title: Robust Component-Based Localizationin Sparse Networks

abstract: Accurate localization is crucial for wireless ad-hoc and sensor networks. Among the localization schemes, component-based approaches specialize in localization performance. By grouping nodes into increasingly large rigid components, component-based localization algorithms can properly conquer network sparseness and anchor sparseness. However, such design is sensitive to measurement errors. Existing robust localization methods focus on eliminating the positioning error of a single node. Indeed, a single node has two dimensions of freedom in 2D space and only suffers from one type of transformation: translation. As a rigid 2D structure, a component suffers from three possible transformations: translation, rotation, and reflection. A high degree of freedom brings about complicated cases of error productions and difficulties on error controlling. This study is the first work addressing how to deal with ranging noises for component-based methods. By exploiting a set of robust patterns, we present an Error-TOlerant Component-based algorithm (ETOC) that not only inherits the high-performance characteristic of component-based methods, but also achieves robustness of the result. We evaluate ETOC through a real-world sensor network consisting of 120 TelosB motes as well as extensive large-scale simulations. Experiment results show that, comparing with the-state-of-the-art designs, ETOC can work properly in sparse networks and provide more accurate localization results.


title: Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

abstract: In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications .


title: Brief detection and co-occurrence of violence, depression and alcohol risk in prenatal care settings

abstract: The purpose of the study was to examine the rates and inter-relationships among violence receipt, alcohol use problems, and depression in women seeking prenatal care. While waiting for their prenatal care appointment, women (n = 1054) completed measures of past year partner and non-partner violence receipt, alcohol misuse (TWEAK and quantity and frequency of alcohol use in past year), and depression (Center for Epidemiological Studies Depression Scale - CESD and prior history of depression). Over 30% of women reported either violence receipt, alcohol use problems or depression risk. Significant inter-relationships among all measured risk variables were found. Although violence receipt was significantly related to alcohol misuse, cigarette use, less education, and scoring above the cutoff on the CESD (>/= 16) was most strongly associated with violence. Practitioners should be well-equipped to provide assessment, interventions, or referrals as needed to the high numbers of women encountered in prenatal care settings experiencing psychosocial and behavioral problems that may affect their pregnancy.


title: Disentangling and Learning Robust Representations with Natural Clustering

abstract: Learning representations that disentangle the underlying factors of variability in data is an intuitive way to achieve generalization in deep models. In this work, we address the scenario where generative factors present a multimodal distribution due to the existence of class distinction in the data. We propose N-VAE, a model which is capable of separating factors of variation which are exclusive to certain classes from factors that are shared among classes. This model implements an explicitly compositional latent variable structure by defining a class-conditioned latent space and a shared latent space. We show its usefulness for detecting and disentangling class-dependent generative factors as well as its capacity to generate artificial samples which contain characteristics unseen in the training data.


title: A NEW POLYNOMIAL-TIME ALGORITHM FOR LINEAR PROGRAMMING

abstract: We present a new polynomial-time algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than the ellipsoid algorithm by a factor of O(n~'~). We prove that given a polytope P and a strictly interior point a E P, there is a projective transformation of the space that maps P, a to P', a' having the following property. The ratio of the radius of the smallest sphere with center a', containing P' to the radius of the largest sphere with center a' contained in P' is O(n). The algorithm consists of repeated application of such projective transformations each followed by optimization over an inscribed sphere to create a sequence of points which converges to the optimal solution in polynomial time. 1. Informal outline


title: Polynomial Lower Bound for Distributed Graph Coloring in a Weak LOCAL Model

abstract: We show an \(\varOmega \big (\varDelta ^{\frac{1}{3}-\frac{\eta }{3}}\big )\) lower bound on the runtime of any deterministic distributed \(\mathcal {O}\big (\varDelta ^{1+\eta }\big )\)-graph coloring algorithm in a weak variant of the \(\mathsf {LOCAL}\) model.


title: Decoding Stacked Denoising Autoencoders

abstract: Data representation in a stacked denoising autoencoder is investigated. Decoding is a simple technique for translating a stacked denoising autoencoder into a composition of denoising autoencoders in the ground space. In the infinitesimal limit, a composition of denoising autoencoders is reduced to a continuous denoising autoencoder, which is rich in analytic properties and geometric interpretation. For example, the continuous denoising autoencoder solves the backward heat equation and transports each data point so as to decrease entropy of the data distribution. Together with ridgelet analysis, an integral representation of a stacked denoising autoencoder is derived.


Prevalence of Nomophobia among University Students: A Comparative Study of Pakistani and Turkish Undergraduate Students


title: A pilot randomized control trial investigating the effect of mindfulness practice on pain tolerance, psychological well-being, and physiological activity

abstract: Abstract Objective To investigate the effect of mindfulness training on pain tolerance, psychological well-being, physiological activity, and the acquisition of mindfulness skills. Methods Forty-two asymptomatic University students participated in a randomized, single-blind, active control pilot study. Participants in the experimental condition were offered six (1-h) mindfulness sessions; control participants were offered two (1-h) Guided Visual Imagery sessions. Both groups were provided with practice CDs and encouraged to practice daily. Pre–post pain tolerance (cold pressor test), mood, blood pressure, pulse, and mindfulness skills were obtained. Results Pain tolerance significantly increased in the mindfulness condition only. There was a strong trend indicating that mindfulness skills increased in the mindfulness condition, but this was not related to improved pain tolerance. Diastolic blood pressure significantly decreased in both conditions. Conclusion Mindfulness training did increase pain tolerance, but this was not related to the acquisition of mindfulness skills.


title: Minimum variance ultrasonic imaging applied to an in situ sparse guided wave array

abstract: Ultrasonic guided wave imaging with a sparse, or spatially distributed, array can detect and localize damage over large areas. Conventional delay-and-sum images from such an array typically have a relatively high noise floor, however, and contain artifacts that often cannot be discriminated from damage. Considered here is minimum variance distortionless response (MVDR) imaging, which is a variation of delay-and-sum imaging whereby weighting coefficients are adaptively computed at each pixel location. Utilization of MVDR significantly improves image quality compared with delay-and-sum imaging, and additional improvements are obtained from incorporation of a priori scattering information in the MVDR method, use of phase information, and instantaneous windowing. Simulated data from a through-hole scatterer are used to illustrate performance improvements, and a performance metric is proposed that allows for quantitative comparisons of images from a known scatterer. Experimental results from a through-hole scatterer are also provided that illustrate imaging efficacy.


title: The method evaluation model: a theoretical model for validating information systems design methods

abstract: Information Systems (IS) design research tends to emphasise the development of new methods (normative research), while addressing the evaluation of methods in only a limited fashion. A possible reason for this is the philosophical and methodological problems involved in validating methods (“knowledge how”) as opposed to theses (“knowledge that”). “Knowledge that” has been the major focus of scientific research, which is generally about establishing the truth of particular propositions (hypotheses). However an entirely different approach is required to validate methodological knowledge. This paper proposes a theoretical model and associated measurement instrument for evaluating IS design methods. The model is based on two previously unrelated areas of theory: the Technology Acceptance Model (TAM) from the IS success literature and Methodological Pragmatism from the philosophy of science. The resulting theoretical model combines two different but related dimensions of method “success”: actual effectiveness and adoption in practice. The model is applicable to all types of IS design methods as well as methods used in other domains. A laboratory experiment and a field experiment are conducted to test the model. The paper also presents some interesting findings about the use of undergraduate students in experimental studies.


title: From past to future: Temporal self-continuity across the life span.

