title: PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

abstract: Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds. Inspired by the outstanding 2D shape descriptor SIFT, we design a module called PointSIFT that encodes information of different orientations and is adaptive to scale of shape. Specifically, an orientation-encoding unit is designed to describe eight crucial orientations, and multi-scale representation is achieved by stacking several orientation-encoding units. PointSIFT module can be integrated into various PointNet-based architecture to improve the representation ability. Extensive experiments show our PointSIFT-based framework outperforms state-of-the-art method on standard benchmark datasets. The code and trained model will be published accompanied by this paper.


title: A Simple but Effective Classification Model for Grammatical Error Correction

abstract: We treat grammatical error correction (GEC) as a classification problem in this study, where for different types of errors, a target word is identified, and the classifier predicts the correct word form from a set of possible choices. We propose a novel neural network based feature representation and classification model, trained using large text corpora without human annotations. Specifically we use RNNs with attention to represent both the left and right context of a target word. All feature embeddings are learned jointly in an end-to-end fashion. Experimental results show that our novel approach outperforms other classifier methods on the CoNLL-2014 test set (F0.5 45.05%). Our model is simple but effective, and is suitable for industrial production.


title: Unveiling the Power of Deep Tracking

abstract: In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking. We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness. We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking. Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy. Extensive experiments are performed on four challenging datasets. On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of 17% in EAO.


title: On the Diversity of Realistic Image Synthesis

abstract: Many image processing tasks can be formulated as translating images between two image domains, such as colorization, super resolution and conditional image synthesis. In most of these tasks, an input image may correspond to multiple outputs. However, current existing approaches only show very minor diversity of the outputs. In this paper, we present a novel approach to synthesize diverse realistic images corresponding to a semantic layout. We introduce a diversity loss objective, which maximizes the distance between synthesized image pairs and links the input noise to the semantic segments in the synthesized images. Thus, our approach can not only produce diverse images, but also allow users to manipulate the output images by adjusting the noise manually. Experimental results show that images synthesized by our approach are significantly more diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks.


title: Analysis of the complete Fischoederius elongatus (Paramphistomidae, Trematoda) mitochondrial genome

abstract: BackgroundFischoederius elongates is an important trematode of Paramphistomes in ruminants. Animals infected with F. elongates often don’t show obvious symptoms, so it is easy to be ignored. However it can cause severe economic losses to the breeding industry. Knowledge of the mitochondrial genome of F. elongates can be used for phylogenetic and epidemiological studies.FindingsThe complete mt genome sequence of F. elongates is 14,120 bp in length and contains 12 protein-coding genes, 22 tRNA genes, two rRNA genes and two non-coding regions (LNR and SNR). The gene arrangement of F. elongates is the same as other trematodes, such as Fasciola hepatica and Paramphistomum cervi. Phylogenetic analyses using concatenated amino acid sequences of the 12 protein-coding genes by Maximum-likelihood and Neighbor-joining analysis method showed that F. elongates was closely related to P. cervi.ConclusionThe complete mt genome sequence of F. elongates should provide information for phylogenetic and epidemiological studies for F. elongates and the family Paramphistomidae.


title: Privacy Impact Assessment Template for Provenance

abstract: Provenance data can be expressed as a graph with links informing who and which activities created, used and modified entities. The semantics of these links and domain specific reasoning can support the inference of additional information about the elements in the graph. If such elements include personal identifiers and/or personal identifiable information, then inferences may reveal unexpected links between elements, thus exposing personal data beyond an individual's intentions. Provenance graphs often entangle data relating to multiple individuals. It is therefore a challenge to protect personal data from unintended disclosure in provenance graphs. In this paper, we provide a Privacy Impact Assessment (PIA) template for identifying imminent privacy threats that arise from provenance graphs in an application-agnostic setting. The PIA template identifies privacy threats, lists potential countermeasures, helps to manage personal data protection risks, and maintains compliance with privacy data protection laws and regulations.


title: Bad Parts: Are Our Manufacturing Systems at Risk of Silent Cyberattacks?

abstract: Recent cyberattacks have highlighted the risk of physical equipment operating outside designed tolerances to produce catastrophic failures. A related threat is cyberattacks that change the design and manufacturing of a machine's part, such as an automobile brake component, so it no longer functions properly. These risks stem from the lack of cyber-physical models to identify ongoing attacks as well as the lack of rigorous application of known cybersecurity best practices. To protect manufacturing processes in the future, research will be needed on a number of critical cyber-physical manufacturing security topics.


title: Cortical Correlates of Low-Level Perception: From Neural Circuits to Percepts

abstract: Low-level perception results from neural-based computations, which build a multimodal skeleton of unconscious or self-generated inferences on our environment. This review identifies bottleneck issues concerning the role of early primary sensory cortical areas, mostly in rodent and higher mammals (cats and non-human primates), where perception substrates can be searched at multiple scales of neural integration. We discuss the limitation of purely bottom-up approaches for providing realistic models of early sensory processing and the need for identification of fast adaptive processes, operating within the time of a percept. Future progresses will depend on the careful use of comparative neuroscience (guiding the choices of experimental models and species adapted to the questions under study), on the definition of agreed-upon benchmarks for sensory stimulation, on the simultaneous acquisition of neural data at multiple spatio-temporal scales, and on the in vivo identification of key generic integration and plasticity algorithms validated experimentally and in simulations.


title: Marker-based tracking with unmanned aerial vehicles

abstract: With the availability of low-cost micro aerial vehicles (MAVs), unmanned aerial vehicles (UAVs) quickly gain popularity and application potential. This requires techniques that can be understood by non-experts and flexibly applied for rapid prototyping. Visual tracking is an essential task with many applications, such as autonomous navigation and scene acquisition. While marker-less methods emerge, marker-based methods still have major advantages including simplicity, robustness, accuracy and performance. In practice, however, multi-marker setups introduce complexity and calibration efforts that can void the advantages. This work proposes a solution for practical, robust and easy-to-use marker-based tracking with independent compound targets. We introduce two novel target designs and describe pose estimation, noise removal and geometric transformations. The concepts are implemented in a tracking library for the Parrot AR. Drone 2.0. We explain its video access and camera calibration, and provide a first set of intrinsic parameters, jointly estimated from 14 units with high accuracy and low variance. The library is applied in a one-day contest on automatic visual navigation of UAVs, where students without technical background and programming skills achieved learning by experience and rapid development. This shows the effectiveness of combining capability with simplicity, and provides a case study on robotics in interdisciplinary education.


title: A speech enhancement algorithm by iterating single- and multi-microphone processing and its application to robust ASR

abstract: We propose a speech enhancement algorithm based on single- and multi-microphone processing techniques. The core of the algorithm estimates a time-frequency mask which represents the target speech and use masking-based beamforming to enhance corrupted speech. Specifically, in single-microphone processing, the received signals of a microphone array are treated as individual signals and we estimate a mask for the signal of each microphone using a deep neural network (DNN). With these masks, in multi-microphone processing, we calculate a spatial covariance matrix of noise and steering vector for beamforming. In addition, we propose a masking-based post-filter to further suppress the noise in the output of beamforming. Then, the enhanced speech is sent back to DNN for mask re-estimation. When these steps are iterated for a few times, we obtain the final enhanced speech. The proposed algorithm is evaluated as a frontend for automatic speech recognition (ASR) and achieves a 5.05% average word error rate (WER) on the real environment test set of CHiME-3, outperforming the current best algorithm by 13.34%.


title: Soft computing for greenhouse climate control

abstract: The methodology proposed in the paper applies artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity in such systems are, in fact, difficult to model and control using traditional techniques. The paper proposes a framework for the development of soft computing-based controllers in modern greenhouses.


