title: Support vector machines and Kernel methods: the new generation of learning machines

abstract: Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized. Although the research is not concluded, already now kernel methods are considered the state of the art in several machine learning tasks. Their ease of use, theoretical appeal, and remarkable performance have made them the system of choice for many learning problems. Successful applications range from text categorization to handwriting recognition to classification of gene-expression data.


title: App store mining is not enough for app improvement

abstract: The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.


title: THE PSYCHOLOGY OF COSMETIC SURGERY: A REVIEW AND RECONCEPTUALIZATION

abstract: Abstract This article discusses the psychology of cosmetic surgery. A review of the research on the psychological characteristics of individuals who seek cosmetic surgery yielded contradictory findings. Interview-based investigations revealed high levels of psychopathology in cosmetic surgery patients, whereas studies that used standardized measurements reported far less disturbance. It is difficult to fully resolve the discrepancy between these two sets of findings. We believe that investigating the construct of body image in cosmetic surgery patients will yield more useful findings. Thus, we propose a model of the relationship between body image dissatisfaction and cosmetic surgery and outline a research agenda based upon the model. Such research will generate information that is useful to the medical and mental health communities and, ultimately, the patients themselves. © 1998 Elsevier Science Ltd


title: MFCC and Prosodic Feature Extraction Techniques: A Comparative Study

abstract: In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC) and long-term features (Prosodic) extraction techniques. In this paper, we explore the usefulness of prosodic features for syllable classification and MFCC for feature extraction of a speech signal followed by comparison between them. The Me1 Frequency Cepstral Coefficients (MFCC) is one of the most important features extraction techniques, which is required among various kinds of speech applications. The MFCC features are extracted from the speaker phonemes in the presegmented speech sentences. Now days Prosodic features are currently used in most emotion recognition algorithms Prosodic features are relatively simple in their structures and known for their effectiveness in some speech recognition tasks. There are various ways of generating prosodic syllable contour features that have recently been applied to enhance systems for speaker recognition.


title: A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features

abstract: In this note we present a generative model of natural images consisting of a deep hierarchy of layers of latent random variables, each of which follows a new type of distribution that we call rectified Gaussian. These rectified Gaussian units allow spike-and-slab type sparsity, while retaining the differentiability necessary for efficient stochastic gradient variational inference. To learn the parameters of the new model, we approximate the posterior of the latent variables with a variational auto-encoder. Rather than making the usual mean-field assumption however, the encoder parameterizes a new type of structured variational approximation that retains the prior dependencies of the generative model. Using this structured posterior approximation, we are able to perform joint training of deep models with many layers of latent random variables, without having to resort to stacking or other layerwise training procedures.


title: V.: Cloud-assisted multiparty computation from fully homomorphic encryption. Cryptology ePrint Archive

abstract: We construct protocols for secure multiparty computation with the help of a computationally powerful party, namely the “cloud”. Our protocols are simultaneously ecient in a number of metrics: • Rounds: our protocols run in 4 rounds in the semi-honest setting, and 5 rounds in the malicious setting. • Communication: the number of bits exchanged in an execution of the protocol is independent of the complexity of function f being computed, and depends only on the length of the inputs and outputs. • Computation: the computational complexity of all parties is independent of the complexity of the function f, whereas that of the cloud is linear in the size of the circuit computing f. In the semi-honest case, our protocol relies on the “ring learning with errors” (RLWE) assumption, whereas in the malicious case, security is shown under the Ring LWE assumption as well as the existence of simulation-extractable NIZK proof systems and succinct non-interactive arguments. In the malicious setting, we also relax the communication and computation requirements above, and only require that they be “small” ‐ polylogarithmic in the computation size and linear in the size of the joint size of the inputs. Our constructions leverage the key homomorphic property of the recent fully homomorphic encryption scheme of Brakerski and Vaikuntanathan (CRYPTO 2011, FOCS 2011). Namely, these schemes allow combining encryptions of messages under dierent keys to produce an encryption (of the sum of the messages) under the sum of the keys. We also design an ecient, non-interactive threshold decryption protocol for these fully homomorphic encryption schemes.


Using Network Analysis to Understand Severe Asthma Phenotypes


Single-bicycle crash types and characteristics


title: Continuous Preintegration Theory for Graph-based Visual-Inertial Navigation.

abstract: In this paper we propose a new continuous preintegration theory for graph-based sensor fusion with an inertial measurement unit (IMU) and a camera (or other aiding sensors). Rather than using discrete sampling of the measurement dynamics as in current methods, we analytically derive the closed-form solutions to the preintegration equations, yielding improved accuracy in state estimation. We advocate two new different inertial models for preintegration: (i) the model that assumes piecewise constant measurements, and (ii) the model that assumes piecewise constant local true acceleration. We show through extensive Monte-Carlo simulations the effect that the choice of preintegration model has on estimation performance. To validate the proposed preintegration theory, we develop both direct and indirect visual-inertial navigation systems (VINS) that leverage our preintegration. In the first, within a tightly-coupled, sliding-window optimization framework, we jointly estimate the features in the window and the IMU states while performing marginalization to bound the computational cost. In the second, we loosely couple the IMU preintegration with a direct image alignment that estimates relative camera motion by minimizing the photometric errors (i.e., raw image intensity difference), allowing for efficient and informative loop closures. Both systems are extensively tested in real-world experiments and are shown to offer competitive performance to state-of-the-art methods.


title: Human versus automatic quality evaluation of NMT and PBSMT

abstract: Neural machine translation (NMT) has recently gained substantial popularity not only in academia, but also in industry. For its acceptance in industry it is important to investigate how NMT performs in comparison to the phrase-based statistical MT (PBSMT) model, that until recently was the dominant MT paradigm. In the present work, we compare the quality of the PBSMT and NMT solutions of KantanMT—a commercial platform for custom MT—that are tailored to accommodate large-scale translation production, where there is a limited amount of time to train an end-to-end system (NMT or PBSMT). In order to satisfy the time requirements of our production line, we restrict the NMT training time to 4 days; to train a PBSMT system typically requires no longer than one day with the current training pipeline of KantanMT. To train both NMT and PBSMT engines for each language pair, we strictly use the same parallel corpora and the same pre- and post-processing steps (when applicable). Our results show that, even with time-restricted training of 4 days, NMT quality substantially surpasses that of PBSMT. Furthermore, we challenge the reliability of automatic quality evaluation metrics based on n-gram comparison (in particular F-measure, BLEU and TER) for NMT quality evaluation. We support our hypothesis with both analytical and empirical evidence. We investigate how suitable these metrics are when comparing the two different paradigms.


title: Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels

abstract: We consider a multiuser multiple-input multiple- output (MIMO) Gaussian broadcast channel (BC), where the transmitter and receivers have multiple antennas. Since the MIMO BC is in general a nondegraded BC, its capacity region remains an unsolved problem. We establish a duality between what is termed the "dirty paper" achievable region (the Caire-Shamai (see Proc. IEEE Int. Symp. Information Theory, Washington, DC, June 2001, p.322) achievable region) for the MIMO BC and the capacity region of the MIMO multiple-access channel (MAC), which is easy to compute. Using this duality, we greatly reduce the computational complexity required for obtaining the dirty paper achievable region for the MIMO BC. We also show that the dirty paper achievable region achieves the sum-rate capacity of the MIMO BC by establishing that the maximum sum rate of this region equals an upper bound on the sum rate of the MIMO BC.


title: The Numerical Simulation of a Bird-Impact on an Aircraft Windshield by Using the SPH Method

abstract: This paper is focused on the development of an effective numerical method to simulate bird-impact aircraft windshield events. A new Smooth Particle Hydrodynamics (SPH) which has been incorporated as a solver option into the explicit finite element program PAM-CRASH was used to model the bird. The deformation between the numerical results and the experimental results is in good agreement. Simulation results of a bird-impact process indicated that the SPH bird model is more suitable to model the break-up of the bird into particles. Failure of the windshield in the experiment was simulated, and the good agreement between the numerical and experiment indicates that the failure model established in the present paper is reasonable. Finally, the energy changes of the bird and the windshield were calculated.


