title: Automatic Cinemagraph Portraits

abstract: Cinemagraphs are a popular new type of visual media that lie in-between photos and video; some parts of the frame are animated and loop seamlessly, while other parts of the frame remain completely still. Cinemagraphs are especially effective for portraits because they capture the nuances of our dynamic facial expressions. We present a completely automatic algorithm for generating portrait cinemagraphs from a short video captured with a hand-held camera. Our algorithm uses a combination of face tracking and point tracking to segment face motions into two classes: gross, large-scale motions that should be removed from the video, and dynamic facial expressions that should be preserved. This segmentation informs a spatially-varying warp that removes the large-scale motion, and a graph-cut segmentation of the frame into dynamic and still regions that preserves the finer-scale facial expression motions. We demonstrate the success of our method with a variety of results and a comparison to previous work.


title: NutriTrack: Android-based food recognition app for nutrition awareness

abstract: One of the emergent concerns of human life is about health and wellness. Undeniably, health and nutrition are one of the valuable aspects of life. Thus, technological innovations to help enhance and even promote health awareness is essential. With the advent of mobile computing, it is much easier to be aware of health information because of its mobility and availability. Much mobile application is being developed to serve as a tool for health monitoring and nutritional guide. Mobile applications have the ability to support health needs like detecting heart rate, classifying food, and many more. Taking advantage of technology, utilization of it hereby addresses certain issue and problems of human life, especially in health. In this study, the researcher's attempts to design and develop an Android-based food recognition application that could be used as a health awareness tool for non-health conscious individual. The application lets the user take the photo of the food and show its nutritional contents. Implementing Mifflin-St Jeor method in determining daily calorie consumption, users shall be aware of their required calorie intake. Moreover, the researchers' have studied its effect on people's health awareness on food nutrition by the randomly selected respondents. Finally, this paper presents an analysis of the impact of the food recognition app to change people's concept of food nutrition.


title: The Four Qualities of Life: Ordering Concepts and Measures of the Good Life

abstract: The terms 'quality-of-life', 'wellbeing' and 'happiness' denote different meanings; sometimes they are used as an umbrella term for all of value, and the other times to denote special merits. ::: This paper is about the specific meanings of the terms. It proposes a classification based on two bi-partitions; between life 'chances' and life 'results', and between 'outer' and 'inner' qualities. Together these dichotomies imply four qualities of life: 1) livability of the environment, 2) life-ability of the individual, 3) external utility of life and 4) inner appreciation of life. ::: This fourfold matrix is applied in three ways: firstly to place related notions and alternative classifications, secondly to explore substantive meanings in various measures for quality of life and thirdly to find out whether quality-of-life can be measured comprehensively. This last question is answered in the negative. Current sum-scores make little sense. The most inclusive summary measure is still how long and happily people live.


title: Budget Feasible Mechanisms

abstract: We study a novel class of mechanism design problems in which the outcomes are constrained by the payments. This basic class of mechanism design problems captures many common economic situations, and yet it has not been studied, to our knowledge, in the past. We focus on the case of procurement auctions in which sellers have private costs, and the auctioneer aims to maximize a utility function on subsets of items, under the constraint that the sum of the payments provided by the mechanism does not exceed a given budget. Standard mechanism design ideas such as the VCG mechanism and its variants are not applicable here. We show that, for general functions, the budget constraint can render mechanisms arbitrarily bad in terms of the utility of the buyer. However, our main result shows that for the important class of sub modular functions, a bounded approximation ratio is achievable. Better approximation results are obtained for subclasses of the sub modular functions. We explore the space of budget feasible mechanisms in other domains and give a characterization under more restricted conditions.


title: Partial Fingerprint Matching through Region-Based Similarity

abstract: Despite advances in fingerprint matching, partial/incomplete/fragmentary fingerprint recognition remains a challenging task. While miniaturization of fingerprint scanners limits the capture of only part of the fingerprint, there is also special interest in processing latent fingerprints which are likely to be partial and of low quality. Partial fingerprints do not include all the structures available in a full fingerprint, hence a suitable matching technique which is independent of specific fingerprint features is required. Common fingerprint recognition methods are based on fingerprint minutiae which do not perform well when applied to low quality images and might not even be suitable for partial fingerprint recognition. To overcome this drawback, in this research, a region-based fingerprint recognition method is proposed in which the fingerprints are compared in a pixel- wise manner by computing their correlation coefficient. Therefore, all the attributes of the fingerprint contribute in the matching decision. Such a technique is promising to accurately recognise a partial fingerprint as well as a full fingerprint compared to the minutiae-based fingerprint recognition methods.The proposed method is based on simple but effective metrics that has been defined to compute local similarities which is then combined into a global score such that it is less affected by distribution skew of the local similarities. Extensive experiments over Fingerprint Verification Competition (FVC) data set proves the superiority of the proposed method compared to other techniques in literature.


title: Opportunistic Routing Algorithm for Relay Node Selection in Wireless Sensor Networks

abstract: Energy savings optimization becomes one of the major concerns in the wireless sensor network (WSN) routing protocol design, due to the fact that most sensor nodes are equipped with the limited nonrechargeable battery power. In this paper, we focus on minimizing energy consumption and maximizing network lifetime for data relay in one-dimensional (1-D) queue network. Following the principle of opportunistic routing theory, multihop relay decision to optimize the network energy efficiency is made based on the differences among sensor nodes, in terms of both their distance to sink and the residual energy of each other. Specifically, an Energy Saving via Opportunistic Routing (ENS_OR) algorithm is designed to ensure minimum power cost during data relay and protect the nodes with relatively low residual energy. Extensive simulations and real testbed results show that the proposed solution ENS_OR can significantly improve the network performance on energy saving and wireless connectivity in comparison with other existing WSN routing schemes.


title: Comparing methods for deriving intensity scores for adjectives

abstract: We compare several different corpusbased and lexicon-based methods for the scalar ordering of adjectives. Among them, we examine for the first time a lowresource approach based on distinctivecollexeme analysis that just requires a small predefined set of adverbial modifiers. While previous work on adjective intensity mostly assumes one single scale for all adjectives, we group adjectives into different scales which is more faithful to human perception. We also apply the methods to both polar and non-polar adjectives, showing that not all methods are equally suitable for both types of adjectives.


title: Towards a system for complex analysis of security events in large-scale networks

abstract: After almost two decades of development, modern Security Information and Event Management (SIEM) systems still face issues with normalisation of heterogeneous data sources, high number of false positive alerts and long analysis times, especially in large-scale networks with high volumes of security events. In this paper, we present our own prototype of SIEM system, which is capable of dealing with these issues. For efficient data processing, our system employs in-memory data storage (SAP HANA) and our own technologies from the previous work, such as the Object Log Format (OLF) and high-speed event normalisation. We analyse normalised data using a combination of three different approaches for security analysis: misuse detection, query-based analytics, and anomaly detection. Compared to the previous work, we have significantly improved our unsupervised anomaly detection algorithms. Most importantly, we have developed a novel hybrid outlier detection algorithm that returns ranked clusters of anomalies. It lets an operator of a SIEM system to concentrate on the several top-ranked anomalies, instead of digging through an unsorted bundle of suspicious events. We propose to use anomaly detection in a combination with signatures and queries, applied on the same data, rather than as a full replacement for misuse detection. In this case, the majority of attacks will be captured with misuse detection, whereas anomaly detection will highlight previously unknown behaviour or attacks. We also propose that only the most suspicious event clusters need to be checked by an operator, whereas other anomalies, including false positive alerts, do not need to be explicitly checked if they have a lower ranking. We have proved our concepts and algorithms on a dataset of 160 million events from a network segment of a big multinational company and suggest that our approach and methods are highly relevant for modern SIEM systems.