title: Predictive Sentiment Analysis of Tweets: A Stock Market Application

abstract: The application addressed in this paper studies whether Twitter feeds, expressing public opinion concerning companies and their products, are a suitable data source for forecasting the movements in stock closing prices. We use the term predictive sentiment analysis to denote the approach in which sentiment analysis is used to predict the changes in the phenomenon of interest. In this paper, positive sentiment probability is proposed as a new indicator to be used in predictive sentiment analysis in finance. By using the Granger causality test we show that sentiment polarity (positive and negative sentiment) can indicate stock price movements a few days in advance. Finally, we adapted the Support Vector Machine classification mechanism to categorize tweets into three sentiment categories (positive, negative and neutral), resulting in improved predictive power of the classifier in the stock market application.


title: VR-STEP: Walking-in-Place using Inertial Sensing for Hands Free Navigation in Mobile VR Environments

abstract: Low-cost smartphone adapters can bring virtual reality to the masses, but input is typically limited to using head tracking, which makes it difficult to perform complex tasks like navigation. Walking-in-place (WIP) offers a natural and immersive form of virtual locomotion that can reduce simulation sickness. WIP, however, is difficult to implement in mobile contexts as it typically relies on bulky controllers or an external camera. We present VR-STEP; a WIP implementation that uses real-time pedometry to implement virtual locomotion. VR-STEP requires no additional instrumentation outside of a smartphone's inertial sensors. A user study with 18 users compares VR-STEP with a commonly used auto-walk navigation method and finds no significant difference in performance or reliability, though VR-STEP was found to be more immersive and intuitive.


title: Security in industrie 4.0 - challenges and solutions for the fourth industrial revolution

abstract: Information technology (IT) is one of the most important drivers of innovation in production and automation. In Germany, the term Industrie 4.0 summarizes various activities and developments involved in the evolution of industrial processes in production, logistics, automation, etc. Many research and development projects work on different aspects of these developments. In the view of politics, industry, and IT enterprises, sufficient IT security is considered an essential prerequisite for the future of production. Although many current IT security solutions can be applied in Industrie 4.0 context, they do not satisfy requirements of processes in Industrie 4.0. Work needs to be done on underlying security mechanisms as well as on security architectures.


title: NAVIG: augmented reality guidance system for the visually impaired

abstract: Navigating complex routes and finding objects of interest are challenging tasks for the visually impaired. The project NAVIG (Navigation Assisted by artificial VIsion and GNSS) is directed toward increasing personal autonomy via a virtual augmented reality system. The system integrates an adapted geographic information system with different classes of objects useful for improving route selection and guidance. The database also includes models of important geolocated objects that may be detected by real-time embedded vision algorithms. Object localization (relative to the user) may serve both global positioning and sensorimotor actions such as heading, grasping, or piloting. The user is guided to his desired destination through spatialized semantic audio rendering, always maintained in the head-centered reference frame. This paper presents the overall project design and architecture of the NAVIG system. In addition, details of a new type of detection and localization device are presented. This approach combines a bio-inspired vision system that can recognize and locate objects very quickly and a 3D sound rendering system that is able to perceptually position a sound at the location of the recognized object. This system was developed in relation to guidance directives developed through participative design with potential users and educators for the visually impaired.


title: A Declarative Approach to Data-Driven Fact Checking

abstract: Fact checking is an essential part of any investigative work. For linguistic, psychological and social reasons, it is an inherently human task. Yet, modern media make it increasingly difficult for experts to keep up with the pace at which information is produced. Hence, we believe there is value in tools to assist them in this process. Much of the effort on Web data research has been focused on coping with incompleteness and uncertainty. Comparatively, dealing with context has received less attention, although it is crucial in judging the validity of a claim. For instance, what holds true in a US state, might not in its neighbors, e.g., due to obsolete or superseded laws. In this work, we address the problem of checking the validity of claims in multiple contexts. We define a language to represent and query facts across different dimensions. The approach is non-intrusive and allows relatively easy modeling, while capturing incompleteness and uncertainty. We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples.


title: Mapping API elements for code migration with vector representations

abstract: Mapping API elements has a significant role in software development, especially in code migration. A manual process of defining the migration is tedious and error-prone while recent approaches to automatically mine API mappings are limited to discover the mappings with textually similar APIs’ names. This leads to the low accuracy in existing migration tools.We propose an approach to automatically mine API mappings which overcomes the lexical mismatch problem. We represent an API by its usages instead of its name.To characterize an API with its context consisting of surrounding APIs in its usages, we take advantage of Word2Vec model to project the APIs of Java JDK and C# .NET into corresponding continuous vector spaces. The semantic relations among APIs will be observed in those continuous space as the geometric arrangements between their representation vectors in two vector spaces.We use a learning approach to derive the linear (e.g., rotating and scaling) transformation function between two vector spaces. Transformation function is trained from human-defined pairs of API mappings from Java to C#. To find the C# API mapping with a given Java API, we use the learned function to compute its transformed vector in the C# vector space. Then, the C# API which has the most similar vector with the transformed vector is considered as the result. Our experiment shows that for just one suggestion, we are able to correctly derive the API in C# in almost 43% of the cases. With 5 suggestions, we can correctly suggest the correct C# API in almost 3 out of 4 cases (73.2%).


Reference Constrained Cartoon Colorization


NAVIGATING THE LANDSCAPE OF COMPUTER AIDED ALGORITHMIC COMPOSITION SYSTEMS : A DEFINITION , SEVEN DESCRIPTORS , AND A LEXICON OF SYSTEMS AND RESEARCH


title: Deep Reinforcement Learning With Visual Attention for Vehicle Classification

abstract: Automatic vehicle classification is crucial to intelligent transportation system, especially for vehicle-tracking by police. Due to the complex lighting and image capture conditions, image-based vehicle classification in real-world environments is still a challenging task and the performance is far from being satisfactory. However, owing to the mechanism of visual attention, the human vision system shows remarkable capability compared with the computer vision system, especially in distinguishing nuances processing. Inspired by this mechanism, we propose a convolutional neural network (CNN) model of visual attention for image classification. A visual attention-based image processing module is used to highlight one part of an image and weaken the others, generating a focused image. Then the focused image is input into the CNN to be classified. According to the classification probability distribution, we compute the information entropy to guide a reinforcement learning agent to achieve a better policy for image classification to select the key parts of an image. Systematic experiments on a surveillance-nature dataset which contains images captured by surveillance cameras in the front view, demonstrate that the proposed model is more competitive than the large-scale CNN in vehicle classification tasks.


title: A probabilistic approach for learning folksonomies from structured data

abstract: Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation [7] to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering.


title: Price-aware real-time ride-sharing at scale: an auction-based approach

abstract: Real-time ride-sharing, which enables on-the-fly matching between riders and drivers (even en-route), is an important problem due to its environmental and societal benefits. With the emergence of many ride-sharing platforms (e.g., Uber and Lyft), the design of a scalable framework to match riders and drivers based on their various constraints while maximizing the overall profit of the platform becomes a distinguishing business strategy. A key challenge of such framework is to satisfy both types of the users in the system, e.g., reducing both riders' and drivers' travel distances. However, the majority of the existing approaches focus only on minimizing the total travel distance of drivers which is not always equivalent to shorter trips for riders. Hence, we propose a fair pricing model that simultaneously satisfies both the riders' and drivers' constraints and desires (formulated as their profiles). In particular, we introduce a distributed auction-based framework where each driver's mobile app automatically bids on every nearby request taking into account many factors such as both the driver's and the riders' profiles, their itineraries, the pricing model, and the current number of riders in the vehicle. Subsequently, the server determines the highest bidder and assigns the rider to that driver. We show that this framework is scalable and efficient, processing hundreds of tasks per second in the presence of thousands of drivers. We compare our framework with the state-of-the-art approaches in both industry and academia through experiments on New York City's taxi dataset. Our results show that our framework can simultaneously match more riders to drivers (i.e., higher service rate) by engaging the drivers more effectively. Moreover, our frame-work schedules shorter trips for riders (i.e., better service quality). Finally, as a consequence of higher service rate and shorter trips, our framework increases the overall profit of the ride-sharing platforms.