title: Attention training and attention state training

abstract: The ability to attend and to exercise cognitive control are vital aspects of human adaptability. Several studies indicate that attention training using computer based exercises can lead to improved attention in children and adults. Randomized control studies of exposure to nature, mindfulness and integrative body-mind training (IBMT) yield improved attention and self-regulation. Here, we ask how attention training and attention state training might be similar and different in their training methods, neural mechanisms and behavioral outcomes. Together these various methods lead to practical ways of improving attention and self-regulation.


title: Combining supervised and unsupervised learning for zero-day malware detection

abstract: Malware is one of the most damaging security threats facing the Internet today. Despite the burgeoning literature, accurate detection of malware remains an elusive and challenging endeavor due to the increasing usage of payload encryption and sophisticated obfuscation methods. Also, the large variety of malware classes coupled with their rapid proliferation and polymorphic capabilities and imperfections of real-world data (noise, missing values, etc) continue to hinder the use of more sophisticated detection algorithms. This paper presents a novel machine learning based framework to detect known and newly emerging malware at a high precision using layer 3 and layer 4 network traffic features. The framework leverages the accuracy of supervised classification in detecting known classes with the adaptability of unsupervised learning in detecting new classes. It also introduces a tree-based feature transformation to overcome issues due to imperfections of the data and to construct more informative features for the malware detection task. We demonstrate the effectiveness of the framework using real network data from a large Internet service provider.


title: A rational analysis of curiosity

abstract: We present a rational analysis of curiosity, proposing that people's curiosity is driven by seeking stimuli that maximize their ability to make appropriate responses in the future. This perspective offers a way to unify previous theories of curiosity into a single framework. Experimental results confirm our model's predictions, showing how the relationship between curiosity and confidence can change significantly depending on the nature of the environment.


title: BetweenIT: An Interactive Tool for Tight Inbetweening

abstract: The generation of inbetween frames that interpolate a given set of key frames is a major component in the production of a 2D feature animation. Our objective is to considerably reduce the cost of the inbetweening phase by offering an intuitive and effective interactive environment that automates inbetweening when possible while allowing the artist to guide, complement, or override the results. Tight inbetweens, which interpolate similar key frames, are particularly time-consuming and tedious to draw. Therefore, we focus on automating these high-precision and expensive portions of the process. We have designed a set of user-guided semi-automatic techniques that fit well with current practice and minimize the number of required artist-gestures. We present a novel technique for stroke interpolation from only two keys which combines a stroke motion constructed from logarithmic spiral vertex trajectories with a stroke deformation based on curvature averaging and twisting warps. We discuss our system in the context of a feature animation production environment and evaluate our approach with real production data.


title: A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution

abstract: We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions â€” information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.


title: Water monitoring system using arduino with labview

abstract: In the many part of the word, availability of potable water is an issue as it is contaminated, there are basic qualitative observations that quickly determine if water is not safe to consume. To prevent the pollution, first part is to detect that pollutant. This system is to develop, implement, monitor and control some parameter of water such as pH level, temperature, turbidity. In this system low cost sensors are used to control and monitor the parameters. Arduino UNO interface with LabVIEW.


title: Performance Capture of Interacting Characters with Handheld

abstract: We present an algorithm for marker-less performance capture of interacting humans using only three hand-held Kinect cameras. Our method reconstructs human skeletal poses, deforming surface geometry and camera poses for every time step of the depth video. Skeletal configurations and camera poses are found by solving a joint energy minimization problem which optimizes the alignment of RGBZ data from all cameras, as well as the alignment of human shape templates to the Kinect data. The energy function is based on a combination of geometric correspondence finding, implicit scene segmentation, and correspondence finding using image features. Only the combination of geometric and photometric correspondences and the integration of human pose and camera pose estimation enables reliable performance capture with only three sensors. As opposed to previous performance capture methods, our algorithm succeeds on general uncontrolled indoor scenes with potentially dynamic background, and it succeeds even if the cameras are moving.


title: Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain

abstract: The advantage of spiking neural networks (SNNs) over their predecessors is their ability to spike, enabling them to use spike timing for coding and efficient computing. A neuromorphic dataset should allow a neuromorphic algorithm to clearly show that a SNN is able to perform better on the dataset than an ANN. We have analyzed both N-MNIST and N-Caltech101 along these lines, but focus our study on N-MNIST. First we evaluate if additional information is encoded in the time domain in a neuromoprhic dataset. We show that an ANN trained with backpropagation on frame based versions of N-MNIST and N-Caltech101 images achieve 99.23% and 78.01% accuracy. These are the best classification accuracies obtained on these datasets to date. Second we present the first unsupervised SNN to be trained on N-MNIST and demonstrate results of 91.78%. We also use this SNN for further experiments on N-MNIST to show that rate based SNNs perform better, and precise spike timings are not important in N-MNIST. N-MNIST does not, therefore, highlight the unique ability of SNNs. The conclusion of this study opens an important question in neuromorphic engineering - what, then, constitutes a good neuromorphic dataset?


title: Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations

abstract: Goal-oriented dialogue systems typically communicate with a backend (e.g. database, Web API) to complete certain tasks to reach a goal. The intents that a dialogue system can recognize are mostly included to the system by the developer statically. For an open dialogue system that can work on more than a small set of well curated data and APIs, this manual intent creation will not scalable. In this paper, we introduce a straightforward methodology for intent creation based on semantic annotation of data and services on the web. With this method, the Natural Language Understanding (NLU) module of a goal-oriented dialogue system can adapt to newly introduced APIs without requiring heavy developer involvement. We were able to extract intents and necessary slots to be filled from schema.org annotations. We were also able to create a set of initial training sentences for classifying user utterances into the generated intents. We demonstrate our approach on the NLU module of a state-of-the art dialogue system development framework.


title: Analytics for the Internet of Things: A Survey

abstract: The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.


title: My Magnetometer Is Telling You Where I've Been?: A Mobile Device Permissionless Location Attack

abstract: Although privacy compromises remain an issue among users and advocacy groups, identification of user location has emerged as another point of concern. Techniques using GPS, Wi-Fi, NFC, Bluetooth tracking and cell tower triangulation are well known. These can typically identify location accurately with meter resolution. Another technique, inferring routes via sensor exploitation, may place a user within a few hundred meters of a general location. Acoustic beacons such as those placed in malls may have more finely grained resolution yet are limited by the sensitivity of the device's microphone to ultrasonic signals and directionality. In this paper we are able to discern user location within commercial GPS resolution by leveraging the ability of mobile device magnetometers to detect externally generated signals in a permissionless attack. We are able to achieve an aggregate location identification success rate of 86% with a bit error rate of 1.5% which is only ten times the stationary error rate. We accomplish this with a signal that is a fraction of the Earth's magnetic field strength. We designed, prototyped, and experimentally evaluated a system where a location ID is transmitted via low power magnetic coil(s) and received by permissionless apps. The system can be located at ingresses and kiosks situated in malls, stores, transportation hubs and other public locations including crosswalks using a location ID that is mapped to the GPS coordinates of the facility hosting the system. We demonstrate that using Android phone magnetometers, we can correctly detect and identify the when and the where of a device when the victim walks at a comfortable pace while their device has all the aforementioned services disabled. In order to address the substantial signal fading effects due to mobility in a very-low power magnetic near field, we developed signal processing and coding techniques and evaluated the prototype on six android devices in an IRB-approved study with six participants.


title: Detection of Malicious Code Variants Based on Deep Learning

abstract: With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.


title: Low RCS microstrip patch array: EM design and performance analysis

abstract: In low observable technology, antennas with low radar cross section (RCS) but with high gain are preferred. In this paper, a low RCS microstrip patch array is designed using the concept of phase cancellation. The patch elements are rotated sequentially or randomly keeping the feed fixed. This geometric arrangement of patch elements reduces the scattering effectively. The gain and the RCS of randomly rotated (RR) microstrip patch array is analyzed in comparison with a uniform array with no element rotation (NR) and sequentially rotated (SR) patch elements. It is shown that the RCS of the RR array is reduced significantly without any adverse effect on its radiation performance.