abstract: Although perceived continuity with one's future self has attracted increasing research interest, age differences in this phenomenon remain poorly understood. The present study is the first to simultaneously examine past and future self-continuity across multiple temporal distances using both explicit and implicit measures and controlling for a range of theoretically implicated covariates in an adult life span sample (N = 91, aged 18-92, M = 50.15, SD = 19.20, 56% female). Perceived similarity to one's self across 6 past and 6 future time points (1 month to 10 years) was assessed with an explicit self-report measure and an implicit me/not me trait rating task. In multilevel analyses, age was significantly associated with greater implicit and explicit self-continuity, especially for more distant intervals. Further, reaction times (RTs) in the implicit task remained stable with temporal distance for older adults but decreased with temporal distance for younger adults, especially for future ratings. This points toward age differences in the underlying mechanisms of self-continuity. Multilevel models examined the role of various covariates including personality, cognition, future horizons, and subjective health and found that none of them could fully account for the observed age effects. Taken together, our findings suggest that chronological age is associated with greater self-continuity although specific mechanisms and correlates may vary by age. (PsycINFO Database Record


title: Crystallization behavior of poly(trimethylene terephthalate)/multi-walled carbon nanotube composites

abstract: Poly(trimethylene terepthalate) (PTT) has in recent years, attracted much interest for its application in fibers and engineering thermoplastics, due to its outstanding properties such as good resilience and elastic recovery [1, 2]. As a typical semicrystalline polymer, the crystallization behavior of PTT is the primary property that affects its physical properties and processing conditions, and thus many articles have appeared describing this topic [3–10]. In previous works, we reported the crystallization kinetics of PTT including the secondary crystallization process, and the crystallization behavior of PTT at high undercoolings was also explored [6, 7]. A series of copolyesters based on PTT with modified thermal properties have been prepared and their composition dependence of thermal properties was also presented [8–10]. Moreover, recently increasing attention has been given to the fabrication and the property studies of polymer/carbon nanotubes (CNTs) composites due to the high aspect ratio, nanosize in diameter, very low density, excellent physical properties of CNTs, and the remarkably enhanced physical properties of the composites [11–15]. However, so far there is a little research on the crystallization behavior of such polymer nanocomposites, especially in the nucleation effect of CNTs on the polymer matrix despite of its potential, academic, and technological values. In this communication, PTT composites with multiwalled carbon nanotubes (MWNTs), in which the nanotube consists of several layers of coaxial carbon tubes [16, 17], were prepared, and the influence of MWNT on crystallization of PTT matrix was discussed. To our knowledge, it is the first time that poly(trimethylene terephthalate)/multiwalled carbon nanotube composites were prepared and their crystallization behavior was presented. PTT ([g] = 0.0795 m/kg at 298 K) used in this study is the same polymer which has been described in one of our previous articles [6]. MWNTs were purchased from Bill Nanotech Co., Ltd (Shenzhen, China). These MWNTs were produced via the chemical vapor deposition (CVD) method and have lengths up to a few microns and diameters between 70 and 100 nm. The purchased MWNTs were purified to remove amorphous carbon and iron impurities [18]. In a typical experiment, an MWNT sample was heated and refluxed in an aqueous HNO3 solution (2.6 M) for 13 h. After filtering through a Teflon-PTFE membrane, the remaining solid was washed with deionized water until the pH was neutral. The purified MWNTs were then functionalized by tetrabutyl titanate to enhance the compatibility of MWNT with PTT. To fabricate PTT–MWNT composites, weighed PTT sample was dissolved in the solution of 1:1 (w/w) 1,1,2,2-tetrachloroethane/phenol at 353 K to form a clear solution. Appropriate amount of functionalized MWNT was weighed according to the required percentage, and was added to the solution. The mixture was refluxed with stir for 4 h to form a homogeneous suspension and then sonicated for 1 h. Finally, the mixture was cast into an aluminum mold in the form of a film. The mold with the suspension was vacuum dried in an oven at 373 K for 48 h. The resultant composite was denoted according to the weight percentage of MWNT in Y. Xu (&) H.-B. Jia J.-N. Piao Department of Polymer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, P.R. China e-mail: xuyong@zuaa.zju.edu.cn


title: See-through-wall imaging using ultra wideband short-pulse radar system

abstract: See-through-wall imaging radar is a unique application of ultra wideband communication that can provide soldiers and law enforcement officers with an enhanced situation awareness. We have developed an ultra-wideband high-resolution short pulse imaging radar system operating around 10 GHz, where two essential considerations are addressed: the effect of penetrating the walls; the pulse fidelity through the UWB components and antennas of the radar. Modeled and measured wall parameters, and the effect of antenna types on signal fidelity are discussed in detail.


title: Morphometry of the pancreas in human foetuses.

abstract: With the use of conventional anatomical dissection, radiography, digital and ::: statistical analysis, morphometry and skeletopy of the pancreas was carried out ::: in 60 human foetuses of both sexes (28 female, 32 male) between the 17 th and ::: 40 th week of intrauterine life. The material was fixed in a 10% formalin solution. ::: The age of the foetuses was determined by crown-rump (CR) lenght measurement ::: on the basis of the Iffy et al. tables. Photographic documentation was ::: made and then digitally processed in the Computer Image Digital Analysis System. ::: The following parameters were taken into account: the length and width ::: of 3 parts of the pancreas, namely the head, corpus and tail. Additionally, radiograms ::: were made to obtain a projection of the gland on the vertebral column. ::: Development of the pancreas was correlated with the age of the foetuses calculated ::: on the basis of crown-rump (CR) lenght measurements. The correlation ::: coefficient with CR was 0.998 for the pancreas length, 0.709 for the width of ::: the head, 0.703 for the width of the corpus and 0.712 for the width of tail. ::: Gender dimorphism was not found (p > 0.05) with regard to the morphometry ::: of the pancreas. In the material under examination the pancreas did not change ::: its position in relation to the vertebral column. The head projected on the vertebral ::: column in the range Th 12 –L 2 (most frequently L 1 –L 2 ), the corpus on Th 12 –L 2 ::: and the tail on Th 11 .


title: Differential susceptibility to the environment: an evolutionary--neurodevelopmental theory.

abstract: Two extant evolutionary models, biological sensitivity to context theory (BSCT) and differential susceptibility theory (DST), converge on the hypothesis that some individuals are more susceptible than others to both negative (risk-promoting) and positive (development-enhancing) environmental conditions. These models contrast with the currently dominant perspective on personal vulnerability and environmental risk: diathesis stress/dual risk. We review challenges to this perspective based on emerging theory and data from the evolutionary, developmental, and health sciences. These challenges signify the need for a


Hierarchical Temporal Memory Concepts , Theory , and Terminology


title: The effects of affective interventions in human–computer interaction

abstract: Abstract The present study investigated the psychophysiological effects of positive and negative affective interventions in human–computer interaction during and after the interventions. Eighteen subjects were exposed to pre-programmed mouse delays in an interactive problem-solving task. Following the mouse delays three types of conditions were used: positive or negative interventions given via speech synthesizer, and no intervention. Facial electromyographic responses were recorded from the zygomaticus major and corrugator supercilii muscle sites. These muscles control smiling and frowning, respectively. Smiling activity was significantly higher during the positive than the other conditions. It was also significantly higher after the positive interventions than the no intervention condition. The frowning activity attenuated significantly more after the positive interventions than the no intervention condition. Following the positive interventions the users' problem solving performance was significantly better than after no intervention. In all, the results suggest that both types of affective intervention had beneficial effects over ignoring the user. The results suggest further that positive intervention may be especially useful.


GPU Real Time Hatching


title: Exploring Security in PROFINET IO

abstract: In this paper we show that it is possible to attack and gain control over PROFINET IO nodes and also that this can be done without any of the communicating peers detecting the attack. Analysis of attacks in both shared and packet switched networks show that the attacker can control the process data and thus the state of the machines connected to the I/O modules. As the security risks are increasing in automation with the level of vertical and horizontal integration, the concept of security modules is proposed towards a method to retrofit security in PROFINET IO. The concept of security modules can be applied without changing anything in the underlying transmission system and is extendable if and when new security threats are identified.


title: Closest pair and the post office problem for stochastic points

abstract: Given a (master) set M of n points in d-dimensional Euclidean space, consider drawing a random subset that includes each point m"[email protected]?M with an independent probability p"i. How difficult is it to compute elementary statistics about the closest pair of points in such a subset? For instance, what is the probability that the distance between the closest pair of points in the random subset is no more than @?, for a given value @?? Or, can we preprocess the master set M such that given a query point q, we can efficiently estimate the expected distance from q to its nearest neighbor in the random subset? These basic computational geometry problems, whose complexity is quite well-understood in the deterministic setting, prove to be surprisingly hard in our stochastic setting. We obtain hardness results and approximation algorithms for stochastic problems of this kind.


title: Elementary Students’ View of Collaborative Knowledge Building in LearningVillages

abstract: LearningVillages (LV) is an online game-based virtual learning environment. It aims at facilitating elementary students to pursue social inquiry learning and hence attain collaborative knowledge building (CKB). LV operates in the form of massively multi-player online role-play gaming. Different villages in this "virtual world" represent different societal issues. To embark on the development of a village, students have to inquire collaboratively into the issue therein. Besides delineating the pedagogical design of LV, this paper also discusses our quantitative study on investigating the CKB affordance of this educational innovation from the student perspective (involving 229 elementary students in Hong Kong). Results showed that LV brought desirable CKB experience to the students in general. On top of that, we found the students with low academic achievement held a more positive perception (i.e. the affordance of LV in facilitating CKB) than the students with high and moderate achievement did.