title: MuCheck: an extensible tool for mutation testing of haskell programs

abstract: This paper presents MuCheck, a mutation testing tool for Haskell programs. MuCheck is a counterpart to the widely used QuickCheck random testing tool for functional programs, and can be used to evaluate the efficacy of QuickCheck property definitions. The tool implements mutation operators that are specifically designed for functional programs, and makes use of the type system of Haskell to achieve a more relevant set of mutants than otherwise possible. Mutation coverage is particularly valuable for functional programs due to highly compact code, referential transparency, and clean semantics; these make augmenting a test suite or specification based on surviving mutants a practical method for improved testing.


title: Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

abstract: Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.


title: Secure outsourced skyline query processing via untrusted cloud service providers

abstract: Recent years have witnessed a growing number of location-based service providers (LBSPs) outsourcing their points of interest (POI) datasets to third-party cloud service providers (CSPs), which in turn answer various data queries from mobile users on their behalf. A main challenge in such systems is that the CSPs cannot be fully trusted, which may return fake query results for various bad motives, e.g., in favor of POIs willing to pay. As an important type of queries, location-based skyline queries (LBSQs) ask for the POIs that are not spatially dominated by any other POI with respect to some query position. In this paper, we propose three novel schemes that enable efficient verification of any LBSQ result returned by an untrusted CSP by embedding and exploring a novel neighboring relationship among POIs. The efficacy and efficiency of our schemes are thoroughly analyzed and evaluated.


title: DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

abstract: Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.


title: A simplified and reliable assay for complex I in human blood lymphocytes.

abstract: Complex I activity of the mitochondrial respiratory chain is difficult to measure in blood lymphocytes because of the limited access of substrates to the enzyme complex in these cells. The results of the present study show that permeabilization of human blood lymphocytes in the presence of protease inhibitors by three cycles of freeze-thawing enables reproducible detection of the rotenone-sensitive complex I activity. To that end, the water-soluble coenzyme Q(10) analogue CoQ(1) and a relatively high concentration of blood lymphocytes were combined in small quartz cuvettes so that the amount of blood needed for this assay remained low. The relationship between the initial rate of NADH oxidation by complex I and the protein concentration was quasi-linear. The fractional inhibition of the total NADH:CoQ(1) oxidoreductase by a saturating concentration of rotenone decreased sharply at CoQ(1) concentrations higher than 20 muM, which is indicative, but does not prove the involvement of a second CoQ(1) binding site at complex I. Since the present complex I assay requires only a small amount of blood, the functionality of this important respiratory chain complex can be assessed in an easy and reliable manner not only in adult patients but also in children suspected to have a mitochondrial disease.


title: Analysis of fMRI Data by Blind Separation Into Independent Spatial Components

abstract: r r Abstract: Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ((1995): Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component ''map''), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon (1994): Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak


title: Ancient DNA Analysis of 8000 B.C. Near Eastern Farmers Supports an Early Neolithic Pioneer Maritime Colonization of Mainland Europe through Cyprus and the Aegean Islands

abstract: The genetic impact associated to the Neolithic spread in Europe has been widely debated over the last 20 years. Within this context, ancient DNA studies have provided a more reliable picture by directly analyzing the protagonist populations at different regions in Europe. However, the lack of available data from the original Near Eastern farmers has limited the achieved conclusions, preventing the formulation of continental models of Neolithic expansion. Here we address this issue by presenting mitochondrial DNA data of the original Near-Eastern Neolithic communities with the aim of providing the adequate background for the interpretation of Neolithic genetic data from European samples. Sixty-three skeletons from the Pre Pottery Neolithic B (PPNB) sites of Tell Halula, Tell Ramad and Dja'de El Mughara dating between 8,700–6,600 cal. B.C. were analyzed, and 15 validated mitochondrial DNA profiles were recovered. In order to estimate the demographic contribution of the first farmers to both Central European and Western Mediterranean Neolithic cultures, haplotype and haplogroup diversities in the PPNB sample were compared using phylogeographic and population genetic analyses to available ancient DNA data from human remains belonging to the Linearbandkeramik-Alfoldi Vonaldiszes Keramia and Cardial/Epicardial cultures. We also searched for possible signatures of the original Neolithic expansion over the modern Near Eastern and South European genetic pools, and tried to infer possible routes of expansion by comparing the obtained results to a database of 60 modern populations from both regions. Comparisons performed among the 3 ancient datasets allowed us to identify K and N-derived mitochondrial DNA haplogroups as potential markers of the Neolithic expansion, whose genetic signature would have reached both the Iberian coasts and the Central European plain. Moreover, the observed genetic affinities between the PPNB samples and the modern populations of Cyprus and Crete seem to suggest that the Neolithic was first introduced into Europe through pioneer seafaring colonization.


title: Design of novel capacitive type torque sensor for robotic applications

abstract: This paper introduces a novel capacitive type torque sensor. The developed torque sensor measures single axis torque value by using two capacitance transducing cells. To make a linear relation between the capacitance and the deformation, the shape of flexure hinge is considered to be deformed constantly. Also, the nonlinear curve fitting method is used to fit the capacitance linearly. Finally, the performance of the sensor is verified in reference to a commercial torque sensor


title: Feature Selection Using a Multilayer Perceptron

abstract: The problem of selecting the best set of features for target r ecognition using a multilayer perceptron is addressed in this paper. A technique has been developed which analyzes the weights in a multilayer perceptron to determine which features the network finds important and which are unimportant. A brief introduction to the use of multilayer perceptrons for classification and the training rules available is followed by the mathematical development of the saliency measure for multilayer perceptrons. The tec hnique is applied to two different image databases and is found to be consistent with statistical techniques and independent of the network initial conditions. The saliency measure is the n used to compare the results of two different training rules on a target recognition problem.


title: Exploring the Neural Algorithm of Artistic Style

abstract: We explore the method of style transfer presented in the article "A Neural Algorithm of Artistic Style" by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge (arXiv:1508.06576). ::: We first demonstrate the power of the suggested style space on a few examples. We then vary different hyper-parameters and program properties that were not discussed in the original paper, among which are the recognition network used, starting point of the gradient descent and different ways to partition style and content layers. We also give a brief comparison of some of the existing algorithm implementations and deep learning frameworks used. ::: To study the style space further we attempt to generate synthetic images by maximizing a single entry in one of the Gram matrices $\mathcal{G}_l$ and some interesting results are observed. Next, we try to mimic the sparsity and intensity distribution of Gram matrices obtained from a real painting and generate more complex textures. ::: Finally, we propose two new style representations built on top of network's features and discuss how one could be used to achieve local and potentially content-aware style transfer.


title: Connected Vehicular Transportation: Data Analytics and Traffic-dependent Networking

abstract: With onboard operating systems becoming increasingly common in vehicles, the real-time broadband infotainment and Intelligent Transportation System (ITS) service applications in fast-motion vehicles become ever demanding, which are highly expected to significantly improve the efficiency and safety of our daily on-road lives. The emerging ITS and vehicular applications, e.g., trip planning, however, require substantial efforts on the real-time pervasive information collection and big data processing so as to provide quick decision making and feedbacks to the fast moving vehicles, which thus impose the significant challenges on the development of an efficient vehicular communication platform. In this article, we present TrasoNET, an integrated network framework to provide realtime intelligent transportation services to connected vehicles by exploring the data analytics and networking techniques. TrasoNET is built upon two key components. The first one guides vehicles to the appropriate access networks by exploring the information of realtime traffic status, specific user preferences, service applications and network conditions. The second component mainly involves a distributed automatic access engine, which enables individual vehicles to make distributed access decisions based on access recommender, local observation and historic information. We showcase the application of TrasoNET in a case study on real-time traffic sensing based on real traces of taxis.


