title: Fused matrix factorization with geographical and social influence in location-based social networks

abstract: Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.


title: Differential Evolutionary Superpixel Segmentation

abstract: Superpixel segmentation has been of increasing importance in many computer vision applications recently. To handle the problem, most state-of-the-art algorithms either adopt a local color variance model or a local optimization algorithm. This paper develops a new approach, named differential evolutionary superpixels, which is able to optimize the global properties of segmentation by means of a global optimizer. We design a comprehensive objective function aggregating within-superpixel error, boundary gradient, and a regularization term. Minimizing the within-superpixel error enforces the homogeneity of superpixels. In addition, the introduction of boundary gradient drives the superpixel boundaries to capture the natural image boundaries, so as to make each superpixel overlaps with a single object. The regularizer further encourages producing similarly sized superpixels that are friendly to human vision. The optimization is then accomplished by a powerful global optimizer—differential evolution. The algorithm constantly evolves the superpixels by mimicking the process of natural evolution, while using a linear complexity to the image size. Experimental results and comparisons with eleven state-of-the-art peer algorithms verify the promising performance of our algorithm.


title: Autonomous Driving in Structured and Unstructured Environments

abstract: Autonomous vehicles are being used increasingly often for a range of tasks, including automated highway driving and automated parking. Such systems are typically either specialized for structured environments and depend entirely on such structure being present in their surroundings, or are specialized for unstructured environments and ignore any structure that may exist. In this paper, we present a hybrid autonomous system that recognizes and exploits structure in the environment in the form of driving lanes, yet also navigates successfully when no such information is present. We believe such an approach is more flexible and more robust than either of its sub-components alone. We demonstrate the effectiveness of our system on both marked roads and unmarked lots.


title: Neuroprotective therapies in glaucoma: I. Neurotrophic factor delivery.

abstract: Glaucoma is a neurodegenerative eye disease that causes permanent blindness at the progressive stage and the number of people affected worldwide is expected to reach over 79 million by 2020. Currently, glaucoma management relies on pharmacological and invasive surgical treatments mainly by reducing the intraocular pressure (IOP), which is the most important risk factor for the progression of the visual field loss. Recent research suggests that neuroprotective or neuroregenerative approaches are necessary to prevent retinal ganglion cells (RGCs) loss and visual impairment over time. Neuroprotection is a new therapeutic strategy that attempts to keep RGCs alive and functional. New gene and cell therapeutics encoding neurotrophic factors (NTFs) are emerging for both neuroprotection and regenerative treatments for retinal diseases. This article briefly reviews the role of NTFs in glaucoma and the potential delivery systems.


title: Congenital hypoplasia of the cerebellum: developmental causes and behavioral consequences

abstract: Over the last sixty years, the spotlight of research has periodically returned to the cerebellum as new techniques and insights have emerged. Because of its simple homogeneous structure, limited diversity of cell types and characteristic behavioural pathologies, the cerebellum is natural home for studies of cell specification, patterning and neuronal migration. However, recent evidence has extended the traditional range of perceived cerebellar function to include modulation of cognitive processes an association with the mechanisms of Autistic Spectrum Disorder. In the light of this emerging frontier, we review the key stages and genetic mechanisms behind cerebellum development. In particular, we discuss the role of the midbrain hindbrain isthmic organizer in the development of the cerebellar vermis and the specification and differentiation of Purkinje cells and granule neurons. These developmental processes are then considered in relation to recent insights into selected human developmental cerebellar defects: Joubert’s syndrome, Dandy Walker malformation and pontocerebellar hypoplasia. Finally, we review current research that opens up the possibility of using the mouse as a genetic model to study the role of the cerebellum in cognitive function.


title: A Data Mining Framework for Constructing Features and Models for Intrusion Detection Systems

abstract: Intrusion detection is an essential component of critical infrastructure protection mechanisms. The traditional pure “knowledge engineering” process of building Intrusion Detection Systems (IDSs) is very slow, expensive, and error-prone. Current IDSs thus have limited extensibility in the face of changed or upgraded network configurations, and poor adaptability in the face of new attack methods. ::: This thesis describes a novel framework, MADAM ID, for Mining Audit Data for Automated Models for Intrusion Detection. Classification rules are inductively learned from audit records and used as intrusion detection models. A critical requirement for the rules to be effective detection models is that an appropriate set of features need to be first constructed and included in the audit records. A key contribution of the thesis is thus in automatic “feature construction”. Using MADAM ID, raw audit data is first preprocessed into records with a set of “intrinsic” (i.e., general purposes) features. Data mining algorithms are then applied to compute the frequent activity patterns from the records, which are automatically analyzed to generate an additional set of features for intrusion detection purposes. ::: We introduce several extensions, namely, axis attribute(s), reference attribute(s), level-wise approximate mining, and mining with relative support, to the basic association rules and frequent episodes algorithms. The extended algorithms use the characteristics of audit data to direct the efficient computation of “relevant” patterns. We develop an encoding algorithm so that frequent patterns can be easily visualized, analyzed, and compared. We devise an algorithm that automatically constructs temporal and statistical features according to the semantics of the patterns. ::: The effectiveness and advantages of our algorithms have been objectively evaluated through the 1998 DARPA Intrusion Detection Evaluation program.


title: A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations

abstract: Developing efficient numerical algorithms for the solution of high dimensional random Partial Differential Equations (PDEs) has been a challenging task due to the well-known curse of dimensionality. We present a new solution framework for these problems based on a deep learning approach. Specifically, the random PDE is approximated by a feed-forward fully-connected deep residual network, with either strong or weak enforcement of initial and boundary constraints. The framework is mesh-free, and can handle irregular computational domains. Parameters of the approximating deep neural network are determined iteratively using variants of the Stochastic Gradient Descent (SGD) algorithm. The satisfactory accuracy of the proposed frameworks is numerically demonstrated on diffusion and heat conduction problems, in comparison with the converged Monte Carlo-based finite element results.


title: Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

abstract: Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end. Therefore, we investigate end-to-end source separation in the time-domain, which allows modelling phase information and avoids fixed spectral transformations. Due to high sampling rates for audio, employing a long temporal input context on the sample level is difficult, but required for high quality separation results because of long-range temporal correlations. In this context, we propose the Wave-U-Net, an adaptation of the U-Net to the one-dimensional time domain, which repeatedly resamples feature maps to compute and combine features at different time scales. We introduce further architectural improvements, including an output layer that enforces source additivity, an upsampling technique and a context-aware prediction framework to reduce output artifacts. Experiments for singing voice separation indicate that our architecture yields a performance comparable to a state-of-the-art spectrogram-based U-Net architecture, given the same data. Finally, we reveal a problem with outliers in the currently used SDR evaluation metrics and suggest reporting rank-based statistics to alleviate this problem.


title: Crowd detection and management using cascade classifier on ARMv8 and OpenCV-Python

abstract: The steady increase in population and overcrowding has become an unavoidable factor in any public gathering or on the street during any festive occasions. The intelligent monitoring technology has been developing in recent years and human tracking has made a lot of progress. In this paper, we propose a method to manage the crowd by keeping in track the count of the people in the scene. In our study, we develop a system using Raspberry Pi 3 board that consists of ARMv8 CPU that detects the human heads and provide a count of humans in the region using OpenCV-Python. A Haar cascade classifier is trained for human head detection. Human tracking is achieved by indicating the direction of movement of the person. The results of the analysis will be helpful in managing the crowd in any area with high density of crowds.


Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Detection, Evaluation, and Treatment of High Blood Cholesterol Education Program (NCEP) Expert Panel on Executive Summary of the Third Report of the National


title: "FRCEF: The new friction reduced and coupling enhanced finger for the Awiwi hand"

abstract: The combination of tendon driven robotic fingers and variable impedance actuation in the DLR hand arm system brings benefits in robustness and dynamics by enabling energy storage. Since the force measurement and motors are in the forearm the tendon path should have low friction for accurate movements and precise finger control. In this paper an enhanced generation of the Awiwi hand finger design is presented. It reduces the friction in the actuation system about 20 percent and increases the maximum fingertip force about 33 percent. A test finger was designed to evaluate different tendon couplings and to test a magnetic sensor to measure the joint position. In a next step a new finger design for DLR hand arm system has been developed. Finally, the low friction and the robustness are proven using several experiments.


title: Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification

abstract: Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example. Such attacks are called evasion attacks. Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars. In this work, we develop new DNNs that are robust to state-of-the-art evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. Specifically, for a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. In contrast, traditional classifiers are point-based classification, i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based classification is significantly more robust than point-based classification to state-of-the-art evasion attacks.


