title: Invertible Autoencoder for domain adaptation

abstract: The unsupervised image-to-image translation aims at finding a mapping between the source ( A ) and target ( B ) image domains, where in many applications aligned image pairs are not available at training. This is an ill-posed learning problem since it requires inferring the joint probability distribution from marginals. Joint learning of coupled mappings F A B : A → B and F B A : B → A is commonly used by the state-of-the-art methods, like CycleGAN to learn this translation by introducing cycle consistency requirement to the learning problem, i.e., F A B ( F B A ( B ) ) ≈ B and F B A ( F A B ( A ) ) ≈ A . Cycle consistency enforces the preservation of the mutual information between input and translated images. However, it does not explicitly enforce F B A to be an inverse operation to F A B . We propose a new deep architecture that we call invertible autoencoder (InvAuto) to explicitly enforce this relation. This is done by forcing an encoder to be an inverted version of the decoder, where corresponding layers perform opposite mappings and share parameters. The mappings are constrained to be orthonormal. The resulting architecture leads to the reduction of the number of trainable parameters (up to 2 times). We present image translation results on benchmark datasets and demonstrate state-of-the art performance of our approach. Finally, we test the proposed domain adaptation method on the task of road video conversion. We demonstrate that the videos converted with InvAuto have high quality and show that the NVIDIA neural-network-based end-to-end learning system for autonomous driving, known as PilotNet, trained on real road videos performs well when tested on the converted ones.


title: Clinical significance of hWAPL polymorphisms in the risk of cervical carcinogenesis

abstract: To investigate the clinical significance of human wings apart-like (hWAPL) genetic polymorphisms in cervical carcinogenesis. hWAPL polymorphisms and human papillomavirus (HPV) types were examined in 175 cervical smears of exfoliated cervical cell samples using a real-time polymerase chain reaction system. A significant difference was detected in the frequency of the CC genotype between the HPV(+) low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) groups [Odds ratio 0.21, 95% confidence interval (CI) 0.0723–0.61; P = 0.0029]. A significant difference was noted in the frequency of the CC genotype between the high-risk HPV-positive LSIL and HSIL groups (odds ratio 0.2955, 95% CI 0.0893–0.9771; P = 0.0414). The CC genotype of hWAPL gene promoter polymorphism may be associated with cervical carcinogenesis.


title: Revealing patterns of Twitter emoji usage in Barcelona and Madrid

abstract: Comunicacio presentada a: 19th International Conference of the Catalan Association for Artificial Intelligence, Barcelona, Catalonia, Spain, October 19–21, 2016


title: Predicting player churn in the wild

abstract: Free-to-Play or “freemium” games represent a fundamental shift in the business models of the game industry, facilitated by the increasing use of online distribution platforms and the introduction of increasingly powerful mobile platforms. The ability of a game development company to analyze and derive insights from behavioral telemetry is crucial to the success of these games which rely on in-game purchases and in-game advertising to generate revenue, and for the company to remain competitive in a global marketplace. The ability to model, understand and predict future player behavior has a crucial value, allowing developers to obtain data-driven insights to inform design, development and marketing strategies. One of the key challenges is modeling and predicting player churn. This paper presents the first cross-game study of churn prediction in Free-to-Play games. Churn in games is discussed and thoroughly defined as a formal problem, aligning with industry standards. Furthermore, a range of features which are generic to games are defined and evaluated for their usefulness in predicting player churn, e.g. playtime, session length and session intervals. Using these behavioral features, combined with the individual retention model for each game in the dataset used, we develop a broadly applicable churn prediction model, which does not rely on game-design specific features. The presented classifiers are applied on a dataset covering five free-to-play games resulting in high accuracy churn prediction.


title: The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

abstract: This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.


title: A computationally efficient limited memory CMA-ES for large scale optimization

abstract: We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimization of non-linear, non-convex optimization problems in continuous domain. Inspired by the limited memory BFGS method of Liu and Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a covariance matrix reproduced from m direction vectors selected during the optimization process. The decomposition of the covariance matrix into Cholesky factors allows to reduce the time and memory complexity of the sampling to O(mn), where $n$ is the number of decision variables. When $n$ is large (e.g., n > 1000), even relatively small values of $m$ (e.g., m=20,30) are sufficient to efficiently solve fully non-separable problems and to reduce the overall run-time.


title: Modeling systems with internal state using evolino

abstract: Existing Recurrent Neural Networks (RNNs) are limited in their ability to model dynamical systems with nonlinearities and hidden internal states. Here we use our general framework for sequence learning, EVOlution of recurrent systems with LINear Outputs (Evolino), to discover good RNN hidden node weights through evolution, while using linear regression to compute an optimal linear mapping from hidden state to output. Using the Long Short-Term Memory RNN Architecture, Evolino outperforms previous state-of-the-art methods on several tasks: 1) context-sensitive languages, 2) multiple superimposed sine waves.


title: TAXONOMY DEVELOPMENT IN INFORMATION SYSTEMS: DEVELOPING A TAXONOMY OF MOBILE APPLICATIONS

abstract: The complexity of the information systems field often lends itself to classification schemes, or taxonomies, which provide ways to understand the similarities and differences among objects under study. Developing a taxonomy, however, is a complex process that is often done in an ad hoc way. This research-in-progress paper uses the design science paradigm to develop a systematic method for taxonomy development in information systems. The method we propose uses an indicator or operational level model that combines both empirical to deductive and deductive to empirical approaches. We evaluate this method by using it to develop a taxonomy of mobile applications, which we have chosen because of their ever-increasing number and variety. The resulting taxonomy contains seven dimensions with fifteen characteristics. We demonstrate the usefulness of this taxonomy by analyzing a range of current and proposed mobile applications. From the results of this analysis we identify combinations of characteristics where applications are missing and thus are candidates for new and potentially useful applications.


title: A Spectral-particle hybrid method for rendering falling snow

abstract: Falling snow has the visual property that it is simultaneously a set of discrete moving particles as well as a dynamic texture. To capture the dynamic texture properties of falling snow using particle systems can, however, require so many particles that it severely impacts rendering rates. Here we address this limitation by rendering the texture properties directly. We use a standard particle system to generate a relatively sparse set of falling snow flakes, and we then composite in a dynamic texture to fill in between the particles. The texture is generated using a novel image-based spectral synthesis method. The spectrum of the falling snow texture is defined by a dispersion relation in the image plane, derived from linear perspective. The dispersion relation relates image speed, image size, and particle depth. In the frequency domain, it relates the wavelength and speed of moving 2D image sinusoids. The parameters of this spectral snow can be varied both across the image and over time. This provides the flexibility to match the direction and speed parameters of the spectral snow to those of the falling particles. Camera motion can also be matched. Our method produces visually pleasing results at interactive rendering rates. We demonstrate our approach by adding snow effects to static and dynamic scenes. An extension for creating rain effects is also presented.


title: Comparison of the C4.5 and a Naive Bayes Classifier for the Prediction of Lung Cancer Survivability

abstract: Numerous data mining techniques have been developed to extract information and identify patterns and predict trends from large data sets. In this study, two classification techniques, the J48 implementation of the C4.5 algorithm and a Naive Bayes classifier are applied to predict lung cancer survivability from an extensive data set with fifteen years of patient records. The purpose of the project is to verify the predictive effectiveness of the two techniques on real, historical data. Besides the performance outcome that renders J48 marginally better than the Naive Bayes technique, there is a detailed description of the data and the required pre-processing activities. The performance results confirm expectations while some of the issues that appeared during experimentation, underscore the value of having domain-specific understanding to leverage any domain-specific characteristics inherent in the data.


title: Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets

abstract: This work investigates how using reduced precision data in Convolutional Neural Networks (CNNs) affects network accuracy during classification. More specifically, this study considers networks where each layer may use different precision data. Our key result is the observation that the tolerance of CNNs to reduced precision data not only varies across networks, a well established observation, but also within networks. Tuning precision per layer is appealing as it could enable energy and performance improvements. In this paper we study how error tolerance across layers varies and propose a method for finding a low precision configuration for a network while maintaining high accuracy. A diverse set of CNNs is analyzed showing that compared to a conventional implementation using a 32-bit floating-point representation for all layers, and with less than 1% loss in relative accuracy, the data footprint required by these networks can be reduced by an average of 74% and up to 92%.


title: Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

abstract: Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.