title: Bayesian Nonparametric Inverse Reinforcement Learning

abstract: Inverse reinforcement learning (IRL) is the task of learning the reward function of a Markov Decision Process (MDP) given the transition function and a set of observed demonstrations in the form of state-action pairs. Current IRL algorithms attempt to find a single reward function which explains the entire observation set. In practice, this leads to a computationally-costly search over a large (typically infinite) space of complex reward functions. This paper proposes the notion that if the observations can be partitioned into smaller groups, a class of much simpler reward functions can be used to explain each group. The proposed method uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to each partition. The simple rewards are interpreted intuitively as subgoals, which can be used to predict actions or analyze which states are important to the demonstrator. Experimental results are given for simple examples showing comparable performance to other IRL algorithms in nominal situations. Moreover, the proposed method handles cyclic tasks (where the agent begins and ends in the same state) that would break existing algorithms without modification. Finally, the new algorithm has a fundamentally different structure than previous methods, making it more computationally efficient in a real-world learning scenario where the state space is large but the demonstration set is small.


title: SoK: Systematic Classification of Side-Channel Attacks on Mobile Devices.

abstract: Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices. ::: In this paper, we propose a new categorization system for side-channel attacks on mobile devices, which is necessary since side-channel attacks have evolved significantly since their introduction during the smartcard era. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive overview of existing attacks and attack strategies provides valuable insights on the evolving field of side-channel attacks on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.


title: Basal ganglia contributions to motor control: a vigorous tutor

abstract: The roles of the basal ganglia (BG) in motor control are much debated. Many influential hypotheses have grown from studies in which output signals of the BG were not blocked, but pathologically disturbed. A weakness of that approach is that the resulting behavioral impairments reflect degraded function of the BG per se mixed together with secondary dysfunctions of BG-recipient brain areas. To overcome that limitation, several studies have focused on the main skeletomotor output region of the BG, the globus pallidus internus (GPi). Using single-cell recording and inactivation protocols these studies provide consistent support for two hypotheses: the BG modulates movement performance (‘vigor’) according to motivational factors (i.e. context-specific cost/reward functions) and the BG contributes to motor learning. Results from these studies also add to the problems that confront theories positing that the BG selects movement, inhibits unwanted motor responses, corrects errors on-line, or stores and produces well-learned motor skills.


title: F-formation Detection: Individuating Free-standing Conversational Groups in Images

abstract: Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy, we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.


title: DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems

abstract: To cope with increasing energy consumption in mobile devices, the mobile cloud offloading has received considerable attention from its ability to offload processing tasks of mobile devices to cloud servers, and previous studies have focused on single type tasks in fixed network environments. However, real network environments are spatio-temporally varying, and typical mobile devices have not only various types of tasks, e.g., network traffic, cloud offloadable/nonoffloadable workloads but also capabilities of CPU frequency scaling and network interface selection between WiFi and cellular. In this paper, we first jointly consider the following three dynamic problems in real mobile environments: 1) cloud offloading policy, i.e., determining to use local CPU resources or cloud resources; 2) allocation of tasks to transmit through networks and to process in local CPU; and 3) CPU clock speed and network interface controls. We propose a DREAM algorithm by invoking the Lyapunov optimization and mathematically prove that it minimizes CPU and network energy for given delay constraints. Trace-driven simulation based on real measurements demonstrates that DREAM can save over 35% of total energy than existing algorithms with the same delay. We also design DREAM architecture and demonstrate the applicability of DREAM in practice.


title: A theoretical comparison of job scheduling algorithms in cloud computing environment

abstract: Cloud computing is a dynamic, scalable and payper-use distributed computing model empowering designers to convey applications amid job designation and storage distribution. Cloud computing encourages to impart a pool of virtualized computer resource empowering designers to convey applications amid job designation and storage distribution. The cloud computing mainly aims to give proficient access to remote and geographically distributed resources. As cloud technology is evolving day by day and confronts numerous challenges, one of them being uncovered is scheduling. Scheduling is basically a set of constructs constructed to have a controlling hand over the order of work to be performed by a computer system. Algorithms are vital to schedule the jobs for execution. Job scheduling algorithms is one of the most challenging hypothetical problems in the cloud computing domain area. Numerous deep investigations have been carried out in the domain of job scheduling of cloud computing. This paper intends to present the performance comparison analysis of various pre-existing job scheduling algorithms considering various parameters. This paper discusses about cloud computing and its constructs in section (i). In section (ii) job scheduling concept in cloud computing has been elaborated. In section (iii) existing algorithms for job scheduling are discussed, and are compared in a tabulated form with respect to various parameters and lastly section (iv) concludes the paper giving brief summary of the work.


title: A natural language interface to a graph-based bibliographic information retrieval system

abstract: Abstract With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities.


title: Anatomical predictors of aphasia recovery: a tractography study of bilateral perisylvian language networks

abstract: Stroke-induced aphasia is associated with adverse effects on quality of life and the ability to return to work. For patients and clinicians the possibility of relying on valid predictors of recovery is an important asset in the clinical management of stroke-related impairment. Age, level of education, type and severity of initial symptoms are established predictors of recovery. However, anatomical predictors are still poorly understood. In this prospective longitudinal study, we intended to assess anatomical predictors of recovery derived from diffusion tractography of the perisylvian language networks. Our study focused on the arcuate fasciculus, a language pathway composed of three segments connecting Wernicke's to Broca's region (i.e. long segment), Wernicke's to Geschwind's region (i.e. posterior segment) and Broca's to Geschwind's region (i.e. anterior segment). In our study we were particularly interested in understanding how lateralization of the arcuate fasciculus impacts on severity of symptoms and their recovery. Sixteen patients (10 males; mean age 60 ± 17 years, range 28-87 years) underwent post stroke language assessment with the Revised Western Aphasia Battery and neuroimaging scanning within a fortnight from symptoms onset. Language assessment was repeated at 6 months. Backward elimination analysis identified a subset of predictor variables (age, sex, lesion size) to be introduced to further regression analyses. A hierarchical regression was conducted with the longitudinal aphasia severity as the dependent variable. The first model included the subset of variables as previously defined. The second model additionally introduced the left and right arcuate fasciculus (separate analysis for each segment). Lesion size was identified as the only independent predictor of longitudinal aphasia severity in the left hemisphere [beta = -0.630, t(-3.129), P = 0.011]. For the right hemisphere, age [beta = -0.678, t(-3.087), P = 0.010] and volume of the long segment of the arcuate fasciculus [beta = 0.730, t(2.732), P = 0.020] were predictors of longitudinal aphasia severity. Adding the volume of the right long segment to the first-level model increased the overall predictive power of the model from 28% to 57% [F(1,11) = 7.46, P = 0.02]. These findings suggest that different predictors of recovery are at play in the left and right hemisphere. The right hemisphere language network seems to be important in aphasia recovery after left hemispheric stroke.


title: Makeup-Go: Blind Reversion of Portrait Edit

abstract: Virtual face beautification (or markup) becomes common operations in camera or image processing Apps, which is actually deceiving. In this paper, we propose the task of restoring a portrait image from this process. As the first attempt along this line, we assume unknown global operations on human faces and aim to tackle the two issues of skin smoothing and skin color change. These two tasks, intriguingly, impose very different difficulties to estimate subtle details and major color variation. We propose a Component Regression Network (CRN) and address the limitation of using Euclidean loss in blind reversion. CRN maps the edited portrait images back to the original ones without knowing beautification operation details. Our experiments demonstrate effectiveness of the system for this novel task.


title: A conceptual framework linking learning design with learning analytics

abstract: In this paper we present a learning analytics conceptual framework that supports enquiry-based evaluation of learning designs. The dimensions of the proposed framework emerged from a review of existing analytics tools, the analysis of interviews with teachers, and user scenarios to understand what types of analytics would be useful in evaluating a learning activity in relation to pedagogical intent. The proposed framework incorporates various types of analytics, with the teacher playing a key role in bringing context to the analysis and making decisions on the feedback provided to students as well as the scaffolding and adaptation of the learning design. The framework consists of five dimensions: temporal analytics, tool-specific analytics, cohort dynamics, comparative analytics and contingency. Specific metrics and visualisations are defined for each dimension of the conceptual framework. Finally the development of a tool that partially implements the conceptual framework is discussed.


