title: Pricing Services Subject to Congestion: Charge Per-Use Fees or Sell Subscriptions?

abstract: Should a firm charge on a per-use basis or sell subscriptions when its service experiences congestion? Queueing-based models of pricing primarily focus on charging a fee per use for the service, in part because per-use pricing enables the firm to regulate congestion---raising the per-use price naturally reduces how frequently customers use a service. The firm has less control over usage with subscription pricing (by definition, with subscription pricing customers are not charged proportional to their actual usage), and this is a disadvantage when customers dislike congestion. However, we show that subscription pricing is more effective at earning revenue. Consequently, the firm may be better off with subscription pricing, even, surprisingly, when congestion is intuitively most problematic for the firm: e.g., as congestion becomes more disliked by consumers. We show that the absolute advantage of subscription pricing relative to per-use pricing can be substantial, whereas the potential advantage of per-use pricing is generally modest. Subscription pricing becomes relatively more attractive if consumers become more heterogeneous in their service rates (e.g., some know they are “heavy” users and others know they are “light” users) as long as capacity is fixed, the potential utilization is high, and the two segments have substantially different usage rates. Otherwise, heterogeneity in usage rates makes subscription pricing less attractive relative to per-use pricing. We conclude that subscription pricing can be effective even if congestion is relevant for the overall quality of a service.


title: Modelling and testing consumer trust dimensions in e-commerce

abstract: Abstract Prior research has found trust to play a significant role in shaping purchase intentions of a consumer. However there has been limited research where consumer trust dimensions have been empirically defined and tested. In this paper we empirically test a path model such that Internet vendors would have adequate solutions to increase trust. The path model presented in this paper measures the three main dimensions of trust, i.e. competence, integrity, and benevolence. And assesses the influence of overall trust of consumers. The paper also analyses how various sources of trust, i.e. consumer characteristics, firm characteristic, website infrastructure and interactions with consumers, influence dimensions of trust. The model is tested using 365 valid responses. Findings suggest that consumers with high overall trust demonstrate a higher intention to purchase online.


title: Attention-based Neural Text Segmentation

abstract: Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal. Previous methods for this task require manual feature engineering, huge memory requirements and large execution times. To the best of our knowledge, this paper is the first one to present a novel supervised neural approach for text segmentation. Specifically, we propose an attention-based bidirectional LSTM model where sentence embeddings are learned using CNNs and the segments are predicted based on contextual information. This model can automatically handle variable sized context information. Compared to the existing competitive baselines, the proposed model shows a performance improvement of \(\sim \)7% in WinDiff score on three benchmark datasets.


title: Smoothed state estimation for nonlinear Markovian switching systems

abstract: The contributions of the work presented here are twofold. First we introduce a computationally efficient refinement of the unscented transformation (UT) which is applicable to nonlinear systems with a linear substructure. The resulting UT is referred to as the marginalised UT and is derived in two forms: the conventional UT marginalised form and the simplex sigma point marginalised form. The second contribution is the application of the proposed marginalised UT to smoothed state estimation in Markovian switching systems with nonlinear dynamic and measurement equations. Three algorithms for smoothing are proposed in this context. The first two algorithms are based on the well-known interacting multiple model (IMM) and hypothesis pruning techniques. The third algorithm uses maximum a posteriori (MAP) estimates of the switching parameter to generate a stochastic dynamic system within which smoothed state estimation is performed. The performances of the smoothing algorithms are analysed and compared in two scenarios involving a target which undergoes coordinated turn maneuvers.


title: Self-calibrating photometric stereo

abstract: We present a self-calibrating photometric stereo method. From a set of images taken from a fixed viewpoint under different and unknown lighting conditions, our method automatically determines a radiometric response function and resolves the generalized bas-relief ambiguity for estimating accurate surface normals and albedos. We show that color and intensity profiles, which are obtained from registered pixels across images, serve as effective cues for addressing these two calibration problems. As a result, we develop a complete auto-calibration method for photometric stereo. The proposed method is useful in many practical scenarios where calibrations are difficult. Experimental results validate the accuracy of the proposed method using various real-world scenes.


title: Birdsnap: Large-Scale Fine-Grained Visual Categorization of Birds

abstract: We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species. We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds. First, we introduce "one-vs-most classifiers." By eliminating highly similar species during training, these classifiers achieve more accurate and intuitive results than common one-vs-all classifiers. Second, we show how to estimate spatio-temporal class priors from observations that are sampled at irregular and biased locations. We show how these priors can be used to significantly improve performance. We then show state-of-the-art recognition performance on a new, large dataset that we make publicly available. These recognition methods are integrated into the online field guide, which is also publicly available.


RtGender: A Corpus for Studying Differential Responses to Gender


title: What is skipped: Finding desirable items in e-commerce search by discovering the worst title tokens

abstract: Given an ecommerce query, how well the titles of items for sale match the user intent is an important signal for ranking the items. A well-known technique for computing this signal is to use a standard machine-learned model that uses words as features, targets user clicks and predicts a score to rank the titles. In this paper, we introduce an alternate modeling technique that applies to queries that are frequent enough to have historical click data. For each such query we build a parameterized model of user behavior that learns what makes users skip a title. The parameters are different for each query. Specifically, our model predicts how desirable an item's title is to the user query by focusing on the worst tokens in the title. The model is learned offline using maximum likelihood based on user behavioral data, significantly improving query processing cost. The model's output score is used as a feature in a machine learned ranker for e-commerce search at eBay. Besides titles, the model design can easily incorporate any attribute of an item including structured content. In this scope, we present our new title desirability model built for nearly 8M queries recently deployed into the eBay search ecosystem and demonstrate its significant performance improvement over a baseline click-based Nive Bayes model through different evaluation approaches including A/B testing and human judgment. The reported performance is based on eBays commercial search engine serving millions of queries each day.


title: Caffe CNN-based classification of hyperspectral images on GPU

abstract: Deep learning techniques based on Convolutional Neural Networks (CNNs) are extensively used for the classification of hyperspectral images. These techniques present high computational cost. In this paper, a GPU (Graphics Processing Unit) implementation of a spatial-spectral supervised classification scheme based on CNNs and applied to remote sensing datasets is presented. In particular, two deep learning libraries, Caffe and CuDNN, are used and compared. In order to achieve an efficient GPU projection, different techniques and optimizations have been applied. The implemented scheme comprises Principal Component Analysis (PCA) to extract the main features, a patch extraction around each pixel to take the spatial information into account, one convolutional layer for processing the spectral information, and fully connected layers to perform the classification. To improve the initial GPU implementation accuracy, a second convolutional layer has been added. High speedups are obtained together with competitive classification accuracies.


title: Is face recognition really a Compressive Sensing problem?

abstract: Compressive Sensing has become one of the standard methods of face recognition within the literature. We show, however, that the sparsity assumption which underpins much of this work is not supported by the data. This lack of sparsity in the data means that compressive sensing approach cannot be guaranteed to recover the exact signal, and therefore that sparse approximations may not deliver the robustness or performance desired. In this vein we show that a simple £2 approach to the face recognition problem is not only significantly more accurate than the state-of-the-art approach, it is also more robust, and much faster. These results are demonstrated on the publicly available YaleB and AR face datasets but have implications for the application of Compressive Sensing more broadly.


title: Reconstructing Interlaced High-Dynamic-Range Video Using Joint Learning

abstract: For extending the dynamic range of video, it is a common practice to capture multiple frames sequentially with different exposures and combine them to extend the dynamic range of each video frame. However, this approach results in typical ghosting artifacts due to fast and complex motion in nature. As an alternative, video imaging with interlaced exposures has been introduced to extend the dynamic range. However, the interlaced approach has been hindered by jaggy artifacts and sensor noise, leading to concerns over image quality. In this paper, we propose a data-driven approach for jointly solving two specific problems of deinterlacing and denoising that arise in interlaced video imaging with different exposures. First, we solve the deinterlacing problem using joint dictionary learning via sparse coding. Since partial information of detail in differently exposed rows is often available via interlacing, we make use of the information to reconstruct details of the extended dynamic range from the interlaced video input. Second, we jointly solve the denoising problem by tailoring sparse coding to better handle additive noise in low-/high-exposure rows, and also adopt multiscale homography flow to temporal sequences for denoising. We anticipate that the proposed method will allow for concurrent capture of higher dynamic range video frames without suffering from ghosting artifacts. We demonstrate the advantages of our interlaced video imaging compared with the state-of-the-art high-dynamic-range video methods.


title: CNN for license plate motion deblurring

abstract: In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks (CNN) in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.


