title: Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD

abstract: Research and clinical investigations in psychiatry largely rely on the de facto assumption that the diagnostic categories identified in the Diagnostic and Statistical Manual (DSM) represent homogeneous syndromes. However, the mechanistic heterogeneity that potentially underlies the existing classification scheme might limit discovery of etiology for most developmental psychiatric disorders. Another, perhaps less palpable, reality may also be interfering with progress—heterogeneity in typically developing populations. In this report we attempt to clarify neuropsychological heterogeneity in a large dataset of typically developing youth and youth with attention deficit/hyperactivity disorder (ADHD), using graph theory and community detection. We sought to determine whether data-driven neuropsychological subtypes could be discerned in children with and without the disorder. Because individual classification is the sine qua non for eventual clinical translation, we also apply support vector machine-based multivariate pattern analysis to identify how well ADHD status in individual children can be identified as defined by the community detection delineated subtypes. The analysis yielded several unique, but similar subtypes across both populations. Just as importantly, comparing typically developing children with ADHD children within each of these distinct subgroups increased diagnostic accuracy. Two important principles were identified that have the potential to advance our understanding of typical development and developmental neuropsychiatric disorders. The first tenet suggests that typically developing children can be classified into distinct neuropsychological subgroups with high precision. The second tenet proposes that some of the heterogeneity in individuals with ADHD might be “nested” in this normal variation.


title: SDN-Based Resource Allocation in MPLS Networks: A Hybrid Approach

abstract: The highly dynamic nature of the current network traffics, makes the network managers to exploit the flexibility of the state-of-the-art paradigm called SDN. In this way, there has been an increasing interest in hybrid networks of SDN-MPLS. In this paper, a new traffic engineering architecture for SDN-MPLS network is proposed. To this end, OpenFlow-enabled switches are applied over the edge of the network to improve flow-level management flexibility while MPLS routers are considered as the core of the network to make the scheme applicable for existing MPLS networks. The proposed scheme re-assigns flows to the Label-Switched Paths (LSPs) to highly utilize the network resources. In the cases that the flow-level re-routing is insufficient, the proposed scheme recomputes and re-creates the undergoing LSPs. To this end, we mathematically formulate two optimization problems: i) flow re-routing, and, ii) LSP re-creation and propose a heuristic algorithm to improve the performance of the scheme. Our experimental results show the efficiency of the proposed hybrid SDN-MPLS architecture in traffic engineering superiors traditionally deployed MPLS networks.


title: A dissimilarity measure for the k-Modes clustering algorithm

abstract: Clustering is one of the most important data mining techniques that partitions data according to some similarity criterion. The problems of clustering categorical data have attracted much attention from the data mining research community recently. As the extension of the k-Means algorithm, the k-Modes algorithm has been widely applied to categorical data clustering by replacing means with modes. In this paper, the limitations of the simple matching dissimilarity measure and Ng's dissimilarity measure are analyzed using some illustrative examples. Based on the idea of biological and genetic taxonomy and rough membership function, a new dissimilarity measure for the k-Modes algorithm is defined. A distinct characteristic of the new dissimilarity measure is to take account of the distribution of attribute values on the whole universe. A convergence study and time complexity of the k-Modes algorithm based on new dissimilarity measure indicates that it can be effectively used for large data sets. The results of comparative experiments on synthetic data sets and five real data sets from UCI show the effectiveness of the new dissimilarity measure, especially on data sets with biological and genetic taxonomy information.


title: Automatic thumbnail cropping and its effectiveness

abstract: Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques 1) based on a general method that detects salient portions of images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.


title: Enterprise Architecture Analysis and Network Thinking: A Literature Review

abstract: Previous works in enterprise architecture (EA) literature has mostly dealt with EA principles, qualitative aspects like EA value and benefits, modeling efforts like Archimate, and methodologies for EA implementation like The Open Group Architecture Framework (TOGAF) and others. Today, these are all common subjects in EA literature. In this paper, we are going in a complementary direction, aiming to contribute towards fostering a set of EA analysis methods based on network measures, considering the enterprise as a complex network. Thus, we present a central question: What is known about the application of network measures in order to analyze components and relationships in the EA context? To answer this question, we perform a broad literature review and create a state-of-the-art description of EA network analysis, applied measures and its main achievements. We also provide a research agenda for the field.


title: Pixelwise Instance Segmentation with a Dynamically Instantiated Network

abstract: Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level. We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label. Most approaches adapt object detectors to produce segments instead of boxes. In contrast, our method is based on an initial semantic segmentation module, which feeds into an instance subnetwork. This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances. This part of our model is dynamically instantiated to produce a variable number of instances per image. Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals. Therefore, unlike some related work, a pixel cannot belong to multiple instances. Furthermore, far more precise segmentations are achieved, as shown by our substantial improvements at high APr thresholds.


title: A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone

abstract: Activity recognition plays an essential role in bridging the gap between the low-level sensor data and the high-level applications in ambient-assisted living systems. With the aim to obtain satisfactory recognition rate and adapt to various application scenarios, a variety of sensors have been exploited, among which, smartphone-embedded inertial sensors are widely applied due to its convenience, low cost, and intrusiveness. In this paper, we explore the power of triaxial accelerometer and gyroscope built-in a smartphone in recognizing human physical activities in situations, where they are used simultaneously or separately. A novel feature selection approach is then proposed in order to select a subset of discriminant features, construct an online activity recognizer with better generalization ability, and reduce the smartphone power consumption. Experimental results on a publicly available data set show that the fusion of both accelerometer and gyroscope data contributes to obtain better recognition performance than that of using single source data, and that the proposed feature selector outperforms three other comparative approaches in terms of four performance measures. In addition, great improvement in time performance can be achieved with an effective feature selector, indicating the way of power saving and its applicability to real-world activity recognition.


title: L-Asparaginase of Leishmania donovani: Metabolic target and its role in Amphotericin B resistance

abstract: Emergence of Amphotericin B (AmB) resistant Leishmania donovani has posed major therapeutic challenge against the parasite. Consequently, combination therapy aimed at multiple molecular targets, based on proteome wise network analysis has been recommended. In this regard we had earlier identified and proposed L-asparaginase of Leishmania donovani (LdAI) as a crucial metabolic target. Here we report that both LdAI overexpressing axenic amastigote and promastigote forms of L. donovani survives better when challenged with AmB as compared to wild type strain. Conversely, qRT-PCR analysis showed an upregulation of LdAI in both forms upon AmB treatment. Our data demonstrates the importance of LdAI in imparting immediate protective response to the parasite upon AmB treatment. In the absence of structural and functional information, we modeled LdAI and validated its solution structure through small angle X-ray scattering (SAXS) analysis. We identified its specific inhibitors through ligand and structure-based approach and characterized their effects on enzymatic properties (Km, Vmax, Kcat) of LdAI. We show that in presence of two of the inhibitors L1 and L2, the survival of L. donovani is compromised whereas overexpression of LdAI in these cells restores viability. Taken together, our results conclusively prove that LdAI is a crucial metabolic enzyme conferring early counter measure against AmB treatment by Leishmania.


title: Comparison of Various Requirements Elicitation Techniques

abstract: No requirements elicitation technique has capability of finding all of the software requirements so we have to use variety of techniques that will help us to cover all the requirements, resulting in more effective elicitation. Each technique has its features which makes it different from all other techniques and which make it suitable for a particular condition. The important thing is to use the most appropriate technique. Before using any technique we should have thorough knowledge of that technique. In this paper, we present an in-depth review of different requirements elicitation techniques. We also present Pros and Cons of different elicitation techniques.


