A Decomposed Dual-Cross Generative Adversarial Network for Image Rain Removal

Rain removal is important for many computer vision applications, such as surveillance, autonomous car, etc. Traditionally, rain removal is regarded as a signal removal problem which usually causes over-smoothing by removing texture details in non-rain background regions. This paper considers the issue of rain removal from a completely different perspective, to treat rain removal as a signal decomposition problem. Specifically, we decompose the rain image into two components, namely non-rain background image and rain streaks image. Then, we introduce an adversarial training mechanism to synthesize non-rain background image and rain streaks image in a Dual-Cross manner, which makes the two adversarial branches interact with each other, archiving a win-win result ultimately. The proposed Decomposed Dual-Cross Generative Adversarial Network (DDC-GAN) shows significantly performance improvement compared with stateof-the-art methods on both synthetic and real-world images in terms of qualitative and quantitative measures (over 3dB gains in PSNR).


Content-based audio segmentation using support vector machines

Audio exists at everywhere, but is often out-of-order. It is necessary to arrange them into regularized classes in order to use them more easily. It is also useful, especially in video content analysis, to segment an audio stream according to audio types. In this paper, we present our work in applying support vector machines (SVMs) in audio segmentation and classification. Five audio classes are considered: silence, music, background sound, pure speech, and non-pure speech which includes speech over music and speech over noise. A SVM learns optimal class boundaries from training data to best separate between two classes. A sound clip is segmented by classifying each sub-clip of one second into one of these five classes. Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.


Building contextual visual vocabulary for large-scale image applications

Not withstanding its great success and wide adoption in Bag-of-visual Words representation, visual vocabulary created from single image local features is often shown to be ineffective largely due to three reasons. First, many detected local features are not stable enough, resulting in many noisy and non-descriptive visual words in images. Second, single visual word discards the rich spatial contextual information among the local features, which has been proven to be valuable for visual matching. Third, the distance metric commonly used for generating visual vocabulary does not take the semantic context into consideration, which renders them to be prone to noise. To address these three confrontations, we propose an effective visual vocabulary generation framework containing three novel contributions: 1) we propose an effective unsupervised local feature refinement strategy; 2) we consider local features in groups to model their spatial contexts; 3) we further learn a discriminant distance metric between local feature groups, which we call discriminant group distance. This group distance is further leveraged to induce visual vocabulary from groups of local features. We name it contextual visual vocabulary, which captures both the spatial and semantic contexts. We evaluate the proposed local feature refinement strategy and the contextual visual vocabulary in two large-scale image applications: large-scale near-duplicate image retrieval on a dataset containing 1.5 million images and image search re-ranking tasks. Our experimental results show that the contextual visual vocabulary shows significant improvement over the classic visual vocabulary. Moreover, it outperforms the state-of-the-art Bundled Feature in the terms of retrieval precision, memory consumption and efficiency.


Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature

A deep learning model adaptive to both sentence-level and articlelevel paraphrase identification is proposed in this paper. It consists of pairwise unit similarity feature and semantic context correlation feature. In this model, sentences are represented by word and phrase embedding while articles are represented by sentence embedding. Those phrase and sentence embedding are learned from parse trees through Weighted Unfolding Recursive Autoencoders (WURAE), an unsupervised learning algorithm. Then, unit similarity matrix is calculated by matching the pairwise lists of embedding. It is used to extract the pairwise unit similarity feature through CNN and k-max pooling layers. In addition, semantic context correlation feature is taken into account, which is captured by the combination of CNN and LSTM. CNN layers learn collocation information between adjacent units while LSTM extracts the long-term dependency feature of the text based on the output of CNN. This model is experimented on a famous English sentence paraphrase corpus, MSRPC, and a Chinese article paraphrase corpus. The results show that the deep semantic feature of text could be extracted based on WURAE, unit similarity and context correlation feature. We release our code of WURAE, deep learning model for paraphrase identification and pre-trained phrase end sentence embedding data for use by the community.


The impact of cyberloafing towards Malaysia employees' productivity: A conceptual framework

The increasing use of the internet has changed the way we live and work. Moreover, in countries where there is a use of internet on a large scale, changes in the content and context of work have been observed. The use of Internet has become a tool to achieve competitive advantage by organizations. Also, it has been associated with many negative consequences such as reduced employees' productivity. Despite its importance, there is a lack of cyberloafing studies in Malaysia. The paper proposes a modified version of Triandis's theory of interpersonal behaviour by incorporating a new independent variable, ability to hide cyberloafing that directly predicts employees cyberloafing. This paper also recommends examining the relationship between cyberloafing and employees' productivity. Some studies believe that cyberloafing is beneficial for employees' productivity but other studies suggest that it is harmful to employees' productivity. By understanding what causes cyberloafing and its impacts, organizations can be more effective in managing the issue.


Competing Memes Propagation on Networks: A Network Science Perspective

In this paper, we study the intertwined propagation of two competing "memes" (or data, rumors, etc.) in a composite network. Within the constraints of this scenario, we ask two key questions: (a) which meme will prevail? and (b) can one influence the outcome of the propagations? Our model is underpinned by two key concepts, a structural graph model (composite network) and a viral propagation model (SI1I2S). Using this framework, we formulate a non-linear dynamic system and perform an eigenvalue analysis to identify the tipping point of the epidemic behavior. Based on insights gained from this analysis, we demonstrate an effective and accurate prediction method to determine viral dominance, which we call the EigenPredictor. Next, using a combination of synthetic and real composite networks, we evaluate the effectiveness of various viral suppression techniques by either a) concurrently suppressing both memes or b) unilaterally suppressing a single meme while leaving the other relatively unaffected.


Sentiment in short strength detection informal text


The dynamics of viral marketing

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.


Comparing and managing multiple versions of slide presentations

Despite the ubiquity of slide presentations, managing multiple presentations remains a challenge. Understanding how multiple versions of a presentation are related to one another, assembling new presentations from existing presentations, and collaborating to create and edit presentations are difficult tasks. In this paper, we explore techniques for comparing and managing multiple slide presentations. We propose a general comparison framework for computing similarities and differences between slides. Based on this framework we develop an interactive tool for visually comparing multiple presentations. The interactive visualization facilitates understanding how presentations have evolved over time. We show how the interactive tool can be used to assemble new presentations from a collection of older ones and to merge changes from multiple presentation authors.


Visual pattern discovery for architecture image classification and product image search

Many objects have repetitive elements, and finding repetitive patterns facilitates object recognition and numerous applications. We devise a representation to describe configurations of repetitive elements. By modeling spatial configurations, visual patterns are more discriminative than local features, and are able to tackle with object scaling, rotation, and deformation. We transfer the pattern discovery problem into finding frequent subgraphs from a graph, and exploit a graph mining algorithm to solve this problem. Visual patterns are then exploited in architecture image classification and product image retrieval, based on the idea that visual pattern can describe elements conveying architecture styles and emblematic motifs of brands. Experimental results show that our pattern discovery approach has promising performance and is superior to the conventional bag-of-words approach.


Autonomous decision-making: a data mining approach

The researchers and practitioners of today create models, algorithms, functions, and other constructs defined in abstract spaces. The research of the future will likely be data driven. Symbolic and numeric data that are becoming available in large volumes will define the need for new data analysis techniques and tools. Data mining is an emerging area of computational intelligence that offers new theories, techniques, and tools for analysis of large data sets. In this paper, a novel approach for autonomous decision-making is developed based on the rough set theory of data mining. The approach has been tested on a medical data set for patients with lung abnormalities referred to as solitary pulmonary nodules (SPNs). The two independent algorithms developed in this paper either generate an accurate diagnosis or make no decision. The methodology discussed in the paper depart from the developments in data mining as well as current medical literature, thus creating a variable approach for autonomous decision-making.


Modeling Propagation Dynamics and Developing Optimized Countermeasures for Rumor Spreading in Online Social Networks

The spread of rumors in Online Social Networks (OSNs) poses great challenges to the social peace and public order. It is imperative to model propagation dynamics of rumors and develop corresponding countermeasures. Most of the existing works either overlook the heterogeneity of social networks or do not consider the cost of countermeasures. Motivated by these issues, this paper proposes a heterogeneous network based epidemic model that incorporates both the network heterogeneity and various countermeasures. Through analyzing the existence and stability of equilibrium solutions of the proposed ODE (Ordinary Differential Equation) system, the critical conditions that determine whether a rumor continuously propagates or becomes extinct are derived. Moreover, we concern about the cost of the main two types of countermeasures, i.e., Blocking rumors at influential users and spreading truth to clarify rumors. Employing the Pontryagin's maximum principle, we obtain the optimized countermeasures that ensures a rumor can become extinct at the end of an expected time period with lowest cost. Both the critical conditions and the optimized countermeasures provide a real-time decision reference to restrain the rumor spreading. Experiments based on Digg2009 dataset are conducted to evaluate the effectiveness of the proposed dynamic model and the efficiency of the optimized countermeasures.


