational elements to yield specific representations. These representations are driven largely by the activity patterns shaped during encoding, but are malleable, subject to the influence of time and interaction of the existing memory with novel information.


title: A Clinical Information System Research Agenda for Kaiser Permanente

abstract: Clinical information systems (CIS) could drive progress in health care in the 21st century: information captured in a CIS could be used within a general “CIS research landscape” (described by us previously) to develop research projects that examine and potentially improve delivery of health care services. The CIS research landscape also identifies aspects of the care delivery system that must be addressed before quality of care can be improved. In addition, the CIS research landscape portrays the research process and how it relates to operational aspects of health care delivery. In this article, we describe how we used the CIS research landscape in conjunction with known operational, financial, technical, governmental, and social constraints of Kaiser Permanente (KP) to develop a specific CIS research agenda. We identified four CIS research priorities: clinical decision support systems, population-based care systems, personal health record systems, and establishment of a functional baseline against which future CIS enhancement can be measured. These research priorities should help guide researchers so they can focus their time, effort, and money on important questions that will inform KP and other health care providers about the use of CIS to improve health care.


title: Adaptive Edge Features Guided Graph Attention Networks

abstract: Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning do not consider incorporating edge features, especially multi-dimensional edge features. In this paper, we propose an attention mechanism which combines both node features and edge features. Guided by the edge features, the attention mechanism on a pair of graph nodes will not only depend on node contents, but also ajust automatically with respect to the properties of the edge connecting these two nodes. Moreover, the edge features are adjusted by the attention function and fed to the next layer, which means our edge features are adaptive across network layers. As a result, our proposed adaptive edge features guided graph attention model can consolidate a rich source of graph information that current state-of-the-art graph learning methods cannot. We apply our proposed model to graph node classification, and experimental results on three citaion network datasets and a biological network dataset show that out method outperforms the current state-of-the-art methods, testifying to the discriminative capability of edge features and the effectiveness of our adaptive edge features guided attention model. Additional ablation experimental study further shows that both the edge features and adaptiveness components contribute to our model.


title: MRI Cross-Modality NeuroImage-to-NeuroImage Translation

abstract: We present a cross-modality generation framework that learns to generate translated modalities from given modalities in MR images without real acquisition. Our proposed method performs NeuroImage-to-NeuroImage translation (abbreviated as N2N) by means of a deep learning model that leverages conditional generative adversarial networks (cGANs). Our framework jointly exploits the low-level features (pixel-wise information) and high-level representations (e.g. brain tumors, brain structure like gray matter, etc.) between cross modalities which are important for resolving the challenging complexity in brain structures. Our framework can serve as an auxiliary method in clinical diagnosis and has great application potential. Based on our proposed framework, we first propose a method for cross-modality registration by fusing the deformation fields to adopt the cross-modality information from translated modalities. Second, we propose an approach for MRI segmentation, translated multichannel segmentation (TMS), where given modalities, along with translated modalities, are segmented by fully convolutional networks (FCN) in a multichannel manner. Both of these two methods successfully adopt the cross-modality information to improve the performance without adding any extra data. Experiments demonstrate that our proposed framework advances the state-of-the-art on five brain MRI datasets. We also observe encouraging results in cross-modality registration and segmentation on some widely adopted brain datasets. Overall, our work can serve as an auxiliary method in clinical diagnosis and be applied to various tasks in medical fields. Keywords: image-to-image, cross-modality, registration, segmentation, brain MRI


title: Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

abstract: Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.


title: A Hybrid Framework for News Clustering Based on the DBSCAN-Martingale and LDA

abstract: Nowadays there is an important need by journalists and media monitoring companies to cluster news in large amounts of web articles, in order to ensure fast access to their topics or events of interest. Our aim in this work is to identify groups of news articles that share a common topic or event, without a priori knowledge of the number of clusters. The estimation of the correct number of topics is a challenging issue, due to the existence of “noise”, i.e. news articles which are irrelevant to all other topics. In this context, we introduce a novel density-based news clustering framework, in which the assignment of news articles to topics is done by the well-established Latent Dirichlet Allocation, but the estimation of the number of clusters is performed by our novel method, called “DBSCAN-Martingale”, which allows for extracting noise from the dataset and progressively extracts clusters from an OPTICS reachability plot. We evaluate our framework and the DBSCAN-Martingale on the 20newsgroups-mini dataset and on 220 web news articles, which are references to specific Wikipedia pages. Among twenty methods for news clustering, without knowing the number of clusters k, the framework of DBSCAN-Martingale provides the correct number of clusters and the highest Normalized Mutual Information.