title: Secure Multiparty Computations on Bitcoin

abstract: Is it possible to design an online protocol for playing a lottery, in a completely decentralized way, that is, without relying on a trusted third party? Or can one construct a fully decentralized protocol for selling secret information, so that neither the seller nor the buyer can cheat in it? Until recently, it seemed that every online protocol that has financial consequences for the participants needs to rely on some sort of a trusted server that ensures that the money is transferred between them. In this work, we propose to use Bitcoin (a digital currency, introduced in 2008) to design such fully decentralized protocols that are secure even if no trusted third party is available. As an instantiation of this idea, we construct protocols for secure multiparty lotteries using the Bitcoin currency, without relying on a trusted authority. Our protocols guarantee fairness for the honest parties no matter how the loser behaves. For example, if one party interrupts the protocol, then her money is transferred to the honest participants. Our protocols are practical (to demonstrate it, we performed their transactions in the actual Bitcoin system) and in principle could be used in real life as a replacement for the online gambling sites.


title: RANDOM GRAPH ISOMORPHISM

abstract: A straightforward linear time canonical labeling algorithm is shown to apply to almost all graphs (i.e. all but $o(2^{( \begin{subarray}{l} n \\ 2 \end{subarray} )} )$) of the $2^{( \begin{subarray}{l} n \\ 2 \end{subarray} )} $ graphs on n vertices). Hence, for almost all graphs X, any graph Y can be easily tested for isomorphism to X by an extremely naive linear time algorithm. This result is based on the following: In almost all graphs on n vertices, the largest $n^{0.15} $ degrees are distinct. In fact, they are pairwise at least $n^{0.03} $ apart.


title: An analysis of the GTZAN music genre dataset

abstract: A significant amount of work in automatic music genre recognition has used a dataset whose composition and integrity has never been formally analyzed. For the first time, we provide an analysis of its composition, and create a machine-readable index of artist and song titles. We also catalog numerous problems with its integrity, such as replications, mislabelings, and distortions.


title: Evaluation of a stair-climbing power wheelchair in 25 people with tetraplegia.

abstract: OBJECTIVE ::: To compare the performance of a power wheelchair with stair-climbing capability (TopChair) and a conventional power wheelchair (Storm3). ::: ::: ::: DESIGN ::: A single-center, open-label study. ::: ::: ::: SETTING ::: A physical medicine and rehabilitation hospital. ::: ::: ::: PARTICIPANTS ::: Patients (N=25) who required power wheelchairs because of severe impairments affecting the upper and lower limbs. ::: ::: ::: INTERVENTIONS ::: Indoor and outdoor driving trials with both devices. Curb-clearing and stair-climbing with TopChair. ::: ::: ::: MAIN OUTCOME MEASURES ::: Trial duration and Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) tool; number of failures during driving trials and ability to climb curbs and stairs. ::: ::: ::: RESULTS ::: All 25 participants successfully completed the outdoor and indoor trials with both wheelchairs. Although differences in times to trial completion were statistically significant, they were less than 10%. QUEST scores were significantly better with the Storm3 than the TopChair for weight (P=.001), dimension (P=.006), and effectiveness (P=.04). Of the 25 participants, 23 cleared a 20-cm curb without help, and 20 climbed up and down 6 steps. Most participants felt these specific capabilities of the TopChair--for example, curb clearing and stair climbing-were easy to use (22/25 for curb, 21/25 for stairs) and helpful (24/25 and 23/25). A few participants felt insecure (4/25 and 6/25, respectively). ::: ::: ::: CONCLUSIONS ::: The TopChair is a promising mobility device that enables stair and curb climbing and warrants further study.


title: Acoustic vowel reduction as a function of sentence accent, word stress, and word class

abstract: Abstract The effect of sentence accent, word stress, and word class (function words versus content words) on the acoustic properties of 9 Dutch vowels in fluent speech was investigated. A list of sentences was read aloud by 15 male speakers. Each sentence contained one syllable of interest. This could be a monosyllabic function word, an unstressed syllable of a content word, or a stressed syllable of a content word. The same syllable occurred in all three conditions. Sentence accent was manipulated with questions that preceded the sentences. A total number of 3465 vowels were segmented from the syllables and analysed. It was found that all three factors mentioned above had a significant effect both on the steady-state formant frequencies ( F 1 and F 2 ) and on the duration of the vowels. Word stress and word class had a stronger effect on the vowels than sentence accent. A listening experiment showed the perceptual significance of the acoustic measurements. It appeared that spectral vowel reduction could be better interpreted as the result of an increased contextual assimilation than as the tendency to centralize. We also studied changes in the dynamics of the formant tracks due to the experimental conditions. It was found that formant tracks of reduced vowels became flatter which supports the view of an increased contextual assimilation. Three simple models of vowel reduction are discussed.


title: Multi-Frequency Electrical Impedance Tomography System With Automatic Self-Calibration for Long-Term Monitoring

abstract: Electrical Impedance Tomography (EIT) is a safe medical imaging technology, requiring no ionizing or heating radiation, as opposed to most other imaging modalities. This has led to a clinical interest in its use for long-term monitoring, possibly at the bedside, for ventilation monitoring, bleeding detection, gastric emptying and epilepsy foci diagnosis. These long-term applications demand auto-calibration and high stability over long time periods. To address this need we have developed a new multi-frequency EIT system called the KHU Mark2.5 with automatic self-calibration and cooperation with other devices via a timing signal for synchronization with other medical instruments. The impedance measurement module (IMM) for flexible configuration as a key component includes an independent constant current source, an independent differential voltmeter, and a current source calibrator, which allows automatic self-calibration of the current source within each IMM. We installed a resistor phantom inside the KHU Mark2.5 EIT system for intra-channel and inter-channel calibrations of all voltmeters in multiple IMMs. We show the deterioration of performance of an EIT system over time and the improvement due to automatic self-calibration. The system is able to maintain SNR of 80 dB for frequencies up to 250 kHz and below 0.5% reciprocity error over continuous operation for 24 hours. Automatic calibration at least every 3 days is shown to maintain SNR above 75 dB and reciprocity error below 0.7% over 7 days at 1 kHz. A clear degradation in performance results with increasing time between automatic calibrations allowing the tailoring of calibration to suit the performance requirements of each application.


title: Efficient deployment of wireless sensor networks targeting environment monitoring applications

abstract: Maximizing network connectivity while maintaining a useful lifetime period without exceeding cost constraints is a challenging design objective for wireless sensor networks. Satisfying such objective becomes even a more intricate task with 3-D setups and harsh operational conditions found in typical large scale environment monitoring applications. While much work has been performed in environment monitoring, only few have addressed the unique characteristics of such applications. In this paper, we introduce a novel 3-D deployment strategy, called Optimized 3-D deployment with Lifetime Constraint (O3DwLC), for relay nodes in environmental applications. The strategy optimizes network connectivity, while guarantying specific network lifetime and limited cost. Key to our contribution is a very limited search space for the optimization problem, in addition to a revised definition for network lifetime that is more appropriate in environment monitoring. The effectiveness of our strategy is validated through extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity.


title: PCM and Memristor based nanocrossbars

abstract: This paper presents performance comparison between two emerging resistive Non-Volatile Memory (NVM) technologies; namely Memristors and Phase Change Memory (PCM); using nanocrossbar architecture. A comparison in terms of leakage current, reading and writing delay, and energy consumption between both non-volatile memory devices, with SRAM based nanocrossbar as benchmark was carried. It was found that Memristive crossbars offer 3 orders of magnitude improvement in the average read cycle, compared to SRAM based crossbars. On the other hand; both PCM based and Memristor based crossbars offered more than 2 orders of magnitude improvement in leakage energy compared to SRAM based crossbars. The aim of this comparison is to provide a fair simulation platform to study and compare PCM crossbar and Memristive crossbar.


title: Fully Stretchable Textile Triboelectric Nanogenerator with Knitted Fabric Structures

abstract: Harvesting human-motion energy for power-integrated wearable electronics could be a promising way to extend the battery-operation time of small low-power-consumption electronics such as various sensors. For this purpose, a fully stretchable triboelectric nanogenerator (S-TENG) that has been fabricated with knitted fabrics and has been integrated with the directly available materials and techniques of the textile industry is introduced. This device has been adapted to cloth movement and can generate electricity under compression and stretching. We investigated plain-, double-, and rib-fabric structures and analyzed their potentials for textile-based energy harvesting. The superior stretchable property of the rib-knitted fabric contributed to a dramatic enhancement of the triboelectric power-generation performance owing to the increased contact surface. The present study shows that, under stretching motions of up to 30%, the S-TENG generates a maximum voltage and a current of 23.50 V and 1.05 μA, respective...