Bus tracking and monitoring using RFID

The rapid growth in population in India causes more crowding at public bus stops. People long wait for arriving of buses and suddenly gather near bus when it arrives and travel in overcrowded buses on footboards which leads to accidents. Theft is another risk of overcrowding. All this happens due to lack of proper information of arriving of buses at many of the bus stops. To solve this problem in a cost effective way, we have developed an IoT based bus tracking system to show the current location of the bus and seat availability in the arriving buses. We have used RFID technology for tracking the bus and Thingspeak web server for displaying location of the bus and seat availability in android application in a smart phone. This system enables the commuters to know the exact location of the bus, and occupancy level in the bus, which enable the commuters to take decision, whether to board this bus or next. This reduces the waiting time and overcrowding at bus stops. It also reduces the risk of accidents and thefts.


On path planning methods for automotive collision avoidance

There is a strong trend for increasingly sophisticated Advanced Driver Assistance Systems (ADAS) such as Autonomous Emergency Braking (AEB) systems, Lane Keeping Aid (LKA) systems, and indeed autonomous driving. This trend generates a need for online maneuver generation, for which numerous approaches can be found in the large body of work related to path planning and obstacle avoidance. In order to ease the challenge of choosing a method, this paper reports quantitative and qualitative insights about three different path planning methods: a state lattice planner, predictive constraint-based planning, and spline-based search tree. Each method is described, implemented and compared on two specific traffic situations. The paper will not provide a final answer about which method is best. This depends on several factors such as computational constraints and the formulation of maneuver optimality that is appropriate for a given assistance or safety function. Instead, the conclusions will highlight qualitative merits and drawbacks for each method, in order to provide guidance for choosing a method for a specific application.


Edge detection using constrained discrete particle swarm optimisation in noisy images

Edge detection algorithms often produce broken edges, especially in noisy images. We propose an algorithm based on discrete particle swarm optimisation (PSO) to detect continuous edges in noisy images. A constrained PSO-based algorithm with a new objective function is proposed to address noise and reduce broken edges. The localisation accuracy of the new algorithm is compared with that of a modified version of the Canny algorithm as a Gaussian-based edge detector, the robust rank order (RRO)-based algorithm as a statistical based edge detector, and our previously developed PSO-based algorithm. Pratt's figure of merit is used as a measure of localisation accuracy for these edge detection algorithms. Experimental results show that the performance of the new algorithm is higher than the Canny and RRO algorithms in the images corrupted by two different types of noise (impulsive and Gaussian noise). The new algorithm also detects edges more accurately and smoothly than our previously developed algorithm in noisy images.


Geo-parsing Messages from Microtext

Widespread use of social media during crises has become commonplace, as shown by the volume of messages during the Haiti earthquake of 2010 and Japan tsunami of 2011. Location mentions are particularly important in disaster messages as they can show emergency responders where problems have occurred. This article explores the sorts of locations that occur in disaster-related social messages, how well off-theshelf software identifies those locations, and what is needed to improve automated location identification, called geo-parsing. To do this, we have sampled Twitter messages from the February 2011 earthquake in Christchurch, Canterbury, New Zealand. We annotated locations in messages manually to make a gold standard by which to measure locations identified by a Named Entity Recognition software. The Stanford NER software found some locations that were proper nouns, but did not identify locations that were not capitalized, local streets and buildings, or nonstandard place abbreviations and mis-spellings that are plentiful in microtext. We review how these problems might be solved in software research, and model a readable crisis map that shows crisis location clusters via enlarged place labels.


Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.


Hybrid PWM Strategy of SVPWM and VSVPWM for NPC Three-Level Voltage-Source Inverter

Neutral-point (NP) voltage drift is the main technical drawback of NP-clamped (NPC) three-level inverters. Traditional space vector pulsewidth modulation (SVPWM) is incapable of controlling the NP voltage for high modulation indexes and low power factors. Virtual SVPWM (VSVPWM) is capable of controlling the NP voltage under full modulation indexes and full power factors. However, this modulation strategy is more complex than SVPWM, increases the switching frequency, and deteriorates the output waveforms of the inverter. A novel PWM concept that includes NP voltage-balancing conditions is proposed. Based on this concept, a hybrid modulation scheme that uses both SVPWM and VSVPWM is presented for complete control of the NP voltage in NPC three-level inverters. The performance of this new modulation approach and its benefits over SVPWM and VSVPWM are verified by simulation and experiments.


Why-type Question Classification in Question Answering System

The fundamental requisite to acquire information on any topic has become increasingly important. The need for Question Answering Systems (QAS) prevalent nowadays, replacing the traditional search engines stems from the user requirement for the most accurate answer to any question or query. Thus, interpreting the information need of the users is quite crucial for designing and developing a question answering system. Question classification is an important component in question answering systems that helps to determine the type of question and its corresponding type of answer. In this paper, we present a new way of classifying Why-type questions, aimed at understanding a questioner’s intent. Our taxonomy classifies Why-type questions into four separate categories. In addition, to automatically detect the categories of these questions by a parser, we differentiate them at lexical level.


Circular Quadruple-Ridged Flared Horn Achieving Near-Constant Beamwidth Over Multioctave Bandwidth: Design and Measurements

A circular quadruple-ridged flared horn achieving almost-constant beamwidth over 6:1 bandwidth is presented. This horn is the first demonstration of a wideband feed for radio telescopes which is capable of accommodating different reflector antenna optics, maintains almost constant gain and has excellent match. Measurements of stand-alone horn performance reveal excellent return loss performance as well as stable radiation patterns over 6:1 frequency range. Physical optics calculations predict an average of 69% aperture efficiency and 13 K antenna noise temperature with the horn installed on a radio telescope.


Automatic Classification of Power Quality Events Using Balanced Neural Tree

This paper proposes an empirical-mode decomposition (EMD) and Hilbert transform (HT)-based method for the classification of power quality (PQ) events. Nonstationary power signal disturbance waveforms are considered as the superimposition of various undulating modes, and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMFs). The HT is applied on all the IMFs to extract instantaneous amplitude and frequency components. This time-frequency analysis results in the clear visual detection, localization, and classification of the different power signal disturbances. The required feature vectors are extracted from the time-frequency distribution to perform the classification. A balanced neural tree is constructed to classify the power signal patterns. Finally, the proposed method is compared with an S-transform-based classifier to show the efficacy of the proposed technique in classifying the PQ disturbances.


Research on nursing handoffs for medical and surgical settings: an integrative review.

AIMS
To synthesize outcomes from research on handoffs to guide future computerization of the process on medical and surgical units.

BACKGROUND
Handoffs can create important information gaps, omissions and errors in patient care. Authors call for the computerization of handoffs; however, a synthesis of the literature is not yet available that might guide computerization.

DATA SOURCES
PubMed, CINAHL, Cochrane, PsycINFO, Scopus and a handoff database from Cohen and Hilligoss.

DESIGN
Integrative literature review.

REVIEW METHODS
This integrative review included studies from 1980-March 2011 in peer-reviewed journals. Exclusions were studies outside medical and surgical units, handoff education and nurses' perceptions.

RESULTS
The search strategy yielded a total of 247 references; 81 were retrieved, read and rated for relevance and research quality. A set of 30 articles met relevance criteria.

CONCLUSION
Studies about handoff functions and rituals are saturated topics. Verbal handoffs serve important functions beyond information transfer and should be retained. Greater consideration is needed on analysing handoffs from a patient-centred perspective. Handoff methods should be highly tailored to nurses and their contextual needs. The current preference for bedside handoffs is not supported by available evidence. The specific handoff structure for all units may be less important than having a structure for contextually based handoffs. Research on pertinent information content for contextually based handoffs is an urgent need. Without it, handoff computerization is not likely to be successful. Researchers need to use more sophisticated experimental research designs, control for individual and unit differences and improve sampling frames.