title: Fundamental Limitations on Search Algorithms: Evolutionary Computing in Perspective

abstract: The past twenty years has seen a rapid growth of interest in stochas- tic search algorithms, particularly those inspired by natural processes in physics and biology. Impressive results have been demonstrated on complex practical op- timisation problems and related search applications taken from a variety of fields, but the theoretical understanding of these algorithms remains weak. This results partly from the insufficient attention that has been paid to results showing certain fundamental limitations on universal search algorithms, including the so-called "No Free Lunch" Theorem. This paper extends these results and draws out some of their implications for the design of search algorithms, and for the construction of useful representations. The resulting insights focus attention on tailoring alg- orithms and representations to particular problem classes by exploiting domain knowledge. This highlights the fundamental importance of gaining a better the- oretical grasp of the ways in which such knowledge may be systematically ex- ploited as a major research agenda for the future.


title: Efficiency comparison between doubler and Dickson charge pumps

abstract: The paper presents a comparison between two of the most popular charge pump structures, the Dickson charge pump and the cascade of voltage doublers. The comparison has been carried out considering power efficiency as the main parameter of interest. The discussion is supported by theoretical analysis and experimental results. To compare the two topologies, two voltage elevators were designed and integrated in a triple-well 0.18-/spl mu/m CMOS technology. The two charge pumps were designed with the same operating clock frequency, the same storage capacitance per stage, and the same number of stages (and, thus, approximately the same area). The comparison showed that the voltage doubler has the higher power efficiency by about 13%.


title: FABS: file and block surveillance system for determining anomalous disk accesses

abstract: Despite increasingly sophisticated security measures, attackers have continued to find ways to gain access to stored data with impacts including data disclosure, modification, or deletion. There currently exist no tools independent of the operating system to monitor storage status. The authors introduced FABS as a comprehensive tool to monitor storage for anomalous accesses. A scalable GUI prototype, VisFlowConnect-SS, which represents storage accesses visually to human operators, was also introduced. The goal is an integrated storage-based monitoring system that provides intrusion detection, minimizes attack damage, and assists with post-attack forensic analysis.


title: Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers

abstract: Fingerprint classification reduces the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. Support vector machines (SVMs) are widely used in pattern classification and have produced high accuracy when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which the SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with nai@?ve Bayes classifiers. This is necessary to break the ties that frequently occur when working with multi-class classification systems that use OVA SVMs. More specifically, it uses representative fingerprint features as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and nai@?ve Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for five-class classification with the statistical significance. The results show the benefits of integrating different fingerprint features as well as the usefulness of the proposed method in multi-class fingerprint classification.


title: How Social and Communication Channels Shape and Challenge a Participatory Culture in Software Development

abstract: Software developers use many different communication tools and channels in their work. The diversity of these tools has dramatically increased over the past decade and developers now have access to a wide range of socially enabled communication channels and social media to support their activities. The availability of such social tools is leading to a participatory culture of software development, where developers want to engage with, learn from, and co-create software with other developers. However, the interplay of these social channels, as well as the opportunities and challenges they may create when used together within this participatory development culture are not yet well understood. In this paper, we report on a large-scale survey conducted with 1,449 GitHub users. We discuss the channels these developers find essential to their work and gain an understanding of the challenges they face using them. Our findings lay the empirical foundation for providing recommendations to developers and tool designers on how to use and improve tools for software developers.


title: What Is Personalization? Perspectives on the Design and Implementation of Personalization in Information Systems

abstract: In e-commerce and mobile commerce, personalization has been recognized as an important approach element in customer relationships and Web strategies. However, there are wide differences in how this concept is defined, characterized, and implemented in the literature. In this article we present a high-level framework for classifying approaches to personalization that delineates fundamental assumptions about personalization in the literature and relates them to strategies for developing personalization systems. The framework consists of 2 parts: (a) a set of perspectives on personalization that guide the design of personalization systems at a general level and (b) a scheme for classifying how personalization can be implemented. The personalization perspectives represent 4 distinct schools of thought on the nature of personalization distilled from the literature of several fields. These perspectives are ideal types and we discuss them in terms of the motivation they supply for personalization, the goals and ...


title: A 100Watt ultra-broadband power amplifier using silicon LDMOSFETs

abstract: We present the design of a high-efficiency power amplifier using silicon LDMOSFETs for the ultra-broadband from 30 to 500MHz. The amplifier has a 4-way push-pull configuration using Guanella's 1∶1 transmission line transformer. To achieve both the high efficiency and the IMD3 required, the bias condition for the class-AB operation has been applied. In addition, a push-pull structure and a negative feedback network have been adopted for the broadband operation which has been implemented using broadband coaxial transmission line transformers and a broadband high-Q RF choke inductor. It is shown from the experimental results presented that the implemented power amplifier exhibits an output power at the 1dB compression point (P1dB) of more than 100W, as well as having a power-added efficiency of more than 35% at P1dB over the entire operational frequency band from 30 to 500MHz. The results also show very flat power gain characteristics of 40±1.5dB from 30MHz to 500MHz.


title: VLSI Implementation of Neural Networks

abstract: Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 microm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.


title: Dual Field Theories In (d-1)+1 Emergent Spacetimes From A Unifying Field Theory In d+2 Spacetime

abstract: According to two-time physics, there is more to space and time than can be garnered with the ordinary formulation of physics. Two-time physics has shown that the standard model of particles and forces is successfully reproduced by a two-time field theory in four space and two time dimensions projected as a holographic image on an emergent space-time in 3 + 1 dimensions. Among the successes of this approach is the resolution of the strong CP problem of QCD as an outcome of the restrictions imposed by the higher symmetry structures in 4 + 2 dimensions. In this paper we launch a program to construct the duals of the standard model as other holographic images of the same 4 + 2 dimensional theory on a variety of emergent spacetimes in 3 + 1 dimensions. These dual field theories are obtained as a family of gauge choices in the master 4 + 2 field theory. In the present paper we deal with some of the simpler gauge choices which lead to interacting Klein-Gordon field theories for the conformal scalar with a predicted SO(d,2) symmetry in a variety of interesting curved spacetimes in (d - 1) + 1 dimensions. More challenging and more interesting gauge choices (including some that relate to mass) which are left to future work are also outlined. Through this approach we discover a new realm of previously unexplored dualities and hidden symmetries that exist both in the macroscopic and microscopic worlds, at the classical and quantum levels. Such phenomena predicted by Two time physics (2T-physics) can in principle be confirmed both by theory and experiment. One time physics (IT-physics) can be used to analyze the predictions but in most instances gives no clue that the predicted phenomena exist in the first place. This point of view suggests a new paradigm for the construction of a fundamental theory that is likely to impact on the quest for unification.


title: Quickest Detection of False Data Injection Attack in Wide-Area Smart Grids

abstract: We consider the sequential (i.e., online) detection of false data injection attacks in smart grid, which aims to manipulate the state estimation procedure by injecting malicious data to the monitoring meters. The unknown parameters in the system, namely the state vector, injected malicious data and the set of attacked meters pose a significant challenge for designing a robust, computationally efficient, and high-performance detector. We propose a sequential detector based on the generalized likelihood ratio to address this challenge. Specifically, the proposed detector is designed to be robust to a variety of attacking strategies, and load situations in the power system, and its computational complexity linearly scales with the number of meters. Moreover, it considerably outperforms the existing first-order cumulative sum detector in terms of the average detection delay and robustness to various attacking strategies. For wide-area monitoring in smart grid, we further develop a distributed sequential detector using an adaptive sampling technique called level-triggered sampling. The resulting distributed detector features single bit per sample in terms of the communication overhead, while preserving the high performance of the proposed centralized detector.


title: Analyzing facebook privacy settings: user expectations vs. reality

abstract: The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy. In this paper, we focus on measuring the disparity between the desired and actual privacy settings, quantifying the magnitude of the problem of managing privacy. We deploy a survey, implemented as a Facebook application, to 200 Facebook users recruited via Amazon Mechanical Turk. We find that 36% of content remains shared with the default privacy settings. We also find that, overall, privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected. Finally, we explore how our results have potential to assist users in selecting appropriate privacy settings by examining the user-created friend lists. We find that these have significant correlation with the social network, suggesting that information from the social network may be helpful in implementing new tools for managing privacy.


title: Creating and evolving developer documentation: understanding the decisions of open source contributors

abstract: Developer documentation helps developers learn frameworks and libraries. To better understand how documentation in open source projects is created and maintained, we performed a qualitative study in which we interviewed core contributors who wrote developer documentation and developers who read documentation. In addition, we studied the evolution of 19 documents by analyzing more than 1500 document revisions. We identified the decisions that contributors make, the factors influencing these decisions and the consequences for the project. Among many findings, we observed how working on the documentation could improve the code quality and how constant interaction with the projects' community positively impacted the documentation.


