title: An analysis on human fall detection using skeleton from Microsoft kinect

abstract: In this paper, we present a novel fall detection system based on the Kinect sensor. The originalities of this system are two-fold. Firstly, based on the observation that using all joints to represent human posture is not pertinent and robust because in several human postures the Kinect is not able to track correctly all joints, we define and compute three features (distance, angle, velocity) on only several important joints. Secondly, in order to distinguish fall with other activities such as lying, we propose to use Support Vector Machine technique. In order to analyze the robustness of the proposed features and joints for fall detection, we have performed intensive experiments on 108 videos of 9 activities (4 falls, 2 falls like and 3 daily activities). The experimental results show that the proposed system is capable of detecting falls accurately and robustly.


Emerging Trends in Smart Home Security, Privacy, and Digital Forensics


title: Lung microbiota promotes tolerance to allergens in neonates via PD-L1

abstract: Epidemiological data point toward a critical period in early life during which environmental cues can set an individual on a trajectory toward respiratory health or disease. The neonatal immune system matures during this period, although little is known about the signals that lead to its maturation. Here we report that the formation of the lung microbiota is a key parameter in this process. Immediately following birth, neonatal mice were prone to develop exaggerated airway eosinophilia, release type 2 helper T cell cytokines and exhibit airway hyper-responsiveness following exposure to house dust mite allergens, even though their lungs harbored high numbers of natural CD4(+)Foxp3(+)CD25(+)Helios(+) regulatory T (Treg) cells. During the first 2 weeks after birth, the bacterial load in the lungs increased, and representation of the bacterial phyla shifts from a predominance of Gammaproteobacteria and Firmicutes towards Bacteroidetes. The changes in the microbiota were associated with decreased aeroallergen responsiveness and the emergence of a Helios(-) Treg cell subset that required interaction with programmed death ligand 1 (PD-L1) for development. Absence of microbial colonization(10) or blockade of PD-L1 during the first 2 weeks postpartum maintained exaggerated responsiveness to allergens through to adulthood. Adoptive transfer of Treg cells from adult mice to neonates before aeroallergen exposure ameliorated disease. Thus, formation of the airway microbiota induces regulatory cells early in life, which, when dysregulated, can lead to sustained susceptibility to allergic airway inflammation in adulthood.


title: Topology and application of bidirectional isolated dc-dc converters

abstract: A bidirectional isolated dc-dc converter manages the power flow between an energy storage device and a dc bus with functions of galvanic isolation and voltage matching. Research and development of these converters focus on improving efficiency and power density. This paper presents and compares various configurations of bidirectional isolated dc-dc converters. It also illustrates the performance of a 6-kW, full-bridge, bidirectional isolated dc-dc converter operating at 4 kHz, which is suitable for output power leveling of photovoltaic generation systems connected to homes and office buildings. The maximum efficiency of the dc-dc converter is measured at 98.1% during battery charging and at 98.2% during battery discharging. The converter maintains a high efficiency of more than 97% for a wide range of power transfer.


title: Advances in phase-aware signal processing in speech communication

abstract: Abstract During the past three decades, the issue of processing spectral phase has been largely neglected in speech applications. There is no doubt that the interest of speech processing community towards the use of phase information in a big spectrum of speech technologies, from automatic speech and speaker recognition to speech synthesis, from speech enhancement and source separation to speech coding, is constantly increasing. In this paper, we elaborate on why phase was believed to be unimportant in each application. We provide an overview of advancements in phase-aware signal processing with applications to speech, showing that considering phase-aware speech processing can be beneficial in many cases, while it can complement the possible solutions that magnitude-only methods suggest. Our goal is to show that phase-aware signal processing is an important emerging field with high potential in the current speech communication applications. The paper provides an extended and up-to-date bibliography on the topic of phase aware speech processing aiming at providing the necessary background to the interested readers for following the recent advancements in the area. Our review expands the step initiated by our organized special session and exemplifies the usefulness of spectral phase information in a wide range of speech processing applications. Finally, the overview will provide some future work directions.


title: Efficient Analysis of Substrate Integrated Waveguide Devices Using Hybrid Mode Matching Between Cylindrical and Guided Modes

abstract: In this paper, a new method to efficiently analyze substrate integrated waveguide (SIW) based devices with multiple accessing ports is presented. The problem is considered as a 2-D electromagnetic problem assuming no field variation normal to the dielectric substrate. The incident and scattered fields from each circular cylinder are expanded with cylindrical modes, and the fields in the waveguide ports are expanded using progressive and regressive modal summations. The addition theorems of Bessel and Hankel functions are used to analyze the full-wave behavior of the SIW device. In order to extract the circuital parameters, the hybrid mode-matching between guided and cylindrical modes is done by projecting continuity equations in a circular boundary containing the whole SIW structure over the inner modes of each region. Applying this new technique, it is possible to analyze multiple port devices by solving a set of integrals that can be easily approached analytically or by using the inverse fast Fourier transform, avoiding the use of non efficient numerical methods. It is shown that the new method runs faster than commercial software packages and other techniques recently published.


title: ATLASCAR - technologies for a computer assisted driving system on board a common automobile

abstract: The future of intelligent vehicles will rely on robust information to allow the proper feedback to the vehicle itself, to issue several kinds of active safety, but before all, to generate information for the driver by calling his or her attention to potential instantaneous or mid-term risks associated with the driving. Before true vehicle autonomy, safety and driver assistance are a priority. Sophisticated sensorial and perceptive mechanisms must be made available for, in a first instance, assisting the driver and, on a latter phase, participate in better autonomy. These mechanisms rely on sensors and algorithms that are mostly available nowadays, but many of them are still unsuited for critical situations. This paper presents a project where engineering and scientific solutions have been devised to settle a full featured real scale platform for the next generation of ITS vehicles that are concerned with the immediate issues of navigation and challenges on the road. The car is now ready and running, and the data gathering has just begun.


title: LOMo: Latent Ordinal Model for Facial Analysis in Videos

abstract: We study the problem of facial analysis in videos. We propose a novel weakly supervised learning method that models the video event (expression, pain etc.) as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for smile, brow lower and cheek raise for pain). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations. In combination with complimentary features, we report state-of-the-art results on these datasets.


title: Deep Descriptor Transforming for Image Co-Localization

abstract: Reusable model design becomes desirable with the rapid expansion of machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detector for the common object in the image co-localization problem. We propose a simple but effective method, named Deep Descriptor Transforming (DDT), for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of images. Empirical studies validate the effectiveness of the proposed DDT method. On benchmark image co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. Moreover, DDT also demonstrates good generalization ability for unseen categories and robustness for dealing with noisy data.


title: Accurate, Robust, and Flexible Real-time Hand Tracking

abstract: We present a new real-time hand tracking system based on a single depth camera. The system can accurately reconstruct complex hand poses across a variety of subjects. It also allows for robust tracking, rapidly recovering from any temporary failures. Most uniquely, our tracker is highly flexible, dramatically improving upon previous approaches which have focused on front-facing close-range scenarios. This flexibility opens up new possibilities for human-computer interaction with examples including tracking at distances from tens of centimeters through to several meters (for controlling the TV at a distance), supporting tracking using a moving depth camera (for mobile scenarios), and arbitrary camera placements (for VR headsets). These features are achieved through a new pipeline that combines a multi-layered discriminative reinitialization strategy for per-frame pose estimation, followed by a generative model-fitting stage. We provide extensive technical details and a detailed qualitative and quantitative analysis.


