title: Trust-PCL: An Off-Policy Trust Region Method for Continuous Control

abstract: Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective for continuous control, they typically require a prohibitively large amount of on-policy interaction with the environment. To address this problem, we propose an off-policy trust region method, Trust-PCL. The algorithm is the result of observing that the optimal policy and state values of a maximum reward objective with a relative-entropy regularizer satisfy a set of multi-step pathwise consistencies along any path. Thus, Trust-PCL is able to maintain optimization stability while exploiting off-policy data to improve sample efficiency. When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO.


title: Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities

abstract: In the field of automated program repair, the redundancy assumption claims large programs contain the seeds of their own repair. However, most redundancy-based program repair techniques do not reason about the repair ingredients– the code that is reused to craft a patch. We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation. Our approach, DeepRepair, relies on deep learning to reason about code similarities. Code fragments at well-defined levels of granularity in a codebase can be sorted according to their similarity to suspicious elements (i.e., code elements that contain suspicious statements) and statements can be transformed by mapping out-of-scope identifiers to similar identifiers in scope. We examined these new search strategies for patch generation with respect to effectiveness from the viewpoint of a software maintainer. Our comparative experiments were executed on six open-source Java projects including 374 buggy program revisions and consisted of 19,949 trials spanning 2,616 days of computation time. Deep-Repair’s search strategy using code similarities generally found compilable ingredients faster than the baseline, jGenProg, but this improvement neither yielded test-adequate patches in fewer attempts (on average) nor found significantly more patches (on average) than the baseline. Although the patch counts were not statistically different, there were notable differences between the nature of DeepRepair patches and jGenProg patches. The results show that our learning-based approach finds patches that cannot be found by existing redundancy-based repair techniques.


title: VideoWhiteboard: video shadows to support remote collaboration

abstract: VideoWhiteboard is a prototype tool to support remote shared drawing activity. It provides a whiteboard-sized shared drawing space for collaborators who are located in remote sites. It allows each user to see the drawings and a shadow of the gestures of collaborators at the remote site. The development of VideoWhiteboard is based on empirical studies of collaborative drawing activity, including experiences in using the VideoDraw shared drawing prototype. VideoWhiteboard enables remote collaborators to work together much as if they were sharing a whiteboard, and in some ways allows them to work together even more closely than if they were in the same room.


title: A Graph Matching Approach Based on Aggregated Search

abstract: Graph datasets are commonly used in a broad range of application domains, which makes the graph querying a crucial task in order to fully exploit the knowledge within these datasets. Using approximate graph matching tools is usually preferred due to the noisy nature of graph datasets, with the aim of overcoming restrictive query answering. In this paper, we propose a novel approach for approximate graph matching using aggregated search, called Label and Structure Similarity Aggregated Search (LaSaS). LaSaS yields effective and efficient graph querying without having a fixed schema of the data graph through: (i) the use of the aggregated search strategy to raise the number of answers, (ii) the use of a new low-cost graph similarity metric that considers similarities of nodes labels and graphs structures to enable finding approximate matches.


title: Volume xx (200y), Number z, pp. 1–12 Scale Space Meshing of Raw Data Point Sets

abstract: This paper develops a scale space strategy for orienting and meshing exactly and completely a raw point set. The scale space is based on the intrinsic heat equation, also called mean curvature motion (MCM). A simple iterative scheme implementing MCM directly on the raw point set is described, and a mathematical proof of its consistency with MCM is given. Points evolved by this MCM implementation can be trivially backtracked to their initial raw position. Therefore, both the orientation and mesh of the data point set obtained at a smooth scale can be transported back on the original. The gain in visual accuracy is demonstrated on archaeological objects by comparison with several state of the art meshing methods.


title: A Latent Dirichlet Model for Unsupervised Entity Resolution

abstract: Entity resolution has received considerable attention in recent years. Given many references to underlying entities, the goal is to predict which references correspond to the same entity. We show how to extend the Latent Dirichlet Allocation model for this task and propose a probabilistic model for collective entity resolution for relational domains where references are connected to each other. Our approach differs from other recently proposed entity resolution approaches in that it is a) generative, b) does not make pair-wise decisions and c) captures relations between entities through a hidden group variable. We propose a novel sampling algorithm for collective entity resolution which is unsupervised and also takes entity relations into account. Additionally, we do not assume the domain of entities to be known and show how to infer the number of entities from the data. We demonstrate the utility and practicality of our relational entity resolution approach for author resolution in two real-world bibliographic datasets. In addition, we present preliminary results on characterizing conditions under which relational information is useful.


title: Motion Tracking with an Active Camera

abstract: This paper describes a method for real-time motion detection using an active camera mounted on a pan/tilt platform. Image mapping is used to align images of different viewpoints so that static camera motion detection can be applied. In the presence of camera position noise, the image mapping is inexact and compensation techniques fail. The use of morphological filtering of motion images is explored to desensitize the detection algorithm to inaccuracies in background compensation, Two motion detection techniques are examined, and experiments to verify the methods are presented. The system successfully extracts moving edges from dynamic images even when the pan/tilt angles between successive frames are as large as 3. >


title: Snap-and-ask: answering multimodal question by naming visual instance

abstract: In real-life, it is easier to provide a visual cue when asking a question about a possibly unfamiliar topic, for example, asking the question, "Where was this crop circle found?". Providing an image of the instance is far more convenient than texting a verbose description of the visual properties, especially when the name of the query instance is not known. Nevertheless, having to identify the visual instance before processing the question and eventually returning the answer makes multimodal question-answering technically challenging. This paper addresses the problem of visual-to-text naming through the paradigm of answering-by-search in a two-stage computational framework, which is composed out of instance search (IS) and similar question ranking (QR). In IS, names of the instances are inferred from similar visual examples searched through a million-scale image dataset. For recalling instances of non-planar and non-rigid shapes, spatial configurations that emphasize topology consistency while allowing for local variations in matches have been incorporated. In QR, the candidate names of the instance are statistically identified from search results and directly utilized to retrieve similar questions from community-contributed QA (cQA) archives. By parsing questions into syntactic trees, a fuzzy matching between the inquirer's question and cQA questions is performed to locate answers and recommend related questions to the inquirer. The proposed framework is evaluated on a wide range of visual instances (e.g., fashion, art, food, pet, logo, and landmark) over various QA categories (e.g., factoid, definition, how-to, and opinion).


title: Dual coordinate descent methods for logistic regression and maximum entropy models

abstract: Most optimization methods for logistic regression or maximum entropy solve the primal problem. They range from iterative scaling, coordinate descent, quasi-Newton, and truncated Newton. Less efforts have been made to solve the dual problem. In contrast, for linear support vector machines (SVM), methods have been shown to be very effective for solving the dual problem. In this paper, we apply coordinate descent methods to solve the dual form of logistic regression and maximum entropy. Interestingly, many details are different from the situation in linear SVM. We carefully study the theoretical convergence as well as numerical issues. The proposed method is shown to be faster than most state of the art methods for training logistic regression and maximum entropy.


title: On stochastic optimal control and reinforcement learning by approximate inference

abstract: We present a reformulation of the stochastic optimal control problem in terms of KL divergence minimisation, not only providing a unifying perspective of previous approaches in this area, but also demonstrating that the formalism leads to novel practical approaches to the control problem. Specifically, a natural relaxation of the dual formulation gives rise to exact iter- ative solutions to the finite and infinite horizon stochastic optimal control problem, while direct application of Bayesian inference methods yields instances of risk sensitive control. We furthermore study corresponding formulations in the reinforcement learning setting and present model free algorithms for problems with both discrete and continuous state and action spaces. Evaluation of the proposed methods on the standard Gridworld and Cart-Pole benchmarks verifies the theoretical insights and shows that the proposed methods improve upon current approaches.


