title: A survey of appearance models in visual object tracking

abstract: Visual object tracking is a significant computer vision task which can be applied to many domains such as visual surveillance, human computer interaction, and video compression. In the literature, researchers have proposed a variety of 2D appearance models. To help readers swiftly learn the recent advances in 2D appearance models for visual object tracking, we contribute this survey, which provides a detailed review of the existing 2D appearance models. In particular, this survey takes a module-based architecture that enables readers to easily grasp the key points of visual object tracking. In this survey, we first decompose the problem of appearance modeling into two different processing stages: visual representation and statistical modeling. Then, different 2D appearance models are categorized and discussed with respect to their composition modules. Finally, we address several issues of interest as well as the remaining challenges for future research on this topic. The contributions of this survey are four-fold. First, we review the literature of visual representations according to their feature-construction mechanisms (i.e., local and global). Second, the existing statistical modeling schemes for tracking-by-detection are reviewed according to their model-construction mechanisms: generative, discriminative, and hybrid generative-discriminative. Third, each type of visual representations or statistical modeling techniques is analyzed and discussed from a theoretical or practical viewpoint. Fourth, the existing benchmark resources (e.g., source code and video datasets) are examined in this survey.


title: Search, interrupted: understanding and predicting search task continuation

abstract: Many important search tasks require multiple search sessions to complete. Tasks such as travel planning, large purchases, or job searches can span hours, days, or even weeks. Inevitably, life interferes, requiring the searcher either to recover the "state" of the search manually (most common), or plan for interruption in advance (unlikely). The goal of this work is to better understand, characterize, and automatically detect search tasks that will be continued in the near future. To this end, we analyze a query log from the Bing Web search engine to identify the types of intents, topics, and search behavior patterns associated with long-running tasks that are likely to be continued. Using our insights, we develop an effective prediction algorithm that significantly outperforms both the previous state-of-the-art method, and even the ability of human judges, to predict future task continuation. Potential applications of our techniques would allow a search engine to pre-emptively "save state" for a searcher (e.g., by caching search results), perform more targeted personalization, and otherwise better support the searcher experience for interrupted search tasks.


title: Diversifying Music Recommendations

abstract: We compare submodular and Jaccard methods to diversify Amazon Music recommendations. Submodularity significantly improves recommendation quality and user engagement. Unlike the Jaccard method, our submodular approach incorporates item relevance score within its optimization function, and produces a relevant and uniformly diverse set.


Evaluation of a Non-Invasive Optical Glucose Monitoring Device


title: Man vs. machine: practical adversarial detection of malicious crowdsourcing workers

abstract: Recent work in security and systems has embraced the use of machine learning (ML) techniques for identifying misbehavior, e.g. email spam and fake (Sybil) users in social networks. However, ML models are typically derived from fixed datasets, and must be periodically retrained. In adversarial environments, attackers can adapt by modifying their behavior or even sabotaging ML models by polluting training data. ::: ::: In this paper, we perform an empirical study of adversarial attacks against machine learning models in the context of detecting malicious crowdsourcing systems, where sites connect paying users with workers willing to carry out malicious campaigns. By using human workers, these systems can easily circumvent deployed security mechanisms, e.g. CAPTCHAs. We collect a dataset of malicious workers actively performing tasks on Weibo, China's Twitter, and use it to develop ML-based detectors. We show that traditional ML techniques are accurate (95%-99%) in detection but can be highly vulnerable to adversarial attacks, including simple evasion attacks (workers modify their behavior) and powerful poisoning attacks (where administrators tamper with the training set). We quantify the robustness of ML classifiers by evaluating them in a range of practical adversarial models using ground truth data. Our analysis provides a detailed look at practical adversarial attacks on ML models, and helps defenders make informed decisions in the design and configuration of ML detectors.


Comparison of Preprocessing Algorithms using an Affordable EEG Headset


title: The Effect Of Hyaluronic Acid To Pain Intensity Rated By Face Pain Scale-Revised On Knee Osteoarthritis Patients

abstract: Introduction: Osteoarthritis (OA), is a chronic degenerative disease of joint associated with cartilage and joints. The most common symptoms of OA patients is joint pain that worsen in activity and relieved with rest. Hyaluronic acid (HA) injection is one of the treatment that used to relieved joint pain in OA patients. ::: Aim : The purpose of this study was to determine the effect of HA injection to pain intensity on knee OA degree 2 and 3 patients in PHC Hospital Surabaya. ::: Methods : Analytic research study design with retrospective cross-sectional study with purposive sampling technique. The respondents in this study were patients with knee OA degree 2 and 3 that had been failed with conservative therapeutics within 3 until 6 months and had been injected with HA in PHC Hospital in Surabaya. The number of respondents that ::: have been collected that fulfill the inclusion and exclusion criteria were 46 respondents. ::: Results : based on data analysis with Wilcoxon Signed Ranks Test there was an effect of HA injection in pain reduction on knee OA degree 2 and 3 patients (p = 0,00001). ::: Conclusion : There is an effect of HA injection in pain reduction on knee OA degree 2 and 3 patients.


title: Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment

abstract: Facial landmark localisation in images captured in-the-wild is an important and challenging problem. The current state-of-the-art revolves around certain kinds of Deep Convolutional Neural Networks (DCNNs) such as stacked U-Nets and Hourglass networks. In this work, we innovatively propose stacked dense U-Nets for this task. We design a novel scale aggregation network topology structure and a channel aggregation building block to improve the model's capacity without sacrificing the computational complexity and model size. With the assistance of deformable convolutions inside the stacked dense U-Nets and coherent loss for outside data transformation, our model obtains the ability to be spatially invariant to arbitrary input face images. Extensive experiments on many in-the-wild datasets, validate the robustness of the proposed method under extreme poses, exaggerated expressions and heavy occlusions. Finally, we show that accurate 3D face alignment can assist pose-invariant face recognition where we achieve a new state-of-the-art accuracy on CFP-FP.


title: Automated Process for Incorporating Drivable Path into Real-Time Semantic Segmentation

abstract: Vision systems are widely used in autonomous vehicle systems due to the rich information that camera sensors provide of the surrounding environment. This paper presents an automatic algorithm to obtain the drivable path of a vehicle operating in urban roads with or without clear lane markings. The developed system projects trajectories obtained during human operation of the vehicle and utilizes these to generate automatic labels for training a semantic based path prediction model. The system segments an urban scenario into 13 categories including vehicles, pedestrian, undrivable road, other categories relevant to urban roads, and a new class for a path proposal. The drivable path information is essential particularly in unstructured scenarios, and is critical for an intelligent vehicle system to make sound driving decisions. The path proposal category is a car-width drivable lane estimated to be safe to drive for the vehicle under consideration. The data collection, model training and inference process requires only images from a monocular camera and odometry from a low-cost IMU combined with a wheel encoder. The algorithm has been successfully demonstrated on the Sydney University campus, which is a challenging environment without clear road markings. The algorithm was demonstrated to run in real-time, proving its applicability for intelligent vehicles.


title: Using artificial intelligence techniques to emulate the creativity of a portrait painter

abstract: We present three new machine learning based artificial intelligence (AI) techniques, which we have added to our parameterised computational painterly rendering framework and show their benefits in computational creativity and non-photorealistic rendering (NPR) of portraits. Traditional portrait artists use a specific but open human creativity, vision, technical and perception methodologies to create a painterly portrait of a live or photographed sitter. By incorporating more open ended creative, semantic and concept blending techniques, these new neural network based AI techniques allow us to better model the creative cognitive thinking process that human painters employ. We analyse these AI based methods for their operating principles and outputs that together along with our parameterised NPR modules can be relevant to the field of computational creativity research and computational painterly rendering.


title: A rapid prototyping software infrastructure for user interfaces in ubiquitous augmented reality

abstract: Recent user interface concepts, such as multimedia, multimodal, wearable, ubiquitous, tangible, or augmented-reality-based (AR) interfaces, each cover different approaches that are all needed to support complex human–computer interaction. Increasingly, an overarching approach towards building what we call ubiquitous augmented reality (UAR) user interfaces that include all of the just mentioned concepts will be required. To this end, we present a user interface architecture that can form a sound basis for combining several of these concepts into complex systems. We explain in this paper the fundamentals of DWARF’s user interface framework (DWARF standing for distributed wearable augmented reality framework) and an implementation of this architecture. Finally, we present several examples that show how the framework can form the basis of prototypical applications.


title: Statistical Machine Translation Features with Multitask Tensor Networks

abstract: We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters jointly. Each of our proposed methods results in significant improvements that are complementary. The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and Chinese-English translation over a state-of-the-art system that already includes neural network features.


