title: Smooth and time-optimal trajectory planning for industrial manipulators along specified paths

abstract: This article presents a method for determining smooth and time-optimal path constrained trajectories for robotic manipulators and investigates the performance of these trajectories both through simulations and experiments. The desired smoothness of the trajectory is imposed through limits on the torque rates. The third derivative of the path parameter with respect to time, the pseudo-jerk, is the controlled input. The limits on the actuator torques translate into state-dependent limits on the pseudo-acceleration. The time-optimal control objective is cast as an optimization problem by using cubic splines to parametrize the state space trajectory. The optimization problem is solved using the flexible tolerance method. The experimental results presented show that the planned smooth trajectories provide superior feasible time-optimal motion. © 2000 John Wiley & Sons, Inc.


A Neural Architecture for Dialectal Arabic Segmentation


title: Traumatic brain injuries

abstract: Traumatic brain injuries (TBIs) are clinically grouped by severity: mild, moderate and severe. Mild TBI (the least severe form) is synonymous with concussion and is typically caused by blunt non-penetrating head trauma. The trauma causes stretching and tearing of axons, which leads to diffuse axonal injury — the best-studied pathogenetic mechanism of this disorder. However, mild TBI is defined on clinical grounds and no well-validated imaging or fluid biomarkers to determine the presence of neuronal damage in patients with mild TBI is available. Most patients with mild TBI will recover quickly, but others report persistent symptoms, called post-concussive syndrome, the underlying pathophysiology of which is largely unknown. Repeated concussive and subconcussive head injuries have been linked to the neurodegenerative condition chronic traumatic encephalopathy (CTE), which has been reported post-mortem in contact sports athletes and soldiers exposed to blasts. Insights from severe injuries and CTE plausibly shed light on the underlying cellular and molecular processes involved in mild TBI. MRI techniques and blood tests for axonal proteins to identify and grade axonal injury, in addition to PET for tau pathology, show promise as tools to explore CTE pathophysiology in longitudinal clinical studies, and might be developed into diagnostic tools for CTE. Given that CTE is attributed to repeated head trauma, prevention might be possible through rule changes by sports organizations and legislators. Traumatic brain injuries (TBIs) result from an external force and can result in a range of clinical manifestations and neuropathological changes, many of which are only beginning to be understood. Here, the authors argue that the cellular and molecular processes involved in TBIs of different severities are probably similar, just to different degrees.


abstract: We propose a new end-to-end single image dehazing method, called Densely Connected Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map, atmospheric light and dehazing all together. The end-to-end learning is achieved by directly embedding the atmospheric scattering model into the network, thereby ensuring that the proposed method strictly follows the physics-driven scattering model for dehazing. Inspired by the dense network that can maximize the information flow along features from different levels, we propose a new edge-preserving densely connected encoder-decoder structure with multi-level pyramid pooling module for estimating the transmission map. This network is optimized using a newly introduced edge-preserving loss function. To further incorporate the mutual structural information between the estimated transmission map and the dehazed result, we propose a joint-discriminator based on generative adversarial network framework to decide whether the corresponding dehazed image and the estimated transmission map are real or fake. An ablation study is conducted to demonstrate the effectiveness of each module evaluated at both estimated transmission map and dehazed result. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods. Code and dataset is made available at: https://github.com/hezhangsprinter/DCPDN


title: Novel Low-Density Signature for Synchronous CDMA Systems Over AWGN Channel

abstract: Novel low-density signature (LDS) structure is proposed for transmission and detection of symbol-synchronous communication over memoryless Gaussian channel. Given N as the processing gain, under this new arrangement, users' symbols are spread over N chips but virtually only dv < N chips that contain nonzero-values. The spread symbol is then so uniquely interleaved as the sampled, at chip rate, received signal contains the contribution from only dc < K number of users, where K denotes the total number of users in the system. Furthermore, a near-optimum chip-level iterative soft-in-soft-out (SISO) multiuser decoding (MUD), which is based on message passing algorithm (MPA) technique, is proposed to approximate optimum detection by efficiently exploiting the LDS structure. Given beta = K/N as the system loading, our simulation suggested that the proposed system alongside the proposed detection technique, in AWGN channel, can achieve an overall performance that is close to single-user performance, even when the system has 200% loading, i.e., when beta = 2. Its robustness against near-far effect and its performance behavior that is very similar to optimum detection are demonstrated in this paper. In addition, the complexity required for detection is now exponential to dc instead of K as in conventional code division multiple access (CDMA) structure employing optimum multiuser detector.


title: Multi-level mapping: Real-time dense monocular SLAM

abstract: We present a method for Simultaneous Localization and Mapping (SLAM) using a monocular camera that is capable of reconstructing dense 3D geometry online without the aid of a graphics processing unit (GPU). Our key contribution is a multi-resolution depth estimation and spatial smoothing process that exploits the correlation between low-texture image regions and simple planar structure to adaptively scale the complexity of the generated keyframe depthmaps to the texture of the input imagery. High-texture image regions are represented at higher resolutions to capture fine detail, while low-texture regions are represented at coarser resolutions for smooth surfaces. The computational savings enabled by this approach allow for significantly increased reconstruction density and quality when compared to the state-of-the-art. The increased depthmap density also improves tracking performance as more constraints can contribute to the pose estimation. A video of experimental results is available at http://groups.csail.mit.edu/rrg/multi_level_mapping.


title: Generating Sentences by Editing Prototypes

abstract: We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perplexity on language modeling and generates higher quality outputs according to human evaluation. Furthermore, the model gives rise to a latent edit vector that captures interpretable semantics such as sentence similarity and sentence-level analogies.


title: Pix2Vox: Context-Aware 3D Reconstruction From Single and Multi-View Images

abstract: Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.


title: Shaping the Narrative Arc: An Information-Theoretic Approach to Collaborative Dialogue

abstract: We consider the problem of designing an artificial agent capable of interacting with humans in collaborative dialogue to produce creative, engaging narratives. In this task, the goal is to establish universe details, and to collaborate on an interesting story in that universe, through a series of natural dialogue exchanges. Our model can augment any probabilistic conversational agent by allowing it to reason about universe information established and what potential next utterances might reveal. Ideally, with each utterance, agents would reveal just enough information to add specificity and reduce ambiguity without limiting the conversation. We empirically show that our model allows control over the rate at which the agent reveals information and that doing so significantly improves accuracy in predicting the next line of dialogues from movies. We close with a case-study with four professional theatre performers, who preferred interactions with our model-augmented agent over an unaugmented agent.


title: Robust Inference via Generative Classifiers for Handling Noisy Labels

abstract: Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a novel inference method, termed Robust Generative classifier (RoG), applicable to any discriminative (e.g., softmax) neural classifier pre-trained on noisy datasets. In particular, we induce a generative classifier on top of hidden feature spaces of the pre-trained DNNs, for obtaining a more robust decision boundary. By estimating the parameters of generative classifier using the minimum covariance determinant estimator, we significantly improve the classification accuracy with neither re-training of the deep model nor changing its architectures. With the assumption of Gaussian distribution for features, we prove that RoG generalizes better than baselines under noisy labels. Finally, we propose the ensemble version of RoG to improve its performance by investigating the layer-wise characteristics of DNNs. Our extensive experimental results demonstrate the superiority of RoG given different learning models optimized by several training techniques to handle diverse scenarios of noisy labels.


title: The Sustainability and Challenges of Business Incubators in the Western Cape Province, South Africa

abstract: Notwithstanding the growing interest in business incubation programmes and the benefits derived from such programmes, the path is beset by numerous challenges. This paper investigates the challenges faced by business incubators (BIs) as they strive to support their clients. The study utilized a qualitative approach to collect data by way of interviews to gain in-depth knowledge and understanding of the concept and challenges of business incubators. The data were collected using structured and unstructured in-depth personal interviews, which were carried out with the respondents of business incubators in the Western Cape. The research participants for this study were limited to the business incubators on the database of a local organization that promotes small and medium enterprises (SMEs) development strategy and programmes in the Western Cape Province. All five business incubators on the database were deemed suitable for the study. The results indicated that an average of twenty-five entrepreneurs graduated from the incubation programme in the last five years. Furthermore, lack of sponsorship, production space, advanced technological facilities (prototype) and expansion to different areas were found to be among the challenges hindering incubators.