Point-Wise Approach for Yandex Personalized Web Search Challenge


title: Bayesian Neural Networks for Genetic Association Studies of Complex Disease

abstract: Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. Using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting genetic interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships while having the computational efficiency needed to handle large datasets.


title: Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

abstract: Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.


title: A Novel Hybrid Spiking Neuron: Bifurcations, Responses, and On-Chip Learning

abstract: We present a novel hybrid spiking neuron that is a wired system of shift registers and behaves like an analog spiking neuron model. The presented neuron exhibits various bifurcation phenomena and response characteristics to an input spike train. We derive continuous discrete hybrid maps that can describe the neuron dynamics analytically. By using these maps, the typical mechanisms of bifurcations and responses are clarified. We also present a novel field-programmable gate-array-friendly online learning algorithm for the neuron. It is shown that the algorithm enables the neuron to reconstruct the response characteristics of another neuron with unknown parameter values. Typical learning functions are also validated by experimental measurements.


title: THE PHILOSOPHY OF THE SUFFICIENCY ECONOMY: A CONTRIBUTION TO THE THEORY OF DEVELOPMENT

abstract: The King of Thailand’s philosophy of sufficiency economy highlights a balanced way of living. Three principles—moderation, reasonableness, and self-immunity—along with the conditions of morality and knowledge can be applied to any level of the society—from an individual to a country. This article proposes that the philosophy of sufficiency economy conveys new theory in addressing current development challenges, which are issues of institutions, human capital, environmental sustainability and the role of government. The philosophy of sufficiency economy, as a new paradigm of development, aims at improving human well-being as a development goal. in the management of MPAs.


Could The 1-MSB Input Difference Be The Fastest Collision Attack For MD 5 ?


title: An efficient HRTF model for 3-D sound

abstract: A simple model is presented for synthesizing binaural sound from a monaural source. The model produces vertical as well as horizontal and externalization effects. The simplicity of the model permits efficient implementation, allowing for real-time multisource operation. Additionally, the parameters in the model can be adjusted to fit a particular individual's characteristics.


title: SAR ATR performance using a conditionally Gaussian model

abstract: A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program.


title: A novel approach for pre-processing of face detection system based on HSV color space and IWPT

abstract: Face detection system is challenging area of research in the field of security surveillance. Preprocessing of facial image data is very important part of face detection system. Now days various method of facial image data preprocessing are available, but all these method are not up to mark. Now we have a novel approach for preprocessing of face detection system based on HSV color space and integer wavelet packet transform method. And finally in this paper we have used LBP (local binary pattern) and SVM classification process are used.


title: MusicSense: contextual music recommendation using emotional allocation modeling

abstract: In this paper, we present a novel contextual music recommendation approach, MusicSense, to automatically suggest music when users read Web documents such as Weblogs. MusicSense matches music to a document's content, in terms of the emotions expressed by both the document and the music songs. To achieve this, we propose a generative model - Emotional Allocation Modeling - in which a collection of word terms is considered as generated with a mixture of emotions. This model also integrates knowledge discovering from a Web-scale corpus and guidance from psychological studies of emotion. Music songs are also described using textual information extracted from their meta-data and relevant Web pages. Thus, both music songs and Web documents can be characterized as distributions over the emotion mixtures through the emotional allocation modeling. For a given document, the songs with the most matched emotion distributions are finally selected as the recommendations. Preliminary experiments on Weblogs show promising results on both emotion allocation and music recommendation.


title: Tunable Optical Time Delay of Quantum Signals Using a Prism Pair

abstract: We describe a compact, tunable, optical time-delay module that functions by means of total internal reflection within two glass prisms. The delay is controlled by small mechanical motions of the prisms. The device is inherently extremely broad band, unlike time delay modules based on “slow light” methods. In the prototype device that we fabricated, we obtain time delays as large as 1.45 ns in a device of linear dimensions of the order of 3.6 cm. We have delayed pulses with a full width at half-maximum pulse duration of 25 fs, implying a delay bandwidth product (measured in delay time divided by the FWHM pulse width) of 5.8x104. We also show that the dispersion properties of this device are sufficiently small that quantum features of a light pulse are preserved upon delay.


title: Malayalam online handwriting recognition system: A simplified fuzzy ARTMAP approach

abstract: On-line handwriting recognition is the automatic conversion of handwritten text into letter codes as it is written on a special digitizer or PDA which is usable within computer and text-processing applications. Some of the commonly used techniques for online handwritten characters include Hidden Markov Model (HMM), elastic matching, structural analysis, Time delay neural networks, and combination of multiple classifiers. This paper describes Malayalam online handwriting recognition system using SFAM artificial neural network technique. The structural and directional information is extracted/collected from each character/stroke. The extracted features, which form the feature vector, are passed as input to a Simplified Fuzzy ARTMAP (SFAM) artificial neural network (ANN) classifier. The SFAM classifier compares the input data with the trained data and finds the nearest prototype from the database that ‘resonates’ with the input pattern. The labels or recognized characters are assigned their corresponding Unicode code points and displayed using appropriate fonts. Using this technique we have obtained an average accuracy of 98.26%.


title: Designing sound tools and toys for blind and visually impaired children

abstract: In this paper, we highlight the issue of integrating the audio and tactile sensory experience into the design process of toys and computer games. Our approach is to address and incorporate design issues for the sensory impaired at the beginning of the design process, to design for their abilities rather than compensating for their disabilities. The children are engaged as co-designers. The scenarios outlined in this paper are an example of inclusive design.


title: Health Recommender Systems: Concepts, Requirements, Technical Basics and Challenges

abstract: During the last decades huge amounts of data have been collected in clinical databases representing patients' health states (e.g., as laboratory results, treatment plans, medical reports). Hence, digital information available for patient-oriented decision making has increased drastically but is often scattered across different sites. As as solution, personal health record systems (PHRS) are meant to centralize an individual's health data and to allow access for the owner as well as for authorized health professionals. Yet, expert-oriented language, complex interrelations of medical facts and information overload in general pose major obstacles for patients to understand their own record and to draw adequate conclusions. In this context, recommender systems may supply patients with additional laymen-friendly information helping to better comprehend their health status as represented by their record. However, such systems must be adapted to cope with the specific requirements in the health domain in order to deliver highly relevant information for patients. They are referred to as health recommender systems (HRS). In this article we give an introduction to health recommender systems and explain why they are a useful enhancement to PHR solutions. Basic concepts and scenarios are discussed and a first implementation is presented. In addition, we outline an evaluation approach for such a system, which is supported by medical experts. The construction of a test collection for case-related recommendations is described. Finally, challenges and open issues are discussed.


title: ASSET MANAGEMENT: REMEDY FOR ADDRESSING THE FISCAL CHALLENGES FACING HIGHWAY INFRASTRUCTURE

abstract: Abstract In recent years, increasing claims have been advanced for asset management as a promising new approach to infrastructure management. If these claims are seen in the light of past management fads, such as zero-based-budgeting (ZBB), management-by-objectives (MBO), total quality management (TQM), and business process reengineering (BPR), asset management may be considered the latest in a long line of management fads being marketed by consultants to transportation agency managers. However, asset management can be an effective response to the fiscal challenges confronting the United States' highway infrastructure. This paper explores asset management in light of recent developments in the funding, condition, documentation, and management of the US highway infrastructure. The paper begins with a discussion of capital biases associated with traditional US federal highway funding programs. It then describes the advent of innovative financing approaches that have evolved in response to the inability of the Federal Highway Trust Fund to meet burgeoning highway renewal and replacement needs, caused in part by widespread deferred maintenance of the highway system. The paper suggests that public sources of funding for the highway system will not be adequate to renovate or replace current highways and build new capacity. Closing the expected shortfall in public highway funding will require sustained infusions of private sector funding. However, the financing of highways through public–private partnerships will require state and local transportation agencies to radically change the ways in which the highway infrastructure is managed. Within the context of gradual changes in highway management and financing, the paper suggests a critical role for asset management – in demonstrating prudent stewardship of highway infrastructure and facilitating private sector confidence in highway investments. The paper examines the implications for innovative highway financing resulting from recent developments in asset management: • further devolution of highway program and funding responsibilities to state and local levels of government; • developments in asset management processes and practices; and • the infrastructure reporting requirements recently developed by the Governmental Accounting Standards Board (GASB). The infrastructure reporting requirements of GASB's Statement No. 34 are intended to increase accountability for publicly owned infrastructure and promote improved management of long-lasting capital assets. GASB 34 also provides a basis for enabling public agencies to finance implementation of asset management techniques and renewal of infrastructure assets through securitization. Covenants associated with securitized highway bonds would provide the fiscal discipline needed to ensure that responsible agencies abide by the terms of the bond agreements – thereby assuring preventive asset maintenance and freeing up resources for debt service payments. Shadow tolling provides a useful mechanism for generating a positive revenue stream to support securitized highway bonds. The paper concludes that asset management is a proven and essential process for helping the US rebuild and expand its highway infrastructure.