title: Modification of EEG power spectra and EEG connectivity in autobiographical memory: a sLORETA study

abstract: The aim of the present study was to explore the modifications of scalp EEG power spectra and EEG connectivity during the autobiographical memory test (AM-T) and during the retrieval of an autobiographical event (the high school final examination, Task 2). Seventeen healthy volunteers were enrolled (9 women and 8 men, mean age 23.4 ± 2.8 years, range 19–30). EEG was recorded at baseline and while performing the autobiographical memory (AM) tasks, by means of 19 surface electrodes and a nasopharyngeal electrode. EEG analysis was conducted by means of the standardized LOw Resolution Electric Tomography (sLORETA) software. Power spectra and lagged EEG coherence were compared between EEG acquired during the memory tasks and baseline recording. The frequency bands considered were as follows: delta (0.5–4 Hz); theta (4.5–7.5 Hz); alpha (8–12.5 Hz); beta1 (13–17.5 Hz); beta2 (18–30 Hz); gamma (30.5–60 Hz). During AM-T, we observed a significant delta power increase in left frontal and midline cortices (T = 3.554; p < 0.05) and increased EEG connectivity in delta band in prefrontal, temporal, parietal, and occipital areas, and for gamma bands in the left temporo-parietal regions (T = 4.154; p < 0.05). In Task 2, we measured an increased power in the gamma band located in the left posterior midline areas (T = 3.960; p < 0.05) and a significant increase in delta band connectivity in the prefrontal, temporal, parietal, and occipital areas, and in the gamma band involving right temporo-parietal areas (T = 4.579; p < 0.05). These results indicate that AM retrieval engages in a complex network which is mediated by both low- (delta) and high-frequency (gamma) EEG bands.


title: Smart mobile SoCs driving the semiconductor industry: Technology trend, challenges and opportunities

abstract: The explosive growth of smart mobile wireless devices in recent years has fundamentally transformed the semiconductor industry. Mobile system-on-chips (SoCs) has become the leading product driver for technology definition and manufacturing for the semiconductor industry. This trend was first observed in 28 nm and will continue for 20 nm, 16/14 nm, and 10 nm adoption and production ramp. Recent mobile SoC performance increase was achieved mainly through silicon technology scaling, and from single to dual- and quad-core. For mobile SoCs to continue offering new and exciting user-experiences, and longer battery life, a holistic approach in orthogonal system scaling to break out of the box of (speed*density/power/cost) constrains is mandated. Examples in the new paradigm of mobile heterogeneous computing are: energy-efficient transistors/memories/interconnects in expanding and boosting existing SoC functionalities (e.g., SiGe/III-V FinFET, GAA-FET, TFET, and RRAM/MRAM etc.), all-inclusive technology/design co-optimization to extract more values from silicon tech, RFFE system integration, and the multi-die integration by system partitioning that allows each component to be optimized and integrated closely together for lower cost and power, higher performance, and reduced form factor. Numerous challenges are ahead yet tremendous opportunities exist for collaborative and multiplicative innovations in the industry to enable the continued growth of mobile SoCs.


title: Researching violence with children: experiences and lessons from the UK and South Africa

abstract: The impact of violence on children's health and development has had growing attention in global and national politics. Research on children's experiences of violence has increased in recent years and this paper aims to add to this literature by highlighting key messages and learning points from the experiences of researchers who have worked with children and violence across the different contexts of the UK and South Africa. As qualitative and quantitative researchers, our concepts, aims, methods,resources and approaches were very different but we all faced similar challenges in working with children and violence in contexts were adults' views about what violence counted predominated. We argue that children's participation in research and highlighting children's own understandings, agency and negotiations in relation to violence are crucial for challenging sometimes unhelpful taken for granted views about the impact of violence on children's lives.


title: Classification of Botnet Detection Based on Botnet Architechture

abstract: Nowadays, Botnets pose a major threat to the security of online ecosystems and computing assets. A Botnet is a network of computers which are compromised under the influence of Bot (malware) code. This paper clarifies Botnet phenomenon and discusses Botnet mechanism, Botnet architecture and Botnet detection techniques. Botnet detection techniques can be categorized into six classes: honey pot based, signature-based, mining-based, anomaly-based, DNS-based and network-based. It provides a brief comparison of the above mentioned Botnet detection techniques. Finally, we discuss the importance of honey pot research to detect the infection vector and dealing with new Botnet approaches in the near future.


title: Selecting housekeeping genes as references for the normalization of quantitative PCR data in breast cancer

abstract: OBJECTIVE ::: The common reference genes of choice in relative gene expression studies based on quantitative real time polymerase chain reaction, ACTB and B2M, were shown to be regulated differently in respect to tissue type. In this study, the stability of the selected housekeeping genes for normalizing the qPCR data were identified in the tumor and its adjacent tissues in invasive breast cancer, and the variability of their levels according to the stages and the histopathologic subtypes was analyzed. ::: ::: ::: METHODS ::: Four housekeeping genes: PUM1, RPL13A, B2M, and ACTB were analyzed in 99 surgically excised tissue specimens (50 tumor, 45 tumor adjacent and 4 normal breast tissues). Three of the most common softwares (GeNorm, NormFinder, and BestKeeper) were used for calculation purposes. ::: ::: ::: RESULTS ::: When all of the tissue samples were included in analyses, PUM1 was the most stable gene according to calculations made with both NormFinder and BestKeeper; while PUM1/RPL13A combination was the most stable by GeNorm software. The PUM1 gene was also identified as the most stable gene among the four in all sample groups (in both Estrogen Receptor positive and Estrogen Receptor negative subgroups of invasive breast carcinoma and in normal breast tissue) according to calculations made using the NormFinder software. ::: ::: ::: CONCLUSION ::: While suggesting PUM1 is one of the most stable single gene and the PUM1/RPL13A pair as one of the best housekeeping genes for the normalization of expression studies in invasive breast tumor studies, it will be more practical to evaluate stability once more and decide upon the reference gene accordingly within the sample group itself.


title: Finite-difference time-domain (fdtd) matlab codes for first- and second-order em differential equations [testing ourselves]

abstract: A set of two-dimensional (2D) electromagnetic (EM) MATLAB codes, using both first-order coupled differential (Maxwell) equations and second-order decoupled (wave) equations, are developed for both transverse-magnetic (TM) and transverse-electric (TE) polarizations. Second-order MUR type absorbing boundary conditions are used to simulate free space. Metamaterial (MTM) modeling is also included. Performance tests in terms of computational times, memory requirements, and accuracies were done for simple EM scenarios with magnetic field, current, and voltage comparisons. The codes may be used for teaching and research purposes.


Intrusion Detection System using K-means , PSO with SVM Classifier : A Survey


title: Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models

abstract: Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names. Variability, particularly systematic variability in location names (Carroll, 1983), challenges the identification task. Some of this variability can be anticipated as operations within a statistical language model, in this case drawn from gazetteers such as OpenStreetMap (OSM), Geonames, and DBpedia. This permits evaluation of an observed n-gram in Twitter targeted text as a legitimate location name variant from the same location-context. Using n-gram statistics and location-related dictionaries, our Location Name Extraction tool (LNEx) handles abbreviations and automatically filters and augments the location names in gazetteers (handling name contractions and auxiliary contents) to help detect the boundaries of multi-word location names and thereby delimit them in texts. ::: We evaluated our approach on 4,500 event-specific tweets from three targeted streams to compare the performance of LNEx against that of ten state-of-the-art taggers that rely on standard semantic, syntactic and/or orthographic features. LNEx improved the average F-Score by 33-179%, outperforming all taggers. Further, LNEx is capable of stream processing.


title: Risk Assessment Uncertainties in Cybersecurity Investments

abstract: When undertaking cybersecurity risk assessments, it is important to be able to assign numeric values to metrics to compute the final expected loss that represents the risk that an organization is exposed to due to cyber threats. Even if risk assessment is motivated by real-world observations and data, there is always a high chance of assigning inaccurate values due to different uncertainties involved (e.g., evolving threat landscape, human errors) and the natural difficulty of quantifying risk. Existing models empower organizations to compute optimal cybersecurity strategies given their financial constraints, i.e., available cybersecurity budget. Further, a general game-theoretic model with uncertain payoffs (probability-distribution-valued payoffs) shows that such uncertainty can be incorporated in the game-theoretic model by allowing payoffs to be random. This paper extends previous work in the field to tackle uncertainties in risk assessment that affect cybersecurity investments. The findings from simulated examples indicate that although uncertainties in cybersecurity risk assessment lead, on average, to different cybersecurity strategies, they do not play a significant role in the final expected loss of the organization when utilising a game-theoretic model and methodology to derive these strategies. The model determines robust defending strategies even when knowledge regarding risk assessment values is not accurate. As a result, it is possible to show that the cybersecurity investments’ tool is capable of providing effective decision support.