H YBRID T ECHNIQUE FOR K NOWLEDGE REPRESENTATION & A C OMPARATIVE S TUDY


title: Face liveness detection under bad illumination conditions

abstract: Spoofing face recognition systems with photos or videos of someone else is not difficult. Sometimes, all one needs is to display a picture on a laptop monitor or a printed photograph to the biometric system. In order to detect this kind of spoofs, in this paper we present a solution that works either with printed or LCD displayed photographs, even under bad illumination conditions without extra-devices or user involvement. Tests conducted on large databases show good improvements of classification accuracy as well as true positive and false positive rates compared to the state-of-the-art.


title: Hard Evidence on Soft Skills

abstract: This paper summarizes recent evidence on what achievement tests measure; how achievement tests relate to other measures of "cognitive ability" like IQ and grades; the important skills that achievement tests miss or mismeasure, and how much these skills matter in life. Achievement tests miss, or perhaps more accurately, do not adequately capture, soft skills--personality traits, goals, motivations, and preferences--that are valued in the labor market, in school, and in many other domains. The larger message of this paper is that soft skills predict success in life, that they causally produce that success, and that programs that enhance soft skills have an important place in an effective portfolio of public policies.


title: Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation

abstract: Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95%, which is comparable to those obtained by the known methods.


title: Pinpointing Vulnerabilities

abstract: Memory-based vulnerabilities are a major source of attack vectors. They allow attackers to gain unauthorized access to computers and their data. Previous research has made significant progress in detecting attacks. However, developers still need to locate and fix these vulnerabilities, a mostly manual and time-consuming process. They face a number of challenges. Particularly, the manifestation of an attack does not always coincide with the exploited vulnerabilities, and many attacks are hard to reproduce in the lab environment, leaving developers with limited information to locate them. In this paper, we propose Ravel, an architectural approach to pinpoint vulnerabilities from attacks. Ravel consists of an online attack detector and an offline vulnerability locator linked by a record & replay mechanism. Specifically, Ravel records the execution of a production system and simultaneously monitors it for attacks. If an attack is detected, the execution is replayed to reveal the targeted vulnerabilities by analyzing the program's memory access patterns under attack. We have built a prototype of Ravel based on the open-source FreeBSD operating system. The evaluation results in security and performance demonstrate that Ravel can effectively pinpoint various types of memory vulnerabilities and has low performance overhead.


title: Towards development of health Data Warehouse: Bangladesh perspective

abstract: Health informatics is one of the top most focuses of researchers now a days. Availability of timely and accurate data is essential for informed medical decision making. Health care organizations face a common problem with large amount of data they have in numerous systems. Such systems are unstructured and unorganized, requires computational time for data integration. Researchers, medical practitioners, health care providers and patients will not be able to utilize the knowledge stored in different repositories unless synthesize the information from disparate sources. This problem can be solved by Data warehousing. Data warehousing techniques share a common set of tasks, include requirements analysis, data design, architectural design, implementation and deployment. Developing Clinical data warehouse is complex and time consuming but is essential to deliver quality patient care. Data integration tasks of medical data store are much challenging when designing clinical data warehouse architecture. This research identifies prospects and complexities of Health data warehousing and Mining in Bangladesh perspective and proposes a data-warehousing model suitable for integrating data from different health care sources.


title: Deformable Models in Medical Image Analysis : A Survey

abstract: This article surveys deformable models, a promising and vigorously researched computerassisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching, and motion tracking.


title: WAIS-IV Profile of Cognition in Schizophrenia

abstract: The Wechsler Adult Intelligence Scale (WAIS) has been used extensively to study impairment across a range of cognitive domains in schizophrenia. However, cognitive performance among those with the illness has yet to be examined using the newest edition of this measure. Hence, the current study aims first, to provide WAIS-IV normative data for Canadian individuals with schizophrenia of low average intelligence; second, to examine schizophrenia performance on all WAIS-IV subtest, index and general intelligence scores relative to healthy comparison subjects; and third, to revalidate the pattern of impairment identified in this clinical group using the WAIS-III, where processing speed (PS) was most affected, followed by working memory (WM), perceptual reasoning (PR) and verbal comprehension (VC). The WAIS-IV was administered to outpatients with schizophrenia and their performance compared with age, gender, and education matched controls. WAIS-IV schizophrenia performance data are provided. Analyses revealed s...


title: Graph Regularized Nonnegative Matrix Factorization for Data Representation

abstract: Matrix factorization techniques have been frequently applied in information retrieval, computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. On the other hand, from the geometric perspective, the data is usually sampled from a low-dimensional manifold embedded in a high-dimensional ambient space. One then hopes to find a compact representation,which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In this paper, we propose a novel algorithm, called Graph Regularized Nonnegative Matrix Factorization (GNMF), for this purpose. In GNMF, an affinity graph is constructed to encode the geometrical information and we seek a matrix factorization, which respects the graph structure. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.


title: Bandlimited Reconstruction of Multidimensional Images From Irregular Samples

abstract: We examine different sampling lattices and their respective bandlimited spaces for reconstruction of irregularly sampled multidimensional images. Considering an irregularly sampled dataset, we demonstrate that the non-tensor-product bandlimited approximations corresponding to the body-centered cubic and face-centered cubic lattices provide a more accurate reconstruction than the tensor-product bandlimited approximation associated with the commonly-used Cartesian lattice. Our practical algorithm uses multidimensional sinc functions that are tailored to these lattices and a regularization scheme that provides a variational framework for efficient implementation. Using a number of synthetic and real data sets we record improvements in the accuracy of reconstruction in a practical setting.


title: Interactive global illumination

abstract: Interactive graphics has been limited to simple direct illumination that commonly results in an artificial appearance. A more realistic appearance by simulating global illumination effects has been too costly to compute at interactive rates. In this paper we describe a new Monte Carlo-based global illumination algorithm. It achieves performance of up to 10 frames per second while arbitrary changes to the scene may be applied interactively. The performance is obtained through the effective use of a fast, distributed ray-tracing engine as well as a new interleaved sampling technique for parallel Monte Carlo simulation. A new filtering step in combination with correlated sampling avoids the disturbing noise artifacts common to Monte Carlo methods.


title: Dynamic programming for a Markov-switching jump-diffusion

abstract: Abstract We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markov-switching jump–diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman’s optimality principle (or dynamic programming principle) and obtain the corresponding Hamilton–Jacobi–Belman equation, which turns out to be a partial integro-differential equation due to the extra terms arising from the Levy process and the Markov process. As an application of our results, we study a finite horizon consumption–investment problem for a jump–diffusion financial market consisting of one risk-free asset and one risky asset whose coefficients are assumed to depend on the state of a continuous time finite state Markov process. We provide a detailed study of the optimal strategies for this problem, for the economically relevant families of power utilities and logarithmic utilities.


title: Manga109 dataset and creation of metadata

abstract: We have created Manga109, a dataset of a variety of 109 Japanese comic books publicly available for use for academic purposes. This dataset provides numerous comic images but lacks the annotations of elements in the comics that are necessary for use by machine learning algorithms or evaluation of methods. In this paper, we present our ongoing project to build metadata for Manga109. We first define the metadata in terms of frames, texts and characters. We then present our web-based software for efficiently creating the ground truth for these images. In addition, we provide a guideline for the annotation with the intent of improving the quality of the metadata.


title: EEG Mouse:A Machine Learning-Based Brain Computer Interface

abstract: The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time. Keywords—EEG; BCI; Data Mining; Machine Learning; SVMs; NNs; DWT; Feature Extraction


title: Single-phase inverter for solar energy conversion controlled with DSpace DS1104

abstract: The present paper presents a system intended to be used in the development of photovoltaic inverters with maximum power point tracking capability. A simulation model was implemented in MATLAB/SIMULINK and an experimental model composed of a DSpace 1104 controller board, a Semikron inverter, a coupling transformer, an LC filter and two 75W solar panels was developed. The system facilitates the development and testing of digital control and MPPT algorithms for single-phase inverters connected to the utility grid.


title: Exhaustive search, combinatorial optimization and enumeration: Exploring the potential of raw computing power

abstract: For half a century since computers came into existence, the goal of finding elegant and efficient algorithms to solve "simple" (well-defined and well-structured) problems has dominated algorithm design. Over the same time period, both processing and storage capacity of computers have increased by roughly a factor of a million. The next few decades may well give us a similar rate of growth in raw computing power, due to various factors such as continuing miniaturization, parallel and distributed computing. If a quantitative change of orders of magnitude leads to qualitative advances, where will the latter take place? Only empirical research can answer this question. ::: ::: Asymptotic complexity theory has emerged as a surprisingly effective tool for predicting run times of polynomial-time algorithms. For NP-hard problems, on the other hand, it yields overly pessimistic bounds. It asserts the non-existence of algorithms that are efficient across an entire problem class, but ignores the fact that many instances, perhaps including those of interest, can be solved efficiently. For such cases we need a complexity measure that applies to problem instances, rather than to over-sized problem classes. ::: ::: Combinatorial optimization and enumeration problems are modeled by state spaces that usually lack any regular structure. Exhaustive search is often the only way to handle such "combinatorial chaos". Several general purpose search algorithms are used under different circumstances. We describe reverse search and illustrate this technique on a case study of enumerative optimization: enumeratingt he k shortest Euclidean spanning trees.