title: EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening

abstract: BACKGROUND ::: Annual retinal screening of patients with diabetes is the standard clinical practice to prevent visual impairment and blindness from diabetic retinopathy. Telemedicine-based diabetic retinopathy screening (DRS) in primary care settings can effectively detect sight-threatening retinopathy and significantly increase compliance with annual retinal exams. EyePACS is a license-free Web-based DRS system designed to simplify the process of image capture, transmission, and review. The system provides a flexible platform for collaboration among clinicians about diabetic retinopathy. ::: ::: ::: METHODS ::: Primary clinic personnel (i.e., nursing, technical, or administrative staff) are trained and certified by the EyePACS program to acquire retinal images from standard digital retinal cameras. Relevant clinical data and eight high-resolution images per patient (two external and six retinal images) are encrypted and transmitted to a secure Internet server, using a standard computer and Web browser. Images are then interpreted by certified EyePACS reviewers or local eye care providers who are certified through the EyePACS Retinopathy Grading System. Reports indicating retinopathy level and referral recommendations are transmitted back to primary care providers through the EyePACS Web site or through interfaces between EyePACS and Health Level 7-compliant electronic medical records or chronic disease registries. ::: ::: ::: RESULTS ::: The pilot phase of the EyePACS DRS program in California (2005-2006) recorded 3562 encounters. Since 2006, EyePACS has been expanded to over 120 primary care sites throughout California and elsewhere recording over 34,000 DRSs. The overall rate of referral is 8.21% for sight-threatening retinopathy and 7.83% for other conditions (e.g., cataract and glaucoma). ::: ::: ::: CONCLUSION ::: The use of license-free Web-based software, standard interfaces, and flexible protocols has allowed primary care providers to adopt retinopathy screening with minimal effort and resources.


title: Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations

abstract: Objective ::: To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women. ::: ::: ::: Materials and Methods ::: We annotated 891 interventional cancer trials from ClinicalTrials.gov based on their eligibility for human immunodeficiency virus (HIV)-positive patients using their eligibility criteria. These annotations were used to develop classifiers based on regular expressions and machine learning (ML). After evaluating classification of cancer trials for eligibility of HIV-positive patients, we sought to evaluate the generalizability of our approach to more general diseases and conditions. We annotated the eligibility criteria for 1570 of the most recent interventional trials from ClinicalTrials.gov for HIV-positive and pregnancy eligibility, and the classifiers were retrained and reevaluated using these data. ::: ::: ::: Results ::: On the cancer-HIV dataset, the baseline regex model, the bag-of-words ML classifier, and the ML classifier with named entity recognition (NER) achieved macro-averaged F2 scores of 0.77, 0.87, and 0.87, respectively; the addition of NER did not result in a significant performance improvement. On the general dataset, ML + NER achieved macro-averaged F2 scores of 0.91 and 0.85 for HIV and pregnancy, respectively. ::: ::: ::: Discussion and Conclusion ::: The eligibility status of specific patient populations, such as persons living with HIV and pregnant women, for clinical trials is of interest to both patients and clinicians. We show that it is feasible to develop a high-performing, automated trial classification system for eligibility status that can be integrated into consumer-facing search engines as well as patient-trial matching systems.


title: What good are character strengths beyond subjective well-being? The contribution of the good character on self-reported health-oriented behavior, physical fitness, and the subjective health status

abstract: Character strengths were tested in relation to subjective (life satisfaction) and physical (self-evaluated health and physical fitness) well-being (440 adults). Health-behaviors and the mediating role of health-behaviors in explaining the relationship between character and well-being were also considered. Emotional, intellectual strengths and strengths of the heart were positively associated with life satisfaction and physical fitness. Emotional strengths correlated positively with the self-evaluation of feeling healthy. All strengths (except for modesty and religiousness) were related to health-behaviors; e.g. healthy eating and watching ones food consumption were associated with self-regulation and intellectual strengths. Health behaviors partially mediated the relation of broader strengths factors on subjective and physical well-being. The largest indirect effects were found for leading an active way of life. The study suggests that there are positive relations between character strengths and subjective but also physical well-being. Furthermore, potential mechanisms, which might account for these relations (i.e. health-behaviors) are assessed.


title: Home Alone Faint Detection Surveillance System Using Thermal Camera

abstract: Fainting is the unwanted event occur amongst the senior citizens, patients or pregnant women. This event can cause physical injuries or even mental problems. In this paper, we proposed a simple faint detection algorithm apply into a thermal imaging camera to monitor the fainting event. In addition, this system is to allow the immediate treatment can be carried out for life saving purposes when there are people fainting. Experimental results on sample images show that the proposed surveillance system achieved high accuracy of 96.15% for poor lighting condition and 86.19% in indoor in detecting the human faint event.


title: Unsupervised learning of hierarchical representations with convolutional deep belief networks

abstract: There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks (DBNs); however, scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network, a hierarchical generative model that scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique that shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images.


title: A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition

abstract: In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation. We describe the temporal evolution of facial landmarks as parametrized trajectories on the Riemannian manifold of positive semidefinite matrices of fixed-rank. Our representation has the advantage to bring naturally a second desirable quantity when comparing shapes – the spatial covariance – in addition to the conventional affine-shape representation. We derive then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the manifold. Specifically, our approach involves three steps: 1) facial landmarks are first mapped into the Riemannian manifold of positive semidefinite matrices of rank 2, to build time-parameterized trajectories; 2) a temporal alignment is performed on the trajectories, providing a geometry-aware (dis-)similarity measure between them; 3) finally, pairwise proximity function SVM (ppfSVM) is used to classify them, incorporating the latter (dis-)similarity measure into the kernel function. We show the effectiveness of the proposed approach on four publicly available benchmarks (CK+, MMI, Oulu-CASIA, and AFEW). The results of the proposed approach are comparable to or better than the state-of-the-art methods when involving only facial landmarks.


title: A Credit Assignment Compiler for Joint Prediction

abstract: Many machine learning applications involve jointly predicting multiple mutually dependent output variables. Learning to search is a family of methods where the complex decision problem is cast into a sequence of decisions via a search space. Although these methods have shown promise both in theory and in practice, implementing them has been burdensomely awkward. In this paper, we show the search space can be defined by an arbitrary imperative program, turning learning to search into a credit assignment compiler. Altogether with the algorithmic improvements for the compiler, we radically reduce the complexity of programming and the running time. We demonstrate the feasibility of our approach on multiple joint prediction tasks. In all cases, we obtain accuracies as high as alternative approaches, at drastically reduced execution and programming time.


title: THE FIVE-FACTOR MODEL OF PERSONALITY AND AGGRESSIVENESS IN PRISONERS AND ATHLETES

abstract: The study examined the type of relationship between the trait of aggressiveness and the dimensions of the Five-Factor Model (FFM) of personality on samples of prisoners (N=106) and athletes (N=109). The relationship between the five factors of personality and six measures of aggressiveness was determined by the Pearson’ s correlation coefficient, whereas multiple regression analysis was used to determine the relations between the five personality factors and the total measure of aggressiveness separately for each sample. In the prisoners it was found that aggressiveness is significantly correlated with agreeableness, conscientiousness and emotional stability, whereas in the athletes a significant correlation was found between aggressiveness and extraversion, agreeableness and emotional stability. Multiple regression analysis showed that all the factors, except agreeableness, were significant predictors of aggressiveness in the prisoners, whereas in the athletes only emotional stability was a significant predictor.


title: A Hierarchical Game Framework for Resource Management in Fog Computing

abstract: Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.


title: RCV1: A New Benchmark Collection for Text Categorization Research

abstract: Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data was produced. Drawing on interviews with Reuters personnel and access to Reuters documentation, we describe the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data. We refer to the original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection's properties, suggesting new directions for research, and providing baseline results for future studies. We make available detailed, per-category experimental results, as well as corrected versions of the category assignments and taxonomy structures, via online appendices.