title: Product Feature Extraction with a Combined Approach

abstract: Product review mining is the process of extracting opinions of customers in reviews which are expressed by natural language. As the first phrase of product review mining, product feature extraction decides the quality of subsequent phrases. In this paper, we build a combined approach based on bootstrapping and ID3, ID3 is used as a feature selection algorithm in the iteration of bootstrapping. Given the seed set and classification feature set, the combined approach can automatically extract textual patterns with different structures, and avoid the design of textual pattern structures and the design of similarity function among textual patterns. We implement an automated product feature extraction system with the combined approach. Compare to previous study, our system achieves higher precision and better portability.


title: Drishti: an integrated navigation system for visually impaired and disabled

abstract: Drishti is a wireless pedestrian navigation system. It integrates several technologies including wearable computers, voice recognition and synthesis, wireless networks, Geographic Information System (GIS) and Global positioning system (GPS). Drishti augments contextual information to the visually impaired and computes optimized routes based on user preference, temporal constraints (e.g. traffic congestion), and dynamic obstacles (e.g. ongoing ground work, road blockade for special events). The system constantly guides the blind user to navigate based on static and dynamic data. Environmental conditions and landmark information queried from a spatial database along their route are provided on the fly through detailed explanatory voice cues. The system also provides capability for the user to add intelligence, as perceived by, the blind user, to the central server hosting the spatial database. Our system is supplementary to other navigational aids such as canes, blind guide dogs and wheel chairs.


title: Enriching very large ontologies using the WWW

abstract: This paper explores the possibility to exploit text on the world wide web in order to enrich the concepts in existing ontologies. First, a method to retrieve documents from the WWW related to a concept is described. These document collections are used 1) to construct topic signatures (lists of topically related words) for each concept in WordNet, and 2) to build hierarchical clusters of the concepts (the word senses) that lexicalize a given word. The overall goal is to overcome two shortcomings of WordNet: the lack of topical links among concepts, and the proliferation of senses. Topic signatures are validated on a word sense disambiguation task with good results, which are improved when the hierarchical clusters are used.


title: A Multipatch Malaria Model with Logistic Growth Populations

abstract: In this paper, we propose a multi-patch model to study the effects of population dispersal on the spatial spread of malaria between patches. The basic reproduction number [Formula: see text] is derived and it is shown that the disease-free equilibrium is locally asymptotically stable if [Formula: see text] and unstable if [Formula: see text]. Bounds on the disease-free equilibrium and [Formula: see text] are given. A sufficient condition for the existence of an endemic equilibrium when [Formula: see text] is obtained. For the two-patch submodel, the dependence of [Formula: see text] on the movement of exposed, infectious, and recovered humans between the two patches is investigated. Numerical simulations indicate that travel can help the disease to become endemic in both patches, even though the disease dies out in each isolated patch. However, if travel rates are continuously increased, the disease may die out again in both patches.


title: A microfabricated wedge-shaped adhesive array displaying gecko-like dynamic adhesion, directionality and long lifetime

abstract: Gecko adhesion has become a paradigmatic example of bio-inspired engineering, yet among the many gecko-like synthetic adhesives (GSAs), truly gecko-like performance remains elusive. Many GSAs have previously demonstrated one or two features of the gecko adhesive. We present a new wedge-shaped GSA that exhibits several gecko-like properties simultaneously: directional features; zero force at detachment; high ratio of detachment force to preload force; non-adhesive default state; and the ability to maintain performance while sliding, even after thousands of cycles. Individual wedges independently detach and reattach during sliding, resulting in high levels of shear and normal adhesion during drag. This behaviour provides a non-catastrophic failure mechanism that is desirable for applications such as climbing robots where sudden contact failure would result in serious falls. The effects of scaling patch sizes up to tens of square centimetres are also presented and discussed. Patches of 1 cm2 had an adhesive pressure of 5.1 kPa while simultaneously supporting 17.0 kPa of shear. After 30 000 attachment/detachment cycles, a patch retained 67 per cent of its initial adhesion and 76 per cent of its initial shear without cleaning. Square-based wedges of 20 μm and 50 μm are manufactured in a moulding process where moulds are fabricated using a dual-side, dual-angle lithography process on quartz wafers with SU-8 photoresist as the mould material and polydimethylsiloxane as the cast material.


title: Scalability Problems of Simple Genetic Algorithms

abstract: Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm-namely elitism, niching, and restricted mating are not significantly improving the scalability problems.


title: Bayesian Locality Sensitive Hashing for Fast Similarity Search

abstract: Given a collection of objects and an associated similarity measure, the all-pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user-specified threshold. Locality-sensitive hashing (LSH) based methods have become a very popular approach for this problem. However, most such methods only use LSH for the first phase of similarity search - i.e. efficient indexing for candidate generation. In this paper, we present BayesLSH, a principled Bayesian algorithm for the subsequent phase of similarity search - performing candidate pruning and similarity estimation using LSH. A simpler variant, BayesLSH-Lite, which calculates similarities exactly, is also presented. Our algorithms are able to quickly prune away a large majority of the false positive candidate pairs, leading to significant speedups over baseline approaches. For BayesLSH, we also provide probabilistic guarantees on the quality of the output, both in terms of accuracy and recall. Finally, the quality of BayesLSH's output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation, unlike standard approaches. For two state-of-the-art candidate generation algorithms, AllPairs and LSH, BayesLSH enables significant speedups, typically in the range 2x-20x for a wide variety of datasets.


title: Characterizing RNA Secondary-Structure Features and Their Effects on Splice-Site Prediction

abstract: RNA molecules are distinguished by their sequence composition and by their three-dimensional shape, called the secondary structure. The secondary structure of a pre-mRNA sequence may have a strong influence on gene splicing. In our previous work, we showed that a splice-site model employing sequence features built using our feature generation algorithm was very effective in predicting splice sites. The generated sequence features also contained biologically relevant features. In this paper, we extend the feature generation algorithm to construct secondary-structure features. These features capture the nucleotide pairing tendency in the splice-site neighborhood. We extend the splice-site model to include both pre-mRNA sequence and structure characteristics. The new model significantly outperforms the sequence-based features model. The identified secondary-structure features capture biologically relevant signals such as splicing silencers. We also found these signals to prefer specific regions around the splice-site neighborhood and we detail their preference.


title: The state of CRM adoption by the financial services in the UK: an empirical investigation

abstract: In recent years, organisations have begun to realise the importance of knowing their customers better. Customer relationship management (CRM) is an approach to managing customer related knowledge of increasing strategic significance. The successful adoption of IT-enabled CRM redefines the traditional models of interaction between businesses and their customers, both nationally and globally. It is regarded as a source for competitive advantage because it enables organisations to explore and use knowledge of their customers and to foster profitable and long-lasting one-to-one relationships. This paper discusses the results of an exploratory survey conducted in the UK financial services sector; it discusses CRM practice and expectations, the motives for implementing it, and evaluates post-implementation experiences. It also investigates the CRM tools functionality in the strategic, process, communication, and business-to-customer (B2C) organisational context and reports the extent of their use. The results show that despite the anticipated potential, the benefits from such tools are rather small.


title: A thermal Object Tracking benchmark

abstract: Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.


title: Capacity management for streaming applications over cloud infrastructures with micro billing models

abstract: Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming data at unprecedented rates. Typical applications include smart cities & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Analysis of stream data involves: (i) execution of a number of operations on a time/sample window – e.g. min./max./avg., filtering, etc, (ii) a need to combine a number of such operations together, (iii) event-driven execution of operations, generally over short time durations, (iv) operation correlations across multiple data streams. The use of such operations does not fit well in the per-hour or per-minute cloud billing models currently available from cloud providers – with some notable exceptions (e.g. Amazon AWS). In this paper we discuss how micro-billing and sub-second resource allocation can be used in the context of streaming applications and how micro-billing models bring challenges to capacity management on cloud infrastructures.