The University of Maryland’s Chinese-English Neural Machine Translation Systems at WMT18


title: Improved Sampling Techniques for Learning an Imbalanced Data Set

abstract: This paper presents the performance of a classifier built using the stackingC algorithm in nine different data sets. Each data set is generated using a sampling technique applied on the original imbalanced data set. Five new sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling Undersampling, and Combined-Lax Random Undersampling Oversampling) that were based on the three sampling techniques (i.e., Random Undersampling, Random Oversampling, and Synthetic Minority Oversampling Technique) usually used as solutions in imbalance learning. The metrics used to evaluate the classifier's performance were F-measure and G-mean. F-measure determines the performance of the classifier for every class, while G-mean measures the overall performance of the classifier. The results using F-measure showed that for the data without a sampling technique, the classifier's performance is good only for the majority class. It also showed that among the eight sampling techniques, RU and LRU have the worst performance while other techniques (i.e., RO, C-LRUO and C-LROU) performed well only on some classes. The best performing techniques in all data sets were SMOTE, SMOTERandRep, and LRO having the lowest F-measure values between 0.5 and 0.65. The results using G-mean showed that the oversampling technique that attained the highest G-mean value is LRO (0.86), next is C-LROU (0.85), then SMOTE (0.84) and finally is SMOTERandRep (0.83). Combining the result of the two metrics (F-measure and G-mean), only the three sampling techniques are considered as good performing (i.e., LRO, SMOTE, and SMOTERandRep).


title: Using a three-axis accelerometer and GPS module in a smart phone to measure walking steps and distance

abstract: In this paper we use a smart phone with three-axis accelerometer features together with a multiple judgment method to improve the accuracy of measuring both walking and running steps. In the past, using only a single characteristic for ascertaining whether there is a walking step resulted in large errors while on the other hand the multiple characteristics of acceleration variation are used to decide the presence of walking steps, thus effectively enhancing accuracy. By using a GPS module this design estimates both the user walking distance and the user path as depicted on the GPS map, and then allows the user to estimate the amount of exercise.


title: The Pet-friendly Veterinary Practice: A Guide for Practitioners

abstract: Low-stress handling is important for the safety of the veterinary staff and for the welfare of the patient. The commitment to ensuring the emotional well-being of the patient should be equal to that shown toward the physical well-being of the animals under a veterinarian’s care. Before handling animals it is essential to assess the environment and the patient’s response to it. Taking the time to create a behavior handling plan makes future visits easier and bonds clients to the practice. Understanding how and when to use handling tools is key to making patient visits safer, more humane, and more efficient.


title: Visual navigation of a mobile robot with laser-based collision avoidance

abstract: In this paper, we propose and validate a framework for visual navigation with collision avoidance for a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such a path, the robot is able to avoid obstacles which were not present during teaching, and which are sensed by an on-board range scanner. Our control scheme guarantees that obstacle avoidance and navigation are achieved simultaneously. In fact, in the presence of obstacles, the camera pan angle is actuated to maintain scene visibility while the robot circumnavigates the obstacle. The risk of collision and the eventual avoiding behaviour are determined using a tentacle-based approach. The framework can also deal with unavoidable obstacles, which make the robot decelerate and eventually stop. Simulated and real experiments show that with our method, the vehicle can navigate along a visual path while avoiding collisions.


title: Hidden Markov models based on multi-space probability distribution for pitch pattern modeling

abstract: This paper discusses a hidden Markov model (HMM) based on multi-space probability distribution (MSD). The HMMs are widely-used statistical models to characterize the sequence of speech spectra and have successfully been applied to speech recognition systems. From these facts, it is considered that the HMM is useful for modeling pitch patterns of speech. However, we cannot apply the conventional discrete or continuous HMMs to pitch pattern modeling since the observation sequence of the pitch pattern is composed of one-dimensional continuous values and a discrete symbol which represents "unvoiced". MSD-HMM includes discrete HMMs and continuous mixture HMMs as special cases, and further can model the sequence of observation vectors with variable dimension including zero-dimensional observations, i.e., discrete symbols. As a result, MSD-HMMs can model pitch patterns without heuristic assumption. We derive a reestimation algorithm for the extended HMM and show that it can find a critical point of the likelihood function.


title: Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation

abstract: Customization requires not only an implementation of proper manufacturing systems but also a proper strategy regarding when firms should offer customized products and what the nature of customization should be. This paper questions 1)whether customization is better than no customization, and, if so, 2) what kind of customization strategy firms should adopt under competition. We find that customization is not optimal when the cost of soliciting customer preference information is sufficiently high. When competing firms choose to customize, we show that firms target only certain customer segments with customized products. We also find that the optimal customization strategy may require firms to offer only a few discrete product varieties. Despite the concern that customization may initiate price wars because customization reduces product differentiation, we find that customization does not escalate the price competition, because aggressive price competition exacerbates cannibalization. Although customers within the product line of a firm are charged higher prices, we show that on average customers are better off when firms adopt customization. However, unless the customization is quite cheap, when firms choose to customize, we find that firms cannot generate more profits than when firms offer only a single product


title: Dataset Popularity Prediction for Caching of CMS Big Data

abstract: The Compact Muon Solenoid (CMS) experiment at the European Organization for Nuclear Research (CERN) deploys its data collections, simulation and analysis activities on a distributed computing infrastructure involving more than 70 sites worldwide. The historical usage data recorded by this large infrastructure is a rich source of information for system tuning and capacity planning. In this paper we investigate how to leverage machine learning on this huge amount of data in order to discover patterns and correlations useful to enhance the overall efficiency of the distributed infrastructure in terms of CPU utilization and task completion time. In particular we propose a scalable pipeline of components built on top of the Spark engine for large-scale data processing, whose goal is collecting from different sites the dataset access logs, organizing them into weekly snapshots, and training, on these snapshots, predictive models able to forecast which datasets will become popular over time. The high accuracy achieved indicates the ability of the learned model to correctly separate popular datasets from unpopular ones. Dataset popularity predictions are then exploited within a novel data caching policy, called PPC (Popularity Prediction Caching). We evaluate the performance of PPC against popular caching policy baselines like LRU (Least Recently Used). The experiments conducted on large traces of real dataset accesses show that PPC outperforms LRU reducing the number of cache misses up to 20% in some sites.


title: Semantic locking in object-oriented database systems

abstract: Object-oriented databases are being increasingly used to model non-standard applications that emphasize modularity, composition, and rapid prototyping. A semantic locking protocol is presented for transaction management for such object-oriented databases. In particular, the protocol incorporates the semantics of complex objects, nested executions and dynamic conflicts resulting from referentially shared objects.


title: Using text mining and sentiment analysis for online forums hotspot detection and forecast

abstract: Text sentiment analysis, also referred to as emotional polarity computation, has become a flourishing frontier in the text mining community. This paper studies online forums hotspot detection and forecast using sentiment analysis and text mining approaches. First, we create an algorithm to automatically analyze the emotional polarity of a text and to obtain a value for each piece of text. Second, this algorithm is combined with K-means clustering and support vector machine (SVM) to develop unsupervised text mining approach. We use the proposed text mining approach to group the forums into various clusters, with the center of each representing a hotspot forum within the current time span. The data sets used in our empirical studies are acquired and formatted from Sina sports forums, which spans a range of 31 different topic forums and 220,053 posts. Experimental results demonstrate that SVM forecasting achieves highly consistent results with K-means clustering. The top 10 hotspot forums listed by SVM forecasting resembles 80% of K-means clustering results. Both SVM and K-means achieve the same results for the top 4 hotspot forums of the year.


title: Movement Direction and Distance Classification Using a Single PIR Sensor

abstract: Pyroelectric infrared sensors (PIR) are excellent devices to detect humananimal presence with a small form factor and rugged design, for cost-effective surveillance. They are widely used to trigger an intruder alarm and activate household appliances upon the presence of a human. However, the analog output from the sensor is proportional to several spatial and temporal relationships between an object in the field of view of the sensor, the sensitivity of the sensor, PIR lens features, and the environmental heat conditions. This article shows the use of only one PIR sensor in conjunction with signal processing and machine learning algorithms to clearly estimate the presence, direction, and distance of the human movements in a hallway. Simulation results show that more than 99 and 93 accuracy, respectively, in classifying direction and distance of the movements in the field of view of the PIR sensor module. Moreover, hardware and software resources are used to address the limited computational capabilities of commercially available general purpose microcontroller and sensory components to reduce the cost of the entire PIR system module. This article uses only one PIR sensor module with many well-known machine learning and computer vision methods for analog pattern recognition and movement classification. Primarily, this information is used to monitor occupational analysis of a particular floor in a building. However, the application domain can be extended to any one of the earlier mentioned areas in this abstract.


title: Learning Bayesian network parameters under equivalence constraints

abstract: We propose a principled approach for learning parameters in Bayesian networks from incomplete datasets, where the examples of a dataset are subject to equivalence constraints. These equivalence constraints arise from datasets where examples are tied together, in that we may not know the value of a particular variable, but whatever that value is, we know it must be the same across different examples. We formalize the problem by defining the notion of a constrained dataset and a corresponding constrained likelihood that we seek to optimize. We further propose a new learning algorithm that can effectively learn more accurate Bayesian networks using equivalence constraints, which we demonstrate empirically. Moreover, we highlight how our general approach can be brought to bear on more specialized learning tasks, such as those in semi-supervised clustering and topic modeling, where more domain-specific approaches were previously developed.