title: A Generalized Linear Model for Principal Component Analysis of Binary Data

abstract: We investigate a generalized linear model for dimensionality reduction of binary data. The model is related to principal component analysis (PCA) in the same way that logistic regression is related to linear regression. Thus we refer to the model as logistic PCA. In this paper, we derive an alternating least squares method to estimate the basis vectors and generalized linear coefficients of the logistic PCA model. The resulting updates have a simple closed form and are guaranteed at each iteration to improve the model’s likelihood. We evaluate the performance of logistic PCA—as measured by reconstruction error rates—on data sets drawn from four real world applications. In general, we find that logistic PCA is much better suited to modeling binary data than conventional PCA.


title: Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series

abstract: New remote sensing sensors acquire now high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of any classification framework to obtain up-to-date and accurate land cover maps of the Earth's soils. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS enables the monitoring of vegetation dynamics. Although some traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not fully take advantage of the temporal domain. Conversely, deep-learning based methods have been successfully used to make the most of sequential data such as text and audio data. For the first time, this paper explores the use of Convolutional Neural Networks (CNNs) with convolutions applied in the temporal dimension for SITS classification. The goal is to quantitatively and qualitatively evaluate the contribution of temporal CNNs for SITS classification. More precisely, this paper proposes a set of experiments performed on a million Formosat-2 time series. The experimental results show that temporal CNNs are 2 to 3 % more accurate than RF. The experiments also highlight some counter-intuitive results on pooling layers: contrary to image classification, their use decreases accuracy. Moreover, we provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as the batch size. Finally, the visual quality of the land cover maps produced by the temporal CNN is assessed.


title: Reconfigurable Circularly-Polarized Patch Antenna With Conical Beam

abstract: A design method is described for reconfigurable circularly-polarized (CP) microstrip array antennas with conical-beam radiation. The proposed antenna structure consists of four L-shaped patch antennas arranged in a square-ring formation. Each patch antenna is shorted to the ground plane through conducting walls. With a top-loaded monopole feed, two orthogonal resonant modes, loop mode and monopole mode, can be excited simultaneously. The effects of the shorting walls on the resonant frequencies of the two modes are investigated, and it is found that the resonant frequency (fmono) of the monopole mode has a more obvious variation than that (floop) of the loop mode when the width or the number of the shorting walls is changed. In addition, if the two modes are properly coupled, the microstrip array can generate a CP radiation; meanwhile, the polarization sense is left-hand CP if floop > fmono but is right-hand CP if floop <; fmono. A reconfigurable antenna prototype is fabricated, and the experimental results show that the prototype not only has the characteristic of omnidirectional radiation but also possesses the ability of switching between left-hand and right-hand CP. Simulated results carried out by HFSS are also provided and they agree well with the measured results.


The Relationship Between Operations Strategy and Competitive Strategy In Improving Firm Performance : A Literatur Review


Characters or Morphemes: How to Represent Words?


Accurate SHRG-Based Semantic Parsing


Exemplar Encoder-Decoder for Neural Conversation Generation


title: Review of iOS Malware Analysis

abstract: The security architecture and distribution of iOS was introduced. The threats to user's data security and privacy from iOS malicious applications were analyzed. The reasons that the number of iOS malicious applications is less than Android malicious applications were discussed. The attacking methods of emerging iOS malicious applications were summed up. And prospect for future mitigation and confrontation strategies was given.


Twitter Emotion Analysis in Earthquake Situations


title: Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

abstract: OBJECTIVES ::: The aim of this study was to evaluate the diagnostic accuracy of a multipurpose image analysis software based on deep learning with artificial neural networks for the detection of breast cancer in an independent, dual-center mammography data set. ::: ::: ::: MATERIALS AND METHODS ::: In this retrospective, Health Insurance Portability and Accountability Act-compliant study, all patients undergoing mammography in 2012 at our institution were reviewed (n = 3228). All of their prior and follow-up mammographies from a time span of 7 years (2008-2015) were considered as a reference for clinical diagnosis. After applying exclusion criteria (missing reference standard, prior procedures or therapies), patients with the first diagnosis of a malignoma or borderline lesion were selected (n = 143). Histology or clinical long-term follow-up served as reference standard. In a first step, a breast density-and age-matched control cohort was selected (n = 143) from the remaining patients with more than 2 years follow-up (n = 1003). The neural network was trained with this data set. From the publicly available Breast Cancer Digital Repository data set, patients with cancer and a matched control cohort were selected (n = 35 × 2). The performance of the trained neural network was also tested with this external data set. Three radiologists (3, 5, and 10 years of experience) evaluated the test data set. In a second step, the neural network was trained with all cases from January to September and tested with cases from October to December 2012 (screening-like cohort). The radiologists also evaluated this second test data set. The areas under the receiver operating characteristic curve between readers and the neural network were compared. A Bonferroni-corrected P value of less than 0.016 was considered statistically significant. ::: ::: ::: RESULTS ::: Mean age of patients with lesion was 59.6 years (range, 35-88 years) and in controls, 59.1 years (35-83 years). Breast density distribution (A/B/C/D) was 21/59/42/21 and 22/60/41/20, respectively. Histologic diagnoses were invasive ductal carcinoma in 90, ductal in situ carcinoma in 13, invasive lobular carcinoma in 13, mucinous carcinoma in 3, and borderline lesion in 12 patients. In the first step, the area under the receiver operating characteristic curve of the trained neural network was 0.81 and comparable on the test cases 0.79 (P = 0.63). One of the radiologists showed almost equal performance (0.83, P = 0.17), whereas 2 were significantly better (0.91 and 0.94, P < 0.016). In the second step, performance of the neural network (0.82) was not significantly different from the human performance (0.77-0.87, P > 0.016); however, radiologists were consistently less sensitive and more specific than the neural network. ::: ::: ::: CONCLUSIONS ::: Current state-of-the-art artificial neural networks for general image analysis are able to detect cancer in mammographies with similar accuracy to radiologists, even in a screening-like cohort with low breast cancer prevalence.


title: Multilabel classification with label correlations and missing labels

abstract: Many real-world applications involve multilabel classification, in which the labels can have strong interdependencies and some of them may even be missing. Existing multilabel algorithms are unable to handle both issues simultaneously. In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations. By integrating out the missing information, it also provides a disciplined approach to the handling of missing labels. The inference procedure is simple, and the optimization subproblems are convex. Experiments on a number of real-world data sets with both complete and missing labels demonstrate that the proposed algorithm can consistently outperform state-of-the-art multilabel classification algorithms.


title: Linear Time Maximally Stable Extremal Regions

abstract: In this paper we present a new algorithm for computing Maximally Stable Extremal Regions (MSER), as invented by Matas et al. The standard algorithm makes use of a union-find data structure and takes quasi-linear time in the number of pixels. The new algorithm provides exactly identical results in true worst-case linear time. Moreover, the new algorithm uses significantly less memory and has better cache-locality, resulting in faster execution. Our CPU implementation performs twice as fast as a state-of-the-art FPGA implementation based on the standard algorithm. ::: ::: The new algorithm is based on a different computational ordering of the pixels, which is suggested by another immersion analogy than the one corresponding to the standard connected-component algorithm. With the new computational ordering, the pixels considered or visited at any point during computation consist of a single connected component of pixels in the image, resembling a flood-fill that adapts to the grey-level landscape. The computation only needs a priority queue of candidate pixels (the boundary of the single connected component), a single bit image masking visited pixels, and information for as many components as there are grey-levels in the image. This is substantially more compact in practice than the standard algorithm, where a large number of connected components must be considered in parallel. The new algorithm can also generate the component tree of the image in true linear time. The result shows that MSER detection is not tied to the union-find data structure, which may open more possibilities for parallelization.