title: Uncertainty about managers’ reporting objectives and investors’ response to earnings reports: Evidence from the 2006 executive compensation disclosures

abstract: We examine whether the information content of the earnings report, as captured by the earnings response coefficient (ERC), increases when investors’ uncertainty about the manager's reporting objectives decreases, as predicted in Fischer and Verrecchia (2000). We use the 2006 mandatory compensation disclosures as an instrument to capture a decrease in investors’ uncertainty about managers’ incentives and reporting objectives. Employing a difference-in-differences design and exploiting the staggered adoption of the new rules, we find a statistically and economically significant increase in ERC for treated firms relative to control firms, largely driven by profit firms. Cross-sectional tests suggest that the effect is more pronounced in subsets of firms most affected by the new rules. Our findings represent the first empirical evidence of a role of compensation disclosures in enhancing the information content of financial reports.


title: Quantitative phase imaging via Fourier ptychographic microscopy

abstract: Fourier ptychographic microscopy (FPM) is a recently developed imaging modality that uses angularly varying illumination to extend a system's performance beyond the limit defined by its optical components. The FPM technique applies a novel phase-retrieval procedure to achieve resolution enhancement and complex image recovery. In this Letter, we compare FPM data to theoretical prediction and phase-shifting digital holography measurement to show that its acquired phase maps are quantitative and artifact-free. We additionally explore the relationship between the achievable spatial and optical thickness resolution offered by a reconstructed FPM phase image. We conclude by demonstrating enhanced visualization and the collection of otherwise unobservable sample information using FPM's quantitative phase.


title: Sensor-based stroke detection and stroke type classification in table tennis

abstract: In this paper we present a sensor-based table tennis stroke detection and classification system. We attached inertial sensors to table tennis rackets and collected data of 8 different basic stroke types from 10 amateur and professional players. Firstly, single strokes were detected by a event detection algorithm. Secondly, features were computed and used as input for stroke type classification. Multiple classifiers were compared regarding classification rates and computational effort. The overall sensitivity of the stroke detection was 95.7% and the best classifier reached a classification rate of 96.7%. Therefore, our presented approach is able to detect table tennis strokes in time-series data and to classify each stroke into correct stroke type categories. The system has the potential to be implemented as an embedded real-time application for other racket sports, to analyze training exercises and competitions, to present match statistics or to support the athletes' training progress. To our knowledge, this is the first paper that addresses a wearable support system for table tennis, and our future work aims at using the presented results to build a complete match analysis system for this sport.


title: Artificial Intelligence learning based on proportional navigation guidance

abstract: Artificial Intelligence (AI) is a vast domain with variety of applications. This research proposes the use of AI for guidance of an Unmanned Aerial Vehicle (UAV) to a maneuvering target. The AI agent is trained using machine learning algorithm. The PN guidance algorithm is used as a basis for training the agent. An orthogonal array based training strategy is proposed that provides a better training and reduced miss distance values. The AI based guidance provides performance equal to the PN Guidance law and in some cases it outperforms PN guidance law.


CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection


Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution


title: Survey on semantic image segmentation techniques

abstract: Semantic image segmentation is a vast area of interest for computer vision and machine learning researchers. Many vision applications need accurate and efficient image segmentation and segment classification mechanisms for assessing the visual contents and perform the real-time decision making. The application area includes remote sensing, autonomous driving, indoor navigation, video surveillance and virtual or augmented reality systems etc. The segmentation and classification of objects generate the specific performance parameters for various applications which require detailed domain analysis. There are broad range of applications where remote sensing image scene classification play an important role and has been receiving remarkable attention. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This survey paper provides a review of different traditional methods of image segmentation and classification. By comparing these methods with semantic image segmentation using deep learning it is assumed to show the far better result.


title: Real-time lane detection and tracking system using simple filter and Kalman filter

abstract: In this paper, we propose a real-time lane detection and tracking algorithm using a simple filter and Kalman filter to develop a Lane Departure Warning System(LDWS) that can be implemented in an embedded system. Our LDWS was realized on the I.MX6Q board, mounted on a test vehicle, and traveled about 1,000km to derive the experimental results. Experimental results show that our LDWS operates at 97% detection rate in the day time and 95% in the night time. The average processing time of the our LDWS is about 15frame per seconds.


title: Deep Reinforcement Learning for Sponsored Search Real-time Bidding

abstract: Bidding optimization is one of the most critical problems in online advertising. Sponsored search (SS) auction, due to the randomness of user query behavior and platform nature, usually adopts keyword-level bidding strategies. In contrast, the display advertising (DA), as a relatively simpler scenario for auction, has taken advantage of real-time bidding (RTB) to boost the performance for advertisers. In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB. SS-RTB has a much more complex dynamic environment, due to stochastic user query behavior and more complex bidding policies based on multiple keywords of an ad. Most previous methods for DA cannot be applied. We propose a reinforcement learning (RL) solution for handling the complex dynamic environment. Although some RL methods have been proposed for online advertising, they all fail to address the "environment changing'' problem: the state transition probabilities vary between two days. Motivated by the observation that auction sequences of two days share similar transition patterns at a proper aggregation level, we formulate a robust MDP model at hour-aggregation level of the auction data and propose a control-by-model framework for SS-RTB. Rather than generating bid prices directly, we decide a bidding model for impressions of each hour and perform real-time bidding accordingly. We also extend the method to handle the multi-agent problem. We deployed the SS-RTB system in the e-commerce search auction platform of Alibaba. Empirical experiments of offline evaluation and online A/B test demonstrate the effectiveness of our method.