Snow Simulation


Dynamic measurement errors correction adaptive to noises of a sensor


Ten Years of Knowledge Harvesting: Lessons and Challenges


title: A switching mode power supply with digital pulse density modulation control

abstract: The aim of this work is the design of a digital pulse density modulation control for switch mode power supplies. It is based on a third order sigma-delta modulator controlling a DC- DC buck-converter to be used in integrated circuit applications, but can be straightforwardly applied to other type of converters. The performance of the controller are compared with that than an equivalent digital pulse width modulator.


title: Future Energy Systems: Integrating Renewable Energy Sources into the Smart Power Grid Through Industrial Electronics

abstract: This paper discusses about integrating renewable energy sources into the smart power grid through industrial electronics. This paper discusses photovoltaic power, wind energy conversion, hybrid energy systems, and tidal energy conversion.


title: Exploiting Fine-Grained Co-Authorship for Personalized Citation Recommendation

abstract: In the era of big scholarly data, citation recommendation is playing an increasingly significant role as it solves information overload issues by automatically suggesting relevant references that align with researchers’ interests. Many state-of-the-art models have been utilized for citation recommendation, among which graph-based models have garnered significant attention, due to their flexibility in integrating rich information that influences users’ preferences. Co-authorship is one of the key relations in citation recommendation, but it is usually regarded as a binary relation in current graph-based models. This binary modeling of co-authorship is likely to result in information loss, such as the loss of strong or weak relationships between specific research topics. To address this issue, we present a fine-grained method for co-authorship modeling that incorporates the co-author network structure and the topics of their published articles. Then, we design a three-layered graph-based recommendation model that integrates fine-grained co-authorship as well as author–paper, paper–citation, and paper-keyword relations. Our model effectively generates query-oriented recommendations using a simple random walk algorithm. Extensive experiments conducted on a subset of the anthology network data set for performance evaluation demonstrate that our method outperforms other models in terms of both Recall and NDCG.


title: A HOG-based Real-time and Multi-scale Pedestrian Detector Demonstration System on FPGA

abstract: Pedestrian detection will play a major role in future driver assistance and autonomous driving. One powerful algorithm in this field uses HOG features to describe the specific properties of pedestrians in images. To determine their locations, features are extracted and classified window-wise from different scales of an input image. The results of the classification are finally merged to remove overlapping detections. The real-time execution of this method requires specific FPGA- or ASIC-architectures. Recent work focused on accelerating the feature extraction and classification. Although merging is an important step in the algorithm, it is only rarely considered in hardware implementations. A reason for that could be its complexity and irregularity that is not trivial to implement in hardware. In this paper, we present a new bottom-up FPGA architecture that maps the full HOG-based algorithm for pedestrian detection including feature extraction, SVM classification, and multi-scale processing in combination with merging. For that purpose, we also propose a new hardware-optimized merging method. The resulting architecture is highly efficient. Additionally, we present an FPGA-based full real-time and multi-scale pedestrian detection demonstration system.


title: Bioinspired tracking control of high speed nonholonomic ground vehicles

abstract: The behavior of nature's predators is considered for designing a high speed tracking controller for nonholonomic vehicles, whose dynamics are represented using a unicycle model. To ensure that the vehicle behaves intuitively and mimics the biologically inspired predator-prey interaction, saturation constraints based on Ackermann steering kinematics are added. A new strategy for mapping commands back into a viable envelope is introduced, and the restrictions are accounted for using Lyapunov stability criteria. Following verification of the saturation constraints, the proposed algorithm was implemented on a testing platform. Stable trajectories of up to 9m/s were achieved. The results presented show that the algorithm demonstrates significant promise in high speed trajectory tracking with obstacle avoidance.


title: Neural network system for face recognition

abstract: An image recognition method based on neural network system is proposed. This method uses the permutative coding technique for image preprocessing and neural classifier for image recognition. We have proposed the permutative coding technique to make recognition process invariant to small displacements of the object in the image. The system was tested on the ORL database. This database contains 400 face images of 40 persons. 200 images are used for training and 200 for recognition. The error rate of 0.1% for face recognition was obtained. This method was tested also with 40, 80, 120 and 160 images for system training and the rest images for recognition. The error rates 16.1%, 7.09%, 2.15% and 1.4% were obtained respectively.


title: Computing pure nash equilibria in graphical games via markov random fields

abstract: We present a reduction from graphical games to Markov random fields so that pure Nash equilibria in the former can be found by statistical inference on the latter. Our result, when combined with the junction tree algorithm for statistical inference, yields a unified proof of all previously known tractable cases of the NP-complete problem of finding pure Nash equilibria in graphical games, but also implies efficient algorithms for new classes, such as the games with O(log n) treewidth. Furthermore, this important problem becomes susceptible to a wealth of sophisticated and empirically successful techniques from Machine Learning.


title: Illumination robust interest point detection

abstract: Most interest point detection algorithms are highly sensitive to illumination variations. This paper presents a method to find interest points robustly even under large non-uniform photometric changes. The method, which we call illumination robust feature extraction transform (IRFET), determines salient interest points in an image by calculating and analyzing contrast signatures. A contrast signature shows the response of an interest point detector with respect to a set of contrast stretching functions. The IRFET is generic and can be used with most interest point detectors. In this paper, we demonstrate that the IRFET improves the repeatability rate of the Harris corner detector significantly (by around 25% on average in the experiments).


title: Students’ opinions on blended learning and its implementation in terms of their learning styles

abstract: The purpose of this article is to examine students' views on the blended learning method and its use in relation to the students' individual learning style. The study was conducted with 31 senior students. Web based media together with face to face classroom settings were used in the blended learning framework. A scale of Students' Views on Blended Learning and its implementation, Kolb's Learning Style Inventory, Pre-Information Form and open ended questions were used to gather data. The majority of the students' fell into assimilators, accommodators and convergers learning styles. Results revealed that students' views on blended learning method and its use are quite positive.


title: A critical review of the effects of de-noising algorithms on MRI brain tumor segmentation

abstract: One can find in the literature numerous techniques to reduce noise in Magnetic Resonance Images (MRI). This paper critically reviews modern de-noising algorithms (Gaussian filter, anisotropic diffusion, wavelet, and non-local mean) in terms of their efficiency, statistical assumptions, and their ability to improve brain tumor segmentation results. We will show that although different techniques do reduce the noise, many generate artifacts that are incompatible with precise brain tumor segmentation. We also show that the non-local means algorithm is the best de-noising technique for brain tumor segmentation.


title: Digital halftoning with space filling curves

abstract: This paper introduces a new digital halftoning technique that uses space filling curves to generate aperiodic patterns of clustered dots. This method allows the parameterization of the size of pixel clusters, which can vary in one pixel steps. The algorithm unifies, in this way, the dispersed and clustered-dot dithering techniques.


title: Emerging Applications of Interferometric Synthetic Aperture Radar (InSAR) in

abstract: Interferometric synthetic aperture radar (InSAR) is a powerful geodetic tool used to construct digital elevation models of the earth’s topography and to image centimeter–scale displacements associated with crustal deformation and the flow of ice sheets. The past decade has seen significant improvements in our understanding of earthquakes, volcanoes, and glaciers as a direct result of this technology. Geomorphology and hydrology can also benefit from InSAR. A small but growing body of work shows that it is possible to interferometrically measure centimeter–scale motions associated with unstable slopes, land subsidence, fluctuating soil moisture and water levels, and deforming river–ice cover. Maps of interferometric correlation provide information about the structure of snow and reveal areas disturbed by erosion, sedimentation, flooding, snowfall, and aufeis growth. At present, such techniques are underdeveloped and largely overlooked by the geographic and radar communities. This article reviews these emer...


title: Latency reduction with short processing time and short TTI length

abstract: Latency is an important component in fifth generation (5G) study. In this paper, we focus on the latency reduction with short processing time and short TTI length. The maximum time advance (TA) and transport block size (TBS) are introduced to shorten the processing time. Short processing time with frame structure unchanged results into less standardization efforts but is hard to support stricter requirement on latency such as less than 1ms. Short TTI length is taken into account and 2-symbol length can achieve a tradeoff between overhead and performance gain. A short PDCCH design is proposed and a two-stage DCI is introduced to handle the large number of blind decodes within a subframe due to the change of TTI length. Both short processing time and short TTI length are an effective way to reduce latency.


title: Compressed Linear Algebra for Large-Scale Machine Learning

abstract: Large-scale machine learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications to converge to an optimal model. It is crucial for performance to fit the data into single-node or distributed main memory. General-purpose, heavy- and lightweight compression techniques struggle to achieve both good compression ratios and fast decompression speed to enable block-wise uncompressed operations. Hence, we initiate work on compressed linear algebra (CLA), in which lightweight database compression techniques are applied to matrices and then linear algebra operations such as matrix-vector multiplication are executed directly on the compressed representations. We contribute effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Our experiments show that CLA achieves in-memory operations performance close to the uncompressed case and good compression ratios that allow us to fit larger datasets into available memory. We thereby obtain significant end-to-end performance improvements up to 26x or reduced memory requirements.