Outsourcing Information Technology: How Culture and Attitude Affect Client-vendor Relationships


title: Market forecasting using Hidden Markov Models

abstract: Working on the daily closing prices and logreturns, in this paper we deal with the use of Hidden Markov Models (HMMs) to forecast the price of the EUR/USD Futures. The aim of our work is to understand how the HMMs describe different financial time series depending on their structure. Subsequently, we analyse the forecasting methods exposed in the previous literature, putting on evidence their pros and cons.


title: Probabilistic CFG With Latent Annotations

abstract: This paper defines a generative probabilistic model of parse trees, which we call PCFG-LA. This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables. Fine-grained CFG rules are automatically induced from a parsed corpus by training a PCFG-LA model using an EM-algorithm. Because exact parsing with a PCFG-LA is NP-hard, several approximations are described and empirically compared. In experiments using the Penn WSJ corpus, our automatically trained model gave a performance of 86.6% (F1, sentences ≤ 40 words), which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection.


title: A few chirps about twitter

abstract: Web 2.0 has brought about several new applications that have enabled arbitrary subsets of users to communicate with each other on a social basis. Such communication increasingly happens not just on Facebook and MySpace but on several smaller network applications such as Twitter and Dodgeball. We present a detailed characterization of Twitter, an application that allows users to send short messages. We gathered three datasets (covering nearly 100,000 users) including constrained crawls of the Twitter network using two different methodologies, and a sampled collection from the publicly available timeline. We identify distinct classes of Twitter users and their behaviors, geographic growth patterns and current size of the network, and compare crawl results obtained under rate limiting constraints.


title: Performance Evaluation of LoRa Considering Scenario Conditions

abstract: New verticals within the Internet of Things (IoT) paradigm such as smart cities, smart farming, or goods monitoring, among many others, are demanding strong requirements to the Radio Access Network (RAN) in terms of coverage, end-node’s power consumption, and scalability. The technologies employed so far to provide IoT scenarios with connectivity, e.g., wireless sensor network and cellular technologies, are not able to simultaneously cope with these three requirements. Thus, a novel solution known as Low Power - Wide Area Network (LP-WAN) has emerged as a promising alternative to provide with low-cost and low-power-consumption connectivity to end-nodes spread in a wide area. Concretely, the Long-Range Wide Area Network (LoRaWAN) technology is one of the LP-WAN platforms that is receiving greater attention from both the industry and the academia. For that reason, in this work, a comprehensive performance evaluation of LoRaWAN under different environmental conditions is presented. The results are obtained from three real scenarios, namely, urban, suburban, and rural, considering both dynamic and static conditions, hence a discussion about the most proper LoRaWAN physical-layer configuration for each scenario is provided. Besides, a theoretical coverage study is also conducted by the use of a radio planning tool considering topographic maps and a precise propagation model. From the attained results, it can be concluded that it is necessary to evaluate the propagation conditions of the deployment scenario prior to the system implantation in order to reach a compromise between the robustness of the network and the transmission data-rate.


title: Mathematical Programming for Data Mining: Formulations and Challenges

abstract: This article is intended to serve as an overview of a rapidly emerging research and applications area. In addition to providing a general overview, motivating the importance of data mining problems within the area of knowledge discovery in databases, our aim is to list some of the pressing research challenges, and outline opportunities for contributions by the optimization research communities. Towards these goals, we include formulations of the basic categories of data mining methods as optimization problems. We also provide examples of successful mathematical programming approaches to some data mining problems.


title: Pencil-Drawn Chemiresistive Sensor for Free Chlorine in Water

abstract: Free chlorine concentration in drinking water is an important control parameter throughout the distribution network, since both too-low and too-high concentrations can lead to health hazards, with 0.52 ppm being a typically acceptable range. Although colorimetric and electrochemical methods exist for measuring free chlorine, they require either the addition of chemicals during manual sampling or maintenance intensive instrumentation, such as reference electrodes. We have previously demonstrated a simple, reliable, reagent-fee solid-state chemiresistive sensor for continuous monitoring of aqueous free chlorine concentrations based on a functionalized carbon nanotube film. Here, we show that the same sensing principle can be translated to lower cost materialsas simple as a line drawn between two contacts using a free IKEA pencil. While IKEA (HB grade, about 70 graphite) pencils work well enough for quantitative free chlorine sensors, the use of 9B grade pencils (90 graphite) results in higher sensitivity. Bare pencil line sensors show a nonselective response to a wide range of aqueous species; pencil lines functionalized with a redox-active aniline oligomer (phenyl-capped aniline tetramer), however, are highly selective to oxidant species, namely free chlorine, in a demonstrated range of 0.0660 ppm. The resulting sensors are cost-effective, durable, resettable, reusable, and resistant to fouling. In contrast to electrochemical sensors, they do not require the use of a reference electrode. They can be operated continuously online for drinking water quality monitoring.


title: 3d semantic representation of actions from effcient stereo-image-sequence segmentation on GPUs

abstract: A novel real-time framework for model-free stereo-video segmentation and stereo-segment tracking is presented, combining real-time optical flow and stereo with image segmentation running separately on two GPUs. The stereosegment tracking algorithm achieves a frame rate of 23 Hz for regular videos with a frame size of 256 x 320 pixels and nearly real time for stereo videos. The computed stereo segments are used to construct 3D segment graphs, from which main graphs, representing a relevant change in the scene, are extracted, which allow us to represent a movie of e.g. 396 original frames by only 12 graphs, each containing only a small number of nodes, providing a condensed description of the scene while preserving data-intrinsic semantics. Using this method, human activities, e.g., handling of objects, can be encoded in an efficient way. The method has potential applications for manipulation action recognition and learning, and provides a vision-front end for applications in cognitive robotics.


title: ApproxANN: An approximate computing framework for artificial neural network

abstract: Artificial Neural networks (ANNs) are one of the most well-established machine learning techniques and have a wide range of applications, such as Recognition, Mining and Synthesis (RMS). As many of these applications are inherently error-tolerant, in this work, we propose a novel approximate computing framework for ANN, namely ApproxANN. When compared to existing solutions, ApproxANN considers approximation for both computation and memory accesses, thereby achieving more energy savings. To be specific, ApproxANN characterizes the impact of neurons on the output quality in an effective and efficient manner, and judiciously determine how to approximate the computation and memory accesses of certain less critical neurons to achieve the maximum energy efficiency gain under a given quality constraint. Experimental results on various ANN applications with different datasets demonstrate the efficacy of the proposed solution.


title: A Beginner's Guide to Factor Analysis: Focusing on Exploratory Factor Analysis

abstract: The following paper discusses exploratory factor analysis and gives an overview of the statistical technique and how it is used in various research designs and applications. A basic outline of how the technique works and its criteria, including its main assumptions are discussed as well as when it should be used. Mathematical theories are explored to enlighten students on how exploratory factor analysis works, an example of how to run an exploratory factor analysis on SPSS is given, and finally a section on how to write up the results is provided. This will allow readers to develop a better understanding of when to employ factor analysis and how to interpret the tables and graphs in the output.


An Object Oriented Approach to Web-Based Application Design


title: A data-driven model for large wildfire behaviour prediction in Europe

abstract: Abstract The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) in close collaboration with the Member States and neighbour countries. EFFIS is intended as complementary system to national and regional systems in the countries, providing harmonised information required for international collaboration on forest fire prevention and fighting and in cases of trans-boundary fire events. However, one missing component in the system is a wildfire behaviour model able to cover the whole Europe. We propose a new general conceptualisation for wildfire prediction. It relies on an array-based and semantically enhanced (Semantic Array Programming) application of the Dynamic Data Driven Application Systems (DDDAS) concept, so as to predict spread of large fires at European level. The proposed mathematical framework is designed to simulate with an ensemble strategy the wildfire dynamics under given sequences of actions for controlling the fire spread and updated data- driven information. First results on data and software uncertainties associated with the problem have been presented with a real case study in Spain.


title: Notes on Noise Contrastive Estimation and Negative Sampling

abstract: Estimating the parameters of probabilistic models of language such as maxent models and probabilistic neural models is computationally difficult since it involves evaluating partition functions by summing over an entire vocabulary, which may be millions of word types in size. Two closely related strategies---noise contrastive estimation (Mnih and Teh, 2012; Mnih and Kavukcuoglu, 2013; Vaswani et al., 2013) and negative sampling (Mikolov et al., 2012; Goldberg and Levy, 2014)---have emerged as popular solutions to this computational problem, but some confusion remains as to which is more appropriate and when. This document explicates their relationships to each other and to other estimation techniques. The analysis shows that, although they are superficially similar, NCE is a general parameter estimation technique that is asymptotically unbiased, while negative sampling is best understood as a family of binary classification models that are useful for learning word representations but not as a general-purpose estimator.


title: Stock price prediction using linear regression based on sentiment analysis

abstract: Stock price prediction is a difficult task, since it very depending on the demand of the stock, and there is no certain variable that can precisely predict the demand of one stock each day. However, Efficient Market Hypothesis (EMH) said that stock price also depends on new information significantly. One of many information sources is people's opinion in social media. People's opinion about products from certain companies may determine the company's reputation and thus affecting people's decision to buy the stock of the company. When using opinion as primary data, it is necessary to make a suitable analysis of it. A famous example using opinion as data is sentiment analysis. Sentiment analysis is a process to determine emotion/feeling within people opinion about something, in this case products of some companies. There are some researches about sentiment analysis used to predict the stock prices. Bollen on his research concludes that people opinion on social media such as Twitter can predict DJIA value with 87.6% accuracy. This shows that there is a relation between sentiment analysis and stock prices. Our purpose on this research is to predict the Indonesian stock market using simple sentiment analysis. Naive Bayes and Random Forest algorithm are used to classify tweet to calculate sentiment regarding a company. The results of sentiment analysis are used to predict the company stock price. We use linear regression method to build the prediction model. Our experiment shows that prediction models using previous stock price and hybrid feature as predictor gives the best prediction with 0.9989 and 0.9983 coefficient of determination.


title: Classification of the Aging Lips: A Foundation for an Integrated Approach to Perioral Rejuvenation

abstract: Background ::: Although perioral aging is highly individual with several distinct processes taking part simultaneously, there is scarce systematic information which helps to indicate the right rejuvenation approach among the multitude of proposed procedures. Existing data about perioral aging has not yet been transformed into a consistent therapeutic concept. The intention of this study was to provide a simple, yet reproducible classification and to offer appropriate rejuvenation approaches.