title: EVOLUTION AND MEMES: THE HUMAN BRAIN AS A SELECTIVE IMITATION DEVICE

abstract: The meme is an evolutionary replicator, defined as information copied from person-to-person by imitation. I suggest that taking memes into account may provide a better understanding of human evolution in the following way. Memes appeared in human evolution when our ancestors became capable of imitation. From this time on, two replicators memes and genes coevolved. Successful memes changed the selective environment, favoring genes for the ability to copy them. I have called this process memetic drive. Meme-gene coevolution produced a big brain that is especially good at copying certain kinds of memes. This is an example of the more general process in which a replicator and its replication machinery evolve together. The human brain has been designed not just for the benefit of human genes, but for the replication of memes. It is a selective imitation device. Some problems of definition are discussed and suggestions made for future research.


title: How can computers be partners in the creative process: classification and commentary on the special issue

abstract: The different ways that computers can be involed in creative work are examined. A classification based on four categories of human-computer interaction to promote creativity is proposed: computers may facilitate (a) the management of creative work, (b) communication between individuals collaborating on creative projects, (c) the use of creativity enhancement techniques, (d) the creative act through integrated human-computer cooperation during idea production. The papers in the Special Issue are discussed according to this classification. Issues to be considered in future work on human-computer interactions for promoting creativity are discussed.


title: RTE Template Structure for AUTOSAR based Embedded Software Platform

abstract: In this paper, we present a design of RTE template structure for AUTOSAR-based vehicle applications. Due to an increase in software complexity in recent years, much greater efforts are necessary to manage and develop software modules in automotive industries. To deal with this issue, an automotive Open system architecture (AUTOSAR) partnership was launched. This embedded platform standardizes software architectures and provides a methodology supporting distributed process. The RTE that is located at the heart of AUTOSAR implements the virtual function bus functionality for a particle electronic control unit (ECU). It enables to communicate between application software components. The purpose of this paper is to design a RTE structure with the AUTOSAR standard concept. As a future work, this research will be further extended, and we will propose the development of a RTE generator that is drawn on an AUTOSAR RTE template structure.


title: Personalization of Health Interventions using Cluster-Based Reinforcement Learning

abstract: Research has shown that personalization of health interventions can contribute to an improved effectiveness. Reinforcement learning algorithms can be used to perform such tailoring. In this paper, we present a cluster-based reinforcement learning approach which learns optimal policies for groups of users. Such an approach can speed up the learning process while still giving a level of personalization. We apply both online and batch learning to learn policies over the clusters and introduce a publicly available simulator which we have developed to evaluate the approach. The results show batch learning significantly outperforms online learning. Furthermore, near-optimal clustering is found which proves to be beneficial in learning significantly better policies compared to learning per user and learning across all users.


title: Low power and area efficient Wallace tree multiplier using carry select adder with binary to excess-1 converter

abstract: Multipliers are major blocks in the most of the digital and high performance systems such as Microprocessors, Signal processing Circuits, FIR filters etc. In the present scenario, Fast multipliers with less power consumption are leading with their performance. Wallace tree multiplier with carry select adder (CSLA) is one of the fastest multiplier but utilizes more area. To improve the performance of this multiplier, CSLA is replaced by binary excess-1 counter(BEC) which not only reduces the area at gate level but also reduces power consumption. Area and power calculations for the Wallace tree multiplier using CSLA with BEC are giving good results compared to regular Wallace tree multiplier


title: Cyber/Physical Co-verification for Developing Reliable Cyber-physical Systems

abstract: Cyber-Physical Systems (CPS) tightly integrate cyber and physical components and transcend discrete and continuous domains. It is greatly desired that the physical components being controlled and the software implementation of control algorithms can be verified together. We present an efficient approach to reachability analysis of Hybrid Automata Pushdown System (HAPS) models for cyber/physical co-verification of CPS. We have realized this approach and applied it to real-world control systems. The evaluation has shown that HAPS is an effective model for co-verification of CPS and our approach has major potential in verifying system-level properties of CPS, therefore improving the reliability of CPS.


title: Active Community Detection in Massive Graphs

abstract: A canonical problem in graph mining is the detection of dense communities. This problem is exacerbated for a graph with a large order and size -- the number of vertices and edges -- as many community detection algorithms scale poorly. In this work we propose a novel framework for detecting active communities that consist of the most active vertices in massive graphs. The framework is applicable to graphs having billions of vertices and hundreds of billions of edges. Our framework utilizes a parallelizable trimming algorithm based on a locality statistic to filter out inactive vertices, and then clusters the remaining active vertices via spectral decomposition on their similarity matrix. We demonstrate the validity of our method with synthetic Stochastic Block Model graphs, using Adjusted Rand Index as the performance metric. We further demonstrate its practicality and efficiency on a most recent real-world Hyperlink Web graph consisting of over 3.5 billion vertices and 128 billion edges.