title: Random-effects models for longitudinal data.

abstract: Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a distribution specified at the second stage. A general family of models is discussed, which includes both growth models and repeated-measures models as special cases. A unified approach to fitting these models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed. Two examples are taken from a current epidemiological study of the health effects of air pollution.


title: Boosting Moving Average Reversion Strategy for Online Portfolio Selection: A Meta-learning Approach

abstract: In this paper, we study the online portfolio selection problem from the perspective of meta learning for mean reversion. The online portfolio selection problem aims to maximize the final accumulated wealth by rebalancing the portfolio at each time period based on the portfolio prices announced before. Mean Reversion is a typical principle in portfolio theory and strategies that utilize this principle achieve the superior empirical performances so far. However there are some important limits of existing Mean Reversion strategies: First, the mean reversion strategies have to set a fixed window size, where the optimal window size can only be chosen in hindsight. Second, most existing mean reversion techniques ignore the temporal heterogeneity of historical price relatives from different periods. Moreover, most mean reversion methods suffer from noises and outliers in the data, which greatly affects the performances. In order to tackle the limits of previous approaches, we exploit mean reversion principle from a meta learning perspective and propose a boosting method for price relative prediction. More specifically, we generate several experts where each expert follows a specific mean reversion policy and predict the final price relatives with meta learning techniques. The sampling of multiple experts involves mean reversion strategies with various window sizes; while the meta learning technique brings temporal heterogeneity and stronger robustness for prediction. We adopt online passive-aggressive learning for portfolio optimization with the predicted price relatives. Extensive experiments have been conducted on real-world datasets and our approach outperforms the state-of-the-art approaches significantly.


title: HEVC/H.265 vs. VP9 state-of-the-art video coding comparison for HD and UHD applications

abstract: High Efficiency Video Coding (HEVC) and VP9 are the latest state-of-the-art high-efficiency video coding standards. Both standards aim to decrease the bit-rate while improving the compression efficiency. HEVC/H.265 video standard was collaboratively developed by ITU-T and ISO/IEC with up to 50% bit-rate reduction as compared to its predecessor H.264/AVC. On the other hand, VP9 is an open source video standard developed by Google. While maintaining the same video quality, VP9 has achieved 50% bit-rate reduction as compared to its predecessor VP8. This research paper focuses on the comparison of HEVC and VP9 based on both subjective as well as objective assessments. First, detailed differences in design and specification of the two standards are discussed. Later experiment results are provided, which compare the coding efficiency of both standards on various high-definition 720p, 1090p, and 2160p sample test bench videos. According to our experimental results, it has been empirically proved that HEVC/H.265 outperforms VP9 in bit-rate savings.


title: A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network

abstract: The scholarly literature is expanding at a rate that necessitates intelligent algorithms for search and navigation.For the most part, the problem of delivering scholarly articles has been solved. If one knows the title of an article, locating it requires little effort and, paywalls permitting, acquiring a digital copy has become trivial. However, the navigational aspect of scientific search – finding relevant, influential articles that one does not know exist – is in its early development. In this paper, we introduce Eigenfactor Recommends – a citation-based method for improving scholarly navigation. The algorithm uses the hierarchical structure of scientific knowledge, making possible multiple scales of relevance for different users. We implement the method and generate more than 300 million recommendations from more than 35 million articles from various bibliographic databases including the AMiner dataset. We find little overlap with co-citation, another well-known citation recommender, which indicates potential complementarity. In an online A-B comparison using SSRN, we find that our approach performs as well as co-citation, but this new approach offers much larger recommendation coverage. We make the code and recommendations freely available at babel.eigenfactor.org and provide an API for others to use for implementing and comparing the recommendations on their own platforms.


title: A Medium-Voltage Hybrid DC Circuit Breaker—Part II: Ultrafast Mechanical Switch

abstract: This paper presents the test results of an ultrafast (less than 2 ms) medium-voltage hybrid dc circuit breaker prototype that consists of three switching devices: a 15-kV silicon carbide (SiC) emitter turn-off thyristor as the main breaker (MB), a fast acting mechanical switch, and a commutating switch (CS) to quickly divert the primary current to the MB for arcless interruption. The hybrid dc circuit breaker prototype can interrupt a circuit in less than 2 ms in dc power systems up to 10 kV, such as in electric ships. The ultrafast operations and extremely low loss can effectively limit the fault current level and switching transients in all medium-voltage systems, and can provide intelligent and fast protection function for smart power distribution and critical loads in a modernized grid. The design considerations of the three switching devices of the hybrid dc circuit breaker are presented. This paper focuses on the ultrafast mechanical switch and the testing of the hybrid dc circuit breaker, while a companion paper addresses the high-voltage solid-state main switch and the low-voltage solid-state CS.


title: Efficient sparse matrix-vector multiplication on CUDA

abstract: The massive parallelism of graphics processing units (GPUs) oers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In this paper we discuss data structures and algorithms for SpMV that are eciently implemented on the CUDA platform for the ne-grained parallel architecture of the GPU. Given the memory-bound nature of SpMV, we emphasize memory bandwidth eciency and compact storage formats. We consider a broad spectrum of sparse matrices, from those that are well-structured and regular to highly irregular matrices with large imbalances in the distribution of nonzeros per matrix row. We develop methods to exploit several common forms of matrix structure while oering alternatives which accommodate greater irregularity. On structured, grid-based matrices we achieve performance of 36 GFLOP/s in single precision and 16 GFLOP/s in double precision on a GeForce GTX 280 GPU. For unstructured nite-element matrices, we observe performance in excess of 15 GFLOP/s and 10 GFLOP/s in single and double precision respectively. These results compare favorably to prior state-of-the-art studies of SpMV methods on conventional multicore processors. Our double precision SpMV performance is generally two and a half times that of a Cell BE with 8 SPEs and more than ten times greater than that of a quad-core Intel Clovertown system.


title: DEVELOPMENT OF FOUR WHEEL STEERING SYSTEM FOR A CAR

abstract: Production cars are designed to understeer and rarely do they oversteer. If a car could automatically compensate for an understeer/oversteer problem, the driver would enjoy nearly neutral steering under varying operating conditions. Four-wheel steering is a serious effort on the part of automotive design engineers to provide near-neutral steering. Also in situations like low speed cornering, vehicle parking and driving in city conditions with heavy traffic in tight spaces, driving would be very difficult due to vehicle’s larger wheelbase and track width. Hence there is a requirement of a mechanism which result in less turning radius and it can be achieved by implementing four wheel steering mechanism instead of regular two wheel steering. In this project Maruti Suzuki 800 is considered as a benchmark vehicle. The main aim of this project is to turn the rear wheels out of phase to the front wheels. In order to achieve this, a mechanism which consists of two bevel gears and intermediate shaft which transmit 100% torque as well turns rear wheels in out of phase was developed. The mechanism was modelled using CATIA and the motion simulation was done using ADAMS. A physical prototype was realised. The prototype was tested for its cornering ability through constant radius test and was found 50% reduction in turning radius and the vehicle was operated at low speed of 10 kmph. Nomenclature L Wheelbase (mm) R Turning radius (mm) a2 Distance of CG from rear axle (mm) δif Inner angle of front tire (degree) δof Outer angle of front tire (degree) δir Inner angle of rear tire (degree) δif Outer angle of rear tire (degree) R1 Distance between instantaneous centre and the axis of the vehicle (mm) Wf Load on front axle (kg) W Total weight of car (kg) C1 Distance of instantaneous centre from front axle axis (mm) C2 Distance of instantaneous centre from rear axle axis (mm) wf Front track width (mm) wr Rear track width (mm) δ Total steering angle of the vehicle (degree) δi Total inner angle of the vehicle (degree) δo Total outer angle of the vehicle (degree)


title: Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification

abstract: The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.


title: Public Perception of Unmanned Aerial Vehicles

abstract: This study explored public risk perception of unmanned aerial vehicles (UAV) for civilian commercial operations, including cargo and passenger transportation. Civilian operators are considering the use of UAVs to increase safety while reducing labor costs, but negative perceptions may delay the advancement of these aircraft. To document perception of UAV or UAS aircraft for commercial operations, a questionnaire was distributed to adults (ages 18+) who use commercial air travel. The survey classified age group, gender, and UAV familiarity as demographic variables. This study assessed risk perception of UAVs and its correlation to the variables through statistical analysis to identify whether there was an association between the demographic variables and risk perception. Upon analysis, it was determined that the data from the sample showed no strong evidence that demographic variables influenced risk perception. Many respondents’ risks perceptions involved technology reliability and higher perceived safety with a human pilot onboard.