title: Artificial intelligence based smart diagnosis of alzheimer's disease and mild cognitive impairment

abstract: Modern pattern recognition and artificial intelligence systems can help in providing better health care and medical solutions. The performance of human diagnosis degrades due to fatigue, cognitive biases, systems faults, and distractions. However, artificial intelligence based diagnosis systems are less error prone and give safe support to clinicians in detection and decision making. This work presents a smart and reliable way of diagnosing Alzheimer's disease (AD) and its possible early stage i.e., mild cognitive impairment. Alzheimer's is a neurodegenerative disease and leads to severe memory loss and inability to cope with daily life tasks. The diagnosis of AD from structural images requires great skill and is challenging for human diagnostics. The presented framework is based on deep learning and detects Alzheimer's and its initial stages accurately from structural MRI scans. The framework analyzes four different classes simultaneously in a single setup. The testing accuracy of diagnosis obtained by the method is 98.88%. Experiments are also performed on binary data and transfer learning is applied for multiclass classification achieving 99.7% accuracy. Once a good trained model is obtained, the decision for an unseen test scan is given within a few seconds. These models are free from the factors that are responsible for causing human diagnostic errors. Therefore, these models are dependable and can provide much faster diagnosis.


title: Modeling the Performance of Image Restoration From Motion Blur

abstract: When dealing with motion blur there is an inevitable trade-off between the amount of blur and the amount of noise in the acquired images. The effectiveness of any restoration algorithm typically depends on these amounts, and it is difficult to find their best balance in order to ease the restoration task. To face this problem, we provide a methodology for deriving a statistical model of the restoration performance of a given deblurring algorithm in case of arbitrary motion. Each restoration-error model allows us to investigate how the restoration performance of the corresponding algorithm varies as the blur due to motion develops. Our modeling treats the point-spread-function trajectories as random processes and, following a Monte-Carlo approach, expresses the restoration performance as the expectation of the restoration error conditioned on some motion-randomness descriptors and on the exposure time. This allows to coherently encompass various imaging scenarios, including camera shake and uniform (rectilinear) motion, and, for each of these, identify the specific exposure time that maximizes the image quality after deblurring.


title: A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles

abstract: Power train electrification is promoted as a potential alternative to reduce carbon intensity of transportation. Lithium-ion batteries are found to be suitable for hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), and temperature control on lithium batteries is vital for long-term performance and durability. Unfortunately, battery thermal management (BTM) has not been paid close attention partly due to poor understanding of battery thermal behaviour. Cell performance change dramatically with temperature, but it improves with temperature if a suitable operating temperature window is sustained. This paper provides a review on two aspects that are battery thermal model development and thermal management strategies. Thermal effects of lithium-ion batteries in terms of thermal runaway and response under cold temperatures will be studied, and heat generation methods are discussed with aim of performing accurate battery thermal analysis. In addition, current BTM strategies utilised by automotive suppliers will be reviewed to identify the imposing challenges and critical gaps between research and practice. Optimising existing BTMs and exploring new technologies to mitigate battery thermal impacts are required, and efforts in prioritising BTM should be made to improve the temperature uniformity across the battery pack, prolong battery lifespan, and enhance the safety of large packs.


title: High-resolution acoustic imaging in air by synthetic aperture using pixel-wise matched kernels

abstract: This paper introduces a device and needed signal processing for high-resolution acoustic imaging in air. The device employs off the shelf audio hardware and linear frequency modulated (LFM) pulse waveform. The image formation is based on the principle of synthetic aperture. The proposed implementation uses inverse filtering method with a unique kernel function for each pixel and focuses a synthetic aperture with no approximations. The method is solid for both far-field and near-field and easily adaptable for different synthetic aperture formation geometries. The proposed imaging is demonstrated via an inverse synthetic aperture formation where the object rotation by a stepper motor provides the required change in aspect angle. Simulated and empirical results are presented. Measurements have been done using a conventional speaker and microphones in an ordinary room with near-field distance and strong static echoes present. The resulting high-resolution 2-D spatial distribution of the acoustic reflectivity provides valuable information for many applications such as object recognition.


title: Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

abstract: This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy.


title: Associations of interleukin-6 gene polymorphisms with cancer risk: Evidence based on 49,408 cancer cases and 61,790 controls

abstract: Many molecular epidemiologic studies have shown that interleukin-6 (IL-6) polymorphisms are significantly associated with susceptibility for various cancers. However, the conclusions of these studies are inconsistent. The purpose of the present study was to explore the association between three common IL-6 loci (rs1800795, rs1800796, and rs1800797) and the risk for various cancers. We systematically searched the PubMed, Web of Science, Wanfang and China national knowledge infrastructure (CNKI) databases for relevant publications and obtained 108 eligible studies, involving 49,408 cancer patients and 61,790 cancer-free controls. Odds ratio (OR), 95% confidence interval (CI), and false positive reporting probability (FPRP) were used to evaluate cancer risk. All statistical analyses were performed using the R software meta package. We observed a non-significant association between rs1800795 and overall cancer risk, while rs1800797 was found to have a false positive association with overall risk of cancer. Subgroup analyses of rs1800797 also suggested non-significant association and rs1800795 played a protective role in liver cancer. Rs1800796 was found to be associated with overall cancer risk, particularly in Asian patients and those with prostate cancer. These findings provide evidence that IL-6 polymorphisms may affect cancer risk.


title: Long short-term memory networks in memristor crossbars

abstract: Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units have led to major advances in artificial intelligence. However, state-of-the-art LSTM models with significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from both limited memory capacity and limited data communication bandwidth. Here we demonstrate experimentally that the synaptic weights shared in different time steps in an LSTM can be implemented with a memristor crossbar array, which has a small circuit footprint, can store a large number of parameters and offers in-memory computing capability that contributes to circumventing the ‘von Neumann bottleneck’. We illustrate the capability of our crossbar system as a core component in solving real-world problems in regression and classification, which shows that memristor LSTM is a promising low-power and low-latency hardware platform for edge inference.


title: Segmentation of Moving Objects by Long Term Video Analysis

abstract: Motion is a strong cue for unsupervised object-level grouping. In this paper, we demonstrate that motion will be exploited most effectively, if it is regarded over larger time windows. Opposed to classical two-frame optical flow, point trajectories that span hundreds of frames are less susceptible to short-term variations that hinder separating different objects. As a positive side effect, the resulting groupings are temporally consistent over a whole video shot, a property that requires tedious post-processing in the vast majority of existing approaches. We suggest working with a paradigm that starts with semi-dense motion cues first and that fills up textureless areas afterwards based on color. This paper also contributes the Freiburg-Berkeley motion segmentation (FBMS) dataset, a large, heterogeneous benchmark with 59 sequences and pixel-accurate ground truth annotation of moving objects.


title: A Subhertz Nanopower Low-Pass Filter

abstract: This brief presents a first-order low-pass filter topology capable of providing cutoff frequencies down to 2 mHz with power consumption of 5 nW. The circuit is intended for signal conditioning applications, particularly for use with very low frequency physiological signals in low-power portable medical equipment. To achieve a low-frequency cutoff, the filter is based around the use of a clocked transconductor, which provides low transconductance while using a relatively high bias current level. The circuit is implemented in a 0.35-μm technology with a 1-V supply and has 32- μVRMS measured noise and a 64-dB dynamic range. In terms of power consumption and cutoff frequency the reported filter outperforms previous filters from the literature.


title: Acute effects of violent video-game playing on blood pressure and appetite perception in normal-weight young men: a randomized controlled trial

abstract: Watching television and playing video game being seated represent sedentary behaviours and increase the risk of weight gain and hypertension. We investigated the acute effects of violent and non-violent video-game playing on blood pressure (BP), appetite perception and food preferences. Forty-eight young, normal-weight men (age: 23.1±1.9 years; body mass index: 22.5±1.9 kg/m(2)) participated in a three-arm, randomized trial. Subjects played a violent video game, a competitive, non-violent video game or watched TV for 1 h. Measurements of BP, stress and appetite perception were recorded before a standardized meal (∼300 kcal) and then repeated every 15 min throughout the intervention. Violent video-game playing was associated with a significant increase in diastolic BP (Δ±s.d.=+7.5±5.8 mm Hg; P=0.04) compared with the other two groups. Subjects playing violent video games felt less full (P=0.02) and reported a tendency towards sweet food consumption. Video games involving violence appear to be associated with significant effects on BP and appetite perceptions compared with non-violent gaming or watching TV.


title: Nutrition for working and service dogs.

abstract: Conformation, genetics, and behavioral drive are the major determinants of success in canine athletes, although controllable variables, such as training and nutrition, play an important role. The scope and breadth of canine athletic events has expanded dramatically in the past 30 years, but with limited research on performance nutrition. There are considerable data examining nutritional physiology in endurance dogs and in sprinting dogs; however, nutritional studies for agility, field trial, and detection are rare. This article highlights basic nutritional physiology and interventions for exercise, and reviews newer investigations regarding aging working and service dogs, and canine detection activities.