title: Triple bands antenna and high efficiency rectifier design for RF energy harvesting at 900, 1900 and 2400 MHz

abstract: We present the design and development of a novel antenna that effectively radiates at three frequency bands to harvest RF energy from cellular network frequency bands (900 MHz and 1900 MHz) and Wi-Fi sources (2.4 GHz) available in ambience. The antenna is designed using the combination of three different design techniques including composite right/left hand transmission line (CRLH). In addition, a high efficiency, sensitive triple-band rectifier is also proposed in this work. The rectifier provides a maximum conversion efficiency of 80%, 46% and 42% at 940MHz, 1.95GHz and 2.44 GHz, respectively. The experimental results demonstrate that the proposed triple-band RF energy harvesting system can collect 6.6 times more power than the single 900MHz band one and 3.4 times more power than three individual bands combined.


title: Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network

abstract: Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six-degree-of freedom (6 DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, the idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases reflection distortions, noise, sudden camera movements and lack of distinguishable features.


title: An improved one-time password authentication scheme

abstract: The rapid development of the Internet facilitates our lives in many aspects. More and more business will be done through Internet. Under such circumstances, enough attention must be given to the information security, of which the identity authentication is one important problem. In the traditional authentication scheme, the user provides the username and static password to service provider, but there are some inherent shortcomings of this method—static passwords maybe guessed, forgotten, and eavesdropped. One-Time Password (OTP) is considered as the strongest authentication scheme among all password-based solutions. In this paper, a novel two-factor authentication scheme based OTP is proposed. The scheme not only satisfies the mutual authentication between the user and service provider, but also presents higher security and lower computational cost than traditional schemes based OTP.


title: Localization in the brain: new solutions emerging

abstract: Descriptions of neuroanatomical locations are often ambiguous. With the greatly increasing volumes of imaging data that are being produced and the increasing need to generate databases for the efficient analysis of these data, neuroscientists need to avoid such confusion in nomenclature. Here, I discuss the theory and practice on assigning locations to anatomical data, with a focus on data that have been collected from microscopic sections.


of Architecture of Application Systems A Proposal of BPMN Extensions for the Manufacturing


title: An efficient multi-dimensional index for cloud data management

abstract: Recently, the cloud computing platform is getting more and more attentions as a new trend of data management. Currently there are several cloud computing products that can provide various services. However, currently the cloud platforms only support simple keyword-based queries and can't answer complex queries efficiently due to lack of efficient index techniques. In this paper we propose an efficient approach to build multi-dimensional index for Cloud computing system. We use the combination of R-tree and KD-tree to organize data records and offer fast query processing and efficient index maintenance. Our approach can process typical multi-dimensional queries including point queries and range queries efficiently. Besides, frequent change of data on big amount of machines makes the index maintenance a challenging problem, and to cope with this problem we proposed a cost estimation-based index update strategy that can effectively update the index structure. Our experiments show that our indexing techniques improve query efficiency by an order of magnitude compared with alternative approaches, and scale well with the size of the data. Our approach is quite general and independent from the underlying infrastructure and can be easily carried over for implementation on various Cloud computing platforms.


title: The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries

abstract: A predictive spatio-temporal query retrieves the set of moving objects that will intersect a query window during a future time interval. Currently, the only access method for processing such queries in practice is the TPR-tree. In this paper we first perform an analysis to determine the factors that affect the performance of predictive queries and show that several of these factors are not considered by the TPR-tree, which uses the insertion/deletion algorithms of the R*-tree designed for static data. Motivated by this, we propose a new index structure called the TPR*- tree, which takes into account the unique features of dynamic objects through a set of improved construction algorithms. In addition, we provide cost models that determine the optimal performance achievable by any data-partition spatio-temporal access method. Using experimental comparison, we illustrate that the TPR*-tree is nearly-optimal and significantly outperforms the TPR-tree under all conditions.


title: TabletGaze: dataset and analysis for unconstrained appearance-based gaze estimation in mobile tablets

abstract: We study gaze estimation on tablets; our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet are not constrained. We collected a large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. We made three major observations on the collected data and employed a baseline algorithm for analyzing the impact of several factors on gaze estimation accuracy. The baseline algorithm is based on multilevel HoG feature and Random Forests regressor, which achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various practical factors such as person dependency, dataset size, race, wearing glasses and user posture on the gaze estimation accuracy.


title: Recurrent Neural Networks for Word Alignment Model

abstract: This study proposes a word alignment model based on a recurrent neural network (RNN), in which an unlimited alignment history is represented by recurrently connected hidden layers. We perform unsupervised learning using noise-contrastive estimation (Gutmann and Hyvarinen, 2010; Mnih and Teh, 2012), which utilizes artificially generated negative samples. Our alignment model is directional, similar to the generative IBM models (Brown et al., 1993). To overcome this limitation, we encourage agreement between the two directional models by introducing a penalty function that ensures word embedding consistency across two directional models during training. The RNN-based model outperforms the feed-forward neural network-based model (Yang et al., 2013) as well as the IBM Model 4 under Japanese-English and French-English word alignment tasks, and achieves comparable translation performance to those baselines for Japanese-English and Chinese-English translation tasks.


title: ESR spectra of O 2 − on SnO2. effect of adsorbed CO on the conditions of stabilization of O 2 −

abstract: The physical adsorption of CO molecules on SnO2 is shown to be accompanied by a reversible change in the ESR parameters of CO 2 − anion radicals. The effect of CO is different for SnO2 samples with different life histories.


title: A Survey on Deep Learning Toolkits and Libraries for Intelligent User Interfaces

abstract: This paper provides an overview of prominent deep learning toolkits and, in particular, reports on recent publications that contributed open source software for implementing tasks that are common in intelligent user interfaces (IUI). We provide a scientific reference for researchers and software engineers who plan to utilise deep learning techniques within their IUI research and development projects.


title: WISERNet: Wider Separate-then-reunion Network for Steganalysis of Color Images

abstract: Until recently, deep steganalyzers in the spatial domain have been all designed for gray-scale images. In this paper, we propose the wider separate-then-reunion network (WISERNet) for steganalysis of color images. We provide theoretical rationale to claim that the summation in normal convolution is one sort of linear collusion attack which reserves strong correlated patterns while impairs uncorrelated noises. Therefore, in the bottom convolutional layer which aims at suppressing correlated image contents, we adopt separate channel-wise convolution without summation instead. Conversely, in the upper convolutional layers, we believe that the summation in normal convolution is beneficial. Therefore, we adopt united normal convolution in those layers and make them remarkably wider to reinforce the effect of linear collusion attack. As a result, our proposed wide-and-shallow, separate-then-reunion network structure is specifically suitable for color image steganalysis. We have conducted extensive experiments on color image datasets generated from BOSSBase raw images and another large-scale dataset that contains 100, 000 raw images, with different demosaicking algorithms and down-sampling algorithms. The experimental results show that our proposed network outperforms other state-of-the-art color image steganalytic models either hand crafted or learned using deep networks in the literature by a clear margin. Specifically, it is noted that the detection performance gain is achieved with less than half the complexity compared to the most advanced deep-learning steganalyzer as far as we know, which is scarce in the literature.


title: Koszul Equivalences in $A_\infty$-Algebras

abstract: We prove a version of Koszul duality and the induced derived equivalence for Adams connected $A_\infty$-algebras that generalizes the classical Beilinson-Ginzburg-Soergel Koszul duality. As an immediate consequence, we give a version of the Bern\v{s}te{\u\i}n-Gel'fand-Gel'fand correspondence for Adams connected $A_\infty$-algebras. ::: We give various applications. For example, a connected graded algebra $A$ is Artin-Schelter regular if and only if its Ext-algebra $\Ext^\ast_A(k,k)$ is Frobenius. This generalizes a result of Smith in the Koszul case. If $A$ is Koszul and if both $A$ and its Koszul dual $A^!$ are noetherian satisfying a polynomial identity, then $A$ is Gorenstein if and only if $A^!$ is. The last statement implies that a certain Calabi-Yau property is preserved under Koszul duality.