title: From LTE to 5G for Connected Mobility

abstract: Long Term Evolution, the fourth generation of mobile communication technology, has been commercially deployed for about five years. Even though it is continuously updated through new releases, and with LTE Advanced Pro Release 13 being the latest one, the development of the fifth generation has been initiated. In this article, we measure how current LTE network implementations perform in comparison with the initial LTE requirements. The target is to identify certain key performance indicators that have suboptimal implementations and therefore lend themselves to careful consideration when designing and standardizing next generation wireless technology. Specifically, we analyze user and control plane latency, handover execution time, and coverage, which are critical parameters for connected mobility use cases such as road vehicle safety and efficiency. We study the latency, handover execution time, and coverage of four operational LTE networks based on 19,000 km of drive tests covering a mixture of rural, suburban, and urban environments. The measurements have been collected using commercial radio network scanners and measurement smartphones. Even though LTE has low air interface delays, the measurements reveal that core network delays compromise the overall round-trip time design requirement. LTE's breakbefore- make handover implementation causes a data interruption at each handover of 40 ms at the median level. While this is in compliance with the LTE requirements, and lower values are certainly possible, it is also clear that break-before-make will not be sufficient for connected mobility use cases such as road vehicle safety. Furthermore, the measurements reveal that LTE can provide coverage for 99 percent of the outdoor and road users, but the LTE-M or NarrowBand-IoT upgrades, as of LTE Release 13, are required in combination with other measures to allow for additional penetration losses, such as those experienced in underground parking lots. Based on the observed discrepancies between measured and standardized LTE performance, in terms of latency, handover execution time, and coverage, we conclude the article with a discussion of techniques that need careful consideration for connected mobility in fifth generation mobile communication technology.


title: A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty

abstract: With the abundance of exceptionally High Dimensional data, feature selection has become an essential element in the Data Mining process. In this paper, we investigate the problem of efficient feature selection for classification on High Dimensional datasets. We present a novel filter based approach for feature selection that sorts out the features based on a score and then we measure the performance of four different Data Mining classification algorithms on the resulting data. In the proposed approach, we partition the sorted feature and search the important feature in forward manner as well as in reversed manner, while starting from first and last feature simultaneously in the sorted list. The proposed approach is highly scalable and effective as it parallelizes over both attribute and tuples simultaneously allowing us to evaluate many of potential features for High Dimensional datasets. The newly proposed framework for feature selection is experimentally shown to be very valuable with real and synthetic High Dimensional datasets which improve the precision of selected features. We have also tested it to measure classification accuracy against various feature selection process.


title: A Framework for Iterative Hash Functions: HAIFA

abstract: Since the seminal works of Merkle and Damgard on the iter- ation of compression functions, hash functions were built from compres- sion functions using the Merkle-Damgard construction. Recently, several flaws in this construction were identified, allowing for second pre-image attacks and chosen target pre-image attacks on such hash functions even when the underlying compression functions are secure. In this paper we propose the HAsh Iterative FrAmework (HAIFA). Our framework can fix many of the flaws while supporting several additional properties such as defining families of hash functions and supporting variable hash size. HAIFA allows for an online computation of the hash function in one pass with a fixed amount of memory independently of the size of the message. Besides our proposal, the recent attacks initiated research on the way compression functions are to be iterated. We show that most recent pro- posals such as randomized hashing, the enveloped Merkle-Damgard, and the RMC and ROX modes can be all be instantiated as part of the HAsh Iterative FrAmework (HAIFA).


title: A 486k S/s CMOS time-domain smart temperature sensor with −0.85°C/0.78°C voltage-calibrated error

abstract: This paper presents the first voltage-calibrated CMOS time-domain smart temperature sensor to reduce the cost of mass production. A digitally adjustable relaxation oscillator designed as the temperature sensor vibrates between CMOS-based CTAT, PTAT voltage references with mutual curvature compensation to generate linear temperature-dependent output pulses. Voltage instead of temperature calibration is adopted to alleviate the impact of process variation and TDC is used for output coding. Fabricated in a TSMC 0.18-μm standard CMOS process, the proposed sensor is able to operate at a high speed of 486k samples/sec for SoC thermal monitoring. The active area is merely 0.122 mm2 and the inaccuracy is measured to be less than ±1°C for 15 test chips in a wide temperature range of −40°C to 120°C. The performance is even superior to some chips with one- or two-point temperature calibrations [1-4]. A milestone is established for time-domain smart temperature sensor to get rid of the heavy burden of fixed-temperature calibration with reason error budget.


title: What (or Who) Is Public?: Privacy Settings and Social Media Content Sharing

abstract: When social networking sites give users granular control over their privacy settings, the result is that some content across the site is public and some is not. How might this content--or characteristics of users who post publicly versus to a limited audience--be different? If these differences exist, research studies of public content could potentially be introducing systematic bias. Via Mechanical Turk, we asked 1,815 Facebook users to share recent posts. Using qualitative coding and quantitative measures, we characterize and categorize the nature of the content. Using machine learning techniques, we analyze patterns of choices for privacy settings. Contrary to expectations, we find that content type is not a significant predictor of privacy setting; however, some demographics such as gender and age are predictive. Additionally, with consent of participants, we provide a dataset of nearly 9,000 public and non-public Facebook posts.


title: Scratch: programming for all

abstract: "Digital fluency" should mean designing, creating, and remixing, not just browsing, chatting, and interacting.


title: CUBIC: a new TCP-friendly high-speed TCP variant

abstract: CUBIC is a congestion control protocol for TCP (transmission control protocol) and the current default TCP algorithm in Linux. The protocol modifies the linear window growth function of existing TCP standards to be a cubic function in order to improve the scalability of TCP over fast and long distance networks. It also achieves more equitable bandwidth allocations among flows with different RTTs (round trip times) by making the window growth to be independent of RTT -- thus those flows grow their congestion window at the same rate. During steady state, CUBIC increases the window size aggressively when the window is far from the saturation point, and the slowly when it is close to the saturation point. This feature allows CUBIC to be very scalable when the bandwidth and delay product of the network is large, and at the same time, be highly stable and also fair to standard TCP flows. The implementation of CUBIC in Linux has gone through several upgrades. This paper documents its design, implementation, performance and evolution as the default TCP algorithm of Linux.


Efficient pedestrian detection in urban traffic scenes


Deep Learning 3D Shapes Using Alt-az Anisotropic 2-Sphere Convolution


title: “Change the mind and you change the brain”: effects of cognitive-behavioral therapy on the neural correlates of spider phobia

abstract: Abstract Questions pertaining to the neurobiological effects of psychotherapy are now considered among the most topical in psychiatry. With respect to this issue, positron emission tomography (PET) findings indicate that cognitive and behavioral modifications, occurring in a psychotherapeutic context, can lead to regional brain metabolic changes in patients with major depression or obsessive-compulsive disorder. The goal of the present functional magnetic resonance imaging (fMRI) study, which constitutes the first neuroimaging investigation of the effects of cognitive-behavioral therapy (CBT) using an emotional activation paradigm, was to probe the effects of CBT on the neural correlates of spider phobia. In order to do so, fMRI was used in subjects suffering from spider phobia (n = 12) to measure, before and after effective CBT, regional brain activity during the viewing of film excerpts depicting spiders. Normal control subjects were also scanned (once) while they were exposed to the same film excerpts. Results showed that, in phobic subjects before CBT, the transient state of fear triggered, during the viewing of the phobogenic stimuli, was correlated with significant activation of the right dorsolateral prefrontal cortex (Brodmann area—BA 10), the parahippocampal gyrus, and the visual associative cortical areas, bilaterally. For normal control subjects (n = 13), only the left middle occipital gyrus and the right inferior temporal gyrus were significantly activated. In phobic subjects before CBT, the activation of the dorsolateral prefrontal cortex (BA 10) may reflect the use of metacognitive strategies aimed at self-regulating the fear triggered by the spider film excerpts, whereas the parahippocampal activation might be related to an automatic reactivation of the contextual fear memory that led to the development of avoidance behavior and the maintenance of spider phobia. After successful completion of CBT, no significant activation was found in the dorsolateral prefrontal cortex (BA 10) or the parahippocampal gyrus. These findings suggest that a psychotherapeutic approach, such as CBT, has the potential to modify the dysfunctional neural circuitry associated with anxiety disorders. They further indicate that the changes made at the mind level, within a psychotherapeutic context, are able to functionally “rewire” the brain.