title: A Tutorial for Information Theory in Neuroscience

abstract: Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.


title: An efficient lane detection algorithm for lane departure detection

abstract: In this paper, we propose an efficient lane detection algorithm for lane departure detection; this algorithm is suitable for low computing power systems like automobile black boxes. First, we extract candidate points, which are support points, to extract a hypotheses as two lines. In this step, Haar-like features are used, and this enables us to use an integral image to remove computational redundancy. Second, our algorithm verifies the hypothesis using defined rules. These rules are based on the assumption that the camera is installed at the center of the vehicle. Finally, if a lane is detected, then a lane departure detection step is performed. As a result, our algorithm has achieved 90.16% detection rate; the processing time is approximately 0.12 milliseconds per frame without any parallel computing.


title: The Diagnosis and Management of Mild Cognitive Impairment

abstract: Importance Cognitive decline is a common and feared aspect of aging. Mild cognitive impairment (MCI) is defined as the symptomatic predementia stage on the continuum of cognitive decline, characterized by objective impairment in cognition that is not severe enough to require help with usual activities of daily living. Objective To present evidence on the diagnosis, treatment, and prognosis of MCI and to provide physicians with an evidence-based framework for caring for older patients with MCI and their caregivers. Evidence Acquisition We searched PubMed for English-language articles in peer-reviewed journals and the Cochrane Library database from inception through July 2014. Relevant references from retrieved articles were also evaluated. Findings The prevalence of MCI in adults aged 65 years and older is 10% to 20%; risk increases with age and men appear to be at higher risk than women. In older patients with MCI, clinicians should consider depression, polypharmacy, and uncontrolled cardiovascular risk factors, all of which may increase risk for cognitive impairment and other negative outcomes. Currently, no medications have proven effective for MCI; treatments and interventions should be aimed at reducing cardiovascular risk factors and prevention of stroke. Aerobic exercise, mental activity, and social engagement may help decrease risk of further cognitive decline. Although patients with MCI are at greater risk for developing dementia compared with the general population, there is currently substantial variation in risk estimates (from Conclusions and Relevance Cognitive decline and MCI have important implications for patients and their families and will require that primary care clinicians be skilled in identifying and managing this common disorder as the number of older adults increases in coming decades. Current evidence supports aerobic exercise, mental activity, and cardiovascular risk factor control in patients with MCI.


title: An Empirical Study of Mobile Ad Targeting

abstract: Advertising, long the financial mainstay of the web ecosystem, has become nearly ubiquitous in the world of mobile apps. While ad targeting on the web is fairly well understood, mobile ad targeting is much less studied. In this paper, we use empirical methods to collect a database of over 225,000 ads on 32 simulated devices hosting one of three distinct user profiles. We then analyze how the ads are targeted by correlating ads to potential targeting profiles using Bayes' rule and Pearson's chi squared test. This enables us to measure the prevalence of different forms of targeting. We find that nearly all ads show the effects of application- and time-based targeting, while we are able to identify location-based targeting in 43% of the ads and user-based targeting in 39%.


title: Above the glass ceiling: When are women and racial/ethnic minorities promoted to CEO?

abstract: Using a dataset of all CEO transitions in Fortune 500 companies over a 15-year period, we analyze mechanisms that shape the promotion probabilities and leadership tenure of women and racial/ethnic minority CEOs. Consistent with the theory of the glass cliff, we find that occupational minorities�defined as white women and men and women of color�are more likely than white men to be promoted CEO of weakly performing firms. Though we find no significant differences in tenure length between occupational minorities and white men, we find that when firm performance declines during the tenure of occupational minority CEOs, these leaders are likely to be replaced by white men. We term this phenomenon the �savior effect.


title: Generalized Deformable Spatial Pyramid: Geometry-preserving dense correspondence estimation

abstract: We present a Generalized Deformable Spatial Pyramid (GDSP) matching algorithm for calculating the dense correspondence between a pair of images with large appearance variations. The main challenges of the problem generally originate in appearance dissimilarities and geometric variations between images. To address these challenges, we improve the existing Deformable Spatial Pyramid (DSP) [10] model by generalizing the search space and devising the spatial smoothness. The former is leveraged by rotations and scales, and the latter simultaneously considers dependencies between high-dimensional labels through the pyramid structure. Our spatial regularization in the high-dimensional space enables our model to effectively preserve the meaningful geometry of objects in the input images while allowing for a wide range of geometry variations such as perspective transform and non-rigid deformation. The experimental results on public datasets and challenging scenarios show that our method outperforms the state-of-the-art methods both qualitatively and quantitatively.


title: An FPGA-based AES-CCM Crypto Core For IEEE 802.11i Architecture

abstract: The widespread adoption of IEEE 802.11 wireless networks has brought its security paradigm under active research. One of the important research areas in this field is the realization of fast and secure implementations of cryptographic algorithms. Under this work, such an implementation has been done for Advanced Encryption Standard (AES) on fast, efficient and low power Field Programmable Gate Arrays (FPGAs) whereby computational intensive cryptographic processes are offloaded from the main processor thus results in achieving highspeed secure wireless connectivity. The dedicated resources of Spartan-3 FPGAs have been effectively utilized to develop wider logic function which minimizes the critical paths by confining logic to single Configurable Logic Block (CLB), thus improving the performance, density and power consumption of the design. The resultant design consumes only 4 Block RAMs and 487 Slices to fit both AES cores and its key scheduling.


Sustainability of health information systems: a three-country qualitative study in southern Africa


title: Frequency-Reconfigurable Low-Profile Circular Monopolar Patch Antenna

abstract: A novel frequency-reconfigurable antenna is presented based on a circular monopolar patch antenna. The antenna comprises of a center-fed circular patch surrounded by four sector-shaped patches. Eight varactor diodes are introduced to bridge the gaps between the circular patch and the sector-shaped patches. By changing the reverse bias voltage of the varactor diodes, the antenna can be switched in the operating frequency. A fully functional prototype is developed and tested, which exhibits a continuously tunable frequency band from 1.64 GHz to 2.12 GHz. The measured efficiency rises from about 45% to about 85% as the operating frequency increases from 1.64 GHz to 2.12 GHz. Stable monopole like radiation patterns are achieved at all operating frequencies. In addition, the antenna owns a low-profile structure (0.04 free-space wavelengths at 1.9 GHz). The frequency selective feature and stable radiation patterns make the antenna potentially suitable for future wireless communication systems, such as cognitive radio.


title: Patterns in property specifications for finite-state verification

abstract: Model checkers and other finite-state verification tools allow developers to detect certain kinds of errors automatically. Nevertheless, the transition of this technology from research to practice has been slow. While there are a number of potential causes for reluctance to adopt such formal methods, we believe that a primary cause is that practitioners are unfamiliar with specification processes, notations, and strategies. In a recent paper, we proposed a pattern-based approach to the presentation, codification and reuse of property specifications for finite-state verification. Since then, we have carried out a survey of available specifications, collecting over 500 examples of property specifications. We found that most are instances of our proposed patterns. Furthermore, we have updated our pattern system to accommodate new patterns and variations of existing patterns encountered in this survey. This paper reports the results of the survey and the current status of our pattern system.


title: Fast segmentation of 3D point clouds for ground vehicles

abstract: This paper describes a fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects. Our approach is targeted at high-speed autonomous ground robot mobility, so real-time performance of the segmentation method plays a critical role. This is especially true as segmentation is considered only a necessary preliminary for the more important task of object classification that is itself computationally very demanding. Efficiency is achieved in our approach by splitting the segmentation problem into two simpler subproblems of lower complexity: local ground plane estimation followed by fast 2D connected components labeling. The method's performance is evaluated on real data acquired in different outdoor scenes, and the results are compared to those of existing methods. We show that our method requires less runtime while at the same time yielding segmentation results that are better suited for later classification of the identified objects.