title: Online Sequential Extreme Learning Machine With Kernels

abstract: The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets.


title: 10-Gbps, 5.3-mW Optical Transmitter and Receiver Circuits in 40-nm CMOS

abstract: We describe transmitter and receiver circuits for a 10-Gbps single-ended optical link in a 40-nm CMOS technology. The circuits are bonded using low-parasitic micro-solder bumps to silicon photonic devices on a 130-nm SOI platform. The transmitter drives oval resonant ring modulators with a 2-V swing and employs static thermal tuners to compensate for optical device process variations. The receiver is based on a transimpedance amplifier (TIA) with 4-kΩ gain and designed for an input power of - 15 dBm, a photodiode responsivity of 0.7 A/W, and an input extinction ratio of 6 dB. It employs a pair of interleaved clocked sense-amplifiers for voltage slicing and uses a DLL with phase adjustment for centering the clock in the data eye. Periodic calibration allows for adjustment of both voltage and timing margins. At 10 Gbps, the transmitter extinction ratio exceeds 7 dB and, excluding thermal tuning and laser power, it consumes 1.35 mW. At the same datarate, the receiver consumes 3.95 mW. On-chip PRBS generators and checkers with 231-1 sequences confirm operation at a BER better than 10-12.


title: QuickTime VR: an image-based approach to virtual environment navigation

abstract: Traditionally, virtual reality systems use 3D computer graphics to model and render virtual environments in real-time. This approach usually requires laborious modeling and expensive special purpose rendering hardware. The rendering quality and scene complexity are often limited because of the real-time constraint. This paper presents a new approach which uses 360-degree cylindrical panoramic images to compose a virtual environment. The panoramic image is digitally warped on-the-fly to simulate camera panning and zooming. The panoramic images can be created with computer rendering, specialized panoramic cameras or by "stitching" together overlapping photographs taken with a regular camera. Walking in a space is currently accomplished by "hopping" to different panoramic points. The image-based approach has been used in the commercial product QuickTime VR, a virtual reality extension to Apple Computer's QuickTime digital multimedia framework. The paper describes the architecture, the file format, the authoring process and the interactive players of the VR system. In addition to panoramic viewing, the system includes viewing of an object from different directions and hit-testing through orientation-independent hot spots. CR


title: Advances in opportunistic radio technologies for TVWS

abstract: Cognitive radio has been an active research area in wireless communications over the past 10 years. TV Digital Switch Over resulted in new regulatory regimes, which offer the first large-scale opportunity for cognitive radio and networks. This article considers the most recent regulatory rules for TV White Space opportunistic usage, and proposes technologies to operate in these bands. It addresses techniques to assess channel vacancy by the cognitive radio, focusing on the two incumbent systems of the TV bands, namely TV stations and wireless microphones. Spectrum-sensing performance is discussed under TV White Space regulation parameters. Then, modulation schemes for the opportunistic radio are discussed, showing the limitations of classical multi-carrier techniques and the advantages of filter bank modulations. In particular, the low adjacent band leakage of filter bank is addressed, and its benefit for spectrum pooling is stressed as a means to offer broadband access through channel aggregation.


title: The Role of Business Model and Its Elements in Computer Game Start-ups

abstract: In this multiple case study we interviewed six Finnish computer game start-ups to find out what elements are included in their business models. We identified the key elements and used the analytical hierarchy process to rank the elements. We found out that computer game start-ups see their business model as a synonym to a revenue model and/or a business plan. In an in-depth analysis we identified nine key elements (human capital, marketing, key partners, financing, customer relationship, key activities, innovation process, key resources and customer segment) that have operative importance for these companies. These elements are the building blocks of a business model in the computer game start-up domain. The findings provide improved knowledge on how the business models of game start-ups could be constructed.


title: Young children's productivity with word order and verb morphology.

abstract: Four studies examined English-speaking children's productivity with word order and verb morphology. Two- and 3-year olds were taught novel transitive verbs with experimentally controlled argument structures. The younger children neither used nor comprehend word order with these verbs; older children comprehended and used word order correctly to mark agents and patients of the novel verbs. Children as young as 2 years 1 months added -ing but not -ed to verb stems; older children were productive with both inflections. These studies demonstrate that the present progressive inflection is used productively before the regular past tense marker and suggest that productivity with word order may be independent of developments in verb morphology. The findings are discussed in terms of M. Tomasello's (1992a) Verb Island hypothesis and M. Rispoli's (1991) notion of the mosaic acquisition of grammatical relations


title: Attention training in socially anxious children: A multiple baseline design analysis

abstract: Abstract Current evidence has established an association between anxiety and attentional threat biases. Emerging research suggests these attentional biases may play a causal role in anxiety development. Clinical researchers have begun to develop treatments specifically designed to address these attentional processes. As such, Attention training (ATT) has been shown to be effective in the treatment of anxiety in adults. The current study represents an early attempt to implement ATT to treat social anxiety disorder (SOC) in children. Two boys meeting criteria for DSM-IV SOC participated in the study, along with their parents. Both boys received 10, 10-min sessions of ATT. A multiple-baseline design was used. Following treatment, both boys evidenced reductions in social anxiety. The current study provides preliminary evidence regarding the utility of ATT for treatment of childhood SOC. Future research is needed to further examine the use of this treatment strategy with youth and to explore the mechanisms of change.


title: Fog Computing in Healthcare–A Review and Discussion

abstract: Fog computing is an architectural style in which network components between devices and the cloud execute application-specific logic. We present the first review on fog computing within healthcare informatics, and explore, classify, and discuss different application use cases presented in the literature. For that, we categorize applications into use case classes and list an inventory of application-specific tasks that can be handled by fog computing. We discuss on which level of the network such fog computing tasks can be executed, and provide tradeoffs with respect to requirements relevant to healthcare. Our review indicates that: 1) there is a significant number of computing tasks in healthcare that require or can benefit from fog computing principles; 2) processing on higher network tiers is required due to constraints in wireless devices and the need to aggregate data; and 3) privacy concerns and dependability prevent computation tasks to be completely moved to the cloud. These findings substantiate the need for a coherent approach toward fog computing in healthcare, for which we present a list of recommended research and development actions.


title: Ontology‐based activity recognition in intelligent pervasive environments

abstract: Purpose – This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.Design/methodology/approach – The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and w...


title: Multicommunicating: A Practice Whose Time Has Come?

abstract: We offer the concept of multicommunicating to describe overlapping conversations, an increasingly common occurrence in the technology-enriched workplace. We define multicommunicating, distinguish it from other behaviors, and develop propositions for future research. Our work extends the literature on technology-stimulated restructuring and reveals one of the opportunities provided by lean media—specifically, an opportunity to multicommunicate. We conclude that the concept of multicommunicating has value both to the scholar and to the practicing manager.


title: Particularism and pleasure

abstract: Book synopsis: Particularism is a justly popular ‘cutting-edge’ topic in contemporary ethics across the world. Many moral philosophers do not, in fact, support particularism (instead defending "generalist" theories that rest on particular abstract moral principles), but nearly all would take it to be a position that continues to offer serious lessons and challenges that cannot be safely ignored. Given the high standard of the contributions, and that this is a subject where lively debate continues to flourish, Challenging Moral Particularism will become required reading for professionals and advanced students working in the area.


title: A diversity-penalizing ensemble training method for deep learning.