Towards The Deep Model: Understanding Visual Recognition Through Computational Models

Introduction Vision, due to its significance in surviving and socializing, is one of the most important and  extensively studied sensory functions in the human brain. In order to fully understand visual  information processing, or more specifically, visual recognition, David Marr proposed the  Tri-level Hypothesis [29], in that three levels of the system should be studied: the computational  goal of the system, the internal representation or the algorithm the system uses to achieve the  goal, and the neural substrates that implement the system. It is well-known that visual  recognition in the human brain is implemented by the ventral visual pathway [32], which  receives visual information from the retina and goes through a layered structure including V1  (also known as the primary visual cortex), V2, V4, before reaching the inferior temporal cortex  (IT). The topographic mapping between the retina and the human visual cortex follows a  log-polar transformation, in which the Cartesian coordinates of the retina are transformed to  polar coordinates (polar angle and eccentricity) in the human visual cortex. From V1 to V4, each  of the visual areas contains a complete polar angle and eccentricity map, and have a increasing  receptive field (RF) size and respond to increasing complex visual features [12,22]. In higher  level visual areas such as the ventral temporal cortex (VTC), studies using functional magnetic  resonance imaging (fMRI) and diffusion tensor imaging (DTI) have found there exists visual  category-selective regions, such as the Fusiform Face Area (FFA, [26]) for face processing, the  Parahippocampal Place Area (PPA, [13]) for scene perception, and the Lateral Occipital  Complex (LOC, [19]) for general object recognition. Grill-Spector and Malach [20] proposed that  layer-based hierarchical processing, together with the functional specialization, are the two  organizing principles of the human visual cortex. More recent studies have shown that the  central-biased face recognition pathway (FFA) and the peripheral-biased scene recognition  pathway (PPA) are both functionally and anatomically segregated by mid-fusiform sulcus (MFS),  which enables parallel fast processing of visual recognition tasks in the ventral pathway [18, 43,  21, 44].


UniqueID: Decentralized Proof-of-Unique-Human

Bitcoin and Ethereum are novel mechanisms for decentralizing the concept of money and computation. Extending decentralization to the human identity concept, we can think of using blockchain for creating a list of verified human identities with a one-person-one-ID property. UniqueID is a Decentralized Autonomous Organization(DAO) for maintaining human identities such that every physical human entity can have no more that one account. One part of this identity is simply the user’s claim on one of his unique, permanent, and measurable characteristics -biometrics. Blockchain has proved its integrity as a platform for storing and performing computations on such claims. The biggest challenge here is to ensure that the user has submitted his own valid biometric data. Human verifiers can check if there is any inconsistency in other users’ data, by peer-to-peer checks. For preventing bad behavior and centralization in the verification process, UniqueID benefits from novel governance mechanisms to choose verifiers and punish unjust ones. Also, there are incentives for honest verifiers and users by newly generated tokens. We show how the users’ privacy can be preserved by using stateof-the-art cryptographic techniques, and so they can use their identity without any concerns for votings, financial and banking purposes, social media accounts, reputation systems etc.


On the multi-stage influence maximization problem

The influence maximization problem turns up in many online social networks (OSN) in which each participant can potentially influence the decisions made by others in the network. Relationships can be friendships, family relationships or even professional relationships. Such influences can be used to achieve some objective and the influence maximization problem attempts to make decisions so as to maximize the effect of these influences. Past work focused on a static problem whereby one tries to identify the participants who are the most influential. Recently, a multi-stage version of the problem was proposed in which outcomes of influence attempts are observed before additional participants are chosen. For example, in online advertising, one can provide an impression to a particular subject and if that subject clicks on the impression, then their friends are informed in the hope that this information will increase the chances that they also click on the impression and eventually purchase the product. This problem is computationally intensive; in this paper we investigate various optimization methods for finding its solution that yield close to optimal results while taking less computation time. These include greedy and particle swarm optimization algorithms.


Conflict in Adult Close Relationships : An Attachment Perspective

Relationship researchers have focused on the frequency of conflict in couples' relationships and the manner in which couples engage in and try to resolve conflicts. conflict, under some conditions, may facilitate the development and maintenance of associated with patterns of behavior (e.g., negative affect reciprocity, demand-withdraw) and thought that tend to escalate conflict and make it more difficult to negotiate a facilitates intimacy or exacerbates distress may depend on individual differences in the way in which people interpret and respond to conflict. framework for understanding different responses to conflict. People are thought to differ in their working models of attachment, which include expectations, beliefs, and goals These working models are likely to shape people's thoughts, feelings, and behavior during conflict. For example, a person who expects close others to be generally responsive and available is likely to interpret and respond to conflict very differently from a person who expects close others to be rejecting and unavailable. Attachment theory may be able to inform the literature on 3 conflict in close relationships by suggesting how individuals might differ in how they construe conflict. At the same time, the study of relationship conflict provides a useful context for testing important aspects of attachment theory. Conflict may be particularly likely to reveal attachment processes because (a) it may act as a stressor on the relationship and thereby activate the attachment system (Simpson, Rholes, & Phillips, 1996), (b) it challenges partners' abilities to regulate their emotions and behavior (Kobak & Duemmler, 1994), which are thought to be connected to attachment processes and (c) it may trigger behaviors (e.g., personal disclosures) that typically promote intimacy, thereby providing evidence relevant to different attachment goals such as achieving intimacy or maintaining self-reliance (Pietromonaco & Feldman Barrett, 1997). In this chapter, we first discuss how conflict can be conceptualized within an attachment framework. Specifically, we propose that conflict may pose a threat to the attachment bond, but that it also may provide an opportunity for perceiving or experiencing greater intimacy. Furthermore, the degree to which people perceive conflict as a threat, opportunity, or both will depend on the content (e.g., expectations, beliefs, goals) of their working models of attachment. Next, we identify a set of predictions that follow from this framework, and evaluate extent to which empirical findings support these predictions; in particular, we attempt to integrate divergent findings in the empirical literature. Finally, we outline several critical …


Evaluation of cross-language voice conversion based on GMM and straight

Voice conversion is a technique for producing utterances using any target speakers’ voice from a single source speaker’s utterance. In this paper, we apply cross-language voice conversion between Japanese and English to a system based on a Gaussian Mixture Model (GMM) method and STRAIGHT, a high quality vocoder. To investigate the effects of this conversion system across different languages, we recorded two sets of bilingual utterances and performed voice conversion experiments using a mapping function which converts parameters of acoustic features for a source speaker to those of a target speaker. The mapping functions were trained using bilingual databases of both Japanese and English speech. In an objective evaluation using Mel cepstrum distortion (Mel CD), it was confirmed that the system can perform cross-language voice conversion with the same performance as that within a single-language.


A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters

One of the most difficult problems in the design of an anomaly b sed intrusion detection system (IDS) that uses clustering is th at of labelling the obtained clusters, i.e. determining which of them correspond t ”good” behaviour on the network/host and which to ”bad” behaviour. In this pap er, a new clusters’ labelling strategy, which makes use of a clustering quality index is proposed for application in such an IDS. The aim of the new labelling algor ithm is to detect compact clusters containing very similar vectors and these are highly likely to be attack vectors. Two clustering quality indexes have been te sted and compared: the Silhouette index and the Davies-Bouldin index. Experiment al results comparing the effectiveness of a multiple classifier IDS with the two in dexes implemented show that the system using the Silhouette index produces sli ghtly more accurate results than the system that uses the Davies-Bouldin index. However, the computation of the Davies-Bouldin index is much less complex th an the computation of the Silhouette index, which is a very important advantage regarding eventual real-time operation of an IDS that employs clustering.


Detecting social media mobile botnets using user activity correlation and artificial immune system

With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.


A miniaturized wideband dual-polarized linear array with balanced antipodal Vivaldi antenna

Previous studies on Vivaldi wideband phased arrays are mainly focused on Two-dimensional scanning phased arrays. A miniaturized balanced antipodal Vivaldi antenna (BAVA) is presented in this paper. A novel vertical parasitic metal strip loading is employed in the dual-polarized linear phased arrays, it to make a compact structure. With the arc-shaped slots and metal strip loads, the radiation performance in lower operating band can be greatly enhanced. The proposed antenna is simulated in the infinite condition through periodic boundary with the size of 100mm (length) *100mm (width) *125mm (depth). The antenna achieves an impedance bandwidth when scanning to ±50° for VSWR≤3 achieves 4:1 (0.5GHz-2GHz) bandwidth and 5:1 (0.4GHz-2GHz) bandwidth in the vertical polarization and horizontal polarization, respectively, and the isolation is less than -18dB over the operating frequency range.