EVALUATION OF ENERGY HARVESTING CONCEPTS FOR TIRE PRESSURE MONITORING SYSTEMS


title: Acupuncture and homeostasis: physiological, physical (postural) and psychological.

abstract: When the subject of homeostasis is discussed, one automatically thinks of physiological homeostasis, in particular such things as fluid and electrolyte balance, and the maintenance of body temperature, blood pressure and blood sugar, etc. In fact, two other concepts of homeostasis exist in the literature, namely physical or postural homeostasis, and also psychological homeostasis. The former applies to the situation where the line of gravity of the body in the erect posture passes in the frontal or coronal plane directly through the center of the axis of the atlanto-occipital, shoulder, hip, knee and ankle joints. Only a very small proportion of people attain this ideal balanced posture. Psychological homeostasis refers to emotional control or tranquility. It has been stated that the biological function of human emotion is primarily homeostatic. Repression is a homeostatic mechanism. Evidence is presented to show that a feedback type of relationship probably exists between these forms of homeostasis, and any of the body-mind type of therapies (including acupuncture and T'ai chi ch'uan) thus have a three-fold effect on man, i.e. on his physiology, his posture and his mind.


VisHap: Augmented Reality Combining Haptics and Vision


title: Enhanced fingerprint fuzzy vault based on distortion invariant minutiae structures

abstract: The fuzzy vault is a promising cryptosystem for the protection of fingerprint templates. However it requires the alignment of the query template to the enrolled one. Most existing implementations either rely on the extraction of the core which is not a guaranteed task, or use publicly available helper data which may leak information about the protected minutiae. We propose a new alignment approach for the fingerprint fuzzy vault based on distortion invariant minutiae features. Experimental results show improvements in genuine and false accept rates over previous implementations.


title: Design methodology of Miller frequency compensation with current buffer/amplifier

abstract: Current buffers/amplifiers are used in series to the Miller compensation capacitor with the aim of eliminating the positive zero introduced by the forward path. They are increasingly adopted because of their low-voltage features, high-speed performance and, recently, for their suitability to be used with large capacitive loads (when a current gain is introduced). The authors propose a novel and simple design approach for the frequency compensation of a two-stage amplifier exploiting a current buffer/amplifier. The procedure has been profitably applied to a class-AB two-stage CMOS operational transconductance amplifier, having a 100 pF load. In particular, three compensation networks were designed using a 1.3 pF, 0.6 pF and 250 fF compensation capacitor alternatively. Moreover, the adopted compensation topology provides an improvement in terms of power supply rejection ratio, which was also analytically demonstrated. Simulations that are in very good agreement with theoretical results are also given.


title: Morphology of the mentalis muscle and its relationship with the orbicularis oris and incisivus labii inferioris muscles.

abstract: The mentalis muscle (MT) is the only elevator of the lower lip and the chin, and it provides the major vertical support for the lower lip. However, there are few reports on the relationship between the MT and its surrounding muscles. The aim of this study was to clarify the morphology of the MT, especially in relation to the orbicularis oris muscle and the incisivus labii inferioris muscle (ILI), thereby providing data to understand the function of the MT in relation to the surrounding muscles. The MT was examined in 40 specimens of embalmed adult Korean cadavers. The medial fibers of both MTs descended anteromedially and crossed together, forming a dome-shaped chin prominence in all specimens. The lateral fibers of the MT descended inferomedially in 38 specimens (95%) and inferolaterally in 2 specimens (5%). The upper fibers of the MT were short and ran horizontally, whereas the lower fibers were long and descended inferomedially or vertically. The upper fibers of the MT were intermingled with the inferior margin of the orbicularis oris muscle in all specimens. The originating muscle fibers of the ILI were intermingled with the upper lateral MT in all specimens. Some of the ILI fibers extended inferomedially to the middle or lower portion of the MT in 22 specimens (55%). The results of this study constitute new anatomical knowledge regarding the MT and will be useful to surgeons performing various surgical procedures of the chin area.


title: FEDERAL: A Framework for Distance-Aware Privacy-Preserving Record Linkage

abstract: In privacy-preserving record linkage, a number of data custodians encode their records and submit them to a trusted third-party who is responsible for identifying those records that refer to the same real-world entity. In this paper, we propose FEDERAL, a novel record linkage framework that implements methods for anonymizing both string and numerical data values, which are typically present in data records. These methods rely on a strong theoretical foundation for rigorously specifying the dimensionality of the anonymization space, into which the original values are embedded, to provide accuracy and privacy guarantees under various models of privacy attacks. A key component of the applied embedding process is the threshold that is required by the distance computations, which we prove can be formally specified to guarantee accurate results. We evaluate our framework using three real-world data sets with varying characteristics. Our experimental findings show that FEDERAL offers a complete and effective solution for accurately identifying matching anonymized record pairs (with recall rates constantly above 93 percent) in large-scale privacy-preserving record linkage tasks.


title: Delayed rehabilitation lessens brain injury and improves recovery after intracerebral hemorrhage in rats

abstract: Abstract Rehabilitation improves recovery after intracerebral hemorrhage (ICH) in rats. In some cases, brain damage is attenuated. In this study, we tested whether environmental enrichment (EE) combined with skilled reach training improves recovery and lessens brain injury after ICH in rats. Collagenase was injected stereotaxically to produce a moderate-sized striatal ICH. One week after ICH rats were either placed into a rehabilitation (REHAB) or control (CONT) condition. The REHAB rats received 15 h of EE and four 15-minute reach-training sessions daily over 5 days a week for 2 weeks. The CONT rats stayed in standard group cages. Skilled reaching (staircase test), walking (horizontal ladder) and forelimb use bias (cylinder test) were assessed at 4 and 6 weeks after ICH. Lesion volume, corpus callosum volume and cortical thickness were calculated 46 days after ICH. The REHAB treatment reduced lesion volume by 28% (p = 0.019) without affecting the corpus callosum volume (p = 0.405) or cortical thickness (p = 0.300), thus indicating that protection was due to lessening striatal injury. As well, REHAB significantly improved skilled reaching ability in the staircase apparatus at 4 (p = 0.002) and 6 weeks (p


title: A Unified Model for Word Sense Representation and Disambiguation

abstract: Most word representation methods assume that each word owns a single semantic vector. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be resolved by word sense disambiguation. In this paper, we present a unified model for joint word sense representation and disambiguation, which will assign distinct representations for each word sense. 1 The basic idea is that both word sense representation (WSR) and word sense disambiguation (WSD) will benefit from each other: (1) highquality WSR will capture rich information about words and senses, which should be helpful for WSD, and (2) high-quality WSD will provide reliable disambiguated corpora for learning better sense representations. Experimental results show that, our model improves the performance of contextual word similarity compared to existing WSR methods, outperforms stateof-the-art supervised methods on domainspecific WSD, and achieves competitive performance on coarse-grained all-words WSD.


title: Is dark silicon useful?: harnessing the four horsemen of the coming dark silicon apocalypse

abstract: Due to the breakdown of Dennardian scaling, the percentage of a silicon chip that can switch at full frequency is dropping exponentially with each process generation. This utilization wall forces designers to ensure that, at any point in time, large fractions of their chips are effectively dark or dim silicon, i.e., either idle or significantly underclocked. As exponentially larger fractions of a chip's transistors become dark, silicon area becomes an exponentially cheaper resource relative to power and energy consumption. This shift is driving a new class of architectural techniques that "spend" area to "buy" energy efficiency. All of these techniques seek to introduce new forms of heterogeneity into the computational stack. We envision that ultimately we will see widespread use of specialized architectures that leverage these techniques in order to attain orders-of-magnitude improvements in energy efficiency. However, many of these approaches also suffer from massive increases in complexity. As a result, we will need to look towards developing pervasively specialized architectures that insulate the hardware designer and the programmer from the underlying complexity of such systems. In this paper, I discuss four key approaches--the four horsemen--that have emerged as top contenders for thriving in the dark silicon age. Each class carries with its virtues deep-seated restrictions that requires a careful understanding of the underlying tradeoffs and benefits.


Automatic Identification of Narrative Diegesis and Point of View


title: Private web search

abstract: Web search is currently a source of growing concern about personal privacy. It is an essential and central part of most users' activity online and therefore one through which a significant amount of personal information may be revealed.To help users protect their privacy, we have designed and implemented Private WebSearch (PWS), a usable client-side tool that minimizes the information that users reveal to a search engine. Our tool protects users against attacks that involve active components and timing information, to which more general Web-browsing privacy tools (including the combination of FoxTor and Privoxy) are vulnerable. PWS is a Firefox plugin that functions as an HTTP proxy and as a client for the Tor anonymity network. It configures Firefox so that search queries executed from the PWS search box are routed through the HTTP proxy and Tor client, filtering potentially sensitive or identifying components of the request and response.


title: A survey of grammatical inference in software engineering

abstract: Grammatical inference - used successfully in a variety of fields such as pattern recognition, computational biology and natural language processing - is the process of automatically inferring a grammar by examining the sentences of an unknown language. Software engineering can also benefit from grammatical inference. Unlike these other fields, which use grammars as a convenient tool to model naturally occurring patterns, software engineering treats grammars as first-class objects typically created and maintained for a specific purpose by human designers. We introduce the theory of grammatical inference and review the state of the art as it relates to software engineering. We survey grammatical inference as it relates to software engineering.A background on the theory of grammatical inference is provided.We explore a variety of applications in software engineering.These include programming languages, DSLs, visual languages, and execution traces.