abstract: A common way to improve the performance of deep learning is to train an ensemble of neural networks and combine them during decoding. However, this is computationally expensive in test time. In this paper, we propose an diversity-penalizing ensemble training (DPET) procedure, which trains an ensemble of DNNs, whose parameters were differently initialized, and penalizes differences between each individual DNN’s output and their average output. This way each model learns to emulate the average of the whole ensemble of models, and in test time we can use one arbitrarily chosen member of the ensemble. Experimental results on a variety of speech recognition tasks show that this technique is effective, and gives us most of the WER improvement of the ensemble method while being no more expensive in test time than using a single model.


title: Optimization by simulated annealing

abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.


title: Learning Robust Deep Face Representation

abstract: With the development of convolution neural network, more and more researchers focus their attention on the advantage of CNN for face recognition task. In this paper, we propose a deep convolution network for learning a robust face representation. The deep convolution net is constructed by 4 convolution layers, 4 max pooling layers and 2 fully connected layers, which totally contains about 4M parameters. The Max-Feature-Map activation function is used instead of ReLU because the ReLU might lead to the loss of information due to the sparsity while the Max-Feature-Map can get the compact and discriminative feature vectors. The model is trained on CASIA-WebFace dataset and evaluated on LFW dataset. The result on LFW achieves 97.77% on unsupervised setting for single net.


title: Teeth-Brushing Recognition Based on Deep Learning

abstract: In this paper, we propose a multi-stream deep learning framework to tackle the activity recognition problem of sixteen kinds of Bass brushing methods by brushing photos and sensor data. This is a challenge task because the model needs to infer the relevance between images and sensor data. In order to solve this problem, CNN model is exploited to learn the spatial features from images and the LSTM model is used to learn the temporal features from sensor data. Then, a fusion scheme is proposed for prediction. Experimental results show that our model achieves high accuracy by using both images and sensor data under the constraint that the dataset is still quite limited.


title: Deep Convolutional Network Based on Pyramid Architecture

abstract: Deep convolutional network demonstrates that the classification accuracy can be remarkably improved by increasing the number of network layers, however, increases the accuracy by 1% of costs nearly doubling the number of layers. Meanwhile, gradient dispersion will occur in the training process, which leads to performance degradation. In order to solve the problem of training difficulty with the increased number of layers, we focus on network architecture and propose a deep convolutional network based on the pyramid structure. In the network architecture, as the number of layers increased, the feature map dimensions (i.e., the number of channels) are gradually increased at each layer to distribute the burden concentrated at locations of structural units affected by downsampling, such that all units are equally distributed. By exploring the sequence between the stacked elements inside the structural unit, we design a pyramidal building block, as its shape gradually widens from the top downwards, which is called the deep pyramid convolutional network (DPCNet). Experimental results on CIFAR-10 and CIFAR-100 datasets have shown that DPCNet has the superior generalization capability and can effectively improve the image classification accuracy.


title: SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning

abstract: In Deep Learning, Stochastic Gradient Descent (SGD) is usually selected as a training method because of its efficiency; however, recently, a problem in SGD gains research interest: sharp minima in Deep Neural Networks (DNNs) have poor generalization; especially, large-batch SGD tends to converge to sharp minima. It becomes an open question whether escaping sharp minima can improve the generalization. To answer this question, we propose SmoothOut framework to smooth out sharp minima in DNNs and thereby improve generalization. In a nutshell, SmoothOut perturbs multiple copies of the DNN by noise injection and averages these copies. Injecting noises to SGD is widely used in the literature, but SmoothOut differs in lots of ways: (1) a de-noising process is applied before parameter updating; (2) noise strength is adapted to filter norm; (3) an alternative interpretation on the advantage of noise injection, from the perspective of sharpness and generalization; (4) usage of uniform noise instead of Gaussian noise. We prove that SmoothOut can eliminate sharp minima. Training multiple DNN copies is inefficient, we further propose an unbiased stochastic SmoothOut which only introduces the overhead of noise injecting and de-noising per batch. An adaptive variant of SmoothOut, AdaSmoothOut, is also proposed to improve generalization. In a variety of experiments, SmoothOut and AdaSmoothOut consistently improve generalization in both small-batch and large-batch training on the top of state-of-the-art solutions.


title: Extended Target Tracking Using Gaussian Processes with High-Resolution Automotive Radar

abstract: In this paper, an implementation of an extended target tracking filter using measurements from high-resolution automotive Radio Detection and Ranging (RADAR) is proposed. Our algorithm uses the Cartesian point measurements from the target's contour as well as the Doppler range rate provided by the RADAR to track a target vehicle's position, orientation, and translational and rotational velocities. We also apply a Gaussian Process (GP) to model the vehicle's shape. To cope with the nonlinear measurement equation, we implement an Extended Kalman Filter (EKF) and provide the necessary derivatives for the Doppler measurement. We then evaluate the effectiveness of incorporating the Doppler rate on simulations and on 2 sets of real data.


An Implementation of Active Contour and Kalman Filter for Road Tracking


title: Cytolytic T lymphocytes recognize an antigen encoded by MAGE-A10 on a human melanoma.

abstract: From melanoma patient LB1751, cytolytic T lymphocytes (CTL) were generated that lysed specifically autologous tumor cells. To establish whether these CTL recognized one of the Ags that had previously been defined, a CTL clone was stimulated with cells expressing various MAGE genes. It produced TNF upon stimulation with target cells expressing MAGE-A10. The Ag was found to be nonapeptide GLYDGMEHL (codons 254-262), which is presented by HLA-A2.1. This is the first report on the generation of anti-MAGE CTL by autologous mixed lymphocyte-tumor cell culture (MLTC) from a melanoma patient other than patient MZ2, from whom the first MAGE gene was identified. MAGE genes are expressed in many tumors but not by normal tissues except male germline cells and placenta, which do not express HLA molecules. Therefore, the identification of an antigenic peptide derived from MAGE-A10 adds to the repertoire of tumor-specific shared Ags available for anti-tumoral vaccination trials.


title: Impact of ICT Innovative Momentum on Real-Time Accounting

abstract: Background: Enterprises are entering into the era of the real-time economy, also called the “now economy”, which can be characterized by a substantive acceleration of business measurement, assessment and decision processes. The real-time reporting, as a phenomenon of the now economy, presents a new challenge to the Accounting Information Systems. The current long wave of prosperity is characterized by an innovative momentum of ICT, with several disruptive innovations, far from being completely utilized.


title: 5-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression

abstract: Carriers of the short allele of a functional 5' promoter polymorphism of the serotonin transporter gene have increased anxiety-related temperamental traits, increased amygdala reactivity and elevated risk of depression. Here, we used multimodal neuroimaging in a large sample of healthy human subjects to elucidate neural mechanisms underlying this complex genetic association. Morphometrical analyses showed reduced gray matter volume in short-allele carriers in limbic regions critical for processing of negative emotion, particularly perigenual cingulate and amygdala. Functional analysis of those regions during perceptual processing of fearful stimuli demonstrated tight coupling as a feedback circuit implicated in the extinction of negative affect. Short-allele carriers showed relative uncoupling of this circuit. Furthermore, the magnitude of coupling inversely predicted almost 30% of variation in temperamental anxiety. These genotype-related alterations in anatomy and function of an amygdala-cingulate feedback circuit critical for emotion regulation implicate a developmental, systems-level mechanism underlying normal emotional reactivity and genetic susceptibility for depression.


title: Interactive Storytelling: A Player Modelling Approach

abstract: In recent years, the fields of Interactive Storytelling and Player Modelling have independently enjoyed increased interest in both academia and the computer games industry. The combination of these technologies, however, remains largely unexplored. In this paper, we present PaSSAGE (Player-Specific Stories via Automatically Generated Events), an interactive storytelling system that uses player modelling to automatically learn a model of the player's preferred style of play, and then uses that model to dynamically select the content of an interactive story. Results from a user study evaluating the entertainment value of adaptive stories created by our system as well as two fixed, pre-authored stories indicate that automatically adapting a story based on learned player preferences can increase the enjoyment of playing a computer role-playing game for certain types of players.