Deep Light Source Estimation for Mixed Reality


title: Sign language ‘heard’ in the auditory cortex

abstract: The upper regions of the brain's temporal lobe are important both for hearing and for comprehending spoken language. We have discovered that these regions can be activated by sign language in congenitally deaf subjects, even though the temporal lobe normally functions as an auditory area. This finding indicates that, in deaf people, the brain region usually reserved for hearing may be activated by other sensory modalities, providing striking evidence of neural plasticity.


title: Fast 3D point cloud segmentation using supervoxels with geometry and color for 3D scene understanding

abstract: Segmentation of 3D colored point clouds is a research field with renewed interest thanks to recent availability of inexpensive consumer RGB-D cameras and its importance as an unavoidable low-level step in many robotic applications. However, 3D data's nature makes the task challenging and, thus, many different techniques are being proposed, all of which require expensive computational costs. This paper presents a novel fast method for 3D colored point cloud segmentation. It starts with supervoxel partitioning of the cloud, i.e., an oversegmentation of the points in the cloud. Then it leverages on a novel metric exploiting both geometry and color to iteratively merge the supervoxels to obtain a 3D segmentation where the hierarchical structure of partitions is maintained. The algorithm also presents computational complexity linear to the size of the input. Experimental results over two publicly available datasets demonstrate that our proposed method outperforms state-of-the-art techniques.


title: High-Efficiency Current-Fed Dual Active Bridge DC–DC Converter With ZVS Achievement Throughout Full Range of Load Using Optimized Switching Patterns

abstract: For current-fed dual active bridge bidirectional dc–dc converters, all the possible switching patterns are summarized in view of the combinations of both side pulse-width modulation duty cycles and phase-shift angle. A control strategy is proposed for the current-fed dual active bridge converter to operate with the optimized patterns. The equivalent duty cycle of the secondary side is smaller than that of the primary side by a fixed value, which is optimized based on the soft-switching achievement and the circulating current minimization. The closed-loop control is easy to be implemented since there are only two independent variables, one-side duty cycle and the phase-shift angle. With the proposed control, zero-voltage switching can be achieved for all power switches throughout full range of load even at no-load condition. The typical working modes with the proposed control are given. The optimal design of the system parameters including the fixed time delta and dead time is illustrated. The converter has a very good performance, not only under steady states but also in transients. The conversion efficiency is high. The effectiveness of the proposed control is verified by the experimental results of a 1-kW laboratory prototype.


title: Secure Multiparty Computation for Privacy-Preserving Data Mining

abstract: In this paper, we survey the basic paradigms and notions of secure mul- tiparty computation and discuss their relevance to the fleld of privacy-preserving data mining. In addition to reviewing deflnitions and constructions for secure mul- tiparty computation, we discuss the issue of e-ciency and demonstrate the di-cul- ties involved in constructing highly e-cient protocols. We also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacy-preserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.


title: Application-aware Memory System for Fair and Efficient Execution of Concurrent GPGPU Applications

abstract: The available computing resources in modern GPUs are growing with each new generation. However, as many general purpose applications with limited thread-scalability are tuned to take advantage of GPUs, available compute resources might not be optimally utilized. To address this, modern GPUs will need to execute multiple kernels simultaneously. As current generations of GPUs (e.g., NVIDIA Kepler, AMD Radeon) already enable concurrent execution of kernels from the same application, in this paper we address the next logical step: executing multiple concurrent applications in GPUs. We show that while this paradigm has a potential to improve the overall system performance, negative interactions among concurrently executing applications in the memory system can severely hamper the performance and fairness among applications. We show that the current application agnostic GPU memory system design can (1) lead to sub-optimal GPU performance; and (2) create significant imbalance in performance slowdowns across kernels. Thus, we argue that GPU memory system should be augmented with application awareness. As one example to the applicability of this concept, we augment the memory system hardware with application awareness such that requests from different applications can be scheduled in a round robin (RR) fashion while still preserving the benefits of the current first-ready FCFS (FR-FCFS) memory scheduling policy. Evaluations with different multi-application workloads demonstrate that the proposed memory scheduling policy, first-ready round-robin FCFS (FR-RR-FCFS), improves fairness and delivers better system performance compared to the existing FR-FCFS memory scheduling scheme.


title: Best-Buddies Similarity for robust template matching

abstract: We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)—pairs of points in source and target sets, where each point is the nearest neighbor of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset.


title: Development of an Implicit Overall Well-Being Measure Using the Implicit Association Test

abstract: Usually, well-being has been measured by means of questionnaires or scales. Although most of these methods have a high level of reliability and validity, they present some limitations. In order to try to improve well-being assessment, in the present work, the authors propose a new complementary instrument: The Implicit Overall Well-Being Measure (IOWBM). The Implicit Association Test (IAT) was adapted to measure well-being by assessing associations of the self with well-being-related words. In the first study, the IOWBM showed good internal consistency and adequate temporal reliability. In the second study, it presented weak correlations with explicit well-being measures. The third study examined the validity of the measure, analyzing the effect of traumatic memories on implicit well-being. The results showed that people who remember a traumatic event presented low levels of implicit well-being compared with people in the control condition.


title: Discovering and Profiling Overlapping Communities in Location-Based Social Networks

abstract: With the recent surge of location-based social networks (LBSNs), such as Foursquare and Facebook Places, huge digital footprints of people's locations, profiles, and online social connections become accessible to service providers. Unlike social networks (e.g., Flickr, Facebook) that have explicit groups for users to subscribe to or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed. In the meantime, the diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user check-in traces at venues and user/venue attributes, we come out with a novel multimode multi-attribute edge-centric coclustering framework to discover the overlapping and hierarchical communities of LBSNs users. By employing both intermode and intramode features, the proposed framework is not only able to group like-minded users from different social perspectives but also discover communities with explicit profiles indicating the interests of community members. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset.


title: The Effect of 635 nm Red Laser Irradiation on Proliferation of Bone Marrow Stem Cells

abstract: Photobiomodulation effects of Low-level light irradiation (LLLI) on regeneration have been reported in skin, nerve, and skeletal muscle tissues and bone. Bone Mesenchymal stem cells (BMSCs) are derived from bone marrow, which exhibited a ?broblast-like appearance, and could differentiate in vitro into different lineages. However, there is a reciprocal relationship between growth and osteogenic differentiation in MSCs. Therefore, it’s important to investigate the effect of LLLI on BMSCs. The aim of our study was to investigate the proliferation effect of 635 nm red laser light on bone marrow MSCs with or without osteogenic supplements. Bone marrow was collected from the 4-week-old Sprague–Dawley rats femur and tibiae. MSCs with and without osteogenic supplements both were divided into three groups. A continuous 635 nm wavelength red light diode laser (a power output of 960 mW) was used in the study. The size of light spot was 35mm in diameter. Irradiation was performed every other day since the half of medium was changed to osteogenic differentiation media (ODM). The first irradiation day was set as 0 day. The duration of each irradiation for red light was calculated at 10 seconds for 1 J/cm2, 20 seconds for 2 J/cm2. Two of these groups were used as controls: MSCs incubated in DMEM without irradiation (control 1), MSCs incubated in ODM without irradiation (control 2). Cellular proliferation was evaluated by using WST-8. Cell viability was assessed with WST-8 kit at 2, 4, 6 and 8 days, respectively. At 4, 6 and 8 days, groups cultured with DMEM showed significantly higher viabilities than that in groups with ODM. In groups with DMEM, red light at all doses significantly stimulated cell viability as compared with the control 1. Groups irradiated at 1 and 2 J/cm2 had more effective proliferation on 4 (P


title: Enhanced resting-state connectivity of amygdala in the immediate aftermath of acute psychological stress

abstract: Recent neuroimaging studies investigating responses to stressful stimuli may importantly further our understanding of psychological trauma etiology. However, theory posits that sustained activation of these stress circuits after the stressful event may play an equally important role in the development of stress-related psychopathology. Importantly, such post-stress network changes remain poorly characterized. The amygdala with its connections is crucially positioned in the central stress circuitry that mediates the initial stress response. Hence, we investigated post-stress amygdala-centered connectivity patterns in order to characterize the aftermath of acute, experimentally-induced stress in healthy humans. We recorded resting-state functional MRI in 26 female participants following a period of moderate psychological stress induced by means of aversive (vs. emotionally neutral) movie watching with a self-referencing instruction. Next, we implemented a seedregion analysis calculating the voxel-wise correlation with the anatomically extracted time-series of the amygdala. Various stress measures confirmed successful stress induction. Moreover, we demonstrated enhanced functional coupling of the amygdala with dorsal anterior cingulate cortex, anterior insula, and a dorso-rostral pontine region, which appears to overlap with the anatomical location of the locus coeruleus (LC), when contrasting the stress with the control group. Thus, we show that the aftermath of acute stress is qualified by prolonged activation in an amygdala-connectivity network. This pattern of co-activation may indicate an extended state of hypervigilance that promotes sustained salience and mnemonic processing after stress. Characterization of the post-stress brain state may provide initial insight into the early phases of psychological trauma formation.