Towards Accurate 3 D Person Detection and Localization from RGB-D in Cluttered Environments


title: EEG non-linear feature extraction using correlation dimension and Hurst exponent.

abstract: In this work, we evaluated the differences between epileptic electroencephalogram (EEG) and interictal EEG by computing some non-linear features. Correlation dimension (CD) and Hurst exponent (H) were calculated for 100 segments of epileptic EEG and 100 segments of interictal EEG. A comparison was made between epileptic EEG and interictal EEG in those non-linear parameters. Results show that the mean values of CD are 2.64 for epileptic EEG and 4.55 for interictal EEG. We also calculated approximate entropy (ApEn) of those EEG signals. The mean values of ApEn are 0.90 for epileptic EEG and 4.55 for interictal EEG. The values of CD and ApEn of epileptic EEG are generally lower than those of interictal EEG, indicating less complexity of EEG signals during seizures. The mean values of Hurst exponent are 0.19 for epileptic EEG and 0.29 for interictal EEG. Hurst exponents for epileptic EEG and interictal EEG are both <0.5. This indicates that both epileptic and interictal EEGs show long-range anticorrelation. The value of Hurst exponent of epileptic EEG signals is lower than that of interictal EEG signals, showing that the degree of anticorrelation of epileptic EEG signals is larger than that of interictal EEG. Hence, the non-linear parameters such as CD and Hurst exponent can help interpret epileptic and interictal EEGs and their neurodynamics.


title: Quo vadis? The challenges of recombinant protein folding and secretion in Pichia pastoris

abstract: The development of Pichia pastoris as a production platform for recombinant proteins has been a remarkable success story over the last three decades. Stable cheap production processes and the good protein secretion abilities were pacemakers of this development. However, limitations of protein folding, glycosylation or secretion have been identified quite early on. With the availability of genome sequences and the development of systems biology characterization in the last 5 years, remarkable success in strain improvement was achieved. Here, we focus on recent developments of characterization and improvement of P. pastoris production strains regarding protein folding, intracellular trafficking, glycosylation and proteolytic degradation.


title: Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications

abstract: Parallel control and management have been proposed as a new mechanism for conducting operations of complex systems, especially those that involved complexity issues of both engineering and social dimensions, such as transportation systems. This paper presents an overview of the background, concepts, basic methods, major issues, and current applications of Parallel transportation Management Systems (PtMS). In essence, parallel control and management is a data-driven approach for modeling, analysis, and decision-making that considers both the engineering and social complexity in its processes. The developments and applications described here clearly indicate that PtMS is effective for use in networked complex traffic systems and is closely related to emerging technologies in cloud computing, social computing, and cyberphysical-social systems. A description of PtMS system architectures, processes, and components, including OTSt, Dyna CAS, aDAPTS, iTOP, and TransWorld is presented and discussed. Finally, the experiments and examples of real-world applications are illustrated and analyzed.


title: An Innovative Hybrid Wind-Solar and Battery-Supercapacitor Microgrid System—Development and Optimization

abstract: This paper presents a methodology for the joint capacity optimization of renewable energy (RE) sources, i.e., wind and solar, and the state-of-the-art hybrid energy storage system (HESS) comprised of battery energy storage (BES) and supercapacitor (SC) storage technology, employed in a grid-connected microgrid (MG). The problem involves multiple fields, i.e., RE, battery technology, SC technology, and control theory, and requires an efficient and precise co-ordination between sub-fields to harness the full benefits, making the problem labyrinthine. The optimization problem is formulated, and it involves a variety of realistic constraints from both hybrid generation and storage, and an objective function is proposed to: 1) minimize the cost; 2) improve the reliability; and 3) curtail greenhouse gases (GHG) emissions. The complex optimization problem is solved innovatively in piecewise fashion to decrease the complexity and computational time. First, sizes of solar photovoltaic (PV) and wind turbine (WT) are determined using an innovative search algorithm, and in the second step, the size of HESS is calculated, finally the optimal solution is determined. A comparison based upon cost, reliability, and GHG emissions is presented which plainly shows the effectiveness of the proposed methodology. The technique is also applied to determine the size of an MG employing PV, WT, and BES operating in grid-connected mode. And a brief cost analysis, reliability assessment, and emission reduction are given for three scenarios: 1) MG with HESS; 2) MG with BES; and 3) MG with conventional generation. It is shown that an MG with HESS is not only economical but also more reliable and has lower GHG emissions.


title: Opinion Word Expansion and Target Extraction through Double Propagation

abstract: Analysis of opinions, known as opinion mining or sentiment analysis, has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this article, we study two important problems, namely, opinion lexicon expansion and opinion target extraction. Opinion targets (targets, for short) are entities and their attributes on which opinions have been expressed. To perform the tasks, we found that there are several syntactic relations that link opinion words and targets. These relations can be identified using a dependency parser and then utilized to expand the initial opinion lexicon and to extract targets. This proposed method is based on bootstrapping. We call it double propagation as it propagates information between opinion words and targets. A key advantage of the proposed method is that it only needs an initial opinion lexicon to start the bootstrapping process. Thus, the method is semi-supervised due to the use of opinion word seeds. In evaluation, we compare the proposed method with several state-of-the-art methods using a standard product review test collection. The results show that our approach outperforms these existing methods significantly.


title: Training Deep Neural Networks on Noisy Labels with Bootstrapping

abstract: Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting. The performance depends critically on the amount of labeled examples, and in current practice the labels are assumed to be unambiguous and accurate. However, this assumption often does not hold; e.g. in recognition, class labels may be missing; in detection, objects in the image may not be localized; and in general, the labeling may be subjective. In this work we propose a generic way to handle noisy and incomplete labeling by augmenting the prediction objective with a notion of consistency. We consider a prediction consistent if the same prediction is made given similar percepts, where the notion of similarity is between deep network features computed from the input data. In experiments we demonstrate that our approach yields substantial robustness to label noise on several datasets. On MNIST handwritten digits, we show that our model is robust to label corruption. On the Toronto Face Database, we show that our model handles well the case of subjective labels in emotion recognition, achieving state-of-the- art results, and can also benefit from unlabeled face images with no modification to our method. On the ILSVRC2014 detection challenge data, we show that our approach extends to very deep networks, high resolution images and structured outputs, and results in improved scalable detection.