title: Formalizing the structural semantics of domain-specific modeling languages

abstract: Model-based approaches to system design are now widespread and successful. These approaches make extensive use of model structure to describe systems using domain-specific abstractions, to specify and implement model transformations, and to analyze structural properties of models. In spite of its general importance the structural semantics of modeling languages are not well-understood. In this paper we develop the formal foundations for the structural semantics of domain-specific modeling languages (DSML), including the mechanisms by which metamodels specify the structural semantics of DSMLs. Additionally, we show how our formalization can complement existing tools, and how it yields algorithms for the analysis of DSMLs and model transformations.


title: Enacted Routines in Agile and Waterfall Processes

abstract: Agile methods form an alternative to waterfall methodologies. Little is known about activity composition, the proportion of varying activities in agile processes and the extent to which the proportions of activities differ from "waterfall" processes. In the current study, we examine the variation in per formative routines in one large agile and traditional lifecycle project using an event sequencing method. Our analysis shows that the enactment of waterfall and agile routines differ significantly suggesting that agile process is composed of fewer activities which are repeated iteratively1.


title: A Probabilistic Model of Redundancy in Information Extraction

abstract: Unsupervised Information Extraction (UIE) is the task of extracting knowledge from text without using hand-tagged training examples. A fundamental problem for both UIE and supervised IE is assessing the probability that extracted information is correct. In massive corpora such as the Web, the same extraction is found repeatedly in different documents. How does this redundancy impact the probability of correctness? ::: ::: This paper introduces a combinatorial "balls-andurns" model that computes the impact of sample size, redundancy, and corroboration from multiple distinct extraction rules on the probability that an extraction is correct. We describe methods for estimating the model's parameters in practice and demonstrate experimentally that for UIE the model's log likelihoods are 15 times better, on average, than those obtained by Pointwise Mutual Information (PMI) and the noisy-or model used in previous work. For supervised IE, the model's performance is comparable to that of Support Vector Machines, and Logistic Regression.


title: Hybrid Genetic Algorithm, Simulated Annealing and Tabu Search Methods for Vehicle Routing Problems with Time Windows

abstract: The Vehicle Routing Problem with Time Windows (VRPTW) involves servicing a set of customers, with earliest and latest time deadlines, with varying demands using capacitated vehicles with limited travel times. The objective of the problem is to service all customers while minimizing the number of vehicles and travel distance without violating the capacity and travel time of the vehicles and customer time constraints. In this paper we describe a λ-interchange mechanism that moves customers between routes to generate neighborhood solutions for the VRPTW. The λ-interchange neighborhood is searched using Simulated Annealing and Tabu Search strategies. The initial solutions to the VRPTW are obtained using the Push-Forward Insertion heuristic and a Genetic Algorithm based sectoring heuristic. The hybrid combination of the implemented heuristics, collectively known as the GenSAT system, were used to solve 60 problems from the literature with customer sizes varying from 100 to 417 customers. The computational results of GenSAT obtained new best solutions for 40 test problems. For the remaining 20 test problems, 11 solutions obtained by the GenSAT system equal previously known best solutions. The average performance of GenSAT is significantly better than known competing heuristics. For known optimal solutions to the VRPTW problems, the GenSAT system obtained the optimal number of vehicles.


title: Understanding the Political Representativeness of Twitter Users

abstract: In this article, we analyze the structure and content of the political conversations that took place through the microblogging platform Twitter in the context of the 2011 Spanish legislative elections and the 2012 U.S. presidential elections. Using a unique database of nearly 70 million tweets collected during both election campaigns, we find that Twitter replicates most of the existing inequalities in public political exchanges. Twitter users who write about politics tend to be male, to live in urban areas, and to have extreme ideological preferences. Our results have important implications for future research on the relationship between social media and politics, since they highlight the need to correct for potential biases derived from these sources of inequality.


title: Mining Photo-sharing Websites to Study Ecological Phenomena

abstract: The popularity of social media websites like Flickr and Twitter has created enormous collections of user-generated content online. Latent in these content collections are observations of the world: each photo is a visual snapshot of what the world looked like at a particular point in time and space, for example, while each tweet is a textual expression of the state of a person and his or her environment. Aggregating these observations across millions of social sharing users could lead to new techniques for large-scale monitoring of the state of the world and how it is changing over time. In this paper we step towards that goal, showing that by analyzing the tags and image features of geo-tagged, time-stamped photos we can measure and quantify the occurrence of ecological phenomena including ground snow cover, snow fall and vegetation density. We compare several techniques for dealing with the large degree of noise in the dataset, and show how machine learning can be used to reduce errors caused by misleading tags and ambiguous visual content. We evaluate the accuracy of these techniques by comparing to ground truth data collected both by surface stations and by Earth-observing satellites. Besides the immediate application to ecology, our study gives insight into how to accurately crowd-source other types of information from large, noisy social sharing datasets.


title: On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data

abstract: Single-cell RNA-Sequencing (scRNA-Seq) has become the most widely used high-throughput method for transcription profiling of individual cells. Systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies. Surprisingly, these issues have received minimal attention in published studies based on scRNA-Seq technology. We examined data from five published studies and found that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we found that the proportion of genes reported as expressed explains a substantial part of observed variability and that this quantity varies systematically across experimental batches. Furthermore, we found that the implemented experimental designs confounded outcomes of interest with batch effects, a design that can bring into question some of the conclusions of these studies. Finally, we propose a simple experimental design that can ameliorate the effect of theses systematic errors have on downstream results.


title: The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy

abstract: Abstract Of the offensive yet non-pathological personalities in the literature, three are especially prominent: Machiavellianism, subclinical narcissism, and subclinical psychopathy. We evaluated the recent contention that, in normal samples, this ‘Dark Triad’ of constructs are one and the same. In a sample of 245 students, we measured the three constructs with standard measures and examined a variety of laboratory and self-report correlates. The measures were moderately inter-correlated, but certainly were not equivalent. Their only common Big Five correlate was disagreeableness. Subclinical psychopaths were distinguished by low neuroticism; Machiavellians, and psychopaths were low in conscientiousness; narcissism showed small positive associations with cognitive ability. Narcissists and, to a lesser extent, psychopaths exhibited self-enhancement on two objectively scored indexes. We conclude that the Dark Triad of personalities, as currently measured, are overlapping but distinct constructs.


title: Embedded data acquisition system for beehive monitoring

abstract: This paper considers architecture and functionality of the embedded data acquisition system for automated beehive monitoring. A description of constructed sensor subsystems is given. Proposed solution acquires hive temperature, humidity and weight referring this data to the mobile application via wireless network. The system also performs an analysis of collected bee noises with artificial neural network to recognize bee piping and informs the beekeeper to prevent unwanted swarming. Schematics and algorithms of proposed system are represented as well as obtained experimental results.


title: Autonomous acquisition of multimodal information for online object concept formation by robots

abstract: This paper proposes a robot that acquires multimodal information, i.e. auditory, visual, and haptic information, fully autonomous way using its embodiment. We also propose an online algorithm of multimodal categorization based on the acquired multimodal information and words, which are partially given by human users. The proposed framework makes it possible for the robot to learn object concepts naturally in everyday operation in conjunction with a small amount of linguistic information from human users. In order to obtain multimodal information, the robot detects an object on a fla surface. Then the robot grasps and shakes it for gaining haptic and auditory information. For obtaining visual information, the robot uses a hand held small observation table, so that the robot can control the viewpoints for observing the object. As for the multimodal concept formation, the multimodal LDA using Gibbs sampling is extended to the online version in this paper. The proposed algorithms are implemented on a real robot and tested using real everyday objects in order to show validity of the proposed system.


title: Noise Estimation from a Single Image

abstract: In order to work well, many computer vision algorithms require that their parameters be adjusted according to the image noise level, making it an important quantity to estimate. We show how to estimate an upper bound on the noise level from a single image based on a piecewise smooth image prior model and measured CCD camera response functions. We also learn the space of noise level functions how noise level changes with respect to brightness and use Bayesian MAP inference to infer the noise level function from a single image. We illustrate the utility of this noise estimation for two algorithms: edge detection and featurepreserving smoothing through bilateral filtering. For a variety of different noise levels, we obtain good results for both these algorithms with no user-specified inputs.