An Attention-Based Syntax-Tree and Tree-LSTM Model for Sentence Summarization


title: Soft segmented inchworm robot with dielectric elastomer muscles

abstract: Robotic devices typically utilize rigid components in order to produce precise and robust operation. Rigidity becomes a ::: significant impediment, however, when navigating confined or constricted environments e.g. search-and-rescue, ::: industrial pipe inspection. In such cases adaptively conformable soft structures become optimal. Dielectric elastomers ::: (DEs) are well suited for developing such soft robots since they are inherently compliant and can produce large musclelike ::: actuation strains. In this paper, a soft segmented inchworm robot is presented that utilizes pneumatically-coupled DE ::: membranes to produce inchworm-like locomotion. The robot is constructed from repeated body segments, each with a ::: simple control architecture, so that the total length can be readily adapted by adding or removing segments. Each ::: segment consists of a soft inflatable shell (internal pressure in range of 1.0-15.9 mBar) and a pair of antagonistic DE ::: membranes (VHB 4905). Experimental testing of a single body segment is presented and the relationship between drive ::: voltage, pneumatic pressure and active displacement is characterized. This demonstrates that pneumatic coupling of DE ::: membranes induces complex non-linear electro-mechanical behaviour as drive voltage and pneumatic pressure are ::: altered. Locomotion of a two-segment inchworm robot prototype with a passive length of 80 mm is presented. Artificial ::: setae are included on the body shell to generate anisotropic friction for locomotion. A maximum locomotion speed of 4.1 ::: mm/s was recorded at a drive frequency of 1.5 Hz, which compares favourably to biological counterparts. Future ::: development of the soft inchworm robot are discussed including reflexive low-level control of individual segments.


title: Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

abstract: Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP. We demonstrate and quantify the benefit of the action-dependent baseline through both theoretical analysis as well as numerical results, including an analysis of the suboptimality of the optimal state-dependent baseline. The result is a computationally efficient policy gradient algorithm, which scales to high-dimensional control problems, as demonstrated by a synthetic 2000-dimensional target matching task. Our experimental results indicate that action-dependent baselines allow for faster learning on standard reinforcement learning benchmarks and high-dimensional hand manipulation and synthetic tasks. Finally, we show that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.


title: Smart-home automation using IoT-based sensing and monitoring platform

abstract: In order to help maintain comfortable living conditions within a home, home monitoring and automation are utilized. The standards of human's comfort in homes can be categorized into several types. Among these categories, the most significant ones are the thermal comfort, which is related to temperature and humidity, followed by the visual comfort, related to colors and light, and hygienic comfort, associated with air quality. A system can be set to monitor these parameters to help maintain them within an acceptable range. Additionally, making the house smart is to allow for intelligent automatic executing of several commands after analyzing the collected data. Automation can be accomplished by using the Internet of Things (IoT). This gives the inhabitant accesses to certain data in the house and the ability to control some parameters remotely. This paper presents the complete design of an IoT based sensing and monitoring system for smart home automation. The proposed design uses the EmonCMS platform for collecting and visualizing monitored data and remote controlling of home appliances and devices. The selected platform is very flexible and user-friendly. The sensing of different variables inside the house is conducted using the NodeMCU-ESP8266 microcontroller board, which allows realtime data sensing, processing and uploading/downloading to/from the EmonCMS cloud server.


title: Access to another mind: Naturalistic theories require naturalistic data

abstract: If there is to be a natural theory of consciousness that would satisfy both philosophers and scientists, it must be based on naturalistic data and minimal clutter accumulated from semantic arguments. Carruthers offers a 'natural' theory of consciousness that is rather myopic. To explore the evolutionary basis of consciousness, a natural theory should include comparative psychological and neurological data that encompass nonlinguistic measures. Such an approach could provide a clearer picture of the adaptive function, mechanisms, and origins of consciousness.


title: ECG to identify individuals

abstract: The electrocardiogram (ECG also called EKG) trace expresses cardiac features that are unique to an individual. The ECG processing followed a logical series of experiments with quantifiable metrics. Data filters were designed based upon the observed noise sources. Fiducial points were identified on the filtered data and extracted digitally for each heartbeat. From the fiducial points, stable features were computed that characterize the uniqueness of an individual. The tests show that the extracted features are independent of sensor location, invariant to the individual's state of anxiety, and unique to an individual.


title: Misuse, Abuse and Reuse: Economic Utility Functions for Characterising Security Requirements

abstract: Negative use cases — in the form of 'misuse' or 'abuse' cases — have found a broad following within the security community due to their ability to make explicit the knowledge, assumptions and desires of stakeholders regarding real and perceived threats to systems. As an accepted threat modelling tool, they have become a standard part of many Secure Software Engineering (SSE) processes. Despite this widespread adoption, aspects of the original misuse case concept have yet to receive a formal treatment in the literature. This paper considers the application of economic utility functions within the negative use case development process, as a means of addressing existing challenges. We provide a simple demonstration of how existing practice might integrate economic factors to describe the business, management and functional concerns that surround system security and software development.


title: Fingerstroke time estimates for touchscreen-based mobile gaming interaction.

abstract: The growing popularity of gaming applications and ever-faster mobile carrier networks have called attention to an intriguing issue that is closely related to command input performance. A challenging mirroring game service, which simultaneously provides game service to both PC and mobile phone users, allows them to play games against each other with very different control interfaces. Thus, for efficient mobile game design, it is essential to apply a new predictive model for measuring how potential touch input compares to the PC interfaces. The present study empirically tests the keystroke-level model (KLM) for predicting the time performance of basic interaction controls on the touch-sensitive smartphone interface (i.e., tapping, pointing, dragging, and flicking). A modified KLM, tentatively called the fingerstroke-level model (FLM), is proposed using time estimates on regression models.


title: Automatic era: Presentation slides from Academic paper

abstract: Presentations are a productive approach to pass the blueprint of a work to the group of onlookers; here the framework is intended to make the presentation slides consequently from PDF document. Such produced slide can be utilized as draft slides. This draft slides will set up their last slide in a simple way, which will spare moderator's opportunity. To make presentation slide automatic, framework utilizes SVR strategy and ILP technique. SVR is used to calculate important sentences in the paper and concentrate the key elements. ILP is a strategy that connected on generated slide from the whole paper which performs sentence arrangement. Specialized papers are in PDF position having the content segment, graphical components like table, flowchart and so forth. Here the framework concentrates on content component and also graphical components to assemble presentation slide as draft slide and make slides more justifiable and distinctive.


title: Floor-plan reconstruction from panoramic images

abstract: The capturing of panoramic 360° images has become a popular photographic technique. While a panoramic image gives an impressive view of the environment, many people have difficulties to understand the spatial scene arrangement from this flat image. In this paper, we present a new visualization technique for panoramic images based on a coarse reconstruction of the indoor environment, in which the panorama was captured. Applications of this are, for example, real-estate or hotel advertising, featuring virtual tours through the apartment. We use a semi-automatic reconstruction process, in which the user marks the room corners in the panoramic images. These can be translated into viewing-angle measurements, from which our algorithms can compute the exact sizes of the walls, based on a pre-defined geometric model.


title: Metamer-set-based approach to estimating surface reflectance from camera RGB.

abstract: We present an approach to estimating the reflectance of a surface given its camera response. In this approach we first solve the general form of this problem: we calculate the set of all possible surface reflectances, called the metamer set, and then choose a member from this set. Three possibilities in choosing a single reflectance are described here. First, we assume that all reflectances are equally likely and minimize worst-case error. Second, we adopt the assumption that reflectances follow a normal probability distribution and maximize this probability. Finally, we assume that reflectances are smooth and maximize this property. The results of our experiments show that there is significant benefit from the proposed approach in terms of the accuracy of the estimation compared with that of standard estimation methods. Moreover, the present approach introduces a notion of robustness of estimates in the form of error bars.


title: Hardware Implementation of (63,51) BCH Encoder and Decoder For WBAN Using LFSR and BMA

abstract: Error Correcting Codes are required to have a reliable communication through a medium that has an unacceptable bit error rate and low signal to noise ratio. In IEEE 802.15.6 2.4GHz Wireless Body Area Network (WBAN), data gets corrupted during the transmission and reception due to noises and interferences. Ultra low power operation is crucial to prolong the life of implantable devices. Hence simple block codes like BCH (63, 51, 2) can be employed in the transceiver design of 802.15.6 Narrowband PHY. In this paper, implementation of BCH (63, 51, t = 2) Encoder and Decoder using VHDL is discussed. The incoming 51 bits are encoded into 63 bit code word using (63, 51) BCH encoder. It can detect and correct up to 2 random errors. The design of an encoder is implemented using Linear Feed Back Shift Register (LFSR) for polynomial division and the decoder design is based on syndrome calculator, inversion-less Berlekamp-Massey algorithm (BMA) and Chien search algorithm. Synthesis and simulation were carried out using Xilinx ISE 14.2 and ModelSim 10.1c. The codes are implemented over Virtex 4 FPGA device and tested on DN8000K10PCIE Logic Emulation Board. To the best of our knowledge, it is the first time an implementation of (63, 51) BCH encoder and decoder carried out.