Associations Between Materialism, Gratitude, and Well-Being in Children of Overseas Filipino Workers


title: Discriminative k-means for clustering

abstract: We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical results have shown its favorable performance in comparison with several other popular clustering algorithms. However, the inherent relationship between subspace selection and clustering in this framework is not well understood, due to the iterative nature of the algorithm. We show in this paper that this iterative subspace selection and clustering is equivalent to kernel K-means with a specific kernel Gram matrix. This provides significant and new insights into the nature of this subspace selection procedure. Based on this equivalence relationship, we propose the Discriminative K-means (DisKmeans) algorithm for simultaneous LDA subspace selection and clustering, as well as an automatic parameter estimation procedure. We also present the nonlinear extension of DisKmeans using kernels. We show that the learning of the kernel matrix over a convex set of pre-specified kernel matrices can be incorporated into the clustering formulation. The connection between DisKmeans and several other clustering algorithms is also analyzed. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets.


title: AppsPlayground: automatic security analysis of smartphone applications

abstract: Today's smartphone application markets host an ever increasing number of applications. The sheer number of applications makes their review a daunting task. We propose AppsPlayground for Android, a framework that automates the analysis smartphone applications. AppsPlayground integrates multiple components comprising different detection and automatic exploration techniques for this purpose. We evaluated the system using multiple large scale and small scale experiments involving real benign and malicious applications. Our evaluation shows that AppsPlayground is quite effective at automatically detecting privacy leaks and malicious functionality in applications.


title: Base force/torque sensing for position based Cartesian impedance control

abstract: In this paper, a position based impedance controller (i.e. admittance controller) is designed by utilizing measurements of a force/torque sensor, which is mounted at the robot's base. In contrast to conventional force/torque sensing at the end-effector, placing the sensor at the base allows to implement a compliant behavior of the robot not only with respect to forces acting on the end-effector but also with respect to forces acting on the robot's structure. The resulting control problem is first analyzed in detail for the simplified one-degree-of-freedom case in terms of stability and passivity. Then, an extension to the Cartesian admittance control of a robot manipulator is discussed. Furthermore, it is shown how the steady state properties of the underlying position controller can be taken into account in the design of the outer admittance controller. Finally, a simulation study of the Cartesian admittance controller applied to a three-degrees-of-freedom manipulator is presented.


title: A Fixed-frequency hysteretic controlled buck DC-DC converter with improved load regulation

abstract: Hysteretic control is widely adopted in the power management units (PMUs) for modern electronic devices due to their simple and stable control architecture, as well as the fast load transient response. However, the switching frequency of a traditional hysteretic converter is not fixed. It changes with operating condition and component aging, and hence brings in electro-magnetic interference (EMI) problems and multiphase interleaving difficulties. In this work, a hysteretic buck converter with frequency-locking capability is presented, where the switching frequency is adjusted by tuning the hysteretic window. In addition, the current-mode hysteretic control in this work utilizes a simple feedback network, which consists of only passive components and yet provides significant improvement on output voltage regulation. To achieve good reliability at high temperature and high voltage, the proposed buck converter has been fabricated in a 1μm SOI process with a chip area of 6mm ::: 2 ::: . Experimental results measured at 1MHz fixed switching frequency with 12V-to-5V voltage conversion and 0.9A load current shows a peak efficiency of 91%.


title: A 3-Vppd 730-mW Linear Driver IC Using InP HBTs for Advanced Optical Modulations

abstract: This paper presents a 3-Vppd 730-mW linear optical modulator driver IC for advanced modulations suitable for optical communications systems operating at 100-Gb/b/ch and beyond. The driver was designed and fabricated using 0.5-um-emitter InP HBT technology, which yields a peak ft of 290 GHz, a peak fmax of 320 GHz, and BVceo of approximately 4 V. The linear driver has a two-stage lumped configuration comprising a variable-gain amplifier (VGA) and an output cascode amplifier. It provides linear differential output amplitude of up to 3 Vppd and consumes only 730 mW. It has a large bandwidth of 37.8 GHz and a maximum differential gain of 16.2 dB with 6-dB variable gain range. Total harmonic distortion (THD) is as low as 1.2% at 1 GHz and better than 3.8% up to 16 GHz. The driver provides very clear binary and multilevel output and can be applied to optical transmissions of 100-Gb/s/ch and beyond.


title: Mapping of seafloor gravity anomalies by underwater gravity measurement system using autonomous underwater vehicle for exploration of seafloor deposits

abstract: It is known that there are seafloor mineral deposits around Japan islands. Gravity survey is one of powerful methods to obtain density structure in crust, especially for exploration of underground deposits. We have developed an underwater gravity measurement system for exploration below a seafloor using an Autonomous Underwater Vehicle. In 2014 and 2015, mapping of seafloor gravity anomalies by the underwater gravity measurement system for actual seafloor hydrothermal deposits was performed using the Urashima in Izena Caldera, the middle Okinawa Trough, where seafloor deposits were found. We obtained Bouguer gravity anomalies and found two small high gravity areas. We interpreted that high gravity anomalies originate from hydrothermal deposits with high density. The gravity survey by using AUV is useful to estimate buried mineral deposits below seafloor.


title: Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions

abstract: This paper examines the potential effectiveness of the following three precollision system (PCS) algorithms: 1) forward collision warning only; 2) forward collision warning and precrash brake assist; and 3) forward collision warning, precrash brake assist, and autonomous precrash brake. Real-world rear-end crashes were extracted from a nationally representative sample of collisions in the United States. A sample of 1396 collisions, corresponding to 1.1 million crashes, were computationally simulated as if they occurred, with the driver operating a precollision-system-equipped vehicle. A probability-based framework was developed to account for the variable driver reaction to the warning system. As more components were added to the algorithms, greater benefits were realized. The results indicate that the exemplar PCS investigated in this paper could reduce the severity (i.e., ΔV) of the collision between 14% and 34%. The number of moderately to fatally injured drivers who wore their seat belts could have been reduced by 29% to 50%. These collision-mitigating algorithms could have prevented 3.2% to 7.7% of rear-end collisions. This paper shows the dramatic reductions in serious and fatal injuries that a PCS, which is one of the first intelligent vehicle technologies to be deployed in production cars, can bring to highway safety when available throughout the fleet. This paper also presents the framework of an innovative safety benefits methodology that, when adapted to other emerging active safety technologies, can be employed to estimate potential reductions in the frequency and severity of highway crashes.


title: The blister test for interface toughness measurement

abstract: Abstract The blister test for the measurement of interface fracture toughness for a thin film bonded to an elastic substrate has been analyzed. The load applied in order to debond the film is either hydrostatic pressure or a point load. Results for the energy release rate and mixity of modes 1 and 2 for the interface crack are presented for cases where the debond has been initiated and extends several film thicknesses. Analytical results are given in the two extremes of very small film deflections (weak interfaces) and very large film deflections (tough interfaces). Numerical results are presented in the intermediate zone based on the non-linear von Karman equations. Arbitrary and independent values of the elastic constants of the film and the substrate are allowed in the analysis. These basic crack tip quantities, energy release rate and mode mixity, may be used in a proper fracture criterion which incorporates the strong dependence of mode mixity for interface cracks.


title: Cognitive Control Predicts Use of Model-based Reinforcement Learning

abstract: Accounts of decision-making and its neural substrates have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental work suggest that this classic distinction between behaviorally and neurally dissociable systems for habitual and goal-directed or more generally, automatic and controlled choice may arise from two computational strategies for reinforcement learning RL, called model-free and model-based RL, but the cognitive or computational processes by which one system may dominate over the other in the control of behavior is a matter of ongoing investigation. To elucidate this question, we leverage the theoretical framework of cognitive control, demonstrating that individual differences in utilization of goal-related contextual information-in the service of overcoming habitual, stimulus-driven responses-in established cognitive control paradigms predict model-based behavior in a separate, sequential choice task. The behavioral correspondence between cognitive control and model-based RL compellingly suggests that a common set of processes may underpin the two behaviors. In particular, computational mechanisms originally proposed to underlie controlled behavior may be applicable to understanding the interactions between model-based and model-free choice behavior.


title: Beta event-related desynchronization as an index of individual differences in processing human facial expression: further investigations of autistic traits in typically developing adults

abstract: The human mirror neuron system (hMNS) has been associated with various forms of social cognition and affective processing including vicarious experience. It has also been proposed that a faulty hMNS may underlie some of the deficits seen in the autism spectrum disorders (ASDs). In the present study we set out to investigate whether emotional facial expressions could modulate a putative EEG index of hMNS activation (mu suppression) and if so, would this differ according to the individual level of autistic traits [high versus low Autism Spectrum Quotient (AQ) score]. Participants were presented with 3 s films of actors opening and closing their hands (classic hMNS mu-suppression protocol) while simultaneously wearing happy, angry, or neutral expressions. Mu-suppression was measured in the alpha and low beta bands. The low AQ group displayed greater low beta event-related desynchronization (ERD) to both angry and neutral expressions. The high AQ group displayed greater low beta ERD to angry than to happy expressions. There was also significantly more low beta ERD to happy faces for the low than for the high AQ group. In conclusion, an interesting interaction between AQ group and emotional expression revealed that hMNS activation can be modulated by emotional facial expressions and that this is differentiated according to individual differences in the level of autistic traits. The EEG index of hMNS activation (mu suppression) seems to be a sensitive measure of the variability in facial processing in typically developing individuals with high and low self-reported traits of autism.


title: Vacuum cleaner use in autoerotic death.

abstract: A case of a fatal cardiac episode resulting from an unusual autoerotic practice involving the use of a vacuum cleaner, is presented. Scene investigation and autopsy findings are discussed. Language: en


title: Key Factors in the Use and Frequency of Use of Microcomputers by College Students.

abstract: There has been a steady increase in the use of microcomputers on college campuses in recent years, and as a result, more interest in studying microcomputer use. The purpose of this research is to examine some of the factors that predict which undergraduates use microcomputers and how much they use them. To this end, the Student Affairs Research and Evaluation Office at the University of Massachusetts at Amherst conducted a telephone survey of a random sample of 308 undergraduates. The results show that three variables—computer access, expected future computer use, and prior computer use before entering college—explain almost one-half of the variance in identifying microcomputer users and nearly one-fifth of the variance in explaining frequency of use.