title: ARTIFICIAL NEURAL NETWORK APPLICATIONS IN GEOTECHNICAL ENGINEERING

abstract: Over the last few years or so, the use of artificial neural networks ( ANNs) has increased in many areas of engineering. In particular, ANNs have been applied to many geotechnical engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in pile capacity prediction, modelling soil behaviour, site characterisation, earth retaining structures, settlement of structures, slope stability, design of tunnels and underground openings, liquefaction, soil permeability and hydraulic conductivity, soil compaction, soil swelling and classification of soils. The objective of this paper is to provide a general view of some ANN applications for solving some types of geotechnical engineering problems. It is not intended to describe the ANNs modelling issues in geotechnical engineering. The paper also does not intend to cover every single application or scientific paper that found in the literature. For brevity, some works are selected to be described in some detail, while others are acknowledged for reference purposes. The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches. The engineering properties of soil and rock exhibit varied and uncertain behaviour due to the complex and imprecise physical processes associated with the formation of these materials (Jaksa 1995). This is in contrast to most other civil engineering materials, such as steel, concrete and timber, which exhibit far greater homogeneity and isotropy. In order to cope with the complexity of geotechnical behaviour, and the spatial variability of these materials, traditional forms of engineering design models are justifiably simplified. An alternative approach, which has been shown to have some degree of success, is based on the data alone to determine the structure and parameters of the model . The technique is known as artificial neural networks ( ANNs) and is well suited to model complex problems where the relationship between the model variables is unknown (Hubick 1992). This paper is intended to be for readers in the field of geotechnical engineering who are not familiar with artificial neural networks. The paper aims to detail some features associated with ANNs through a review for some of their applications to-date in geotechnical engineering. It is hoped that this review may attract more geotechnical engineers to pay better attention to this promising tool. The paper starts with a brief overview of the structure and operation of the ANNs and gives a general overview of most ANN applications that have appeared in the geotechncial engineering literature. Finally, the paper discusses the relative success of ANNs in predicting various geotechnical engineering properties and behaviour.


title: On Service-Chaining Strategies using Virtual Network Functions in Operator Networks

abstract: Abstract Network functions (e.g., firewalls, load balancers, etc.) have been traditionally provided through proprietary hardware appliances. Often, hardware appliances need to be hardwired back-to-back to form a service chain providing chained network functions. These hardware appliances cannot be provisioned on-demand since they are statically embedded in the network topology, making creation, insertion, modification, upgrade, and removal of service chains complex, and also slowing down service innovation. Hence, network operators are starting to deploy Virtual Network Functions (VNFs), which are virtualized over commodity hardware. VNFs can be deployed in Data Centers (DCs) or in Network Function Virtualization (NFV)-capable network elements (nodes) such as routers and switches. NFV-capable nodes and DCs together form a Network-enabled Cloud (NeC) that helps to facilitate the dynamic service chaining required to support today’s evolving network traffic and its service demands. In this study, we focus on the VNF service-chain placement and traffic routing problem, and build a model for placing a VNF service chain while minimizing network-resource consumption. Further, we study the network-resource consumption of various service-chaining strategies, which can be a NeC having a varying number of NFV-capable nodes or a proprietary hardware solution. Our results indicate that a NeC having a DC and NFV-capable nodes can significantly reduce network-resource consumption.


title: Efficient and robust feature selection via joint l21-norms minimization. NIPS

abstract: Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint l2,1-norm minimization on both loss function and regularization. The l2,1-norm based loss function is robust to outliers in data points and the l2,1-norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.


title: Automotive functional safety = safety + security

abstract: Standard approaches to functional safety as described in the automotive functional safety standard ISO 26262 are focused on reducing the risk of hazards due to random hardware faults or systematic failures during design (e.g. software bugs). However, as vehicle systems become increasingly complex and ever more connected to the internet of things, a third source of hazard must be considered, that of intentional manipulation of the electrical/electronic control systems either via direct physical contact or via the systems' open interfaces. This article describes how the process prescribed by the ISO 26262 can be extended with methods from the domain of embedded security to protect the systems against this third source of hazard.


title: `Putting the Face to the Voice' Matching Identity across Modality

abstract: Speech perception provides compelling examples of a strong link between auditory and visual modalities [1, 2]. This link originates in the mechanics of speech production, which, in shaping the vocal tract, determine the movement of the face as well as the sound of the voice [3, 4]. In this paper, we present evidence that equivalent information about identity is available cross-modally from both the face and voice. Using a delayed matching to sample task, XAB, we show that people can match the video of an unfamiliar face, X, to an unfamiliar voice, A or B, and vice versa, but only when stimuli are moving and are played forward. The critical role of time-varying information is underlined by the ability to match faces to voices containing only the coarse spatial and temporal information provided by sine wave speech [5]. The effect of varying sentence content across modalities was small, showing that identity-specific information is not closely tied to particular utterances. We conclude that the physical constraints linking faces to voices result in bimodally available dynamic information, not only about what is being said, but also about who is saying it.


title: Web Text Corpus For Natural Language Processing

abstract: Web text has been successfully used as training data for many NLP applications. While most previous work accesses web text through search engine hit counts, we created a Web Corpus by downloading web pages to create a topic-diverse collection of 10 billion words of English. We show that for context-sensitive spelling correction the Web Corpus results are better than using a search engine. For thesaurus extraction, it achieved similar overall results to a corpus of newspaper text. With many more words available on the web, better results can be obtained by collecting much larger web corpora.


title: Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition

abstract: Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability. In this paper, we introduce a new layer normalization technique called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling in speech recognition. By dynamically generating the scaling and shifting parameters in layer normalization, DLN adapts neural acoustic models to the acoustic variability arising from various factors such as speakers, channel noises, and environments. Unlike other adaptive acoustic models, our proposed approach does not require additional adaptation data or speaker information such as i-vectors. Moreover, the model size is fixed as it dynamically generates adaptation parameters. We apply our proposed DLN to deep bidirectional LSTM acoustic models and evaluate them on two benchmark datasets for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The experimental results show that our DLN improves neural acoustic models in terms of transcription accuracy by dynamically adapting to various speakers and environments.


title: Adaptive Coherent Lp-Norm Combining

abstract: In this paper, we introduce an adaptive L p -norm metric for robust coherent diversity combining in non-Gaussian noise and interference. We derive a general closed-form expression for the asymptotic bit error rate (BER) for L p -norm combining in independent non-identically distributed Ricean fading and non-Gaussian noise and interference with finite moments. Based on this asymptotic BER expression, the metric parameters can be adapted to the underlying type of noise and interference using a finite difference stochastic approximation (FDSA) algorithm. Simulation results confirm the validity of the derived asymptotic BER expression and the excellent performance of the proposed adaptive L p -norm metric.


title: How anthropogenic shifts in plant community composition alter soil food webs

abstract: There are great concerns about the impacts of soil biodiversity loss on ecosystem functions and services such as nutrient cycling, food production, and carbon storage. A diverse community of soil organisms that together comprise a complex food web mediates such ecosystem functions and services. Recent advances have shed light on the key drivers of soil food web structure, but a conceptual integration is lacking. Here, we explore how human-induced changes in plant community composition influence soil food webs. We present a framework describing the mechanistic underpinnings of how shifts in plant litter and root traits and microclimatic variables impact on the diversity, structure, and function of the soil food web. We then illustrate our framework by discussing how shifts in plant communities resulting from land-use change, climatic change, and species invasions affect soil food web structure and functioning. We argue that unravelling the mechanistic links between plant community trait composition and soil food webs is essential to understanding the cascading effects of anthropogenic shifts in plant communities on ecosystem functions and services.