title: Visualizing real-time multivariate data using preattentive processing

abstract: A new method is presented for visualizing data as they are generated from real-time applications. These techniques allow viewers to perform simple data analysis tasks such as detection of data groups and boundaries, target detection, and estimation. The goal is to do this rapidly and accurately on a dynamic sequence of data frames. Our techniques take advantage of an ability of the human visual system called preattentive processing. Preattentive processing refers to an initial organization of the visual system based on operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, orientation, intensity, size, curvature, and line length. We believe that studies from preattentive processing should be used to assist in the design of visualization tools, especially those for which high speed target, boundary, and region detection are important. Previous work has shown that results from research in preattentive processing can be used to build visualization tools that allow rapid and accurate analysis of individual, static data frames. We extend these techniques to a dynamic real-time environment. This allows users to perform similar tasks on dynamic sequences of frames, exactly like those generated by real-time systems such as visual interactive simulation. We studied two known preattentive features, hue and curvature. The primary question investigated was whether rapid and accurate target and boundary detection in dynamic sequences is possible using these features. Behavioral experiments were run that simulated displays from our preattentive visualization tools. Analysis of the results of the experiments showed that rapid and accurate target and boundary detection is possible with both hue and curvature. A second question, whether interactions occur between the two features in a real-time environment, was answered positively.


title: The hippocampus as a "stupid," domain-specific module: Implications for theories of recent and remote memory, and of imagination.

abstract: The hippocampus and surrounding regions of the medial temporal lobe play a central role in all neuropsychological theories of memory. It is still a matter of debate, however, how best to characterise the functions of these regions, the hippocampus in particular. In this article, I examine the proposal that the hippocampus is a “stupid” module whose specific domain is consciously apprehended information. A number of interesting consequences for the organisation of memory and the brain follow from this proposal and the assumptions it entails. These, in turn, have important implications for neuropsychological theories of recent and remote episodic, semantic, and spatial memory and for the functions that episodic memory may serve in perception, comprehension, planning, imagination, and problem solving. I consider these implications by selectively reviewing the literature and primarily drawing on research my collaborators and I have conducted.


title: Epenthetic vowels in Japanese: A perceptual illusion?

abstract: In four cross-linguistic experiments comparing French and Japanese hearers, we found that the phonotactic properties of Japanese (very reduced set of syllable types) induce Japanese listeners to perceive ``illusory'' vowels inside consonant clusters in VCCV stimuli. In Experiments 1 and 2, we used a continuum of stimuli ranging from no vowel (e.g. ebzo) to a full vowel between the consonants (e.g. ebuzo). Japanese, but not French participants, reported the presence of a vowel [u] between consonants, even in stimuli with no vowel. A speeded ABX discrimination paradigm was used in Experiments 3 and 4, and revealed that Japanese participants had trouble discriminating between VCCV and VCuCV stimuli. French participants, in contrast had problems discriminating items that differ in vowel length (ebuzo vs. ebuuzo), a distinctive contrast in Japanese but not in French. We conclude that models of speech perception have to be revised to account for phonotactically-based assimilations.


title: Improving On-policy Learning with Statistical Reward Accumulation

abstract: Deep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided by the environment, typically in the form of discounted cumulative returns. Such reward signals represent the immediate feedback of a particular action performed by an agent. However, tasks with sparse reward signals are still challenging to on-policy methods. In this paper, we introduce an effective characterization of past reward statistics (which can be seen as long-term feedback signals) to supplement this immediate reward feedback. In particular, value functions are learned with multi-critics supervision, enabling complex value functions to be more easily approximated in on-policy learning, even when the reward signals are sparse. We also introduce a novel exploration mechanism called "hot-wiring" that can give a boost to seemingly trapped agents. We demonstrate the effectiveness of our advantage actor multi-critic (A2MC) method across the discrete domains in Atari games as well as continuous domains in the MuJoCo environments. A video demo is provided at https://youtu.be/zBmpf3Yz8tc.


title: A Survey on Sentiment and Emotion Analysis for Computational Literary Studies

abstract: Emotions have often been a crucial part of compelling narratives: literature tells about people with goals, desires, passions, and intentions. In the past, classical literary studies usually scrutinized the affective dimension of literature within the framework of hermeneutics. However, with emergence of the research field known as Digital Humanities (DH) some studies of emotions in literary context have taken a computational turn. Given the fact that DH is still being formed as a science, this direction of research can be rendered relatively new. At the same time, the research in sentiment analysis started in computational linguistic almost two decades ago and is nowadays an established field that has dedicated workshops and tracks in the main computational linguistics conferences. This leads us to the question of what are the commonalities and discrepancies between sentiment analysis research in computational linguistics and digital humanities? In this survey, we offer an overview of the existing body of research on sentiment and emotion analysis as applied to literature. We precede the main part of the survey with a short introduction to natural language processing and machine learning, psychological models of emotions, and provide an overview of existing approaches to sentiment and emotion analysis in computational linguistics. The papers presented in this survey are either coming directly from DH or computational linguistics venues and are limited to sentiment and emotion analysis as applied to literary text.


title: A New Optimization Layer for Real-Time Bidding Advertising Campaigns

abstract: While it is relatively easy to start an online advertising campaign, obtaining a high Key Performance Indicator (KPI) can be challenging. A large body of work on this subject has already been performed and platforms known as DSPs are available on the market that deal with such an optimization. From the advertiser's point of view, each DSP is a different black box, with its pros and cons, that needs to be configured. In order to take advantage of the pros of every DSP, advertisers are well-advised to use a combination of them when setting up their campaigns. In this paper, we propose an algorithm for advertisers to add an optimization layer on top of DSPs. The algorithm we introduce, called SKOTT, maximizes the chosen KPI by optimally configuring the DSPs and putting them in competition with each other. SKOTT is a highly specialized iterative algorithm loosely based on gradient descent that is made up of three independent sub-routines, each dealing with a different problem: partitioning the budget, setting the desired average bid, and preventing under-delivery. In particular, one of the novelties of our approach lies in our taking the perspective of the advertisers rather than the DSPs. Synthetic market data is used to evaluate the efficiency of SKOTT against other state-of-the-art approaches adapted from similar problems. The results illustrate the benefits of our proposals, which greatly outperforms the other methods.


title: Matthew Thomson Human Brands: Investigating Antecedents to Consumers ' Strong Attachments to Celebrities

abstract: Abstract This article explores recent advances in self-determination research to address why consumers develop strong attachments to “human brands,” a term that refers to any well-known persona who is the subject of marketing communications efforts. Study 1 uses a survey that is analyzed with structural equation modeling. Study 2 is qualitative and offers corroborating evidence for the proposed theoretical model. Study 3 extends the model with a more naturalistic sample and tests several alternative hypotheses using hierarchical regression. The results suggest that when a human brand enhances a person's feelings of autonomy and relatedness and does not suppress feelings of competence, the person is likely to become more strongly attached to it. This article documents that strong attachments are predictive of satisfied, trusting, and committed relationships and proposes that attachment strength may be a parsimonious proxy for consumer–brand relationship strength. The results imply that benefits would accru...


title: Deep Learning for Video Classification and Captioning

abstract: Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data. In this paper, we focus on reviewing two lines of research aiming to stimulate the comprehension of videos with deep learning: video classification and video captioning. While video classification concentrates on automatically labeling video clips based on their semantic contents like human actions or complex events, video captioning attempts to generate a complete and natural sentence, enriching the single label as in video classification, to capture the most informative dynamics in videos. In addition, we also provide a review of popular benchmarks and competitions, which are critical for evaluating the technical progress of this vibrant field.


title: Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

abstract: Research within the field of hydrology often focuses on the statistical problem of comparing stochastic to machine learning (ML) forecasting methods. The performed comparisons are based on case studies, while a study providing large-scale results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 extensive computational experiments based on simulations. Each of these experiments uses 2000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the forecasting performance of the methods using 18 metrics. The results indicate that stochastic and ML methods may produce equally useful forecasts.


title: Hierarchical Cloth Simulation using Deep Neural Networks

abstract: Fast and reliable physically-based simulation techniques are essential for providing flexible visual effects for computer graphics content. In this paper, we propose a fast and reliable hierarchical cloth simulation method, which combines conventional physically-based simulation with deep neural networks (DNN). Simulations of the coarsest level of the hierarchical model are calculated using conventional physically-based simulations, and more detailed levels are generated by inference using DNN models. We demonstrate that our method generates reliable and fast cloth simulation results through experiments under various conditions.


title: Compact SIW Annular Ring Slot Antenna With Multiband Multimode Characteristics

abstract: A substrate-integrated waveguide (SIW) cavity-backed annular ring slot antenna with multiband multimode characteristics is proposed in this communication. The antenna consists of an SIW cavity and an annular slot on top surface as the radiator. Two patch-like radiation patterns and one monopolar radiation pattern at three different frequencies are excited by the single fed antenna. Moreover, each operating frequency can be individually adjusted. A prototype is fabricated and measured to validate the theoretical analysis. Good agreement between the simulated and measured results of the input reflection coefficients and radiation patterns is achieved. Compared with other designs, the proposed antenna has features of simple structure, multiband multimode and frequency-tunable characteristics.


title: A Pen-Pressure-Sensitive Capacitive Touch System Using Electrically Coupled Resonance Pen

abstract: A touch system sensing pen-pressure of the proposed electrically coupled resonance (ECR) pen is implemented, which can replace costly digitizer system containing electro magnetic resonance (EMR) and capacitive touch system. The proposed system detects the location of the ECR pen and finger using proposed position sensor, and senses pen-pressure of ECR pen using proposed pen-pressure sensor. For the position sensor, to detect even small variation of the mutual capacitance on touch screen panel (TSP) of the pen, a simultaneous driving scheme is proposed with modified Hadamard matrix, resulting in highly increased dynamic range and SNR. In the proposed pen-pressure sensor, the resonant frequency of the ECR pen is measured by a frequency to voltage converter based sensor. The measured SNR for the pen position is 49 dB with 1 mm ${\mmb\varphi}$ metal pillar, and 6.5-bit resolution is achieved for pen-pressure sensor in $6\sigma$ criteria.