Understanding the Internet Banking Adoption : a Unified Theory of Acceptance and Use of Technology and Perceived Risk Application 1


title: Argumentation Accelerated Reinforcement Learning for RoboCup Keepaway-Takeaway

abstract: Multi-Agent Learning (MAL) is a complex problem, especially in real-time systems where both cooperative and competitive learning are involved. We study this problem in the RoboCup Soccer Keepaway-Takeaway game and propose Argumentation Accelerated Reinforcement Learning (AARL) for this game. AARL incorporates heuristics, represented by arguments in Value-Based Argumentation, into Reinforcement Learning (RL) by using Heuristically Accelerated RL techniques. We empirically study for a specific setting of the Keepaway-Takeaway game the suitability of AARL, in comparison with standard RL and hand-coded strategies, to meet the challenges of MAL.


title: Survey Bloom filter applications in network security: A state-of-the-art survey

abstract: Undoubtedly, dealing with security issues is one of the most important and complex tasks various networks face today. A large number of security algorithms have been proposed to enhance security in various types of networks. Many of these solutions are either directly or indirectly based on Bloom filter (BF), a space- and time-efficient probabilistic data structure introduced by Burton Bloom in 1970. Obviously, Bloom filters and their variants are getting more and more consideration in network security area. This paper provides an up-to-date survey of the application of BFs and their variants to improve performance of the approaches proposed to address security problems with different types of networks.


title: A Probabilistic Approach to Collaborative Multi-Robot Localization

abstract: This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.


A propositional modal logic of time intervals


title: Error and Attack Tolerance of Complex Networks

abstract: Communication/transportation systems are often subjected to failures and attacks. Here we represent such systems as networks and we study their ability to resist failures (attacks) simulated as the breakdown of a group of nodes of the network chosen at random (chosen accordingly to degree or load). We consider and compare the results for two different network topologies: the Erdos–Renyi random graph and the Barabasi–Albert scale-free network. We also discuss briefly a dynamical model recently proposed to take into account the dynamical redistribution of loads after the initial damage of a single node of the network.


title: Differential Cryptanalysis of Feal and N-Hash

abstract: In [1,2] we introduced the notion of differential cryptanalysis and described its application to DES[8] and several of its variants. In this paper we show the applicability of differential cryptanalysis to the Feal family of encryption algorithms and to the N-Hash hash function.


title: On HMM static hand gesture recognition

abstract: A real time static isolated gesture recognition application using a Hidden Markov Model approach with features extracted from gestures' silhouettes is presented. Nine different hand poses with various degrees of rotation are considered. The system, both simple and effective, uses color images of the hands to be recognized directly from the camera and is capable of processing 23 frames per second on a Quad Core Intel Processor.


title: A Detection Method for Anomaly Flow in Software Defined Network

abstract: As a new type of network structure, the Software Defined Network (SDN) provides a new solution for network flow management and optimization, which has made the accurate detection of anomaly SDN flows a hot research topic. This paper presents an SDN-based flow detection method, builds structures for detecting anomaly SDN flows and performs classification detection on the flows using the double P-value of transductive confidence machines for K-nearest neighbors algorithm. The experimental results show that the algorithm proposed achieves a lower false positive rate, higher precision, and better adaptation to the SDN environment than do other algorithms of the same type.


title: Residual LSTM Attention Network for Object Tracking

abstract: In this letter, we propose an attention network for object tracking. To construct the proposed attention network for sequential data, we combine long–short term memory (LSTM) and a residual framework into a residual LSTM (RLSTM). The LSTM, which learns temporal correlation, is used for a temporal learning of object tracking. In the proposed RLSTM method, the residual framework, which achieves the highest accuracy in ImageNet large scale visual recognition competition (ILSVRC) 2016, learns the variations of spatial inputs and thus achieves the spatio-temporal attention of the target object. Also, a rule-based RLSTM learning is used for robust attention. Experimental results on large tracking benchmark datasets object tracking benchmark (OTB)-2013, OTB-100, and OTB-50 show that the proposed RLSTM tracker achieves the highest performance among existing trackers including the Siamese trackers, attention trackers, and correlation trackers, and also has comparable performance with the state-of-the-art deep trackers.


title: A Machine-Learning Item Recommendation System for Video Games

abstract: Video-game players generate huge amounts of data, as everything they do within a game is recorded. In particular, among all the stored actions and behaviors, there is information on the in-game purchases of virtual products. Such information is of critical importance in modern free-to-play titles, where gamers can select or buy a profusion of items during the game in order to progress and fully enjoy their experience. To try to maximize these kind of purchases, one can use a recommendation system so as to present players with items that might be interesting for them. Such systems can better achieve their goal by employing machine learning algorithms that are able to predict the rating of an item or product by a particular user. In this paper we evaluate and compare two of these algorithms, an ensemble-based model (extremely randomized trees) and a deep neural network, both of which are promising candidates for operational video-game recommender engines. Item recommenders can help developers improve the game. But, more importantly, it should be possible to integrate them into the game, so that users automatically get personalized recommendations while playing. The presented models are not only able to meet this challenge, providing accurate predictions of the items that a particular player will find attractive, but also sufficiently fast and robust to be used in operational settings.


A TOSCA-based Programming Model for Interacting Components of Automatically Deployed Cloud and IoT Applications


title: MEMS Shear Stress Sensors: Promise and Progress

abstract: Abstract : This paper reviews existing microelectromechanical systems (MEMS)-based shear stress sensors. The promise and progress of MEMS scaling advantages to improve the spatial and temporal resolution and accuracy of shear stress measurement is critically reviewed. The advantages and limitations of existing devices are discussed. Finally, unresolved technical issues are summarized for future sensor development.


title: Evaluation instrument for e-government websites

abstract: An instrument for evaluating the quality of government websites is described. It takes the form of a questionnaire resident within spreadsheet or database software so that an automatically determined evaluation may be indicated following input of data. The 106 questions included are derived from previous research regarding significant components of successful websites, along with interviews with developers of portals at different levels of Australian government. The instrument may be easily tailored to accommodate varying requirements at different levels of government, and used as a basis for extended internal evaluation of their own websites by agencies. The instrument also embodies explanations of the survey questions that are asked, along with advice on how to determine sought-after information from sites.


title: Proximal binaural sound can induce subjective frisson

abstract: Auditory frisson is the experience of feeling of cold or shivering related to sound in the absence of a physical cold stimulus. Multiple examples of frisson-inducing sounds have been reported, but the mechanism of auditory frisson remains elusive. Typical frisson-inducing sounds may contain a looming effect, in which a sound appears to approach the listener's peripersonal space. Previous studies on sound in peripersonal space have provided objective measurements of sound-inducing effects, but few have investigated the subjective experience of frisson-inducing sounds. Here we explored whether it is possible to produce subjective feelings of frisson by moving a noise sound (white noise, rolling beads noise, or frictional noise produced by rubbing a plastic bag) stimulus around a listener's head. Our results demonstrated that sound-induced frisson can be experienced stronger when auditory stimuli are rotated around the head (binaural moving sounds) than the one without the rotation (monaural static sounds), regardless of the source of the noise sound. Pearson's correlation analysis showed that several acoustic features of auditory stimuli, such as variance of interaural level difference (ILD), loudness, and sharpness, were correlated with the magnitude of subjective frisson. We had also observed that the subjective feelings of frisson by moving a musical sound had increased comparing with a static musical sound.


title: Design of Low-Profile Metamaterial-Loaded Substrate Integrated Waveguide Horn Antenna and Its Array Applications

abstract: A metamaterial-loaded substrate integrated wave-guide (SIW) horn antenna and its array application are researched in this communication. The mushroom-type metamaterial is placed in front of SIW horn aperture, and much improvement in impedance matching is achieved on a 1/ $20~\lambda _{0}$ -thickness substrate. Due to the backward wave generated by the metamaterial, backward endfire radiation pattern is observed for the proposed antenna. Experimental results indicate that a fractional impedance bandwidth of 10.6% is obtained for a fabricated prototype. Furthermore, a shunt four-element array and a monopulse four-element array are designed based on the novel SIW horn element to further enhance the radiation performance. Results show that performances of wide operating bandwidth, low profile, and backward endfire radiation pattern are maintained for the designed two arrays.


title: Homomorphic encryption method applied to Cloud Computing

abstract: When the data transferred to the Cloud we use standard encryption methods to secure this data, but when we want to do the calculations on data located on a remote server, it is necessary that the Cloud provider has access to the raw data, and then it will decrypt them. In this paper we propose the application of a method to perform the operation on encrypted data without decrypted and provide the same result as well that the calculations were carried out on raw data.


title: Visible embodiment: Gestures as simulated action

abstract: Spontaneous gestures that accompany speech are related to both verbal and spatial processes. We argue that gestures emerge from perceptual and motor simulations that underlie embodied language and mental imagery. We first review current thinking about embodied cognition, embodied language, and embodied mental imagery. We then provide evidence that gestures stem from spatial representations and mental images. We then propose the gestures-as-simulated-action framework to explain how gestures might arise from an embodied cognitive system. Finally, we compare this framework with other current models of gesture production, and we briefly outline predictions that derive from the framework.