A CONSTRUCTIVE APPROACH TO SPARSE LINEAR REGRESSION IN HIGH-DIMENSIONS


title: Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition

abstract: With the availability of speech data obtained from different devices and varied acquisition conditions, we are often faced with scenarios, where the intrinsic discrepancy between the training and the test data has an adverse impact on affective speech analysis. To address this issue, this letter introduces an Adaptive Denoising Autoencoder based on an unsupervised domain adaptation method, where prior knowledge learned from a target set is used to regularize the training on a source set. Our goal is to achieve a matched feature space representation for the target and source sets while ensuring target domain knowledge transfer. The method has been successfully evaluated on the 2009 INTERSPEECH Emotion Challenge’s FAU Aibo Emotion Corpus as target corpus and two other publicly available speech emotion corpora as sources. The experimental results show that our method significantly improves over the baseline performance and outperforms related feature domain adaptation methods.


title: Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV

abstract: The use of Unmanned Aerial Vehicles (UAV) has been increasing over the last few years in many sorts of applications due mainly to the decreasing cost of this technology. One can see the use of the UAV in several civilian applications such as surveillance and search and rescue. Automatic detection of pedestrians in aerial images is a challenging task. The computing vision system must deal with many sources of variability in the aerial images captured with the UAV, e.g., low-resolution images of pedestrians, images captured at distinct angles due to the degrees of freedom that a UAV can move, the camera platform possibly experiencing some instability while the UAV flies, among others. In this work, we created and evaluated different implementations of Pattern Recognition Systems (PRS) aiming at the automatic detection of pedestrians in aerial images captured with multirotor UAV. The main goal is to assess the feasibility and suitability of distinct PRS implementations running on top of low-cost computing platforms, e.g., single-board computers such as the Raspberry Pi or regular laptops without a GPU. For that, we used four machine learning techniques in the feature extraction and classification steps, namely Haar cascade, LBP cascade, HOG + SVM and Convolutional Neural Networks (CNN). In order to improve the system performance (especially the processing time) and also to decrease the rate of false alarms, we applied the Saliency Map (SM) and Thermal Image Processing (TIP) within the segmentation and detection steps of the PRS. The classification results show the CNN to be the best technique with 99.7% accuracy, followed by HOG + SVM with 92.3%. In situations of partial occlusion, the CNN showed 71.1% sensitivity, which can be considered a good result in comparison with the current state-of-the-art, since part of the original image data is missing. As demonstrated in the experiments, by combining TIP with CNN, the PRS can process more than two frames per second (fps), whereas the PRS that combines TIP with HOG + SVM was able to process 100 fps. It is important to mention that our experiments show that a trade-off analysis must be performed during the design of a pedestrian detection PRS. The faster implementations lead to a decrease in the PRS accuracy. For instance, by using HOG + SVM with TIP, the PRS presented the best performance results, but the obtained accuracy was 35 percentage points lower than the CNN. The obtained results indicate that the best detection technique (i.e., the CNN) requires more computational resources to decrease the PRS computation time. Therefore, this work shows and discusses the pros/cons of each technique and trade-off situations, and hence, one can use such an analysis to improve and tailor the design of a PRS to detect pedestrians in aerial images.


title: Medical Body Area Networks: Mobility and channel Modeling

abstract: Body Area Networks (BANs) have become the focus of both research and industry in the last five years. Furthermore, IEEE has published the official standard in 2012. BANs come in several variants such as Medical (or hospital) BANs (MBANs) and wireless BANs (WBANs). They are also located at the crossing of several domains for example wearables, Internet of Things (IoT), sensors nets, and so on. In this paper we propose contributions at two BAN levels. We first present a system level solution for MBAN mobility and handover (HO). Second, we give a BAN channel model which consists of an extension of our work formerly applied to Wi-Fi and Wimax.


title: Mesotherapy using dutasteride-containing preparation in treatment of female pattern hair loss: photographic, morphometric and ultrustructural evaluation.

abstract: BACKGROUND ::: Treatment of female pattern hair loss (FPHL) is frustrating for both patients and doctors. Mesotherapy is a novel treatment for hair fall and its efficacy in FPHL has not been evaluated. ::: ::: ::: OBJECTIVE ::: Evaluation of the efficacy and safety of mesotherapy using dutasteride-containing preparation in treatment of FPHL. ::: ::: ::: METHODS ::: This study included 126 female patients with FPHL. They were classified into two groups; group I (86 patients) injected with dutasteride-containing preparation and group II (40 control patients) injected with saline. Patients received 12 sessions and were evaluated at the 18th week by: photographic assessment, hair pull test, hair diameter and patient self-assessment. Ultrastructural evaluation was done for three patients. ::: ::: ::: RESULTS ::: After mesotherapy with dutasteride-containing preparation, photographic improvement occurred in 62.8% of patients compared with 17.5% in control group (P < 0.05), mean number of epilated hairs was significantly decreased (P < 0.05), mean hair diameter was significantly increased (P < 0.05). Patient self-assessment showed statistically significant improvement compared with the controls (P < 0.05). There was a negative correlation between degree of improvement and duration of FPHL (P < 0.05). Side effects were minimal with no statistically significant difference between the two groups (P > 0.05). Ultrastructural examination of pretreated hairs revealed absent cuticle in one patient and focal destruction of the cuticle in the second patient, which reappeared in both after therapy. ::: ::: ::: CONCLUSION ::: We concluded that mesotherapy with dutasteride-containing preparation was effective, tolerable and minimally invasive treatment modality in FPHL with better response for shorter duration of the disease.


title: An algebraic approach to IP traceback

abstract: We present a new solution to the problem of determining the path a packet traversed over the Internet (called the traceback problem) during a denial-of-service attack. This article reframes the traceback problem as a polynomial reconstruction problem and uses algebraic techniques from coding theory and learning theory to provide robust methods of transmission and reconstruction.


title: A comparison between several NoSQL databases with comments and notes

abstract: This paper is trying to comment on the various NoSQL (Not only Structured Query Language) systems and to make a comparison (using multiple criteria) between them. The NoSQL databases were created as a mean to offer high performance (both in terms of speed and size) and high availability at the price of loosing the ACID (Atomic, Consistent, Isolated, Durable) trait of the traditional databases in exchange with keeping a weaker BASE (Basic Availability, Soft state, Eventual consistency) feature. Remains to be seen which of the multiple solutions created since the official appearance of the NoSQL concept (which was defined in 1998 and reintroduced in 2009, around which moment several NoSQL solutions emerged; at the present moment there are known over 120 such solutions) are really delivering on these promises of higher performance (although several of them are already used with very good results).


The Dual-Tree Complex Wavelet Transform – A Coherent Framework for Multiscale Signal and Image Processing


title: A BRIEF SURVEY ON BIO INSPIRED OPTIMIZATION ALGORITHMS FOR MOLECULAR DOCKING

abstract: The idea in molecular docking is to design pharmaceuticals computationally by identifying potential drug candidates targeted against proteins. The candidates can be found using a docking algorithm that tries to identify the bound conformation of a small molecule ligand to a macromolecular target at its active site, which is the region of an enzyme where natural substrate binds. Mathematically, molecular docking can be formulated as an optimization problem in which the objective is to minimize the intermolecular bound conformational energy of two interacting molecules. In this brief survey, after a concise introduction on nature inspired computing, the application of bio inspired optimization algorithms in molecular docking has been studied.


title: Human emotion recognition using real 3D visual features from Gabor library

abstract: Emotional state recognition is an important component for efficient human-computer interaction. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. The general 3D based methods only consider geometric information for feature extraction. In this paper, we present a real 3D visual features based method for human emotion recognition. 3D geometric information plus colour/density information of the facial expressions are extracted by 3D Gabor library to construct visual feature vectors. The filter's scale, orientation, and shape of the library are specified according to the appearance patterns of the 3D facial expressions. An improved kernel canonical correlation analysis (IKCCA) algorithm is proposed for final decision. From training samples, the semantic ratings that describe the different facial expressions are computed by IKCCA to generate a seven dimensional semantic expression vector. It is applied for learning the correlation with different testing samples. According to this correlation, we estimate the associated expression vector and perform expression classification. From experiment results, our proposed method demonstrates impressive performance.