A Study of Ferroresonance & Mitigation Techniques April


title: CRISPR as a driving force: the Model T of biotechnology

abstract: The CRISPR system for gene editing can break, repair, and replace targeted sections of DNA. Although CRISPR gene editing has important therapeutic potential, it raises several ethical concerns. Some bioethicists worry CRISPR is a prelude to a dystopian future, while others maintain it should not be feared because it is analogous to past biotechnologies. In the scientific literature, CRISPR is often discussed as a revolutionary technology. In this paper we unpack the framing of CRISPR as a revolutionary technology and contrast it with framing it as a value-threatening biotechnology or business-as-usual. By drawing on a comparison between CRISPR and the Ford Model T, we argue CRISPR is revolutionary as a product, process, and as a force for social change. This characterization of CRISPR offers important conceptual clarity to the existing debates surrounding CRISPR. In particular, conceptualizing CRISPR as a revolutionary technology structures regulatory goals with respect to this new technology. Revolutionary technologies have characteristic patterns of implementation, entrenchment, and social impact. As such, early identification of technologies as revolutionary may help construct more nuanced and effective ethical frameworks for public policy.


title: Solving LEGO brick layout problem using Evolutionary Algorithms

abstract: LEGO® presented the following problem at the SCAI'01 conference in February 2001: Given any 3D body, how can it be built from LEGO bricks? We apply methods of Evolutionary Algorithms (EA) to solve this optimization problem. For this purpose several specific operators are defined, applied, and their use is compared. In addition, mutation operators with dynamic rate of mutation updated based on their contribution to progress of evolution are proposed. Different population organization strategies are compared. Early results indicate that EA is suitable for solving this hard optimization problem.


title: Fully autonomous hip exoskeleton saves metabolic cost of walking

abstract: We have developed a hip exoskeleton for seniors with difficulties in walking due to muscle weakness. The exoskeleton is lightweight and moderate in assistance power compared to other hip exoskeletons in the literature. Its controller estimates user gait phase, walking speed, and ground inclinations to generate assistance torque adaptively. To assess the physiological effect of the gait assistance, we compared metabolic energy consumption for 5 adults for walking on a treadmill with and without the exoskeleton at the same speed: the exoskeleton reduced metabolic cost of walking by 13% (p = 0:0024). The step length and the stride time increased under the assistance. Our analysis for the result suggests that the efficiency of hip exoskeletons on saving metabolic energy can be twice as high as that of ankle exoskeletons possibly because muscle-tendon unit in the hip joint is less energy-efficient than in the ankle joint.


title: All-hex meshing using closed-form induced polycube

abstract: The polycube-based hexahedralization methods are robust to generate all-hex meshes without internal singularities. They avoid the difficulty to control the global singularity structure for a valid hexahedralization in frame-field based methods. To thoroughly utilize this advantage, we propose to use a frame field without internal singularities to guide the polycube construction. Theoretically, our method extends the vector fields associated with the polycube from exact forms to closed forms, which are curl free everywhere but may be not globally integrable. The closed forms give additional degrees of freedom to deal with the topological structure of high-genus models, and also provide better initial axis alignment for subsequent polycube generation. We demonstrate the advantages of our method on various models, ranging from genus-zero models to high-genus ones, and from single-boundary models to multiple-boundary ones.


title: Simulation planning and rostering: a simulation approach to manpower planning

abstract: This paper describes the development of a computerized line maintenance simulation model for strategic manpower planning at Continental Airlines for one of their major maintenance stations at Newark airport. The simulation model provides guidelines to the development of enhanced staffing models and a better understanding of resource requirements on a daily basis. The proposed simulation model could be used as a tool to support the management of the line maintenance department in solving various capacity planning issues related to the manpower requirement and scheduling. The recent capabilities of simulation modeling, namely optimization modeling is adopted in search of enhanced shift schedule of technicians that would improve the efficiency of the existing system.


title: Freezing Behavior as a Response to Sexual Visual Stimuli as Demonstrated by Posturography

abstract: Posturographic changes in motivational conditions remain largely unexplored in the context of embodied cognition. Over the last decade, sexual motivation has been used as a good canonical working model to study motivated social interactions. The objective of this study was to explore posturographic variations in response to visual sexual videos as compared to neutral videos. Our results support demonstration of a freezing-type response in response to sexually explicit stimuli compared to other conditions, as demonstrated by significantly decreased standard deviations for (i) the center of pressure displacement along the mediolateral and anteroposterior axes and (ii) center of pressure's displacement surface. These results support the complexity of the motor correlates of sexual motivation considered to be a canonical functional context to study the motor correlates of motivated social interactions.


title: Maturation of the adolescent brain

abstract: Adolescence is the developmental epoch during which children become adults - intellectually, physically, hormonally, and socially. Adolescence is a tumultuous time, full of changes and transformations. The pubertal transition to adulthood involves both gonadal and behavioral maturation. Magnetic resonance imaging studies have discovered that myelinogenesis, required for proper insulation and efficient neurocybernetics, continues from childhood and the brain's region-specific neurocircuitry remains structurally and functionally vulnerable to impulsive sex, food, and sleep habits. The maturation of the adolescent brain is also influenced by heredity, environment, and sex hormones (estrogen, progesterone, and testosterone), which play a crucial role in myelination. Furthermore, glutamatergic neurotransmission predominates, whereas gamma-aminobutyric acid neurotransmission remains under construction, and this might be responsible for immature and impulsive behavior and neurobehavioral excitement during adolescent life. The adolescent population is highly vulnerable to driving under the influence of alcohol and social maladjustments due to an immature limbic system and prefrontal cortex. Synaptic plasticity and the release of neurotransmitters may also be influenced by environmental neurotoxins and drugs of abuse including cigarettes, caffeine, and alcohol during adolescence. Adolescents may become involved with offensive crimes, irresponsible behavior, unprotected sex, juvenile courts, or even prison. According to a report by the Centers for Disease Control and Prevention, the major cause of death among the teenage population is due to injury and violence related to sex and substance abuse. Prenatal neglect, cigarette smoking, and alcohol consumption may also significantly impact maturation of the adolescent brain. Pharmacological interventions to regulate adolescent behavior have been attempted with limited success. Since several factors, including age, sex, disease, nutritional status, and substance abuse have a significant impact on the maturation of the adolescent brain, we have highlighted the influence of these clinically significant and socially important aspects in this report. Language: en


title: Computational Creativity: Coming of Age

abstract: This editorial provides an introduction to current AI research on computationally created artefacts as well as to the papers and topics covered by this special issue on computational creativity


title: Dermoscopic characteristics of nodular squamous cell carcinoma and keratoacanthoma

abstract: BACKGROUND ::: Nodular squamous cell carcinoma (SCC) and keratoacanthoma (KA) may mimic a variety of other benign and malignant non-pigmented nodules. ::: ::: ::: OBJECTIVES ::: To analyze the dermoscopic characteristics of nodular SCC and KA. ::: ::: ::: PATIENTS/METHODS ::: Retrospective analysis of 50 nodular SCCs and 8 KAs collected from a tertiary dermatology referral center and a private dermatology practice in Melbourne, Australia, between 1 September 2009 and 1 October 2012. All lesions were nodules; defined as firm, elevated, round, palpable tumors with a diameter of 5 mm or more. Clinical and dermoscopic images were evaluated by two examiners in consensus. ::: ::: ::: RESULTS ::: Signs of keratinization were frequently observed and included keratin crust/scale (90% of SCCs, 100% of KAs), central keratin mass (32% of SCCs, 88% of KAs), white structureless areas (66% of SCCs, 50% of KAs), white circles (32% of SCCs, 38% of KAs) and white keratin pearls (14% of SCCs, 12% of KAs). Hemorrhage was present in 72% of SCCs and 88% of KAs and preferentially occurred centrally and in areas of keratinization. For nodular SCCs and KAs, we observed glomerular vessels (42% and 25% respectively), linear irregular vessels (36% and 25% respectively), atypical vessels (30% and 38% respectively) and hairpin vessels (30% and 25% respectively). ::: ::: ::: CONCLUSIONS ::: Hemorrhage, keratinization and vascular features (glomerular, hairpin and linear irregular morphologies) are useful in diagnosing both nodular SCC and KA. Further research on the comparative dermoscopic characteristics of a range of amelanotic nodules is important in order to improve diagnosis of these clinically challenging tumors.