title: Learning linear threshold functions in the presence of classification noise

abstract: I show that the linear threshold functions are polynomially learnable in the presence of classification noise, i.e., polynomial in n , 1/e, 1/δ, and 1/s, where n is the number of Boolean attributes, e and δ are the usual accuracy and confidence parameters, and s indicates the minimum distance of any example from the target hyperplane, which is assumed to be lower than the average distance of the examples from any hyperplane. This result is achieved by modifying the Perceptron algorithm—for each update, a weighted average of a sample of misclassified examples and a correction vector is added to the current weight vector. Similar modifications are shown for the Weighted Majority algorithm. The correction vector is simply the mean of the normalized examples. In the special case of Boolean threshold functions, the modified Perceptron algorithm performs O ( n 2 e −2 ) iterations over O ( n 4 e −2 ln( n /(δe))) examples. This improves on the previous classification-noise result of Angluin and Laird to a much larger concept class with a similar number of examples, but with multiple iterations over the examples.


title: Algorithm 908: Online Exact Summation of Floating-Point Streams

abstract: We present a novel, online algorithm for exact summation of a stream of floating-point numbers. By “online” we mean that the algorithm needs to see only one input at a time, and can take an arbitrary length input stream of such inputs while requiring only constant memory. By “exact” we mean that the sum of the internal array of our algorithm is exactly equal to the sum of all the inputs, and the returned result is the correctly-rounded sum. The proof of correctness is valid for all inputs (including nonnormalized numbers but modulo intermediate overflow), and is independent of the number of summands or the condition number of the sum. The algorithm asymptotically needs only 5 FLOPs per summand, and due to instruction-level parallelism runs only about 2--3 times slower than the obvious, fast-but-dumb “ordinary recursive summation” loop when the number of summands is greater than 10,000. Thus, to our knowledge, it is the fastest, most accurate, and most memory efficient among known algorithms. Indeed, it is difficult to see how a faster algorithm or one requiring significantly fewer FLOPs could exist without hardware improvements. An application for a large number of summands is provided.


title: Fuzzy Description Logic Programs

abstract: Description Logic Programs (DLPs), which combine the expressive power of classical description logics and logic programs, are emerging as an important ontology description language paradigm. In this study, we present fuzzy DLPs, which extend DLPs by allowing the representation of vague/imprecise information.


title: Lane Boundary Geometry Extraction from Satellite Imagery

abstract: Automated driving is becoming a reality. In this new reality, High Definition (HD) Maps play an important role in path planning and vehicle localization. Lane boundary geometry is one of the key components of an HD Map. Such maps are typically created from ground level LiDAR and imagery data, which, while useful in many ways, have many limitations such as prohibitive cost, infrequent update, traffic occlusions, and incomplete coverage. In this paper, we propose a novel method to automatically extract lane boundaries from satellite imagery using pixel-wise segmentation and machine learning. We then convert these unstructured lines into a structured road model by using a hypothesis linking algorithm, which addresses the aforementioned limitations. We also publish a dataset consisting of satellite imagery and the corresponding lane boundaries for future authors to train, test, and evaluate algorithms.


title: Series Feed Networks for Dual-Polarized Frequency Scanning Phased Array Antenna Based on Composite Right/Left-Handed Transmission Line

abstract: In this paper, series feed networks based on composite right/left-handed transmission line for dual-polarized frequency scanning phased array antenna are presented. The proposed all-passive feed networks provide unique frequency dispersion and adjustable current amplitude distribution, which enable continuous backward-to-forward beam scanning capability as well as the directivity, beamwidth, and sidelobe level controllability of the incorporated array antenna. The working mechanisms are demonstrated through broadband dual-polarized aperture-coupled antenna elements and series feed networks that support uniform/binomial current amplitude distributions. Backward-to-forward frequency beam scanning and wide range beam shaping capability for dual-polarization states of the proposed phased array are validated in both simulation and measurement.


title: Recognizing Stances in Online Debates

abstract: This paper presents an unsupervised opinion analysis method for debate-side classification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classification task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods.


title: Mortality salience and the uncanny valley

abstract: It seems natural to assume that the more closely robots come to resemble people, the more likely they are to elicit the kinds of responses people direct toward each other. However, subtle flaws in appearance and movement only seem eerie in very human-like robots. This uncanny phenomenon may be symptomatic of entities that elicit a model of a human other but do not measure up to it. If so, a very human-like robot may provide the best means of finding out what kinds of behavior are perceived as human, since deviations from a human other are more obvious. In pursuing this line of inquiry, it is essential to identify the mechanisms involved in evaluations of human likeness. One hypothesis is that an uncanny robot elicits an innate fear of death and culturally-supported defenses for coping with death's inevitability. An experiment, which borrows from the methods of terror management research, was performed to test this hypothesis. Across all questions subjects who were exposed to a still image of an uncanny humanlike robot had on average a heightened preference for worldview supporters and a diminished preference for worldview threats relative to the control group


title: UQLIPS: A Real-time Near-duplicate Video Clip Detection System

abstract: Near-duplicate video clip (NDVC) detection is an important problem with a wide range of applications such as TV broadcast monitoring, video copyright enforcement, content-based video clustering and annotation, etc. For a large database with tens of thousands of video clips, each with thousands of frames, can NDVC search be performed in real-time? In addition to considering inter-frame similarity (i.e., spatial information), what is the impact of frame sequence similarity (i.e., temporal information) on search speed and accuracy? UQLIPS is a prototype system for online NDVC detection. The core of UQLIPS comprises two novel complementary schemes for detecting NDVCs. Bounded Coordinate System (BCS), a compact representation model ignoring temporal information, globally summarizes each video to a single vector which captures the dominating content and content changing trends of each clip. The other proposal, named FRAme Symbolization (FRAS), maps each clip to a sequence of symbols, and takes temporal order and sequence context information into consideration. Using a large collection of TV commercials, UQLIPS clearly demonstrates that it is feasible to perform real-time NDVC detection with high accuracy.


title: High frequency bus converter with low loss integrated matrix transformer

abstract: The trend in isolated DC/DC converters is increasing output power demands and higher operating frequencies. Improved topologies and semiconductors can allow for lower loss at higher frequencies. A major barrier to further improvement is the transformer design. With high current levels and high frequency effects the transformers can become the major loss component in the circuit. High values of transformer leakage inductance can also greatly degrade the performance of the converter. Matrix transformers offer the ability to reduce winding loss and leakage inductance. This paper will study the impact of increased switching frequencies on transformer size and explore the use of matrix transformers in high current high frequency isolated applications. This paper will also propose an improved integrated matrix transformer design that can decrease core loss and further improve the performance of matrix transformers.


title: Modeling of the HP-1430A feedthrough wide-band (28-ps) sampling head

abstract: The theory and construction of the HP-1430A feed-through sampling head are reviewed, and a model for the sampling head is developed from dimensional and electrical measurements in conjunction with electromagnetic, electronic, and network theory. The model was used to predict the sampling-head step response needed for the deconvolution of true input waveforms. The dependence of the sampling-head step response on the sampling diode bias is investigated. Calculations based on the model predict step response transition durations of 27.5 to 30.5 ps for diode reverse bias values of -1.76 to -1.63 V.


title: Mobile banking services

abstract: Adopting new and innovative mobile financial applications and service provisioning methods.


title: Big data and e-government: issues, policies, and recommendations

abstract: The promises and potential of Big Data in transforming digital government services, governments, and the interaction between governments, citizens, and the business sector, are substantial. From "smart" government to transformational government, Big Data can foster collaboration; create real-time solutions to challenges in agriculture, health, transportation, and more; and usher in a new era of policy- and decision-making. There are, however, a range of policy challenges to address regarding Big Data, including access and dissemination; digital asset management, archiving and preservation; privacy; and security. This paper selectively reviews and analyzes the U.S. policy context regarding Big Data and offers recommendations aimed at facilitating Big Data initiatives.


title: The motivation to express prejudice.