title: Defect Detection in Solar Modules Using ICA Basis Images

abstract: Solar power has become an attractive alternative of electricity energy. Solar cells that form the basis of a solar power system are mainly based on multicrystalline silicon. A set of solar cells are assembled and interconnected into a large solar module to offer a large amount of electricity power for commercial applications. Many defects in a solar module cannot be visually observed with the conventional CCD imaging system. This paper aims at defect inspection of solar modules in electroluminescence (EL) images. The solar module charged with electrical current will emit infrared light whose intensity will be darker for intrinsic crystal grain boundaries and extrinsic defects including micro-cracks, breaks and finger interruptions. The EL image can distinctly highlight the invisible defects but also create a random inhomogeneous background, which makes the inspection task extremely difficult. The proposed method is based on independent component analysis (ICA), and involves a learning and a detection stage. The large solar module image is first divided into small solar cell subimages. In the training stage, a set of defect-free solar cell subimages are used to find a set of independent basis images using ICA. In the inspection stage, each solar cell subimage under inspection is reconstructed as a linear combination of the learned basis images. The coefficients of the linear combination are used as the feature vector for classification. Also, the reconstruction error between the test image and its reconstructed image from the ICA basis images is also evaluated for detecting the presence of defects. Experimental results have shown that the image reconstruction with basis images distinctly outperforms the ICA feature extraction approach. It can achieve a mean recognition rate of 93.4% for a set of 80 test samples.


title: How to develop lean project management?

abstract: Lean project management will improve existing concepts and standards on project management. Here we focus of extensions of project management standards to shape lean project management.


title: The general design of a seafloor surveying AUV system

abstract: Owing to the exceptional ability of obtaining maximum data quality by bringing advanced payload sensors close to the seafloor, AUVs have been widely applied to seafloor survey task in recent years. Tianjin University developed a seafloor mapping AUV in 2010. In this paper, the energy consumption and endurance of the seafloor mapping AUV are predicted. To optimize the maneuverability at the early design stage, the hydrodynamic characteristics of the vehicle are calculated and evaluated. A propeller-gear-motor combination thruster is developed to ensure maneuverability and realize high resolution trajectory tracing at a low speed. An aided inertial navigation system integrating a number of surface and subsea navigation sensors is mounted on the vehicle to achieve better surveying data quality. Several trials were conducted and the general design of the whole system is verified.


title: Moore's law continues into the 1x-nm era

abstract: After much-publicised delays, Intel introduced their 14-nm second-generation trigate products in 2014. The following year TSMC and Samsung also launched their 16/14-nm finFET processes, notably in the Apple A9 and Exynos processors. In parallel NAND flash moved solidly into the 1x-nm generation and the first 3D V-NAND product went into volume production; and DRAM became a 2x-nm product. Chipworks, as a supplier of competitive intelligence to the semiconductor and electronics industries, monitors the evolution of chip processes as they come into commercial production. Chipworks has obtained parts from the leading edge manufacturers, and performed structural analyses to examine the features and manufacturing processes of the devices. The paper discusses some of the different structures we have seen during the evolution of these technologies, and examines the latest 14 and 20-nm logic parts that have been introduced, as well as some details of the latest flash and DRAM products.


title: Internet of Things: Security vulnerabilities and challenges

abstract: Internet of Things (IoT) has been given a lot of emphasis since the 90s when it was first proposed as an idea of interconnecting different electronic devices through a variety of technologies. However, during the past decade IoT has rapidly been developed without appropriate consideration of the profound security goals and challenges involved. This study explores the security aims and goals of IoT and then provides a new classification of different types of attacks and countermeasures on security and privacy. It then discusses future security directions and challenges that need to be addressed to improve security concerns over such networks and aid in the wider adoption of IoT by masses.


title: Smart trafile monitoring and control architecture and design

abstract: The road traffic volume is the main criteria in the road planning and traffic management. Previous studies have discussed on the smart traffic monitoring, and little attention has been paid to the solution of smart traffic control. The objective of this paper is to propose a smart traffic monitoring and control architecture and also the respective algorithm using sensors. This paper assumed that sensors are strategically placed along the road to detect traffic volume. These information will then be transmitted via Low Power Wide Area Network (LoPWAN) to a dedicated control centre. There will be server in the control centre that controls the traffic lights scheduling at the junctions. The proposed solution can be further developed and tested using hardware.


title: Understanding Linux Malware

abstract: For the past two decades, the security community has been fighting malicious programs for Windows-based operating systems. However, the recent surge in adoption of embedded devices and the IoT revolution are rapidly changing the malware landscape. Embedded devices are profoundly different than traditional personal computers. In fact, while personal computers run predominantly on x86-flavored architectures, embedded systems rely on a variety of different architectures. In turn, this aspect causes a large number of these systems to run some variants of the Linux operating system, pushing malicious actors to give birth to ""Linux malware."" To the best of our knowledge, there is currently no comprehensive study attempting to characterize, analyze, and understand Linux malware. The majority of resources on the topic are available as sparse reports often published as blog posts, while the few systematic studies focused on the analysis of specific families of malware (e.g., the Mirai botnet) mainly by looking at their network-level behavior, thus leaving the main challenges of analyzing Linux malware unaddressed. This work constitutes the first step towards filling this gap. After a systematic exploration of the challenges involved in the process, we present the design and implementation details of the first malware analysis pipeline specifically tailored for Linux malware. We then present the results of the first large-scale measurement study conducted on 10,548 malware samples (collected over a time frame of one year) documenting detailed statistics and insights that can help directing future work in the area.


Advances in protein hydrogen exchange by mass spectrometry


title: Study on Security Problems and Key Technologies of the Internet of Things

abstract: The IOT is a huge and widely distributed the Internet that things connect things.It connects all the articles to the internet through information sensing devices. It is the second information wave after Computer, Internet and mobile communication network. With the rapid development of the Internet of Things, its security problems have become more concentrated. This paper addresses the security issues and key technologies in IOT. It elaborated the basic concepts and the principle of the IOT and combined the relevant characteristics of the IOT as well as the International main research results to analysis the security issues and key technologies of the IOT which in order to plays a positive role in the construction and the development of the IOT through the research.


title: Andragogy and Self-Directed Learning : Pillars of Adult Learning Theory

abstract: Andragogy and self-directed learning continue to be important to our present-day understanding of adult learning.


title: Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking

abstract: Offline training for object tracking has recently shown great potentials in balancing tracking accuracy and speed. However, it is still difficult to adapt an offline trained model to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance object tracking. The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online. In particular, by exploiting the offline trained general attention, the target adapted residual attention, and the channel favored feature attention, the RASNet not only mitigates the over-fitting problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation of representation learning and discriminator learning. The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking. Experimental results on two latest benchmarks, OTB-2015 and VOT2017, show that the RASNet tracker has the state-of-the-art tracking accuracy while runs at more than 80 frames per second.