title: Retail pharmacy staff perceptions of design strengths and weaknesses of electronic prescribing

abstract: OBJECTIVE ::: This paper explored pharmacy staff perceptions of the strengths and weaknesses of electronic prescribing (e-prescribing) design in retail pharmacies using the sociotechnical systems framework. This study examined how adoption of e-prescribing technology is affecting clinical practice and patient care. ::: ::: ::: MATERIALS AND METHODS ::: Direct observations and think aloud protocols were used to collect data from seven retail pharmacies. ::: ::: ::: RESULTS ::: Pharmacists and pharmacy technicians reported strengths of e-prescribing design that facilitated pharmacy work which included: legibility, ease of archiving, quick access to prescriptions and consistency in the format of electronic prescriptions (e-prescriptions). Design weaknesses and potential hazards to patient care associated with e-prescribing systems were due to differences between pharmacy and prescriber computer systems which resulted in the selection of wrong patient or drug (name, directions, dose, strength, formulation, package sizes). There were unique strengths and weaknesses in the design of e-prescriptions peculiar to the three pharmacy computer systems examined in this study. ::: ::: ::: DISCUSSION ::: Findings from this study can help inform policy on creating e-prescribing design standards for pharmacy. e-Prescribing system developers can use the results of this study to identify and apply the most usable features of the three main pharmacy computer systems to design systems that support dispensing efficiency and safety. ::: ::: ::: CONCLUSIONS ::: This is the first study to highlight design flaws with e-prescribing in retail pharmacies. The sociotechnical systems framework was useful in providing an indepth understanding of the pharmacist and pharmacy technician's interface with e-prescribing technology. This information can be used by policy makers to create e-prescribing standards for pharmacies.


title: Vectorization in PyPy's Tracing Just-In-Time Compiler

abstract: PyPy is a widely known virtual machine for the Python programming language. PyPy itself is implemented in the statically typed subset of Python called RPython. RPython includes a tracing Just-In-Time (JIT) compiler and is capable of generating the compiler for a language from the specification of the interpreter for that language. In PyPy 4.0.0 we extended the tracing JIT compiler to support vectorization of loops and emit code for the SSE4 vector operations of the x86 instruction set. This article presents the details of the new vectorizer of PyPy. The vectorizer uses a loop unrolling approach to vectorization. It has been designed for efficient compilation as the compilation is done during the execution of the application. The scientific library NumPy introduced arrays which are homogeneous, primitive typed and contiguous in memory. These kind of arrays are used to avoid the problems with dynamic typing. Our contribution to PyPy's new vectorizer supports scalar and constant expansion, accumulator splitting for reductions, guard strengthening and array bounds check removal. The empirical evaluation shows that the vectorizer can gain speedups close to the theoretical optimum of the SSE4 instruction set.


title: Vulnerabilities of biometric systems integrated in mobile devices: An evaluation

abstract: With the growing number of people that own a smartphone with a fingerprint sensor, it is necessary to be able to assess and compare different smartphones' ability to reject false fingerprints, as attackers could have access to sensitive data (bank accounts, pictures, documents). For this end, a common ground is needed to perform comparable security evaluations. This paper unifies several methodologies to evaluate the security of fingerprint biometric systems embedded in mobile devices. Then, this methodology is applied on 5 different smartphones for a security evaluation and their ability' to reject false fingerprints is compared.


Shortest-Path Graph Kernels for Document Similarity.


title: Coreference in Spoken vs. Written Texts: a Corpus-based Analysis

abstract: This paper describes an empirical study of coreference in spoken vs. written text. We focus on the comparison of two particular text types, interviews and popular science texts, as instances of spoken and written texts since they display quite different discourse structures. We believe in fact, that the correlation of difficulties in coreference resolution and varying discourse structures requires a deeper analysis that accounts for the diversity of coreference strategies or their sub-phenomena as indicators of text type or genre. In this work, we therefore aim at defining specific parameters that classify differences in genres of spoken and written texts such as the preferred segmentation strategy, the maximal allowed distance in or the length and size of coreference chains as well as the correlation of structural and syntactic features of coreferring expressions. We argue that a characterization of such genre dependent parameters might improve the performance of current state-of-art coreference resolution technology.


title: The beauty of capturing faces: Rating the quality of digital portraits

abstract: Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment. In this paper, we design a specific framework to automatically evaluate the beauty of digital portraits. To this end, we procure a large dataset of face images annotated not only with aesthetic scores but also with information about the traits of the subject portrayed. We design a set of visual features based on portrait photography literature, and extensively analyze their relation with portrait beauty, exposing interesting findings about what makes a portrait beautiful. We find that the beauty of a portrait is linked to its artistic value, and independent from age, race and gender of the subject. We also show that a classifier trained with our features to separate beautiful portraits from non-beautiful portraits outperforms generic aesthetic classifiers.


title: Localization of a Stable Neural Correlate of Associative Memory

abstract: Do learning and retrieval of a memory activate the same neurons? Does the number of reactivated neurons correlate with memory strength? We developed a transgenic mouse that enables the long-lasting genetic tagging of c- fos –active neurons. We found neurons in the basolateral amygdala that are activated during Pavlovian fear conditioning and are reactivated during memory retrieval. The number of reactivated neurons correlated positively with the behavioral expression of the fear memory, indicating a stable neural correlate of associative memory. The ability to manipulate these neurons genetically should allow a more precise dissection of the molecular mechanisms of memory encoding within a distributed neuronal network.


title: A dynamic logistics coordination model for evacuation and support in disaster response activities

abstract: Abstract This paper describes an integrated location-distribution model for coordinating logistics support and evacuation operations in disaster response activities. Logistics planning in emergencies involves dispatching commodities (e.g., medical materials and personnel, specialised rescue equipment and rescue teams, food, etc.) to distribution centres in affected areas and evacuation and transfer of wounded people to emergency units. During the initial response time it is also necessary to set up temporary emergency centers and shelters in affected areas to speed up medical care for less heavily wounded survivors. In risk mitigation studies for natural disasters, possible sites where these units can be situated are specified according to risk based urban structural analysis. Logistics coordination in disasters involves the selection of sites that result in maximum coverage of medical need in affected areas. Another important issue that arises in such emergencies is that medical personnel who are on duty in nearby hospitals have to be re-shuffled to serve both temporary and permanent emergency units. Thus, an optimal medical personnel allocation must be determined among these units. The proposed model also considers this issue. The proposed model is a mixed integer multi-commodity network flow model that treats vehicles as integer commodity flows rather than binary variables. This results in a more compact formulation whose output is processed to extract a detailed vehicle route and load instruction sheet. Post processing is achieved by a simple routing algorithm that is pseudo-polynomial in the number of vehicles utilized, followed by the solution of a linear system of equations defined in a very restricted domain. The behavior and solvability of the model is illustrated on an earthquake scenario based on Istanbul’s risk grid as well as larger size hypothetical disaster scenarios.


title: REVIEW : KNOWLEDGE MANAGEMENT AND KNOWLEDGE MANAGEMENT SYSTEMS : CONCEPTUAL FOUNDATIONS AND RESEARCH ISSUES 1 , 2 By :

abstract: Knowledge is a broad and abstract notion that has defined epistemological debate in western philosophy since the classical Greek era. In the past few years, however, there has been a growing interest in treating knowledge as a significant organizational resource. Consistent with the interest in organizational knowledge and knowledge management (KM), IS researchers have begun promoting a class of information systems, referred to as knowledge management systems (KMS). The objective of KMS is to support creation, transfer, and application of knowledge in organizations. Knowledge and knowledge management are complex and multi-faceted concepts. Thus, effective development and implementation of KMS requires a foundation in several rich literatures.


title: Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling

abstract: Previous studies in Open Information Extraction (Open IE) are mainly based on extraction patterns. They manually define patterns or automatically learn them from a large corpus. However, these approaches are limited when grasping the context of a sentence, and they fail to capture implicit relations. In this paper, we address this problem with the following methods. First, we exploit long short-term memory (LSTM) networks to extract higher-level features along the shortest dependency paths, connecting headwords of relations and arguments. The path-level features from LSTM networks provide useful clues regarding contextual information and the validity of arguments. Second, we constructed samples to train LSTM networks without the need for manual labeling. In particular, feedback negative sampling picks highly negative samples among non-positive samples through a model trained with positive samples. The experimental results show that our approach produces more precise and abundant extractions than state-of-the-art open IE systems. To the best of our knowledge, this is the first work to apply deep learning to Open IE.


Dynamic Adaptation and Opponent Exploitation in Computer Poker


title: A Unifying Framework for Vector-valued Manifold Regularization and Multi-view Learning

abstract: This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. Our formulation includes as special cases Vector-valued Manifold Regularization and Multi-view Learning, thus provides in particular a unifying framework linking these two important learning approaches. In the case of least square loss function, we provide a closed form solution with an efficient implementation. Numerical experiments on challenging multi-class categorization problems show that our multi-view learning formulation achieves results which are comparable with state of the art and are significantly better than single-view learning.