T-KVM : A Trusted architecture for KVM ARM v 7 and v 8 Virtual Machines Securing Virtual Machines by means of KVM , TrustZone , TEE and SELinux


title: A Stakeholder Model of Organizational Leadership

abstract: Organizations are evolving from the bureaucratic form based upon hierarchy to the new-form or radix organization that has the value chain as its relatively fluid foundation. This article explores the relationship between the radix organization and leadership, viewed through an organization-environment coevolution framework. It explicates the changes in the leader's role-sets and relationships brought about with the evolution from bureaucracy to the radix organization, developing a model of leadership that is referred to as the stakeholder model of organizational leadership. Stakeholder theory provides the appropriate theoretical basis for this model, as it offers the flexibility to accommodate various leader relationships. The stakeholder model of organizational leadership helps to predict leader effectiveness in organizations characterized by fuzzy organizational boundaries, flattened hierarchies, and work relationships sometimes brought about through contracts instead of employment.


title: Adaptive 3D Face Reconstruction from Unconstrained Photo Collections

abstract: Given a photo collection of “unconstrained” face images of one individual captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of the individual along with albedo information. Unlike prior work on face reconstruction that requires large photo collections, we formulate an approach to adapt to photo collections with a high diversity in both the number of images and the image quality. To achieve this, we incorporate prior knowledge about face shape by fitting a 3D morphable model to form a personalized template, following by using a novel photometric stereo formulation to complete the fine details, under a coarse-to-fine scheme. Our scheme incorporates a structural similarity-based local selection step to help identify a common expression for reconstruction while discarding occluded portions of faces. The evaluation of reconstruction performance is through a novel quality measure, in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on synthetic, Internet, and personal photo collections.


title: Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

abstract: Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.


title: Attachment and reflective function: Their role in self-organization

abstract: The paper traces the relationship between attachment processes and the development of the capacity to envision mental states in self and others. We suggest that the ability to mentalize, to represent behavior in terms of mental states, or to have “a theory of mind” is a key determinant of self-organization which is acquired in the context of the child’s early social relationships. Evidence for an association between the quality of attachment relationship and reflective function in the parent and the child is reviewed and interpreted in the context of current models of theory of mind development. A model of the development of self-organization is proposed which has at its core the caregiver’s ability to communicate understanding of the child’s intentional stance. The implications of the model for pathological self-development are explored, with specific reference to the consequences of maltreatment.


Zigbee : “ Wireless Control That Simply Works ”


title: Enabling and measuring electronic customer relationship management readiness

abstract: This work provides a comprehensive customer-focused evaluation framework that businesses can use to assess their electronic customer relationship management (e-CRM) readiness. The framework is intended to provide a big picture of the overall composition of e-CRM, to facilitate gap analysis, and to support a monitoring and feedback process. Knowledge management, trust, and technology are identified as key enablers of e-CRM. Finally, we propose weighting and rating scales to aid in assessing customer relationship management readiness, and provide examples of their use.


Proof of Stake versus Proof of Work White Paper


title: Container-Based Orchestration in Cloud: State of the Art and Challenges

abstract: How to effectively manage increasingly complex enterprise computing environments is one of the hardest challenges that most organizations have to face in the era of cloud computing, big data and IoT. Advanced automation and orchestration systems are the most valuable solutions helping IT staff to handle large-scale cloud data centers. Containers are the new revolution in the cloud computing world, they are more lightweight than VMs, and can radically decrease both the start up time of instances and the processing and storage overhead with respect to traditional VMs. The aim of this paper is to provide a comprehensive description of cloud orchestration approaches with containers, analyzing current research efforts, existing solutions and presenting issues and challenges facing this topic.


title: A complete anytime algorithm for number partitioning

abstract: Abstract Given a set of numbers, the two-way number partitioning problem is to divide them into two subsets, so that the sum of the numbers in each subset are as nearly equal as possible. The problem is NP-complete. Based on a polynomial-time heuristic due to Karmarkar and Karp, we present a new algorithm, called Complete Karmarkar-Karp (CKK), that optimally solves the general number-partitioning problem, and significantly outperforms the best previously-known algorithms for large problem instances. For numbers with twelve significant digits or less, CKK can optimally solve two-way partitioning problems of arbitrary size in practice. For numbers with greater precision, CKK first returns the Karmarkar-Karp solution, then continues to find better solutions as time allows. Over seven orders of magnitude improvement in solution quality is obtained in less than an hour of running time. Rather than building a single solution one element at a time, or modifying a complete solution, CKK constructs subsolutions, and combines them together in all possible ways. This approach may be effective for other NP-hard problems as well.


title: Cryptanalysis of Vigenere cipher using Cuckoo Search

abstract: Since the key space of Vigenere cryptosystem is very large, therefore, brute force does not help in the analysis of the cryptosystem in real time. Statistical techniques are also not helpful when the key size is not small.The aim of this work was to examine the feasibility of Cuckoo Search algorithm as a cryptanalysis tool of classical cryptosystems especially Vigenere cipher. We applied Genetic Algorithm, Particle Swarm Optimization and Cuckoo Search techniques for the analysis of Vigenere cipher. After doing lots of experiments we observed that GA and PSO techniques can recover the entire key of Vigenere Cipher correctly for keys of small lengths. However, Cuckoo Search can recover more than 90% of the key characters for keys of size up to 25 characters because GA and PSO get trapped in local minima while Cuckoo Search finds the global optimum solution following characteristics of Levy flights.Experimental results show the superiority of CS in fast convergence and the accuracy in the cryptanalysis of Vigenere cipher over PSO and GA. Evolutionary techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) are promising nature-inspired meta-heuristic optimization algorithms. Cuckoo Search combined with Levy flights behavior and Markov chain random walk can search global optimal solution very quickly. The aim of this paper is to investigate the applicability of Cuckoo Search algorithm in cryptanalysis of Vigenere cipher. It is shown that optimal solutions obtained by CS are better than the best solutions obtained by GA or PSO for the analysis of the Vigenere cipher. The results show that a Cuckoo Search based attack is very effective on the Vigenere cryptosystem.


title: Detecting insider threats by monitoring system call activity

abstract: One approach to detecting insider misbehavior is to monitor system call activity and watch for danger signs or unusual behavior. We describe an experimental system designed to test this approach. We tested the system's ability to detect common insider misbehavior by examining file system and process-related system calls. Our results show that this approach can detect many such activities.


title: Computational Approaches to Figurative Language

abstract: The heading figurative language subsumes multiple phenomena that can be used to perform most linguistic functions including predication, modification, and reference. Figurative language can tap into conceptual and linguistic knowledge (as in the case of idioms, metaphor, and some metonymies) as well as evoke pragmatic factors in interpretation (as in indirect speech acts, humor, irony, or sarcasm). Indeed, the distinction between the terms literal and figurative is far from clear cut. While there is a continuum from literal to figurative, certain phenomena such as metaphor and metonymy are considered prototypical instances of figurative language. To date, no comprehensive computational system addressing all of figurative language has been implemented, or even designed. Most computational work has focused on the conceptual and linguistic underpinnings of figurative language. Our discussion will focus on the two types of figurative language phenomena that have been studied computationally: metonymy (Section 2) and metaphor (Section 3). Each section introduces the phenomenon by way of examples and provides an overview of computational approaches. Section 4 concludes with a brief discussion of computational attempts to model other types of figurative language including humor and irony.


title: Massivizing online games using cloud computing: A vision

abstract: Online gaming, a large market with hundreds of millions of active players, is still struggling to scale without risky investments in infrastructure. In this work, we propose a cloudbased platform to massivize online gaming - the challenges and opportunities of scaling on-demand, while paying only for what is used. We discuss the major aspects of cloud-based gaming, virtual-world management, game-data processing, and game-content generation.


title: If multi-agent learning is the answer, what is the question?

abstract: The area of learning in multi-agent systems is today one of the most fertile grounds for interaction between game theory and artificial intelligence. We focus on the foundational questions in this interdisciplinary area, and identify several distinct agendas that ought to, we argue, be separated. The goal of this article is to start a discussion in the research community that will result in firmer foundations for the area.


title: Similarities and Differences in Chinese and Caucasian Adults' Use of Facial Cues for Trustworthiness Judgments

abstract: Background ::: All cultural groups in the world place paramount value on interpersonal trust. Existing research suggests that although accurate judgments of another's trustworthiness require extensive interactions with the person, we often make trustworthiness judgments based on facial cues on the first encounter. However, little is known about what facial cues are used for such judgments and what the bases are on which individuals make their trustworthiness judgments.