title: Contact-free Measurement of Heart Rate Variability via a Microwave Sensor

abstract: Measures of heart rate variability (HRV) are widely used to assess autonomic nervous system (ANS) function. HRV can be recorded via electrocardiography (ECG), which is both non-invasive and widely available. However, ECG needs three electrodes touching the body of the subjects, which makes them feel nervous and uncomfortable, thus potentially affecting the recording. Contact-free detection of the heartbeat via a microwave sensor constitutes another means of determining the timing of cardiac cycles by continuous monitoring of mechanical contraction of the heart. This technique can measure the heartbeat without any electrodes touching human body and penetrate the clothes at some distances, which in some instances may prove a practical basis for HRV analysis. Comparison of 5-minute recordings demonstrated that there were no significant differences in the temporal, frequency domains and in non-linear dynamic analysis of HRV measures derived from heartbeat and ECG, which suggested this technique may prove a practical alternative to ECG for HRV analysis.


title: GPU centric extensions for parallel strongly connected components computation

abstract: Finding Strongly Connected Components (SCC) of a directed graph is a fundamental graph problem. Many of the state-of-the-art sequential algorithms use depth-first search (DFS) to find SCCs. Since, in general DFS is hard to parallelize, researchers rely on other approaches such as Forward-Backward-Trim and Coloring. In this work, we have extended the two state-of-the-art multicore parallel algorithms to take advantage of the highly powerful GPU devices. Unfortunately, state-of-the-art parallel implementations have three performance limitations: (i) selection of ineffective pivots for running multiple forward-backward, (ii) load imbalance across threads while computing reachability closures, and (iii) serializability bottleneck while computing forward-backward sets. We address the first limitation using an improved pivot selection scheme. This improves the chances of finding the largest SCC in real-world graphs. We handle the other two limitations by adding parallelism during closure computation. This improves load balance across warp-threads and reduces thread-serialization. In effect, the resultant codes perform much better compared to the state-of-the-art. The evaluation results show that our methods achieve up to 8× speedup over Tarjan's sequential algorithm and up to 2× speedup over a previous CUDA implementation.


title: Functional and clinical neuroanatomy of morality

abstract: Morality is among the most sophisticated features of human judgement, behaviour and, ultimately, mind. An individual who behaves immorally may violate ethical rules and civil rights, and may threaten others’ individual liberty, sometimes becoming violent and aggressive. In recent years, neuroscience has shown a growing interest in human morality, and has advanced our understanding of the cognitive and emotional processes involved in moral decisions, their anatomical substrates and the neurology of abnormal moral behaviour. In this article, we review research findings that have provided a key insight into the functional and clinical neuroanatomy of the brain areas involved in normal and abnormal moral behaviour. The ‘moral brain’ consists of a large functional network including both cortical and subcortical anatomical structures. Because morality is a complex process, some of these brain structures share their neural circuits with those controlling other behavioural processes, such as emotions and theory of mind. Among the anatomical structures implicated in morality are the frontal, temporal and cingulate cortices. The prefrontal cortex regulates activity in subcortical emotional centres, planning and supervising moral decisions, and when its functionality is altered may lead to impulsive aggression. The temporal lobe is involved in theory of mind and its dysfunction is often implicated in violent psychopathy. The cingulate cortex mediates the conflict between the emotional and the rational components of moral reasoning. Other important structures contributing to moral behaviour include the subcortical nuclei such as the amygdala, hippocampus and basal ganglia. Brain areas participating in moral processing can be influenced also by genetic, endocrine and environmental factors. Hormones can modulate moral behaviour through their effects on the brain. Finally, genetic polymorphisms can predispose to aggressivity and violence, arguing for a genetic-based predisposition to morality. Because abnormal moral behaviour can arise from both functional and structural brain abnormalities that should be diagnosed and treated, the neurology of moral behaviour has potential implications for clinical practice and raises ethical concerns. Last, since research has developed several neuromodulation techniques to improve brain dysfunction (deep brain stimulation, transcranial magnetic stimulation and transcranial direct current stimulation), knowing more about the ‘moral brain’ might help to develop novel therapeutic strategies for neurologically based abnormal moral behaviour. ::: ::: * Abbreviations ::: : COMT ::: : catechol-O-methyltransferase ::: tDCS ::: : transcranial direct current stimulation ::: MAOA ::: : monoamine oxidase A


title: Microplastics Alter the Properties and Sinking Rates of Zooplankton Faecal Pellets

abstract: Plastic debris is a widespread contaminant, prevalent in aquatic ecosystems across the globe. Zooplankton readily ingest microscopic plastic (microplastic, < 1 mm), which are later egested within their faecal pellets. These pellets are a source of food for marine organisms, and contribute to the oceanic vertical flux of particulate organic matter as part of the biological pump. The effects of microplastics on faecal pellet properties are currently unknown. Here we test the hypotheses that (1) faecal pellets are a vector for transport of microplastics, (2) polystyrene microplastics can alter the properties and sinking rates of zooplankton egests and, (3) faecal pellets can facilitate the transfer of plastics to coprophagous biota. Following exposure to 20.6 μm polystyrene microplastics (1000 microplastics mL–1) and natural prey (∼1650 algae mL–1) the copepod Calanus helgolandicus egested faecal pellets with significantly (P < 0.001) reduced densities, a 2.25-fold reduction in sinking rates, and a higher pr...


title: Cuboid dielectric resonator antenna array placed on # slots for LTE femtocell base stations

abstract: A Cuboid Dielectric Resonator Antenna fed by microstrip feed line is studied. The dielectric resonator of the antenna is made by a material with high dielectric constant ∊ r =30. The impedance matching of dielectric resonator can be improved by using a # slot in the ground. In additional, a two elements array is used to enhance the performance of the antenna. This proposed antenna is suitable for long-term evolution (LTE) band 7 and LTE band 38 femtocell base stations with impedance bandwidth more than 200 MHz.


title: Deep convolutional priors for indoor scene synthesis

abstract: We present a convolutional neural network based approach that learns object placement priors from a semantically-enriched top-down representation of indoor scenes.


title: Pseudo Block Coded Single-Carrier Transmission Using Frequency-Domain Block Signal Detection

abstract: Recently, we proposed a pseudo block coded single-carrier (PBC-SC) transmission using minimum mean square error (MMSE) based frequency-domain equalization and decoding (FDED) and show that MMSE based FDED achieves BER performance superior to 2-step decoding (which performs frequency-domain equalization and hard decision decoding separately). However, there still exists a large BER performance gap between MMSE based FDED and MF bound due to the presence of residual inter-symbol interference (ISI) after MMSE based FDED. In this paper, in order to narrow the performance gap, we propose frequency-domain block signal detection (FDBD) aided FDEDs for PBC-SC transmission: frequency-domain iterative interference cancellation (FDIC), maximum likelihood block sig-nal detection employing QR decomposition and M algorithm (QRM-MLBD) and belief propagation (BP). We compare, by computer simulation, the BER performances and the computa-tional complexities of three FDBD aided FDED schemes. It is shown that QRM-MLBD and BP can achieve almost the same BER performance as MF bound with much lower computational complexity than MMSE based FDED.


title: Extractive Multi-Document Summarization Using population-based Multicriteria Optimization

abstract: Abstract Multi-document summarization is the process of extracting salient information from a set of source texts and present that information to the user in a condensed form. In this paper, we propose a multi-document summarization system which generates an extractive generic summary with maximum relevance and minimum redundancy by representing each sentence of the input document as a vector of words in Proper Noun, Noun, Verb and Adjective set. Five features, such as TF_ISF, Aggregate Cross Sentence Similarity, Title Similarity, Proper Noun and Sentence Length associated with the sentences, are extracted, and scores are assigned to sentences based on these features. Weights that can be assigned to different features may vary depending upon the nature of the document, and it is hard to discover the most appropriate weight for each feature, and this makes generation of a good summary a very tough task without human intelligence. Multi-document summarization problem is having large number of decision parameters and number of possible solutions from which most optimal summary is to be generated. Summary generated may not guarantee the essential quality and may be far from the ideal human generated summary. To address this issue, we propose a population-based multicriteria optimization method with multiple objective functions. Three objective functions are selected to determine an optimal summary, with maximum relevance, diversity, and novelty, from a global population of summaries by considering both the statistical and semantic aspects of the documents. Semantic aspects are considered by Latent Semantic Analysis (LSA) and Non Negative Matrix Factorization (NMF) techniques. Experiments have been performed on DUC 2002, DUC 2004 and DUC 2006 datasets using ROUGE tool kit. Experimental results show that our system outperforms the state of the art works in terms of Recall and Precision.


title: What and Where: 3D Object Recognition with Accurate Pose

abstract: Many applications of 3D object recognition, such as augmented reality or robotic manipulation, require an accurate solution for the 3D pose of the recognized objects. This is best accomplished by building a metrically accurate 3D model of the object and all its feature locations, and then fitting this model to features detected in new images. In this chapter, we describe a system for constructing 3D metric models from multiple images taken with an uncalibrated handheld camera, recognizing these models in new images, and precisely solving for object pose. This is demonstrated in an augmented reality application where objects must be recognized, tracked, and superimposed on new images taken from arbitrary viewpoints without perceptible jitter. This approach not only provides for accurate pose, but also allows for integration of features from multiple training images into a single model that provides for more reliable recognition.