Constructivist learning environments on the web: engaging students in meaningful learning


title: Active Learning with Amazon Mechanical Turk

abstract: Supervised classification needs large amounts of annotated training data that is expensive to create. Two approaches that reduce the cost of annotation are active learning and crowdsourcing. However, these two approaches have not been combined successfully to date. We evaluate the utility of active learning in crowdsourcing on two tasks, named entity recognition and sentiment detection, and show that active learning outperforms random selection of annotation examples in a noisy crowdsourcing scenario.


title: Psychological Language on Twitter Predicts County-Level Heart Disease Mortality:

abstract: Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.


title: Plan Selection Based on Query Clustering

abstract: Query optimization is a computationally intensive process, especially for complex queries. We present here a tool, called PLASTIC, that can be used by query optimizers to amortize the optimization cost. Our scheme groups similar queries into clusters and uses the optimizer-generated plan for the cluster representative to execute all future queries assigned to the cluster. Query similarity is evaluated based on a comparison of query structures and the associated table schemas and statistics, and a classifier is employed for efficient cluster assignments. Experiments with a variety of queries on a commercial optimizer show that PLASTIC predicts the correct plan choice in most cases, thereby providing significantly improved query optimization times. Further, when errors are made, the additional execution cost incurred due to the sub-optimal plan choices is marginal.


title: #Irony or #Sarcasm - A Quantitative and Qualitative Study Based on Twitter

abstract: Current study is with the aim to identify similarities and distinctions between irony and sarcasm by adopting quantitative sentiment analysis as well as qualitative content analysis. The result of quantitative sentiment analysis shows that sarcastic tweets are used with more positive tweets than ironic tweets. The result of content analysis corresponds to the result of quantitative sentiment analysis in identifying the aggressiveness of sarcasm. On the other hand, from content analysis it shows that irony owns two senses. The first sense of irony is equal to aggressive sarcasm with speaker awareness. Thus, tweets of first sense of irony may attack a specific target, and the speaker may tag his/her tweet irony because the tweet itself is ironic. These tweets though tagged as irony are in fact sarcastic tweets. Different from this, the tweets of second sense of irony is tagged to classify an event to be ironic. However, from the distribution in sentiment analysis and examples in content analysis, irony seems to be more broadly used in its second sense.


title: Clinical trial methodology to assess the efficacy/effectiveness of long-acting antipsychotics: Randomized controlled trials vs naturalistic studies

abstract: Schizophrenia presents unique difficulties in clinical trial design associated with the condition's variable presentation and clinical course, and multiple features influencing affect, cognition, volition and perception. Randomized controlled trials (RCTs) are explanatory studies using a carefully selected patient population, predefined assessment intervals and, generally, symptom-focused endpoints. Naturalistic studies are pragmatic, with no active intervention, and outcomes that are generally those used in clinical practice (e.g. hospitalization, relapse rate). Both naturalistic studies and RCTs have pros and cons, making it difficult for physicians in clinical practice to apply research findings to their own treatment decisions. The choice of clinical trial design can have a significant impact on the comparative effectiveness or efficacy of drugs. This is particularly true for studies comparing long-acting injectable (LAI) antipsychotics with oral antipsychotics in schizophrenia, in which RCTs generally show no benefit for LAIs over oral drugs, whereas observational studies do. The more pragmatic the study design, the more likely it is to show a benefit for LAIs versus oral therapy. This article reviews the pros and cons of different study types, using published examples. Criteria are outlined to help physicians design appropriate prospective studies in schizophrenia including the relevant pragmatic and/or explanatory features, as required.


title: PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs

abstract: The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal variations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial appearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures.


title: Threat, Authoritarianism, and Selective Exposure to Information

abstract: We examined the hypothesis that threat alters the cognitive strategies used by high authoritarians in seeking out new political information from the environment. In a laboratory experiment, threat was manipulated through a “mortality salience” manipulation used in research on terror management theory (Pyszczynski, Solomon & Greenberg, 2003). Subjects (N = 92) were then invited to read one of three editorial articles on the topic of capital punishment. We found that in the absence of threat, both low and high authoritarians were responsive to salient norms of evenhandedness in information selection, preferring exposure to a two-sided article that presents the merits of both sides of an issue to an article that selectively touts the benefits of the pro or con side of the issue. However, in the presence of threat, high but not low authoritarians became significantly more interested in exposure to an article containing uniformly pro-attitudinal arguments, and significantly less interested in a balanced, two-sided article. Finally, a path analysis indicated that selective exposure to attitude-congruent information led to more internally consistent policy attitudes and inhibited attitude change. Discussion focuses on the role of threat in conditioning the cognitive and attitudinal effects of authoritarianism.


title: Explaining Explanations to Society

abstract: There is a disconnect between explanatory artificial intelligence (XAI) methods and the types of explanations that are useful for and demanded by society (policy makers, government officials, etc.) Questions that experts in artificial intelligence (AI) ask opaque systems provide inside explanations, focused on debugging, reliability, and validation. These are different from those that society will ask of these systems to build trust and confidence in their decisions. Although explanatory AI systems can answer many questions that experts desire, they often don't explain why they made decisions in a way that is precise (true to the model) and understandable to humans. These outside explanations can be used to build trust, comply with regulatory and policy changes, and act as external validation. In this paper, we focus on XAI methods for deep neural networks (DNNs) because of DNNs' use in decision-making and inherent opacity. We explore the types of questions that explanatory DNN systems can answer and discuss challenges in building explanatory systems that provide outside explanations for societal requirements and benefit.


title: Feature Selection Based on Structured Sparsity: A Comprehensive Study

abstract: Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have been widely studied in the last few years, and a number of algorithms have been proposed. However, there is no comprehensive study concerning the connections between different SSFS methods, and how they have evolved. In this paper, we attempt to provide a survey on various SSFS methods, including their motivations and mathematical representations. We then explore the relationship among different formulations and propose a taxonomy to elucidate their evolution. We group the existing SSFS methods into two categories, i.e., vector-based feature selection (feature selection based on lasso) and matrix-based feature selection (feature selection based on ${l_{r,p}}$ -norm). Furthermore, FS has been combined with other machine learning algorithms for specific applications, such as multitask learning, multilabel learning, multiview learning, classification, and clustering. This paper not only compares the differences and commonalities of these methods based on regression and regularization strategies, but also provides useful guidelines to practitioners working in related fields to guide them how to do feature selection.


title: Quantifying the total wetted surface area of the world fleet: a first step in determining the potential extent of ships’ biofouling

abstract: Ships’ hulls can transport aquatic nuisance species, but there is little quantitative information about the magnitude of vessel biofouling on a global or regional scale. There does not exist a robust method to estimate the wetted surface area (WSA) of a particular fleet of ships, especially across the diversity of possible vessel types. An estimate of the total WSA of ship arrivals into a port or region is essential to determine the potential scope of biofouling and to inform management strategies to reduce the future invasions. Multiple statistical models were developed so commonly available ships’ parameters could be used to estimate the WSA for a given set of fleet data. Using individual ship characteristics and publicly available data from ~120,000 active commercial ships in the world fleet, the method results in a total global minimum WSA estimate of approximately 325 × 106 m2. The size of the global fleet employed here is greater than the commonly cited vessel number of approximately 80,000–90,000, as we include ships <100 gross tons. Over 190,000 vessels were initially identified, representing a WSA of 571 × 106 m2, but active status of only 120,000 vessels could be confirmed. Reliable data were unavailable on the operating status of many additional and especially smaller vessels. This approach, along with a contemporary and comprehensive estimate of global WSA, when combined with knowledge of the different operational profiles of ships that may reduce biofouling (port residence times, steaming speeds, extent of antifouling coatings, cleaning frequency, etc.), can inform current numerical models and risk assessments of bioinvasions.


title: Securing Web Accounts Using Graphical Password Authentication through Watermarking

abstract: Today, most Internet applications still establish user authentication with traditional text based passwords. Designing a secure as well as a user- friendly password-based method has been on the agenda of security researchers for a long time. On one hand, there are password manager programs which facilitate generating site-specific strong passwords from a single user password to eliminate the memory burden due to multiple passwords. On the other hand, there are studies exploring the viability of graphical passwords as a more secure and user-friendly alternative. In this project, we propose a new graphical password scheme for accessing web accounts called "Secure Web Account Access through Recognition Based Graphical Password by Watermarking". Here user selects number of images as a password and while login user needs to enter the random code generated below each image, which has been set as a password. Here the security of the system is very high and every time user needs to enter different set of code for authentication i.e. every time new password gets generated making Dictionary attacks, Brute Force attack, and other attacks infeasible.