Automatic Differentiation in Myia


title: Humans prefer curved visual objects

abstract: People constantly make snap judgments about objects encountered in the environment. Such rapid judgments must be based on the physical properties of the targets, but the nature of these properties is yet unknown. We hypothesized that sharp transitions in contour might convey a sense of threat, and therefore trigger a negative bias. Our results were consistent with this hypothesis. The type of contour a visual object possesses—whether the contour is sharp angled or curved—has a critical influence on people's attitude toward that object.


title: Subjective well-being amongst migrant children in China: unravelling the roles of social support and identity integration

abstract: Background ::: Migrant children refer to rural children who accompany one or both parents to urban area. Empirical evidence showed that compared with their urban counterparts, migrant children had poorer developmental, emotional and psychological health. ::: ::: Method ::: A sample of 1306 migrant children were recruited to examine the characteristics of migrant children and investigate the effects of identity integration, support and socioeconomic factors (e.g. age, gender, type of school, family socioeconomic status, city type) on their subjective wellbeing. ::: ::: Results ::: Children with higher levels of identity integration, social support, family socioeconomic status, who attended public school and who lived in the third-tiered city of Weihai demonstrated better subjective wellbeing. Social support remained a strong predictor for subjective wellbeing, despite a significant mediating effect of identity integration. ::: ::: Conclusions ::: These results highlight the need for policymakers and practitioners alike to address individual factors pertaining to psychological adjustments, as well as social determinants of subjective wellbeing in the context of migration.


title: Building firewall over the software-defined network controller

abstract: Many have recognized the need to restructure the current internet work into a much more dynamic networking environment. It is difficult for today's inflexible infrastructure to cope with the fast changing demands of the users. As a result, Software-Defined Network (SDN) was introduced around 2005 to transform today's network to have centralized management, rapid innovation, and programmability by decoupling the control and data planes. This study focuses on developing a firewall application that runs over an OpenFlow-based SDN controller to show that most of the firewall functionalities are able to be built on software, without the aid of a dedicated hardware. Among many OpenFlow controllers that already exist for the public, we have chosen POX written in Python for the experiment; and to create the SDN network topology, we have used VirtualBox and Mininet. In this study, we cover the implementation detail of our firewall application, as well as the experimentation result.


title: On data placement strategies in distributed RDF stores

abstract: In the last years, scalable RDF stores in the cloud have been developed, where graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. One main challenge in these RDF stores is the data placement strategy that can be formalized in terms of graph covers. These graph covers determine whether (a) different query results may be computed on several compute nodes in parallel (vertical parallelization) and (b) individual query results can be produced only from triples assigned to few --- ideally one --- storage node (horizontal containment). We analyse the impact of three most commonly used graph cover strategies in these terms and found out that balancing query workload reduces the query execution time more than reducing data transfer over network. To this end, we present our novel benchmark and open source evaluation platform.


title: A Deep Generative Framework for Paraphrase Generation

abstract: Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generating paraphrases automatically. Our proposed method is based on a combination of deep generative models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases, given an input sentence. Traditional VAEs when combined with recurrent neural networks can generate free text but they are not suitable for paraphrase generation for a given sentence. We address this problem by conditioning the both, encoder and decoder sides of VAE, on the original sentence, so that it can generate the given sentence's paraphrases. Unlike most existing models, our model is simple, modular and can generate multiple paraphrases, for a given sentence. Quantitative evaluation of the proposed method on a benchmark paraphrase dataset demonstrates its efficacy, and its performance improvement over the state-of-the-art methods by a significant margin, whereas qualitative human evaluation indicate that the generated paraphrases are well-formed, grammatically correct, and are relevant to the input sentence. Furthermore, we evaluate our method on a newly released question paraphrase dataset, and establish a new baseline for future research.


title: A substrate integrated waveguide slot array antenna at Ka-band

abstract: Substrate integrated waveguide (SIW) is a new high Q, low loss, low cost, easy processing and integrating planar waveguide structure, which can be widely used in microwave and millimeter-wave integrated circuit. A five-elements resonant slot array antenna at 35GHz has been designed in this paper with a bandwidth of 500MHz (S 11 <−15dB), gain of 11.5dB and sidelobe level (SLL) of −23.5dB (using Taylor weighted), which has a small size, low cost and is easy to integrate, etc.


title: Autoencoder for words

abstract: This paper presents a training method that encodes each word into a different vector in semantic space and its relation to low entropy coding. Elman network is employed in the method to process word sequences from literary works. The trained codes possess reduced entropy and are used in ranking, indexing, and categorizing literary works. A modification of the method to train the multi-vector for each polysemous word is also presented where each vector represents a different meaning of its word. These multiple vectors can accommodate several different meanings of their word. This method is applied to the stylish analyses of two Chinese novels, Dream of the Red Chamber and Romance of the Three Kingdoms.


title: Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs

abstract: Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.


title: Distantly Supervised POS Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of Hittite

abstract: This paper presents a statistical approach to automatic morphosyntactic annotation of Hittite transcripts. Hittite is an extinct Indo-European language using the cuneiform script. There are currently no morphosyntactic annotations available for Hittite, so we explored methods of distant supervision. The annotations were projected from parallel German translations of the Hittite texts. In order to reduce data sparsity, we applied stemming of German and Hittite texts. As there is no off-the-shelf Hittite stemmer, a stemmer for Hittite was developed for this purpose. The resulting annotation projections were used to train a POS tagger, achieving an accuracy of 69\% on a test sample. To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.


title: Self-training from labeled features for sentiment analysis

abstract: Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort. In this paper, we propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon with preferences on expectations of sentiment labels of those lexicon words being expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie-review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than existing weakly-supervised sentiment classification methods despite using no labeled documents.


title: Visualization of Myocardial Perfusion Derived from Coronary Anatomy

abstract: Visually assessing the effect of the coronary artery anatomy on the perfusion of the heart muscle in patients with coronary artery disease remains a challenging task. We explore the feasibility of visualizing this effect on perfusion using a numerical approach. We perform a computational simulation of the way blood is perfused throughout the myocardium purely based on information from a three-dimensional anatomical tomographic scan. The results are subsequently visualized using both three-dimensional visualizations and bullpsilas eye plots, partially inspired by approaches currently common in medical practice. Our approach results in a comprehensive visualization of the coronary anatomy that compares well to visualizations commonly used for other scanning technologies. We demonstrate techniques giving detailed insight in blood supply, coronary territories and feeding coronary arteries of a selected region. We demonstrate the advantages of our approach through visualizations that show information which commonly cannot be directly observed in scanning data, such as a separate visualization of the supply from each coronary artery. We thus show that the results of a computational simulation can be effectively visualized and facilitate visually correlating these results to for example perfusion data.