title: Reference reconciliation in complex information spaces

abstract: Reference reconciliation is the problem of identifying when different references (i.e., sets of attribute values) in a dataset correspond to the same real-world entity. Most previous literature assumed references to a single class that had a fair number of attributes (e.g., research publications). We consider complex information spaces: our references belong to multiple related classes and each reference may have very few attribute values. A prime example of such a space is Personal Information Management, where the goal is to provide a coherent view of all the information on one's desktop.Our reconciliation algorithm has three principal features. First, we exploit the associations between references to design new methods for reference comparison. Second, we propagate information between reconciliation decisions to accumulate positive and negative evidences. Third, we gradually enrich references by merging attribute values. Our experiments show that (1) we considerably improve precision and recall over standard methods on a diverse set of personal information datasets, and (2) there are advantages to using our algorithm even on a standard citation dataset benchmark.


title: THE RECENT USE OF HEART RATE VARIABILITY FOR RESEARCH

abstract: Heart rate variability (HRV) is a non-invasive method that analysis cardiac autonomic regulation1,2.The use of this method was previously validated in physiological experiments3,4,which performed pharmacological stimulation of the sympathetic and parasympathetic nervous systems and made relationship with the traditional time and frequency domain analysis of HRV, confirming the use of this method to evaluate cardiac autonomic regulation. Moreover, HRV has also been used to predict mortality after cardiacevents5 and it has been considered as a marker of stress6. An easy and economic access to HRV analysis through heart rate monitors, which are well recognized in the literature7, 8 helped researches to use this method to evaluate cardiac autonomic regulation.In the last 15 years this method has received great attention.After a careful search on Pubmed/Medline, the term “heart rate variability” was present in 1,013 manuscripts in 2014, in 2013 this number was 972 and in 2012 it was 7299. We realize that each year the number of groups that use HRV in their studies has considerably increased. In this context, I raise points that are worth to mention. Firstly, advantages includeadditional information for cardiac autonomic responses in pathological situations10-12, during physiological stress13,14, association with social function15 for instance. It is currently clear that autonomic nervous system is related to several conditions. Nevertheless, before starting to study HRV analysis,relevant details of this method should be discussed in order to avoid misunderstanding of results. For example, HRV consider the interval between consecutive heart beats2, reflecting parasympathetic and sympathetic cardiac autonomic modulation, it does not directly measures sympathetic or parasympathetic activity. There are different methods that quantify it, i.e. plasma levels of norepinephrine and epinephrine16, renal sympathetic nerve activity17and parasympathetic nerve activity18. In 1996, the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology published methodological considerations regarding traditional time and frequency domain analysis of HRV2. This seminal publication aimed to permit appropriate comparisons.Since then, new methods related to the chaos theory applied on organic systems and heart rate dynamics has been investigated19. This year the European Society of Cardiology together with the European Heart Rhythm Association and co-endorsed by the Asia Pacific Heart Rhythm Society reviewed the literature abovementioned in 199619. They addressed the use of non-conventional methods (nonlinear) which had been used in a significant number of patients (more than 200 cases) finding only 21 studies from the thousands already published. They described in details nonlinear methods as Detrended fluctuation analysis with a1 and a2 components, Hurst Exponent, Correlation Dimension and Largest Lyapunov Exponent. In this sense, I take this opportunity to reinforce attention of researchers around the world to keep being extremely careful when using HRV for their analysis. Important factors are necessary to consider, such as sample frequency to use when digitalizing the electrocardiogram, filtering records, caution with stationary data andwhether to interpolate an electrocardiogram to enhance accuracy of R wave. Therefore, investigators around the world will keep helping us to discover the unlimited mysteries of science. The current number of the Journal of Human Growth and Development is composed by a total of 15 original manuscripts. The journal keeps


Traffic Anomaly Detection Using KMeans Clustering


title: iLock: Immediate and Automatic Locking of Mobile Devices against Data Theft

abstract: Mobile device losses and thefts are skyrocketing. The sensitive data hosted on a lost/stolen device are fully exposed to the adversary. Although password-based authentication mechanisms are available on mobile devices, many users reportedly do not use them, and a device may be lost/stolen while in the unlocked mode. This paper presents the design and evaluation of iLock, a secure and usable defense against data theft on a lost/stolen mobile device. iLock automatically, quickly, and accurately recognizes the user's physical separation from his/her device by detecting and analyzing the changes in wireless signals. Once significant physical separation is detected, the device is immediately locked to prevent data theft. iLock relies on acoustic signals and requires at least one speaker and one microphone that are available on most COTS (commodity-off-the-shelf) mobile devices. Extensive experiments on Samsung Galaxy S5 show that iLock can lock the device with negligible false positives and negatives.


title: Slingshot: deploying stateful services in wireless hotspots

abstract: Given a sufficiently good network connection, even a handheld computer can run extremely resource-intensive applications by executing the demanding portions on a remote server. At first glance, the increasingly ubiquitous deployment of wireless hotspots seems to offer the connectivity needed for remote execution. However, we show that the backhaul connection from the hotspot to the Internet can be a prohibitive bottleneck for interactive applications. To eliminate this bottleneck, we propose a new architecture, called Slingshot, that replicates remote application state on surrogate computers co-located with wireless access points. The first-class replica of each application executes on a remote server owned by the handheld user; this offers a safe haven for application state in the event of surrogate failure. Slingshot deploys second-class replicas on nearby surrogates to improve application response time. A proxy on the handheld broadcasts each application request to all replicas and returns the first response it receives. We have modified a speech recognizer and a remote desktop to use Slingshot. Our results show that these applications execute 2.6 times faster with Slingshot than with remote execution.


title: Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

abstract: In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.


Applications of causally defined direct and indirect effects in mediation analysis using SEM


title: Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation

abstract: Skin cancer is a major public health problem, as is the most common type of cancer and represents more than half of cancer diagnoses worldwide. Early detection influences the outcome of the disease and motivates our work. We investigate the composition of CNN committees and data augmentation for the the ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards Melanoma Detection) facing the peculiarities of dealing with such a small, unbalanced, biological database. For that, we explore committees of Convolutional Neural Networks trained over the ISBI challenge training dataset artificially augmented by both classical image processing transforms and image warping guided by specialist knowledge about the lesion axis and improve the final classifier invariance to common melanoma variations.


title: The high prevalence of thyroid dysfunction in psychiatric inpatients.

abstract: INTRODUCTION ::: The relation of the neuroendocrine system with the psychiatric disorders has been described by different authors throughout history. ::: ::: ::: OBJECTIVES ::: To evaluate the presence of thyroid disfunction in psychiatric inpatients. ::: ::: ::: METHODS ::: Observacional, descriptive, retrospective study of all the psychiatric inpatients in the UHP of Vigo during the year 2006, in which the presence of thyroid dysfunction is evaluated immediately when they are admitted. ::: ::: ::: RESULTS ::: Prevalence of thyroide dysfunction is 7.5%, representing the hypothyroidism a 6.4%, and the hyperthyroidism a 1.1%. The presence of thyroid dysfunction calculated by sex was significantly superior (p=0.010) for the feminine sex (10.1%), that masculine sex (2.3%). No specific associations were found between psychiatric diagnosis, years of evolution, toxic consume or treatment and endocrine abnormalities. ::: ::: ::: CONCLUSIONS ::: Given the high prevalence of thyroid dysfunction in psychiatric inpatients obtained in our study we advised the screening of thyroid hormones to an under-population in which is feminine sex, people who present mood disorders,unipolar or bipolar disorder, especially in rapid cycling or mixed episodies; in refractay mood disease, in the psychotics patients and whom they are to treatment with psychiatric drugs, especially lithium.


title: Automated Functional Safety Analysis of Automated Driving Systems

abstract: In this paper, we present a method to assess functional safety of architectures for Automated Driving Systems (ADS). The ISO 26262 standard defines requirements and processes in support of achieving functional safety of passenger vehicles, but does not address in particular autonomous driving functions. Autonomous driving will bring with it a number of fundamental changes affecting functional safety. First, there will no longer be a driver capable of controlling the vehicle in case of a failure of the ADS. Second, the hardware and software architectures will become more complex and flexible than those used for conventional vehicles. We present an automated method to assert functional safety of ADS systems in the spirit of ISO 26262 in light of these changes. The approach is model-based and implemented in the QuantUM analysis tool. We illustrate its use in functional safety analysis using a proposed practical ADS architecture and address, in particular, architectural variant analysis.