title: Image Enhancement Network Trained by Using HDR images

abstract: In this paper, a novel image enhancement network is proposed, where HDR images are used for generating training data for our network. Most of conventional image enhancement methods, including Retinex based methods, do not take into account restoring lost pixel values caused by clipping and quantizing. In addition, recently proposed CNN based methods still have a limited scope of application or a limited performance, due to network architectures. In contrast, the proposed method have a higher performance and a simpler network architecture than existing CNN based methods. Moreover, the proposed method enables us to restore lost pixel values. Experimental results show that the proposed method can provides higher-quality images than conventional image enhancement methods including a CNN based method, in terms of TMQI and NIQE.


title: Risk-Sensitive Reinforcement Learning

abstract: Most reinforcement learning algorithms optimize the expected return of a Markov Decision Problem. Practice has taught us the lesson that this criterion is not always the most suitable because many applications require robust control strategies which also take into account the variance of the return. Classical control literature provides several techniques to deal with risk-sensitive optimization goals like the so-called worst-case optimality criterion exclusively focusing on risk-avoiding policies or classical risk-sensitive control, which transforms the returns by exponential utility functions. While the first approach is typically too restrictive, the latter suffers from the absence of an obvious way to design a corresponding model-free reinforcement learning algorithm. ::: ::: Our risk-sensitive reinforcement learning algorithm is based on a very different philosophy. Instead of transforming the return of the process, we transform the temporal differences during learning. While our approach reflects important properties of the classical exponential utility framework, we avoid its serious drawbacks for learning. Based on an extended set of optimality equations we are able to formulate risk-sensitive versions of various well-known reinforcement learning algorithms which converge with probability one under the usual conditions.


title: Optimized simulated annealing for Ising spin glasses

abstract: Abstract We present several efficient implementations of the simulated annealing algorithm for Ising spin glasses on sparse graphs. In particular, we provide a generic code for any choice of couplings, an optimised code for bipartite graphs, and highly optimised implementations using multi-spin coding for graphs with small maximum degree and discrete couplings with a finite range. The latter codes achieve up to 50 spin flips per nanosecond on modern Intel CPUs. We also compare the performance of the codes to that of the special purpose D-Wave devices built for solving such Ising spin glass problems. Program summary Program title: SimAn v1.0 Catalogue identifier: AEVZ_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEVZ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 14999 No. of bytes in distributed program, including test data, etc.: 26594 Distribution format: tar.gz Programming language: C++, OpenMP for parallelization. Computer: Any PC. Operating system: Linux/OS X/UNIX. Has the code been vectorised or parallelized?: Parallelized using OpenMP. RAM: Variable, from a few megabytes. Classification: 4.13, 6.5, 23. Nature of problem: Ising spin glass ground states on sparse graphs. Solution method: Simulated annealing. Running time: From milliseconds to seconds.


title: Estrus Detection in Dairy Cows from Acceleration Data using Self-learning Classification Models

abstract: Automatic estrus detection techniques in dairy cows have been present by different traits. Pedometers and accelerators are the most common sensor equipment. Most of the detection methods are associated with the supervised classification technique, which the training set becomes a crucial reference. The training set obtained by visual observation is subjective and time consuming. Another limitation of this approach is that it usually does not consider the factors affecting successful alerts, such as the discriminative figure, activity type of cows, the location and direction of the sensor node placed on the neck collar of a cow. This paper presents a novel estrus detection method that uses k-means clustering algorithm to create the training set online for each cow. And the training set is finally used to build an activity classification model by SVM. The activity index counted by the classification results in each sampling period can measure cow’s activity variation for assessing the onset of estrus. The experimental results indicate that the peak of estrus time are higher than that of non-estrus time at least twice in the activity index curve, and it can enhance the sensitivity and significantly reduce the error rate.


title: Immediate effect of specific nostril manipulating yoga breathing practices on autonomic and respiratory variables.

abstract: The effect of right, left, and alternate nostril yoga breathing (i.e., RNYB, LNYB, and ANYB, respectively) were compared with breath awareness (BAW) and normal breathing (CTL). Autonomic and respiratory variables were studied in 21 male volunteers with ages between 18 and 45 years and experience in the yoga breathing practices between 3 and 48 months. Subjects were assessed in five experimental sessions on five separate days. The sessions were in fixed possible sequences and subjects were assigned to a sequence randomly. Each session was for 40 min; 30 min for the breathing practice, preceded and followed by 5 min of quiet sitting. Assessments included heart rate variability, skin conductance, finger plethysmogram amplitude, breath rate, and blood pressure. Following RNYB there was a significant increase in systolic, diastolic and mean pressure. In contrast, the systolic and diastolic pressure decreased after ANYB and the systolic and mean pressure were lower after LNYB. Hence, unilateral nostril yoga breathing practices appear to influence the blood pressure in different ways. These effects suggest possible therapeutic applications.


title: Marginal improvement of AUFLS using ROCOF

abstract: This paper proposes a scheme that integrates rate of change of frequency (ROCOF) and under frequency (UF) elements to design more effective load shedding response. A new load block activation algorithm is also introduced that improves the overall load shedding (LS) performance. The combination of ROCOF and UF criteria is used to overcome the LS block discrimination problem and to provide a flexible and decentralised solution to mitigation. The proposed method is verified through simulations on the IEEE 39-bus test system.


title: 3-D Markerless Tracking of Human Gait by Geometric Trilateration of Multiple Kinects

abstract: In this paper, we develop an integrated markerless gait tracking system with three Kinect v2 sensors. A geometric principle-based trilateration method is proposed for optimizing the accuracy of the measured gait data. To tackle the data synchronization problem among the Kinect clients and the server, a synchronization mechanism based on network time protocol (NTP) is designed for synchronizing the server and Kinect clients’ clocks. Furthermore, a time schedule is designed for timing each Kinect client's data transmission. In the experiment, participants are asked to perform a 60-s walk, while the proposed tracking system obtains the participant's gait data. Six joints (including left hip, right hip, left knee, right knee, left ankle, and right ankle) of the participants are tracked where the obtained gait data are described as 6000 movements of joint positions (1000 movements for each joint). The results show that the trilateration tracking result by the three Kinect sensors has a much higher accuracy compared with the accuracy measured by a single Kinect sensor. Within a randomly sampled time period (67.726 s in the experiment), 98.37 $\%$ of the frames generated by the gait tracking system have timing errors less than 1 ms, which is much better than the default NTP service embedded in the Windows 8.1 operating system. The accuracy of the proposed system is quantitatively evaluated and verified by a comparison with a commercial medical system (Delsys Trigno Smart Sensor System).


title: Color point cloud registration with 4D ICP algorithm

abstract: This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the Iterative Closest Point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in {x, y, z, hue} 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP


title: Signal Processing and Imaging with Ultrasonic Guided Waves: Goals, Challenges and Recent Progress

abstract: Ultrasonic guided waves have the potential for both rapid inspection and in situ monitoring of plate-like structures, and effective signal processing and imaging algorithms are essential to achieve necessary performance. Although guided waves can propagate long distances and still remain sensitive to damage, their dispersive nature and sensitivity to varying environmental and operational conditions offer significant challenges. This paper addresses two guided wave applications that have recently been the subject of significant research. The first is acquisition and analysis of full or partial guided wavefield data such as can be obtained by either a scanning laser vibrometer or a scanned air-coupled transducer. This application is motivated by the need for a rapid alternative to traditional bulk wave inspections that does not require extensive teardown or liquid couplants. The second is in situ monitoring using a spatially distributed array of simple piezoelectric transducers. This application is motivated by the need for long term monitoring of critical structures to both lower maintenance costs and prevent catastrophic failures. Recent results are presented from current and past projects of the QUEST Laboratory at Georgia Tech.


title: Three-dimensional optical holography using a plasmonic metasurface

abstract: Holographic techniques allow for the construction of 3D images by controlling the wave front of light beams. Huang et al. develop ultrathin plasmonic metasurfaces to provide 3D optical holographic image reconstruction in the visible and near-infrared regions for circularly polarized light.


title: A 1.5 V, 1.5 GHz CMOS low noise amplifier

abstract: A 1.5-GHz low noise amplifier (LNA), intended for use in a global positioning system (GPS) receiver, has been implemented in a standard 0.6-/spl mu/m CMOS process. The amplifier provides a forward gain (S21) of 22 dB with a noise figure of only 3.5 dB while drawing 30 mW from a 1.5 V supply. In this paper, we present a detailed analysis of the LNA architecture, including a discussion on the effects of induced gate noise in MOS devices.