Recognizing Syntactic Errors in Written Philippine English


title: Sequence assembly demystified

abstract: As the use of next-generation sequencing has proliferated, so has the range of sequencing applications and software tools that are available for assembling sequences. To help readers to make informed choices about assembly techniques, this Review discusses the available options and practical trade-offs.


title: Blended Learning Approach to Develop the Teachers’ TPACK

abstract: A theoretical framework has emerged recently to guide research in the teachers’ use of ICT and it is the technological pedagogical content knowledge (TPACK). Early research indicates that Blended learning is increasingly being adopted at all levels of educational system. It is considered as a way to foster engaging in interactive learning experiences. The purpose of this article was to determine the levels of ICT knowledge on e-course design through blended learning approach among science teachers of secondary schools in Yemen. The study was conducted on the sample of 60 science teacher trainees in Ibb city. The ICT knowledge scale was used based on TPACK. To analyze the data t-test was used. The findings in this study indicated that TPACK has provided a valuable tool for assessing teacher knowledge in the area of technology integration, the teachers’ ICT knowledge was above average in two groups, and there is significant difference between experimental and control groups on ICT knowledge scale. Recommendations are made for future research on online collaboration activities to raise awareness of factors related to online group work and to determine the in-service training needs of teachers on ICT use to follow-up support and to ensure successful utilization of new technologies.


title: Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

abstract: There has been a recent emergence of sampling-based techniques for estimating epistemic uncertainty in deep neural networks. While these methods can be applied to classification or semantic segmentation tasks by simply averaging samples, this is not the case for object detection, where detection sample bounding boxes must be accurately associated and merged. A weak merging strategy can significantly degrade the performance of the detector and yield an unreliable uncertainty measure. This paper provides the first in-depth investigation of the effect of different association and merging strategies. We compare different combinations of three spatial and two semantic affinity measures with four clustering methods for MC Dropout with a Single Shot Multi-Box Detector. Our results show that the correct choice of affinity-clustering combination can greatly improve the effectiveness of the classification and spatial uncertainty estimation and the resulting object detection performance. We base our evaluation on a new mix of datasets that emulate near open-set conditions (semantically similar unknown classes), distant open-set conditions (semantically dissimilar unknown classes) and the common closed-set conditions (only known classes).


Neuro-IoU: Learning a Surrogate Loss for Semantic Segmentation


title: Formal Barriers to Longest-Chain Proof-of-Stake Protocols

abstract: The security of most existing cryptocurrencies is based on a concept called Proof-of-Work, in which users must solve a computationally hard cryptopuzzle to authorize transactions ("one unit of computation, one vote''). This leads to enormous expenditure on hardware and electricity in order to collect the rewards associated with transaction authorization. Proof-of-Stake is an alternative concept that instead selects users to authorize transactions proportional to their wealth ("one coin, one vote"). Some aspects of the two paradigms are the same. For instance, obtaining voting power in Proof-of-Stake has a monetary cost just as in Proof-of-Work: a coin cannot be freely duplicated any more easily than a unit of computation. However some aspects are fundamentally different. In particular, exactly because Proof-of-Stake is wasteless, there is no inherent resource cost to deviating (commonly referred to as the "Nothing-at-Stake'' problem). In contrast to prior work, we focus on incentive-driven deviations (any participant will deviate if doing so yields higher revenue) instead of adversarial corruption (an adversary may take over a significant fraction of the network, but the remaining players follow the protocol). The main results of this paper are several formal barriers to designing incentive-compatible proof-of-stake cryptocurrencies (that don't apply to proof-of-work).


BitStore: An Incentive-Compatible Solution for Blocked Downloads in BitTorrent


title: Gromov-wasserstein averaging of kernel and distance matrices

abstract: This paper presents a new technique for computing the barycenter of a set of distance or kernel matrices. These matrices, which define the interrelationships between points sampled from individual domains, are not required to have the same size or to be in row-by-row correspondence. We compare these matrices using the softassign criterion, which measures the minimum distortion induced by a probabilistic map from the rows of one similarity matrix to the rows of another; this criterion amounts to a regularized version of the Gromov-Wasserstein (GW) distance between metric-measure spaces. The barycenter is then defined as a Frechet mean of the input matrices with respect to this criterion, minimizing a weighted sum of softassign values. We provide a fast iterative algorithm for the resulting nonconvex optimization problem, built upon state-of-the-art tools for regularized optimal transportation. We demonstrate its application to the computation of shape barycenters and to the prediction of energy levels from molecular configurations in quantum chemistry.


title: AMT: a Property-based Monitoring Tool for Analog Systems ⋆

abstract: In this paper we describe AMT, a tool for monitoring temporal properties of continuous signals. We first introduce STL/PSL, a specification formalism based on the industrial standard language PSL and the real-time temporal logic MITL, extended with constructs that allow describing behaviors of real-valued variables. The tool automatically builds property observers from an STL/PSL specification and checks, in an offline or incremental fashion, whether simulation traces satisfy the property. The AMT tool is validated through a Flash memory case-study.


title: North-Holland THE SHORTEST-PATH PROBLEM FOR GRAPHS WITH RANDOM ARC-LENGTHS A.M. FRIEZE

abstract: Abstract We consider the problem of finding the shortest distance between all pairs of vertices in a complete digraph on n vertices, whose arc-lengths are non-negative random variables. We describe an algorithm which solves this problem in O(n(m+nlogn)) expected time, where m is the expected number of arcs with finite lenght. If m is small enough, this represents a small improvement over the bound in Bloniarz [3]. We consider also the case when the arc-lengths are random variables which are independently distributed with distribution function F, where F(0)=0 and F is differentiable at 0; for this case, we describe an algorithm which runs in O(n2logn) expected time. In our treatment of the shortest-path problem we consider the following problem in combinatorial probability theory. A town contains n people, one of whom knows a rumour. At the first stage he tells someone chosen randomly from the town; at each stage, each person who knows the rumour tells someone else, chosen randomly from the town and indeependently of all other choices. Let Sn be the number of stages before the whole town rnows the rumour. We show that Sn/log2n → 1 + loge2 in probability as n → ∞, and estimate the probabilities of large deviations in Sn.


title: Tensor-based document retrieval over Neo4j with an application to PubMed mining

abstract: PubMed mining is currently at the epicenter of intense interdisciplinary research. Text mining methodologies provide a way to retrieve and analyze emotionally charged words, punctuation, and syntax. Moreover, they can analyze scientific literature and process document collections. Moving beyond traditional document-term matrix representation, an architecture for content based retrieval from PubMed is proposed whose core is a document-term-author third order tensor. This methodology has been implemented in Python over Neo4j and has been applied to a PubMed document article collection.


title: Vishleshan: Performance Comparison and Programming Process Mining Algorithms in Graph-Oriented and Relational Database Query Languages

abstract: Process-Aware Information System (PAIS) are IT systems that manages, supports business processes and generate large event logs from execution of business processes. Process Mining consists of analyzing event logs generated by PAISs and discover business process models and check for conformance between the discovered and actual models. The large volume of event logs generated are stored in databases. Relational databases perform well for certain class of applications. However, there are certain class of applications for which relational databases are not able to scale. Several NoSQL databases have emerged to encounter the challenges of scalability in traditional databases. Discovering social network from event logs is one of the most challenging and important Process Mining task. Similar-Task algorithm is one of the most widely used Organizational Mining techniques. Our objective is to investigate which of the databases (Relational or Graph) perform better for Organizational Mining under Process Mining. We implement Similar-Task algorithm on relational and NoSQL (graph oriented) databases using only query language constructs. We conduct empirical analysis on a large real world data set to compare the performance of row-oriented database and NoSQL graph-oriented database.