title: Speed control of DC motor using IGBT

abstract: DC Motor speed control is carried out by use of Four Quadrant Chopper drive. Insulated Gate Bipolar Transistors are used for speed control of the motor and the IGBT triggering is carried out by use of PWM converters under various loading conditions and by varying armature voltage and field voltage. The above mentioned experiment was again carried out using Thyristors and a comparative study was made.


title: Consciousness of subjective time in the brain

abstract: “Mental time travel” refers to conscious experience of remembering the personal past and imagining the personal future. Little is known about its neural correlates. Here, using functional magnetic resonance imaging, we explored the hypothesis that mental time travel into “nonpresent” times (past and future) is enabled by a special conscious state (chronesthesia). Well-trained subjects repeatedly imagined taking one and the same short walk in a familiar environment, doing so either in the imagined past, present, or future. In an additional condition, they recollected an instance in which they actually performed the same short walk in the same familiar setting. This design allowed us to measure brain activity correlated with “pure” conscious states of different moments of subjective time. The results showed that the left lateral parietal cortex was differentially activated by nonpresent subjective times compared with the present (past and future > present). A similar pattern was observed in the left frontal cortex, cerebellum, and thalamus. There was no evidence that the hippocampal region is involved in subjective time travel. These findings provide support for theoretical ideas concerning chronesthesia and mental time travel.


title: Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems

abstract: Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item- and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use.


title: Exploring folksonomy for personalized search

abstract: As a social service in Web 2.0, folksonomy provides the users the ability to save and organize their bookmarks online with "social annotations" or "tags". Social annotations are high quality descriptors of the web pages' topics as well as good indicators of web users' interests. We propose a personalized search framework to utilize folksonomy for personalized search. Specifically, three properties of folksonomy, namely the categorization, keyword, and structure property, are explored. In the framework, the rank of a web page is decided not only by the term matching between the query and the web page's content but also by the topic matching between the user's interests and the web page's topics. In the evaluation, we propose an automatic evaluation framework based on folksonomy data, which is able to help lighten the common high cost in personalized search evaluations. A series of experiments are conducted using two heterogeneous data sets, one crawled from Del.icio.us and the other from Dogear. Extensive experimental results show that our personalized search approach can significantly improve the search quality.


title: A robust eye gaze estimation using geometric eye features

abstract: Gaze estimation is the process of determining the point of gaze in the space, or the visual axis of an eye. It plays an important role in representing human attention; therefore, it can be most appropriately used in Human Computer Interaction as a means of an advance computer input. Here, the focus is to develop a gaze estimation method for Human Computer Interaction using an ordinary webcam mounted on the top of the computer screen without any additional or specialized hardware. The eye center coordinates are obtained with the geometrical eye model and edge gradients. To improve the reliability, the estimates from two eye centers are combined to reduce the noise and improve the accuracy. Facial land marking is done to identify a precise reference point on the face between the nose. The ellipse fitting and RANSAC method is used to estimate the gaze coordinates and to reject the outliers. This approach can estimate the gaze coordinates with high degree of accuracy even when significant numbers of outliers are present in the data set. Several refinements such as feedback and masking, queuing and averaging are proposed to make the system more stable and useful practically. The results show that the proposed method can be successfully applied to commercial gaze tracking systems using ordinary webcams.


title: Understanding the Impressions, Motivations, and Barriers of One Time Code Contributors to FLOSS Projects: A Survey

abstract: Successful Free/Libre Open Source Software (FLOSS) projects must attract and retain high-quality talent. Researchers have invested considerable effort in the study of core and peripheral FLOSS developers. To this point, one critical subset of developers that have not been studied are One-Time code Contributors (OTC) - those that have had exactly one patch accepted. To understand why OTCs have not contributed another patch and provide guidance to FLOSS projects on retaining OTCs, this study seeks to understand the impressions, motivations, and barriers experienced by OTCs. We conducted an online survey of OTCs from 23 popular FLOSS projects. Based on the 184 responses received, we observed that OTCs generally have positive impressions of their FLOSS project and are driven by a variety of motivations. Most OTCs primarily made contributions to fix bugs that impeded their work and did not plan on becoming long term contributors. Furthermore, OTCs encounter a number of barriers that prevent them from continuing to contribute to the project. Based on our findings, there are some concrete actions FLOSS projects can take to increase the chances of converting OTCs into long-term contributors.


title: Challenges and Experience in Prototyping a Multi-Modal Stream Analytic and Monitoring Application on System S

abstract: In this paper, we describe the challenges of prototyping a reference application on System S, a distributed stream processing middleware under development at IBM Research. With a large number of stream PEs (Processing Elements) implementing various stream analytic algorithms, running on a large-scale, distributed cluster of nodes, and collaboratively digesting several multi-modal source streams with vastly differing rates, prototyping a reference application on System S faces many challenges. Specifically, we focus on our experience in prototyping DAC (Disaster Assistance Claim monitoring), a reference application dealing with multi-modal stream analytic and monitoring. We describe three critical challenges: (1) How do we generate correlated, multi-modal source streams for DAC? (2) How do we design and implement a comprehensive stream application, like DAC, from many divergent stream analytic PEs? (3) How do we deploy DAC in light of source streams with extremely different rates? We report our experience in addressing these challenges, including modeling a disaster claim processing center to generate correlated source streams, constructing the PE flow graph, utilizing programming supports from System S, adopting parallelism, and exploiting resource-adaptive computation.


title: AppGen: interactive material modeling from a single image

abstract: We present AppGen, an interactive system for modeling materials from a single image. Given a texture image of a nearly planar surface lit with directional lighting, our system models the detailed spatially-varying reflectance properties (diffuse, specular and roughness) and surface normal variations with minimal user interaction. We ask users to indicate global shading and reflectance information by roughly marking the image with a few user strokes, while our system assigns reflectance properties and normals to each pixel. We first interactively decompose the input image into the product of a diffuse albedo map and a shading map. A two-scale normal reconstruction algorithm is then introduced to recover the normal variations from the shading map and preserve the geometric features at different scales. We finally assign the specular parameters to each pixel guided by user strokes and the diffuse albedo. Our system generates convincing results within minutes of interaction and works well for a variety of material types that exhibit different reflectance and normal variations, including natural surfaces and man-made ones.


title: Using Smart City Technology to Make Healthcare Smarter

abstract: Smart cities use information and communication technologies (ICTs) to scale services include utilities and transportation to a growing population. In this paper, we discuss how smart city ICTs can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.