title: Early word-learning entails reference, not merely associations

abstract: Recent years have witnessed a resurgence of classic tensions concerning the fundamental nature of human knowledge and the processes underlying its acquisition. This tension, especially evident in research on the acquisition of words and concepts, arises when researchers pit one type of content against another (perceptual versus conceptual) and one type of process against another (associative versus theory-based). But these dichotomies are false; they rest upon insufficient consideration of the structure and diversity of the words and concepts that we naturally acquire. As infants and young children establish categories and acquire words to describe them, they take advantage of both perceptual and conceptual information, and relate this to both the (rudimentary) theories they hold and the statistics that they witness.


title: A fast computation of pairwise sequence alignment scores between a protein and a set of single-locus variants of another protein

abstract: Recently we have developed a new algorithm, PROVEAN ( Pro tein V ariation E ffect An alyzer), for predicting the functional effect of protein sequence variations, including single amino acid substitutions and small insertions and deletions [2]. The prediction is based on the change, caused by a given variation, in the similarity of the query sequence to a set of its related protein sequences. For this prediction, the algorithm is required to compute a semi-global pairwise sequence alignment score between the query sequence and each of the related sequences. Using dynamic programming, it takes O(n · m) time to compute alignment score between the query sequence Q of length n and a related sequence S of length m. Thus given l different variations in Q, in a naive way it would take O(l · n · m) time to compute the alignment scores between each of the variant query sequences and S. In this paper, we present a new approach to efficiently compute the pairwise alignment scores for l variations, which takes O((n + l) · m) time when the length of variations is bounded by a constant. In this approach, we further utilize the solutions of overlapping subproblems, which are already used by dynamic programming approach. Our algorithm has been used to build a new database for precomputed prediction scores for all possible single amino acid substitutions, single amino acid insertions, and up to 10 amino acids deletions in about 91K human proteins (including isoforms), where l becomes very large, that is, l = O(n). The PROVEAN source code and web server are available at http://provean.jcvi.org.


title: A simulation study on air traffic control strategies

abstract: Air Traffic Control (ATC) plays a crucial role in the modern air transportation system. As a decentralized system, every control sector in the ATC network system needs to use all sorts of available information to manage local air traffic in a safe, smooth and cost-efficient way. A key issue is: how each individual ATC sector should use global traffic information to make local ATC decisions, such that the global air traffic, not just the local, can be improved. This paper reports a simulation study on ATC strategies aiming to address the above issue. The coming-in traffic to sectors is the focus, and the ATC strategy means how to define and apply various local ATC rules, such as first-come-first-served rule, to the coming-in traffic according to the global traffic information. A simplified ATC network model is set up and a software simulation system is then developed. The simulation results reveal that, even for a same set of ATC rules, a bad strategy of applying them can cause heavy traffic congestion, while a good strategy can significantly reduce delays, improve safety, and increase efficiency of using airspace.


title: Human-Robot dialogue for joint construction tasks

abstract: We describe a human-robot dialogue system that allows a human to collaborate with a robot agent on assembling construction toys. The human and the robot are fully equal peers in the interaction, rather than simply partners. Joint action is supported at all stages of the interaction: the participants agree on a construction task, jointly decide how to proceed to proceed with the task, and also implement the selected plans jointly. The symmetry provides novel challenges for a dialogue system, and also makes it possible for findings from human-human joint-action dialogues to be easily implemented and tested.


title: Encoding Voxels with Deep Learning.

abstract: Humans achieve fast and accurate recognition of complex objects through the ventral visual stream, a system of interconnected brain regions capable of hierarchical processing of increasingly complex features. In the feedforward view of the ventral visual stream, processing starts at the primary


title: Tracking actual usage: the attention metadata approach

abstract: The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioural information of users in learning contexts that can be used to deal with the information overload in user centric ways. We introduce a schema and framework for capturing and managing such contextualized attention metadata in this paper. Schema and framework are designed to enable collecting and merging observations about the attention users give to content and their contexts. The contextualized attention metadata schema enables the correlation of the observations, thus reflects the relationships that exists between the user, her context and the content she works with. We illustrate with a simple demo application how contextualized attention metadata can be collected from several tools, the merging of the data streams into a repository and finally the correlation of the data.


title: Probability-Based Parameter Selection for Black-Box Fuzz Testing

abstract: Abstract : Dynamic, randomized-input functional testing, or black-box fuzz testing, is an effective technique for finding security vulnerabilities in software applications. Parameters for an invocation of black-box fuzz testing generally include known-good input to use as a basis for randomization (i.e., a seed file) and a specification of how much of the seed file to randomize (i.e., the range).This report describes an algorithm that applies basic statistical theory to the parameter selection problem and automates selection of seed files and ranges. This algorithm was implemented in an open-source, file-interface testing tool and was used to find and mitigate vulnerabilities in several software applications. This report generalizes the parameter selection problem, explains the algorithm, and analyzes empirical data collected from the implementation. Results of using the algorithm show a marked improvement in the efficiency of discovering unique application errors over basic parameter selection techniques.


title: Calling while driving: effects of providing remote traffic context

abstract: Cell phone conversations distract drivers. This research explores the possibility of reducing distracting by providing callers with remote information about the driver's traffic. We asked whether providing such contextual information would change the caller's conversation such that drivers would be less distracted. In Experiment 1 we examined this question in a low-fidelity driving simulator; in Experiment 2 we examined this question in a higher fidelity simulator. In both experiments, remote callers and passengers were distracting. Providing traffic information to the remote caller significantly reduced crashes in the low fidelity tests and significantly reduced passing in the high fidelity tests, compared with the control conditions. We consider the implications for development of remote displays or signals to promote driving safety.


Multilingual Semantic Parsing And Code-Switching


title: A comparison of fin geometries for heatsinks in laminar forced convection: Part I - Round, elliptical, and plate fins in staggered and in-line configurations

abstract: In this study, the heat transfer performance of various commonly used fin geometries is compared. Realistic, manufacturable geometries are considered for minimizing thermal resistance at moderate laminar air velocities and pressure gradients. These consist of plate fins or pin fins, which can be round, elliptical, or square. The plate fins can be continuous (parallel plates) or staggered. The pin fins can be inline or staggered arrays. In order to compare these various geometries, a set of standard conditions was required. The basis of comparison was chosen to be a circular array of 1mm diameter pin fins with a 2mm pitch. The pitch-to-width of the other geometries were chosen to provide equal ratios of fin cross-sectional area to base area. The pitch was fixed to provide the same wetted area per unit volume as that of the nominal case. Analysis was simplified by three major assumptions. Heat transfer and fluid flow were assumed to be two-dimensional, with identical velocity and pressure distribution in the z-direction and similar temperature profiles. Flow and heat transfer were also assumed to be periodically developed, thereby permitting all but the first few rows of fins to be simulated by a model with similar velocity and temperature profiles at inlet and outlet. The ratio of solid to fluid thermal conductivity for aluminum and air is quite high, around 7000, permitting the fins to be modeled as isothermal surfaces rather than conjugate solids. Each of these three assumptions reduced model complexity by about an order of magnitude. The CFD simulations were carried out on a two-dimensional computational domain bounded by planes of symmetry parallel to the flow. The air approach velocity was in the range of 0.5 to 5m/s. comparison of heat transfer performance and pressure drop is presented. In general, the staggered plate fin geometry showed the highest heat transfer for a given combination of pressure gradient and flow rate. This method can be used to compare any repeating fin geometry in a ducted forced convection environment.


title: Integrating Feature Modeling into UML.

abstract: Feature modeling is an important approach to dealing with variability at an abstract level in a hierarchical manner extensively used in software product lines. For its use in conjunction with other UML models and MDA approach, it is important to correctly integrate feature modeling into UML. In this paper, we present an approach to integrating feature modeling into UML that respects abstractness of feature modeling elements. This is achieved by deriving feature modeling elements from the deeper levels of the UML metamodel. We applied this approach to the essential elements of feature modeling in the cardinality-based Czarnecki-Eisenecker notation and selected elements specific to other notations. Since UML modeling tools do not support such modifications of the UML metatmodel, in order to facilitate experimentation with combining elements from different notations, we implemented this feature modeling extension as a UML profile (in Enterprise Architect and Omondo UML).


title: DEEP: A Biofeedback Virtual Reality Game for Children At-risk for Anxiety

abstract: Anxiety disorders are among the most frequently diagnosed mental health problems in children, leading to potentially devastating outcomes on a personal level and high costs for society. Although evidence-based interventions are readily available, their outcomes are often disappointing and variable. In particular, existing interventions are not effective long-term nor tailored to differences in individual responsiveness. We therefore need a new approach to the prevention and treatment of anxiety in children and a commensurate scientific methodology to uncover individual profiles of change. We argue that applied games have a great deal of potential for both. The current paper presents results from a recent pilot study using a biofeedback virtual reality game (DEEP). DEEP integrates established therapeutic principles with an embodied and intuitive learning process towards improved anxiety regulation skills.