abstract: Contemporary prejudice research focuses primarily on people who are motivated to respond without prejudice and the ways in which unintentional bias can cause these people to act in a manner inconsistent with this motivation. However, some real-world phenomena (e.g., hate speech, hate crimes) and experimental findings (e.g., Plant & Devine, 2001, 2009) suggest that some prejudice is intentional. These phenomena and findings are difficult to explain solely from the motivations to respond without prejudice. We argue that some people are motivated to express prejudice, and we develop the Motivation to Express Prejudice Scale (MP) to measure this motivation. In 7 studies involving more than 6,000 participants, we demonstrate that, across scale versions targeted at Black people and gay men, the MP has good reliability and convergent, discriminant, and predictive validity. In normative climates that prohibit prejudice, the internal and external motivations to express prejudice are functionally nonindependent, but they become more independent when normative climates permit more prejudice toward a target group. People high in the motivation to express prejudice are relatively likely to resist pressure to support programs promoting intergroup contact and to vote for political candidates who support oppressive policies. The motivation to express prejudice predicted these outcomes even when controlling for attitudes and the motivations to respond without prejudice. This work encourages contemporary prejudice researchers to give greater consideration to the intentional aspects of negative intergroup behavior and to broaden the range of phenomena, target groups, and samples that they study. (PsycINFO Database Record (c) 2015 APA, all rights reserved)


title: Training with Exploration Improves a Greedy Stack-LSTM Parser

abstract: We adapt the greedy Stack-LSTM dependency parser of Dyer et al. (2015) to support a training-with-exploration procedure using dynamic oracles(Goldberg and Nivre, 2013) instead of cross-entropy minimization. This form of training, which accounts for model predictions at training time rather than assuming an error-free action history, improves parsing accuracies for both English and Chinese, obtaining very strong results for both languages. We discuss some modifications needed in order to get training with exploration to work well for a probabilistic neural-network.


title: Monocular visual odometry in urban environments using an omnidirectional camera

abstract: We present a system for monocular simultaneous localization and mapping (mono-SLAM) relying solely on video input. Our algorithm makes it possible to precisely estimate the camera trajectory without relying on any motion model. The estimation is completely incremental: at a given time frame, only the current location is estimated while the previous camera positions are never modified. In particular, we do not perform any simultaneous iterative optimization of the camera positions and estimated 3D structure (local bundle adjustment). The key aspect of the system is a fast and simple pose estimation algorithm that uses information not only from the estimated 3D map, but also from the epipolar constraint. We show that the latter leads to a much more stable estimation of the camera trajectory than the conventional approach. We perform high precision camera trajectory estimation in urban scenes with a large amount of clutter. Using an omnidirectional camera placed on a vehicle, we cover one of the longest distance ever reported, up to 2.5 kilometers.


title: The hippocampal–prefrontal pathway: The weak link in psychiatric disorders?

abstract: Abstract While the hippocampal formation and the prefrontal cortex each have a well-established role in cognitive and mnemonic processes, the extent and manner in which these structures interact to achieve these functions has not been fully delineated. Recent research in rodents compellingly supports the idea that the projection of neurons extending from the CA1 region of the hippocampus and from the subiculum to the prefrontal cortex, referred to here as the H-PFC pathway, is critically involved in aspects of cognition related to executive function and to emotional regulation. Concurrently, it is becoming evident that persons suffering from schizophrenia, depression, and post-traumatic stress disorder display structural anomalies and aberrant functional coupling within the hippocampal–prefrontal circuit. Considering that these disorders involve varying degrees of cognitive impairment and emotional dysregulation, dysfunction in the H-PFC pathway might therefore be the common element of their pathophysiology. This overlap might also be intertwined with the pathway's evident susceptibility to stress and with its relationship to the amygdala. In consequence, the H-PFC pathway is a potentially crucial element of the pathophysiology of several psychiatric diseases, and it offers a specific target for therapeutic intervention, which is consistent with the recent emphasis on reframing psychiatric diseases in terms of brain circuits.


title: Advancing Nonvolatile Computing With Nonvolatile NCFET Latches and Flip-Flops

abstract: Nonvolatile computing has been proven to be effective in dealing with power supply outages for on-chip check-pointing in emerging energy-harvesting Internet-of-Things applications. It also plays an important role in power-gating to cut off leakage power for higher energy efficiency. However, existing on-chip state backup solutions for D flip–flop (DFF) have a bottleneck of significant energy and/or latency penalties which limit the overall energy efficiency and computing progress. Meanwhile, these solutions rely on external control that limits compatibility and increases system complexity. This paper proposes an approach to fundamentally advancing the nonvolatile computing paradigm by intrinsically nonvolatile area-efficient latches and flip–flops designs using negative capacitance FET. These designs consume fJ-level energy and ns-level intrinsic latency for a backup plus restore operation, e.g., 2.4 fJ in energy and 1.1 ns in time for one proposed nonvolatile DFF with a supply power of 0.80 V.


title: Why do players buy in-game content? An empirical study on concrete purchase motivations

abstract: Selling in-game content has become a popular revenue model for game publishers. While prior research has investigated latent motivations as determinants of in-game content purchases, the prior literature has not focused on more concrete reasons to purchase in-game content that stem from how the games are being designed. We form an inventory of reasons (19) to buy in-game content via triangulating from analyses of top-grossing free-to-play games, from a review of existing research, and from industry expert input. These reasons were operationalized into a survey (N=519). Firstly, we explored how these motivations converged into categories. The results indicated that the purchasing reasons converged into six dimensions: 1) Unobstructed play, 2) Social interaction, 3) Competition, 4) Economical rationale, 5) Indulging the children, and 6) Unlocking content. Secondly, we investigated the relationship between these factors and how much players spend money on in-game content. The results revealed that the purchase motivations of unobstructed play, social interaction, and economical rationale were positively associated with how much money players spend on in-game content. The results imply that the way designers implement artificial limitations and obstacles as well as social interaction affects how much players spend money on in-game content. Inventory/questionnaire of in-game purchase motivations (19) was formed.Motivational dimensions of in-game purchases were investigated via survey (N=519).Nineteen motivations converged onto six main dimensions:Unobstructing, Social, Competition, Economical, Children, and Unlocking content.Unobstructing, Social and Economical motivations predicted in-game purchases.


title: Quantifying ruggedness of continuous landscapes using entropy

abstract: A major unsolved problem in the field of optimisation and computational intelligence is how to determine which algorithms are best suited to solving which problems. This research aims to analytically characterise individual problems as a first step towards attempting to link problem types with the algorithms best suited to solving them. In particular, an information theoretic technique for analysing the ruggedness of a fitness landscape with respect to neutrality was adapted to work in continuous landscapes and to output a single measure of ruggedness. Experiments run on test functions with increasing ruggedness show that the proposed measure of ruggedness produced relative values consistent with a visual inspection of the problem landscapes. Combined with other measures of complexity, the proposed ruggedness measure could be used to more broadly characterise the complexity of fitness landscapes in continuous domains.


title: Developing a Research Agenda for Human-Centered Data Science

abstract: The study and analysis of large and complex data sets offer a wealth of insights in a variety of applications. Computational approaches provide researchers access to broad assemblages of data, but the insights extracted may lack the rich detail that qualitative approaches have brought to the understanding of sociotechnical phenomena. How do we preserve the richness associated with traditional qualitative methods while utilizing the power of large data sets? How do we uncover social nuances or consider ethics and values in data use? These and other questions are explored by human-centered data science, an emerging field at the intersection of human-computer interaction (HCI), computer-supported cooperative work (CSCW), human computation, and the statistical and computational techniques of data science. This workshop, the first of its kind at CSCW, seeks to bring together researchers interested in human-centered approaches to data science to collaborate, define a research agenda, and form a community.


title: State of the art: Dynamic symbolic execution for automated test generation

abstract: Dynamic symbolic execution for automated test generation consists of instrumenting and running a program while collecting path constraint on inputs from predicates encountered in branch instructions, and of deriving new inputs from a previous path constraint by an SMT (Satisfiability Modulo Theories) solver in order to steer next executions toward new program paths. It has been introduced into several applications, such as automated test generation, automated filter generation and malware analysis mainly for its two intrinsic properties: low false positives and high code-coverage. In this paper, we focus on the topics that are closely related to automated test generation. Our contributions are five-fold. First, we summarize the theoretical foundation of dynamic symbolic execution. Second, we highlight the challenges when turning ideas into reality. Besides, we describe the state-of-the-art solutions including advantages and disadvantages for those challenges. In addition, twelve typical tools are analyzed and many properties of those tools are censused. Finally, we outline the prospects of this research field in detail.


title: Multi-Agent Q-Learning for Minimizing Demand-Supply Power Deficit in Microgrids

abstract: We consider the problem of minimizing the difference in the demand and the supply of power using microgrids. We setup multiple microgrids, that provide electricity to a village. They have access to the batteries that can store renewable power and also the electrical lines from the main grid. During each time period, these microgrids need to take decision on the amount of renewable power to be used from the batteries as well as the amount of power needed from the main grid. We formulate this problem in the framework of Markov Decision Process (MDP), similar to the one discussed in [1]. The power allotment to the village from the main grid is fixed and bounded, whereas the renewable energy generation is uncertain in nature. Therefore we adapt a distributed version of the popular Reinforcement learning technique, Multi-Agent Q-Learning to the problem. Finally, we also consider a variant of this problem where the cost of power production at the main site is taken into consideration. In this scenario the microgrids need to minimize the demand-supply deficit, while maintaining the desired average cost of the power production.