l / VARIATION IN AMERICAN ENGLISH : A CORPUS


title: Real-time multiview analysis of soccer matches for understanding interactions between ball and players

abstract: In the last decade, several research efforts have been undertaken in soccer video analysis. This increasing interest is motivated by the possible applications over a wide spectrum of topics: indexing, summarization, video enhancement, team and players statistics, tactic analysis, referee's support, etc. Soccer video analysis requires different challenging tasks: ball and players have to be localized in each frame, tracked over time and, above all, their interactions have to be detected and analyzed. The latter task is fundamental, especially for statistics and referee decision support purposes, but, unfortunately, it has not received adequate attention from the scientific community. In this paper a multi-view system able to understand in real time the interactions between the ball and the players is presented. 3D ball trajectories are extracted by triangulation from multiple cameras and used to detect the interactions between the players and the ball. Inference processes are then introduced to determine the player kicking the ball and to fix the instant of the interaction. The system has been tested during several matches of the Italian first division soccer championship and experimental proofs of its effectiveness are reported.


title: Binary Similarity Detection Using Machine Learning

abstract: Finding similar procedures in stripped binaries has various use cases in the domains of cyber security and intellectual property. Previous works have attended this problem and came up with approaches that either trade throughput for accuracy or address a more relaxed problem. In this paper, we present a cross-compiler-and-architecture approach for detecting similarity between binary procedures, which achieves both high accuracy and peerless throughput. For this purpose, we employ machine learning alongside similarity by composition: we decompose the code into smaller comparable fragments, transform these fragments to vectors, and build machine learning-based predictors for detecting similarity between vectors that originate from similar procedures. We implement our approach in a tool called Zeek and evaluate it by searching similarities in open source projects that we crawl from the world-wide-web. Our results show that we perform 250X faster than state-of-the-art tools without harming accuracy.


Overview of TAC-KBP2017 13 Languages Entity Discovery and Linking


title: Cloaking Malware with the Trusted Platform Module

abstract: The Trusted Platform Module (TPM) is commonly thought of as hardware that can increase platform security. However, it can also be used for malicious purposes. The TPM, along with other hardware, can implement a cloaked computation, whose memory state cannot be observed by any other software, including the operating system and hypervisor. We show that malware can use cloaked computations to hide essential secrets (like the target of an attack) from a malware analyst. ::: ::: We describe and implement a protocol that establishes an encryption key under control of the TPM that can only be used by a specific infection program. An infected host then proves the legitimacy of this key to a remote malware distribution platform, and receives and executes an encrypted payload in a way that prevents software visibility of the decrypted payload. We detail how malware can benefit from cloaked computations and discuss defenses against our protocol. Hardening legitimate uses of the TPM against attack improves the resilience of our malware, creating a Catch-22 for secure computing technology.


title: Micro- and macro-level churn analysis of large-scale mobile games

abstract: As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenue. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro-level (between an app and a specific user) and macro-level (between an app and all its users). Accurate micro-level churn prediction and macro-level churn ranking will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macro-level churn ranking. For micro-level churn prediction, in view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user–app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To address macro-level churn ranking, we propose to construct a relationship graph with estimated micro-level churn probabilities as edge weights and adapt link analysis algorithms on the graph. We devise a simple algorithm SimSum and adapt two more advanced algorithms PageRank and HITS. The performance of our solutions to the two-level churn analysis problem is evaluated on real-world data collected from the Samsung Game Launcher platform. The data includes tens of thousands of mobile games and hundreds of millions of user–app interactions. The experimental results with this data demonstrate the superiority of our proposed models against existing state-of-the-art methods.


Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI


title: 3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation

abstract: Semantic segmentation of 3D unstructured point clouds remains an open research problem. Recent works predict semantic labels of 3D points by virtue of neural networks but take limited context knowledge into consideration. In this paper, a novel end-to-end approach for unstructured point cloud semantic segmentation, named 3P-RNN, is proposed to exploit the inherent contextual features. First the efficient pointwise pyramid pooling module is investigated to capture local structures at various densities by taking multi-scale neighborhood into account. Then the two-direction hierarchical recurrent neural networks (RNNs) are utilized to explore long-range spatial dependencies. Each recurrent layer takes as input the local features derived from unrolled cells and sweeps the 3D space along two directions successively to integrate structure knowledge. On challenging indoor and outdoor 3D datasets, the proposed framework demonstrates robust performance superior to state-of-the-arts.