A Deep Learning Approach to Persian Plagiarism Detection


title: Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond

abstract: Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in coming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.


title: MODIS NDVI time series clustering under dynamic time warping

abstract: For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.


title: Ligature based Urdu Nastaleeq sentence recognition using gated bidirectional long short term memory

abstract: Bidirectional long short term memory (BLSTM) architecture—a special case of recurrent neural network—is successfully applied for recognition of Urdu Nastaleeq sentence images based on character information. In such cases, manual labeling of characters in sentences for a large dataset is an intensive job, because identical characters observe different shapes at different positions inside ligatures and words. On the other hand, labeling any dataset with ligatures is a relatively easier and more accurate phenomenon. In the current paper, we propose a novel gated BLSTM (GBLSTM) model for recognition of printed Urdu Nastaleeq text based on ligature information. Our proposed model incorporates raw pixel values as features instead of human crafted features, because of the latter being more error prone. The model is trained on un-degraded and tested on unseen artificially degraded versions of Urdu printed text images dataset. The recognition accuracy of the proposed GBLSTM model is 96.71% that is higher than the prevalent Urdu optical character recognition systems.


title: A drone with insect-inspired folding wings

abstract: Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. To further enhance their use, we are investigating the development of a new class of drones, foldable sensorized hubs that can quickly take off from rescuers' hands as soon as they are taken out of a pocket or a backpack. With this aim, this paper presents the development of a foldable wing inspired by insects. The wing can be packaged for transportation or deployed for flight in half a second with a simple action from the user. The wing is manufactured as a thick origami structure with a foldable multi-layer material. The prototype of the foldable wing is experimentally characterized and validated in flight on a mini-drone.


title: End-to-End Learning for Structured Prediction Energy Networks

abstract: Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate structured outputs is given by a deep network, and predictions are formed by gradient-based optimization. This paper presents end-to-end learning for SPENs, where the energy function is discriminatively trained by back-propagating through gradient-based prediction. In our experience, the approach is substantially more accurate than the structured SVM method of Belanger and McCallum (2016), as it allows us to use more sophisticated non-convex energies. We provide a collection of techniques for improving the speed, accuracy, and memory requirements of end-to-end SPENs, and demonstrate the power of our method on 7-Scenes image denoising and CoNLL-2005 semantic role labeling tasks. In both, inexact minimization of non-convex SPEN energies is superior to baseline methods that use simplistic energy functions that can be minimized exactly.


title: Key words: health information; internet PROMISES OF HEALTH INFORMATION ON THE INTERNET

abstract: Disseminating health and medical information on the Internet can improve knowledge transfer from health professionals to the population, and help individuals to maintain and improve their health. There are currently several medical information websites that directly target the general population with the aim of providing information about health problems, self-care and prevention. However, this new technology also hides several shortcomings, such as: (i) uneven quality of medical information available on the Internet; (ii) difficulties in finding, understanding and using this information; (iii) lack of access for the unconnected population; and (iv) the potential for harm and risks of over-consumption. To be able to overcome these dangers, it is important that public health practitioners and health professionals be involved in the design, dissemination and evaluation of Web-based health and medical information.


title: PMSM Drive Current and Voltage Limiting as a Constraint Optimal Control Problem

abstract: This paper proposes an algorithm for optimal current trajectory control of (interior) permanent magnet synchronous motor (PMSM) drives, considering the motor/inverter current and voltage limitations. In contrast to common voltage feedback flux-weakening strategies, it applies a differential rather than a conventional integral approach to regulate the current vector. Linearized motor equations are used to express torque and voltage amplitude changes relative to the actual operating point as a function of $dq$ -current increments. Optimal $dq$ -current step adjustments are calculated from these approximate voltage/torque increment lines according to machine state feedback. This allows for the constraint optimization problem to be solved in real time. Each iteration, the $dq$ -current setpoint vector is incrementally shifted in an optimal direction, rapidly converging to a solution within the voltage/current limits, which minimizes error on torque request with maximum efficiency. Extensive experimental results on an interior PMSM setup demonstrate the strategy’s dynamic performance and robustness.


Effects of different forms of yeast Saccharomyces cerevisiae on growth performance, intestinal development, and systemic immunity in early-weaned piglets


title: A Knowledge-Based Topic Modeling Approach for Automatic Topic Labeling

abstract: Probabilistic topic models, which aim to discover latent topics in text corpora define each document as a multinomial distributions over topics and each topic as a multinomial ::: distributions over words. Although, humans can infer a proper label for each topic by looking at top representative words of the topic but, it is not applicable for machines. Automatic Topic Labeling techniques try to address the problem. The ultimate goal of topic labeling techniques are to assign interpretable labels for the learned topics. In this paper, we are taking concepts of ontology into consideration instead of words alone to improve the quality of generated labels for each topic. Our work is different in comparison with the previous efforts in this area, where topics are usually represented with a batch of selected words from topics. We have highlighted some aspects of our approach including: 1) we have incorporated ontology concepts with statistical topic modeling in a unified framework, where each topic is a multinomial probability distribution over the concepts and each concept is represented as a distribution over words; and 2) a topic labeling model according to the meaning of the concepts of the ontology included in the learned topics. The best topic labels are selected with respect to the semantic similarity of the concepts and their ontological categorizations. We demonstrate the effectiveness of considering ontological concepts as richer aspects between topics and words by comprehensive experiments on two different data sets. In another word, representing topics via ontological concepts shows an effective way for generating descriptive and representative labels for the discovered topics.


title: Navigation in a small world

abstract: It is easier to find short chains between points in some networks than others. The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology1,2 and is a feature of a range of networks arising in nature and technology3,4,5. Experimental study of the phenomenon1 revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed2,3,4, and here I investigate the second by modelling how individuals can find short chains in a large social network.


title: Topic modeling with network regularization

abstract: In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method bridges topic modeling and social network analysis, which leverages the power of both statistical topic models and discrete regularization. The output of this model well summarizes topics in text, maps a topic on the network, and discovers topical communities. With concrete selection of a topic model and a graph-based regularizer, our model can be applied to text mining problems such as author-topic analysis, community discovery, and spatial text mining. Empirical experiments on two different genres of data show that our approach is effective, which improves text-oriented methods as well as network-oriented methods. The proposed model is general; it can be applied to any text collections with a mixture of topics and an associated network structure.


title: Betaxolol hydrochloride ophthalmic suspension 0.25% and timolol gel-forming solution 0.25% and 0.5% in pediatric glaucoma: a randomized clinical trial.