title: Locally Weighted Naive Bayes

abstract: Despite its simplicity, the naive Bayes classifier has surprised machine learning researchers by exhibiting good performance on a variety of learning problems. Encouraged by these results, researchers have looked to overcome naive Bayes' primary weakness-attribute independence-and improve the performance of the algorithm. This paper presents a locally weighted version of naive Bayes that relaxes the independence assumption by learning local models at prediction time. Experimental results show that locally weighted naive Bayes rarely degrades accuracy compared to standard naive Bayes and, in many cases, improves accuracy dramatically. The main advantage of this method compared to other techniques for enhancing naive Bayes is its conceptual and computational simplicity.


title: Face-TLD: Tracking-Learning-Detection applied to faces

abstract: A novel system for long-term tracking of a human face in unconstrained videos is built on Tracking-Learning-Detection (TLD) approach. The system extends TLD with the concept of a generic detector and a validator which is designed for real-time face tracking resistent to occlusions and appearance changes. The off-line trained detector localizes frontal faces and the online trained validator decides which faces correspond to the tracked subject. Several strategies for building the validator during tracking are quantitatively evaluated. The system is validated on a sitcom episode (23 min.) and a surveillance (8 min.) video. In both cases the system detects-tracks the face and automatically learns a multi-view model from a single frontal example and an unlabeled video.


title: Shot Boundary Detection Method for News Video

abstract: It is very important to detect shot boundary accurately and quickly in a large number of news video data. Therefore, we proposed a new method with dual-detection model. The method is divided into two stages, i.e. pre-detection and re-detection. In the pre-detection stage, the uneven blocked differences based on the feature of human vision are presented and used in adaptive binary search to detect shot boundaries. In the re-detection round, Speeded Up Robust Features (SURF) method is applied to exclude false detections. The experimental results show that this method can detect abrupt boundaries of news video quickly and accurately.


title: A BUSINESS MODEL TYPE FOR THE INTERNET OF THINGS

abstract: The increasing pervasiveness of digital technologies, often referred to as the "Internet of Things" (IoT), offers a wealth of new services and business model opportunities across an ecosystem of partners -- and so it forces companies to rethink their current business models. To date, literature does yet not provide actionable, field-tested model theories for capturing, visualizing and analyzing firms' business models in digitally intensive business environments.The present paper (research in progress) therefore addresses the need for a business model type for the Internet of Things, which recognizes the affordances and impacts of digitization in order to allow companies to truly tap into new business model opportunities. We describe the design and evaluation of a type model, which enables researchers and practitioners alike to capture, visualize and analyze firms' current and future business models in IoT in a structured and actionable way. For our study we elected an iterative design science research approach, which prioritizes the utility of prototype artifacts. We feel confident of reaching at an empirically tested business model type, drawn from both Strategic Management and Information Systems research.


title: A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network

abstract: As information technology has advanced, people are turning more frequently to electronic media for communication, and social relationships are increasingly found in online channels. Discovering the latent communities therein is a useful way to better understand the properties of a virtual social network. Traditional community-detection tasks only consider the structural characteristics of a social organization, but more information about nodes and edges such as semantic information cannot be exploited. What is more, the typical size of virtual spaces is now counted in millions, if not billions, of nodes and edges, most existing algorithms are incapable to analyze such large scale dense networks. In this paper, we first introduce an interesting social network model (Interest Network) in which links between two IDs are built if they both participate to the discussions about one or more topics/stories. In this case, we say both of the connected two IDs have the similar interests. Then, the edges of the initial network are updated using the attitude consistency information of the connected ID pairs. For a given ID pair i and j, they may together reply to some topics/IDs. The implicit orientations/attitudes of these two IDs to their together-reply topics/IDs may not be the same. We use a simple statistical method to calculate the attitude consistency, the value of which is between 0 and 1, and the higher value corresponds to a greater degree of consistency of the given ID pair to topics/IDs. The updated network is called Similar-View Network (SVN). In the second part, a fast parallel modularity optimization algorithm (FPMQA) that performs the analogous greedy optimization as CNM and FUC is used to conduct community discovering. By using the parallel manner and sophisticated data structures, its running time is essentially fast, O(k^m^a^x(k^m^a^x+logk^m^a^x)). Finally, we propose an evaluation metric, which is based on the reliable ground truths, for online network community detection. In the experimental work, we evaluate our method using real datasets and compare our approach with several previous methods; the results show that our method is more effective and accurate in find potential online communities.


title: A Digital 1.6 pJ/bit Chip Identification Circuit Using Process Variations

abstract: -A 128-bit, 1.6 pJ/bit, 96% stable chip ID generation circuit utilizing process variations is designed in a 0.13 μm CMOS process. The circuit consumes 162 nW from a 1 V supply at low readout frequencies and 1.6 μW at 1 Mb/s. Cross-coupled logic gates were employed to simultaneously generate, amplify, and digitize the random circuit offset to create a stable unique digital chip ID code. A thorough statistical analysis is presented in order to explore the ID circuit reliability and stability. Two ID generators with different layout techniques were designed and fabricated to provide a performance comparison of power consumption, ID stability, and ID statistical robustness.


Advantages Of Active Steering For Vehicle Dynamics Control


title: DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition

abstract: Abstract Inspired by the theory of Leitner׳s learning box from the field of psychology, we propose DropSample , a new method for training deep convolutional neural networks (DCNNs), and apply it to large-scale online handwritten Chinese character recognition (HCCR). According to the principle of DropSample , each training sample is associated with a quota function that is dynamically adjusted on the basis of the classification confidence given by the DCNN softmax output. After a learning iteration, samples with low confidence will have a higher frequency of being selected as training data; in contrast, well-trained and well-recognized samples with very high confidence will have a lower frequency of being involved in the ongoing training and can be gradually eliminated. As a result, the learning process becomes more efficient as it progresses. Furthermore, we investigate the use of domain-specific knowledge to enhance the performance of DCNN by adding a domain knowledge layer before the traditional CNN. By adopting DropSample together with different types of domain-specific knowledge, the accuracy of HCCR can be improved efficiently. Experiments on the CASIA-OLHDWB 1.0, CASIA-OLHWDB 1.1, and ICDAR 2013 online HCCR competition datasets yield outstanding recognition rates of 97.33%, 97.06%, and 97.51% respectively, all of which are significantly better than the previous best results reported in the literature.


title: Analyzing Shopper's Behavior through WiFi Signals

abstract: Substantial progress in WiFi-based indoor localization has proven that pervasiveness of WiFi can be exploited beyond its traditional use of internet access to enable a variety of sensing applications. Understanding shopper's behavior through physical analytics can provide crucial insights to the business owner in terms of effectiveness of promotions, arrangement of products and efficiency of services. However, analyzing shopper's behavior and browsing patterns is challenging. Since video surveillance can not used due to high cost and privacy concerns, it is necessary to design novel techniques that can provide accurate and efficient view of shopper's behavior. In this work, we propose WiFi-based sensing of shopper's behavior in a retail store. Specifically, we show that various states of a shopper such as standing near the entrance to view a promotion or walking quickly to proceed towards the intended item can be accurately classified by profiling Channel State Information (CSI) of WiFi. We recognize a few representative states of shopper's behavior at the entrance and inside the store, and show how CSI-based profile can be used to detect that a shopper is in one of the states with very high accuracy (≈ 90%). We discuss the potential and limitations of CSI-based sensing of shopper's behavior and physical analytics in general.


title: Distributionally Robust Convex Optimization

abstract: Distributionally robust optimization is a paradigm for decision making under uncertainty where the uncertain problem data are governed by a probability distribution that is itself subject to uncertainty. The distribution is then assumed to belong to an ambiguity set comprising all distributions that are compatible with the decision maker's prior information. In this paper, we propose a unifying framework for modeling and solving distributionally robust optimization problems. We introduce standardized ambiguity sets that contain all distributions with prescribed conic representable confidence sets and with mean values residing on an affine manifold. These ambiguity sets are highly expressive and encompass many ambiguity sets from the recent literature as special cases. They also allow us to characterize distributional families in terms of several classical and/or robust statistical indicators that have not yet been studied in the context of robust optimization. We determine conditions under which distributionally robust optimization problems based on our standardized ambiguity sets are computationally tractable. We also provide tractable conservative approximations for problems that violate these conditions.