CYBER ATTACKS AND DEFENSE FRAMEWORK FOR UNMANNED AERIAL SYSTEMS ( UAS ) ENVIRONMENT


title: Data Reduction and Data Mining Framework for Digital Forensic Evidence: Storage, Intelligence, Review and Archive

abstract: The volume of digital forensic evidence is rapidly increasing, leading to large backlogs. In this paper, a Digital Forensic Data Reduction and Data Mining Framework is proposed. Initial research with sample data from South Australia Police Electronic Crime Section and Digital Corpora Forensic Images using the proposed framework resulted in significant reduction in the storage requirements — the reduced subset is only 0.196 percent and 0.75 percent respectively of the original data volume. The framework outlined is not suggested to replace full analysis, but serves to provide a rapid triage, collection, intelligence analysis, review and storage methodology to support the various stages of digital forensic examinations. Agencies that can undertake rapid assessment of seized data can more effectively target specific criminal matters. The framework may also provide a greater potential intelligence gain from analysis of current and historical data in a timely manner, and the ability to undertake research of trends over time.


51 UNDERSTANDING AND APPROACH TO TREATMENT OF SCARS AND ADHESIONS


title: Efficient publish/subscribe through a self-organizing broker overlay and its application to SIENA

abstract: Recently many scalable and efficient solutions for event dissemination in publish/subscribe (pub/sub) systems have appeared in the literature. This dissemination is usually done over an overlay network of brokers and its cost can be measured as the number of messages sent over the overlay to allow the event to reach all intended subscribers. Efficient solutions to this problem are often obtained through smart dissemination algorithms that avoid flooding events on the overlay. In this paper, we propose a complementary approach that obtains efficient event dissemination by reorganizing the overlay network topology. More specifically, this reorganization is done through a self-organizing algorithm executed by brokers whose aim is to directly connect, through overlay links, pairs of brokers matching same events. In this way, on average, the number of brokers involved in an event dissemination decreases, thus reducing its cost. Even though the paradigm of the self-organizing algorithm is general and then applicable to any overlay-based pub/sub system, its concrete implementation depends on the specific system. As a consequence, we studied the effect of the introduction of the self-organizing algorithm in the context of a specific system implementing a tree-based routing strategy, namely SIENA, showing the actual performance benefits through an extensive simulation study. In particular, performance results point out the capacity of the algorithm to converge to an overlay topology accommodating efficient event with respect to (w.r.t) dissemination a specific scenario. Moreover, the algorithm shows a significant capacity to adapt the overlay network topology to continuously changing scenarios while keeping an efficient behavior w.r.t. event dissemination.


title: Airwriting: Hands-Free Mobile Text Input by Spotting and Continuous Recognition of 3d-Space Handwriting with Inertial Sensors

abstract: We present an input method which enables complex hands-free interaction through 3d handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. Motion sensing is done wirelessly by accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a Support Vector Machine to identify data segments which contain handwriting. The recognition stage uses Hidden Markov Models (HMM) to generate the text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary with over 8000 words. A statistical language model is used to enhance recognition performance and restrict the search space. We report the results from a nine-user experiment on sentence recognition for person dependent and person independent setups on 3d-space handwriting data. For the person independent setup, a word error rate of 11% is achieved, for the person dependent setup 3% are achieved. We evaluate the spotting algorithm in a second experiment on a realistic dataset including everyday activities and achieve a sample based recall of 99\% and a precision of 25%. We show that additional filtering in the recognition stage can detect up to 99% of the false positive segments.


title: Maternal-foetal bonding: the impact of domestic violence on the bonding process between a mother and child

abstract: Objectives: To test the hypothesis that domestic violence has an impact on the maternal-foetal bond and the maternal-infant bond, and to investigate any relationship between the two bonding states (taking into account post-natal depression).


title: Reference Trajectory Generation for Rehabilitation Robots: Complementary Limb Motion Estimation

abstract: For gait rehabilitation robots, an important question is how to ensure stable gait, while avoiding any interaction forces between robot and human in case the patient walks correctly. To achieve this, the definition of ldquocorrectrdquo gait needs to adapted both to the individual patient and to the situation. Recently, we proposed a method for online trajectory generation that can be applied for hemiparetic subjects. Desired states for one (disabled) leg are generated online based on the movements of the other (sound) leg. An instantaneous mapping between legs is performed by exploiting physiological interjoint couplings. This way, the patient generates the reference motion for the affected leg autonomously. The approach, called Complementary Limb Motion Estimation (CLME), is implemented on the LOPES gait rehabilitation robot and evaluated with healthy subjects in two different experiments. In a previously described study, subjects walk only with one leg, while the robot's other leg acts as a fake prosthesis, to simulate complete loss of function in one leg. This study showed that CLME ensures stable gait. In a second study, to be presented in this paper, healthy subjects walk with both their own legs to assess the interference with self-determined walking. Evaluation criteria are: Power delivered to the joints by the robot, electromyography (EMG) distortions, and kinematic distortions, all compared to zero torque control, which is the baseline of minimum achievable interference. Results indicate that interference of the robot is lower with CLME than with a fixed reference trajectory, mainly in terms of lowered exchanged power and less alteration of EMG. This implies that subjects can walk more naturally with CLME, and they are assisted less by the robot when it is not needed. Future studies with patients are yet to show whether these properties of CLME transfer to the clinical domain.


title: Multivis: Content-based social network exploration through multi-way visual analysis

abstract: With the explosion of social media, scalability becomes a key challenge. There are two main aspects of the problems that arise: 1) data volume: how to manage and analyze huge datasets to efficiently extract patterns, 2) data understanding: how to facilitate understanding of the patterns by users? To address both aspects of the scalability challenge, we present a hybrid approach that leverages two complementary disciplines, data mining and information visualization. In particular, we propose 1) an analytic data model for content-based networks using tensors; 2) an efficient high-order clustering framework for analyzing the data; 3) a scalable context-sensitive graph visualization to present the clusters. We evaluate the proposed methods using both synthetic and real datasets. In terms of computational efficiency, the proposed methods are an order of magnitude faster compared to the baseline. In terms of effectiveness, we present several case studies of real corporate social networks.


title: Matching Anonymized and Obfuscated Time Series to Users’ Profiles

abstract: Many popular applications use traces of user data to offer various services to their users. However, even if user data are anonymized and obfuscated, a user’s privacy can be compromised through the use of statistical matching techniques that match a user trace to prior user behavior. In this paper, we derive the theoretical bounds on the privacy of users in such a scenario. We build on our recent study in the area of location privacy, in which we introduced formal notions of location privacy for anonymization-based location privacy-protection mechanisms. Here, we derive the fundamental limits of user privacy when both anonymization and obfuscation-based protection mechanisms are applied to users’ time series of data. We investigate the impact of such mechanisms on the tradeoff between privacy protection and user utility. We first study achievability results for the case where the time-series of users are governed by an independent and identically distributed (i.i.d.) process. The converse results are proved both for the i.i.d. case as well as the more general Markov chain model. We demonstrate that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.


title: Leaky-Wave Radiation Behavior From a Double Periodic Composite Right/Left-Handed Substrate Integrated Waveguide

abstract: A double periodic composite right/left-handed ( DP-CRLH ) substrate integrated waveguide (SIW) is proposed based on the transmission line theory. A new leaky-wave radiation is observed at low frequency below the left-handed passband in addition to the composite right/left-handed property. First, the equivalent circuit of the proposed structure is analyzed, and dispersion characteristics are obtained including the expression for the cutoff frequencies. It is noted that transmission lines double periodically loaded with either alternate capacitances, or alternate inductances will contribute the new leaky-wave behavior. Next, SIW implementation of a DP-CRLH transmission line is designed and illustrated with simulated and experimentally demonstrated results. The electric field distribution plots are extracted from a full-wave simulation tool and exhibit the forward and backward wave propagation in the new leaky-wave region and the typical left-handed region, respectively. Finally, a compact leaky-wave antenna based on the DP-CRLH SIW is designed, and the radiation characteristics are tested and demonstrated to exhibit forward radiation at the new leaky-wave region from 5.2 to 5.8 GHz (covering IEEE 802.11a band) and frequency scanning from backward to forward directions in the left-handed and conventional right-handed region from 12.6 to 17.8 GHz.