abstract: PURPOSE ::: To describe the safety profile and clinical response on elevated intraocular pressure (IOP) of betaxolol hydrochloride ophthalmic suspension 0.25% (betaxolol) and timolol maleate ophthalmic gel-forming solution (TGFS) (0.25% and 0.5%), in subjects under 6 years of age. ::: ::: ::: METHODS ::: Subjects were randomized to betaxolol 0.25% (twice daily) or TGFS (daily) (0.25% or 0.5%) in this double-masked study. IOPs were obtained at the same time of day (9 AM) at 2 baseline visits and weeks 2, 6, and 12. Mean change from baseline in IOP was the primary efficacy parameter. ::: ::: ::: RESULTS ::: One hundred five subjects were randomized (34 to betaxolol, 35 to TGFS 0.25%, 36 to TGFS 0.5%). Betaxolol, TGFS 0.25%, and TGFS 0.5% produced statistically significant mean reductions in IOP; mean reductions after 12 weeks of treatment were 2.3, 2.9, and 3.7 mm Hg, respectively. In subjects who were not being treated with topical IOP-lowering medication at baseline, mean IOP reductions after 12 weeks of treatment were 3.1, 4.8, and 3.8 mm Hg, respectively. In patients discontinuing 1 or more topical IOP-lowering medications at baseline, mean IOP reductions at Week 12 were 1.8, 1.8, and 3.7 mm Hg, respectively. Responder rates (> or =15% reduction from baseline) for betaxolol, TGFS 0.25%, and TGFS 0.5% were 38.2, 45.7, and 47.2%, respectively. Adverse events were predominantly nonserious and did not interrupt patient continuation in the study. ::: ::: ::: CONCLUSIONS ::: Betaxolol ophthalmic suspension 0.25%, TGFS 0.25%, and TGFS 0.5% were well tolerated. Despite low responder rates, all 3 treatments produced statistically significant mean reductions in IOP in pediatric glaucoma subjects.


Lower Sensitivity to Happy and Angry Facial Emotions in Young Adults with Psychiatric Problems


Mechanical Properties of Titanium Nitride Nanocomposites Produced by Chemical Precursor Synthesis Followed by High-P,T Treatment


title: Optical Based Noninvasive Glucose Monitoring Sensor Prototype

abstract: Diabetes mellitus claims millions of lives every year. It affects the body in various ways by leading to many serious illnesses and premature mortality. Heart and kidney diseases, which are caused by diabetes, are increasing at an alarming rate. In this paper, we report a study of a noninvasive measurement technique to determine the glucose levels in the human body. Current existing methods to quantify the glucose level in the blood are predominantly invasive that involve taking the blood samples using finger pricking. In this paper, we report a spectroscopy-based noninvasive glucose monitoring system to measure glucose concentration. Near-infrared transmission spectroscopy is used and in vitro experiments are conducted, as well as in vivo . Our experimental study confirms a correlation between the sensor output voltage and glucose concentration levels. We report a low-cost prototype of spectroscopy-based noninvasive glucose monitoring system that demonstrates promising results in vitro and establishes a relationship between the optical signals and the changing levels of blood–glucose concentration.


End-to-End Learning of Task-Oriented Dialogs


title: Quantum image processing

abstract: This paper presents a number of problems concerning the practical (real) implementation of the techniques known as quantum image processing. The most serious problem is the recovery of the outcomes after the quantum measurement, which will be demonstrated in this work that is equivalent to a noise measurement, and it is not considered in the literature on the subject. It is noteworthy that this is due to several factors: (1) a classical algorithm that uses Dirac's notation and then it is coded in MATLAB does not constitute a quantum algorithm, (2) the literature emphasizes the internal representation of the image but says nothing about the classical-to-quantum and quantum-to-classical interfaces and how these are affected by decoherence, (3) the literature does not mention how to implement in a practical way (at the laboratory) these proposals internal representations, (4) given that quantum image processing works with generic qubits, this requires measurements in all axes of the Bloch sphere, logically, and (5) among others. In return, the technique known as quantum Boolean image processing is mentioned, which works with computational basis states (CBS), exclusively. This methodology allows us to avoid the problem of quantum measurement, which alters the results of the measured except in the case of CBS. Said so far is extended to quantum algorithms outside image processing too.


title: When a Knowledge Base Is Not Enough: Question Answering over Knowledge Bases with External Text Data

abstract: One of the major challenges for automated question answering over Knowledge Bases (KBQA) is translating a natural language question to the Knowledge Base (KB) entities and predicates. Previous systems have used a limited amount of training data to learn a lexicon that is later used for question answering. This approach does not make use of other potentially relevant text data, outside the KB, which could supplement the available information. We introduce a new system, Text2KB, that enriches question answering over a knowledge base by using external text data. Specifically, we revisit different phases in the KBQA process and demonstrate that text resources improve question interpretation, candidate generation and ranking. Building on a state-of-the-art traditional KBQA system, Text2KB utilizes web search results, community question answering and general text document collection data, to detect question topic entities, map question phrases to KB predicates, and to enrich the features of the candidates derived from the KB. Text2KB significantly improves performance over the baseline KBQA method, as measured on a popular WebQuestions dataset. The results and insights developed in this work can guide future efforts on combining textual and structured KB data for question answering.


title: Hubs in space: Popular nearest neighbors in high-dimensional data

abstract: Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dimensionality curse, referred to as hubness, that affects the distribution of k-occurrences: the number of times a point appears among the k nearest neighbors of other points in a data set. Through theoretical and empirical analysis involving synthetic and real data sets we show that under commonly used assumptions this distribution becomes considerably skewed as dimensionality increases, causing the emergence of hubs, that is, points with very high k-occurrences which effectively represent "popular" nearest neighbors. We examine the origins of this phenomenon, showing that it is an inherent property of data distributions in high-dimensional vector space, discuss its interaction with dimensionality reduction, and explore its influence on a wide range of machine-learning tasks directly or indirectly based on measuring distances, belonging to supervised, semi-supervised, and unsupervised learning families.


title: Multiple Instance Learning Convolutional Neural Networks for object recognition

abstract: Convolutional Neural Networks (CNN) have demonstrated its successful applications in computer vision, speech recognition, and natural language processing. For object recognition, CNNs might be limited by its strict label requirement and an implicit assumption that images are supposed to be target-object-dominated for optimal solutions. However, the labeling procedure, necessitating laying out the locations of target objects, is very tedious, making high-quality large-scale dataset prohibitively expensive. Data augmentation schemes are widely used when deep networks suffer the insufficient training data problem. All the images produced through data augmentation share the same label, which may be problematic since not all data augmentation methods are label-preserving. In this paper, we propose a weakly supervised CNN framework named Multiple Instance Learning Convolutional Neural Networks (MILCNN) to solve this problem. We apply MILCNN framework to object recognition and report state-of-the-art performance on three benchmark datasets: CIFAR10, CIFAR100 and ILSVRC2015 classification dataset.


title: A Feature Learning and Object Recognition Framework for Underwater Fish Images

abstract: Live fish recognition is one of the most crucial elements of fisheries survey applications where the vast amount of data is rapidly acquired. Different from general scenarios, challenges to underwater image recognition are posted by poor image quality, uncontrolled objects and environment, and difficulty in acquiring representative samples. In addition, most existing feature extraction techniques are hindered from automation due to involving human supervision. Toward this end, we propose an underwater fish recognition framework that consists of a fully unsupervised feature learning technique and an error-resilient classifier. Object parts are initialized based on saliency and relaxation labeling to match object parts correctly. A non-rigid part model is then learned based on fitness, separation, and discrimination criteria. For the classifier, an unsupervised clustering approach generates a binary class hierarchy, where each node is a classifier. To exploit information from ambiguous images, the notion of partial classification is introduced to assign coarse labels by optimizing the benefit of indecision made by the classifier. Experiments show that the proposed framework achieves high accuracy on both public and self-collected underwater fish images with high uncertainty and class imbalance.


title: Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection

abstract: Paraphrase detection is the task of examining two sentences and determining whether they have the same meaning. In order to obtain high accuracy on this task, thorough syntactic and semantic analysis of the two statements is needed. We introduce a method for paraphrase detection based on recursive autoencoders (RAE). Our unsupervised RAEs are based on a novel unfolding objective and learn feature vectors for phrases in syntactic trees. These features are used to measure the word- and phrase-wise similarity between two sentences. Since sentences may be of arbitrary length, the resulting matrix of similarity measures is of variable size. We introduce a novel dynamic pooling layer which computes a fixed-sized representation from the variable-sized matrices. The pooled representation is then used as input to a classifier. Our method outperforms other state-of-the-art approaches on the challenging MSRP paraphrase corpus.