title: An Adaptive Voltage-Sensor-Based MPPT for Photovoltaic Systems With SEPIC Converter Including Steady-State and Drift Analysis

abstract: An adaptive voltage-sensor-based maximum power point tracking algorithm employing a variable scaling factor for a single-ended primary-inductance converter is presented. In this method, only a voltage divider circuit is used to sense the photovoltaic (PV) panel voltage. This method can effectively improve both transient and steady-state performance by varying the scaling factor as compared with the fixed step size and adaptive step size with fixed scaling factor. For sudden change in solar insolation or in start-up, this method leads to faster tracking, whereas in steady state, it leads to lower oscillations around maximum power point. The steady-state behavior and drift phenomena are also addressed in this paper to determine the tracking efficiency. The duty cycle is generated directly without using any proportional–integral control loop to simplify the control circuit. MATLAB/Simulink is used for simulation studies, and a microcontroller is used as a digital platform to implement the proposed algorithm for experimental validation. The proposed system is implemented and tested successfully on a PV panel in the laboratory.


title: From Cyborgs to Cyberbodies: The Evolution of the Concept of Techno-Body in Modern Medicine

abstract: This paper focuses on the ways in which the introduction of technologies in modern medicine is changing collective notions of the body. In particular, it describes two popular and imaginative conceptualizations of the body that have been inspired by progresses made by medical technologies during last century: the cyborg, and the cyberbody. Although these two models stem from the same post-modern philosophical "post-body", "post-biological," or "post-human" visions, they are characterized by a fundamental distinction. While the cyborg, at least in its original conception, is linked to the “wild wired world”, the world of cells, neurons, blood and biological processes, the cyberbody can be defined as a wireless, inorganic entity, made of pure bits of information. However, both definitions assume that people no longer has a direct "sense of body", but a mediated sense of body. Further steps in this direction may be determined by the emerging technological paradigm of Ambient Intelligence. In this vision, people will be surrounded by intelligent and intuitive interfaces embedded in everyday objects around us and an environment recognizing and responding to the presence of individuals in an invisible way by year 2010. Although the Ambient Intelligence scenario is still in an early phase of development, it is somehow predictable that technological innovations that this paradigm will bring into medicine are likely to foster the production of a new collective notion of the body based on the “digital me”: a virtual reality representation of the patient as a virtual person, integrating all the diagnostic and clinical information of the patient into a single record continuous across time. In addition to explore this perspective from a theoretical viewpoint, implications for medical practice are discussed.


title: Merkel cells and neurons keep in touch

abstract: The Merkel cell–neurite complex is a unique vertebrate touch receptor comprising two distinct cell types in the skin. Its presence in touch-sensitive skin areas was recognized more than a century ago, but the functions of each cell type in sensory transduction have been unclear. Three recent studies demonstrate that Merkel cells are mechanosensitive cells that function in touch transduction via Piezo2. One study concludes that Merkel cells, rather than sensory neurons, are principal sites of mechanotransduction, whereas two other studies report that both Merkel cells and neurons encode mechanical inputs. Together, these studies settle a long-standing debate on whether or not Merkel cells are mechanosensory cells, and enable future investigations of how these skin cells communicate with neurons.


title: Identifying Top Sellers In Underground Economy Using Deep Learning-Based Sentiment Analysis

abstract: The underground economy is a key component in cyber carding crime ecosystems because it provides a black marketplace for cyber criminals to exchange malicious tools and services that facilitate all stages of cyber carding crime. Consequently, black market sellers are of particular interest to cybersecurity researchers and practitioners. Malware/carding sellers are critical to cyber carding crime since using malwares to skim credit/debit card information and selling stolen information are two major steps of conducting such crime. In the underground economy, the malicious product/service quality is reflected by customers' feedback. In this paper, we present a deep learning-based framework for identifying top malware/carding sellers. The framework uses snowball sampling, thread classification, and deep learning-based sentiment analysis to evaluate sellers' product/service quality based on customer feedback. The framework was evaluated on a Russian carding forum and top malware/carding sellers from it were identified. Our framework contributes to underground economy research as it provides a scalable and generalizable framework for identifying key cybercrime facilitators.


title: Fast and Flexible Conversion of Geohash Codes to and from Latitude/Longitude Coordinates

abstract: Insights extracted from spatial queries in geodatabase systems introduce significant opportunities for business intelligence. However, geodatabases are unable to keep up with the required performance due to the massive (and sky-rocketing) amounts of data generated from embedded location-enabled devices. In this paper, we focus on geographic information systems that make use of geohash, specifically, we tackle the kernel of converting geohash codes to and from longitude/latitude pairs. We present the first hardware implementation of a geohash conversion engine operating at wire speed. The presented geohash converter is further enhanced with runtime flexibility with respect to characteristics of the data it can process, furthermore, the architecture allows the user to compromise on performance when limited by hardware resources (design time flexibility). Experimental results of the geohash conversion engine on a Xilinx XC7K325T FPGA show >13X (end-to-end) speedup compared to optimized industry-grade software running on 16 CPU hardware threads.


title: Somatotopic Representation of Action Words in Human Motor and Premotor Cortex

abstract: Abstract Since the early days of research into language and the brain, word meaning was assumed to be processed in specific brain regions, which most modern neuroscientists localize to the left temporal lobe. Here we use event-related fMRI to show that action words referring to face, arm, or leg actions (e.g., to lick, pick, or kick ), when presented in a passive reading task, differentially activated areas along the motor strip that either were directly adjacent to or overlapped with areas activated by actual movement of the tongue, fingers, or feet. These results demonstrate that the referential meaning of action words has a correlate in the somatotopic activation of motor and premotor cortex. This rules out a unified "meaning center" in the human brain and supports a dynamic view according to which words are processed by distributed neuronal assemblies with cortical topographies that reflect word semantics.


title: Artificial Intelligence in Life Extension: from Deep Learning to Superintelligence

abstract: Abstract: In this paper we focus on the most efficacious AI applications for life extension and anti-aging at three expected stages of AI development: narrow AI, AGI and superintelligence. First, we overview the existing research and commercial work performed by a select number of startups and academic projects. We find that at the current stage of “narrow” AI, the most promising areas for life extension are geroprotector-combination discovery, detection of aging biomarkers, and personalized anti-aging therapy. These advances could help currently living people reach longevity escape velocity and survive until more advanced AI appears. When AI comes close to human level, the main contribution to life extension will come from AI integration with humans through brain-computer interfaces, integrated AI assistants capable of autonomously diagnosing and treating health issues, and cyber systems embedded into human bodies. Lastly, we speculate about the more remote future, when AI reaches the level of superintelligence and such life-extension methods as uploading human minds and creating nanotechnological bodies may become possible, thus lowering the probability of human death close to zero. We conclude that medical AI based superintelligence is intrinsically safer than, say, military AI, as it may help humans to evolve into part of the future superintelligence via brain augmentation, uploading, and a network of self-improving humans. Medical AI’s value system is focused on human benefit.


title: Performance Evaluation of GNSS for Train Localization

abstract: Global Navigation Satellite Systems (GNSS) are applicable to deliver train locations in real time. This train localization function should comply with railway functional safety standards; thus, the GNSS performance needs to be evaluated in consistent with railway EN 50126 standard [Reliability, Availability, Maintainability, and Safety (RAMS)]. This paper demonstrates the performance of the GNSS receiver for train localization. First, the GNSS performance and railway RAMS properties are compared by definitions. Second, the GNSS receiver measurements are categorized into three states (i.e., up, degraded, and faulty states). The relations between the states are illustrated in a stochastic Petri net model. Finally, the performance properties are evaluated using real data collected on the railway track in High Tatra Mountains in Slovakia. The property evaluation is based on the definitions represented by the modeled states.