title: A Wide Range High Power Supply Rejection Ratio and Transient Enhanced Low Drop-Out Regulator

abstract: A novel low dropout regulator (LDO) is proposed in this paper which has high power supply rejection ratio (PSRR) in a wide frequency range and its transient response performance is also improved. In order to get good performance on PSRR, a full differential folded cascode structure error amplifier is used, which also increases the precision of the proposed LDO. A transient enhancement circuit unit (TEU) is used in the proposed LDO to achieve a good transient response performance and proved to be effective by the simulation results. Frequency compensation technologies are also used to ensure the LDO system is stable when it works normally. The LDO structure is implemented and simulated with HSPICE using a standard 0.35μm CMOS process. Simulation results show that the circuit provides a high PSRR which is up to 51.5dB at 100 KHz and 50dB at 1MHz, the load transient response time and overshoot and undershoot voltage are all decreased when the load current changes from 1mA to 100mA in 0.1μs and recover to 1mA several microseconds later.


title: Sequential Heterogeneous Attribute Embedding for Item Recommendation

abstract: Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to combine recurrent neural networks which have recently shown effectiveness in item recommendations in applications such as video and music browsing. To overcome the challenges and to harvest the advantages of sequence models, we present a novel approach, Heterogeneous Attribute Recurrent Neural Networks (HA-RNN), which incorporates heterogeneous attributes and captures sequential dependencies in both items and attributes. HA-RNN extends recurrent neural networks with 1) a hierarchical attribute combination input layer and 2) an output attribute embedding layer. Experiments on two large-scale datasets show significant improvements over the state-of-the-art models. Ablation experiments demonstrate the crucialness of the two components to address heterogeneous attribute challenges including variable lengths and attribute sparseness. Furthermore, our exploratory studies also shed light on why sequence modeling works well.


title: Opinion Mining and Sentiment Analysis: A Survey

abstract:  Abstract: Opinion Mining and Sentiment Analysis is the field of study that analyzes customer opinions, feedback, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in Natural Language Processing (NLP) and is also widely studied in data mining, Web mining, and text mining. The growing importance of sentiment analysis coincides with the growth of social media and Web 2.0 technologies such as reviews, discussion forums, blogs, micro-blogs, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. In this paper we do a survey of papers on Opinion Mining and Sentiment Analysis and detail the techniques used.


title: Neuroeconomics: crosscurrents in research on decision-making. Trends in Cognitive Sciences 10

abstract: Despite substantial advances, the question of how we make decisions and judgments continues to pose important challenges for scientific research. Historically, different disciplines have approached this problem using different techniques and assumptions, with few unifying efforts made. However, the field of neuroeconomics has recently emerged as an inter-disciplinary effort to bridge this gap. Research in neuroscience and psychology has begun to investigate neural bases of decision predictability and value, central parameters in the economic theory of expected utility. Economics, in turn, is being increasingly influenced by a multiple-systems approach to decision-making, a perspective strongly rooted in psychology and neuroscience. The integration of these disparate theoretical approaches and methodologies offers exciting potential for the construction of more accurate models of decision-making.


title: Cost-Effective Resource Provisioning for MapReduce in a Cloud

abstract: This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services such as a generic compute cloud or a dedicated MapReduce cloud, Cura has a number of unique benefits. First, Cura is designed to provide a cost-effective solution to efficiently handle MapReduce production workloads that have a significant amount of interactive jobs. Second, unlike existing services that require customers to decide the resources to be used for the jobs, Cura leverages MapReduce profiling to automatically create the best cluster configuration for the jobs. While the existing models allow only a per-job resource optimization for the jobs, Cura implements a globally efficient resource allocation scheme that significantly reduces the resource usage cost in the cloud. Third, Cura leverages unique optimization opportunities when dealing with workloads that can withstand some slack. By effectively multiplexing the available cloud resources among the jobs based on the job requirements, Cura achieves significantly lower resource usage costs for the jobs. Cura’s core resource management schemes include cost-aware resource provisioning, VM-aware scheduling and online virtual machine reconfiguration. Our experimental results using Facebook-like workload traces show that our techniques lead to more than 80 percent reduction in the cloud compute infrastructure cost with upto 65 percent reduction in job response times.


title: FreeD: a freehand digital sculpting tool

abstract: In this paper, we present an approach to combining digital fabrication and craft, emphasizing the user experience. While many researchers strive to enable makers to design and produce 3D objects, our research seeks to present a new fabrication approach to make unique, one-of-a-kind artifacts. To that end, we developed the FreeD, a hand-held digital milling device. The system is guided and monitored by a computer while preserving the maker's freedom to sculpt and carve, and to manipulate the work in many creative ways. Relying on a predesigned 3D model, the computer gets into action only when the milling bit risks the object's integrity, by slowing down the spindle's speed or by drawing back the shaft, while the rest of the time it allows complete gestural freedom. We describe the key concepts of our work and its motivation, present the FreeD's architecture and technology, and discuss two projects made with the tool.


title: MRI based medical image analysis: Survey on brain tumor grade classification

abstract: Abstract A review on the recent segmentation and tumor grade classification techniques of brain Magnetic Resonance (MR) Images is the objective of this paper. The requisite for early detection of a brain tumor and its grade is the motivation for this study. In Magnetic Resonance Imaging (MRI), the tumor might appear clear but physicians need quantification of the tumor area for further treatment. This is where the digital image processing methodologies along with machine learning aid further diagnosis, treatment, prior and post-surgical procedures, synergizing between the radiologist and computer. These hybrid techniques provide a second opinion and assistance to radiologists in understanding medical images hence improving diagnostic accuracy. This article aims to retrospect the current trends in segmentation and classification relevant to tumor infected human brain MR images with a target on gliomas which include astrocytoma. The methodologies used for extraction and grading of tumors which can be integrated into the standard clinical imaging protocols are elucidated. Lastly, a crucial assessment of the state of the art, future developments and trends are dissertated.


title: Integrating Technical and Legal Concepts of Privacy

abstract: We provide an overview of legal and technical concepts of privacy protection. Data protection guarantees exist in European Union at a primary law level since 2009, when the Lisbon Treaty has become effective. The new regulation called general data protection regulation adopted on April 27, 2016 will enter into effect on May 25, 2018. Regarding data protection, the US follows a very different approach. The cornerstone of the technical framework of data protection is the concept of differential privacy for which the privacy’s provable guarantees hold even against worst-case scenarios when the access to arbitrary auxiliary information is provided. Research directions for integrating legal and technical concepts of privacy are discussed. Although both fields, legal and technical, approach privacy from different perspectives, we expect that these approaches will converge to a common framework. The privacy-preserving framework, therefore, should be a balance between: 1) ambiguous legal concepts and precise mathematical notions of privacy; 2) the desired privacy level; 3) the information loss, which is measured by data utility metrics; and 4) the complexity and the practical feasibility of the proposed technique. Key notions of fairness are incompatible with each other, and hence any privacy-preserving framework should also include the trade-offs between conditions imposed in the definitions of fairness. The complex assembly of legal and technical components implementing various privacy requirements in the privacy-preserving framework, should be realized within the privacy-by-design concept.


title: A Novel Slot Array Antenna With a Substrate-Integrated Coaxial Line Technique

abstract: In this letter, a novel slot array antenna with a substrate-integrated coaxial line (SICL) technique is proposed. The proposed antenna has radiation slots etched homolaterally along the mean line in the top metallic layer of SICL and achieves a compact transverse dimension. A prototype with 5 $\times$ 10 longitudinal slots is designed and fabricated with a multilayer liquid crystal polymer (LCP) process. A maximum gain of 15.0 dBi is measured at 35.25 GHz with sidelobe levels of $-$ 28.2 dB ( E -plane) and $-$ 33.1 dB ( H -plane). The close correspondence between experimental results and designed predictions on radiation patterns has validated the proposed excogitation in the end.


title: Dynamics of personal social relationships in online social networks: a study on twitter

abstract: The growing popularity of Online Social Networks (OSN) is generating a large amount of communication records that can be easily accessed and analysed to study human social behaviour. This represents a unique opportunity to understand properties of social networks that were impossible to assess in the past. Although analyses on OSN conducted hitherto revealed some important global properties of the networks, there is still a lack of understanding of the mechanisms underpinning these properties, their relation to human behaviour, and their dynamic evolution over time. These aspects are clearly important to understand and characterise OSN and to identify the evolutionary strategy that favoured the diffusion of the use of online communications in our society. In this paper we analyse a data set of Twitter communication records, studying the dynamic processes that govern the maintenance of online social relationships. The results reveal that people in Twitter have highly dynamic social networks, with a large percentage of weak ties and high turnover. This suggests that this behaviour can be the product of an evolutionary strategy aimed at coping with the extremely challenging conditions imposed by our society, where dynamism seems to be the key to success.