title: Introduction to Support Vector Machines 1 Description

abstract: Support Vector Machines (SVM’s) are a relatively new learning method used for binary classification. The basic idea is to find a hyperplane which separates the d-dimensional data perfectly into its two classes. However, since example data is often not linearly separable, SVM’s introduce the notion of a “kernel induced feature space” which casts the data into a higher dimensional space where the data is separable. Typically, casting into such a space would cause problems computationally, and with overfitting. The key insight used in SVM’s is that the higher-dimensional space doesn’t need to be dealt with directly (as it turns out, only the formula for the dot-product in that space is needed), which eliminates the above concerns. Furthermore, the VC-dimension (a measure of a system’s likelihood to perform well on unseen data) of SVM’s can be explicitly calculated, unlike other learning methods like neural networks, for which there is no measure. Overall, SVM’s are intuitive, theoretically wellfounded, and have shown to be practically successful. SVM’s have also been extended to solve regression tasks (where the system is trained to output a numerical value, rather than “yes/no” classification).


title: Composing Simple Image Descriptions using Web-scale N-grams

abstract: Studying natural language, and especially how people describe the world around them can help us better understand the visual world. In turn, it can also help us in the quest to generate natural language that describes this world in a human manner. We present a simple yet effective approach to automatically compose image descriptions given computer vision based inputs and using web-scale n-grams. Unlike most previous work that summarizes or retrieves pre-existing text relevant to an image, our method composes sentences entirely from scratch. Experimental results indicate that it is viable to generate simple textual descriptions that are pertinent to the specific content of an image, while permitting creativity in the description -- making for more human-like annotations than previous approaches.


title: Massive Machine-type Communications in 5G: Physical and MAC-layer solutions

abstract: MTC are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into mMTC and uMTC. While mMTC is about wireless connectivity to tens of billions of machinetype terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy-efficient. In this article, we introduce the PHY and MAC layer solutions developed within METIS to address this challenge.


title: Automatic Detection of Epileptiform Discharges in the EEG

abstract: The diagnosis of epilepsy generally includes a visual inspection of EEG recorded data by the Neurologist, with the purpose of checking the occurrence of transient waveforms called interictal epileptiform discharges. These waveforms have short duration (less than 100 ms), so the inspection process is usually time-consuming, particularly for ambulatory long term EEG records. Therefore, an automatic detection system of epileptiform discharges can be a valuable tool for a Neurology service. The proposed approach is the development of a multi stage detection algorithm, which processes the complete EEG signals and applies decision criteria to selected waveforms. It employs EEG analysis techniques such as Wavelet Transform and Mimetic Analysis, complemented with a classification based on Fuzzy Logic. In order to evaluate the algorithm's performance, data were collected from several epileptic patients, with epileptiform activity marked by a Neurologist. The average values obtained for both Sensitivity and Specificity were respectively higher than 80 and 70 percent.


title: Rank aggregation methods for the Web

abstract: We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. We develop a set of techniques for the rank aggregation problem and compare their performance to that of well-known methods. A primary goal of our work is to design rank aggregation techniques that can e ectively combat \spam," a serious problem in Web searches. Experiments show that our methods are simple, e cient, and e ective.


title: Illumination Decomposition for Photograph With Multiple Light Sources

abstract: Illumination decomposition for a single photograph is an important and challenging problem in image editing operation. In this paper, we present a novel coarse-to-fine strategy to perform illumination decomposition for photograph with multiple light sources. We first reconstruct the lighting environment of the image using the estimated geometry structure of the scene. With the position of lights, we detect the shadow regions as well as the highlights in the projected image for each light. Then, using the illumination cues from shadows, we estimate the coarse illumination decomposed image emitted by each light source. Finally, we present a light-aware illumination optimization model, which efficiently produces the finer illumination decomposition results, as well as recover the texture detail under the shadow. We validate our approach on a number of examples, and our method effectively decomposes the input image into multiple components corresponding to different light sources.


title: Autonomous exploration of mobile robots through deep neural networks

abstract: The exploration problem of mobile robots aims to allow mobile robots to explore an unknown environment. We describe an indoor exploration algorithm for mobile robots using a hierarchical structure that fuses several convolutional neural network layers with decision-making process. The whole system is trained end to end by taking only visual information (RGB-D information) as input and generates a sequence of main moving direction as output so that the robot achieves autonomous exploration ability. The robot is a TurtleBot with a Kinect mounted on it. The model is trained and tested in a real world environment. And the training data set is provided for download. The outputs of the test data are compared with the human decision. We use Gaussian process latent variable model to visualize the feature map of last convolutional layer, which proves the effectiveness of this deep convolution neural network mode. We also present a novel and lightweight deep-learning library libcnn especially for deep-learning proc...


COYOTE III : DEVELOPMENT OF A MODULAR AND HIGHLY MOBILE MICRO ROVER


title: Interactive facial feature localization

abstract: We address the problem of interactive facial feature localization from a single image. Our goal is to obtain an accurate segmentation of facial features on high-resolution images under a variety of pose, expression, and lighting conditions. Although there has been significant work in facial feature localization, we are addressing a new application area, namely to facilitate intelligent high-quality editing of portraits, that brings requirements not met by existing methods. We propose an improvement to the Active Shape Model that allows for greater independence among the facial components and improves on the appearance fitting step by introducing a Viterbi optimization process that operates along the facial contours. Despite the improvements, we do not expect perfect results in all cases. We therefore introduce an interaction model whereby a user can efficiently guide the algorithm towards a precise solution. We introduce the Helen Facial Feature Dataset consisting of annotated portrait images gathered from Flickr that are more diverse and challenging than currently existing datasets. We present experiments that compare our automatic method to published results, and also a quantitative evaluation of the effectiveness of our interactive method.


A high efficiency DC-DC Converter using a new in-package structure of Bonding-Wire inductor


title: Index prediction with neuro-genetic hybrid network: A comparative analysis of performance

abstract: Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analyzed are artificial neural network (ANN) trained with gradient descent (GD) technique, ANN trained with genetic algorithm (GA) and functional link neural network (FLANN) trained with GA. The stock price index of Bombay stock exchange data has been considered to train these models and to compare their relative performance. Experimental results and analysis has been presented to show the performance of different models.


Study on Causes and Effects of Employee Turnover in Construction Industry


title: Simulation of Linear induction Motor using Model Predictive Control in synchronously rotating reference frame

abstract: In this paper, the simulation of dynamic model of Linear induction Motor (LIM) using Model predictive control (MPC) is presented. Various control methods have been previously proposed for the control of three phase two level inverter. A Model Predictive Control method is a simple and powerful control scheme used for the control of voltage of a three-phase inverter. A discrete time model of the inverter, filter and the load is used by the controller to predict the behavior of the output voltage for each possible switching state. A MPC is an optimization process that uses a cost function to minimize the error between the measured output voltage and the reference voltage. Switching state of the voltage vector that gives the minimum error is selected and given to the inverter during the next sampling instant. The proposed predictive control method performance for LIM at various sampling frequencies is simulated using MATLAB/Simulink tools for Total Harmonic Distortion (THD) analysis.


title: A line in the sand: A wireless sensor network for target detection, classification, and tracking

abstract: Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensor nodes at MacDill Air Force Base in Tampa, FL, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.


title: Introduction Statistical Assumptions as Empirical Commitments

abstract: Researchers who study punishment and social control, like those who study other social phenomena, typically seek to generalize their findings from the data they have to some larger context: in statistical jargon, they generalize from a sample to a population. Generalizations are one important product of empirical inquiry. Of course, the process by which the data are selected introduces uncertainty. Indeed, any given dataset is but one of many that could have been studied. If the dataset had been different, the statistical summaries would have been different, and so would the conclusions, at least by a little.


title: Agile project manager behavior: The taxonomy

abstract: The past few years have witnessed dynamic changes in the field of software project management. These provided evidence of the strength of agile methodologies as a strategy that can speed up the development of rhythms and growth of innovation. Managing a project involves a complexity of requirements and developmental processes. This provides challenges to the project manager as he is accountable for the failure of a completed project. As such he is required to tackle any problems by adopting agile methodology during the development process. In Malaysia there is little research done to examine the behavior of the agile project manager. This study aims to analyze the behavior when project managers adopt agile in managing the development projects. Moreover, a review of the relevant literature has helped in developing an understanding of the agile project manager's behavior as this is necessary for them to become more agile inside development projects. For that reason, this paper has identified seven behaviors that the manager needs to adopt during software development process. These include Leadership, Openness, Results Orientation, Ethics, Communication, Strategic and Creative and Innovative. This paper contributes to the relevant theory by developing taxonomy of the agile project manager's behavior. Practitioners can use this taxonomy as a sensitizing device that helps the manager to consider behavior that promotes success of their projects. This is important to ensure an increase productivity and profitability, which are the business strategies of software development projects.