title: Smartphone nabbing: Analysis of intrusion detection and prevention systems

abstract: In the past few years, the usage of Smartphones has increased at exponential rates. The pace of change in the smartphone market is astounding with the advent of new technologies and features that are changing the way we perceive these handheld devices. People are becoming more and more dependent on their Smartphones for activities such as sending mails, browsing the internet, performing online transactions, social networking, online shopping, paying various bills etc. While on one hand we have numerous advantages of Smartphones, on the other hand we have a lot of mobile phone intrusions, malwares, and attacks to deal with. Most of the threats are caused due to malicious applications that are installed by the users due to their unawareness regarding mobile security. It is a known fact that the smartphones have limited computational power to execute highly complex algorithms in order to perform various intrusion detection mechanisms. Hence the notions of information security need to be incorporated in these devices as well. In this paper we have performed an analysis on the existing methods of intrusion detection for securing our Smartphones. The paper also discusses various mobile malwares and the reasons to secure our Smartphones.


title: Understanding the Blockchain Using Enterprise Ontology

abstract: Blockchain technology is regarded as highly disruptive, but there is a lack of formalization and standardization of terminology. Not only because there are several (sometimes propriety) implementation platforms, but also because the academic literature so far is predominantly written from either a purely technical or an economic application perspective. The result of the confusion is an offspring of blockchain solutions, types, roadmaps and interpretations. For blockchain to be accepted as a technology standard in established industries, it is pivotal that ordinary internet users and business executives have a basic yet fundamental understanding of the workings and impact of blockchain. This conceptual paper provides a theoretical contribution and guidance on what blockchain actually is by taking an ontological approach. Enterprise Ontology is used to make a clear distinction between the datalogical, infological and essential level of blockchain transactions and smart contracts.


title: Weight Sharing is Crucial to Succesful Optimization

abstract: Exploiting the great expressive power of Deep Neural Network architectures, relies on the ability to train them. While current theoretical work provides, mostly, results showing the hardness of this task, empirical evidence usually differs from this line, with success stories in abundance. A strong position among empirically successful architectures is captured by networks where extensive weight sharing is used, either by Convolutional or Recurrent layers. Additionally, characterizing specific aspects of different tasks, making them "harder" or "easier", is an interesting direction explored both theoretically and empirically. We consider a family of ConvNet architectures, and prove that weight sharing can be crucial, from an optimization point of view. We explore different notions of the frequency, of the target function, proving necessity of the target function having some low frequency components. This necessity is not sufficient - only with weight sharing can it be exploited, thus theoretically separating architectures using it, from others which do not. Our theoretical results are aligned with empirical experiments in an even more general setting, suggesting viability of examination of the role played by interleaving those aspects in broader families of tasks.


title: Transformational leadership and innovative work behavior among nursing staff

abstract: The importance of innovation within organizations has been demonstrated on numerous occasions, which has subsequently led to the identification of effective leadership as a potential catalyst. Most of us would acknowledge that effective leadership plays a pivotal role to engender innovativeness among nursing staff. Although research has identified some leadership styles to foster a nurse's innovative work behavior, a comprehensive model explaining the effect of transformational leadership on nurses’ innovative work behavior is still unclear. This research built and tested a theoretical model linking transformational leadership and innovative work behavior via several intervening variables. Data were collected from 587 nurses and 164 doctors (nursing supervisors) through structured questionnaires from public sector hospitals in Pakistan. Results of the study indicated that, as anticipated, transformational leadership positively affected psychological empowerment of nurses, which in turn influenced both intrinsic motivation and knowledge sharing behavior. These latter two variables then had a positive influence on innovative work behavior. Empowerment role identity moderated the link between transformational leadership and psychological empowerment, whereas willingness to rely on leader (reliance-based trust) and willingness to share sensitive information with leader (disclosure-based trust) moderated the connection between knowledge sharing behavior and innovative work behavior. These results imply that transformational leadership through psychological empowerment, knowledge sharing, and intrinsic motivation fosters nurse's innovative work behavior. The results also show that the relationship between transformational leadership and innovative work behavior is stronger among nurses who frequently share their knowledge about best practices and mistakes with co-workers.


title: Microstrip Patch Antennas With Multiple Parasitic Patches and Shorting Vias for Bandwidth Enhancement

abstract: Two novel microstrip patch antennas with multiple parasitic patches and shorting vias have been presented for the bandwidth enhancement. Based on the conventional triangular patch antenna, two more resonances can be obtained with the introduction of multiple parasitic patches, and consequently, the antenna bandwidth can be broadened. Parametric analysis of the patches has been studied for the verification of bandwidth enhancement. An example of the proposed antenna with multiple parasitic patches is designed, fabricated, and tested. The measured bandwidth with $\vert S_{11}\vert dB ranges from 5.46 to 6.27 GHz (13.8%), and good far-field radiation patterns can be obtained within the frequency band. In addition, two shorting vias are inserted into the above proposed antenna to decrease the input impedance, resulting in further bandwidth enhancement of the antenna. This antenna is fabricated and tested as well, which achieves a measured 10-dB impedance bandwidth of 17.4% from 5.5 to 6.55 GHz.


title: Public-key encryption in a multi-user setting: Security proofs and improvements

abstract: This paper addresses the security of public-key cryptosystems in a "multi-user" setting, namely in the presence of attacks involving the encryption of related messages under different public keys, as exemplified by Hastad's classical attacks on RSA. We prove that security in the single-user setting implies security in the multi-user setting as long as the former is interpreted in the strong sense of "indistinguishability," thereby pin-pointing many schemes guaranteed to be secure against Hastad-type attacks. We then highlight the importance, in practice, of considering and improving the concrete security of the general reduction, and present such improvements for two Diffie-Hellman based schemes, namely El Gamal and Cramer-Shoup.


title: hRESTS: An HTML Microformat for Describing RESTful Web Services

abstract: The Web 2.0 wave brings, among other aspects, the programmable Web: increasing numbers of Web sites provide machine-oriented APIs and Web services. However, most APIs are only described with text in HTML documents. The lack of machine-readable API descriptions affects the feasibility of tool support for developers who use these services. We propose a microformat called hRESTS (HTML for RESTful Services) for machine-readable descriptions of Web APIs, backed by a simple service model. The hRESTS microformat describes main aspects of services, such as operations, inputs and outputs. We also present two extensions of hRESTS: SA-REST, which captures the facets of public APIs important for mashup developers, and MicroWSMO, which provides support for semantic automation.


title: Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows

abstract: The Vehicle Routing Problem with Time windows (VRPTW) is an extension of the capacity constrained Vehicle Routing Problem (VRP). The VRPTW is NP-Complete and instances with 100 customers or more are very hard to solve optimally. We represent the VRPTW as a multi-objective problem and present a genetic algorithm solution using the Pareto ranking technique. We use a direct interpretation of the VRPTW as a multi-objective problem, in which the two objective dimensions are number of vehicles and total cost (distance). An advantage of this approach is that it is unnecessary to derive weights for a weighted sum scoring formula. This prevents the introduction of solution bias towards either of the problem dimensions. We argue that the VRPTW is most naturally viewed as a multi-objective problem, in which both vehicles and cost are of equal value, depending on the needs of the user. A result of our research is that the multi-objective optimization genetic algorithm returns a set of solutions that fairly consider both of these dimensions. Our approach is quite effective, as it provides solutions competitive with the best known in the literature, as well as new solutions that are not biased toward the number of vehicles. A set of well-known benchmark data are used to compare the effectiveness of the proposed method for solving the VRPTW.


title: 1 Hybrid modelling of a sugar boiling process

abstract: The first and maybe the most important step in designing a model-based predictive controller is to develop a model that is as accurate as possible and that is valid under a wide range of operating conditions. The sugar boiling process is a strongly nonlinear and nonstationary process. The main process nonlinearities are represented by the crystal growth rate. This paper addresses the development of the crystal growth rate model according to two approaches. The first approach is classical and consists of determining the parameters of the empirical expressions of the growth rate through the use of a nonlinear programming optimization technique. The second is a novel modeling strategy that combines an artificial neural network (ANN) as an approximator of the growth rate with prior knowledge represented by the mass balance of sucrose crystals. The first results show that the first type of model performs local fitting while the second offers a greater flexibility. The two models were developed with industrial data collected from a 60 m3 batch evaporative crystallizer.