title: Evolutionary model building under streaming data for classification tasks: opportunities and challenges

abstract: Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal `start' or `end'; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.


title: A novel adaptive beamforming algorithm for a smart antenna system in a CDMA mobile communication environment

abstract: An alternative way of adaptive beamforming is presented. The main contribution of the new technique is in its simplicity with a minimal loss of accuracy. The total computational load for computing a suboptimal weight vector from each new signal vector is about O(2N/sup 2/+5N). It can further be reduced down to O(3N) by approximating the autocorrelation matrix with the instantaneous signal vector at each snapshot. The required condition on the adaptive gain for the proposed algorithm to converge is derived analytically. The proposed beamforming algorithm is applied to the base station of a code-division-multiple access (CDMA) mobile communication system. The performance of the proposed method is shown in multipath fading communication channels in terms of the signal-to-interference+noise ratio (SINR), the bit error rate (BER), and the achievable capacity of a given CDMA cell/sector.


title: Symmetry and sexual dimorphism in human faces: interrelated preferences suggest both signal quality

abstract: Symmetry and masculinity in human faces have been proposed to be cues to the quality of the owner. Accordingly, symmetry is generally found attractive in male and female faces, and femininity is attractive in female faces. Women's preferences for male facial masculinity vary in ways that may maximize genetic benefits to women's offspring. Here we examine same- and opposite-sex preferences for both traits (Study 1) and intercorrelations between preferences for symmetry and sexual dimorphism in faces (Study 1 and Study 2) using computer-manipulated faces. For symmetry, we found that male and female judges preferred symmetric faces more when judging faces of the opposite-sex than when judging same-sex faces. A similar pattern was seen for sexual dimorphism (i.e., women preferred more masculine male faces than men did), but women also showed stronger preferences for femininity in female faces than men reported. This suggests that women are more concerned with female femininity than are men. We also found that in women, preferences for symmetry were positively correlated with preferences for masculinity in male faces and that in men preferences for symmetry were positively correlated with preferences for femininity in female faces. These latter findings suggest that symmetry and sexual dimorphism advertise a common quality in faces or that preferences for these facial cues are dependent on a common quality in the judges. Collectively, our findings support the view that preferences for symmetry and sexual dimorphism are related to mechanisms involved in sexual selection and mate choice rather than functionless by-products of other perceptual mechanisms. Copyright 2008, Oxford University Press.


title: A Bayesian approach to joint feature selection and classifier design

abstract: This paper adopts a Bayesian approach to simultaneously learn both an optimal nonlinear classifier and a subset of predictor variables (or features) that are most relevant to the classification task. The approach uses heavy-tailed priors to promote sparsity in the utilization of both basis functions and features; these priors act as regularizers for the likelihood function that rewards good classification on the training data. We derive an expectation- maximization (EM) algorithm to efficiently compute a maximum a posteriori (MAP) point estimate of the various parameters. The algorithm is an extension of recent state-of-the-art sparse Bayesian classifiers, which in turn can be seen as Bayesian counterparts of support vector machines. Experimental comparisons using kernel classifiers demonstrate both parsimonious feature selection and excellent classification accuracy on a range of synthetic and benchmark data sets.


title: Algorithmic detection of semantic similarity

abstract: Automatic extraction of semantic information from text and links in Web pages is key to improving the quality of search results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata --- namely topical directories --- to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of URLs in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective for relevance ranking.


title: A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

abstract: Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sentiment. We found that the sentiment of an emoticon is in substantial agreement with the sentiment of the entire tweet. Thus, emoticons are useful as predictors of tweet sentiment and should not be ignored in sentiment classification. However, the sentiment expressed by an emoticon agrees with the sentiment of the accompanying text only slightly better than random. Thus, using the text accompanying emoticons to train sentiment models is not likely to produce the best results, a fact that we show by comparing lexicons generated using emoticons with others generated using simple textual features.


title: Advanced Steel Microstructural Classification by Deep Learning Methods

abstract: The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.


title: Finite Generalization of the Offline Spectral Learning

abstract: We study the problem of offline learning discrete functions on polynomial threshold units over specified set of polynomial. Our approach is based on the generalization of the classical “Relaxation” method of solving linear inequalities. We give theoretical reason justifying heuristic modification improving the performance of spectral learning algorithm. We demonstrate that if the normalizing factor satisfies sufficient conditions, then the learning procedure is finite and stops after some steps, producing the weight vector of the polynomial threshold unit realizing the given threshold function. Our approach can be applied in hybrid systems of computational intelligence.


title: A Cross Correlation Approach for Breaking of Text CAPTCHA

abstract: Online web service providers generally protect themselves through CAPTCHA. A CAPTCHA is a type of challenge-response test used in computing as an attempt to ensure that the response is generated by a person. CAPTCHAS are mainly instigated as distorted text which the handler must correctly transcribe. Numerous schemes have been proposed till date in order to prevent attacks by Bots. This paper also presents a cross correlation based approach in breaking of famous service provider's text CAPTCHA i.e. PayPal.com and the other one is of India's most visited website IRCTC.co.in. The procedure can be fragmented down into 3 firmly tied tasks: pre-processing, segmentation, and classification. The pre-processing of the image is performed to remove all the background noise of the image. The noise in the CAPTCHA are unwanted on pixels in the background. The segmentation is performed by scanning the image for on pixels. The organization is performed by using the association values of the inputs and templates. Two types of templates have been used for classification purpose. One is the standard templates which give 30% success rate and other is the noisy templates made from the captcha images and success rate achieved with these is 100%.


title: COVER FEATURE FUTURE OF AI

abstract: Recent increases in computing power, coupled with rapid growth in the availability and quantity of data have rekindled our interest in the theory and applications of artificial intelligence (AI). However, for AI to be confidently rolled out by industries and governments, users want greater transparency through explainable AI (XAI) systems. The author introduces XAI concepts, and gives an overview of areas in need of further exploration—such as type-2 fuzzy logic systems—to ensure such systems can be fully understood and analyzed by the lay user.


title: High Speed True Random Number Generator Based on FPGA

abstract: TRN (True Random Number) plays a key role in the communication system and so on. The TRNG (True Random Number Generator) based on FPGA (Field Programmable Gate Array) usually has a high speed and quality. In this article, we study several existing ways to generate the TRN in the FPGA using the oscillator rings which consist of an odd number of NOT gates, the structure of the oscillator rings and the sampling frequency are analyzed. An improved structure based on the oscillator rings is introduced here by which we can get higher speed and quality TRNG with fewer resources compared with the several methods mentioned in the references. The TRNG consists of 16 oscillator rings, each contains 3 NOT gates. The sampling frequency can reach 300M (Million bits per second). The design is implemented on the Altera Stratix II FPGA. The results passed the tests of the NIST (National Institute of Standards and Technology) randomness test suite. The restart experiments have shown that the TRNG outputs behaved truly random and not pseudorandom.


title: Instant Scene Modeler for Crime Scene Reconstruction

abstract: Crime scene reconstruction is a labor-intensive but an essential task of police investigation. Currently, the police spend a lot of time at the crime scene taking many measurements and photos, and then spend more time at the computer recreating the crime scene drawings manually. Automatic generation of photo-realistic three-dimensional calibrated models using a hand-held device is highly desirable for crime scene reconstruction as well as for many other applications. In this paper, we present the instant Scene Modeler (iSM), a 3D imaging system that automatically creates 3D models using an off-the-shelf hand-held stereo camera. The user points the camera at a scene of interest and the system will create a photo-realistic 3D calibrated model automatically within minutes. Forensics field tests have been carried out with promising results and the advantages of 3D imaging for this application are described.