title: Social Determinants of Health Equity

abstract: Language is important. The call for papers in this supplement was entitled health equity. Yet the call asked for papers that address disparities in health. In the United States, disparities, most often, has been used to refer to racial/ethnic differences in health, or more commonly health care. We note that the call in this supplement expands the focus and highlights differences by socioeconomic status and geographic location, among others. By tradition, in the United Kingdom we have used the term inequalities to describe the differences in health between groups defined on the basis of socioeconomic conditions. ::: ::: To reduce health inequalities requires action to reduce socioeconomic and other inequalities. There are other factors that influence health, but these are outweighed by the overwhelming impact of social and economic factors—the material, social, political, and cultural conditions that shape our lives and our behaviors. Much of the evidence describing this was set out in the World Health Organization Global Commission on the Social Determinants of Health.1 ::: ::: In fact, so close is the link between social conditions and health, that the magnitude of health inequalities is an indicator of the impact of social and economic inequalities on people’s lives. Health then becomes an important further cause for concern about the rapid increase in inequalities of wealth and income in our societies. Increasingly, we are using the language of health inequity to describe those health inequalities that, though avoidable, are not avoided and hence are unfair. ::: ::: Two particular issues stand in the way before we can act on knowledge of social determinants of health to address health equities: lifestyle drift and overconcentration on health care.2 Lifestyle drift describes the tendency in public health to focus on individual behaviors, such as smoking, diet, alcohol, and drugs, that are undoubted causes of health inequities, but to ignore the drivers of these behaviors—the causes of the causes. ::: ::: Too often health is equated only with health care. Lack of access to health care has dominated the debate in the United States because of egregious inequities in access, despite spending far more on health care than any other country. A recent study by the Commonwealth Fund found that compared with other countries the US health system performed relatively poorly in terms of cost, equity, and efficiency.3 ::: ::: The Veterans Health Administration, however, does have a strong focus on equity. The Office of Health Equity ensures that the health care provision for veterans provides equitable care appropriate for the individual’s circumstance and irrespective of geography, gender, race/ethnicity, age, culture, or sexual orientation. There is importance, too, in incorporating socioeconomic factors into provision of equitable access and care. The Office of Health Equity also brings an equity focus into organizational discussions of policy, decision-making, resource allocation, practice, and performance plans throughout the Veterans Health Administration—a health equity in all policies approach that could be extended to other relevant organizations and stakeholders. ::: ::: Universal access to high quality care and a focus on equitable outcomes, then, is central to challenging health inequities. So too is challenging inequities in social conditions which lead to health inequalities. Attempts have been made to apportion determinants of health status of populations—see Figure 1, showing the relatively significant proportion of inequity attributed to social determinants. ::: ::: ::: ::: FIGURE 1— ::: ::: Estimates of the contribution of the main drivers of health status. ::: ::: ::: ::: The Robert Wood Johnson Foundation in the United States also sets out how social factors have as much, or even more impact on health as the medical care system, and it urges leaders across the United States to shift funding priorities to emphasize 3 areas essential to improving the nation’s health: Increasing access to early childhood development programs; revitalizing low-income neighborhoods; and broadening the mission of health care providers beyond medical treatment.5 Important goals, too, for the Veterans Health Administration. ::: ::: In our English review of health inequalities, in 2010, we enlisted the help of 80 or so experts and set out a large evidence base, which demonstrated the most important influences on health and health inequalities.6 We made recommendations in six priority areas. None was in health care because there is evidence of reasonably equitable, universal access to health care in England. ::: ::: The six priority areas were: quality of experiences in the early years, education and building personal and community resilience, good quality employment and working conditions, having sufficient income to lead a healthy life, healthy environments, and priority public health conditions—taking a social determinants approach to tackling smoking, alcohol, and obesity. ::: ::: At the heart of our approach is the finding that health inequalities are not limited to poor health for the worst off, or the most socially disadvantaged. There is a striking social gradient in health and disease running from top to bottom of society.7 The social gradient has now been shown to be widespread across the world in countries at low, middle, and high income.6 Figure 2 shows this gradient in England for life expectancy and healthy life expectancy. ::: ::: ::: ::: FIGURE 2— ::: ::: Life expectancy and disability-free life expectancy (DFLE) at birth by neighborhood income and deprivation: 1999–2003. ::: ::: ::: ::: There has been considerable progress in the recognition and adoption of the social determinants of health approach to health equity. Internationally, organizations such as the United Nations have expressed their broad commitment to health equity through action on the social determinants, and the European Union and World Health Organization have also acted on the social determinants of health and adopted this approach at the heart of their health improvement and health equity strategies. There have also been advancements at the national level—in many countries national governments have acted. There have been some great strides by local governments and authorities too. In England, 75% of local authorities have adopted this approach. ::: ::: However, and it is a significant however, there are many further challenges to greater health equity and to the social determinants of health.


title: An efficient 4‐node 24 D.O.F. thick shell finite element with 5‐point quadrature

abstract: A 4‐node flat shell quadrilateral finite element with 6 degrees of freedom per node, denoted QC5D‐SA, is presented. The element is an assembly of a modification of the drilling degree of freedom membrane presented by Ibrahimbegovic et al., and the assumed strain plate element presented by Bathe and Dvorkin. The part of the stiffness matrix associated with in—plane displacements and rotations is integrated over the element domain by a modified 5‐point reduced integration scheme, resulting in greater efficiency without the sacrifice of rank sufficiency. The scheme produces a soft higher order deformation mode which increases numerical accuracy. A large number of standard benchmark problems are analyzed. Some examples show that the effectiveness of a previously proposed “membrane locking correction” technique is significantly reduced when employing distorted elements. However, the element is shown to be generally accurate and in many cases superior to existing elements.


title: Modeling and Evaluating a Robust Feedback-Based Reputation System for E-Commerce Platforms

abstract: Despite the steady growth of e-commerce communities in the past two decades, little has changed in the way these communities manage reputation for building trust and for protecting their member's financial interests against fraud. As these communities mature and the defects of their reputation systems are revealed, further potential for deception against their members is created, that pushes the need for novel reputation mechanisms. Although a high volume of research works has explored the concepts of reputation and trust in e-communities, most of the proposed reputation systems target decentralized e-communities, focusing on issues related with the decentralized reputation management; they have not thus been integrated in e-commerce platforms. This work's objective is to provide an attackresilient feedback-based reputation system for modern e-commerce platforms, while minimizing the incurred financial burden of potent security schemes. Initially, we discuss a series of attacks and issues in reputation systems and study the different approaches of these problems from related works, while also considering the structural properties, defense mechanisms and policies of existing platforms. Then we present our proposition for a robust reputation system which consists of a novel reputation metric and attack prevention mechanisms. Finally, we describe the simulation framework and tool that we have implemented for thoroughly testing and evaluating the metric's resilience against attacks and present the evaluation experiments and their results. We consider the presented simulation framework as the second contribution of our article, aiming at facilitating the simulation and elaborate evaluation of reputation systems which specifically target e-commerce platforms by thoroughly presenting it, exhibiting its usage and making it available to the research community.


title: Radio Interferometric Calibration Using a Riemannian Manifold

abstract: In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such as the Levenberg-Marquardt algorithm in Euclidean space. In this paper, we reformulate radio interferometric calibration as a nonlinear optimization problem on a Riemannian manifold. The reformulated calibration problem is solved using the Riemannian trust-region method. We show that calibration on a Riemannian manifold has faster convergence with reduced computational cost compared to conventional calibration in Euclidean space.


title: A 24 GHz VCO with 20 % tuning range in 130-nm CMOS using SOP Technology

abstract: A 24 GHz System-on-Package (SOP) VCO is demonstrated. The core operates at 6 GHz and employs a high-Q on-carrier inductor. Using two cascaded on-chip frequency doublers the centre frequency is 24.6 GHz with a 20 % tuning range. The phase noise is below −107 dBc/Hz at 1 MHz offset over the tuning range, with a FOM between 188 and 192 dB at a power consumption of 6.9 mW.


title: Ensemble One-Dimensional Convolution Neural Networks for Skeleton-Based Action Recognition

abstract: In this paper, we proposed a effective but extensible residual one-dimensional convolution neural network as base network, based on the this network, we proposed four subnets to explore the features of skeleton sequences from each aspect. Given a skeleton sequences, the spatial information are encoded into the skeleton joints coordinate in a frame and the temporal information are present by multiple frames. Limited by the skeleton sequence representations, two-dimensional convolution neural network cannot be used directly, we chose one-dimensional convolution layer as the basic layer. Each sub network could extract discriminative features from different aspects. Our first subnet is a two-stream network which could explore both temporal and spatial information. The second is a body-parted network, which could gain micro spatial features and macro temporal features. The third one is an attention network, the main contribution of which is to focus the key frames and feature channels which high related with the action classes in a skeleton sequence. One frame-difference network, as the last subnet, mainly processes the joints changes between the consecutive frames. Four subnets ensemble together by late fusion, the key problem of ensemble method is each subnet should have a certain performance and between the subnets, there are diversity existing. Each subnet shares a wellperformance basenet and differences between subnets guaranteed the diversity. Experimental results show that the ensemble network gets a state-of-the-art performance on three widely used datasets.


A Frequent Concepts Based Document Clustering Algorithm


title: Beauty and Healing: Examining Sociocultural Expectations of the Embodied Goddess

abstract: Studies indicate mental health improvement can occur via religious communities offering social support and other resources. Many people from many cultures regard medicine as a supernatural or magical treatment that can somehow lead to a better state of living. In medical advertising, female role portrayal involves the blending of beauty, ritual and attractiveness in combination with the best product image. A Chinese saying suggests that, “A girl will doll herself up for him who loves her.” Female role attraction is a very important ethical subject in gender issues. Moving forward in time, female role visualization and consumption in medical advertising reveal depictions that encouraged women to do some self-searching and find, or develop, inner strength. This study is designed to examine female role portrayals in a restricted patriarchal society. The results indicate that the ideology of motherhood is an accepted social orientation that the public readily identifies with. Results further indicate that beautification through medical products incorporates an emotional element of religious healing and that the objectification of beauty in the media reveals a possible neglect of women’s internal beauty.


title: Poster: ParkMaster: Leveraging Edge Computing in Visual Analytics

abstract: In this work we propose ParkMaster, a low-cost crowdsourcing architecture which exploits machine learning techniques and vision algorithms to evaluate parking availability in cities. While the user is normally driving ParkMaster enables off the shelf smartphones to collect information about the presence of parked vehicles by running image recognition techniques on the phones camera video streaming. The paper describes the design of ParkMaster's architecture and shows the feasibility of deploying such mobile sensor system in nowadays smartphones, in particular focusing on the practicability of running vision algorithms on phones.