title: A nanophotonic interconnect for high-performance many-core computation

abstract: Silicon nanophotonics holds the promise of revolutionizing computing by enabling parallel architectures that combine unprecedented performance and ease of use with affordable power consumption. Here we describe the results of a detailed multiyear design study of dense wavelength division multiplexing (DWDM) on-chip and off-chip interconnects and the device technologies that could improve computing performance by a factor of 20 above industry projections over the next decade.


title: Millimetre-wave beam-switching rotman lens antenna designs on multi-layered LCP substrates

abstract: Compact and low-cost antenna arrays with beamsteering or beam-switching are needed for emerging millimeter-wave communication systems such as 5G and WLAN at the 60 GHz frequency band. The Rotman lens is a planar lens which can be used in conjunction with an antenna array to provide several switchable beam directions. The Rotman lens is a truetime delay device which makes it ideal for wideband systems. The proposed concept can be integrated on a printed circuit board construction based on liquid crystal polymer (LCP) base materials. The design, manufacturing and measurement results are presented for a 60 GHz Rotman lens implementation. The presented design can electrically switch between five different beam directions −30°, −15°, 0°, 15° and 30°.


title: Associations between young adults' use of sexually explicit materials and their sexual preferences, behaviors, and satisfaction.

abstract: This study examined how levels of sexually explicit material (SEM) use during adolescence and young adulthood were associated with sexual preferences, sexual behaviors, and sexual and relationship satisfaction. Participants included 782 heterosexual college students (326 men and 456 women; M(age) = 19.9) who completed a questionnaire online. Results revealed high frequencies and multiple types and contexts of SEM use, with men's usage rates systematically higher than women's. Regression analyses revealed that both the frequency of SEM use and number of SEM types viewed were uniquely associated with more sexual experience (a higher number of overall and casual sexual intercourse partners, as well as a lower age at first intercourse). Higher frequencies of SEM use were associated with less sexual and relationship satisfaction. The frequency of SEM use and number of SEM types viewed were both associated with higher sexual preferences for the types of sexual practices typically presented in SEM. These findings suggest that SEM use can play a significant role in a variety of aspects of young adults' sexual development processes.


title: Non-local means denoising based on SVD basis images

abstract: With the assumption that natural images contain considerable amount of self-similarity, non-local means image de-noising uses patches similarity in order to filter noisy images. Although the output of the Non local means algorithm is very desirable in removing the low level of noise, when the noise increases, the performance deteriorates. This is because the similarity cannot be evaluated perfectly through noisy patches. To solve this problem, in the proposed approach, the similarity evaluation for each patch is performed based on the structural information. This is due to the fact that the HVS (Human Visual System) is highly adopted to extract structural information from a viewing scene. In this paper, a modified non-local means filter is introduced in order to find better similar patches especially in the case of high level of noise. The experimental results show appropriate performance of the presented algorithm both visually and quantitatively.


title: Automated composition of e-services: lookaheads

abstract: The e-services paradigm promises to enable rich, flexible, and dynamic inter-operation of highly distributed, heterogeneous network-enabled services. Among the challenges, a fundamental question concerns the design and analysis of composite e-services. This paper proposes techniques towards automated design of composite e-services. We consider the Roman model which represents e-services as activity-based finite state automata. For a given set of existing e-services and a desired e-service, does there exist a "mediator" which delegates activities in the desired e-service to existing e-services? The question was raised in an early study by Berardi et. al. for a restricted subclass of delegators which does not take into consideration of future activities. In this paper, we define a more general class of delegators called "lookahead" delegators and we show that the hierarchy based on the amount of lookahead is strict. We, then, study the complexity of constructing such delegators. We prove that in the case of deterministic e-services, a k-lookahead delegator can be computed in time polynomial in the size of target and subcontractor e-services, and exponential in k and the number of subcontractor e-services. We also present Wozart, an automated mediator construction tool implemented to realize our approaches.


A study of on-line legal service adoption in Hong Kong


title: DCworms – A tool for simulation of energy efficiency in distributed computing infrastructures

abstract: Abstract In the recent years, energy-efficiency of computing infrastructures has gained a great attention. For this reason, proper estimation and evaluation of energy that is required to execute data center workloads became an important research problem. In this paper we present a Data Center Workload and Resource Management Simulator (DCworms) which enables modeling and simulation of computing infrastructures to estimate their performance, energy consumption, and energy-efficiency metrics for diverse workloads and management policies. We discuss methods of power usage modeling available in the simulator. To this end, we compare results of simulations to measurements of real servers. To demonstrate DCworms capabilities we evaluate impact of several resource management policies on overall energy-efficiency of specific workloads executed on heterogeneous resources.


title: Constant Time Graph Neural Networks

abstract: The recent advancements in graph neural networks (GNNs) have led to state-of-the-art performances in various applications, including chemo-informatics, question-answering systems, and recommender systems. However, scaling up these methods to huge graphs, such as social networks and Web graphs, remains a challenge. In particular, the existing methods for accelerating GNNs either are not theoretically guaranteed in terms of the approximation error or incur at least a linear time computation cost. In this study, we reveal the query complexity of the uniform node sampling scheme for Message Passing Neural Networks including GraphSAGE, graph attention networks (GATs), and graph convolutional networks (GCNs). Surprisingly, our analysis reveals that the complexity of the node sampling method is completely independent of the number of the nodes, edges, and neighbors of the input and depends only on the error tolerance and confidence probability while providing a theoretical guarantee for the approximation error. To the best of our knowledge, this is the first paper to provide a theoretical guarantee of approximation for GNNs within constant time. Through experiments with synthetic and real-world datasets, we investigated the speed and precision of the node sampling scheme and validated our theoretical results.


title: BIOMETRIC AUTHENTICATION USING BRAIN RESPONSES TO VISUAL STIMULI

abstract: This paper studies the suitability of brain activity, namely electroencephalogram signals, as raw material for conducting biometric authentication of individuals. Brain responses were extracted with visual stimulation, leading to biological brain responses known as Visual Evoked Potentials. We evaluated a novel method, using only 8 occipital electrodes and the energy of differential EEG signals, to extract information about the subjects for further use as their biometric features. To classify the features obtained from each individual, we used a one-class classifier per subject and we tested four types of classifiers: K-Nearest Neighbor, Support Vector Data Description and two other classifiers resulting from the combination of the two ones previously mentioned. After testing these four classifiers with features of 70 subjects, the results showed that visual evoked potentials are suitable for an accurate biometric authentication.


title: Contextual Information Improves OOV Detection in Speech

abstract: Out-of-vocabulary (OOV) words represent an important source of error in large vocabulary continuous speech recognition (LVCSR) systems. These words cause recognition failures, which propagate through pipeline systems impacting the performance of downstream applications. The detection of OOV regions in the output of a LVCSR system is typically addressed as a binary classification task, where each region is independently classified using local information. In this paper, we show that jointly predicting OOV regions, and including contextual information from each region, leads to substantial improvement in OOV detection. Compared to the state-of-the-art, we reduce the missed OOV rate from 42.6% to 28.4% at 10% false alarm rate.