Demo Abstract: Cross-level Simulation in COOJA


title: Filling holes in triangular meshes by curve unfolding

abstract: We propose a novel approach to automatically fill holes in triangulated models. Each hole is filled using a minimum energy surface that is obtained in three steps. First, we unfold the hole boundary onto a plane using energy minimization. Second, we triangulate the unfolded hole using a constrained Delaunay triangulation. Third, we embed the triangular mesh as a minimum energy surface in ℝ3. The running time of the method depends primarily on the size of the hole boundary and not on the size of the model, thereby making the method applicable to large models. Our experiments demonstrate the applicability of the algorithm to the problem of filling holes bounded by highly curved boundaries in large models.


title: Recognizing Micro-Actions and Reactions from Paired Egocentric Videos

abstract: We aim to understand the dynamics of social interactions between two people by recognizing their actions and reactions using a head-mounted camera. Our work will impact several first-person vision tasks that need the detailed understanding of social interactions, such as automatic video summarization of group events and assistive systems. To recognize micro-level actions and reactions, such as slight shifts in attention, subtle nodding, or small hand actions, where only subtle body motion is apparent, we propose to use paired egocentric videos recorded by two interacting people. We show that the first-person and second-person points-of-view features of two people, enabled by paired egocentric videos, are complementary and essential for reliably recognizing micro-actions and reactions. We also build a new dataset of dyadic (two-persons) interactions that comprises more than 1000 pairs of egocentric videos to enable systematic evaluations on the task of micro-action and reaction recognition.


title: Investigating Web APIs on the World Wide Web

abstract: The world of services on the Web, thus far limited to "classical" Web services based on WSDL and SOAP, has been increasingly marked by the domination of Web APIs, characterised by their relative simplicity and their natural suitability for the Web. Currently, the development of Web APIs is rather autonomous, guided by no established standards or rules, and Web API documentation is commonly not based on an interface description language such as WSDL, but is rather given directly in HTML as part of a web page. As a result, the use of Web APIs requires extensive manual effort and the wealth of existing work on supporting common service tasks, including discovery, composition and invocation, can hardly be reused or adapted to APIs. Before we can achieve a higher level of automation and can make any significant improvement to current practices and technologies, we need to reach a deeper understanding of these. Therefore, in this paper we present a thorough analysis of the current landscape of Web API forms and descriptions, which has up-to-date remained unexplored. We base our findings on manually examining a body of publicly available APIs and, as a result, provide conclusions about common description forms, output types, usage of API parameters, invocation support, level of reusability, API granularity and authentication details. The collected data provides a solid basis for identifying deficiencies and realising how we can overcome existing limitations. More importantly, our analysis can be used as a basis for devising common standards and guidelines for Web API development.


title: Learning a Super Mario controller from examples of human play

abstract: Imitating human-like behaviour in action games is a challenging but intriguing task in Artificial Intelligence research, with various strategies being employed to solve the human-like imitation problem. In this research we consider learning human-like behaviour via Markov decision processes without being explicitly given a reward function, and learning to perform the task by observing expert's demonstration. Individual players often have characteristic styles when playing the game, and this method attempts to find the behaviours which make them unique. During play sessions of Super Mario we calculate player's behaviour policies and reward functions by applying inverse reinforcement learning to the player's actions in game. We conduct an online questionnaire which displays two video clips, where one is played by a human expert and the other is played by the designed controller based on the player's policy. We demonstrate that by using apprenticeship learning via Inverse Reinforcement Learning, we are able to get an optimal policy which yields performance close to that of an human expert playing the game, at least under specific conditions.


title: PRISM interaction for enhancing control in immersive virtual environments

abstract: When directly manipulating 3D objects in an immersive environment we cannot normally achieve the accuracy and control that we have in the real world. This reduced accuracy stems from hand instability. We present PRISM, which dynamically adjusts the C/D ratio between the hand and the controlled object to provide increased control when moving slowly and direct, unconstrained interaction when moving rapidly. We describe PRISM object translation and rotation and present user studies demonstrating their effectiveness. In addition, we describe a PRISM-enhanced version of ray casting which is shown to increase the speed and accuracy of object selection.


title: Using machine learning algorithms to find patterns in

abstract: We use a machine learning algorithm called Adaboost to find direction-of-change patterns for the S&P 500 index using daily prices from 1962 to 2004. The patterns are able to identify periods to take long and short positions in the index. This result, however, can largely be explained by first-order serial correlation in stock index returns.


title: A hybrid recommendation technique based on product category attributes

abstract: Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers' preferences and desires. Extracting users' preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users' unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user's preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.


title: Deep Learning Human Mind for Automated Visual Classification

abstract: What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories. Afterwards, we train a Convolutional Neural Network (CNN)-based regressor to project images onto the learned manifold, thus effectively allowing machines to employ human brain-based features for automated visual classification. We use a 32-channel EEG to record brain activity of seven subjects while looking at images of 40 ImageNet object classes. The proposed RNN based approach for discriminating object classes using brain signals reaches an average accuracy of about 40%, which outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain-driven approach obtains competitive performance, comparable to those achieved by powerful CNN models, both on ImageNet and CalTech 101, thus demonstrating its classification and generalization capabilities. This gives us a real hope that, indeed, human mind can be read and transferred to machines.


title: Archeoguide: system architecture of a mobile outdoor augmented reality system

abstract: We present the system architecture of a mobile outdoor augmented reality system for the Archeoguide project. We begin with a short introduction to the project. Then we present the hardware we chose for the mobile system and we describe the system architecture we designed for the software implementation. We conclude this paper with the first results obtained from experiments we made during our trials at ancient Olympia in Greece.


title: Bystander behavior in bullying situations: basic moral sensitivity, moral disengagement and defender self-efficacy.

abstract: The aim of the present study was to investigate how basic moral sensitivity in bullying, moral disengagement in bullying and defender self-efficacy were related to different bystander behaviors in bullying. Therefore, we examined pathways that linked students' basic moral sensitivity, moral disengagement, and defender self-efficacy to different bystander behaviors in bullying situations. Three hundred and forty-seven teenagers completed a bullying survey. Findings indicated that compared with boys, girls expressed higher basic moral sensitivity in bullying, lower defender self-efficacy and moral disengagement in bullying. Results from the SEM showed that basic moral sensitivity in bullying was negatively related to pro-bully behavior and positively related to outsider and defender behavior, mediated by moral disengagement in bullying, which in turn was positively related to pro-bully behavior and negatively related to outsider and defender behavior. What differed in the relations between outsider and defender behaviors was the degree of defender self-efficacy.