title: A novel ZVT-ZCT PWM DC-DC converter

abstract: In this paper, a new active snubber cell is proposed to contrive a new family of pulse width modulated (PWM) converters. This snubber cell provides zero voltage transition (ZVT) turn on and zero current transition (ZCT) turn off together for the main switch of a converter. Also, the snubber cell is implemented by using only one quasi resonant circuit without an important increase in the cost and complexity of the converter. New ZVT-ZCT-PWM converter equipped with the proposed snubber cell provides most the desirable features of both ZVT and ZCT converters presented previously, and overcomes most the drawbacks of these converters. Subsequently, the new converter can operate with soft switching successfully at very wide line and load ranges and at considerably high frequencies. Moreover, all semiconductor devices operate under soft switching, the main devices do not have any additional voltage and current stresses, and the stresses on the auxiliary devices are at low levels. Also, the new converter has a simple structure, low cost and ease of control. In this study, a detailed steady state analysis of the new converter is presented, and this theoretical analysis is verified exactly by a prototype of a 1-kW and 100-kHz boost converter.


title: Feedback directed implicit parallelism

abstract: In this paper we present an automated way of using spare CPU resources within a shared memory multi-processor or multi-core machine. Our approach is (i) to profile the execution of a program, (ii) from this to identify pieces of work which are promising sources of parallelism, (iii) recompile the program with this work being performed speculatively via a work-stealing system and then (iv) to detect at run-time any attempt to perform operations that would reveal the presence of speculation. We assess the practicality of the approach through an implementation based on GHC 6.6 along with a limit study based on the execution profiles we gathered. We support the full Concurrent Haskell language compiled with traditional optimizations and including I/O operations and synchronization as well as pure computation. We use 20 of the larger programs from the 'nofib' benchmark suite. The limit study shows that programs vary a lot in the parallelism we can identify: some have none, 16 have a potential 2x speed-up, 4 have 32x. In practice, on a 4-core processor, we get 10-80% speed-ups on 7 programs. This is mainly achieved at the addition of a second core rather than beyond this. This approach is therefore not a replacement for manual parallelization, but rather a way of squeezing extra performance out of the threads of an already-parallel program or out of a program that has not yet been parallelized.


title: Prenatal stress-induced alterations in major physiological systems correlate with gut microbiota composition in adulthood

abstract: Early-life adverse experiences, including prenatal stress (PNS), are associated with a higher prevalence of neurodevelopmental, cardiovascular and metabolic disorders in affected offspring. Here, in a rat model of chronic PNS, we investigate the impact of late gestational stress on physiological outcomes in adulthood. Sprague-Dawley pregnant dams were subjected to repeated restraint stress from embryonic day 14 to day 20, and their male offspring were assessed at 4 months of age. PNS induced an exaggeration of the hypothalamic-pituitary-adrenal (HPA) axis response to stress, as well as an elevation of blood pressure and impairment of cognitive function. Altered respiratory control was also observed, as demonstrated by increased variability in basal respiratory frequency and abnormal frequency responses to both hypoxic and hypercapnic challenges. PNS also affected gastrointestinal neurodevelopment and function, as measured by a decrease in the innervation density of distal colon and an increase in the colonic secretory response to catecholaminergic stimulation. Finally, PNS induced long lasting alterations in the intestinal microbiota composition. 16S rRNA gene 454 pyrosequencing revealed a strong trend towards decreased numbers of bacteria in the Lactobacillus genus, accompanied by elevated abundance of the Oscillibacter, Anaerotruncus and Peptococcus genera in PNS animals. Strikingly, relative abundance of distinct bacteria genera significantly correlated with certain respiratory parameters and the responsiveness of the HPA axis to stress. Together, these findings provide novel evidence that PNS induces long-term maladaptive alterations in the gastrointestinal and respiratory systems, accompanied by hyper-responsiveness to stress and alterations in the gut microbiota.


Sweet Anticipation: Music and the Psychology of Expectation


title: Conditional Adversarial Domain Adaptation

abstract: Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation. Existing adversarial domain adaptation methods may not effectively align different domains of multimodal distributions native in classification problems. In this paper, we present conditional adversarial domain adaptation, a principled framework that conditions the adversarial adaptation models on discriminative information conveyed in the classifier predictions. Conditional domain adversarial networks (CDANs) are designed with two novel conditioning strategies: multilinear conditioning that captures the cross-covariance between feature representations and classifier predictions to improve the discriminability, and entropy conditioning that controls the uncertainty of classifier predictions to guarantee the transferability. With theoretical guarantees and a few lines of codes, the approach has exceeded state-of-the-art results on five datasets.


title: A Quality Framework for Agile Requirements: A Practitioner's Perspective

abstract: Verification activities are necessary to ensure that the requirements are specified in a correct way. However, until now requirements verification research has focused on traditional up-front requirements. Agile or just-in-time requirements are by definition incomplete, not specific and might be ambiguous when initially specified, indicating a different notion of ‘correctness’. We analyze how verification of agile requirements quality should be performed, based on literature of traditional and agile requirements. This leads to an agile quality framework, instantiated for the specific requirement types of feature requests in open source projects and user stories in agile projects. We have performed an initial qualitative validation of our framework for feature requests with eight practitioners from the Dutch agile community, receiving overall positive feedback.


title: Comparison of surface normal estimation methods for range sensing applications

abstract: As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the ‘right’ algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot.


title: Retrieval medicine: a review and guide for UK practitioners. Part 1: clinical guidelines and evidence base.

abstract: It has been proposed that formalisation of training to encompass prehospital and retrieval medicine should be considered in the UK, using those currently involved in immediate care as the core providers of these services.(1) Although there is an overlap in some aspects of "prehospital" and "retrieval" medicine, there are some distinct differences, both in terms of the skill base and service provision required. Retrieval medicine is the term used to indicate the use of an expert team to assess, stabilise and transport patients with severe injury or critical illness. Implicit in this process is the early provision of specialised advice to the health providers at the patient's side. In the UK, there is currently no national and often no regional strategy to coordinate the provision of secondary retrieval services for critically ill patients. International models do exist, which may be of help in this respect.


title: Joint Spectrum and Energy Efficiency in Device to Device Communication Enabled Wireless Networks

abstract: This paper presents a joint spectrum and energy efficiency maximization mechanism for device to device communication enabled wireless networks. Most approaches in the literature fail to take into account link heterogeneity in terms of their demands and the traffic they inject into the network. This paper presents a linear optimization formulation to allocate bandwidth to links, not only based on network topology but also based on traffic injection patterns of devices and link demands. We derive bounds on the achievable spectrum efficiency. The achieved spectrum efficiency and energy efficiency are further enhanced by deploying power control using $ {\mathcal {M}-}$ matrix theory. The proposed approach is shown to provide an improvement between 25% to up to four orders of magnitude in spectrum efficiency and between 25% to up to five orders of magnitude in energy efficiency compared to existing mechanisms, depending on the network topology and traffic injection patterns of the nodes.