title: Optical wireless communications — An emerging technology

abstract: This book focuses on optical wireless communications (OWC), an emerging technology with huge potential for the provision of pervasive and reliable next-generation communications networks. It shows how the development of novel and efficient wireless technologies can contribute to a range of transmission links essential for the heterogeneous networks of the future to support various communications services and traffic patterns with ever-increasing demands for higher data-transfer rates. The book starts with a chapter reviewing the OWC field, which explains different sub-technologies (visible-light, ultraviolet (UV) and infrared (IR) communications) and introduces the spectrum of application areas (indoor, vehicular, terrestrial, underwater, intersatellite, deep space, etc.). This provides readers with the necessary background information to understand the specialist material in the main body of the book, which is in four parts. The first of these deals with propagation modelling and channel characterization of OWC channels at different spectral bands and with different applications. The second starts by providing a unified information-theoretic treatment of OWC and then discusses advanced physical-layer methodologies (including, but not limited to: advanced coding, modulation diversity, cooperation and multi-carrier techniques) and the ultimate limitations imposed by practical constraints. On top of the physical layer come the upper-layer protocols and cross-layer designs that are the subject of the third part of the book. The last part of the book features a chapter-by-chapter assessment of selected OWC applications. Optical Wireless Communications is a valuable reference guide for academic researchers and practitioners concerned with the future development of the worlds communication networks. It succinctly but comprehensively presents the latest advances in the field.


title: On cross-validation and stacking: building seemingly predictive models on random data

abstract: A number of times when using cross-validation (CV) while trying to do classification/probability estimation we have observed surprisingly low AUC's on real data with very few positive examples. AUC is the area under the ROC and measures the ranking ability and corresponds to the probability that a positive example receives a higher model score than a negative example. Intuition seems to suggest that no reasonable methodology should ever result in a model with an AUC significantly below 0.5. The focus of this paper is not on the estimator properties of CV (bias/variance/significance), but rather on the properties of the 'holdout' predictions based on which the CV performance of a model is calculated. We show that CV creates predictions that have an 'inverse' ranking with AUC well below 0.25 using features that were initially entirely unpredictive and models that can only perform monotonic transformations. In the extreme, combining CV with bagging (repeated averaging of out-of-sample predictions) generates 'holdout' predictions with perfectly opposite rankings on random data. While this would raise immediate suspicion upon inspection, we would like to caution the data mining community against using CV for stacking or in currently popular ensemble methods. They can reverse the predictions by assigning negative weights and produce in the end a model that appears to have close to perfect predictability while in reality the data was random.


title: Association Rules Mining : A Recent Overview

abstract: In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the al- gorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions that have yet to be explored.


title: Fingerprint Quality Indices for Predicting Authentication Performance

abstract: The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.


title: Mechanical Stress Reduction of Rotor Core of Interior Permanent Magnet Synchronous Motor

abstract: In this paper, the bridge shape of interior permanent magnet synchronous motor (IPMSM) is designed for integrated starter and generator (ISG) which is applied in hybrid electric vehicle (HEV). Mechanical stress of rotor core which is caused by centrifugal force is the main issue when IPMSM is operated at high speed. The bridge is thin area in rotor core where is mechanically weak point and the shape of bridge significantly affects leakage flux and electromagnetic performance. Therefore, bridge should be designed considering both mechanic and electromagnetic characteristics. In the design process, we firstly find a shape of bridge has low leakage flux and mechanical stress. Next, the calculation of mechanical stress and the electromagnetic characteristics are performed by finite element analysis (FEA). The mechanical stress in rotor core is not maximized in steady high speed but dynamical high momentum. Therefore, transient FEA is necessary to consider the dynamic speed changing in real speed profile for durability experiment. Before the verification test, fatigue characteristic is investigated by using S-N curve of rotor core material. Lastly, the burst test of rotor is performed and the deformation of rotor core is compared between prototype and designed model to verify the design method.


title: Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention

abstract: Image captioning has been recently gaining a lot of attention thanks to the impressive achievements shown by deep captioning architectures, which combine Convolutional Neural Networks to extract image representations and Recurrent Neural Networks to generate the corresponding captions. At the same time, a significant research effort has been dedicated to the development of saliency prediction models, which can predict human eye fixations. Even though saliency information could be useful to condition an image captioning architecture, by providing an indication of what is salient and what is not, research is still struggling to incorporate these two techniques. In this work, we propose an image captioning approach in which a generative recurrent neural network can focus on different parts of the input image during the generation of the caption, by exploiting the conditioning given by a saliency prediction model on which parts of the image are salient and which are contextual. We show, through extensive quantitative and qualitative experiments on large-scale datasets, that our model achieves superior performance with respect to captioning baselines with and without saliency and to different state-of-the-art approaches combining saliency and captioning.


title: Continuous Integration and Delivery Traceability in Industry: Needs and Practices

abstract: The importance of traceability in software development has long been recognized, not only for reasons of legality and certification, but also to enable the development itself. At the same time, organizations are known to struggle to live up to traceability requirements, and there is an identified lack of studies on traceability practices in the industry, not least in the area of tooling and infrastructure. This paper identifies traceability as a key challenge in achieving continuous integration and delivery and documents an industry developed framework -- Eiffel -- designed to provide real time traceability in continuous integration and delivery. The efficacy of the Eiffel framework is then investigated through comparison with previous traceability methods.