title: Running crabs, walking crinoids, grazing gastropods: behavioral diversity and evolutionary implications of the Cabeço da Ladeira Lagerstätte (Middle Jurassic, Portugal) Caranguejos que correm, crinóides que caminham, gastrópodes que se alimentam: diversidade comportamental no Lagerstätte do Cabeço d

abstract: Ichnology is a powerful tool for understanding the evolutionary paths of animal clades, through the paleobiology of behavior preserved in Lagerstatten such as the Cabeco da Ladeira (Portugal) site. Here, the peritidal carbonates of the Chao das Pias Formation (Middle Jurassic, upper Bajocian) record the development of microbial mats in a tidal flat. Episodically, the flat was expanded during periods of equinoctial spring tides that may have been responsible for the in situ killing of several members of echinoderm clades, and the fine preservation of their endoskeleton, in addition to the behavior of an arthropod-dominated endo- and epifauna. Mat-preserved shallow tiers with a moderate ichnodiversity attributed to the Cruziana ichnofacies include trackways of crabs ( Laterigradus lusitanica igen. nov., isp. nov.) and shrimps (Diplopodichnus isp.); trails of fishes (? Undichna ), gastropods ( Archaeonassa fossulata ), isocrinid crinoids ( Krinodromos bentou igen. nov., isp. nov.) and insects ( Haplotichnus indianensis ); and burrows of shrimps or lobsters ( Thalassinoides suevicus ) and other arthropods (Asterosoma ludwigae , Gyrochorte comosa ), or polychaetes ( Rhizocorallium commune ). This is implied already above (igen. nov., etc.) and seems not needed. The crab trackways and gastropod bulldozing trails, extending up to 12.3 m, are among the longest invertebrate cursorial traces ever found in the fossil record. The mortichnial crawling trail of a crinoid is the first evidence of locomotion for this group in the fossil record, and a rare unambiguous example of this ethology. Extensive continuous trackways attributed to amphibious crabs record, with great detail, typical gaits of underwater punting and walking sideways on dry land. The earliest evidences of sidewalking behavior may also be evidence for the development of the crab form during their rise in the Jurassic.


title: InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

abstract: This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound to the mutual information objective that can be optimized efficiently, and show that our training procedure can be interpreted as a variation of the Wake-Sleep algorithm. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing fully supervised methods.


title: A resource allocation game with restriction mechanism in VANET cloud

abstract: Summary ::: In Vehicular Ad hoc Networks (VANETs), because of the selfishness of the vehicles, the resource allocation in VANET has become one of the primary tasks. Exploiting the Road Side Units (RSUs), which constructs the Cloud Computing environment, provides more data access opportunities and stable communication time for the vehicles. We investigated the cloud resource allocation for data access with noncooperative game based on a Gauss–Seidel iteration method. We further proposed a repeated game scheme, which can approximately achieve the near Pareto-optimal flow allocation among the vehicles. Considering the vehicles' irrational behavior, a punishment strategy was designed to prevent the vehicles from behavior deviation. The analysis based on these models lays a theoretical method foundation on cloud resource allocation process. The validity of the modeling and the accuracy of the analysis were verified through the extensive simulations, which also guide the future design of more sophisticated cloud resource allocation schemes. Copyright © 2016 John Wiley & Sons, Ltd.


title: Ingestible Sensors

abstract: Ingestible sensing capsules are fast emerging as a critical technology that has the ability to greatly impact health, nutrition, and clinical areas. These ingestible devices are noninvasive and hence are very attractive for customers. With widespread access to smart phones connected to the Internet, the data produced by this technology can be readily seen and reviewed online, and accessed by both users and physicians. The outputs provide invaluable information to reveal the state of gut health and disorders as well as the impact of food, medical supplements, and environmental changes on the gastrointestinal tract. One unique feature of such ingestible sensors is that their passage through the gut lumen gives them access to each individual organ of the gastrointestinal tract. Therefore, ingestible sensors offer the ability to gather images and monitor luminal fluid and the contents of each gut segment including electrolytes, enzymes, metabolites, hormones, and the microbial communities. As such, an incredible wealth of knowledge regarding the functionality and state of health of individuals through key gut biomarkers can be obtained. This Review presents an overview of the gut structure and discusses current and emerging digestible technologies. The text is an effort to provide a comprehensive overview of ingestible sensing capsules, from both a body physiology point of view as well as a technological view, and to detail the potential information that they can generate.


title: Fast Supervised Discrete Hashing

abstract: Learning-based hashing algorithms are “hot topics” because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called “fast supervised discrete hashing” (FSDH) based on “supervised discrete hashing” (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.


title: A 2.3-mW, 5-Gb/s decision-feedback equalizing receiver front-end with static-power-free signal summation and CDR-based precursor ISI reduction

abstract: This paper demonstrates a low-power decision feedback equalization (DFE) receiver with 0.46-mW/Gbps efficiency that can compensate up to -15-dB of channel loss while operating at 5-Gb/s. The key is the use of a clocked comparator design that can also perform the signal summations required for 1-tap direct-feedback DFE, additional DFE with an infinite impulse response (IIR) filter, and offset compensation, all without dissipating any static current. As a result, the power-hungry, high-bandwidth stages used in the prior backplane DFE receivers such as the continuous-time linear equalizer (CTLE), current-mode summing circuits, and fast selection logic for loop-unrolling DFE are all removed in this design. The pre-cursor ISIs are mitigated with passive inductive peaking at the termination loads and CDR timing adjustment, instead of a CTLE. A prototype DFE receiver fabricated in a 65nm LP CMOS demonstrates the measured eye opening of 112-mVpp and 0.66-UIpp while operating at 5-Gb/s with a -15-dB-loss channel.


title: Demo: WEBee: Physical-Layer Cross-Technology Communication via Emulation

abstract: The applicability of existing Cross-Technology Communication (CTC) methods, which rely on packet-level modulation, is severely limited due to their very low throughput, e.g., tens of bps. Our work, named as WEBee, opens a promising direction for high throughput CTC via physical-level emulation. Specifically, WEBee synthesizes the time-domain signals by choosing appropriate frequency-domain components fed into the subcarriers of WiFi OFDM. WE-Bee can emulate the desired physical-layer ZigBee signals by manipulating only the data bits in WiFi packet payload, requiring neither hardware nor firmware changes in commodity technologies. Moreover, WEBee enables the parallel CTC, where one WiFi frame emulates two ZigBee frames simultaneously. To evaluate the performance, we implemented WEBee on commodity devices (the Atheros AR2425 WiFi card, BCM 4330 WiFi card and CC2420, CC2530 ZigBee devices). Our comprehensive evaluation reveals that WEBee can achieve the CTC between WiFi and ZigBee with a reliable throughput of 126Kbps in noisy environment, 16,000x faster than current state-of-the-art CTC methods.


title: A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction

abstract: Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction.


title: 3DTI amphitheater: a manageable 3DTI environment with hierarchical stream prioritization

abstract: In this paper we present the 3DTI Amphitheater, a live broadcasting system for dissemination of 3DTI (3D Tele-immersive) content. The virtual environment constructed by the system mimics an amphitheater in the real world, where performers interact with each other in the central circular stage, and the audience is placed in virtual seats that surround the stage. Users of the Amphitheater can be geographically dispersed and the streams created by the performer sites are disseminated in a P2P network among the participants. To deal with the high bandwidth demand and strict latency bound of the service, we identify the hierarchical priority of streams in construction of the content dissemination forest. Result shows that the Amphitheater outperforms prior 3DTI systems by boosting the application QoS by a factor of 2.8 while sustaining the same hundred-scale audience group.


title: Applications, Architectures, and Protocol Design Issues for Mobile Social Networks: A Survey

abstract: The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking. The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users. This paper presents a comprehensive survey on the MSN specifically from the perspectives of applications, network architectures, and protocol design issues. First, major applications of the MSN are reviewed. Next, different architectures of the MSN are presented. Each of these different architectures supports different data delivery scenarios. The unique characteristics of social relationship in MSN give rise to different protocol design issues. These research issues (e.g., community detection, mobility, content distribution, content sharing protocols, and privacy) and the related approaches to address data delivery in the MSN are described. At the end, several important research directions are outlined.


title: Optimization of corona electrode position in roll-type electrostatic separators

abstract: The position of the corona electrode(s) is known to be a key factor of electrostatic separation efficiency, as it influences both the charging conditions of the granular materials on the surface of the roll electrode connected to the ground, and the magnitude of the electric forces exerted on the particles. Response surface methodology was employed for the design of the experiments performed on a laboratory roll-type corona-electrostatic separators, with samples of chopped electric wire wastes typically processed by such techniques. The results of the electrostatic separation tests are discussed in relation to the data obtained from two other experiments, in which corona current and particle charge measurements were performed for various positions of the standard wire-type electrode. The conclusions of this study enabled the formulation of several recommendations for the improvement of the outcome of industrial separation processes (i.e., maximize the weight percentage as well as the purity of the recovered materials).