title: Large-Scale Automated Refactoring Using ClangMR

abstract: Maintaining large code bases can be a challenging endeavour. As new libraries, APIs and standards are introduced, old code is migrated to use them. To provide as clean and succinct an interface as possible for developers, old APIs are ideally removed as new ones are introduced. In practice, this becomes difficult as automatically finding and transforming code in a semantically correct way can be challenging, particularly as the size of a code base increases. In this paper, we present a real-world implementation of a system to refactor large C++ code bases efficiently. A combination of the Clang compiler framework and the MapReduce parallel processor, ClangMR enables code maintainers to easily and correctly transform large collections of code. We describe the motivation behind such a tool, its implementation and then present our experiences using it in a recent API update with Google's C++ code base.


title: Unsupervised adaptive neural-fuzzy inference system for solving differential equations

abstract: There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential equations. The presented solution of differential equation consists of two parts; the first part satisfies the initial/boundary condition and has no adjustable parameter whereas the second part is an ANFIS which has no effect on initial/boundary conditions and its adjustable parameters are the weights of ANFIS. The algorithm is applied to solve differential equations and the results demonstrate its accuracy and convince us to use ANFIS in solving various differential equations.


title: On the Ergodic Capacity of MIMO Free-Space Optical Systems over Turbulence Channels

abstract: The free-space optical (FSO) communications can achieve high capacity with huge unlicensed optical spectrum and low operational costs. The corresponding performance analysis of FSO systems over turbulence channels is very limited, especially when using multiple apertures at both transmitter and receiver sides. This paper aim to provide the ergodic capacity characterization of multiple-input multiple-output (MIMO) FSO systems over atmospheric turbulence-induced fading channels. The fluctuations of the irradiance of optical channels distorted by atmospheric conditions is usually described by a gamma-gamma ($\Gamma \Gamma$) distribution, and the distribution of the sum of $\Gamma \Gamma$ random variables (RVs) is required to model the MIMO optical links. We use an $\alpha$-$\mu$ distribution to efficiently approximate the probability density function (PDF) of the sum of independent and identical distributed $\Gamma\Gamma$ RVs through moment-based estimators. Furthermore, the PDF of the sum of independent, but not necessarily identically distributed $\Gamma \Gamma$ RVs can be efficiently approximated by a finite weighted sum of PDFs of $\Gamma \Gamma$ distributions. Based on these reliable approximations, novel and precise analytical expressions for the ergodic capacity of MIMO FSO systems are derived. Additionally, we deduce the asymptotic simple expressions in high signal-to-noise ratio regimes, which provide useful insights into the impact of the system parameters on the ergodic capacity. Finally, our proposed results are validated via Monte-Carlo simulations.


title: Fine Hand Segmentation using Convolutional Neural Networks

abstract: We propose a method for extracting very accurate masks of hands in egocentric views. Our method is based on a novel Deep Learning architecture: In contrast with current Deep Learning methods, we do not use upscaling layers applied to a low-dimensional representation of the input image. Instead, we extract features with convolutional layers and map them directly to a segmentation mask with a fully connected layer. We show that this approach, when applied in a multi-scale fashion, is both accurate and efficient enough for real-time. We demonstrate it on a new dataset made of images captured in various environments, from the outdoors to offices.


title: A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion

abstract: Akaike’s information criterion (AIC) is increasingly being used in analyses in the field of ecology. This measure allows one to compare and rank multiple competing models and to estimate which of them best approximates the “true” process underlying the biological phenomenon under study. Behavioural ecologists have been slow to adopt this statistical tool, perhaps because of unfounded fears regarding the complexity of the technique. Here, we provide, using recent examples from the behavioural ecology literature, a simple introductory guide to AIC: what it is, how and when to apply it and what it achieves. We discuss multimodel inference using AIC—a procedure which should be used where no one model is strongly supported. Finally, we highlight a few of the pitfalls and problems that can be encountered by novice practitioners.


title: Receiving mutual impedance of a compact patch antenna array with two elements in a rich multipath environment

abstract: The performance of an antenna is changed whenever it is placed within the field of another antenna because of the existence of mutual coupling. The concept of receiving mutual impedance was introduced to characterize the mutual coupling effect in receiving antenna arrays and has been successfully applied in many occasions, such as direction finding, adaptive nulling, and medical imaging. In this paper, the definition of receiving mutual impedance is extended to characterize the mutual impedance coupling effect in two patch antennas operating in a rich multipath environment. In the extended definition, the two antennas are excited by a larger number plane waves coming from different directions. The behaviour of the receiving mutual impedances when the antennas are excited by different number of plane waves is studied and compared with the case of omni-directional antennas. This extends the use of receiving mutual impedance to non-omni-directional antennas for operation in multipath environments and has an implication for receiving antenna array design for multi-antenna systems such as MIMO systems.


title: Neural Network based Approach for Recognition of Text Images

abstract: Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size of the characters. Multiple types of handwriting styles from different persons are considered in this work. An image with higher resolution will certainly take much longer time to compute than a lower resolution image. In the practical image acquisition systems and conditions, shape distortion is common processes because different people’s handwriting has different shape of characters. The process of recognizing character recognition in this work has been divided into 2 phases. In the first phase, Image preprocessing is done in which image is firstly converted into binary form based on some threshold value obtained through Otsu’s method. After that removal of noise is done using median filter. After that feature extraction takes place that is done here through Fourier descriptor method using Fourier transform and correlation between template made through training data and test data is obtained. A multilayer feed forward neural network is created and trained through Back Propagation algorithm. After the training, testing is done to match the pattern with test data. Results for various convergence objective of neural network are obtained and analyzed.


title: Stopping GAN Violence: Generative Unadversarial Networks

abstract: While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs.


title: Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution

abstract: Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health record(EHR)s are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource-intense and not readily scalable. Informatics-based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer(BC) receptor status phenotypes from structured and unstructured EHR data using rule-based algorithms, including natural language processing(NLP). Overall, use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor-positive BC cases were ascertained with high precision(P = 0.976) and recall(R = 0.987) compared to gold standard chart-reviewed patients. However, status negation(R = 0.591), decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding perspectives offered) are necessary to derive robust EHR-based precision medicine phenotypes. ::: ::: This article is protected by copyright. All rights reserved


title: Learning Multi-modal Similarity

abstract: In many applications involving multi-media data, the definition of similarity between items is integral to several key tasks, e.g., nearest-neighbor retrieval, classification, and recommendation. Data in such regimes typically exhibits multiple modalities, such as acoustic and visual content of video. Integrating such heterogeneous data to form a holistic similarity space is therefore a key challenge to be overcome in many real-world applications. We present a novel multiple kernel learning technique for integrating heterogeneous data into a single, unified similarity space. Our algorithm learns an optimal ensemble of kernel transfor- mations which conform to measurements of human perceptual similarity, as expressed by relative comparisons. To cope with the ubiquitous problems of subjectivity and inconsistency in multi- media similarity, we develop graph-based techniques to filter similarity measurements, resulting in a simplified and robust training procedure.


title: Multiple-Loop Design Technique for High-Performance Low-Dropout Regulator

abstract: A new multiple-loop design technique for high-performance low-dropout (LDO) regulator designs has been proposed and successfully implemented in many commercial products for portable smart phone and tablet PC applications. The proposed LDO is composed of five loops that allows designers to obtain a good tradeoff between quiescent current and other performances, such as undershoot, overshoot, and so on. A total of one bandgap reference and 38 LDOs (including n-type and p-type LDOs, which will be named NLDO and PLDO, respectively) were integrated in one power-management IC chip for powering an application processor inside mobile devices. The proposed LDO has been fabricated based on 0.13- $\mu {m}$ CMOS process and supplies various current capacities from 50 to 600 mA; 38 LDOs have been designed and supply different output voltage levels from 0.7 to 3.0 V. One of the proposed NLDOs consumes $14~\mu {A}$ of the quiescent current and features under 56/24 mV of undershoot/overshoot at VOUT =1 V as the load current steps up from 0 to 300 mA with 300 mA/1 $\mu {s}$ on a 1- $\mu {F}$ output capacitor. The measured output load and line regulations are 1.8 and 0.4 mV, respectively. The measured integrated output noise from 10 Hz to 100 kHz at $\text{I}_{\mathrm{ LOAD}}=10\%$ of maximum current shows 80 $\mu {V}$ rms. The package chip size is $6.25 \times 6.25$ mm2 with 169 balls.