Biologically feasible deep learning with segregated dendrites


title: Automatic student plagiarism detection : future perspectives

abstract: The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in the recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detection tools revealed that these contain limitations in detecting complex forms of plagiarism such as extensive paraphrasing and use of technical tricks, such as replacing original characters with similar-looking characters from foreign alphabets. ::: ::: This article investigates limitations in automatic detection of student plagiarism and proposes ways on how these issues could be tackled in future systems by applying various natural language processing and information retrieval technologies. A classification of types of plagiarism is presented, and an analysis is provided of the most promising technologies that have the potential of dealing with the limitations of current state-of-the-art systems. Furthermore, the article concludes with a discussion on legal and ethical issues related to the use of plagiarism detection software. The article, hence, provides a "roadmap" for developing the next generation of plagiarism detection systems.


title: Differences in Critical Success Factors in ERP Systems Implementation in Australia and China: A Cultural Analysis

abstract: Enterprise Resource Planning (ERP) systems are integrated, enterprise-wide systems that provide automated support for standard business processes within organisations. They have been adopted by organisations throughout the world with varying degrees of success. Implementing ERP systems is a complex, lengthy and expensive process. In this paper we synthesise an ERP systems implementation process model and a set of critical success factors for ERP systems implementation. Two case studies of ERP systems implementation, one in Australia and one in China are reported. The case studies identify which critical success factors are important in which process model phases. Case study analysis then explains the differences between the Australian and Chinese cases using national cultural characteristics. Outcomes of the research are important for multinational organisations implementing ERP systems and for consulting companies assisting with ERP systems implementation in different countries.


title: Diagnosing and predicting wind turbine faults from SCADA data using support vector machines

abstract: National Research Foundation Singapore (Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program)


title: Improved a-Si:H TFT pixel electrode circuits for active-matrix organic light emitting displays

abstract: Two improved four thin-film-transistors (TFTs) pixel electrode circuits based on hydrogenated amorphous silicon (a-Si:H) technology have been designed. Both circuits can provide a constant output current level and can be automatically adjusted for TFT threshold voltage variations. The circuit simulation results indicate that an excellent linearity between the output current and input current can be established. An output current level higher than /spl sim/5 /spl mu/A can be achieved with these circuits. This current level can provide a pixel electrode brightness higher than 1000 cd/m/sup 2/ with the organic light-emitting device (OLED) having an external quantum efficiency of 1%. These pixel electrode circuits can potentially be used for the active-matrix organic light-emitting displays (AM-OLEDs).


title: Biometric Template Selection and Update: A Case Study in Fingerprints

abstract: A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a claimed identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.


title: SHE-PWM based multilevel T-type inverter topology for single-phase photovoltaic applications

abstract: A total harmonic distortion (THD) optimization based 5-level selective harmonic elimination pulse width modulated (SHE-PWM) single-phase reduced device count (RDC) voltage source inverter (VSI) is presented in this paper. A MATLAB program is developed for offline calculation of switching angles based on quarter wave (QW) symmetry of the output and overall THD optimization method of SHE-PWM. The inverter circuit operation and SHE-PWM scheme to generate 5-level voltage output along with performance parameters are provided. The theoretical discussion in the paper is also supported by simulation and experimental results.


A New Fast Vertical Method for Mining Frequent Patterns


title: All-Digital Circuit-Level Dynamic Variation Monitor for Silicon Debug and Adaptive Clock Control

abstract: A 45 nm microprocessor integrates an all-digital dynamic variation monitor (DVM) to continuously measure the impact of dynamic parameter variations on circuit-level performance to enhance silicon debug and adaptive clock control. The DVM consists of a tunable replica circuit, a time-to-digital converter, and multiplexers to measure circuit delay or frequency changes with less than a 1% measured resolution error while capturing clock-to-data correlations. In validating the DVM with microprocessor maximum clock frequency (FMAX) measurements, an on-die noise injector circuit induces a supply voltage (VCC) droop at a particular cycle in the test program. The FMAX measurement is then repeated for over a thousand iterations while shifting the droop placement to a different cycle per iteration. Silicon measurements demonstrate the DVM capability of tracking the worst case FMAX reduction to within 1% for a wide range of VCC droop profiles. Furthermore, silicon measurements reveal that FMAX is highly sensitive to the placement and magnitude of a high-frequency VCC droop during program execution, thus highlighting the value of the DVM for silicon debug. In addition, the DVM interfaces with an adaptive clock control circuit to dynamically adjust the clock frequency by changing the divide ratio in the phase-locked loop in response to persistent variations, enabling the microprocessor to adapt to the operating environment for maximum efficiency.


title: The connectomics of brain disorders

abstract: Pathological perturbations of the brain can be described and modelled using network science. In this Review, Fornito, Zalesky and Breakspear discuss adaptive and maladaptive neural responses to such insults and consider how connectomics can be used to map, track and predict disease progression.


2 D Shading for Cel Animation Matis Hudon


title: GeoSparkViz: a scalable geospatial data visualization framework in the apache spark ecosystem

abstract: Data Visualization allows users to summarize, analyze and reason about data. A map visualization tool first loads the designated geospatial data, processes the data and then applies the map visualization effect. Guaranteeing detailed and accurate geospatial map visualization (e.g., at multiple zoom levels) requires extremely high-resolution maps. Classic solutions suffer from limited computation resources and hence take a tremendous amount of time to generate maps for large-scale geospatial data. The paper presents G eo S park V iz a large-scale geospatial map visualization framework. G eo S park V iz extends a cluster computing system (Apache Spark in our case) to provide native support for general cartographic design. The proposed system seamlessly integrates with a Spark-based spatial data management system, G eo S park . It provides the data scientist a holistic system that allows her to perform data management and visualization on spatial data and reduces the overhead of loading the intermediate spatial data generated during the data management phase to the designated map visualization tool. G eo S park V iz also proposes a map tile data partitioning method that achieves load balancing for the map visualization workloads among all nodes in the cluster. Extensive experiments show that G eo S park V iz can generate a high-resolution (i.e., Gigapixel image) Heatmap of 1.7 billion Open-StreetMaps objects and 1.3 billion NYC taxi trips in ≈4 and 5 minutes on a four-node commodity cluster, respectively.


title: Built-in Vulnerabilities to Imperceptible Adversarial Perturbations

abstract: Designing models that are robust to small adversarial perturbations of their inputs has proven remarkably difficult. In this work we show that the reverse problem---making models more vulnerable---is surprisingly easy. After presenting some proofs of concept on MNIST, we introduce a generic tilting attack that injects vulnerabilities into the linear layers of pre-trained networks without affecting their performance on natural data. We illustrate this attack on a multilayer perceptron trained on SVHN and use it to design a stand-alone adversarial module which we call a steganogram decoder. Finally, we show on CIFAR-10 that a state-of-the-art network can be trained to misclassify images in the presence of imperceptible backdoor signals. These different results suggest that adversarial perturbations are not always informative of the true features used by a model.


title: Collision avoidance among AGVs at junctions

abstract: The movement of a vehicle in an automated guided vehicle system (AGVS) depends on the status of other vehicles present in the system. One of the objectives of automation is to have collision free movement to increase the safety. In this paper, two controllers are suggested that by design guarantee no collision between two vehicles at the junctions. The control laws obtained are used for finding out the initial conditions that will be safe under both of these laws, or at least under one law. Accordingly, the set of conditions for which collision can not be avoided is determined.


CMOS Voltage and Current Reference Circuits consisting of Subthreshold MOSFETs – Micropower Circuit Components for Power-Aware LSI Applications –


title: Toponym resolution in text: annotation, evaluation and applications of spatial grounding

abstract: In Information Extraction (IE), processing of named entities in text has traditionally been seen as a two-step process comprising a flat text span recognition sub-task and an atomic classification sub-task; relating the text span to a model of the world has been ignored by evaluations such as DARPA/NIST's MUC or ACE. However, spatial and temporal expressions refer to events in space-time, and the grounding of events is a precondition for accurate reasoning. Thus, automatic grounding can improve many applications such as automatic map drawing (e.g. for choosing a focus) and question answering (e.g., for questions like How far is London from Edinburgh, given a story in which both occur and can be resolved). Whereas temporal grounding has received considerable attention in the recent Past [2, 3], robust spatial grounding has long been neglected. Concentrating on geographic names for populated places, I define the task of automatic Toponym Resolution (TR) as computing the mapping from occurrences of names for places as found in a text to a representation of the extensional semantics of the location referred to (its referent), such as a geographic latitude/longitude footprint. The task of mapping from names to locations is hard due to insufficient and noisy databases, and a large degree of ambiguity: common words need to be distinguished from proper names (geo/non-geo ambiguity), and the mapping between names and locations is ambiguous London can refer to the capital of the UK or to London, Ontario, Canada, or to about forty other Londons on earth). In addition, names of places and the boundaries referred to change over time, and databases are incomplete.