title: Neural systems underlying British Sign Language and audio-visual English processing in native users

abstract: In order to understand the evolution of human language, it is necessary to explore the neural systems that support language processing in its many forms. In particular, it is informative to separate those mechanisms that may have evolved for sensory processing (hearing) from those that have evolved to represent events and actions symbolically (language). To what extent are the brain systems that support language processing shaped by auditory experience and to what extent by exposure to language, which may not necessarily be acoustically structured? In this first neuroimaging study of the perception of British Sign Language (BSL), we explored these questions by measuring brain activation using functional MRI in nine hearing and nine congenitally deaf native users of BSL while they performed a BSL sentence-acceptability task. Eight hearing, non-signing subjects performed an analogous task that involved audio-visual English sentences. The data support the argument that there are both modality-independent and modality-dependent language localization patterns in native users. In relation to modality-independent patterns, regions activated by both BSL in deaf signers and by spoken English in hearing non-signers included inferior prefrontal regions bilaterally (including Broca's area) and superior temporal regions bilaterally (including Wernicke's area). Lateralization patterns were similar for the two languages. There was no evidence of enhanced right-hemisphere recruitment for BSL processing in comparison with audio-visual English. In relation to modality-specific patterns, audio-visual speech in hearing subjects generated greater activation in the primary and secondary auditory cortices than BSL in deaf signers, whereas BSL generated enhanced activation in the posterior occipito-temporal regions (V5), reflecting the greater movement component of BSL. The influence of hearing status on the recruitment of sign language processing systems was explored by comparing deaf and hearing adults who had BSL as their first language (native signers). Deaf native signers demonstrated greater activation in the left superior temporal gyrus in response to BSL than hearing native signers. This important finding suggests that left- temporal auditory regions may be privileged for processing heard speech even in hearing native signers. However, in the absence of auditory input this region can be recruited for visual processing.


title: V.D.: Folksonomies, the semantic web, and movie recommendation

abstract: While the Semantic Web has evolved to support the meaningful exchange of heterogeneous data through shared and controlled conceptualisations, Web 2.0 has demonstrated that large-scale community tagging sites can enrich the semantic web with readily accessible and valuable knowledge. In this paper, we investigate the integration of a movies folksonomy with a semantic knowledge base about user-movie rentals. The folksonomy is used to enrich the knowledge base with descriptions and categorisations of movie titles, and user interests and opinions. Using tags harvested from the Internet Movie Database, and movie rating data gathered by Netflix, we perform experiments to investigate the question that folksonomy-generated movie tag-clouds can be used to construct better user profiles that reflect a user's level of interest in different kinds of movies, and therefore, provide a basis for prediction of their rating for a previously unseen movie.


title: Code Syntax-Comparison Algorithm based on Type-Redefinition-Preprocessing and Rehash

abstract: The code comparison technology plays an important role in the fields of software security protection and plagiarism detection. Nowadays, there are mainly FIVE approaches of plagiarism detection, file-attribute-based, text-based, token-based, syntax-based and semantic-based. The prior three approaches have their own limitations, while the technique based on syntax has its shortage of detection ability and low efficiency that all of these approaches cannot meet the requirements on large-scale software plagiarism detection. Based on our prior research, we propose an algorithm on type redefinition plagiarism detection, which could detect the level of simple type redefinition, repeating pattern redefinition, and the redefinition of type with pointer. Besides, this paper also proposes a code syntax-comparison algorithm based on rehash classification, which enhances the node storage structure of the syntax tree, and greatly improves the efficiency.


title: Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares

abstract: Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.


title: Does food marketing need to make us fat? A review and solutions

abstract: Food marketing is often singled out as the leading cause of the obesity epidemic. The present review examines current food marketing practices to determine how exactly they may be influencing food intake, and how food marketers could meet their business objectives while helping people eat healthier. Particular attention is paid to the insights provided by recently published studies in the areas of marketing and consumer research, and those insights are integrated with findings from studies in nutrition and related disciplines. The review begins with an examination of the multiple ways in which 1) food pricing strategies and 2) marketing communication (including branding and food claims) bias food consumption. It then describes the effects of newer and less conspicuous marketing actions, focusing on 3) packaging (including the effects of package design and package-based claims) and 4) the eating environment (including the availability, salience, and convenience of food). Throughout, this review underscores the promising opportunities that food manufacturers and retailers have to make profitable “win-win” adjustments to help consumers eat better.


title: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

abstract: Motivation: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282--283, Bioinformatics, 18, 77--82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST. ::: ::: Availability: http://cd-hit.org ::: ::: Contact: [email protected]


title: Convolutional face finder: a neural architecture for fast and robust face detection

abstract: In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.


title: MSKD: multi-split KD-tree design on GPU

abstract: We present a novel parallel acceleration structure construction and traversal algorithm designed to efficiently exploit the massive parallel computing cores on the Graphic Processing Unit(GPU) to improve the render performance. Our associated data structure is called multi-split KD-tree or MSKD, which focuses on fast generating and efficiently traveling multiple child nodes hierarchy in parallel. At build-time, we introduce a multi-split node generation method to split along three-dimension axes into eight child nodes once, and gather quickly high-quality child nodes even at early construction phase. During traversal, we propose a progressive traversal to fast decide the visiting order for multiple child nodes. Then, we use a dynamic ray transfer to adaptively drive the traversal tasks execution on the GPU. Our experiments with this hierarchy show the construction and traversal performance improvement for ray tracing using MSKD compared to previous methods.


title: Roadside unit deployment for information dissemination in a VANET: an evolutionary approach

abstract: A VANET is a network where each node represents a vehicle equipped with wireless communication technology. This type of network enhances road safety, traffic efficiency, Internet access and many others applications to minimize environmental impact and in general maximize the benefits for the road users. This paper studies a relevant problem in VANETs, known as the deployment of RSUs. A RSU is an access points, used together with the vehicles, to allow information dissemination in the roads. Knowing where to place these RSUs so that a maximum number of vehicles circulating is covered is a challenge. We model the problem as a Maximum Coverage with Time Threshold Problem (MCTTP), and use a genetic algorithm to solve it. The algorithm is tested in four real-world datasets, and compared to a greedy approach previously proposed in the literature. The results show that our approach finds better results than the greedy in all scenarios, with gains up to 11 percentage points.


title: Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

abstract: Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph.


title: Summarising Legal Texts: Sentential Tense And Argumentative Roles

abstract: We report on the SUM project which applies automatic summarisation techniques to the legal domain. We pursue a methodology based on Teufel and Moens (2002) where sentences are classified according to their argumentative role. We describe some experiments with judgments of the House of Lords where we have performed automatic linguistic annotation of a small sample set in order to explore correlations between linguistic features and argumentative roles. We use state-of-the-art NLP techniques to perform the linguistic annotation using XML-based tools and a combination of rule-based and statistical methods. We focus here on the predictive capacity of tense and aspect features for a classifier.


title: Requirements engineering: from craft to discipline

abstract: Getting the right software requirements under the right environment assumptions is a critical precondition for developing the right software. This task is intrinsically difficult. We need to produce a complete, adequate, consistent, and well-structured set of measurable requirements and assumptions from incomplete, imprecise, and sparse material originating from multiple, often conflicting sources. The system we need to consider comprises software and environment components including people and devices. A rich system model may significantly help us in this task. Such model must integrate the intentional, structural, functional, and behavioral facets of the system being conceived. Rigorous techniques are needed for model construction, analysis, exploitation, and evolution. Such techniques should support early and incremental reasoning about partial models for a variety of purposes including satisfaction arguments, property checks, animations, the evaluation of alternative options, the analysis of risks, threats, and conflicts, and traceability management. The tension between technical precision and practical applicability calls for a suitable mix of heuristic, deductive, and inductive forms of reasoning on a suitable mix of declarative and operational specifications. Formal techniques should be deployed only when and where needed, and kept hidden wherever possible. The paper provides a retrospective account of our research efforts and practical experience along this route. Problem-oriented abstractions, analyzable models, and constructive techniques were permanent concerns.


title: Extracting sclera features for cancelable identity verification

abstract: In this paper, we propose a novel sclera template generation, manipulation, and matching scheme for cancelable identity verification. Essentially, a region indicator matrix is generated based on an angular grid reference frame. For binary feature template generation, a random matrix and a local binary patterns (LBP) operator are utilized. Subsequently, the template is manipulated by user-specific random sequence attachment and bit shifting. Finally, matching is performed by a normalized Hamming distance comparison. Some experimental results on UBIRIS v1 database are included with discussion.


title: A safe approximate algorithm for interprocedural aliasing

abstract: During execution, when two or more names exist for the same location at some program point, we call them aliases . In a language which allows arbitrary pointers, the problem of determining aliases at a program point is r-space-hard [Lan92]. We present an algorithm for the Conditional May Alias problem, which can be used to safely approximate Interprocedural May Alias in the presence of pointers. This algorithm is as precise as possible in the worst case and has been implemented in a prototype analysis tool for C programs. Preliminary speed and precision results are presented.