The role of genetic variation in the causation of mental illness: an evolution-informed framework


title: SecureCloud: Towards a Comprehensive Security Framework for Cloud Computing Environments

abstract: Cloud computing has recently gained tremendous momentum but still is in its infancy. It has the potential for significant cost reduction and the increased operating efficiencies in computing. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper, we propose a comprehensive security framework for cloud computing environments. We also discuss challenges, existing solutions, approaches, and future work needed to provide a trustworthy cloud computing environment.


title: Evolving Graphs and Networks with Edge Encoding: Preliminary Report

abstract: A clustering storage system, which enables to operate a plurality of storage system units as a single storage system, presents connection information between clustering storage systems to a user and receives data to be backed up and an instruction about a path between the clustering storage systems from the user. The clustering storage system uses load information or a transfer speed of a data connection line between the storage system units to determine a path between the clustering storage systems used for backup and a storage system unit for holding a backup copy so that the data transfer between the storage system units becomes minimum. Besides, the clustering storage system mainly uses the determined path for backing up the data.


title: Automated Plant Species Identification: Challenges and Opportunities

abstract: The number of species of macro organisms on the planet is estimated at about 10 million. This staggering diversity and the need to better understand it led inevitably to the development of classification schemes called biological taxonomies. Unfortunately, in addition to this enormous diversity, the traditional identification and classification workflows are both slow and error-prone; classification expertise is in the hands of a small number of expert taxonomists; and to make things worse, the number of taxonomists has steadily declined in recent years. Automated identification of organisms has therefore become not just a long time desire but a need to better understand, use, and save biodiversity. This paper presents a survey of recent efforts to use computer vision and machine learning techniques to identify organisms. It focuses on the use of leaf images to identify plant species. In addition, it presents the main technical and scientific challenges as well as the opportunities for herbaria and cybertaxonomists to take a quantum leap towards identifying biodiversity efficiently and empowering the general public by putting in their hands automated identification tools.


title: End-node Fingerprinting for Malware Detection on HTTPS Data

abstract: One of the current challenges in network intrusion detection research is the malware communicating over HTTPS protocol. Usually the task is to detect infected end-nodes with this type of malware by monitoring network traffic. The challenge lies in a very limited number of weak features that can be extracted from the network traffic capture of encrypted HTTP communication. This paper suggests a novel fingerprinting method that addresses this problem by building a higher-level end-node representation on top of the weak features. Conducted large-scale experiments on real network data show superior performance of the proposed method over the state-of-the-art solution in terms of both a lower number of produced false alarms (precision) and a higher number of detected infections (recall).


title: Adult Attachment as a Risk Factor for Intimate Partner Violence: The "Mispairing" of Partners' Attachment Styles

abstract: This study examined the relationship between intimate partner violence and adult attachment in a sample of 70 couples. The attachment style of each partner and the interaction of the partners’ attachment styles were examined as predictors of intimate partner violence. Additional analyses were conducted to examine violence reciprocity and to explore differences in the relationship between attachment and violence using continuous and dichotomous violence measures. Results of hierarchical regression analyses indicated the “mispairing” of an avoidant male partner with an anxious female partner was associated with both male and female violence. When controlling for partner violence, the relationship between attachment and violence was significant for males only. In addition, analyses using a dichotomized violence variable produced different results from analyses using a continuous violence measure. Clinical implications include focusing on the discrepancy between partners’ needs for intimacy and distance within the couple as a strategy for treating intimate partner violence.


title: An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification

abstract: This paper presents a survey as well as an empirical comparison and evaluation of seven kernels on graphs and two related similarity matrices, that we globally refer to as "kernels on graphs" for simplicity. They are the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regularized Laplacian kernel, the commute-time (or resistance-distance) kernel, the random-walk-with-restart similarity matrix, and finally, a kernel first introduced in this paper (the regularized commute-time kernel) and two kernels defined in some of our previous work and further investigated in this paper (the Markov diffusion kernel and the relative-entropy diffusion matrix). The kernel-on-graphs approach is simple and intuitive. It is illustrated by applying the nine kernels to a collaborative-recommendation task, viewed as a link prediction problem, and to a semisupervised classification task, both on several databases. The methods compute proximity measures between nodes that help study the structure of the graph. Our comparisons suggest that the regularized commute-time and the Markov diffusion kernels perform best on the investigated tasks, closely followed by the regularized Laplacian kernel.


title: Image Mining: Trends and Developments

abstract: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining.


title: Large Scale Online Learning of Image Similarity Through Ranking

abstract: Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, the approaches that exist today for learning such semantic similarity do not scale to large data sets. This is both because typically their CPU and storage requirements grow quadratically with the sample size, and because many methods impose complex positivity constraints on the space of learned similarity functions. ::: ::: The current paper presents OASIS, an Online Algorithm for Scalable Image Similarity learning that learns a bilinear similarity measure over sparse representations. OASIS is an online dual approach using the passive-aggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost. Our experiments show that OASIS is both fast and accurate at a wide range of scales: for a data set with thousands of images, it achieves better results than existing state-of-the-art methods, while being an order of magnitude faster. For large, web scale, data sets, OASIS can be trained on more than two million images from 150K text queries within 3 days on a single CPU. On this large scale data set, human evaluations showed that 35% of the ten nearest neighbors of a given test image, as found by OASIS, were semantically relevant to that image. This suggests that query independent similarity could be accurately learned even for large scale data sets that could not be handled before.


title: Modulatory Effects of Gut Microbiota on the Central Nervous System: How Gut Could Play a Role in Neuropsychiatric Health and Diseases

abstract: Gut microbiome is an integral part of the Gut-Brain axis. It is becoming increasingly recognized that the presence of a healthy and diverse gut microbiota is important to normal cognitive and emotional processing. It was known that altered emotional state and chronic stress can change the composition of gut microbiome, but it is becoming more evident that interaction between gut microbiome and central nervous system is bidirectional. Alteration in the composition of the gut microbiome can potentially lead to increased intestinal permeability and impair the function of the intestinal barrier. Subsequently, neuro-active compounds and metabolites can gain access to the areas within the central nervous system that regulate cognition and emotional responses. Deregulated inflammatory response, promoted by harmful microbiota, can activate the vagal system and impact neuropsychological functions. Some bacteria can produce peptides or short chain fatty acids that can affect gene expression and inflammation within the central nervous system. In this review, we summarize the evidence supporting the role of gut microbiota in modulating neuropsychological functions of the central nervous system and exploring the potential underlying mechanisms.


title: Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud

abstract: Human pattern recognition skills are remarkable and in many situations far exceed the ability of automated mining algorithms. By building domain-specific interfaces that present information visually, we can combine human detection with machines‘ far greater computational capacity. We illustrate our ideas by describing a suite of visual interfaces we built for telephone fraud detection.


title: From evolutionary computation to the evolution of things

abstract: Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.


title: Fast symbolic road marking and stop-line detection for vehicle localization

abstract: This paper proposes a fast method for detecting symbolic road markings (SRMs) and stop-lines. The proposed method efficiently restricts the search area based on the lane detection results and finds SRMs and stop-lines in a cost-effective manner. The SRM detector generates multiple SRM candidates using a top-hat filter and projection histogram and classifies their types using a histogram of oriented gradient (HOG) feature and total error rate (TER)-based classifier. The stop-line detector creates stop-line candidates via random sample consensus (RANSAC)-based parallel line pair estimation and verifies them using the HOG feature and TER-based classifier. The proposed method achieves reasonable detection rates and extremely low false positive rates along with a fast computing time.