title: Culture, Control and Perception of Relationships in the Environment

abstract: East Asian cognition has been held to be relatively holistic; that is, attention is paid to the field as a whole. Western cognition, in contrast, has been held to be object focused and control oriented. In this study East Asians (mostly Chinese) and Americans were compared on detection of covariation and field dependence. The results showed the following: (a) Chinese participants reported stronger association between events, were more responsive to differences in covariation, and were more confident about their covariation judgments; (b) these cultural differences disappeared when participants believed they had some control over the covariation judgment task; (c) American participants made fewer mistakes on the Rod-and-Frame Test, indicating that they were less field dependent; (d) American performance and confidence, but not that of Asians, increased when participants were given manual control of the test. Possible origins of the perceptual differences are discussed.


title: Using analytical centrifugation to characterize the dispersibility and particle size distributions of organic/inorganic composite coatings

abstract: In this study, we used a LUMiSizer® analytical centrifuge to determine the particle dispersions and size distributions of organic/inorganic composite coatings. The centrifuge was capable of recording both the measure space and time-resolved extinction profiles (STEP™-Technology) for up to 12 samples simultaneously, making it a powerful tool for the characterization and quality control of coatings. While the variation of the light extinction curves after centrifugal segregation provided a qualitative description, a rigorous formulation can provide detailed quantitative characterization. The technique used in this study allowed demixing to be accelerated and direct quantification of the dispersibility of the coatings. Because the dispersants and binders stabilized the organic/inorganic composite coatings, the dispersibility strongly depended on their concentration. The dispersibility of the coatings increased upon increasing the dispersant concentration and decreasing the concentration of the binder. Furthermore, the inorganic particles exhibited good dispersibility, without large degrees of aggregation, in the coating dispersions. We also used the LUMiSizer® to determine the particle size distribution and shelf life of coatings; the results were comparable with those obtained using dynamic light scattering, scanning electron microscope and zeta potential analysis.


title: Stereo HDR Disparity Map Computation Using Structured Light

abstract: In this paper, we present work in progress towards the generation of a ground truth data set for High Dynamic Range (HDR) stereo matching. The development and evaluation of novel stereo matching algorithms that are tailored to the characteristics of HDR images would greatly benefit from the availability of such reference data. We describe our laboratory setup and processing steps for acquiring multi-exposed stereo images along with a corresponding reference disparity map computed by a structured light approach. We discuss the special requirements and challenges which HDR test scenes impose on the computation of the reference disparities and show some preliminary results.


title: Collaborative Filtering with the Simple Bayesian Classifier

abstract: Many collaborative filtering enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. They recommend items to a user based on the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the Simple Bayesian Classifier. We defme two variants of the recommendation problem for the Simple Bayesian Classifier. In our approach, we calculate the similarity between users from negative ratings and positive ratings separately. We evaluated these algorithms using databases of movie recommendations and joke recommendations. Our empirical results show that one of our proposed Bayesian approaches significandy outperforms a correlation-based collaborative filtering algorithm. The other model outperforms as well although it shows similar performance to the correlation-based approach in some parts of our experiments.


title: Understanding Insider Threat: A Framework for Characterising Attacks

abstract: The threat that insiders pose to businesses, institutions and governmental organisations continues to be of serious concern. Recent industry surveys and academic literature provide unequivocal evidence to support the significance of this threat and its prevalence. Despite this, however, there is still no unifying framework to fully characterise insider attacks and to facilitate an understanding of the problem, its many components and how they all fit together. In this paper, we focus on this challenge and put forward a grounded framework for understanding and reflecting on the threat that insiders pose. Specifically, we propose a novel conceptualisation that is heavily grounded in insider-threat case studies, existing literature and relevant psychological theory. The framework identifies several key elements within the problem space, concentrating not only on noteworthy events and indicators- technical and behavioural- of potential attacks, but also on attackers (e.g., the motivation behind malicious threats and the human factors related to unintentional ones), and on the range of attacks being witnessed. The real value of our framework is in its emphasis on bringing together and defining clearly the various aspects of insider threat, all based on real-world cases and pertinent literature. This can therefore act as a platform for general understanding of the threat, and also for reflection, modelling past attacks and looking for useful patterns.


title: Learning to Decode for Future Success

abstract: We introduce a simple, general strategy to manipulate the behavior of a neural decoder that enables it to generate outputs that have specific properties of interest (e.g., sequences of a pre-specified length). The model can be thought of as a simple version of the actor-critic model that uses an interpolation of the actor (the MLE-based token generation policy) and the critic (a value function that estimates the future values of the desired property) for decision making. We demonstrate that the approach is able to incorporate a variety of properties that cannot be handled by standard neural sequence decoders, such as sequence length and backward probability (probability of sources given targets), in addition to yielding consistent improvements in abstractive summarization and machine translation when the property to be optimized is BLEU or ROUGE scores.


title: What motivates customers to participate in social commerce? The impact of technological environments and virtual customer experiences

abstract: A research model is proposed to examine the factors that influence participation behavior of a customer in social commerce.Social commerce intention is determined by customers' virtual experiences, namely, social support, social presence and flow.Customers' virtual experiences are influenced by perceived interactivity, personalization and sociability features of the social commerce environment.The results of the model tests using Renren and SinaWeibo samples are significantly different. Social commerce, as a relatively new phenomenon, has attracted little research attention. This study aims to provide initial insights into the dynamics of customer participation in social commerce. Based on the stimulus-organism-response paradigm, this study develops a model to investigate the effects of technological features (perceived interactivity, perceived personalization and perceived sociability) of social commerce on customers' virtual experiences (social support, social presence and flow) and subsequently their participation intention. The results indicate that social commerce intention is determined by social support, social presence and flow experiences. These experiences, in turn, are influenced by perceived interactivity, personalization and sociability features.


title: Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices

abstract: Background ::: End-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient’s hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm.


Approaches for Neural-Network Language Model Adaptation


title: Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring

abstract: This paper analyzes pedestrians' behavioral patterns in the pedestrianized shopping environment in the historical center of Barcelona, Spain. We employ a Bluetooth detection technique to capture a large-scale dataset of pedestrians' behavior over a one-month period, including during a key sales period. We focused on comparing particular behaviors before, during, and after the discount sales by analyzing this large-scale dataset, which is different but complementary to the conventionally used small-scale samples. Our results uncover pedestrians actively exploring a wider area of the district during a discount period compared to weekdays, giving rise to strong underlying mobility patterns.


title: Order preserving hashing for approximate nearest neighbor search

abstract: In this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. The problem of mapping the NN points into different hash codes is taken as a classification problem in which the points are categorized into several groups according to the hamming distances to the query. The hash functions are optimized from the classifiers pooled over the training points. Experimental results demonstrate the superiority of our approach over existing state-of-the-art hashing techniques.


title: Applications of crowdsourcing in health: an overview

abstract: Background ::: Crowdsourcing is a nascent phenomenon that has grown exponentially since it was coined in 2006. It involves a large group of people solving a problem or completing a task for an individual or, more commonly, for an organisation. While the field of crowdsourcing has developed more quickly in information technology, it has great promise in health applications. This review examines uses of crowdsourcing in global health and health, broadly. ::: ::: ::: Methods ::: Semantic searches were run in Google Scholar for "crowdsourcing," "crowdsourcing and health," and similar terms. 996 articles were retrieved and all abstracts were scanned. 285 articles related to health. This review provides a narrative overview of the articles identified. ::: ::: ::: Results ::: Eight areas where crowdsourcing has been used in health were identified: diagnosis; surveillance; nutrition; public health and environment; education; genetics; psychology; and, general medicine/other. Many studies reported crowdsourcing being used in a diagnostic or surveillance capacity. Crowdsourcing has been widely used across medical disciplines; however, it is important for future work using crowdsourcing to consider the appropriateness of the crowd being used to ensure the crowd is capable and has the adequate knowledge for the task at hand. Gamification of tasks seems to improve accuracy; other innovative methods of analysis including introducing thresholds and measures of trustworthiness should be considered. ::: ::: ::: Conclusion ::: Crowdsourcing is a new field that has been widely used and is innovative and adaptable. With the exception of surveillance applications that are used in emergency and disaster situations, most uses of crowdsourcing have only been used as pilots. These exceptions demonstrate that it is possible to take crowdsourcing applications to scale. Crowdsourcing has the potential to provide more accessible health care to more communities and individuals rapidly and to lower costs of care.