title: A reactive molecular dynamics simulation of the silica-water interface

abstract: We report our study of a silica-water interface using reactive molecular dynamics. This first-of-its-kind simulation achieves length and time scales required to investigate the detailed chemistry of the system. Our molecular dynamics approach is based on the ReaxFF force field of van Duin et al. [J. Phys. Chem. A 107, 3803 (2003)]. The specific ReaxFF implementation (SERIALREAX) and force fields are first validated on structural properties of pure silica and water systems. Chemical reactions between reactive water and dangling bonds on a freshly cut silica surface are analyzed by studying changing chemical composition at the interface. In our simulations, reactions involving silanol groups reach chemical equilibrium in ∼250 ps. It is observed that water molecules penetrate a silica film through a proton-transfer process we call “hydrogen hopping,” which is similar to the Grotthuss mechanism. In this process, hydrogen atoms pass through the film by associating and dissociating with oxygen atoms within bulk...


title: PIECE 2.0: an update for the plant gene structure comparison and evolution database

abstract: PIECE (Plant Intron Exon Comparison and Evolution) is a web-accessible database that houses intron and exon information of plant genes. PIECE serves as a resource for biologists interested in comparing intron-exon organization and provides valuable insights into the evolution of gene structure in plant genomes. Recently, we updated PIECE to a new version, PIECE 2.0 (http://probes.pw.usda.gov/piece or http://aegilops.wheat.ucdavis.edu/piece). PIECE 2.0 contains annotated genes from 49 sequenced plant species as compared to 25 species in the previous version. In the current version, we also added several new features: (i) a new viewer was developed to show phylogenetic trees displayed along with the structure of individual genes; (ii) genes in the phylogenetic tree can now be also grouped according to KOG (The annotation of Eukaryotic Orthologous Groups) and KO (KEGG Orthology) in addition to Pfam domains; (iii) information on intronless genes are now included in the database; (iv) a statistical summary of global gene structure information for each species and its comparison with other species was added; and (v) an improved GSDraw tool was implemented in the web server to enhance the analysis and display of gene structure. The updated PIECE 2.0 database will be a valuable resource for the plant research community for the study of gene structure and evolution.


title: Inferring Traffic Cascading Patterns

abstract: There is an underlying cascading behavior over road networks. Traffic cascading patterns are of great importance to easing traffic and improving urban planning. However, what we can observe is individual traffic conditions on different road segments at discrete time intervals, rather than explicit interactions or propagation (e.g., A→B) between road segments. Additionally, the traffic from multiple sources and the geospatial correlations between road segments make it more challenging to infer the patterns. In this paper, we first model the three-fold influences existing in traffic propagation and then propose a data-driven approach, which finds the cascading patterns through maximizing the likelihood of observed traffic data. As this is equivalent to a submodular function maximization problem, we solve it by using an approximate algorithm with provable near-optimal performance guarantees based on its submodularity. Extensive experiments on real-world datasets demonstrate the advantages of our approach in both effectiveness and efficiency.


title: Regional foremost matching for internet scene images

abstract: We analyze the dense matching problem for Internet scene images based on the fact that commonly only part of images can be matched due to the variation of view angle, motion, objects, etc. We thus propose regional foremost matching to reject outlier matching points while still producing dense high-quality correspondence in the remaining foremost regions. Our system initializes sparse correspondence, propagates matching with model fitting and optimization, and detects foremost regions robustly. We apply our method to several applications, including time-lapse sequence generation, Internet photo composition, automatic image morphing, and automatic rephotography.


title: Customer Attrition Analysis for Financial Services Using Proportional Hazard Models

abstract: This paper studies the topic of customer attrition in the context of a European financial services company. More specifically, we investigate predictors of churn incidence as part of customer relationship management (CRM). We contribute to the existing literature: (1) by combining several different types of predictors into one comprehensive retention model including several ‘new’ types of time-varying covariates related to actual customer behaviour; (2) by analysing churn behaviour based on a truly random sample of the total population using longitudinal data from a data warehouse. Our findings suggest that: (1) demographic characteristics, environmental changes and stimulating ‘interactive and continuous’ relationships with customers are of major concern when considering retention; (2) customer behaviour predictors only have a limited impact on attrition in terms of total products owned as well as the interpurchase time.


title: Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton

abstract: AbstractRobotic and functional electrical stimulation (FES) approaches are used for rehabilitation of walking impairment of spinal cord injured individuals. Although devices are commercially available, there are still issues that remain to be solved. Control of hybrid exoskeletons aims at blending robotic exoskeletons and electrical stimulation to overcome the drawbacks of each approach while preserving their advantages. Hybrid actuation and control have a considerable potential for walking rehabilitation but there is a need of novel control strategies of hybrid systems that adequately manage the balance between FES and robotic controllers. Combination of FES and robotic control is a challenging issue, due to the non-linear behavior of muscle under stimulation and the lack of developments in the field of hybrid control. In this article, a cooperative control strategy of a hybrid exoskeleton is presented. This strategy is designed to overcome the main disadvantages of muscular stimulation: electromechanical delay and change in muscle performance over time, and to balance muscular and robotic actuation during walking.Experimental results in healthy subjects show the ability of the hybrid FES-robot cooperative control to balance power contribution between exoskeleton and muscle stimulation. The robotic exoskeleton decreases assistance while adequate knee kinematics are guaranteed. A new technique to monitor muscle performance is employed, which allows to estimate muscle fatigue and implement muscle fatigue management strategies. Kinesis is therefore the first ambulatory hybrid exoskeleton that can effectively balance robotic and FES actuation during walking. This represents a new opportunity to implement new rehabilitation interventions to induce locomotor activity in patients with paraplegia.Acronym list: 10mWT: ten meters walking test; 6MWT: six minutes walking test; FSM: finite-state machine; t-FSM: time-domain FSM; c-FSM: cycle-domain FSM; FES: functional electrical stimulation; HKAFO: hip-knee-ankle-foot orthosis; ILC: iterative error-based learning control; MFE: muscle fatigue estimator; NILC: Normalized stimulation output from ILC controller; PID: Proportional-Integral-derivative Control; PW: Stimulation pulse width; QUEST: Quebec User Evaluation of Satisfaction with assistive Technology; SCI: Spinal cord injury; TTI: torque-time integral; VAS: Visual Analog Scale.


title: Object detection using strongly-supervised deformable part models

abstract: Deformable part-based models [1, 2] achieve state-of-the-art performance for object detection, but rely on heuristic initialization during training due to the optimization of non-convex cost function. This paper investigates limitations of such an initialization and extends earlier methods using additional supervision. We explore strong supervision in terms of annotated object parts and use it to (i) improve model initialization, (ii) optimize model structure, and (iii) handle partial occlusions. Our method is able to deal with sub-optimal and incomplete annotations of object parts and is shown to benefit from semi-supervised learning setups where part-level annotation is provided for a fraction of positive examples only. Experimental results are reported for the detection of six animal classes in PASCAL VOC 2007 and 2010 datasets. We demonstrate significant improvements in detection performance compared to the LSVM [1] and the Poselet [3] object detectors.


title: A dataset to evaluate underwater image restoration methods

abstract: Image restoration methods have been made to repair images that have some kind of degradation. Most of these methods are designed to deal with the degradation caused by the over-land effects. However, when the images was captured in underwater environments, there are different properties that can degrade the image in unusual ways. This work aims to evaluate how the popular image restoration methods behaves when applied in underwater images with the presence of turbidity in the water. For this, we propose a dataset where we are able to control the amount of image degradation due to underwater properties on a scenario with 3D objects that represents the seabed characteristics. After that, we evaluate the restoration of these methods and their behavior through the image degradation due to turbidity.


title: World Models

abstract: We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io/


title: LoRaWAN Authentication in RADIUS

abstract: This document describes a proposal for adding LoRaWAN support in ::: RADIUS. The purpose is to integrate the LoRaWAN network join procedure ::: with an Authentication, Authorization and Accounting (AAA) ::: infrastructure based on RADIUS.