title: RRA: Recurrent Residual Attention for Sequence Learning

abstract: In this paper, we propose a recurrent neural network (RNN) with residual attention (RRA) to learn long-range dependencies from sequential data. We propose to add residual connections across timesteps to RNN, which explicitly enhances the interaction between current state and hidden states that are several timesteps apart. This also allows training errors to be directly back-propagated through residual connections and effectively alleviates gradient vanishing problem. We further reformulate an attention mechanism over residual connections. An attention gate is defined to summarize the individual contribution from multiple previous hidden states in computing the current state. We evaluate RRA on three tasks: the adding problem, pixel-by-pixel MNIST classification and sentiment analysis on the IMDB dataset. Our experiments demonstrate that RRA yields better performance, faster convergence and more stable training compared to a standard LSTM network. Furthermore, RRA shows highly competitive performance to the state-of-the-art methods.


title: Spanner: Google's globally-distributed database

abstract: Spanner is Google's scalable, multi-version, globally-distributed, and synchronously-replicated database. It provides strong transactional semantics, consistent replication, and high performance reads and writes for a variety of Google's applications. I'll discuss the design and implementation of Spanner, as well as some of the lessons we have learned along the way. I'll also discuss some open challenges that we still see in building scalable distributed storage systems.


title: The acoustic emotion gaussians model for emotion-based music annotation and retrieval

abstract: One of the most exciting but challenging endeavors in music research is to develop a computational model that comprehends the affective content of music signals and organizes a music collection according to emotion. In this paper, we propose a novel acoustic emotion Gaussians (AEG) model that defines a proper generative process of emotion perception in music. As a generative model, AEG permits easy and straightforward interpretations of the model learning processes. To bridge the acoustic feature space and music emotion space, a set of latent feature classes, which are learned from data, is introduced to perform the end-to-end semantic mappings between the two spaces. Based on the space of latent feature classes, the AEG model is applicable to both automatic music emotion annotation and emotion-based music retrieval. To gain insights into the AEG model, we also provide illustrations of the model learning process. A comprehensive performance study is conducted to demonstrate the superior accuracy of AEG over its predecessors, using two emotion annotated music corpora MER60 and MTurk. Our results show that the AEG model outperforms the state-of-the-art methods in automatic music emotion annotation. Moreover, for the first time a quantitative evaluation of emotion-based music retrieval is reported.


title: Molecular Simulations of the Ribosome and Associated Translation Factors

abstract: The ribosome is a macromolecular complex which is responsible for protein synthesis in all living cells according to their transcribed genetic information. Using X-ray crystallography and, more recently, cryo-electron microscopy (cryo-EM), the structure of the ribosome was resolved at atomic resolution in many functional and conformational states. Molecular dynamics simulations have added information on dynamics and energetics to the available structural information, thereby have bridged the gap to the kinetics obtained from single-molecule and bulk experiments. Here, we review recent computational studies that brought notable insights into ribosomal structure and function.


title: Multimodal MR Synthesis via Modality-Invariant Latent Representation

abstract: We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modality-invariant latent space. These latent representations are then combined into a single fused representation, which is transformed into the target output modality with a learnt decoder. We avoid the need for curriculum learning by exploiting the fact that the various input modalities are highly correlated. We also show that by incorporating information from segmentation masks the model can both decrease its error and generate data with synthetic lesions. We evaluate our model on the ISLES and BRATS data sets and demonstrate statistically significant improvements over state-of-the-art methods for single input tasks. This improvement increases further when multiple input modalities are used, demonstrating the benefits of learning a common latent space, again resulting in a statistically significant improvement over the current best method. Finally, we demonstrate our approach on non skull-stripped brain images, producing a statistically significant improvement over the previous best method. Code is made publicly available at https://github.com/agis85/multimodal_brain_synthesis .


title: Statistical Machine Translation Between Related and Unrelated Languages

abstract: In this paper we describe an attempt to compare how relatedness of languages can influence the performance of statistical machine translation (SMT). We apply the Moses toolkit on the Czech-English-Russian corpus UMC 0.1 in order to train two translation systems: Russian-Czech and English-Czech. The quality of the translation is evaluated on an independent test set of 1000 sentences parallel in all three languages using an automatic metric (BLEU score) as well as manual judgments. We examine whether the quality of Russian-Czech is better thanks to the relatedness of the languages and similar characteristics of word order and morphological richness. Additionally, we present and discuss the most frequent translation errors for both language pairs.


Dynamic Modeling and Control of a HexaRotor using Linear and Nonlinear Methods


Alibaba-Venus at ActivityNet Challenge 2018-Task C Trimmed Event Recognition ( Moments in Time )


title: Do you identify as a gamer? Gender, race, sexuality, and gamer identity:

abstract: The demand for minority representation in video games often focuses on proving that members of marginalized groups are gamers. In turn, it is asserted that the gaming industry should focus on appealing to these players via targeted content. Being targeted as a gamer, however, does not a gamer make. Identity as a gamer intersects with other identities like gender, race, and sexuality. Negative connotations about gaming lead people to not identify as gamers, and even to not play video games. This article concludes, based on interview data, that those invested in diversity in video games must focus their attention on the construction of the medium, and not the construction of the audience as such. This shift in academic attention is necessary to develop arguments for representation in games that do not rely on marking groups as specific kinds of gaming markets via identifiers like gender, race, and sexuality.


PATTERN OF ACCESS AND ICT USAGE AMONG SCIENCE TEACHERS IN FEDERAL UNITY SCHOOLS IN NIGERIA


Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex


title: Learning geographical preferences for point-of-interest recommendation

abstract: The problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the decision process of a user choose a POI is complex and can be influenced by various factors, such as user preferences, geographical influences, and user mobility behaviors. While there are some studies on POI recommendations, it lacks of integrated analysis of the joint effect of multiple factors. To this end, in this paper, we propose a novel geographical probabilistic factor analysis framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, the user mobility behaviors can be effectively exploited in the recommendation model. Moreover, the recommendation model can effectively make use of user check-in count data as implicity user feedback for modeling user preferences. Finally, experimental results on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art latent factor models with a significant margin.


title: A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

abstract: Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.


title: Economic Small-World Behavior in Weighted Networks

abstract: The small-world phenomenon has been already the subject of a huge variety of papers, showing its appeareance in a variety of systems. However, some big holes still remain to be filled, as the commonly adopted mathematical formulation is valid only for topological networks. In this paper we propose a generalization of the theory of small worlds based on two leading concepts, efficiency and cost, and valid also for weighted networks. Efficiency measures how well information propagates over the network, and cost measures how expensive it is to build a network. The combination of these factors leads us to introduce the concept of economic small worlds, that formalizes the idea of networks that are “cheap” to build, and nevertheless efficient in propagating information, both at global and local scale. In this way we provide an adequate tool to quantitatively analyze the behaviour of complex networks in the real world. Various complex systems are studied, ranging from the realm of neural networks, to social sciences, to communication and transportation networks. In each case, economic small worlds are found. Moreover, using the economic small-world framework, the construction principles of these networks can be quantitatively analyzed and compared, giving good insights on how efficiency and economy principles combine up to shape all these systems.


Machine Reading Approach to Understand Alzheimer ’ s Disease Literature


title: Fuzzy multi-objective mathematical programming on reliability optimization model

abstract: Abstract This paper considers multi-objective reliability optimization problem for system reliability where reliability enhancement is involved with several mutually conflicting objectives. It is conflicting to reduce the cost of the system and improve the reliability of the same system simultaneously. A new fuzzy multi-objective optimization method is introduced and it is used for the optimization decision making of the series and complex system reliability with two objectives. In general, cost of the components of the system’s objective goals has not been stated clearly. This imprecise reliability optimization model is solved through the fuzzy multi-objective optimization method. Examples are shown to illustrate the method.


title: Multiband antenna for Bluetooth/ZigBee/ Wi-Fi/WiMAX/WLAN/X-band applications: Partial ground with periodic structures and EBG

abstract: A compact in size microstrip antenna with multiband resonance features is described in this paper. A circular antenna with the partial ground is used as a primary antenna which resonated for WiMAX band (3.3–3.7 GHz). Further, primary antenna embedded with mushroom shape EBG (Electromagnetic Band Gap) and periodic DGS (Defected Ground Structure) to operate over WLAN band (5.1–5.8 GHz) and X-band from 8–12 GHz. Proposed antenna covers 2.4/3.5/5.5 GHz bands and complete X-band which also reflects that it has novel suitability to use for a ZigBee/Wi-Fi/Bluetooth applications. The proposed antenna is etched on FR-4 substrate and overall dimension is 20×26×1.6 mm3. The projected antenna has wide-ranging bandwidth with VSWR < 2. It provides favorable agreement between measured and simulated results.