title: Hybrid Acoustic-Wave-Lumped-Element Resonators (AWLRs) for High-$Q$ Bandpass Filters With Quasi-Elliptic Frequency Response

abstract: A new class of bandpass filters with quasi-elliptic frequency response, small physical size, and effective quality factors $(Q{\bf s}_{\bf eff})$ of the order of 1000 are presented. They are based on novel hybrid acoustic-wave-lumped-element resonator (AWLR) modules that enable the realization of bandpass filters with fractional bandwidths (FBWs) that are much larger (0.91–5.1 $k_{t}^{2}$ ) than the electromechanical coupling coefficient $(k_{t}^{2})$ of conventional acoustic wave (AW) resonator filters that exhibit FBWs between 0.4–0.8 $k_{t}^{2}$ . In addition, they exhibit $Q{\bf s}_{\bf eff}$ that are 25–50 times larger than in traditional lumped-element filter architectures. Quasi-elliptic single-pole bandpass filters (one pole and two zeros) made up of commercially available one-port surface AW resonators and surface-mount-device components are manufactured and measured at 418 MHz. Various passbands with FBW between 0.07% and 0.4% and insertion loss below 1.25 dB (i.e., $Q{\bf s}_{\bf eff}$ in the range of 1525–5150) are demonstrated. Second-order bandpass filters (two poles and four zeros) with 0.24% FBW and less than 1.15 dB of insertion loss (i.e., $Q{\bf s}_{\bf eff}$ of 4000) are also shown to improve the passband selectivity and out-of-band rejection. These filters feature dynamic stopband characteristics by tuning the position of their transmission zeros. The operating principle, theoretical analysis, and design guidelines of the AWLR module, as well as a comparison with traditional all-AW ladder-type filters, are also reported. An original and rather simple $RLC$ -circuit equivalent of AW resonators that facilitates the incorporation of spurious modes into a simple Butterworth–Van-Dyke model for a more accurate synthesis of AWLR-based filters is extracted from measured two-port S-parameters.


title: The Therapist as Psychobiological Regulator: Dissociation, Affect Attunement and Clinical Process

abstract: Dissociation is defined as a protective coping mechanism employed on a broad spectrum: from day-to-day “spacing out” to psychic numbing to multiplicity. A convergence of recent insights in traumatology, neuroscience, and developmental theory is reviewed. These findings all point to the importance of affect regulation in infant and child development and in the therapeutic relationship, where attunement to implicit communication is crucial. Using such relational and intersubjective organizing principles as the concept of enactment and Beebe and Lachmann’s (Infant research and adult treatment: Co-constructing interactions 2002) “three principles of salience,” I discuss an analytic case in which dissociation is a central dynamic.


title: paths in grid graphs

abstract: A grid graph is a node-induced finite subgraph of the infinite grid. It is rectangular if its set of nodes is the product of two intervals. Given a rectangular grid graph and two of its nodes, we give necessary and sufficient conditions for the graph to have a Hamilton path between these two nodes. In contrast, the Hamilton path (and circuit) problem for general grid graphs is shown to be NP-complete. This provides a new, relatively simple, proof of the result that the Euclidean traveling salesman problem is NP-complete.


title: Construal Level and Procrastination

abstract: According to construal-level theory, events that are distant in time tend to be represented more abstractly than are events that are close in time. This mental association between level of abstractness and temporal distance is proposed to be a bidirectional relationship, such that level of representation of an event should also have effects on the time when the activity is performed. In the present studies, participants were asked to respond to a questionnaire via e-mail within 3 weeks. The questionnaire was designed to induce either an abstract or a concrete construal. Using a variety of manipulations of construal level, the studies supported the predictions of construal-level theory. Individuals were less likely to procrastinate performing the task when the questionnaire induced a more concrete construal. Furthermore, this effect did not depend on the attractiveness, importance, or perceived difficulty of the task.


title: Nonlinear control for output regulation of ball and plate system

abstract: This paper proposes nonlinear control methods for output regulation of ball and plate system. Positions of the ball are regulated with double feedback loops. Recursive backstepping design is employed for the external feedback loop, while switching control scheme is used in the inner feedback loop. System performance was tuned by backstepping parameters. Simulation results show that the proposed nonlinear control works wells in both stabilization and tracking control. Asymptotical stabilities are also achieved under unknown external disturbance in the experiments.


title: Current advances in the fabrication of microneedles for transdermal delivery.

abstract: The transdermal route is an excellent site for drug delivery due to the avoidance of gastric degradation and hepatic metabolism, in addition to easy accessibility. Although offering numerous attractive advantages, many available transdermal systems are not able to deliver drugs and other compounds as desired. The use of hypodermic needles, associated with phobia, pain and accidental needle-sticks has been used to overcome the delivery limitation of macromolecular compounds. The means to overcome the disadvantages of hypodermic needles has led to the development of microneedles for transdermal delivery. However, since the initial stages of microneedle fabrication, recent research has been conducted integrating various fabrication techniques for generating sophisticated microneedle devices for transdermal delivery including progress on their commercialization. A concerted effort has been made within this review to highlight the current advances of microneedles, and to provide an update of pharmaceutical research in the field of microneedle-assisted transdermal drug delivery systems.


title: DESlib: A Dynamic ensemble selection library in Python

abstract: DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) \emph{dcs}, containing the implementation of dynamic classifier selection methods (DCS); (ii) \emph{des}, containing the implementation of dynamic ensemble selection methods (DES); (iii) \emph{static}, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.


title: Effects of micro- and nanoplastics on aquatic ecosystems: Current research trends and perspectives

abstract: Abstract Contamination by bulk plastics and plastic debris is currently the one of the most serious environmental problems in aquatic ecosystems. In particular, small-scale plastic debris such as microplastics and nanoplastics has become leading contributors to the pollution of marine and freshwater ecosystems. Studies are investigating the impacts of micro-and nanoplastics on aquatic organisms and ecosystems worldwide. This review covers 83 studies that investigated the distribution of microplastics and the ecotoxicity of micro- and nanoplastics in marine and freshwater ecosystems. The studies indicated that micro-sized plastics and plastic debris were distributed at various concentrations in aquatic ecosystems around the world. They had various effects on the growth, development, behavior, reproduction, and mortality of aquatic animals. We discuss these studies in detail and suggest directions for future research.


title: Machine Learning for Visual Navigation of Unmanned Ground Vehicles

abstract: The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This manuscript reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the manuscript.


title: Extensive assessment and evaluation methodologies on assistive social robots for modelling human-robot interaction - A review

abstract: Assessment and evaluation methodologies as well as combinations of them, for modelling of Human-Robot Interaction (HRI), are reviewed extensively and thoroughly in this paper. However, based on the types of robots and the kinds of interactions involved in the modelling of HRI, we concentrate just on the assistive social robot types. A comprehensive review has been done on each of these extensive evaluation and assessment methodologies applied for testing the usability of assistive social robots, user acceptance towards robots and robot acceptance in terms of behavioural adaptation during the HRI. The evaluation methodologies are reviewed based on the primary and non-primary basis, while the assessment methodologies are reviewed based on the type(s) of modelling approaches. We then discussed the weaknesses, strengths and uniqueness of each type of the past research work done on the evaluation and assessment methodologies. Comparison and contrast tables are also illustrated. Lastly, this paper provides our recommended directions, new vision, as well as our inspirations and new insights for future researches by highlighting the key areas for enhancing each of the past evaluation and assessment methodologies so that a better modelling approach for HRI can be achieved. Contributions of this review paper are also discussed thoroughly.


title: Integrated anaerobic–aerobic fixed-film reactor for slaughterhouse wastewater treatment

abstract: Abstract An integrated anaerobic–aerobic fixed-film pilot-scale reactor with arranged media was fed during 166 days with slaughterhouse wastewater. Operation temperature was 25 °C and the anaerobic–aerobic volume ratio was decreased from 4:1 to 3:2 and finally to 2:3. Overall organic matter removal efficiencies of 93% were achieved for an average organic loading rate of 0.77 kg COD/m 3 d, and nitrogen removal efficiencies of 67% were achieved for nitrogen loading rates of 0.084 kg N/m 3 d. The high internal recirculation associated to the air-lift effect linked to the aeration of a part of the reactor section caused high mixing between the anaerobic and aerobic zones, so that most organic matter was removed aerobically. The nitrification process achieved an efficiency of 91% for nitrogen loads of 0.15 kg N/m 3 d when the anaerobic–aerobic volume ratio was 2:3 and was limited by dissolved oxygen concentration below 3 mg/l. The influence of the heterotrophic biomass growing in the outer biofilm was checked. Denitrification only implied the 12–34% of the total nitrogen removal and was limited by dissolved oxygen concentration in the anaerobic zone above 0.5 mg/l caused by the mixing regime. Most removed nitrogen was employed in synthesis of heterotrophic bacteria.