The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder.

One hundred children, ages 6-19, who were diagnosed with attention-deficit/hyperactivity disorder (ADHD), either inattentive or combined types, participated in a study examining the effects of Ritalin, EEG biofeedback, and parenting style on the primary symptoms of ADHD. All of the patients participated in a 1-year, multimodal, outpatient program that included Ritalin, parent counseling, and academic support at school (either a 504 Plan or an IEP). Fifty-one of the participants also received EEG biofeedback therapy. Posttreatment assessments were conducted both with and without stimulant therapy. Significant improvement was noted on the Test of Variables of Attention (TOVA; L. M. Greenberg, 1996) and the Attention Deficit Disorders Evaluation Scale (ADDES; S. B. McCarney, 1995) when participants were tested while using Ritalin. However, only those who had received EEG biofeedback sustained these gains when tested without Ritalin. The results of a Quantitative Electroencephalographic Scanning Process (QEEG-Scan; V. J. Monastra et al., 1999) revealed significant reduction in cortical slowing only in patients who had received EEG biofeedback. Behavioral measures indicated that parenting style exerted a significant moderating effect on the expression of behavioral symptoms at home but not at school.


Interval Stabbing Problems in Small Integer Ranges

Given a set I of n intervals, a stabbing query consists of a point q and asks for all intervals in I that contain q. The Interval Stabbing Problem is to find a data structure that can handle stabbing queries efficiently. We propose a new, simple and optimal approach for different kinds of interval stabbing problems in a static setting where the query points and interval ends are in {1, . . . , O(n)}. keywords: interval stabbing, interval intersection, rank space, static, discrete, point enclosure


(Leveled) Fully Homomorphic Encryption without Bootstrapping

We present a novel approach to fully homomorphic encryption (FHE) that dramatically improves performance and bases security on weaker assumptions. A central conceptual contribution in our work is a new way of constructing leveled, fully homomorphic encryption schemes (capable of evaluating arbitrary polynomial-size circuits of a-priori bounded depth), without Gentry’s bootstrapping procedure.
 Specifically, we offer a choice of FHE schemes based on the learning with error (LWE) or Ring LWE (RLWE) problems that have 2 λ security against known attacks. We construct the following.
 (1) A leveled FHE scheme that can evaluate depth-L arithmetic circuits (composed of fan-in 2 gates) using O(λ. L3) per-gate computation, quasilinear in the security parameter. Security is based on RLWE for an approximation factor exponential in L. This construction does not use the bootstrapping procedure.
 (2) A leveled FHE scheme that can evaluate depth-L arithmetic circuits (composed of fan-in 2 gates) using O(λ2) per-gate computation, which is independent of L. Security is based on RLWE for quasipolynomial factors. This construction uses bootstrapping as an optimization.
 We obtain similar results for LWE, but with worse performance. All previous (leveled) FHE schemes required a per-gate computation of Ω(λ3.5), and all of them relied on subexponential hardness assumptions.
 We introduce a number of further optimizations to our scheme based on the Ring LWE assumption. As an example, for circuits of large width (e.g., where a constant fraction of levels have width Ω(λ)), we can reduce the per-gate computation of the bootstrapped version to O(λ), independent of L, by batching the bootstrapping operation. At the core of our construction is a new approach for managing the noise in lattice-based ciphertexts, significantly extending the techniques of Brakerski and Vaikuntanathan [2011b].


Are educational computer microgames engaging and effective for knowledge acquisition at high-schools ? A quasi-experimental study

Curricular schooling can benefit from the usage of educational computer games, but it is difficult to integrate them in the formal schooling system. Here, we investigate one possible approach to this integration, which capitalizes on using a micro-game that can be played with a teacher’s guidance as a supplement after a traditional expository lecture followed by a debriefing. The game’s purpose is to reinforce and integrate part of the knowledge learnt during the lecture. We investigated feasibility of this approach in a quasi-experimental study in 70 min long seminars on the topic of animal learning at 5 classes at 4 different high-schools in the Czech Republic. Each class was divided to two groups randomly. After an expository lecture, the game group played a game called Orbis Pictus Bestialis while the control group received an extra lecture that used media-rich materials. The time allotment was the same in both groups. We investigated the immediate and one month delayed effects of the game on students’ knowledge reinforced and integrated by the game as well as on knowledge learnt during the expository lecture but not strengthened by the game. We also investigated students’ overall appeal towards the seminar and its perceived educational value. Data from 100 students were analysed. The results showed that a) the game-playing is comparable to the traditional form of teaching concerning immediate knowledge gains and has a significant medium positive effect size regarding retention, b) the gameplaying is not detrimental to information transmitted in the expository lecture but not strengthened by the game, c) perceived educational value and the overall appeal were high in the game group, nevertheless the perceived educational value was slightly lower in the game group comparing to the traditional group. Our results suggest that the proposed approach of harnessing educational computer games at high-schools is promising. 2011 Elsevier Ltd. All rights reserved.


Deep learning for health informatics: Recent trends and future directions

Health informatics has emerged as a growing domain of interest among researchers world-wide owing to its major implications on society. Applications of machine learning in healthcare range from disease prediction to patient-level personalized services. The prevalence of big data in healthcare has paved the way for applications based on deep learning techniques in the past few years. This paper reviews recent trends and applications of deep learning applied to the healthcare domain. We highlight recent research work, identify challenges and suggest possible future directions that could be pursued further in this domain.


Phase-Based Methods for Heart Rate Detection Using UWB Impulse Doppler Radar

Ultra-wideband (UWB) pulse Doppler radars can be used for noncontact vital signs monitoring of more than one subject. However, their detected signals typically have low signal-to-noise ratio (SNR) causing significant heart rate (HR) detection errors, as the spurious harmonics of respiration signals and mixed products of respiration and heartbeat signals (that can be relatively higher than heartbeat signals) corrupt conventional fast Fourier transform spectrograms. In this paper, we extend the complex signal demodulation (CSD) and arctangent demodulation (AD) techniques previously used for accurately detecting the phase variations of reflected signals of continuous wave radars to UWB pulse radars as well. These detection techniques reduce the impact of the interfering harmonic signals, thus improving the SNR of the detected vital sign signals. To further enhance the accuracy of the HR estimation, a recently developed state-space method has been successfully combined with CSD and AD techniques and over 10 dB improvements in SNR is demonstrated. The implementation of these various detection techniques has been experimentally investigated and full error and SNR analysis of the HR detection are presented.


Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy

We present an approach for the inspection of vertical pole-like infrastructure using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structures, such as light and power distribution poles, is a time consuming, dangerous and expensive task with high operator workload. To address these issues, we propose a VTOL platform that can operate at close-quarters, whilst maintaining a safe stand-off distance and rejecting environmental disturbances. We adopt an Image based Visual Servoing (IBVS) technique using only two line features to stabilise the vehicle with respect to a pole. Visual, inertial and sonar data are used, making the approach suitable for indoor or GPS-denied environments. Results from simulation and outdoor flight experiments demonstrate the system is able to successfully inspect and circumnavigate a pole.


SIT: A Lightweight Encryption Algorithm for Secure Internet of Things

The Internet of Things (IoT) being a promising technology of the future is expected to connect billions of devices. The increased number of communication is expected to generate mountains of data and the security of data can be a threat. The devices in the architecture are essentially smaller in size and low powered. Conventional encryption algorithms are generally computationally expensive due to their complexity and requires many rounds to encrypt, essentially wasting the constrained energy of the gadgets. Less complex algorithm, however, may compromise the desired integrity. In this paper we propose a lightweight encryption algorithm named as Secure IoT (SIT). It is a 64-bit block cipher and requires 64-bit key to encrypt the data. The architecture of the algorithm is a mixture of feistel and a uniform substitution-permutation network. Simulations result shows the algorithm provides substantial security in just five encryption rounds. The hardware implementation of the algorithm is done on a low cost 8-bit micro-controller and the results of code size, memory utilization and encryption/decryption execution cycles are compared with benchmark encryption algorithms. The MATLAB code for relevant simulations is available online at https://goo.gl/Uw7E0W. Keywords—IoT; Security; Encryption; Wireless Sensor Network WSN; Khazad


Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively1.


The effects of prosthetic foot stiffness on transtibial amputee walking mechanics and balance control during turning.