title: Depth Image Inpainting for RGB-D Camera Based on Light Field EPI

abstract: RGB-D camera is an active depth measurement device. Depth images collected by RGB-D camera usually have depth errors at objects edges and corners in image. Current researches of depth image inpainting methods were focusing on exploiting existed depth data and making use of corresponding RGB image. This paper proposes a method to inpaint depth image by using Light Field EPI (Epipolar Plane Image), which has a special linear structure. We present a software framework to extra objects edges depth data from light field EPI. Then we merge the depth image and light field EPI edges depth data to an integral depth image. Experimental results demonstrate the accurateness of the proposed depth image inpainting method, especially for thin objects and edges in image.


title: A Model Predictive Controller for Minimum Time Cornering

abstract: This paper presents a model predictive control approach to drive the vehicle up to its tires adhesion limits. The main focus is optimality of the closed-loop control to follow a certain track in minimum time. A Linear Time Varying Model Predictive Controller (LTV-MPC) is developed to be able to adapt with high non-linearities of the model, but also having low computational complexity in order to work in real-time. In order to have the best reference trajectory and apply it to the LTV-MPC, the problem is first solved in a nonlinear optimal control framework. This solution represents a benchmark as well, to which the LTV-MPC results are compared. The proposed controller is tested in simulation showing promising results.


title: The Effects of Information Overload on Consumer Confusion: An Examination on User Generated Content

abstract: The aim of this study is to determine the effects of information overload on consumer confusion in User-Generated Content (UGC) environments and to find whether consumers’ final buying decisions are affected by the confusion. In this respect, consumer data gathered online was analyzed by means of Structural Equation Modeling (SEM) on the basis of the theoretical framework. In addition to model tests, a scale was developed to measure ‘information overload’ depending on UGC. The results revealed that depending on the quality of information created in UGC environments, consumers’ perceptions of information overload and consequently their confused reactions are related. The most important dimension of the information overload was found to be the information processing capacity. The level of involvement, the level of internet self-efficacy, and the perceived usefulness of UGC were also related to the degree of information over-load. Statistically meaningful relationships were found between perceived information overload and confusion, and this confusion had a negative effect on consumers’ buying decisions, thus resulting in a decrease in purchasing.


title: Extraction of high quality genomic DNA from microsamples of human blood.

abstract: A simple and efficient method for extracting human genomic DNA from microsamples of blood has been developed. This method used sodium perchlorate, chloroform, polymerised silica gel and a dumbbell-shape tube, instead of proteinase K and phenol. The entire process took less than two hours, and high molecular weight DNA, in high yield and purity, was obtained from a few microlitres of human blood. DNA prepared in this way can be easily digested with restriction endonucleases and has been employed for DNA profiling and the polymerase chain reaction.


title: EFFECT OF INTERNAL CONTROL SYSTEMS ON FINANCIAL PERFORMANCE OF SMALL AND MEDIUM SCALE BUSINESS ENTERPRISES IN KISUMU CITY, KENYA

abstract: There has been controversy as to why there is a declining business survival trend among Small and Medium scale Enterprises despite government’s commitment to availability of funds. Economic Survey 2009 statistics indicate a tremendous growth of Small and Medium scale Enterprises in Kenya over the last ten years; constituting about 96 per cent of all business enterprises in the country; yet 90% of the business start-ups do not operate beyond their third anniversary. The main objective of this study therefore was to investigate the effect of internal control systems on financial performance among Small and Medium scale Enterprises in Kisumu city, Kenya; specifically assessing the relationship between internal control systems and return on investment; and establishing the level of business knowledge of an entrepreneur in internal control systems and its effect on financial performance. The sample was selected from the study population through stratified and simple random sampling techniques. The research was conducted using both quantitative and qualitative approaches; adapting cross-sectional survey research design. The study used both primary and secondary data. Primary data was collected using structured questionnaire and interview, while secondary data was obtained from financial statements of the sampled enterprises. Data was analyzed using descriptive statistics as well as inferential statistics. The study specifically revealed that a significant change in financial performance is linked to internal controls systems. Based on the findings of the study, it is concluded that internal control systems as supported by the study findings significantly influence the financial performance of Small and Medium scale Enterprises. The investigation recommends training on the significance of internal controls among proprietors of Small and Medium scale Enterprises.