title: Monopolar Patch Antenna With Dual-Band and Wideband Operations

abstract: The designs of dual-band and wide-band microstrip patch antennas with conical radiation patterns are presented in this paper. The antenna is composed of a square-ring patch that is shorted to the ground plane through four shorting walls. Three resonant modes with conical radiation patterns can be simultaneously excited in the antenna structure by a patch-loaded coaxial probe inside the square-ring patch, and they can be designed as a dual-band operation. Moreover, by adjusting the width of the shorting walls, the three modes can be coupled together to realize a wide-band operation. From the obtained results, the 10 dB impedance bandwidths at lower and higher operating frequencies are respectively 42 and 8% for the dual-band antenna design, and the wide-band design exhibits an impedance bandwidth of about 70%.


title: GANFuzz: a GAN-based industrial network protocol fuzzing framework

abstract: In this paper, we attempt to improve industrial safety from the perspective of communication security. We leverage the protocol fuzzing technology to reveal errors and vulnerabilities inside implementations of industrial network protocols(INPs). Traditionally, to effectively conduct protocol fuzzing, the test data has to be generated under the guidance of protocol grammar, which is built either by interpreting the protocol specifications or reverse engineering from network traces. In this study, we propose an automated test case generation method, in which the protocol grammar is learned by deep learning. Generative adversarial network(GAN) is employed to train a generative model over real-world protocol messages to enable us to learn the protocol grammar. Then we can use the trained generative model to produce fake but plausible messages, which are promising test cases. Based on this approach, we present an automatical and intelligent fuzzing framework(GANFuzz) for testing implementations of INPs. Compared to prior work, GANFuzz offers a new way for this problem. Moreover, GANFuzz does not rely on protocol specification, so that it can be applied to both public and proprietary protocols, which outperforms many previous frameworks. We use GANFuzz to test several simulators of the Modbus-TCP protocol and find some errors and vulnerabilities.


title: Intrusion-Tolerant Autonomous Driving

abstract: Fully autonomous driving is one if not the killer application for the upcoming decade of real-time systems. However, in the presence of increasingly sophisticated attacks by highly skilled and well equipped adversarial teams, autonomous driving must not only guarantee timeliness and hence safety. It must also consider the dependability of the software concerning these properties while the system is facing attacks. For distributed systems, fault-and-intrusion tolerance toolboxes already offer a few solutions to tolerate partial compromise of the system behind a majority of healthy components operating in consensus. In this paper, we present a concept of an intrusion-tolerant architecture for autonomous driving. In such a scenario, predictability and recovery challenges arise from the inclusion of increasingly more complex software on increasingly less predictable hardware. We highlight how an intrusion tolerant design can help solve these issues by allowing timeliness to emerge from a majority of complex components being fast enough, often enough while preserving safety under attack through pre-computed fail safes.


title: Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

abstract: We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified number of discussion threads predicted to be popular are recommended, chosen from a fixed window of recent comments to track. Novel deep reinforcement learning architectures are studied for effective modeling of the value function associated with actions comprised of interdependent sub-actions. The proposed model, which represents dependence between sub-actions through a bi-directional LSTM, gives the best performance across different experimental configurations and domains, and it also generalizes well with varying numbers of recommendation requests.


title: Active contours using a potential field

abstract: In most implementations of active contours (snakes), the evolution of the snake depends only on image characteristics in the immediate neighborhood of the current snake points. This is true even when there is little edge data available in the current neighborhood, and even when the boundary of interest may be some distance away in the image. This paper proposes a vector potential field at each point in the image that is derived from the "pull" exerted by all edge points in the image; the pull for a given edge is inversely proportional to the square of the distance from the pixel it pulls. This potential field acts as a force, and snake points are moved based on the force at their current location, rather than moving to minimize energy at a candidate position. The resulting algorithm allows edges to influence snake evolution earlier and from a greater distance, and results in faster and better convergence to the final boundary under a variety of image characteristics.


title: Domain and range identifier module for semantic web search engines

abstract: Semantic web search engine is the new generation of conventional web search engine that brings precise and meaningful information from the Internet. These new search engines answer user queries using Semantic Web Documents (SWDs) that are found in ontologies database. It is likely that a query may have more than one range in a domain. The semantic web search engines such as Hakia, Swoogle, and Watson do not identify domains and ranges of the user's query while retrieving search results. Hence, the retrieved search results are not in a single range from a domain of the user query. In this paper, a novel Domain and Range Identifier (DRI) module is proposed that can be incorporated into the existing semantic web search engines to resolve this problem. The DRI module uses ontologies represented as SWDs to validate the domain, and to identify and classify the ranges in that domain. This will help the semantic web search engines to retrieve more focused information based on user's preferences. A Graphical User Interface is developed for users to select a range and to obtain only the relevant search results.


title: “Hello. This is the IRS calling.”: A case study on scams, extortion, impersonation, and phone spoofing

abstract: Fraud has existed long before the advent of modern technology; however, we can increasingly observe how this profit-driven enterprise is entering the cyberspace. Our paper focuses on a case study of two scam schemes targeting international students at Penn State. The scams have been perpetrated in either a physical (i.e., phone scam) or online (i.e., Craigslist scam) form. However, this dichotomy becomes blurry when examining the phone scams more closely since they often employ cyber elements (e.g., phone spoofing, requests of electronic payment) to mask the scammer's tracks and identity. Our study aims to better understand the nature of the scams and how international students contextualize their scam experiences. We place particular emphasis on investigating what students' decision-making processes are behind filing a report about their scam experiences. We also explore the predominantly used reporting avenues by those international students who filed reports. In the first part of our study, we present a qualitative analysis of Penn State campus police reports of scam incidents covering three years of data (2014–2016). Aside from being able to understand the prevalence and details of the experienced scams, the analysis of the data also helps to unpack the motivations behind why international students file reports to entities like campus police particularly in the event that an inchoate crime was experienced. Furthermore, working with the data lays the groundwork for the second half of our study, a 16-person in-depth interview series with international students who experienced a scam while studying at Penn State. The results of our case study will show the fundamental impact of increased awareness in preventing international students from falling victim to the scams they encountered. However, opportunities still remain in terms of effectively increasing knowledge about how such incidents can be officially reported to law enforcement and how currently existing cybercrime reporting mechanisms can be improved to further bolster cybercrime reporting to take place.


title: Beat synchronization predicts neural speech encoding and reading readiness in preschoolers

abstract: Temporal cues are important for discerning word boundaries and syllable segments in speech; their perception facilitates language acquisition and development. Beat synchronization and neural encoding of speech reflect precision in processing temporal cues and have been linked to reading skills. In poor readers, diminished neural precision may contribute to rhythmic and phonological deficits. Here we establish links between beat synchronization and speech processing in children who have not yet begun to read: preschoolers who can entrain to an external beat have more faithful neural encoding of temporal modulations in speech and score higher on tests of early language skills. In summary, we propose precise neural encoding of temporal modulations as a key mechanism underlying reading acquisition. Because beat synchronization abilities emerge at an early age, these findings may inform strategies for early detection of and intervention for language-based learning disabilities.


title: Children's reading impairments: From theory to practice

abstract: This paper outlines the nature and characteristics of children's reading disorders and considers current ideas about the definitions of dyslexia and reading comprehension impairment. We argue that reading skills show continuous variations within the population, making the diagnostic “cut-offs” used in the identification of reading disorders essentially arbitrary. We argue that there is a considerable overlap between children's reading and language disorders and discuss methods for the early identification of children's reading disorders. We argue that interventions for reading disorders need to be evidence based, and review the evidence for the effectiveness of current approaches to intervention. We conclude by considering the extent to which learning to read in different languages may depend on some universal cognitive principles, as well as processes that may differ between alphabetic and nonalphabetic writing systems.


title: Modeling, simulation and control of single actuator magnetic levitation system

abstract: This paper aims at Design, Fabrication and Control of a single actuator magnetic levitation system. A physical design model of Magnetic Levitation System have been presented at the initial phase. Modeling and Simulation of this non-linear magnetic levitation system is proposed with feedback linearization where a non linear state space transformation is used to linearize the system exactly. After this, experimental setup and construction of actual magnetic levitation system is presented. Experimental Levitation data is collected and compared to the theory. Magnetic levitation system considered in this study is taken as a neodymium magnet suspended in a voltage controlled magnetic field. Dynamic behavior of the system was modeled by the study of electromagnetic and mechanical subsystems. State space model was derived from the system equations. For high accuracy in position detection, Hall effect sensor SS49E was utilized. The successful operation of this system was obtained using relatively cheaper and simpler magnetic levitation subsystems and components.