title: A 500nA quiescent current, trim-free, ±1.75% absolute accuracy, CMOS-only voltage reference based on anti-doped N-channel MOSFETs

abstract: In this paper, an ultra low power CMOS-only voltage reference is presented. The reference exploits the work function difference between anti-doped (flipped-gate) and standard-doped nMOS devices. These devices require no additional processing and are realizable from the basic N+ and P+ implants used to implement the standard enhancement mode MOS devices on the process. The reference is implemented as a temperature-compensated ΔVgs between anti-doped and standard-doped nMOS devices. Integrated on 0.18μm CMOS, the reference occupies less than 0.04mm2 on silicon, requires less than 500nA of quiescent current, and has a trim-free accuracy of ±1.75% which is comparable to that of the most well-behaved voltage references employing BJTs.


title: Video Salient Object Detection Using Spatiotemporal Deep Features

abstract: This paper presents a method for detecting salient objects in videos, where temporal information in addition to spatial information is fully taken into account. Following recent reports on the advantage of deep features over conventional handcrafted features, we propose a new set of spatiotemporal deep (STD) features that utilize local and global contexts over frames. We also propose new spatiotemporal conditional random field (STCRF) to compute saliency from STD features. STCRF is our extension of CRF to the temporal domain and describes the relationships among neighboring regions both in a frame and over frames. STCRF leads to temporally consistent saliency maps over frames, contributing to accurate detection of salient objects’ boundaries and noise reduction during detection. Our proposed method first segments an input video into multiple scales and then computes a saliency map at each scale level using STD features with STCRF. The final saliency map is computed by fusing saliency maps at different scale levels. Our experiments, using publicly available benchmark datasets, confirm that the proposed method significantly outperforms the state-of-the-art methods. We also applied our saliency computation to the video object segmentation task, showing that our method outperforms existing video object segmentation methods.


title: Thioredoxin-like protein 2b facilitates colon cancer cell proliferation and inhibits apoptosis via NF-κB pathway

abstract: Abstract Our previous work identified thioredoxin-like protein 2 (Txl-2), a novel thioredoxin family member, as the target of the monoclonal antibody MC3 which can detect colon cancer with high sensitivity and specificity. In the present study, the function of the most abundant isoform Txl-2b in cell proliferation and apoptosis was investigated. Txl-2 overexpression correlated with increased clinical stages. Inhibition of Txl-2b suppressed cell proliferation, induced cell cycle arrest at the G1/S phase, and led to responsiveness to the vincristine-induced apoptosis in SW620 cells. Txl-2b overexpression in LoVo cells had the opposite effect, which was dependent on Trx domain function. In vivo studies validated that Txl-2b expression promoted colon cancer tumorigenesis in nude mice. Further studies revealed that nuclear factor-κB (NF-κB) signaling was activated by Txl-2b. Inhibition of NF-κB activation partly abrogated the pro-proliferation and anti-apoptotic phenotypes mediated by Txl-2b via reduced Cyclin D1, Bcl-2, Bcl-xL and Survivin expression and increased Caspase-3 activation. Overall, our results indicate that Txl-2b expression stimulates cancer cell proliferation, accelerates the cell cycle and contributes to apoptosis resistance in colon cancer and provides a potential therapeutic target for colon cancer treatment.


title: Knowledge management performance evaluation: a decade review from 1995 to 2004

abstract: In this paper, the development of knowledge management (KM) was surveyed, using a literature review and classification of articles from 1995 to 2004. With a keyword index and article abstract, we explored how KM performance evaluation has developed during this period. Based on a scope of 108 articles from 80 academic KM journals (retrieved from six online databases), we surveyed and classified methods of KM measurement, using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-orientated analysis and organizationorientated analysis, together with their measurement matrices for different research and problem domains. Future development directions for KM performance evaluation are presented in our discussion. They include: (1) KM performance measurements have tended towards expertise orientation, while evaluation development is a problemorientated domain; (2) different information technology methodologies, such as expert systems, knowledge-based systems and case-based reasoning may be able to evaluate KM as simply another methodology; (3) the ability to continually change and obtain new understanding is the driving power behind KM methodologies, and should be the basis of KM performance evaluations in the future.


Does loss of body protein determine outcome in patients who are critically ill


title: Do topic shift and query reformulation patterns correlate in academic search

abstract: While it is known that academic searchers differ from typical web searchers, little is known about the search behavior of academic searchers over longer periods of time. In this study we take a look at academic searchers through a large-scale log analysis on a major academic search engine. We focus on two aspects: query reformulation patterns and topic shifts in queries. We first analyze how each of these aspects evolve over time. We identify important query reformulation patterns: revisiting and issuing new queries tend to happen more often over time. We also find that there are two distinct types of users: one type of users becomes increasingly focused on the topics they search for as time goes by, and the other becomes increasingly diversifying. After analyzing these two aspects separately, we investigate whether, and to which degree, there is a correlation between topic shifts and query reformulations. Surprisingly, users’ preferences of query reformulations correlate little with their topic shift tendency. However, certain reformulations may help predict the magnitude of the topic shift that happens in the immediate next timespan. Our results shed light on academic searchers’ information seeking behavior and may benefit search personalization.


title: High-PSR CMOS LDO with embedded ripple feedforward and energy-efficient bandwidth extension

abstract: A CMOS low-dropout regulator (LDO) with high power-supply ripple rejection (PSR) across a wide frequency range is presented and designed in 0.18-μm CMOS technology. Targeting for the application of energy-autonomous biomedical implants, it has a very simple structure and features low power consumption. The high PSR in low frequency range is realized by the proposed embedded supply ripple feed-forward technique. Moreover, the PSR bandwidth is improved up to several MHz by adopting an energy-efficient double zero compensation network. With a 4.7-μF ceramic output capacitor, in the maximum loading condition (i.e., 25 mA), the simulated PSR is no less than −86 dB from DC to 10 kHz, and better than −63 dB up to 10 MHz. The quiescent current is just 10 μA. The line and load regulations are 0.5 mV/V @ 25-mA loading and 0.14 mV/mA @ 1.3-V Vin, respectively. The overshoot and undershoot voltages are less than 2 mV when loading current changes between 0 and 25 mA.


title: Path following control of skid-steered wheeled mobile robots at higher speeds on different terrain types

abstract: A new nonlinear control law for path following with skid-steered mobile robots is proposed. A terrain dependent kinematic model is utilized in path coordinates, and the kinematic parameters are experimentally evaluated. A kinematic path following control is developed using the Lyapunov approach. A separate linear velocity control is then proposed, taking reachable curvatures and actuator saturation into account. The proposed approach is experimentally evaluated in different terrain scenarios, and compared with two other state-of-the-art algorithms. The skid-steered vehicle used for the experiments is the Robotnik Summit XL, a well known commercial mobile robot.


A System for Aspect-based Opinion Mining of Hotel Reviews


title: Bidirectional Single-Stage Grid-Connected Inverter for a Battery Energy Storage System

abstract: The objective of this paper is to propose a bidirectional single-stage grid-connected inverter (BSG-inverter) for the battery energy storage system. The proposed BSG-inverter is composed of multiple bidirectional buck–boost type dc–dc converters (BBCs) and a dc–ac unfolder. Advantages of the proposed BSG-inverter include: single-stage power conversion, low battery and dc-bus voltages, pulsating charging/discharging currents, and individual power control for each battery module. Therefore, the equalization, lifetime extension, and capacity flexibility of the battery energy storage system can be achieved. Based on the developed equations, the power flow of the battery system can be controlled without the need of input current sensor. Also, with the interleaved operation between BBCs, the current ripple of the output inductor can be reduced too. The computer simulations and hardware experimental results are shown to verify the performance of the proposed BSG-inverter.


title: Tax, financial and social regulatory mechanisms within the knowledge-driven economy. Blockchain algorithms and fog computing for the efficient regulation

abstract: This conceptual research reflects the understanding of the wider and more coherent use of crypto-currencies and Blockchain' algorithms for the security and equity of operations. This is especially important for the transactions between the collective actors on different hierarchical positions, e.g., the State and private companies or associations, or individuals. The fog computing' hardware and software are helpful for the aims of improving taxation and ensuring reliability of any transactions. The neuron technologies provide creating and developing a multi-criteria assessment of assets or actions, which represent more correct system of affective and socially acceptable evaluations, than it can be calculated on the basis of the cognitive judgments. The ability to provide a high level of information security through systems like Etherium allows of building a reliable and transparent system of taxation and of regulation of all interactions between social, economic and political agents, including the financial and non-financial measurements.