title: A machine learning approach for tracing regulatory codes to product specific requirements

abstract: Regulatory standards, designed to protect the safety, security, and privacy of the public, govern numerous areas of software intensive systems. Project personnel must therefore demonstrate that an as-built system meets all relevant regulatory codes. Current methods for demonstrating compliance rely either on after-the-fact audits, which can lead to significant refactoring when regulations are not met, or else require analysts to construct and use traceability matrices to demonstrate compliance. Manual tracing can be prohibitively time-consuming; however automated trace retrieval methods are not very effective due to the vocabulary mismatches that often occur between regulatory codes and product level requirements. This paper introduces and evaluates two machine-learning methods, designed to improve the quality of traces generated between regulatory codes and product level requirements. The first approach uses manually created traceability matrices to train a trace classifier, while the second approach uses web-mining techniques to reconstruct the original trace query. The techniques were evaluated against security regulations from the USA government's Health Insurance Privacy and Portability Act (HIPAA) traced against ten healthcare related requirements specifications. Results demonstrated improvements for the subset of HIPAA regulations that exhibited high fan-out behavior across the requirements datasets.


title: Self-monitoring: Appraisal and reappraisal.

abstract: Theory and research on serf-monitoring have accumulated into a sizable literature on the impact of variation in the extent to which people cultivate public appearances in diverse domains of social functioning. Yet self-monitoring and its measure, the Self-Monitoring Scale, are surrounded by controversy generated by conflicting answers to the critical question, Is self-monitoring a unitary phenomenon? A primary source of answers to this question has been largely negleeted---4he Self-Monitoring Scale's relations with external criteria. We propose a quantitative method to examine the self-monitoring literature and thereby address major issues of the controversy. Application of this method reveals that, with important exceptions, a wide range of external criteria tap a dimension directly measured by the Self-Monitoring Scale. We discuss what this appraisal reveals about what self-monitoring is and is not. According to the master actor and stage producer Sir Tyrone Guthrie (1971), good acting involves a talent whose natural medium is life itself, a talent readily observed in everyday social interaction: the ability to convincingly convey internal states through expressive channels--facial expressions, hand gestures, body posture, voice texture, and other paralinguistic cues--in the absence of the internal states. This ability may be drawn on in a wide range of life contexts. Most expressive control may be undertaken "in a good-natured endeavor to lubricate the creaking mechanism of social intercourse" (Guthrie, 1971, p. 7). But, it may also be an essential component in illicit social activities as well, such as lying, concealing one's true intentions, or presenting an inauthentic self. Laboratory studies confirm what Guthrie and others in his profession have known for ages: Individuals vary widely in expressive control (Riggio & Friedman, 1982; Siegman & Reynolds, 1983). This variation raises a number of questions: Why are some people better than others at expressive control? What environmental histories and genetic predispositions foster or inhibit the development of expressive control? What features of personality covary with expressive control, and how are these associations to be understood? Toward what ends is expressive control actually used,


title: Smarter than Genius? Human Evaluation of Music Recommender Systems

abstract: Genius is a popular commercial music recommender system that is based on collaborative filtering of huge amounts of user data. To understand the aspects of music similarity that collaborative filtering can capture, we compare Genius to two canonical music recommender systems: one based purely on artist similarity, the other purely on similarity of acoustic content. We evaluate this comparison with a user study of 185 subjects. Overall, Genius produces the best recommendations. We demonstrate that collaborative filtering can actually capture similarities between the acoustic content of songs. However, when evaluators can see the names of the recommended songs and artists, we find that artist similarity can account for the performance of Genius. A system that combines these musical cues could generate music recommendations that are as good as Genius, even when collaborative filtering data is unavailable.


title: ALE: Additive Latent Effect Models for Grade Prediction

abstract: The past decade has seen a growth in the development and deployment of educational technologies for assisting college-going students in choosing majors, selecting courses and acquiring feedback based on past academic performance. Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e.g., next term). Accurate and timely prediction of students' academic grades is important for developing effective degree planners and early warning systems, and ultimately improving educational outcomes. Existing grade pre- diction methods mostly focus on modeling the knowledge components associated with each course and student, and often overlook other factors such as the difficulty of each knowledge component, course instructors, student interest, capabilities and effort. In this paper, we propose additive latent effect models that incorporate these factors to predict the student next-term grades. Specifically, the proposed models take into account four factors: (i) student's academic level, (ii) course instructors, (iii) student global latent factor, and (iv) latent knowledge factors. We compared the new models with several state-of-the-art methods on students of various characteristics (e.g., whether a student transferred in or not). The experimental results demonstrate that the proposed methods significantly outperform the baselines on grade prediction problem. Moreover, we perform a thorough analysis on the importance of different factors and how these factors can practically assist students in course selection, and finally improve their academic performance.


title: Ultrasound assessment of fascial connectivity in the lower limb during maximal cervical flexion: technical aspects and practical application of automatic tracking

abstract: Background ::: The fascia provides and transmits forces for connective tissues, thereby regulating human posture and movement. One way to assess the myofascial interaction is a fascia ultrasound recording. Ultrasound can follow fascial displacement either manually or automatically through two-dimensional (2D) method. One possible method is the iterated Lucas-Kanade Pyramid (LKP) algorithm, which is based on automatic pixel tracking during passive movements in 2D fascial displacement assessments. Until now, the accumulated error over time has not been considered, even though it could be crucial for detecting fascial displacement in low amplitude movements. ::: ::: The aim of this study was to assess displacement of the medial gastrocnemius fascia during cervical spine flexion in a kyphotic posture with the knees extended and ankles at 90°.


title: Fine-Grained Recognition in the Wild: A Multi-task Domain Adaptation Approach

abstract: While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild. These fully supervised models need additional annotated images to classify objects in every new scenario, a task that is infeasible. However, sources such as e-commerce websites and field guides provide annotated images for many classes. In this work, we study fine-grained domain adaptation as a step towards overcoming the dataset shift between easily acquired annotated images and the real world. Adaptation has not been studied in the fine-grained setting where annotations such as attributes could be used to increase performance. Our work uses an attribute based multi-task adaptation loss to increase accuracy from a baseline of 4.1% to 19.1% in the semi-supervised adaptation case. Prior do- main adaptation works have been benchmarked on small datasets such as [46] with a total of 795 images for some domains, or simplistic datasets such as [41] consisting of digits. We perform experiments on a subset of a new challenging fine-grained dataset consisting of 1,095,021 images of 2, 657 car categories drawn from e-commerce web- sites and Google Street View.


title: Power Budgets for CubeSat Radios to Support Ground Communications and Inter-Satellite Links

abstract: CubeSats are a class of pico-satellites that have emerged over the past decade as a cost-effective alternative to the traditional large satellites to provide space experimentation capabilities to universities and other types of small enterprises, which otherwise would be unable to carry them out due to cost constraints. An important consideration when planning CubeSat missions is the power budget required by the radio communication subsystem, which enables a CubeSat to exchange information with ground stations and/or other CubeSats in orbit. The power that a CubeSat can dedicate to the communication subsystem is limited by the hard constraints on the total power available, which are due to its small size and light weight that limit the dimensions of the CubeSat power supply elements (batteries and solar panels). To date, no formal studies of the communications power budget for CubeSats are available in the literature, and this paper presents a detailed power budget analysis that includes communications with ground stations as well as with other CubeSats. For ground station communications, we outline how the orbital parameters of the CubeSat trajectory determine the distance of the ground station link and present power budgets for both uplink and downlink that include achievable data rates and link margins. For inter-satellite communications, we study how the slant range determines power requirements and affects the achievable data rates and link margins.


title: Gaze gestures or dwell-based interaction?