title: Secured Data Storage Scheme Based on Block Chain for Agricultural Products Tracking

abstract: The Internet of Things technology has been widely used in the quality tracking of agricultural products, however, the safety of storage for tracked data is still a serious challenge. Recently, with the expansion of blockchain technology applied in cross-industry field, the unchangeable features of its stored data provide us new vision about ensuring the storage safety for tracked data. Unfortunately, when the blockchain technology is directly applied in agricultural products tracking and data storage, it is difficult to automate storage and obtain the hash data stored in the blockchain in batches base on the identity. Addressing this issue, we propose a double-chain storage structure, and design a secured data storage scheme for tracking agricultural products based on blockchain. Specifically, the chained data structure is utilized to store the blockchain transaction hash, and together with the chain of the blockchain to form a double-chain storage, which ensures the data of agricultural products will not be maliciously tampered or destructed. Finally, in the practical application system, we verify the correctness and security of the proposed storage scheme.


title: Modeling and Analysis of a Web Service Firewall Using Coloured Petri Nets

abstract: Web services are software components defined by WSDL, registered by UDDI and invoked by SOAP protocols. The port used by Web services and SOAP is not typically blocked by conventional firewalls. Therefore, a new type of firewall named Web service firewall or XML firewall is required. There are a number of commercial Web service firewall products. Several academic projects have also been done on Web service firewalls. This category of Web service firewalls are briefly introduced in this paper and their drawbacks are mentioned. We have proposed a Web service firewall architecture that supports authentication and authorization mechanisms. It also provides prevention of SOAP-based attacks. In this paper, we will present the proposed architectural design for a Web service firewall. A formal model for the access control of the proposed architecture using coloured Petri nets (CPNs) is also presented. The CPN model is used for the analysis of the proposed architectural design. The model can also be served as a high-level design for implementation of the Web service firewall.


title: Sonarbeat: Sonar Phase for Breathing Beat Monitoring with Smartphones

abstract: Vital sign (e.g., breathing rate) monitoring has become increasingly more important because it can offer useful clues to medical conditions such as sleep disorders or anomalies. There is a compelling need for technologies that enable contact-free, easy deployment, and long-term vital sign monitoring for healthcare. In this paper, we present a SonarBeat system to leverage a phase based active sonar to monitor breathing rates with smartphones. We design and implement the SonarBeat system, with components including signal generation, data extraction, received signal preprocessing, and breathing rate estimation, with Andriod smartphones. Our experimental results validate the superior performance of SonarBeat in different indoor environment settings.


title: A 1.7 ps Equivalent Bin Size and 4.2 ps RMS FPGA TDC Based on Multichain Measurements Averaging Method

abstract: A high precision and high resolution time-to-digital converter (TDC) based on multichain measurements averaging method is implemented in a 40 nm fabrication process Virtex-6 FPGA. The results of the detailed theoretical analysis and the simulation with the MATLAB tool based on a complete TDC module show that the resolution limitation determined by the intrinsic cell delay of plain tapped-delay chain can be overcame, which results in an improvement on both resolution and precision without increasing the dead time. The test results agree with the simulation results quite well. In such a TDC, the input signal is connected to multiple tapped-delay chains simultaneously (the number of the chains is M), and each chain is just a plain TDC and generates a timestamp for a hit signal. Therefore, M timestamps should be obtained in total, which, after averaging, give the final timestamp. A TDC with 1.7 ps equivalent bin size, 1.5 ps averaged bin size and 4.2 ps RMS has been implemented with M being 16, which performs much better than the plain TDC constructed of a single tapped delay chain having 42.3 ps equivalent bin size, 24.0 ps averaged bin size resolution and 13.2 ps RMS precision. The comparisons of equivalent bin size and averaged bin size show that the nonlinearity is improved with a larger M. Due to the real time integral nonlinearity (INL) calibration and averaging calculation, the multichain TDC is almost insensitive to the process voltage and temperature (PVT) variations.


title: Convex and Scalable Weakly Labeled SVMs

abstract: In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. Unlike supervised learning, learning with weak labels involves a difficult Mixed-Integer Programming (MIP) problem. Therefore, it can suffer from poor scalability and may also get stuck in local minimum. In this paper, we focus on SVMs and propose the WellSVM via a novel label generation strategy. This leads to a convex relaxation of the original MIP, which is at least as tight as existing convex Semi-Definite Programming (SDP) relaxations. Moreover, the WellSVM can be solved via a sequence of SVM subproblems that are much more scalable than previous convex SDP relaxations. Experiments on three weakly labeled learning tasks, namely, (i) semi-supervised learning; (ii) multi-instance learning for locating regions of interest in content-based information retrieval; and (iii) clustering, clearly demonstrate improved performance, and WellSVM is also readily applicable on large data sets.


title: Measures of emotion: A review

abstract: A consensual, componential model of emotions conceptualises them as experiential, physiological, and behavioural responses to personally meaningful stimuli. The present review examines this model in terms of whether different types of emotion-evocative stimuli are associated with discrete and invariant patterns of responding in each response system, how such responses are structured, and if such responses converge across different response systems. Across response systems, the bulk of the available evidence favours the idea that measures of emotional responding reflect dimensions rather than discrete states. In addition, experiential, physiological, and behavioural response systems are associated with unique sources of variance, which in turn limits the magnitude of convergence across measures. Accordingly, the authors suggest that there is no “gold standard” measure of emotional responding. Rather, experiential, physiological, and behavioural measures are all relevant to understanding emotion and cannot ...


title: Partial Evaluation of Computation Process - An Approach to a Compiler-Compiler

abstract: This paper reports the relationship between formal description of semantics (i.e., interpreter) of a programming language and an actual compiler. The paper also describes a method to automatically generate an actual compiler from a formal description which is, in some sense, the partial evaluation of a computation process. The compiler-compiler inspired by this method differs from conventional ones in that the compiler-compiler based on our method can describe an evaluation procedure (interpreter) in defining the semantics of a programming language, while the conventional one describes a translation process.


title: BER estimation from EVM for QPSK and 16-QAM coherent optical systems

abstract: BER estimation from measured EVM values is shown experimentally for QPSK and 16QAM optical signals with 28 GBd. Various impairments, such as gain imbalance, quadrature error and timing skew, are introduced into the transmitted signal in order to evaluate the robustness of the method. The EVM was measured using two different realtime sampling systems and the EVM measurement accuracy is discussed.


title: MUSIC AND NATIONAL CULTURE: POP MUSIC AND RESISTANCE IN BRAZIL

abstract: In the late 1960s Brazil was experiencing a boom in its television and record industries, as part of the so-called “Economic Miracle” (1968 74) brought about by the military dictatorship’s opening up of the market to international capital. Censorship was introduced more or less simultaneously and responded in part to the military’s recognition of the potential power of the audio-visual media in a country in which over half of the population was illiterate or semi-literate. After the 1964 coup and until the infamous 5 Institutional Act (AI-5), introduced in 1968 to silence opposition to the regime, the left wing cultural production that had characterised the period under the government of the deposed populist president, Joao Goulart, had continued to flourish. Until 1968, the military had largely left the cultural scene alone to face up to the failure of its revolutionary political and cultural projects. Instead the generals focused on the brutal repression of student, trade union and grassroots activists who had collaborated with the cultural left, thus effectively depriving these artists of their public. Chico Buarque, one of the most censored performers of the period, maintains that at this moment he was saved from retreating into an introspective formalism in his songs and musical dramas by the emergence in 1965 of the televised music festivals, which became one of the most talked about events in the country (Buarque, 1979, 48). Sponsored by the television stations, which were themselves closely monitored and regulated by the government, the festivals still provided oppositional songwriters with an opportunity to re-