BACKGROUND
Little evidence exists regarding how prosthesis design characteristics affect performance in tasks that challenge mediolateral balance such as turning. This study assesses the influence of prosthetic foot stiffness on amputee walking mechanics and balance control during a continuous turning task.

METHODS
Three-dimensional kinematic and kinetic data were collected from eight unilateral transtibial amputees as they walked overground at self-selected speed clockwise and counterclockwise around a 1-meter circle and along a straight line. Subjects performed the walking tasks wearing three different ankle-foot prostheses that spanned a range of sagittal- and coronal-plane stiffness levels.

FINDINGS
A decrease in stiffness increased residual ankle dorsiflexion (10-13°), caused smaller adaptations (<5°) in proximal joint angles, decreased residual and increased intact limb body support, increased residual limb propulsion and increased intact limb braking for all tasks. While changes in sagittal-plane joint work due to decreased stiffness were generally consistent across tasks, effects on coronal-plane hip work were task-dependent. When the residual limb was on the inside of the turn and during straight-line walking, coronal-plane hip work increased and coronal-plane peak-to-peak range of whole-body angular momentum decreased with decreased stiffness.

INTERPRETATION
Changes in sagittal-plane kinematics and kinetics were similar to those previously observed in straight-line walking. Mediolateral balance improved with decreased stiffness, but adaptations in coronal-plane angles, work and ground reaction force impulses were less systematic than those in sagittal-plane measures. Effects of stiffness varied with the residual limb inside versus outside the turn, which suggests that actively adjusting stiffness to turn direction may be beneficial.


Software-specific part-of-speech tagging: an experimental study on stack overflow

Part-of-speech (POS) tagging performance degrades on out-of-domain data due to the lack of domain knowledge. Software engineering knowledge, embodied in textual documentations, bug reports and online forum discussions, is expressed in natural language, but is full of domain terms, software entities and software-specific informal languages. Such software texts call for software-specific POS tagging. In the software engineering community, there have been several attempts leveraging POS tagging technique to help solve software engineering tasks. However, little work is done for POS tagging on software natural language texts.
 In this paper, we build a software-specific POS tagger, called S-POS, for processing the textual discussions on Stack Overflow. We target at Stack Overflow because it has become an important developer-generated knowledge repository for software engineering. We define a POS tagset that is suitable for describing software engineering knowledge, select corpus, develop a custom tokenizer, annotate data, design features for supervised model training, and demonstrate that the tagging accuracy of S-POS outperforms that of the Stanford POS Tagger when tagging software texts. Our work presents a feasible roadmap to build software-specific POS tagger for the socio-professional contents on Stack Overflow, and reveals challenges and opportunities for advanced software-specific information extraction.


A Constraint-Based Approach to Constructing Continuous Cartograms

We present a new constraint-based continuous area cartogram construction method that is unique in its ability to preserve essential cues for recognition of region shapes. It automatically achieves desired region areas while maintaining correct map topology. The algorithm is compared with a number of existing methods, and results are shown to be superior in both accuracy and preservation of shape recognition cues. Through hierarchical resolution, we first perform gross adjustments upon a coarsely resampled map and later refine the map at progressively higher levels of detail.


Learning Environmental Sounds with Multi-scale Convolutional Neural Network

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.


Orange: From Experimental Machine Learning to Interactive Data Mining

Orange (www.ailab.si/orange) is a suite for machine learning and data mining. It can be used though scripting in Python or with visual programming in Orange Canvas using GUI components called widgets. In the demonstration we will show how to easily prototype stateof-the-art machine learning algorithms through Orange scripting, and design powerful and interactive data exploration applications through visual programming. 1 Orange, a Component-Based Framework Orange is a comprehensive, component-based framework for machine learning and data mining. It is intended for both experienced users and researchers in machine learning who want to prototype new algorithms while reusing as much of the code as possible, and for those just entering the field who can either write short Python scripts for data analysis or enjoy in the powerful while easy-to-use visual programming environment. Orange includes a range of techniques, such as data management and preprocessing, supervised and unsupervised learning, performance analysis, and a range of data and model visualization techniques. 1.1 Scripting in Python As a framework, Orange is comprised of several layers. The core design principle was to use C++ to code the basic data representation and manipulation and all time-complex procedures, such as most learning algorithms and data preprocessing. Tasks that are less time consuming are coded in Python. Python is a popular object-oriented scripting language known for its simplicity and power, and often used for prototyping and as a ”glue-language” for components written in other languages. The interface between C++ and Python provides a tight integration: Python scripts can access and manipulate Orange objects as if they were implemented in Python. On the other hand, components defined in Python can be used by the C++ core. For instance, one can use classification tree as implemented within Orange (in C++) but prototype a component for attribute selection in Python. For Orange, we took special care to implement machine learning methods so that they are assembled from a set of reusable components one can either use in the new algorithms, or replace them with prototypes written in Python. Just for a taste, here is a simple Python script, which, using Orange, reads the data, reports on the number of instances and attributes, builds two classifiers and outputs predicted and true class of the first five instances. import orange data = orange.ExampleTable(’voting.tab’) print ’Instances:’, len(data), ’Attributes:’, len(data.domain.attributes) nbc = orange.BayesLearner(data) knn = orange.kNNLearner(data, k=10) for i in range(5): print nbc(data[i]), knn(data[i]), ’vs. true class’, data[i].getClass() Another, a bit more complicated script below, implements a classification tree learner where node attributes that split the data are chosen at random by a function randomChoice, which is used in place of data splitting component of Orange’s classification tree inducer. The script builds a standard and random tree from the data, and reports on their sizes. import orange, random def randomChoice(instances, *args): attr = random.choice(instances.domain.attributes) cl = orange.ClassifierFromVar(whichVar=attr, classVar=attr) return cl, attr.values, None, 1 treeLearner = orange.TreeLearner() rndLearner = orange.TreeLearner() rndLearner.split = randomChoice data = orange.ExampleTable(’voting.tab’) tree = treeLearner(data) rndtree = rndLearner(data) print tree.treesize(), ’vs.’, rndtree.treesize() 1.2 Visual Programming Component-based approach was also used for graphical user’s interface (GUI). Orange’s GUI is made of widgets, which are essentially a GUI wrappers around data analysis algorithms implemented in Orange and Python. Widgets communicate through channels, and a particular set of connected widgets is called a schema. Orange schemas can be either set in Python scripts, or, preferably, are put together by means of visual programming in an application called Orange Canvas. Besides ease-of-use and flexibility, data exploration widgets were carefully design to support interaction. Clicking on a classification tree node in the tree visualization widget, for example, outputs the corresponding data instances making them available for further analysis. Any visualization of predictive or visualization models where their elements are associated with particular subsets of instances, attributes, data domains, etc., behave in the similar way. A snapshot of Orange Canvas with an example schema is shown in Fig. 1. Fig. 1. Snapshot of Orange Canvas with a schema that takes a microarray data, performs k-means clustering, and evaluates the performance of two different supervised learning methods when predicting the cluster label. Clustered data is visualized in the Heat Map widget, which sends any selected data subset to the Scatterplot widget. 2 Orange Demonstration at ECML-PKDD The demonstration at ECML-PKDD will feature an introduction to Orange, give a tutorial on how to do machine learning by scripting, and show how to use component-based programming to design a new induction algorithm. The second part of the demonstration will demonstrate Orange widgets and visual programming, and will show hot to use these to build classifiers, find interaction between attributes, and do a range of interesting data visualizations. Demonstration will be live, e.g. we will write scripts and visually program at the stage rather than show transparencies. The demonstration will use the data from medicine (predictive models) and functional genomics (data mining of microarray and sequence data). CD’s with installations and copies of the white-papers will be handed out. 3 On Significance and Contribution Orange is an open-source framework that features both scripting and visual programming. Because of component-based design in C++ and integration with Python, Orange should appeal to machine learning researchers for the speed of execution and ease of prototyping of new methods. Graphical user’s interface is provided through visual programming and carefully designed widgets that support interactive data exploration. Component-based design, both on the level of procedural and visual programming, flexibility in combining components to design new machine learning methods and data mining applications, and user and developer-friendly environment are also the most significant attributes of Orange and those where Orange can make its contribution to the community.