title: Explaining three-dimensional dimensionality reduction plots

abstract: Understanding three-dimensional projections created by dimensionality reduction from high-variate datasets is very challenging. In particular, classical three-dimensional scatterplots used to display such projections do not explicitly show the relations between the projected points, the viewpoint used to visualize the projection, and the original data variables. To explore and explain such relations, we propose a set of interactive visualization techniques. First, we adapt and enhance biplots to show the data variables in the projected three-dimensional space. Next, we use a set of interactive bar chart legends to show variables that are visible from a given viewpoint and also assist users to select an optimal viewpoint to examine a desired set of variables. Finally, we propose an interactive viewpoint legend that provides an overview of the information visible in a given three-dimensional projection from all possible viewpoints. Our techniques are simple to implement and can be applied to any dimensionality reduction technique. We demonstrate our techniques on the exploration of several real-world high-dimensional datasets.


title: Opponent Modeling in Poker

abstract: Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent modeling, unreliable information and deception, much like decision-making applications in the real world. Agent modeling is one of the most difficult problems in decision-making applications and in poker it is essential to achieving high performance. This paper describes and evaluates Loki, a poker program capable of observing its opponents, constructing opponent models and dynamically adapting its play to best exploit patterns in the opponents' play.


title: Context-aware Image Generation by Using Generative Adversarial Networks

abstract: Automatically generating images from text by using generative adversarial networks (GANs) has been actively investigated. To the best of our knowledge, there is no method of generating images with consideration of the given text and its context; therefore, representing a story describing a series of related actions is insufficient for applications such as generating image sequences. In this paper, we propose a method of automatically tuning the noise parameter of GANs and a context-aware GAN model to generate images from a series of text and image pairs. Our method and model can be used for automatically generating visual stories.


title: Dementia Associated with the Acquired Immunodeficiency Syndrome

abstract: Approximately one third of adults and half of children with the acquired immunodeficiency syndrome (AIDS) eventually have neurologic complications, which are directly attributable to infection of the brain by the human immunodeficiency virus type 1 (HIV-1).1,2 Neurologic problems occur even in the absence of opportunistic infection or secondary cancer.3 Important clinical manifestations include impaired mental concentration, slowness of hand movements, and difficulty in walking. This malady has been called the AIDS dementia complex by Price and colleagues3; a more recent term is HIV-1–associated cognitive–motor complex. Pathologically, the signs of HIV-1 infection in the central nervous system (or HIV . . .


title: What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization

abstract: Using different methods for laying out a graph can lead to very different visual appearances, with which the viewer perceives different information. Selecting a “good” layout method is thus important for visualizing a graph. The selection can be highly subjective and dependent on the given task. A common approach to selecting a good layout is to use aesthetic criteria and visual inspection. However, fully calculating various layouts and their associated aesthetic metrics is computationally expensive. In this paper, we present a machine learning approach to large graph visualization based on computing the topological similarity of graphs using graph kernels. For a given graph, our approach can show what the graph would look like in different layouts and estimate their corresponding aesthetic metrics. An important contribution of our work is the development of a new framework to design graph kernels. Our experimental study shows that our estimation calculation is considerably faster than computing the actual layouts and their aesthetic metrics. Also, our graph kernels outperform the state-of-the-art ones in both time and accuracy. In addition, we conducted a user study to demonstrate that the topological similarity computed with our graph kernel matches perceptual similarity assessed by human users.


title: Spatio-Temporal Learning With the Online Finite and Infinite Echo-State Gaussian Processes

abstract: Successful biological systems adapt to change. In this paper, we are principally concerned with adaptive systems that operate in environments where data arrives sequentially and is multivariate in nature, for example, sensory streams in robotic systems. We contribute two reservoir inspired methods: 1) the online echostate Gaussian process (OESGP) and 2) its infinite variant, the online infinite echostate Gaussian process (OIESGP) Both algorithms are iterative fixed-budget methods that learn from noisy time series. In particular, the OESGP combines the echo-state network with Bayesian online learning for Gaussian processes. Extending this to infinite reservoirs yields the OIESGP, which uses a novel recursive kernel with automatic relevance determination that enables spatial and temporal feature weighting. When fused with stochastic natural gradient descent, the kernel hyperparameters are iteratively adapted to better model the target system. Furthermore, insights into the underlying system can be gleamed from inspection of the resulting hyperparameters. Experiments on noisy benchmark problems (one-step prediction and system identification) demonstrate that our methods yield high accuracies relative to state-of-the-art methods, and standard kernels with sliding windows, particularly on problems with irrelevant dimensions. In addition, we describe two case studies in robotic learning-by-demonstration involving the Nao humanoid robot and the Assistive Robot Transport for Youngsters (ARTY) smart wheelchair.


title: A Cognitive-Based Semantic Approach to Deep Content Analysis in Search Engines

abstract: This paper presents an innovative cognitive-based semantic approach that uses rule-based Natural Language Processing (NLP) in conjunction with a world model and cognitive frames to semantically analyze, understand, and rank digital text in search engines. The goal is to improve the relevance, accuracy, and efficiency of information search. The world model represents things existing in the real world (e.g., subject-related ontologies or classifications essential for understanding the topics to be analyzed) whereas cognitive frames specify possible users’ interactions with the world, including things that people should know or do (e.g., tasks, methods, procedures, cognitive processes) in such interactions. Using a rule-based semantic approach in conjunction with a subject-related world model and task-relevant cognitive frames to understand and evaluate text is an advancement in search engine technology. It addresses three limitations of the existing approaches: the inadequate measure of the meaningful content in web pages; a poor understanding of users’ intention and tasks in their search and, the irrelevance and inaccuracy of search results. This method has led to the successful implementation of a fullscale semantic search engine in medicine (available at Seenso.com). The method is applicable and adaptable to other disciplines and other types of computer applications.