abstract: The two cardinal problems recognized with gaze-based interaction techniques are: how to avoid unintentional commands, and how to overcome the limited accuracy of eye tracking. Gaze gestures are a relatively new technique for giving commands, which has the potential to overcome these problems. We present a study that compares gaze gestures with dwell selection as an interaction technique. The study involved 12 participants and was performed in the context of using an actual application. The participants gave commands to a 3D immersive game using gaze gestures and dwell icons. We found that gaze gestures are not only a feasible means of issuing commands in the course of game play, but they also exhibited performance that was at least as good as or better than dwell selections. The gesture condition produced less than half of the errors when compared with the dwell condition. The study shows that gestures provide a robust alternative to dwell-based interaction with the reliance on positional accuracy being substantially reduced.


title: Proactive Work Behavior: Forward-Thinking and Change-Oriented Action in Organizations

abstract: /O Psychology is both a science/practice and an applied/basic research discipline. Appropriately, the APA Handbook of Industrial and Organizational Psychology offers an in-depth examination of the types of behavioral and structural issues that I/O psychologists study every day, from both a theoretical and applied perspective. It explores a natural progression, from how problems are diagnosed to how research is conducted to generate answers to those problems to how interventions are implemented and, finally, to how they are evaluated. It examines what is currently known—including basic historical reviews—and identifies the most pertinent sources of information in both the core and emerging literatures. It pinpoints practical issues, probes unresolved and controversial topics, and looks at future theoretical, research, and practice trends...


title: Adaptive 360-Degree Video Streaming using Scalable Video Coding

abstract: Virtual reality and 360-degree video streaming are growing rapidly, yet, streaming high-quality 360-degree video is still challenging due to high bandwidth requirements. Existing solutions reduce bandwidth consumption by streaming high-quality video only for the user's viewport. However, adding the spatial domain (viewport) to the video adaptation space prevents the existing solutions from buffering future video chunks for a duration longer than the interval that user's viewport is predictable. This makes playback more prone to video freezes due to rebuffering, which severely degrades the user's Quality of Experience especially under challenging network conditions. We propose a new method that alleviates the restrictions on buffer duration by utilizing scalable video coding. Our method significantly reduces the occurrence of rebuffering on links with varying bandwidth without compromising playback quality or bandwidth efficiency compared to the existing solutions. We demonstrate the efficiency of our proposed method using experimental results with real world cellular network bandwidth traces.


title: A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM

abstract: With the rapid development of the peer-to-peer lending industry in China, it has been a crucial task to evaluate the default risk of each loan. Motivated by the research in natural language processing, we make use of the online operation behavior data of borrowers and propose a consumer credit scoring method based on attention mechanism LSTM, which is a novel application of deep learning algorithm. Inspired by the idea of Word2vec, we treat each type of event as a word, construct the Event2vec model to convert each type of event transformation into a vector and, then, use an attention mechanism LSTM network to predict the probability of user default. The method is evaluated on the real dataset, and the results show that the proposed solution can effectively increase the predictive accuracy compared with the traditional artificial feature extraction method and the standard LSTM model.


title: Battery-Management System (BMS) and SOC Development for Electrical Vehicles

abstract: Battery monitoring is vital for most electric vehicles (EVs), because the safety, operation, and even the life of the passenger depends on the battery system. This attribute is exactly the major function of the battery-management system (BMS)-to check and control the status of battery within their specified safe operating conditions. In this paper, a typical BMS block diagram has been proposed using various functional blocks. The state of charge (SOC) estimation has been implemented using Coulomb counting and open-circuit voltage methods, thereby eliminating the limitation of the stand-alone Coulomb counting method. By modeling the battery with SOC as one of the state variables, SOC can be estimated, which is further corrected by the Kalman filtering method. The battery parameters from experimental results are integrated in the model, and simulation results are validated by experiment.


DETERMINATION OF THE BEST GEOSTATISTICAL METHOD FOR CLIMATIC ZONING IN IRAN


title: Identification of Flux Linkage Map of Permanent Magnet Synchronous Machines Under Uncertain Circuit Resistance and Inverter Nonlinearity

abstract: This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq -axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.


title: Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

abstract: We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.


title: Unravelling biology and shifting paradigms in cancer with single-cell sequencing

abstract: The fundamental operative unit of a cancer is the genetically and epigenetically innovative single cell. Whether proliferating or quiescent, in the primary tumour mass or disseminated elsewhere, single cells govern the parameters that dictate all facets of the biology of cancer. Thus, single-cell analyses provide the ultimate level of resolution in our quest for a fundamental understanding of this disease. Historically, this quest has been hampered by technological shortcomings. In this Opinion article, we argue that the rapidly evolving field of single-cell sequencing has unshackled the cancer research community of these shortcomings. From furthering an elemental understanding of intra-tumoural genetic heterogeneity and cancer genome evolution to illuminating the governing principles of disease relapse and metastasis, we posit that single-cell sequencing promises to unravel the biology of all facets of this disease.


title: Verifiable Source Code Documentation in Controlled Natural Language

abstract: Writing documentation about software internals is rarely considered a rewarding activity. It is highly time-consuming and the resulting documentation is fragile when the software is continuously evolving in a multi-developer setting. Unfortunately, traditional programming environments poorly support the writing and maintenance of documentation. Consequences are severe as the lack of documentation on software structure negatively impacts the overall quality of the software product. We show that using a controlled natural language with a reasoner and a query engine is a viable technique for verifying the consistency and accuracy of documentation and source code. Using ACE, a state-of-the-art controlled natural language, we present positive results on the comprehensibility and the general feasibility of creating and verifying documentation. As a case study, we used automatic documentation verification to identify and fix severe flaws in the architecture of a non-trivial piece of software. Moreover, a user experiment shows that our language is faster and easier to learn and understand than other formal languages for software documentation.


title: Variable DC bus control for a bidirectional on-board electric vehicle charger

abstract: In this paper, a variable dc bus control strategy is evaluated for a bidirectional grid connected electric vehicle (EV) charger topology. The EV charger under consideration comprises of an ac-dc 3-φ active rectifier stage followed by a dc-dc dual active bridge (DAB) converter. The voltage of the common dc-link between the ac-dc stage and the dc-dc stage is regulated such that it follows the voltage variations of the battery pack. The proposed control strategy helps to minimize circulating reactive currents in the DAB converter and extends the soft-switching operating range of the same to achieve high system efficiency. An optimal design approach is presented to select the DAB converter parameters that can be used in conjunction with the variable dc bus control strategy. Experimental results on a 3.3 kW EV charger hardware prototype validate the benefits of the variable dc bus control and proposed optimal design methodology.


title: PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture

abstract: A web operating system is an operating system that users can access from any hardware at any location. A peer-to-peer (P2P) grid uses P2P communication for resource management and communication between nodes in a grid and manages resources locally in each cluster, and this provides a proper architecture for a web operating system. Use of semantic technology in web operating systems is an emerging field that improves the management and discovery of resources and services. In this paper, we propose PGSW-OS (P2P grid semantic Web OS), a model based on a P2P grid architecture and semantic technology to improve resource management in a web operating system through resource discovery with the aid of semantic features. Our approach integrates distributed hash tables (DHTs) and semantic overlay networks to enable semantic-based resource management by advertising resources in the DHT based upon their annotations to enable semantic-based resource matchmaking. Our model includes ontologies and virtual organizations. Our technique decreases the computational complexity of searching in a web operating system environment. We perform a simulation study using the Gridsim simulator, and our experiments show that our model provides enhanced utilization of resources, better search expressiveness, scalability, and precision.


title: Gaussian process for nonstationary time series prediction

abstract: In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian process models using a nonstationary covariance function is proposed. Experiments proved the approach effectiveness with an excellent prediction and a good tracking. The conceptual simplicity, and good performance of Gaussian process models should make them very attractive for a wide range of problems.