Analytics-driven data ingestion and derivation in the AWESOME polystore

Polystores, i.e., data management systems that use multiple stores for different data models, are gaining popularity. We are developing a polystore-based system called AWESOME to support social data analytics. The AWESOME polystore can support relational, semistructured, graph and text data and houses a Spark computation engine to produce derived data during ingestion. ADIL, the data ingestion language of AWESOME allows a user to flexibly specify the placement of original and derived data into and across component stores and the computation engine. The paper also outlines a number of optimization strategies for managing data placement in AWESOME.


Good Word Attacks on Statistical Spam Filters

Unsolicited commercial email is a significant problem for users and providers of email services. While statistical spam filters have proven useful, senders of spam are learning to bypass these filters by systematically modifying their email messages. In a good word attack, one of the most common techniques, a spammer modifies a spam message by inserting or appending words indicative of legitimate email. In this paper, we describe and evaluate the effectiveness of active and passive good word attacks against two types of statistical spam filters: naive Bayes and maximum entropy filters. We find that in passive attacks without any filter feedback, an attacker can get 50% of currently blocked spam past either filter by adding 150 words or fewer. In active attacks allowing test queries to the target filter, 30 words will get half of blocked spam past either filter.


Social Networking Sites and Addiction: Ten Lessons Learned

Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.


Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model environmental systems. We review some of them and their environmental applicability, with examples and a reference list. The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems. © 2008 IMACS. Published by Elsevier B.V. All rights reserved.


Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection

This paper compared various MLP activation functions for classification problems. The most well-known (Artificial Neural Network) ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. Selection of the activation functions in the MLP network plays an essential role on the network performance. A lot of studies have been conducted by reseachers to investigate special activation function to solve different kind of problems. Therefore, this paper intends to investigate the activation functions in MLP networks in terms of the accuracy performances. The activation functions under investigation are sigmoid, hyperbolic tangent, neuronal, logarithmic, sinusoidal and exponential. Medical diagnosis data from two case studies, thyroid disease classification and breast cancer classification, have been used to test the performance of the MLP network. The MLP networks are trained using Back Propagation learning algorithm. The performance of the MLP networks are calculated based on the percentage of correct classificition. The results show that the hyperbolic tangent function in MLP network had the capability to produce the highest accuracy for classifying breast cancer data. Meanwhile, for thyroid disease classification, neuronal function is the most suitable function that performed the highest accuracy in MLP network.


Monolithic Low Cost Ka-Band Wilkinson Power Dividers on Flexible Organic Substrates

A millimeter wave Wilkinson power divider is presented on a thin and durable organic substrate, along with an analysis of bend topologies to reduce coupling between the output paths. The divider provides excellent performance across the entire Ka-band from 27 to 40 GHz. Insertion loss is approximately 0.3 dB and isolation between the output ports is around 30 dB at the design frequency.


Liquid crystal polymer (LCP) for microwave/millimeter wave multilayer packaging

This paper presents characterization and analysis of liquid crystal polymer materials for microwave and millimeter multi-layer packaging. Processing techniques have been developed to fabricate interconnects on this new LCP material. The experimental results demonstrate that an interconnect on LCP achieves a measured insertion loss of less than 0.1 dB/mm up to 50 GHz. This material is highly suitable for microwave and millimeter wave packaging.


Wideband coplanar waveguide RF probe pad to microstrip transitions without via holes

A novel via-less coplanar waveguide (CPW) to microstrip transition is discussed and design rules based on simulations and experimental results are presented. This transition demonstrates a maximum insertion loss of 1 dB over the frequency range from 10 GHz to 40 GHz with a value of 0.4 dB at 20 GHz. This transition could find a variety of applications due to its compatibility with RF systems-on-a chip, low loss performance, low cost and its ease of fabrication.


A broadband Wilkinson balun using microstrip metamaterial lines

A metamaterial balun that converts a single-ended input to a differential output over a large bandwidth is presented. The device also exhibits excellent return loss, isolation, and through characteristics over the same frequency band. The balun comprises a Wilkinson divider, followed by a +90/spl deg/ negative-refractive-index (NRI) metamaterial (MM) phase-shifting line along the top branch, and a -90/spl deg/ MM phase-shifting line along the bottom branch. Utilizing MM lines for both the +90/spl deg/ and -90/spl deg/ branches allows the slopes of their phase responses to be matched, resulting in a broadband differential output signal. The theoretical performance of the balun is verified through circuit simulations and measurements of a fabricated prototype at 1.5 GHz. The MM balun exhibits a measured differential output phase bandwidth (180/spl deg//spl plusmn/10/spl deg/) of 1.16 GHz (77%), from 1.17 to 2.33 GHz. The measured isolation and return loss for all three ports remain below -10 dB over a bandwidth in excess of 2 GHz, while the output quantities |S/sub 21/| and |S/sub 31/| remain above -4 dB from 0.5 to 2.5 GHz.


Mop: an efficient and generic runtime verification framework

Monitoring-Oriented Programming (MOP1) [21, 18, 22, 19] is a formal framework for software development and analysis, in which the developer specifies desired properties using definable specification formalisms, along with code to execute when properties are violated or validated. The MOP framework automatically generates monitors from the specified properties and then integrates them together with the user-defined code into the original system.
 The previous design of MOP only allowed specifications without parameters, so it could not be used to state and monitor safety properties referring to two or more related objects. In this paper we propose a parametric specification formalism-independent extension of MOP, together with an implementation of JavaMOP that supports parameters. In our current implementation, parametric specifications are translated into AspectJ code and then weaved into the application using off-the-shelf AspectJ compilers; hence, MOP specifications can be seen as formal or logical aspects.
 Our JavaMOP implementation was extensively evaluated on two benchmarks, Dacapo [14] and Tracematches [8], showing that runtime verification in general and MOP in particular are feasible. In some of the examples, millions of monitor instances are generated, each observing a set of related objects. To keep the runtime overhead of monitoring and event observation low, we devised and implemented a decentralized indexing optimization. Less than 8% of the experiments showed more than 10% runtime overhead; in most cases our tool generates monitoring code as efficient as the hand-optimized code. Despite its genericity, JavaMOP is empirically shown to be more efficient than runtime verification systems specialized and optimized for particular specification formalisms. Many property violations were detected during our experiments; some of them are benign, others indicate defects in programs. Many of these are subtle and hard to find by ordinary testing.


A Low Voltage Delta-Sigma Fractional Frequency Divider for Multi-band WSN Frequency Synthesizers

A 1 V low voltage delta-sigma fractional-N frequency divider for multi-band (780/868/915 MHz and 2.4 GHz) WSN frequency synthesizers is presented. The frequency divider consists of a dual-modulus prescaler, a pulse-swallow counter and a delta-sigma modulator. The high-speed and low-voltage phase-switching dualmodulus prescaler is used in the frequency divider. Low threshold voltage transistors are applied to overcome low voltage supply and forward phase-switching technique is adopted to prevent glitches. The modified deltasigma modulator with long output sequence length and less spurs is adopted to minimize the fractional spurs. The frequency divider is designed in 0.18 μm TSMC RF CMOS technology under 1 V supply instead of the standard 1.8 V supply. The total chip area is 1190 μm × 485 μm including I/O pads. The post simulation results show the frequency divider operates normally over a wide range of 1.3-5.0 GHz and the core circuit (without test buffers) consumes 2.3 mW. Copyright © 2013 IFSA.


A high-accuracy method for fine registration of overlapping point clouds

This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-tosurface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations. 2009 Elsevier B.V. All rights reserved.


Rethink energy accounting with cooperative game theory

Energy accounting determines how much a software principal contributes to the total system energy consumption. It is the foundation for evaluating software and for operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track all hardware usage by software in a heterogeneous multicore system like modern smartphones and tablets.
 In this work, we argue that energy accounting should be formulated as a cooperative game and that the Shapley value provides the ultimate ground truth for energy accounting policies. We reveal the important flaws of existing energy accounting policies based on the Shapley value theory and provide Shapley value-based energy accounting, a practical approximation of the Shapley value, for battery-powered mobile systems. We evaluate this approximation against existing energy accounting policies in two ways: (i) how well they identify the top energy consuming applications, and (ii) how effective they are in system energy management. Using a prototype based on Texas Instruments Pandaboard and smartphone workload, we experimentally demonstrate existing energy accounting policies can deviate by 400% in attributing energy consumption to running applications and can be up to 25% less effective in system energy management when compared to Shapley value-based energy accounting.