First-Order Bayesian Logic


title: Consumer Attitudes Toward Mobile Advertising: An Empirical Study

abstract: The rapid proliferation of mobile phones and other mobile devices has created a new channel for marketing. The use of Short Messaging Service to access customers through their handheld devices is gaining popularity, making the mobile phone the ultimate medium for one-to-one marketing. The present research investigates consumer attitudes toward mobile advertising and the relationship between attitude and behavior. An instrument for measuring attitudes toward mobile advertising is developed. The results of a survey indicate that (1) consumers generally have negative attitudes toward mobile advertising unless they have specifically consented to it, and (2) there is a direct relationship between consumer attitudes and consumer behavior. Thus it is not a good idea to send SMS advertisements to potential customers without prior permission.


title: Pulse Current Generator with Improved Waveform Fidelity for High Voltage Capacitively-Coupled Plasma System

abstract: This proposes a novel structure of power converter solution on the basis of current source type topology for a highly capacitively-coupled plasma application system. In overall system, two current sources are connected in parallel to generate the different output current of high precision in different switching intervals. The overall system consists of four major functional blocks; bias current generator, bias current modulator, slope current generator, and slope current modulator. The proposed solution also employs SiC MOSFET type power semiconductor switch of 1200V/90A in order to meet the fast transient requirement. The proposed converter system successfully satisfies the specification of output voltage of 4.5kV and output current of 40A with the rising/falling time of less than 100ns. This proposed topology makes it possible to accurately shape the output load voltage in a highly capacitively-coupled plasma application system with reduced level of voltage spike and distortion as compared to the state-of-the-arts solutions of voltage source type converters.


title: A survey on forecasting of time series data

abstract: Time series analysis and forecasting future values has been a major research focus since years ago. Time series analysis and forecasting in time series data finds it significance in many applications such as business, stock market and exchange, weather, electricity demand, cost and usage of products such as fuels, electricity, etc. and in any kind of place that has specific seasonal or trendy changes with time. The forecasting of time series data provides the organization with useful information that is necessary for making important decisions. In this paper, a detailed survey of the various techniques applied for forecasting different types of time series dataset is provided. This survey covers the overall forecasting models, the algorithms used within the model and other optimization techniques used for better performance and accuracy. The various performance evaluation parameters used for evaluating the forecasting models are also discussed in this paper. This study gives the reader an idea about the various researches that take place within forecasting using the time series data.


title: LUCSS: Language-based User-customized Colourization of Scene Sketches

abstract: We introduce LUCSS, a language-based system for interactive col- orization of scene sketches, based on their semantic understanding. LUCSS is built upon deep neural networks trained via a large-scale repository of scene sketches and cartoon-style color images with text descriptions. It con- sists of three sequential modules. First, given a scene sketch, the segmenta- tion module automatically partitions an input sketch into individual object instances. Next, the captioning module generates the text description with spatial relationships based on the instance-level segmentation results. Fi- nally, the interactive colorization module allows users to edit the caption and produce colored images based on the altered caption. Our experiments show the effectiveness of our approach and the desirability of its compo- nents to alternative choices.


title: Machine Translation for Manufacturing: A Case Study at Ford Motor Company

abstract: Machine translation (MT) was one of the first applications of artificial intelligence technology that was deployed to solve real-world problems. Since the early 1960s, researchers have been building and utilizing computer systems that can translate from one language to another without requiring extensive human intervention. In the late 1990s, Ford Vehicle Operations began working with Systran Software Inc. to adapt and customize its machine-translation technology in order to translate Ford's vehicle assembly build instructions from English to German, Spanish, Dutch, and Portuguese. The use of machine translation was made necessary by the vast amount of dynamic information that needed to be translated in a timely fashion. The assembly build instructions at Ford contain text written in a controlled language as well as unstructured remarks and comments. The MT system has already translated more than 7 million instructions into these languages and is an integral part of the overall manufacturing process-planning system used to support Ford's assembly plants in Europe, Mexico and South America. In this paper, we focus on how AI techniques, such as knowledge representation and natural language processing can improve the accuracy of machine translation in a dynamic environment such as auto manufacturing.


title: Tropical infectious diseases: Diagnostics for the developing world

abstract: Top of pageAbstract ::: Although 'diseases of affluence', such as diabetes and cardiovascular disease, are increasing in developing countries, infectious diseases still impose the greatest health burden. Annually, just under 1 million people die from malaria, 4.3 million from acute respiratory infections, 2.9 million from enteric infections and 5 million from AIDS and tuberculosis. Other sexually transmitted infections and tropical parasitic infections are responsible for hundreds of thousands of deaths and an enormous burden of morbidity. More than 95% of these deaths occur in developing countries. Simple, accurate and stable diagnostic tests are essential to combat these diseases, but are usually unavailable or inaccessible to those who need them.


title: Dynamic hash tables

abstract: Linear hashing and spiral storage are two dynamic hashing schemes originally designed for external files. This paper shows how to adapt these two methods for hash tables stored in main memory. The necessary data structures and algorithms are described, the expected performance is analyzed mathematically, and actual execution times are obtained and compared with alternative techniques. Linear hashing is found to be both faster and easier to implement than spiral storage. Two alternative techniques are considered: a simple unbalanced binary tree and double hashing with periodic rehashing into a larger table. The retrieval time of linear hashing is similar to double hashing and substantially faster than a binary tree, except for very small trees. The loading times of double hashing (with periodic reorganization), a binary tree, and linear hashing are similar. Overall, linear hashing is a simple and efficient technique for applications where the cardinality of the key set is not known in advance.


title: A study on singularity avoidance and robust control of redundant robot

abstract: Up to the present, a number of performance indices have been proposed for measure of dexterity of manipulators. There are many problems connected to the physical and mathematical meanings of these measures. In this paper, a new performance index considering aspect maintenance, computational burden, representation of an identical unit without regard to the robot and the effectiveness of the algorithm is proposed. This performance index is called the reduced minor index. Inverse kinematics is indispensable to robot control. The singularity-robust inverse is used and it offers a feasible motion close to the desired trajectory even at or in the neighborhood of singular points. Control of the redundant robot utilizing the upper reduced minor index and singularity-robust inverse is applied to the a 4 DOF wire-driven robot. The effectiveness of the proposed control algorithm is illustrated with simulation and experiment results.


title: Fingertip touch improves postural stability in patients with peripheral neuropathy

abstract: Abstract The purpose of this work was to determine whether fingertip touch on a stable surface could improve postural stability during stance in subjects with somatosensory loss in the feet from diabetic peripheral neuropathy. The contribution of fingertip touch to postural stability was determined by comparing postural sway in three touch conditions (light, heavy and none) in eight patients and eight healthy control subjects who stood on two surfaces (firm or foam) with eyes open or closed. In the light touch condition, fingertip touch provided only somatosensory information because subjects exerted less than 1 N of force with their fingertip to a force plate, mounted on a vertical support. In the heavy touch condition, mechanical support was available because subjects transmitted as much force to the force plate as they wished. In the no touch condition, subjects held the right forefinger above the force plate. Antero-posterior (AP) and medio-lateral (ML) root mean square (RMS) of center of pressure (CoP) sway and trunk velocity were larger in subjects with somatosensory loss than in control subjects, especially when standing on the foam surface. The effects of light and heavy touch were similar in the somatosensory loss and control groups. Fingertip somatosensory input through light touch attenuated both AP and ML trunk velocity as much as heavy touch. Light touch also reduced CoP sway compared to no touch, although the decrease in CoP sway was less effective than with heavy touch, particularly on the foam surface. The forces that were applied to the touch plate during light touch preceded movements of the CoP, lending support to the suggestion of a feedforward mechanism in which fingertip inputs trigger the activation of postural muscles for controlling body sway. These results have clinical implications for understanding how patients with peripheral neuropathy may benefit from a cane for postural stability in stance.


title: Efficient Local Statistical Analysis via Integral Histograms with Discrete Wavelet Transform

abstract: Histograms computed from local regions are commonly used in many visualization applications, and allowing the user to query histograms interactively in regions of arbitrary locations and sizes plays an important role in feature identification and tracking. Computing histograms in regions with arbitrary location and size, nevertheless, can be time consuming for large data sets since it involves expensive I/O and scan of data elements. To achieve both performance- and storage-efficient query of local histograms, we present a new algorithm called WaveletSAT, which utilizes integral histograms, an extension of the summed area tables (SAT), and discrete wavelet transform (DWT). Similar to SAT, an integral histogram is the histogram computed from the area between each grid point and the grid origin, which can be be pre-computed to support fast query. Nevertheless, because one histogram contains multiple bins, it will be very expensive to store one integral histogram at each grid point. To reduce the storage cost for large integral histograms, WaveletSAT treats the integral histograms of all grid points as multiple SATs, each of which can be converted into a sparse representation via DWT, allowing the reconstruction of axis-aligned region histograms of arbitrary sizes from a limited number of wavelet coefficients. Besides, we present an efficient wavelet transform algorithm for SATs that can operate on each grid point separately in logarithmic time complexity, which can be extended to parallel GPU-based implementation. With theoretical and empirical demonstration, we show that WaveletSAT can achieve fast preprocessing and smaller storage overhead than the conventional integral histogram approach with close query performance.