CRF Based Point Cloud Segmentation


title: Selective Pooling Vector for Fine-Grained Recognition

abstract: We propose a new framework for image recognition by selectively pooling local visual descriptors, and show its superior discriminative power on fine-grained image classification tasks. The representation is based on selecting the most confident local descriptors for nonlinear function learning using a linear approximation in an embedded higher dimensional space. The advantage of our Selective Pooling Vector over the previous state-of-the-art Super Vector and Fisher Vector representations, is that it ensures a more accurate learning function, which proves to be important for classifying details in fine-grained image recognition. Our experimental results corroborate this claim: with a simple linear SVM as the classifier, the selective pooling vector achieves significant performance gains on standard benchmark datasets for various fine-grained tasks such as the CMU Multi-PIE dataset for face recognition, the Caltech-UCSD Bird dataset and the Stanford Dogs dataset for fine-grained object categorization. On all datasets we outperform the state of the arts and boost the recognition rates to 96.4%, 48.9%, 52.0% respectively.


title: Cultural influences on Facebook practices: A comparative study of college students in Namibia and the United States

abstract: Facebook usage influences and are influenced by cultural practices.In tightly-knit societies such as Namibia, we show that online behavior is more open and transparent.Users in the United States are changing their online behavior to include greater self-censorship.Employer activities on Facebook is driving user behavioral changes in the United States.Facebook is the preferred social media site in both the United States and Namibia. Facebook has been adopted in many countries with over 80% of its user-base being outside of the US and Canada. Yet, despite this global dominance, not much is understood of Facebook usage by individuals in non-western cultures. A cross-cultural study was conducted with undergraduate students in the United States and Namibia to examine Facebook use. The study used a mixed method of online surveys and focus groups in both countries. The research examined issues such as motivations for use, friendships, privacy and trust, and life changing events such as relationships, births, deaths, religion and politics. Findings suggest cultural influence on both online and offline practices as well as appropriation and re-contextualization to fit existing offline cultural practices. While we find that participants from the United States are changing their online behavior toward increased self-censorship, more users from Namibia, where family and community structures are important, continue to engage in online behavior that is more open and transparent. Findings also suggest an expressive privacy paradox for United States participants, who are generally less concerned with updating their privacy settings while simultaneously practicing self-censorship.


title: Closure of nasal septal perforations using bilateral intranasal advancement/rotation flaps

abstract: OBJECTIVES ::: This study aims to report our experience on surgical repair of nasal septal perforations (NSP) using bilateral intranasal advancement/rotation flaps with open septoplasty technique. ::: ::: ::: PATIENTS AND METHODS ::: Medical records of 28 consecutive patients who were operated for NSP in our clinic between January 2009 and February 2013 were retrospectively analyzed. Demographic features of the patients and surgical results were evaluated. ::: ::: ::: RESULTS ::: The most common cause of NSP was previous septal surgery in 18 (64%) of the patients. Nasal crusting was the most common symptom in 21 patients (75%). Perforation size ranged from 10 to 30 mm in diameter. Septoplasty was performed in five patients, while septorhinoplasty was performed in three patients simultaneously to the NSP repair. Nasal septal cartilage and auricular chonchal cartilage were used to support the nasoseptal skeleton, if required. There were no major intra- or postoperative complications. The mean postoperative follow-up period was 23 months. During follow-up, there was mild columellar retraction in five patients (18%). Nasal septal perforation was closed successfully in 24 patients (86%). Pinpoint perforation remained in two patients and perforation size was smaller than 5 mm in two patients. ::: ::: ::: CONCLUSION ::: Closing the NSP with bilateral intranasal advancement/rotation flaps has a comparable high success rate. Therefore, this technique can be easily applied to small-medium sized septal perforations.


title: ToriLLE: Learning Environment for Hand-to-Hand Combat

abstract: We present Toribash Learning Environment (To-riLLE), a learning environment for machine learning agents based on the video game Toribash. Toribash is a MuJoCo-like environment of two humanoid characters fighting each other hand-to-hand, controlled by changing actuation modes of the joints. Competitive nature of Toribash as well its focused domain provide a platform for evaluating self-play methods, and evaluating machine learning agents against human players. In this paper we describe the environment with ToriLLE’s capabilities and limitations, and experimentally show its applicability as a learning environment with baseline and human experiments. The source code of the environment and conducted experiments can be found at https://github.com/Miffyli/ToriLLE.


title: Applying Domain Randomization to Synthetic Data for Object Category Detection

abstract: Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pre-trained on standard datasets and fine-tune them for specific application domains with a small set of domain relevant images. In this work, we show that using synthetic datasets that are not necessarily photo-realistic can be a better alternative to simply fine-tune pre-trained networks. Specifically, our results show an impressive 25% improvement in the mAP metric over a fine-tuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.


title: Text Classification based on Multiple Block Convolutional Highways

abstract: In the Text Classification areas of Sentiment Analysis, Subjectivity/Objectivity Analysis, and Opinion Polarity, Convolutional Neural Networks have gained special attention because of their performance and accuracy. In this work, we applied recent advances in CNNs and propose a novel architecture, Multiple Block Convolutional Highways (MBCH), which achieves improved accuracy on multiple popular benchmark datasets, compared to previous architectures. The MBCH is based on new techniques and architectures including highway networks, DenseNet, batch normalization and bottleneck layers. In addition, to cope with the limitations of existing pre-trained word vectors which are used as inputs for the CNN, we propose a novel method, Improved Word Vectors (IWV). The IWV improves the accuracy of CNNs which are used for text classification tasks.


title: Executive functions in synesthesia

abstract: In grapheme-color synesthesia, a number or letter can evoke two different and possibly conflicting (real and synesthetic) color sensations at the same time. In this study, we investigate the relationship between synesthesia and executive control functions. First, no general skill differences were obtained between synesthetes and non-synesthetes in classic executive control paradigms. Furthermore, classic executive control effects did not interact with synesthetic behavioral effects. Third, we found support for our hypothesis that inhibition of a synesthetic color takes effort and time. Finally, individual differences analyses showed no relationship between the two skills; performance on a ‘normal’ Stroop task does not predict performance on a synesthetic Stroop task. Across four studies, the current results consistently show no clear relationship between executive control functions and synesthetic behavioral effects. This raises the question of which mechanisms are at play in synesthetic ‘management’ during the presence of two conflicting (real and synesthetic) sensations.


title: Crowd modeling and simulation technologies

abstract: As a collective and highly dynamic social group, the human crowd is a fascinating phenomenon that has been frequently studied by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This article assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the time-scale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user. We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in this work. We have also discussed some open problems in the area. This article will provide the researchers with useful information and insights on the state of the art of the technologies in crowd modeling and simulation as well as future research directions.


title: Wideband Dual-Polarized Patch Antenna

abstract: Dual-polarized L-probe patch antennas afford wide impedance bandwidth and increased channel capacity. However, due to the close proximity, the coupling between the two input ports remains an important problem. In this paper, we introduce the use of a novel 180deg broadband microstrip balun which delivers both balanced power splitting and consistent 180deg (plusmn5deg ) phase shifting over a wide band (>50%). We demonstrate that for the quadruple L-probe square patch antenna utilizing a "dual-feed" mechanism, the use of a pair of the proposed 180deg broadband baluns allowed for a wide input port isolation bandwidth (S21<-33 dB) of 46.97%, while also achieving low cross-polarization levels and stable co-polarization patterns


title: Towards A Process View on Critical Success Factors in Big Data Analytics Projects

abstract: The research tries to identify factors that are critical for a Big Data project’s success. In total 27 success factors could be identified throughout the analysis of these published case studies. Subsequently, to the identification the success factors were categorized according to their importance for the project’s success. During the categorization process 6 out of the 27 success factors were declared mission critical. Besides this identification of success factors, this thesis provides a process model, as a suggested way to approach Big Data projects. The process model is divided into separate phases. In addition to a description of the tasks to fulfil, the identified success factors are assigned to the individual phases of the analysis process. Finally, this thesis provides a process model for Big Data projects and also assigns success factors to individual process stages, which are categorized according to their importance for the success of the entire project.


title: End-to-End Learning of Geometry and Context for Deep Stereo Regression

abstract: We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity values are regressed from the cost volume using a proposed differentiable soft argmin operation, which allows us to train our method end-to-end to sub-pixel accuracy without any additional post-processing or regularization. We evaluate our method on the Scene Flow and KITTI datasets and on KITTI we set a new state-of-the-art benchmark, while being significantly faster than competing approaches.


title: Can Intelligence Explode?