A Controlled Pilot-Outcome Study of Sensory Integration ( SI ) in the Treatment of Complex Adaptation to Traumatic Stress

This study tested whether sensory integration (SI) treatment combined with psychotherapy would improve symptom outcome over psychotherapy alone in the treatment of complex posttraumatic stress, as measured by the Disorders of Extreme Stress Not Otherwise Specified (DESNOS) clinical construct in a group of 10 adult patients with histories of childhood abuse. DESNOS symptoms were assessed at three time periods (T1, baseline; T2, after experimental group SI treatment; and T3, after wait-list control group SI treatment) using the Structured Interview for Disorders of Extreme Stress (SIDES). The Sensory Learning Program (SLP), developed by the Sensory Learning Institute of Boulder, Colorado, was used as the SI treatment modality. Results indicated significant differential improvement for the group treated with SLP in SIDES Total Score (T1/T2 and T2/T3), Self Perception (T1/T2 and T2/T3), Affect Regulation (T2/T3), and Alterations in Meaning (T1/T2).


B2C web site quality and emotions during online shopping episodes: An empirical study

This paper explores the impact of the quality of a web site on the cognitive process leading to consumers’ emotions—considered as direct antecedents to shopping behaviors and operationalized as mental states of readiness arising from the appraisal of events. A parsimonious theoretical model was defined and tested with data collected from 215 web-shopping episodes during which consumers were shopping for low-touch products. Analysis of the results showed that web site quality had a positive impact on the cognitive appraisal of situational state, which in turn influenced five of the six emotions of the proposed model: liking, joy, pride, dislike, and frustration. Results also showed that a substantial number of shoppers experienced intensively the emotions of liking and joy. Moreover, this paper highlights several implications that could help managers and webmasters improve the quality of their web sites. # 2006 Elsevier B.V. All rights reserved.


An Efficient Character-Level Neural Machine Translation

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation. However, the use of large vocabulary becomes the bottleneck in both training and improving the performance. In this paper, we propose an efficient architecture to train a deep character-level neural machine translation by introducing a decimator and an interpolator. The decimator is used to sample the source sequence before encoding while the interpolator is used to resample after decoding. Such a deep model has two major advantages. It avoids the large vocabulary issue radically; at the same time, it is much faster and more memory-efficient in training than conventional character-based models. More interestingly, our model is able to translate the misspelled word like human beings.


Naturalizing consciousness: a theoretical framework.

Consciousness has a number of apparently disparate properties, some of which seem to be highly complex and even inaccessible to outside observation. To place these properties within a biological framework requires a theory based on a set of evolutionary and developmental principles. This paper describes such a theory, which aims to provide a unifying account of conscious phenomena.


Image-based Calorie Content Estimation for Dietary Assessment

In this paper, we present an image-analysis based approach to calorie content estimation for dietary assessment. We make use of daily food images captured and stored by multiple users in a public Web service called Food Log. The images are taken without any control or markers. We build a dictionary dataset of 6512 images contained in Food Log the calorie content of which have been estimated by experts in nutrition. An image is compared to the ground truth data from the point of views of multiple image features such as color histograms, color correlograms and SURF fetures, and the ground truth images are ranked by similarities. Finally, calorie content of the input food image is computed by linear estimation using the top n ranked calories in multiple features. The distribution of the estimation shows that 79% of the estimations are correct within ±40% error and 35% correct within ±20% error.


A religious upbringing reduces the influence of genetic factors on disinhibition: evidence for interaction between genotype and environment on personality.

Information on personality, on anxiety and depression and on several aspects of religion was collected in 1974 Dutch families consisting of adolescent and young adult twins and their parents. Analyses of these data showed that differences between individuals in religious upbringing, in religious affiliation and in participation in church activities are not influenced by genetic factors. The familial resemblance for different aspects of religion is high, but can be explained entirely by environmental influences common to family members. Shared genes do not contribute to familial resemblances in religion. The absence of genetic influences on variation in several dimensions of religion is in contrast to findings of genetic influences on a large number of other traits that were studied in these twin families. Differences in religious background are associated with differences in personality, especially in Sensation Seeking. Subjects with a religious upbringing, who are currently religious and who engage in church activities score lower on the scales of the Sensation Seeking Questionnaire. The most pronounced effect is on the Disinhibition scale. The resemblances between twins for the Disinhibition scale differ according to their religious upbringing. Receiving a religious upbringing seems to reduce the influence of genetic factors on Disinhibition, especially in males.


A shape and image merging technique to solve jigsaw puzzles

This paper proposes an algorithm for solving subsets of typical (canonical) jigsaw puzzles. This algorithm combines shape and image matching with a cyclic ‘‘growth’’ process that tries to place pieces in their correct positions. First, the jigsaw pieces are extracted from the input image. Then, the corner points of the jigsaw pieces are detected. Next, piece classification and recognition are performed based on jigsaw piece models. Connection relationships between pieces are calculated and finally recovered by using boundary shape matching and image merging. We tested this algorithm by employing real-world images containing dozens of jigsaw pieces. The experiment s results show this algorithm is efficient and effective. 2003 Elsevier Science B.V. All rights reserved.


Adaptive Droop Control Applied to Voltage-Source Inverters Operating in Grid-Connected and Islanded Modes

This paper proposes a novel control for voltage-source inverters with the capability to flexibly operate in grid-connected and islanded modes. The control scheme is based on the droop method, which uses some estimated grid parameters such as the voltage and frequency and the magnitude and angle of the grid impedance. Hence, the inverter is able to inject independently active and reactive power to the grid. The controller provides a proper dynamics decoupled from the grid-impedance magnitude and phase. The system is also able to control active and reactive power flows independently for a large range of impedance grid values. Simulation and experimental results are provided in order to show the feasibility of the control proposed.


Manipulating Attributes of Natural Scenes via Hallucination

Fig. 1. Given a natural image, the proposed approach can hallucinate different versions of the same scene in a wide range of conditions, e.g.night, sunset, winter, spring, rain, fog or even a combination of those. First, our method utilizes a single generator network to imagine the scene with respect to its semantic layout and the desired set of attributes. Then, it directly transfers the look from the hallucinated output to the input image, without a need to have access to a reference style image.


Reflection Removal Using Low-Rank Matrix Completion

The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose a low-rank matrix completion algorithm to remove reflection artifacts automatically from multiple glass images taken at slightly different camera locations. We assume that the transmitted scenes are more dominant than the reflected scenes in typical glass images. We first warp the multiple glass images to a reference image, where the gradients are consistent in the transmission images while the gradients are varying across the reflection images. Based on this observation, we compute a gradient reliability such that the pixels belonging to the salient edges of the transmission image are assigned high reliability. Then we suppress the gradients of the reflection images and recover the gradients of the transmission images only, by solving a low-rank matrix completion problem in gradient domain. We reconstruct an original transmission image using the resulting optimal gradient map. Experimental results show that the proposed algorithm removes the reflection artifacts from glass images faithfully and outperforms the existing algorithms on typical glass images.


Unveiling clusters of events for alert and incident management in large-scale enterprise it

Large enterprise IT (Information Technology) infrastructure components generate large volumes of alerts and incident tickets. These are manually screened, but it is otherwise difficult to extract information automatically from them to gain insights in order to improve operational efficiency. We propose a framework to cluster alerts and incident tickets based on the text in them, using unsupervised machine learning. This would be a step towards eliminating manual classification of the alerts and incidents, which is very labor intense and costly. Our framework can handle the semi-structured text in alerts generated by IT infrastructure components such as storage devices, network devices, servers etc., as well as the unstructured text in incident tickets created manually by operations support personnel. After text pre-processing and application of appropriate distance metrics, we apply different graph-theoretic approaches to cluster the alerts and incident tickets, based on their semi-structured and unstructured text respectively. For automated interpretation and read-ability on semi-structured text clusters, we propose a method to visualize clusters that preserves the structure and human-readability of the text data as compared to traditional word clouds where the text structure is not preserved; for unstructured text clusters, we find a simple way to define prototypes of clusters for easy interpretation. This framework for clustering and visualization will enable enterprises to prioritize the issues in their IT infrastructure and improve the reliability and availability of their services.


Deep Unsupervised Learning of Visual Similarities

Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.


Overuse lower extremity injuries in sports.

When athletes train harder the risk of injury increases, and there are several common overuse injuries to the lower extremity. Three of the most common lower extremity overuse injuries in sports are discussed including the diagnosis and treatments: medial tibal stress syndrome, iliotibial band syndrome, and stress fractures. The charge of sports medicine professionals is to identify and treat the cause of the injuries and not just treat the symptoms. Symptomatology is an excellent guide to healing and often the patient leads the physician to the proper diagnosis through an investigation of the athlete's training program, past injury history, dietary habits, choice of footwear, and training surface.