title: Modeling customer satisfaction for new product development using a PSO-based ANFIS approach

abstract: When developing new products, it is important to understand customer perception towards consumer products. It is because the success of new products is heavily dependent on the associated customer satisfaction level. If customers are satisfied with a new product, the chance of the product being successful in marketplaces would be higher. Various approaches have been attempted to model the relationship between customer satisfaction and design attributes of products. In this paper, a particle swarm optimization (PSO) based ANFIS approach to modeling customer satisfaction is proposed for improving the modeling accuracy. In the approach, PSO is employed to determine the parameters of an ANFIS from which better customer satisfaction models in terms of modeling accuracy can be generated. A notebook computer design is used as an example to illustrate the approach. To evaluate the effectiveness of the proposed approach, modeling results based on the proposed approach are compared with those based on the fuzzy regression (FR), ANFIS and genetic algorithm (GA)-based ANFIS approaches. The comparisons indicate that the proposed approach can effectively generate customer satisfaction models and that their modeling results outperform those based on the other three methods in terms of mean absolute errors and variance of errors.


title: An approach to extract special skills to improve the performance of resume selection

abstract: In the Internet era, the enterprises and companies receive thousands of resumes from the job seekers. Currently available filtering techniques and search services help the recruiters to filter thousands of resumes to few hundred potential ones. Since these filtered resumes are similar to each other, it is difficult to identify the potential resumes by examining each resume. We are investigating the issues related to the development of approaches to improve the performance of resume selection process. We have extended the notion of special features and proposed an approach to identify resumes with special skill information. In the literature, the notion of special features have been applied to improve the process of product selection in E-commerce environment. However, extending the notion of special features for the development of approach to process resumes is a complex task as resumes contain unformatted text or semi-formatted text. In this paper, we have proposed an approach by considering only skills related information of the resumes. The experimental results on the real world data-set of resumes show that the proposed approach has the potential to improve the process of resume selection.


title: Mini-implant anchorage for en-masse retraction of maxillary anterior teeth: a clinical cephalometric study.

abstract: INTRODUCTION ::: This study was conducted to determine the efficiency of mini-implants as intraoral anchorage units for en-masse retraction of the 6 maxillary anterior teeth when the first premolars are extracted compared with conventional methods of anchorage reinforcement. ::: ::: ::: METHODS ::: Thirty patients requiring high anchorage after extraction of the maxillary first premolars were selected for this study. They were divided into 2 groups of 15 each. In the first group (G1), mini-implants were used for en-masse retraction; in the second group (G2), conventional methods of anchorage preservation were followed. Horizontal, vertical, and angular positions of the maxillary first molar and central incisor were evaluated cephalometrically before and after orthodontic retraction. ::: ::: ::: RESULTS ::: The maxillary first molars in the G1 patients showed net distal movement of 0.55 mm, and mesial movement of 1.95 mm was found in G2. The differences were statistically significant. Distal tipping of the first molar of -0.13 degrees+/-3.63 degrees was seen in G1, and mesial tipping of 3.7 degrees+/-3.9 degrees was observed in G2. No significant differences were found in the rates of incisor retraction between the 2 groups. However, G1 showed more than 2 mm of incisor intrusion; this was statistically significant. ::: ::: ::: CONCLUSIONS ::: Mini-implants are efficient for intraoral anchorage reinforcement for en-masse retraction and intrusion of maxillary anterior teeth. No anchorage loss was seen in either the horizontal or the vertical direction in G1 when compared with G2. However, a statistically significant decrease in intermolar width was noted in G1.


title: Informant reports: A cheap, fast, and easy

abstract: Abstract Despite widespread agreement that multi-method assessments are optimal in personality research, the literature is dominated by a single method: self-reports. This pattern seems to be based, at least in part, on widely held preconceptions about the costs of non-self-report methods, such as informant methods. Researchers seem to believe that informant methods are: (a) time-consuming, (b) expensive, (c) ineffective (i.e., informants will not cooperate), and (d) particularly vulnerable to faking or invalid responses. This article evaluates the validity of these preconceptions in light of recent advances in Internet technology, and proposes some strategies for making informant methods more effective. Drawing on data from three separate studies, I demonstrate that, using these strategies, informant reports can be collected with minimal effort and few monetary costs. In addition, informants are generally very willing to cooperate (e.g., response rates of 76–95%) and provide valid data (in terms of strong consensus and self-other agreement). Informant reports represent a mostly untapped resource that researchers can use to improve the validity of personality assessments and to address new questions that cannot be examined with self-reports alone.


title: A High-Resolution Minimicroscope System for Wireless Real-Time Monitoring

abstract: Objective: Compact, cost-effective, and high-performance microscope that enables the real-time imaging of cells and lab-on-a-chip devices is highly demanded for cell biology and biomedical engineering. This paper aims to present the design and application of an inexpensive wireless minimicroscope with resolution up to 2592 × 1944 pixels and speed up to 90 f/s. Methods: The minimicroscope system was built on a commercial embedded system (Raspberry Pi). We modified a camera module and adopted an inverse dual lens system to obtain the clear field of view and appropriate magnification for tens of micrometer objects. Results: The system was capable of capturing time-lapse images and transferring image data wirelessly. The entire system can be operated wirelessly and cordlessly in a conventional cell culturing incubator. The developed minimicroscope was used to monitor the attachment and proliferation of NIH-3T3 and HEK 293 cells inside an incubator for 50 h. In addition, the minimicroscope was used to monitor a droplet generation process in a microfluidic device. The high-quality images captured by the minimicroscope enabled us an automated analysis of experimental parameters. Conclusion: The successful applications prove the great potential of the developed minimicroscope for monitoring various biological samples and microfluidic devices. Significance: This paper presents the design of a high-resolution minimicroscope system that enables the wireless real-time imaging of cells inside the incubator. This system has been verified to be a useful tool to obtain high-quality images and videos for the automated quantitative analysis of biological samples and lab-on-a-chip devices in the long term.


title: Storage-class memory: The next storage system technology

abstract: The dream of replacing rotating mechanical storage, the disk drive, with solid-state, nonvolatile RAM may become a reality in the near future. Approximately ten new technologies--collectively called storage-class memory (SCM)--are currently under development and promise to be fast, inexpensive, and power efficient. Using SCM as a disk drive replacement, storage system products will have random and sequential I/O performance that is orders of magnitude better than that of comparable disk-based systems and require much less space and power in the data center. In this paper, we extrapolate disk and SCM technology trends to 2020 and analyze the impact on storage systems. The result is a 100- to 1,O00-fold advantage for SCM in terms of the data center space and power required.


title: Multi-style paper pop-up designs from 3D models

abstract: Paper pop-ups are interesting three-dimensional books that fascinate people of all ages. The design and construction of these pop-up books however are done manually and require a lot of time and effort. This has led to computer-assisted or automated tools for designing paper pop-ups. This paper proposes an approach for automatically converting a 3D model into a multi-style paper pop-up. Previous automated approaches have only focused on single-style pop-ups, where each is made of a single type of pop-up mechanisms. In our work, we combine multiple styles in a pop-up, which is more representative of actual artist's creations. Our method abstracts a 3D model using suitable primitive shapes that both facilitate the formation of the considered pop-up mechanisms and closely approximate the input model. Each shape is then abstracted using a set of 2D patches that combine to form a valid pop-up. We define geometric conditions that ensure the validity of the combined pop-up structures. In addition, our method also employs an image-based approach for producing the patches to preserve the textures, finer details and important contours of the input model. Finally, our system produces a printable design layout and decides an assembly order for the construction instructions. The feasibility of our results is verified by constructing the actual paper pop-ups from the designs generated by our system.


title: Commission finds no evidence that Bahraini doctors refused to treat Sunni patients, but appeal continues

abstract: The case against 20 healthcare workers accused of anti-state activities in Bahrain continued this week, with prosecutors presenting guns and knives as incriminatory evidence to a civilian court of appeal. ::: ::: The healthcare workers had hoped that an independent report published on 23 November would help clear their names. But the report, from the Bahrain Independent Commission of Inquiry, while confirming that some of the doctors were tortured in detention, did not reach definitive conclusions about their cases. The report seems not to have influenced their chances of winning an appeal against sentences of up to 15 years handed down by a military court. ::: ::: Medical professionals who have been charged and convicted of alleged crimes against the state were among 58 individuals forensically examined for the independent commission’s report. One doctor reported being forced to eat his own excrement, being interrogated by a military doctor, and being made to sign 30 unknown …


title: Automotive requirements for future mobile networks

abstract: We present future use cases from the field of Advanced Driver Assistance Systems (ADAS) and discuss the imposed requirements on communications systems. The presented use cases show that there exist very strict requirements in terms of data rate, end-to-end latency, and reliability. These requirements cannot be fulfilled by existing technologies. We attempt to emphasize that proper development of beyond 4G solutions will potentially enable a new range of safety and driver assistance services.


title: Measurement fusion using batch estimation for trajectory reconstruction of ballistic targets

abstract: The paper deals with the problem of state estimation of continuous-time nonlinear system using discrete-time measurements from multiple sensors. In particular, the problem of multi-radar tracking of artillery ballistic objects is considered. A novel measurement fusion method using batch filter is proposed, which is based on maximum likelihood estimation (MLE). The performance of the MLE track and measurement fusion techniques are evaluated using a generic simulation scenario involving multisensor weapon locating in urban environment.