title: Deep and Wide Multiscale Recursive Networks for Robust Image Labeling

abstract: Feedforward multilayer networks trained by supervised learning have recently demonstrated state of the art performance on image labeling problems such as boundary prediction and scene parsing. As even very low error rates can limit practical usage of such systems, methods that perform closer to human accuracy remain desirable. In this work, we propose a new type of network with the following properties that address what we hypothesize to be limiting aspects of existing methods: (1) a `wide' structure with thousands of features, (2) a large field of view, (3) recursive iterations that exploit statistical dependencies in label space, and (4) a parallelizable architecture that can be trained in a fraction of the time compared to benchmark multilayer convolutional networks. For the specific image labeling problem of boundary prediction, we also introduce a novel example weighting algorithm that improves segmentation accuracy. Experiments in the challenging domain of connectomic reconstruction of neural circuity from 3d electron microscopy data show that these"Deep And Wide Multiscale Recursive"(DAWMR) networks lead to new levels of image labeling performance. The highest performing architecture has twelve layers, interwoven supervised and unsupervised stages, and uses an input field of view of 157,464 voxels ($54^3$) to make a prediction at each image location. We present an associated open source software package that enables the simple and flexible creation of DAWMR networks.


title: Identifying factors of "organizational information security management"

abstract: Purpose – Despite many technically sophisticated solutions, managing information security has remained a persistent challenge for organizations. Emerging IT/ICT media have posed new security challenges to business information and information assets. It is felt that technical solutions alone are not sufficient to address the information security challenge. It has been argued that organizations also need to consider the management aspects of information security. Consequently, literature, especially in the last decade, has witnessed various scholarly works in this direction. Therefore, a synthesis exercise is required to bring clarity on categorizing the issues of organizational information security management (ISM) to take the research forward. The purpose of this paper is to identify management factors that address organizational information security challenges. Design/methodology/approach – Using a mix method approach, the paper adopts the qualitative (keyword analysis and experts’ opinion) and quantitat...


title: Spatio-Temporal Modeling and Prediction of Visual Attention in Graphical User Interfaces

abstract: We present a computational model to predict users' spatio-temporal visual attention on WIMP-style (windows, icons, menus, pointer) graphical user interfaces. Like existing models of bottom-up visual attention in computer vision, our model does not require any eye tracking equipment. Instead, it predicts attention solely using information available to the interface, specifically users' mouse and keyboard input as well as the UI components they interact with. To study our model in a principled way, we further introduce a method to synthesize user interface layouts that are functionally equivalent to real-world interfaces, such as from Gmail, Facebook, or GitHub. We first quantitatively analyze attention allocation and its correlation with user input and UI components using ground-truth gaze, mouse, and keyboard data of 18 participants performing a text editing task. We then show that our model predicts attention maps more accurately than state-of-the-art methods. Our results underline the significant potential of spatio-temporal attention modeling for user interface evaluation, optimization, or even simulation.


title: Dex: a semantic-graph differencing tool for studying changes in large code bases

abstract: This paper describes an automated tool called Dex (difference extractor) for analyzing syntactic and semantic changes in large C-language code bases. It is applied to patches obtained from a source code repository, each of which comprises the code changes made to accomplish a particular task. Dex produces summary statistics characterizing these changes for all of the patches that are analyzed. Dex applies a graph differencing algorithm to abstract semantic graphs (ASGs) representing each version. The differences are then analyzed to identify higher-level program changes. We describe the design of Dex, its potential applications, and the results of applying it to analyze bug fixes from the Apache and GCC projects. The results include detailed information about the nature and frequency of missing condition defects in these projects.


title: Neural Importance Sampling

abstract: We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend in numerous ways to improve performance and enable its application to integration problems. First, we introduce piecewise-polynomial coupling transforms that greatly increase the modeling power of individual coupling layers. Second, we propose to preprocess the inputs of neural networks using one-blob encoding, which stimulates localization of computation and improves inference. Third, we derive a gradient-descent-based optimization for the Kullback-Leibler and the χ2 divergence for the specific application of Monte Carlo integration with unnormalized stochastic estimates of the target distribution. Our approach enables fast and accurate inference and efficient sample generation independently of the dimensionality of the integration domain. We show its benefits on generating natural images and in two applications to light-transport simulation: first, we demonstrate learning of joint path-sampling densities in the primary sample space and importance sampling of multi-dimensional path prefixes thereof. Second, we use our technique to extract conditional directional densities driven by the product of incident illumination and the BSDF in the rendering equation, and we leverage the densities for path guiding. In all applications, our approach yields on-par or higher performance than competing techniques at equal sample count.


title: On-Device Federated Learning via Blockchain and its Latency Analysis

abstract: In this letter, we propose a block-chained federated learning (BlockFL) architecture, where mobile devices' local learning model updates are exchanged and verified by leveraging blockchain. This enables on-device machine learning without any central coordination, even when each device lacks its own training data samples. We investigate the end-to-end learning completion latency of BlockFL, thereby yielding the optimal block generation rate as well as important insights in terms of network scalability and robustness.


title: Result of randomized control trial to increase breast health awareness among young females in Malaysia

abstract: Breast cancer is the most common cancer and the second principal cause of cancer deaths in women worldwide as well as in Malaysia. Breast self-examination (BSE) has a role in raising breast cancer awareness among women and educational programs play an important role in breast cancer preventive behavior. The aim of this study is to develop, implement and evaluate the effectiveness of Breast Health Awareness program based on health belief model on knowledge of breast cancer and breast-selfexamination and BSE practice among female students in Malaysia. A single-blind randomized controlled trial was carried out among 370 female undergraduate students from January 2011 to April 2012 in two selected public universities in Malaysia. Participants were randomized to either the intervention group or the control group. The educational program was delivered to the intervention group. The outcome measures were assessed at baseline, 6, and 12 months after implementing the health educational program. Chi-square, independent samples t-test and two-way repeated measures ANOVA (GLM) were conducted in the course of the data analyses. Mean scores of knowledge on breast cancer (p<0.003), knowledge on breast self examination (p<0.001), benefits of BSE (p<0.00), barrier of BSE (0.01) and confidence of BSE practice (p<0.00) in the intervention group had significant differences in comparison with those of the control group 6 and 12 months after the intervention. Also, among those who never practiced BSE at baseline, frequency of BSE practice increased 6 and 12 months after the intervention (p<0.05). The Breast Health Awareness program based on health the belief model had a positive effect on knowledge of breast cancer and breast self-examination and practice of BSE among females in Malaysia. The ANZCTR clinical trial registry (ACTRN12616000831482), retrospectively registered on Jun 23, 2016 in ANZCTR.org.au.


title: Biomimetic flexible robot arm design and kinematic analysis of a novel flexible robot arm

abstract: Conventional robot manipulators have singularities in their workspaces and constrained spatial movements. Flexible and soft robots provide a unique solution to overcome this limitation. Flexible robot arms have biologically inspired characteristics as flexible limbs and redundant degrees of freedom. From these special characteristics, flexible manipulators are able to develop abilities such as bend, stretch and adjusting stiffness to traverse a complex maze. Many researchers are working to improve capabilities of flexible arms by improving the number of degrees of freedoms and their methodologies. The proposed flexible robot arm is composed of multiple sections and each section contains three similar segments and a base segment. These segments act as the backbone of the basic structure and each section can be controlled by changing the length of three control wires. These control wires pass through each segment and are held in place by springs. This design provides each segment with 2 DOF. The proposed system single section can be bent 90° with respective to its centre axis. Kinematics of the flexible robot is derived with respect to the base segment.