title: Recent machine learning based approaches for disease detection and classification of agricultural products

abstract: Disease infection to agricultural products like plants, fruits and vegetables, results in degradation of quality and quantity of agriculture products. This directly affects the financial source of agriculturists and the human health. Hence, detection of diseases in plants, fruits and vegetables crops at early stages of development leads to reduce loss of yield and quality. The traditional approaches for disease detection requires continues monitoring and observation of farms either by farmers or by experts. But, it is very costly and time consuming. In last few years, various researchers have focused into this area to provide optimized solution. Popular methods have utilized machine learning, image processing and classification based approaches to identify and detect the diseases on agricultural products. The existing techniques for disease detection have utilized various image processing methods followed by various classification techniques. This paper presents an overview of existing reported techniques useful in detection of diseases of agricultural products. A comparative study of different methods based on the type of agricultural product, methodology and its efficiency together with the advantages and disadvantages is also included in this paper.


title: Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC)

abstract: The complexity of modern HVAC systems leads to device mis-configuration in about 40% of buildings, wasting upto 40% of the energy consumed. Fault detection methods generate excessive alarms leading to operator alert fatigue, faults left unfixed and energy wastage. Sophisticated fault detection techniques developed in the literature are seldom used in practice. We investigate this gap by applying various fault detection techniques on real data from a 145,000 sqft, five floor building. We first find that none of these algorithms are designed to capture control loop configuration faults. We develop a novel algorithm, Model, Cluster and Compare (MCC) that is able to detect anomalies by automatically modeling and clustering similar entities in an HVAC system, in an unsupervised manner, and comparing them. We implemented MCC to detect faults in Variable Air Volume boxes in our building, and demonstrate that it successfully detects non-obvious configuration faults. We propose a two stage approach, where we design intelligent rules (iRules) based on anomaly exemplars from a mix of data driven algorithms. iRules are successful in capturing a large fraction of faults in our building, with only one false alarm and 78 anomalies detected out of 237 zones. Thus, comparative data mining is useful in filtering the large amount of data generated in modern buildings, but that human in the loop systems are better still.


title: Convolutional wasserstein distances: efficient optimal transportation on geometric domains

abstract: This paper introduces a new class of algorithms for optimization problems involving optimal transportation over geometric domains. Our main contribution is to show that optimal transportation can be made tractable over large domains used in graphics, such as images and triangle meshes, improving performance by orders of magnitude compared to previous work. To this end, we approximate optimal transportation distances using entropic regularization. The resulting objective contains a geodesic distance-based kernel that can be approximated with the heat kernel. This approach leads to simple iterative numerical schemes with linear convergence, in which each iteration only requires Gaussian convolution or the solution of a sparse, pre-factored linear system. We demonstrate the versatility and efficiency of our method on tasks including reflectance interpolation, color transfer, and geometry processing.


title: Design and implementation of a low cost multichannel rectified EMG acquisition system

abstract: Main objective of this work is designing and implementation of low cost electromyography (EMG) signal acquisition system which provides multichannel facilities. The major challenges of this work are to meet the quality as well as the cost at the same time. Our designed acquisition system comes with some attractive features like real time data display, offline data store, and simultaneous use of three channels. This multiple channel acquisition system is implemented using instrumentation amplifier (INA118) for pre-amplification stage. For smoothing and rectification purposes operational amplifiers (TL 072) have been used. In this system, 10 bit analog to digital (AD) converter of Arduino UNO has been used. Then the output data are displayed graphically by using open source software named by Serial Oscilloscope. The acquired data is validated by two ways. At first, the acquired data is used to control the prosthetic hand. Else, the acquired data by this designed acquisition system has been compared with certified Biopac Mp36 system and we have achieved 84.1% correlation coefficient. These two validation test proves the effectiveness of our designed system. In addition with that, the cost of this data acquisition is only 40 USD.


title: Multiscale patterning of plasmonic metamaterials

abstract: The interaction of light with surface plasmons—collective oscillations of free electrons—in metallic nanostructures has resulted in demonstrations of enhanced optical transmission1, collimation of light through a subwavelength aperture2, negative permeability and refraction at visible wavelengths3,4, and second-harmonic generation from magnetic metamaterials5. The structures that display these plasmonic phenomena typically consist of ordered arrays of particles or holes with sizes of the order of 100 nm. However, surface plasmons can interact with each other over much longer distances, so the ability to organize nanoscale particles or holes over multiple length scales could lead to new plasmonic metamaterials with novel optical properties6. Here, we present a high-throughput nanofabrication technique—soft interference lithography—that combines the ability of interference lithography7 to produce wafer-scale nanopatterns with the versatility of soft lithography8, and use it to create such plasmonic metamaterials. Metal films perforated with quasi-infinite arrays of 100-nm holes were generated over areas greater than 10 cm2, exhibiting sharp spectral features that changed in relative amplitude and shifted to longer wavelengths when exposed to increased refractive index environments. Moreover, gold nanohole arrays patterned into microscale patches exhibited strikingly different transmission properties; for instance, patches of nanoholes displayed narrow resonances (<14.5 nm full-width-at-half-maximum) that resulted in high refractive index sensitivities far exceeding those reported previously9. Soft interference lithography was also used to produce various infinite and finite-area arrays of nanoparticles, including patterns that contained optically distinct particles side by side and arrays that contained both metallic and dielectric materials.


title: Mechanisms Governing Empowerment Effects: A Self-Efficacy Analysis

abstract: This experiment tested the hypotheses that perceived coping and cognitive control self-efficacy govern the effects of personal empowerment over physical threats. Women participated in a mastery modeling program in which they mastered the physical skills to defend themselves successfully against unarmed sexual assailants. Multifaceted measures of theoretically relevant variables were administered within a staggered intragroup control design to test the immediate and long-term effects of the empowerment program and the mechanisms through which it produced its effects. Mastery modeling enhanced perceived coping and cognitive control efficacy, decreased perceived vulnerability to assault, and reduced the incidence of intrusive negative thinking and anxiety arousal. These changes were accompanied by increased freedom of action and decreased avoidant behavior. Path analyses of causal structures revealed a dual path of regulation of behavior by perceived coping self-efficacy, one mediated through perceived vulnerability and risk discernment and the other through perceived cognitive control self-efficacy and intrusive negative thinking. Converging lines of evidence reveal that personal and social change rely extensively on methods of empowerment (Bandura, 1988a; Rappaport, Swift, & Hess, 1984; Ratcliff, 1984; Silbert, 1984). These approaches achieve their effects by equipping people with the requisite knowledge, skills, and resilient self-beliefs of efficacy to alter aspects of their lives over which they can exercise some control. Studies of various aspects of personal change indicate that methods of empowerment operate through the self-efficacy mechanism (Bandura, 1986). However, the mode of operation and the generality of this mediating mechanism require further verification. Personal empowerment is an important part of efforts to prevent and control sexual abuse. Sexual assault of women is a prevalent problem in the United States. Because any woman is a potential victim, the lives of many women are distressed and constricted by a sense of inefficacy to cope with the threat of sexual assault. Although this research focuses on empowerment of women with self-protective means, the responsibility for controlling sexual abuse does not lie solely with women. Sexual


title: Effective route planning in road networks using multi constraint routing algorithm

abstract: Today, applications that take the congestions caused by traffic jams into account already exist. During rush hour, it is common that multiple congestions occur on the road network. If the number of traffic jams is too big, the radio stations that transmit TMC messages to the end user often only report the longest traffic jams. Those applications use the Traffic Message Channel (TMC) technique to calculate a route around the congestion whenever the application receives a message about a traffic jam being present. The study for finding the optimal shortest path on graphs with nonnegative weights has taken many forms. The weights of a road network are usually either the length of the arcs or the time it takes to traverse the arc from one vertex to another. The latter depending on the length of the arc and the speed a vehicle is allowed to travel. The majority of route planning applications use the shortest path as a synonym for the fastest path. The Multi Constraint algorithm uses three arrays to calculate the shortest path. The first one contains the neighbors of each node. The second array stores the number of neighbors each node has, and the third contains the link weights. In order for the algorithm to work, the road network has to have all these arrays available.