title: Understanding the Influence of User Participation and Involvement on System Success – a Systematic Mapping Study

abstract: User participation and involvement in software development are considered to be essential for a successful software system. Three research areas, human aspects of software engineering, requirements engineering, and information systems, study these topics from various perspectives. We think it is important to analyze user participation and involvement in software engineering comprehensively to encourage further research in this area. We investigate the evidence on effects of user participation and involvement on system success and we explore which methods are available in literature. A systematic mapping study was conducted. The systematic search yielded 3,698 hits, from which we identified 289 unique papers. These papers were reviewed by the first author based on inclusion and exclusion criteria. The second author validated the selection of papers by reviewing the reasons for exclusion and inclusion and the corresponding papers on a sample base. 58 of the 289 papers were selected (22 statistical survey and meta-study papers and 36 methods papers). Based on the empirical evidence of the surveys and meta-studies, we developed a meta-analysis of structural equation models. This overview demonstrates that most papers showed positive correlations between aspects of development processes (including user participation) and human aspects (including user involvement) and system success. The analysis of the proposed solutions from the method papers revealed a wide variety of user participation and involvement practices for most activities within software development.


title: Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age

abstract: With modern computer architecture evolving, two problems conspire against the state-of-the-art approaches in parallel query execution: (i) to take advantage of many-cores, all query work must be distributed evenly among (soon) hundreds of threads in order to achieve good speedup, yet (ii) dividing the work evenly is difficult even with accurate data statistics due to the complexity of modern out-of-order cores. As a result, the existing approaches for plan-driven parallelism run into load balancing and context-switching bottlenecks, and therefore no longer scale. A third problem faced by many-core architectures is the decentralization of memory controllers, which leads to Non-Uniform Memory Access (NUMA). In response, we present the morsel-driven query execution framework, where scheduling becomes a fine-grained run-time task that is NUMA-aware. Morsel-driven query processing takes small fragments of input data (morsels) and schedules these to worker threads that run entire operator pipelines until the next pipeline breaker. The degree of parallelism is not baked into the plan but can elastically change during query execution, so the dispatcher can react to execution speed of different morsels but also adjust resources dynamically in response to newly arriving queries in the workload. Further, the dispatcher is aware of data locality of the NUMA-local morsels and operator state, such that the great majority of executions takes place on NUMA-local memory. Our evaluation on the TPC-H and SSB benchmarks shows extremely high absolute performance and an average speedup of over 30 with 32 cores.


title: A Model-Based Joint Detection and Tracking Approach for Multi-Vehicle Tracking With Lidar Sensor

abstract: This paper presents a method for joint detection and tracking of vehicles with a scanning laser rangefinder. The lidar measurements of an object have the particularity to be spatially distributed, which generally leads to a detection step before any tracking. Differently, the proposed method relies on the raw measurement processing without any detection step, which improves the overall performance in multiobject tracking while providing good estimation accuracies. The solution uses the sequential Monte Carlo methods by incorporating the geometric invariant of the objects of interest (vehicles). This approach also offers an efficient solution to the problem of multitarget tracking by integrating naturally the track management in the filtering process.


title: Aggregation in Natural Language Generation

abstract: In this paper we address the problem of redundancy in text generation. Redundancy typically occurs when the material selected for communication contains information that is duplicated in the text, or else is so closely related that the reader can automatically infer one piece when reading another. Such redundant material is invariably removed by people, and ought to be removed by generator systems, to produce better quality text. We call the process of removing redundancy aggregation. In addressing the problem, three questions arise: Why do people object to redundancy? Which redundant portions are best removed? What mechanisms or rules are used to remove redundant information? In this paper we begin to answer the third question by identifying and describing the aggregation processes generators can use. We first survey the studies we have found on aspects of aggregation. We next outline a study we performed with human subjects. Finally, we define and describe eight aggregation strategies we identified, and discuss several associated issues and open questions.


title: The virtualMe, an integrated virtual teacher framework

abstract: With an ever increasing integration of web technologies into our lives, there is a need to look at how to capitalise on this technology. In a teaching and learning environment students, teachers and administrators are increasingly embedding web technology into their daily work. This paper describes research into an Internet-based, teacher-centred framework that can be used in a teaching and learning environment. A web-based prototype is described that has been used to investigate what features are desirable in a web-based teacher-focused information and knowledge framework, and a survey of existing and potential users is discussed in order to identify the perceived usefulness of the proposed framework.