title: An Active Patch Model for Real World Texture and Appearance Classification

abstract: This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of active patches. Active patches are raw intensity patches which can undergo spatial transformations (e.g., rotation and scaling) and adjust themselves to best match the image regions. The dictionary of active patches is required to be compact and representative, in the sense that we can use it to approximately reconstruct the images that we want to classify. We propose a probabilistic model to quantify the quality of image reconstruction and design a greedy learning algorithm to obtain the dictionary. We classify images using the occurrence frequency of the active patches. Feature extraction is fast (about 100 ms per image) using the GPU. The experimental results show that our method improves the state of the art on a challenging material texture benchmark dataset (KTH-TIPS2). To test our method on less homogeneous or inhomogeneous images, we construct two new datasets consisting of appearance image patches of animals and vehicles cropped from the PASCAL VOC dataset. Our method outperforms competing methods on these datasets.


title: Self-oscillating buck driver for power LEDs

abstract: The self-oscillating converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. It is widely employed in mobile phone charges and as the stand-by power source in offline power supplies for data-processing equipment. However, this circuit almost was not explored for supplier Power LEDs. This paper presents a self-oscillating buck power electronics driver for supply directly Power LEDs, with no additional circuit. A simplified mathematical model of LED was used to characterize the self-oscillating converter for the power LED driver. In order to improve the performance of the proposed buck converter in this work the control of the light intensity of LEDs was done using a microcontroller to emulate PWM modulation with frequency 200 Hz. At using the converter proposed the effects of the LED manufacturing tolerances and drifts over temperature almost has no influence on the LED average current.


title: NOISE-ADAPTIVE DEEP NEURAL NETWORK FOR SINGLE-CHANNEL SPEECH ENHANCEMENT

abstract: We introduce a noise-adaptive feed-forward deep neural network (DNN) for single-channel speech enhancement. The goal is to better exploit individual noise characteristics while training a spectral mapping DNN. To this end, we employ noise-dependent adaptation vectors, which are obtained based on the output of an auxiliary noise classification DNN, to adjust the weights and biases of the spectral mapping DNN. The parameters of the spectral mapping DNN, noise classification DNN and adaptation vectors are estimated jointly during the training stage. During the enhancement stage, we combine a classical unsupervised speech enhancement algorithm with the proposed DNN-based approach to further improve the enhanced speech quality. Experiments show that the proposed method provides better enhancement performance than the selected benchmark algorithms.


title: Healthy Young Minds: The Effects of a 1-hour Classroom Workshop on Mental Illness Stigma in High School Students

abstract: This study aimed to test the effects of a 1-h classroom-based workshop, led by medical students, on mental illness stigma amongst secondary school students. Students (aged 14–17) from three public secondary schools in British Columbia participated in the workshop. A questionnaire measuring stigma (including stereotype endorsement and desire for social distance) was administered immediately before (T1), immediately after (T2), and 1-month after the workshop (T3). A total of 279 students met the study inclusion criteria. Total scores on the stigma scale decreased by 23 % between T1 and T2 (p < 0.01). This was sustained 1-month post-workshop with a 21 % stigma reduction compared to pre-intervention (p < 0.01). This effect was primarily due to improvements in scores that measured desire for social distance. There were no significant changes in scores that measured stereotype endorsement. Adolescents’ stigmatizing attitudes can be effectively reduced through a 1-h easily implementable and cost-effective classroom-based workshop led by medical students.


title: Nonclassical computation: a dynamical systems perspective

abstract: In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-


title: Big data integration with business processes: a literature review

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to improve the understanding of the integration of business process management (BPM), business process re-engineering (BPR) and business process innovation (BPI) with big data. It focusses on synthesizing research published in the period 2006-2016 to establish both what the authors know and do not know about this topic, identifying areas for future research. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The research is based on a review of 49 published papers on big data, BPM, BPR and BPI in the top journals in the field 2006-2016. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: In this paper, the authors have identified the most influential works based on citations and PageRank methods. Through network analysis the authors identify four major clusters that provide potential opportunities for future investigation. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: It is important for practitioners to be aware of the benefits of big data, BPM, BPR and BPI integration. This paper provides valuable insights for practitioners. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This paper is based on a comprehensive literature review, which gives big data researchers the opportunity to understand business processes in depth. In addition, highlighting many gaps in the current literature and developing an agenda for future research, will save time and effort for readers looking to research topics within big data and business processes.


title: Information-Dense Nonlinear Photonic Physical Unclonable Function

abstract: We present a comprehensive investigation into the complexity of a new private key storage apparatus: a novel silicon photonic physical unclonable function (PUF) based on ultrafast nonlinear optical interactions in a chaotic silicon microcavity that is both unclonable and impossible to emulate. This device provides remarkable improvements to total information content (raw cryptographic material), information density, and key generation rates over existing optical scattering PUFs and is also more easily integrated with both CMOS electronics and telecommunications hardware. Our device exploits the natural nonlinear optical behavior of silicon to neutralize commonly used attacks against PUFs and vastly enhance device complexity. We confirm this phenomenon with thorough experimental results on prototype devices and present a detailed estimate of their total information content. Our compact, micron-scale approach represents an entirely new generation of ultrafast and high information density photonic PUF devices that can be directly incorporated into integrated circuits to ensure authenticity and provide secure physical storage of private key material.


title: Unsupervised Discovery Of Morphemes

abstract: We present two methods for unsupervised segmentation of words into morpheme-like units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second method, Maximum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Experiments on both Finnish and English corpora show that the presented methods perform well compared to a current state-of-the-art system.


title: A comparative study of RNN for outlier detection in data mining

abstract: We have proposed replicator neural networks (RNNs) for outlier detection. We compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets in particular test scalability and practicality of application.


title: Learning state representations with robotic priors

abstract: Robot learning is critically enabled by the availability of appropriate state representations. We propose a robotics-specific approach to learning such state representations. As robots accomplish tasks by interacting with the physical world, we can facilitate representation learning by considering the structure imposed by physics; this structure is reflected in the changes that occur in the world and in the way a robot can effect them. By exploiting this structure in learning, robots can obtain state representations consistent with the aspects of physics relevant to the learning task. We name this prior knowledge about the structure of interactions with the physical world robotic priors. We identify five robotic priors and explain how they can be used to learn pertinent state representations. We demonstrate the effectiveness of this approach in simulated and real robotic experiments with distracting moving objects. We show that our method extracts task-relevant state representations from high-dimensional observations, even in the presence of task-irrelevant distractions. We also show that the state representations learned by our method greatly improve generalization in reinforcement learning.


title: Self-Shielded Circularly Polarized Antenna-in-Package Based on Quarter Mode Substrate Integrated Waveguide Subarray

abstract: A self-shielded antenna-in-package (AiP) covering IEEE 802.11a band is proposed in this paper, and the antenna is designed using quarter mode substrate integrated waveguide (QMSIW) subarray technology. Electromagnetic interference (EMI) in a system-in-package integrated with AiP is discussed firstly. Electric field distribution of the proposed AiP is simulated, and it shows that the metallic via-holes array in the QMSIW subarray approximates to the electric wall to isolate the electromagnetic field coupling and then to increase the EMI shielding. The operating principle of the proposed antenna is investigated through an isosceles right triangular waveguide. An antenna prototype is fabricated and measured. The measured results match with the simulated and analyzed results very well. It is demonstrated that the proposed QMSIW subarray with metallic via-holes array gives an attractive and promising way to design the AiP with self-shielded property.


title: Excessive Computer Game Playing: Evidence for Addiction and Aggression?

abstract: Computer games have become an ever-increasing part of many adolescents' day-to-day lives. Coupled with this phenomenon, reports of excessive gaming (computer game playing) denominated as "computer/video game addiction" have been discussed in the popular press as well as in recent scientific research. The aim of the present study was the investigation of the addictive potential of gaming as well as the relationship between excessive gaming and aggressive attitudes and behavior. A sample comprising of 7069 gamers answered two questionnaires online. Data revealed that 11.9% of participants (840 gamers) fulfilled diagnostic criteria of addiction concerning their gaming behavior, while there is only weak evidence for the assumption that aggressive behavior is interrelated with excessive gaming in general. Results of this study contribute to the assumption that also playing games without monetary reward meets criteria of addiction. Hence, an addictive potential of gaming should be taken into consideration regar...