title: General Belief Revision

abstract: In artificial intelligence, a key question concerns how an agent may rationally revise its beliefs in light of new information. The standard (AGM) approach to belief revision assumes that the underlying logic contains classical propositional logic. This is a significant limitation, since many representation schemes in AI don’t subsume propositional logic. In this article, we consider the question of what the minimal requirements are on a logic, such that the AGM approach to revision may be formulated. We show that AGM-style revision can be obtained even when extremely little is assumed of the underlying language and its semantics; in fact, one requires little more than a language with sentences that are satisfied at models, or possible worlds. The classical AGM postulates are expressed in this framework and a representation result is established between the postulate set and certain preorders on possible worlds. To obtain the representation result, we add a new postulate to the AGM postulates, and we add a constraint to preorders on worlds. Crucially, both of these additions are redundant in the original AGM framework, and so we extend, rather than modify, the AGM approach. As well, iterated revision is addressed and the Darwiche/Pearl postulates are shown to be compatible with our approach. Various examples are given to illustrate the approach, including Horn clause revision, revision in extended logic programs, and belief revision in a very basic logic called literal revision.


title: Ontology-Mediated Query Answering with Data-Tractable Description Logics

abstract: Recent years have seen an increasing interest in ontology-mediated query answering, in which the semantic knowledge provided by an ontology is exploited when querying data. Adding an ontology has several advantages (e.g. simplifying query formulation, integrating data from different sources, providing more complete answers to queries), but it also makes the query answering task more difficult. In this chapter, we give a brief introduction to ontology-mediated query answering using description logic (DL) ontologies. Our focus will be on DLs for which query answering scales polynomially in the size of the data, as these are best suited for applications requiring large amounts of data. We will describe the challenges that arise when evaluating different natural types of queries in the presence of such ontologies, and we will present algorithmic solutions based upon two key concepts, namely, query rewriting and saturation. We conclude the chapter with an overview of recent results and active areas of ongoing research.


Artificial Immune Systems


title: The YouTube Lens: Crowdsourced Personality Impressions and Audiovisual Analysis of Vlogs

abstract: Despite an increasing interest in understanding human perception in social media through the automatic analysis of users' personality, existing attempts have explored user profiles and text blog data only. We approach the study of personality impressions in social media from the novel perspective of crowdsourced impressions, social attention, and audiovisual behavioral analysis on slices of conversational vlogs extracted from YouTube. Conversational vlogs are a unique case study to understand users in social media, as vloggers implicitly or explicitly share information about themselves that words, either written or spoken cannot convey. In addition, research in vlogs may become a fertile ground for the study of video interactions, as conversational video expands to innovative applications. In this work, we first investigate the feasibility of crowdsourcing personality impressions from vlogging as a way to obtain judgements from a variate audience that consumes social media video. Then, we explore how these personality impressions mediate the online video watching experience and relate to measures of attention in YouTube. Finally, we investigate on the use of automatic nonverbal cues as a suitable lens through which impressions are made, and we address the task of automatic prediction of vloggers' personality impressions using nonverbal cues and machine learning techniques. Our study, conducted on a dataset of 442 YouTube vlogs and 2210 annotations collected in Amazon's Mechanical Turk, provides new findings regarding the suitability of collecting personality impressions from crowdsourcing, the types of personality impressions that emerge through vlogging, their association with social attention, and the level of utilization of nonverbal cues in this particular setting. In addition, it constitutes a first attempt to address the task of automatic vlogger personality impression prediction using nonverbal cues, with promising results.


title: A Model for Learned Bloom Filters and Related Structures

abstract: Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to model the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, clarifying what guarantees can and cannot be associated with such a structure.


Self-assessed Level of Confidence of Public Health Nurses in Baguio City, Philippines on Global Competencies


title: Penile lengthening and widening without grafting according to a modified ‘sliding’ technique

abstract: Objective ::: To present the feasibility and safety of penile length and girth restoration based on a modified ‘sliding’ technique for patients with severe erectile dysfunction (ED) and significant penile shortening, with or without Peyronie's disease (PD). ::: ::: Patients and Methods ::: Between January 2013 and January 2014, 143 patients underwent our modified ‘sliding’ technique for penile length and girth restoration and concomitant penile prosthesis implantation. It is based on three key elements: (i) the sliding manoeuvre for penile length restoration; (ii) potential complementary longitudinal ventral and/or dorsal tunical incisions for girth restoration; and (iii) closure of the newly created rectangular bow-shaped tunical defects with Buck's fascia only. ::: ::: Results ::: In all, 143 patients underwent the procedure. The causes of penile shortening and narrowing were: PD in 53.8%; severe ED with unsuccessful intracavernosal injection therapy in 21%; post-radical prostatectomy 14.7%; androgen-deprivation therapy, with or without brachytherapy or external radiotherapy, for prostate cancer in 7%; post-penile fracture in 2.1%; post-redo-hypospadias repair in 0.7%; and post-priapism in 0.7%. In patients with ED and PD, the mean (range) deviation of the penile axis was 45 (0‒100)°. The mean (range) subjective penile shortening reported by patients was 3.4 (1‒7) cm and shaft constriction was present in 53.8%. Malleable penile prostheses were used in 133 patients and inflatable penile prostheses were inserted in 10 patients. The median (range) follow-up was 9.7 (6‒18) months. The mean (range) penile length gain was 3.1 (2‒7) cm. No penile prosthesis infection caused device explantation. The average International Index of Erectile Function (IIEF) score increased from 24 points at baseline to 60 points at the 6-month follow-up. ::: ::: Conclusion ::: Penile length and girth restoration based on our modified sliding technique is a safe and effective procedure. The elimination of grafting saves operative time and, consequently, decreases the infection risk and costs associated with surgery.


title: Teachers’ Use of Technology and Constructivism

abstract: Technology has changed the way we teach and the way we learn. Many learning theories can be used to apply and integrate this technology more effectively. There is a close relationship between technology and constructivism, the implementation of each one benefiting the other. Constructivism states that learning takes place in contexts, while technology refers to the designs and environments that engage learners. Recent efforts to integrate technology in the classroom have been within the context of a constructivist framework. The purpose of this paper is to examine the definition of constructivism, incorporating technology into the classroom, successful technology integration into the classroom, factors contributing to teachers’ use of technology, role of technology in a constructivist classroom, teacher’s use of learning theories to enable more effective use of technology, learning with technology: constructivist perspective, and constructivism as a framework for educational technology. This paper explains whether technology by itself can make the education process more effective or if technology needs an appropriate instructional theory to indicate its positive effect on the learner. Index Terms Technology, Definition, Role, Constructivism, Benefits, Factors, Learning Theory


title: Magic finger: always-available input through finger instrumentation

abstract: We present Magic Finger, a small device worn on the fingertip, which supports always-available input. Magic Finger inverts the typical relationship between the finger and an interactive surface: with Magic Finger, we instrument the user's finger itself, rather than the surface it is touching. Magic Finger senses touch through an optical mouse sensor, enabling any surface to act as a touch screen. Magic Finger also senses texture through a micro RGB camera, allowing contextual actions to be carried out based on the particular surface being touched. A technical evaluation shows that Magic Finger can accurately sense 22 textures with an accuracy of 98.9%. We explore the interaction design space enabled by Magic Finger, and implement a number of novel interaction techniques that leverage its unique capabilities.


title: 3D Magnetic Field Sensor Concept for Use in Inertial Measurement Units (IMUs)

abstract: We report on the design, fabrication, and characterization of a microfabricated 3D magnetic field sensor that is suitable for co-integration with inertial sensors to form single-chip inertial measurement units. In contrast to classical resonant MEMS magnetometers, which are based on Lorentz force measurement, our sensor uses permanent magnetic materials and piezoresistive detection with silicon strain gauges of nanometric section, leading to low power consumption and high sensitivity for small sensor size. Thin multilayers of CoFe and PtMn as ferro- and antiferromagnetic materials are integrated within the MEMS fabrication process. Sensitivities of 1.09 V/T for x- and y- components of the magnetic field and 0.124 V/T for z- component of the magnetic field were measured, respectively. To be sensitive to magnetic fields along all three spatial directions, two permanent magnetization directions on the same die are required. Implementation of the two magnetization directions was validated by a measured correlation of 99.7% between x- and y- sensitivity axes. Power consumption of the 3D sensor is for polarization with a 100 μA dc current. With resolutions of 100 nT/√Hz for x- and y-component of the magnetic field and 350 nT/√Hz for z- component, the sensor is suitable for precise measurement of earth magnetic field.