Tree Adjoining Grammar Based Parser for a Hindi Text-to-Scene Conversion System

We have developed Preksha: a Hindi text visualizer. Preksha generates a 3D scene by understanding a given Hindi text. We propose the Earley's parsing algorithm using Tree adjoining grammar (TAG) for Hindi language understanding. A brief overview of the TAG formalism and the Earley's parsing algorithm is presented. Preksha's Hindi language parsing is explainedwith the Lisp notation. The maincontribution of this paper is to apply TAG parsing for morphologically-richfree-order language Hindi for automatic text visualization (ATV).


Autonomous Battery Exchange of UAVs with a Mobile Ground Base

This paper presents the autonomous battery exchange operation for small scale UAVs, using a mobile ground base that carries a robotic arm and a service station containing the battery exchange mechanism. The goal of this work is to demonstrate the means to increase the autonomy and persistence of robotic systems without requiring human intervention. The design and control of the system and its components are presented in detail, as well as the collaborative software framework used to plan and execute complex missions. Finally, the results of autonomous outdoor experiments are presented, in which the ground rover successfully localizes, retrieves, services, and deploys the landed UAV, proving its capacity to extend and enhance autonomous operations.


A Survey Paper on PID Control System

This paper concentrates on the work done on the PID control system specially on time delaying systems like network control systems. The different works which have already been done are summarised here like gain margin, phase margin methods. The absolute focal point on time delayed systemare the delays induced by network and the packet losses. This survey paper covers the study on such methods that compensates these


Flying Swarm of Drones Over Circulant Digraph

Drones are critical to military collaborative engagement, surveillance, and monitoring of areas of interest. They also play an increasingly influential role in commercial applications. We describe a new innovative method for collisionless, survivable, and persistent flying of given <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> drones over <inline-formula><tex-math notation="LaTeX">$2n$</tex-math></inline-formula> waypoints that results in dispersed coverage of a given area of interest, where <inline-formula><tex-math notation="LaTeX">$n > k \geq 3$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> divides <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>. Our method is fundamentally different than any known to date since it does not need sensors to avoid collisions between drones. In addition, the flight of drones does not have to be coordinated.


High-voltage pulse waveform modulator based on solid-state Marx generator

Many research fields need special high-voltage pulses in pulsed power applications, which are different from typical rectangular pulses. In this paper, a high-voltage waveform modulator of two methods using a solid-state Marx generator has been developed and tested. Instead of manufacturing a generator with a specialized structure, the modulator can control a general Marx generator by low-voltage programmable logic devices to produce an irregular but controllable high-voltage triangular pulse with pulse length of ~4.5 μs and peak voltage of ~123 kV.


Treevis.net: A Tree Visualization Reference

Tree visualization is one of the best-studied areas of information visualization; researchers have developed more than 200 visualization and layout techniques for trees. The treevis.net project aims to provide a hand-curated bibliographical reference to this ever-growing wealth of techniques. It offers a visual overview that users can filter to a desired subset along the design criteria of dimensionality, edge representation, and node alignment. Details, including links to the original publications, can be brought up on demand. Treevis.net has become a community effort, with researchers sending in preprints of their tree visualization techniques to be published or pointing out additional information.


Thinking Aloud : Reconciling Theory and Practice —

—M. TED BOREN AND JUDITH RAMEY, ASSOCIATE MEMBER, IEEE Abstract—Thinking-aloud protocols may be the most widely used method in usability testing, but the descriptions of this practice in the usability literature and the work habits of practitioners do not conform to the theoretical basis most often cited for it: Ericsson and Simon’s seminal work PROTOCOL ANALYSIS: VERBAL REPORTS AS DATA [1]. After reviewing Ericsson and Simon’s theoretical basis for thinking aloud, we review the ways in which actual usability practice diverges from this model. We then explore the concept of SPEECH GENRE as an alternative theoretical framework. We first consider uses of this new framework that are consistent with Simon and Ericsson’s goal of eliciting a verbal report that is as undirected, undisturbed, and constant as possible. We then go on to consider how the proposed new approach might handle problems that arise in usability testing that appear to require interventions not supported in the older model.


Cone Trees: animated 3D visualizations of hierarchical information

The task of managing and accessing large information spaces is a problem in large scale cognition. Emerging technologies for 3D visualization and interactive animation offer potential solutions to this problem, especially when the structure of the information can be visualized. We describe one of these Information Visualization techniques, called the Cone Tree, which is used for visualizing hierarchical information structures. The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure. Interactive animation is used to shift some of the user's cognitive load to the human perceptual system.


Stacked Graphs – Geometry & Aesthetics

In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.


Readings in information visualization - using vision to think

Visualization: The use of computer-supported, interactive, visual representations of data to amplify cognition. (6) Cognition is the acquisition or use of knowledge. This definition has the virtue of focusing as much on the purpose of visualization as the means. Hamming (1973) saud, “the purpose of computation is insight, not numbers.” Likewise for visualization, “the purpose of visualization is insight not pictures.” The main goals of this insight are discovery, decision  making, and explanation. (6)


Managing information sharing within an organizational setting: A social network perspective

In the current information-driven and technologically based global economy, organizations are becoming increasingly dependent on the cumulative knowledge of their employees, suppliers, customers, and other key stakeholders. An organization’s ability to share this knowledge among organizational members is key to its competitive advantage (Bock, Zmud, Kim, & Lee, 2007; Brown & Duguid, 2000; Small & Sage, 2006). Information sharing is critical to an organization’s competitiveness and requires a free flow of information among members that is undistorted and up-to-date (Childhouse & Towill, 2003; Li & Lin, 2006; Moberg, Cutler, Gross, & Speh, 2002; Rahman, 2004; Tan, Lyman, & Wisner, 2002). However, extensive information sharing within organizations still appears to be the exception rather than the rule (Bock et al., 2007; Davenport & Prusack, 1998; Li & Lin, 2006). According to Li and Lin (2006), intensified competition and globalized markets are some of the challenges associated with getting products and services to the right place at the right time and at the lowest cost. These challenges, for instance, have forced organizations to realize that it is not enough to improve their efficiencies; rather, their entire supply chains have to be made competitive. One way for organizations to do this is to support information sharing among their work groups (Li & Lin, 2006). This article explores organizational information sharing as it relates to individuals within and between work groups. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example then illustrates how a social network approach (SNA) was used to explore the process of measuring the social


A SECURE ONLINE PAYMENT SYSTEM USING STEGANOGRAPHY AND VISUAL CRYPTOGRAPHY

In This paper we presents a new methodology which provides limited information only that is important in transferring money while shopping online provides security for customers data and increasing in confidence of customer and preventing identity stealing. Here we used cryptography technique for image encryption. Data is encrypted in images shares which are meaningless images. These shares are then transferred over an untrusted communication channel. By combining only these shares give the original secret image. This images may contain ATM card details, credit card information etc. At merchants side merchant have to pay some amount per transaction so it’s merchant’s responsibility to protect customers information from being misused. So here is solution for such operations.


A novel method for eye tracking and blink detection in video frames

This paper presents a novel method for eye tracking and blink detection in the video frames obtained from low resolution consumer grade web cameras. It uses a method involving Haar based cascade classifier for eye tracking and a combination of HOG features with SVM classifier for eye blink detection. The presented method is non intrusive and hence provides a comfortable user interaction. The eye tracking method has an accuracy of 92.3% and the blink detection method has an accuracy of 92.5% when tested using standard databases and a combined accuracy of 86% when tested under real world conditions of a normal room.


THE INSTITUTIONAL FOUNDATIONS OF PUBLIC POLICY : A TRANSACTIONS APPROACH WITH APPLICATION TO ARGENTINA *

Public policies are the outcomes of complex intertemporal exchanges among politicians. The basic institutional characteristics of a country constitute the framework within which those transactions are accomplished. We develop a transactions theory to understand the ways in which political institutions affect the transactions that political actors are able to undertake, and hence the policies that emerge. We argue that Argentina is a case in which the functioning of political institutions has been such that it prevented the capacity to undertake efficient intertemporal political exchanges. We use positive political theory and transaction cost economics to explain the workings of Argentine political institutions, and to show how that maps into low-quality policies.