title: DNA-Droid: A Real-Time Android Ransomware Detection Framework

abstract: Ransomware has become one of the main cyber-threats for mobile platforms and in particular for Android. The number of ransomware attacks are increasing exponentially, while even state of art approaches terribly fail to safeguard mobile devices. The main reason is that ransomware and generic malware characteristics are quite different. Current solutions produce low accuracy and high false positives in presence of obfuscation or benign cryptographic API usage. Moreover, they are inadequate in detecting ransomware attack in early stages before infection happens. In this paper, DNA-Droid, a two layer detection framework is proposed. It benefits of a dynamic analysis layer as a complementary layer on top of a static analysis layer. The DNA-Droid utilizes novel features and deep neural network to achieve a set of features with high discriminative power between ransomware and benign samples. Moreover, Sequence Alignment techniques are employed to profile ransomware families. This helps in detecting ransomware activity in early stages before the infection happens. In order to extract dynamic features, a fully automated Android sandbox is developed which is publicly available for researchers as a web service. The DNA-Droid is tested against thousands of samples. The experimental results shows high precision and recall in detecting even unknown ransomware samples, while keeping the false negative rate below 1.5%.


title: Cone Depth and the Center Vertex Theorem

abstract: We generalize the Tukey depth to use cones instead of halfspaces. We prove a generalization of the center point theorem that for S ⊂ R, there is a point s ∈ S, with depth at least n d+1 for cones of half-angle 45 . This gives a notion of data depth for which an approximate median can always be found among the original set.


title: Correlated Tag Learning in Topic Model

abstract: It is natural to expect that the documents in a corpus will be correlated, and these correlations are reflected by not only the words but also the observed tags in each document. Most previous works model this type of corpus, which are called the semi-structured corpus, without considering the correlations among the tags. In this work, we develop a Correlated Tag Learning (CTL) model for semi-structured corpora based on the topic model to enable the construction of the correlation graph among tags via a logistic normal participation process. For the inference of the CTL model, we devise a variational inference algorithm to approximate the posterior. In experiments, we visualize the tag correlation graph generated by the CTL model on the DBLP corpus and for the tasks of document retrieval and classification, the correlation graph among tags is helpful to improve the generalization performance compared with the state-of-the-art baselines.


title: A High-Performance Line-Start Permanent Magnet Synchronous Motor Amended From a Small Industrial Three-Phase Induction Motor

abstract: Small industrial three-phase induction motors (IMs) normally suffer from poor operational efficiency and power factor. This paper presents a high-performance line-start permanent magnet synchronous motor (LSPMSM) which is developed by simple modifications of an off-the-shelf small industrial three-phase IM with minimized additional costs. Two-dimensional (2-D) dynamic finite element analysis (FEA) models are employed to assess the machine performances, which are validated by comprehensive experimental results. The experimental comparisons between the amended LSPMSM and the original IM have indicated that significant improvements in efficiency and power factor can be achieved by the proposed LSPMSM.


title: PWM in three-phase switching converters-sliding mode solution

abstract: In this paper, the selection of the switching logic for PWM three phase switching converters (inverters and rectifiers) is presented. The approach is based on the deliberate introduction of the sliding mode motion in the control system. It is shown that the selection of the PWM pattern is a direct result of the sliding mode existence in the closed-loop system. The PWM pattern selection for three phase inverters and three phase rectifiers is the same, due to the fact that their switching matrix is the same. Simulation results show that proposed algorithm has an advantage that the current ripple can be set as a fraction of the reference current while the switching frequency does not increase. >


title: Dual-Band Dual-Sense Circularly Polarized Substrate Integrated Waveguide Antenna

abstract: A dual-band dual-sense circularly polarized (CP) antenna is presented in substrate integrated waveguide technology. The proposed antenna consists of four V-shaped asymmetrical resonators that are placed on a circular substrate symmetrically with respect to its center. The antenna is excited by a probe on the central axis. A dual-band CP antenna is designed and fabricated. The antenna provides left-hand circular polarization (LHCP) in the lower band and right-hand circular polarization (RHCP) in the upper band. Total size of the antenna is 1963.5 mm2 on a 0.787 mm thick RT/Duroid 5880 substrate. Measured axial ratios are below 3 dB over 8.78–8.90 and 9.52–9.66 GHz. Over the bands, the return loss is more than 10 dB. Measured cross-polarization levels are 31.1 and 24.65 dB, and front-to-back ratios are 14.93 and 18.35 dB over the LHCP and RHCP bands, respectively. Depending on application requirements, the band ratio can be tuned. Also, the sense of polarization can be interchanged. The antenna does not use any ground plane perturbation. Thus, it can be directly attached to a microwave circuit.


title: Smart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video Data

abstract: Video surveillance system has become a critical part in the security and protection system of modem cities, since smart monitoring cameras equipped with intelligent video analytics techniques can monitor and pre-alarm abnormal behaviors or events. However, with the expansion of the surveillance network, massive surveillance video data poses huge challenges to the analytics, storage and retrieval in the Big Data era. This paper presents a novel intelligent processing and utilization solution to big surveillance video data based on the event detection and alarming messages from front-end smart cameras. The method includes three parts: the intelligent pre-alarming for abnormal events, smart storage for surveillance video and rapid retrieval for evidence videos, which fully explores the temporal-spatial association analysis with respect to the abnormal events in different monitoring sites. Experimental results reveal that our proposed approach can reliably pre-alarm security risk events, substantially reduce storage space of recorded video and significantly speed up the evidence video retrieval associated with specific suspects.


title: Predictive and corrective text input for desktop editor using n-grams and suffix trees

abstract: Even though desktop computers have existed since a long time, the method of typing and feeding input has not changed much. Versions after versions of popular text editors have come, and yet no editor yet has addressed the difficulty that smaller user groups such as senior citizens and handicapped people have while entering input. Also, predictive editors cease to exist in desktop computers even today. This paper explores the use of a new software for input on desktops, which relies on a dynamic predictive algorithm using n-grams and suffix trees to significantly reduce the effort of typing. This paper also compares the newly developed Smart Text Editor to traditional popular editors.


title: Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning

abstract: Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.


title: New Retrieval Approaches Using SMART : TREC 4

abstract: The SMART information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 4, performing runs in the routing, ad-hoc, confused text, interactive, and foreign languange environments


title: Retinal fundus imaging with a plenoptic sensor

abstract: Vitreoretinal surgery is moving towards 3D visualization of the surgical field. This require acquisition system capable of recording such 3D information. We propose a proof of concept imaging system based on a light-field camera where an array of micro-lenses is placed in front of a conventional sensor. With a single snapshot, a stack of images focused at different depth are produced on the fly, which provides enhanced depth perception for the surgeon. ::: Difficulty in depth localization of features and frequent focus-change during surgery are making current vitreoretinal heads-up surgical imaging systems cumbersome to use. To improve the depth perception and eliminate the need to manually refocus on the instruments during the surgery, we designed and implemented a proof-of-concept ophthalmoscope equipped with a commercial light-field camera. The sensor of our camera is composed of an array of micro-lenses which are projecting an array of overlapped micro-images. We show that with a single light-field snapshot we can digitally refocus between the retina and a tool located in front of the retina or display an extended depth-of-field image where everything is in focus. ::: The design and system performances of the plenoptic fundus camera are detailed. We will conclude by showing in vivo data recorded with our device.


title: Sensitivity for Telemed diagnosis of myocardial infarction by use of 12-lead electrocardiogram derived from Frank XYZ leads.

abstract: Another comparison of sensitivity for diagnosis of myocardial infarction in conventional and derived 12-lead ECGs processed by the computer diagnostic program of the Telemed Cardio-Pulmonary System is reasonably consistent with the original trial of these two methods of recording ECG signals reported earlier by Dower and Machado. Other similarities and disparities are cited, together with brief comments about the theoretical and clinical implications of those findings and the relationship to a more rigorous analysis from this laboratory.


title: AESvisual: A Visualization Tool for the AES Cipher

abstract: This paper describes a visualization tool AESvisual that helps students learn and instructors teach the AES cipher. The software allows the user to visualize all the major steps of AES encryption and decryption. The demo mode is useful and efficient for classroom presentation and the practice mode provides the user with an environment to practice AES encryption with error checking. AESvisual is quite versatile, providing support for both beginners learning how to encrypt and decrypt, and also for the more advanced users wishing to see all the details, including the GF(28) addition and multiplication operations. Classroom evaluation of the tool was positive.


title: Security analysis of Internet-of-Things: A case study of august smart lock

abstract: To realize the vision of Internet-of-Things (IoT), numerous IoT devices have been developed for improving daily lives, in which smart home devices are among the most popular ones. Smart locks rely on smartphones to ease the burden of physical key management and keep tracking the door opening/close status, the security of which have aroused great interests from the security community. As security is of utmost importance for the IoT environment, we try to investigate the security of IoT by examining smart lock security. Specifically, we focus on analyzing the security of August smart lock. The threat models are illustrated for attacking August smart lock. We then demonstrate several practical attacks based on the threat models toward August smart lock including handshake key leakage, owner account leakage, personal information leakage, and denial-of-service (DoS) attacks. We also propose the corresponding defense methods to counteract these attacks.