abstract: The technological singularity refers to a hypothetical scenario in which technological advances virtually explode. The most popular scenario is the creation of super-intelligent algorithms that recursively create ever higher intelligences. It took many decades for these ideas to spread from science fiction to popular science magazines and finally to attract the attention of serious philosophers. David Chalmers’ (JCS 2010) article is the first comprehensive philosophical analysis of the singularity in a respected philosophy journal. The motivation of my article is to augment Chalmers’ and to discuss some issues not addressed by him, in particular what it could mean for intelligence to explode. In this course, I will (have to) provide a more careful treatment of what intelligence actually is, separate speed from intelligence explosion, compare what super-intelligent participants and classical human observers might experience and do, discuss immediate implications for the diversity and value of life, consider possible bounds on intelligence, and contemplate intelligences right at the singularity.


title: Catastrophic forgetting and the pseudorehearsal solution in hopfield type networks

abstract: Pseudorehearsal is a mechanism proposed by Robins which alleviates catastrophic forgetting in multi-layer perceptron networks. In this paper, we extend the exploration of pseudorehearsal to a Hopfield-type net. The same general principles apply: old information can be rehearsed if it is available, and if it is not available, then generating and rehearsing approximations of old information that 'map' the behaviour of the network can also be effective at preserving the actual old information itself. The details of the pseudorehearsal mechanism, however, benefit from being adapted to the dynamics of Hopfield nets so as to exploit the extra attractors created in state space during learning. These attractors are usually described as 'spurious' or 'cross-talk', and regarded as undesirable, interfering with the retention of the trained population items. Our simulations have shown that, in another sense, such attractors can in fact be useful in preserving the learned population. In general terms, a solution to th...


title: On the Robustness of Information-Theoretic Privacy Measures and Mechanisms

abstract: Consider a data publishing setting for a dataset composed by both private and non-private features. The publisher uses an empirical distribution, estimated from <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> i.i.d. samples, to design a privacy mechanism which is applied to new fresh samples afterward. In this paper, we study the discrepancy between the privacy-utility guarantees for the empirical distribution, used to design the privacy mechanism, and those for the true distribution, experienced by the privacy mechanism in practice. We first show that, for any privacy mechanism, these discrepancies vanish at speed <inline-formula> <tex-math notation="LaTeX">$O(1/\sqrt {n})$ </tex-math></inline-formula> with high probability. These bounds follow from our main technical results regarding the Lipschitz continuity of the considered information leakage measures. Then we prove that the optimal privacy mechanisms for the empirical distribution approach the corresponding mechanisms for the true distribution as the sample size <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> increases, thereby establishing the statistical consistency of the optimal privacy mechanisms. Finally, we introduce and study <italic>uniform privacy mechanisms</italic> which, by construction, provide privacy to all the distributions within a neighborhood of the estimated distribution and, thereby, guarantee privacy for the true distribution with high probability.


title: Accessible Reproducible Research

abstract: As use of computation in research grows, new tools are needed to expand recording, reporting, and reproduction of methods and data.


title: Context-aware location recommendation by using a random walk-based approach

abstract: The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized recommendations. We build a graph model of LBSNs for performing a random walk approach with restart. Random walk is performed to calculate the recommendation probabilities of the nodes. A list of locations are recommended to users after ordering the nodes according to the estimated probabilities. We compare our algorithm, CLoRW, with popularity-based, friend-based and expert-based baselines, user-based collaborative filtering approach and a similar work in the literature. According to experimental results, our algorithm outperforms these approaches in all of the test cases.


title: A method and implementation for the empirical study of deleted file persistence in digital devices and media

abstract: Digital devices are ubiquitous in modern society. Every action on a digital device, whether initiated by a user, an application, the operating system, or hardware, leaves behind evidence of the activity in the form of digital artifacts such as files, memory content, and network traffic. Such artifacts are used by digital forensics investigators to reconstruct past activity, and by criminals seeking to harvest private or sensitive information. The persistence of an artifact over time directly affects its ability to be recovered at a later date, yet a rigorous, comprehensive theory of digital artifact persistence does not exist. This research proposes and demonstrates a method to facilitate the studies necessary to develop such a theory. We implemented a differential analysis approach in which sequential digital media images are analyzed for deleted file persistence. The contents of files deleted between the first two images are tracked in the remaining images. This data forms a decay curve for each file over time and activity. Since we also have access to system and storage media properties, deleted file properties, and the details of actions taken between images, we can begin to form testable hypotheses about the factors affecting deleted file persistence. We have implemented prototype software to conduct this analysis, and we demonstrate the method on images generated in a controlled environment as well as on a series of realistic system images.


title: Object-Oriented Analysis, Structured Analysis, and Jackson System Development

abstract: Conceptual modeling is the activity of producing a conceptual model of an actual or desired version of a universe of discourse (UoD). In this paper, two methods of conceptual modeling are compared, structured analysis (SA) and object-oriented analysis (OOA). This is done by transforming a model produced by the one into a model produced by the other method, using heuristics from several sources, such as Jackson system development and formal specification. It is shown that SA and OOA diverge in three important respects. First, the ordering of tasks in SA is shown to be virtually opposite to the task ordering in OOA. Second, a model produced by SA mixes information about the communication between objects as well as about the life cycle local to an object, which is separated in a model produced by the OOA method we propose in this paper. Third, the heuristics in SA are shown to be data-oriented, which leads to quite different modularization decisions than the object-oriented heuristics proper to OOA. The different approach taken by OOA on all three points is shown to lead to simpler models that better reflect the structure of the UoD.


title: Approximation Methods for Thin Plate Spline Mappings and Principal Warps

abstract: The thin plate spline (TPS) is an effective tool for modeling coordinate transformations that has been applied successfully in several computer vision applications. Unfortunately the solution requires the inversion of a p × p matrix, where p is the number of points in the data set, thus making it impractical for large scale applications. In practical applications, however, a surprisingly good approximate solution is often possible using only a small subset of corresponding points. We begin by discussing the obvious approach of using this subset to estimate a transformation that is then applied to all the points, and we show the drawbacks of this method. We then proceed to borrow a technique from the machine learning community for function approximation using radial basis functions (RBFs) and adapt it to the task at hand. Using this method, we demonstrate a significant improvement over the naive method. One drawback of this method, however, is that is does not allow for principal warp analysis, a technique for studying shape deformations introduced by Bookstein based on the eigenvectors of the p× p bending energy matrix. To address this, we describe a third approximation method based on a classic matrix completion technique that allows for principal warp analysis as a by-product. By means of experiments on real and synthetic data, we demonstrate the pros and cons of these different approximations so as to allow the reader to make an informed decision suited to his or her application.


title: Android malware detection a survey

abstract: The popularity of Android mobile devices has gone up in our lives and are being used for handling a lot of our personal and confidential information. Hence t hey are now an ideal target for attackers. Android based smart-phone users can download a lot of free applications from Android Application Market/Play Store. These downloaded apps some-times contain malware applications that can take possession of private information from users. Apps in queue for launching on Play Store generally undergo an automatic security test where crawl operations like typing, tapping and swiping are performed periodically and analysis results are generated. In this survey paper, we aim to briefly discuss the different techniques used in Android malware detection and highlight their advantages and limitations.


title: The corporate brand identity matrix

abstract: The lack of a widely agreed framework to help define and align corporate brand identity constitutes a serious managerial problem and a shortcoming of the academic literature. In response, this article provides such a framework, the Corporate Brand Identity Matrix (CBIM), which draws upon the relevant literature, enriched by hands-on corporate experience gained by its application in three international corporate branding case studies. Corporate brand identity is explored internally, externally and by focusing on the ‘brand core’. The CBIM offers academics and managers a theoretical and practical guide to the describing, defining and aligning of corporate brand identity. It is a tailored alternative to existing frameworks, which have often been designed for product brands, not corporate brands.


title: A crossword puzzle generator using genetic algorithms with Wisdom of Artificial Crowds

abstract: NP-hard problems, such as generating crossword puzzles, are candidates for solution by genetic algorithms (GAs). A combination of both a genetic algorithms and a Wisdom of Artificial Crowds (WoAC) aggregation method was developed for the purpose of creating crossword puzzles given a particular outline. The program was written using a GPL Hunspell wrapper NHunspell for word verification and suggestion. In American-style crossword puzzles, we saw performance improvements of around 20% in reaching a local optima, with a 6% improvement in the number of successful words found by a GA + WoAC algorithm over the baseline genetic algorithm.