title: Fog Computing: Comprehensive Approach for Security Data Theft Attack Using Elliptic Curve Cryptography and Decoy Technology

abstract: Fog computing extends cloud computing, provides the services like data, compute, storage and application to end user. It improves the quality of service and also reduces latency. According to Cisco, due to its wide geographical distribution, the Fog computing is well suited for real time analytics and big data. This article, by exploitation advantages of Fog computing Paradigm, analyzes its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks, Wireless Sensor and Actuator Networks, Decentralized Smart Building Control, IoT and Cyber-physical systems and software defined networks, and reviews Comprehensive Approach for security Data Theft Attack the use of Elliptic Curve Cryptography (ECC) and decoy technology in such a constrained environment along with the other two aspects of ECC, namely its security and efficiency. In the article, the performance of ECC is compared with the other PKC applications which should prove that ECC performs well and is more suitable for Fog environments.


title: The uses and abuses of Facebook: A review of Facebook addiction

abstract: Background and aims: Recent research suggests that use of social networking sites can be addictive for some individuals. Due to the link between motivations for media use and the development of addiction, this systematic review examines Facebook-related uses and gratifications research and Facebook addiction research. Method: Searches of three large academic databases revealed 24 studies examining the uses and gratifications of Facebook, and nine studies of Facebook addiction. Results: Comparison of uses and gratifications research reveals that the most popular motives for Facebook use are relationship maintenance, passing time, entertainment, and companionship. These motivations may be related to Facebook addiction through use that is habitual, excessive, or motivated by a desire for mood alteration. Examination of Facebook addiction research indicates that Facebook use can become habitual or excessive, and some addicts use the site to escape from negative moods. However, examination of Facebook addiction measures highlights inconsistency in the field. Discussion: There is some evidence to support the argument that uses and gratifications of Facebook are linked with Facebook addiction. Furthermore, it appears as if the social skill model of addiction may explain Facebook addiction, but inconsistency in the measurement of this condition limits the ability to provide conclusive arguments. Conclusions: This paper recommends that further research be performed to establish the links between uses and gratifications and Facebook addiction. Furthermore, in order to enhance the construct validity of Facebook addiction, researchers should take a more systematic approach to assessment.


title: Artificial Intelligence in Predicting Bladder Cancer Outcome: A Comparison of Neuro-Fuzzy Modeling and Artificial Neural Networks 1

abstract: PURPOSE ::: New techniques for the prediction of tumor behavior are needed, because statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. Whereas artificial neural networks (ANN), the best-studied form of AI, have been used successfully, its hidden networks remain an obstacle to its acceptance. Neuro-fuzzy modeling (NFM), another AI method, has a transparent functional layer and is without many of the drawbacks of ANN. We have compared the predictive accuracies of NFM, ANN, and traditional statistical methods, for the behavior of bladder cancer. ::: ::: ::: EXPERIMENTAL DESIGN ::: Experimental molecular biomarkers, including p53 and the mismatch repair proteins, and conventional clinicopathological data were studied in a cohort of 109 patients with bladder cancer. For all three of the methods, models were produced to predict the presence and timing of a tumor relapse. ::: ::: ::: RESULTS ::: Both methods of AI predicted relapse with an accuracy ranging from 88% to 95%. This was superior to statistical methods (71-77%; P < 0.0006). NFM appeared better than ANN at predicting the timing of relapse (P = 0.073). ::: ::: ::: CONCLUSIONS ::: The use of AI can accurately predict cancer behavior. NFM has a similar or superior predictive accuracy to ANN. However, unlike the impenetrable "black-box" of a neural network, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. This technique could be used widely in a variety of areas of medicine.


title: Toward an empirical verification of the General Theory of Verbal Humor

abstract: A roof panel joint construction and clip device for use therein to secure abutting edges of panels so that the clip and associated fastener is concealed and so that the panels are capable of expanding or contracting longitudinally due to thermal changes. Abutting edges of roof panels are crimped together with a lip portion of the clip positioned therebetween while the clip is secured to a purlin with a flange portion of the clip including an elongated slot and a spring clamping member positioned between the head of a fastener and the flange.


title: Proactive and hybrid wireless network access strategy for Vehicle Cloud networks: An evolutionary game approach

abstract: Vehicular cloud computing is a new paradigm that integrates VANET and cloud computing. It refers to the vehicles' use of outside resources: they access to the conventional cloud or cooperate with other vehicles by sharing their resources (i.e. formation of a temporary vehicular cloud). Both these two ways induce different costs such as expensive cellular links in the case of access to the conventional cloud and intermittent links in the case of vehicles' cooperation. In this paper, we focus on the proactive decision made by the vehicle (i.e. choose the access to the vehicular cloud or to the cloud computing), where this decision depends on many parameters hardly to control. In other words, our goal is to determine the best technology to access the cloud (i.e wifi/LTE). We propose to model the evolution of the vehicles' strategy as an evolutionary game. To avoid the utilization of a centralized controller, we propose a distributed Q-Learning based Vehicular Cloud Access algorithm (QL-VCA) that allows each vehicle to select the way of access independently. The simulation results show that the proposed Q-learning algorithm provides users with better throughput and lower packet loss rate and delay compared to the other scenarios (i.e. using only the LTE or the wifi).


title: Microstrip Patch Array Antenna Using a Parallel and Series Combination Feed Network

abstract: In this paper, we describe $\mathrm {a}4 \times 4$ microstrip patch array antenna with a high gain and low side lobe level (SLL) using a parallel and series combination feed network with different power distribution ratios. Several quarter-wavelength impedance transformers are connected, and the power distribution ratio is controlled by adjusting the characteristic impedance of the feed network. The optimized $4 \times 4$ microstrip patch array antenna has a $\vert \mathrm {S} _{\mathbf {11}} \vert \lt {(!{-}!)}10$ dB impedance bandwidth of 220 MHz which is a fractional bandwidth of 2.1%. At 12.5 GHz, the antenna has a gain of 18.2 dBi and SLLs of–28.0 and–26.5 dB in the x-z and y-z planes, respectively. The size of the array antenna is 100 mm $\times 100$ mm $\times 0.7874$ mm (3.5$\lambda \mathbf {o} { \times 3.5} \lambda \mathbf {o} { \times 0.028} \lambda \mathbf {o}$at 12.5 GHz).


title: A federated recommender system for online learning environments

abstract: From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to provide a Web 2.0 dashboard bringing together open resources from the Cloud and proprietary content from in-house learning management systems. The paper describes the main aspects of the federated recommender system, including its adopted architecture, the common data model used to harvest the different learning platforms, the recommendation algorithm, as well as the recommendation display widget.


title: CT-index: Fingerprint-based graph indexing combining cycles and trees

abstract: Efficient subgraph queries in large databases are a time-critical task in many application areas as e.g. biology or chemistry, where biological networks or chemical compounds are modeled as graphs. The NP-completeness of the underlying subgraph isomorphism problem renders an exact subgraph test for each database graph infeasible. Therefore efficient methods have to be found that avoid most of these tests but still allow to identify all graphs containing the query pattern. We propose a new approach based on the filter-verification paradigm, using a new hash-key fingerprint technique with a combination of tree and cycle features for filtering and a new subgraph isomorphism test for verification. Our approach is able to cope with edge and vertex labels and also allows to use wild card patterns for the search. We present an experimental comparison of our approach with state-of-the-art methods using a benchmark set of both real world and generated graph instances that shows its practicability. Our approach is implemented as part of the Scaffold Hunter software, a tool for the visual analysis of chemical compound databases.


title: Factor Graphs and the Sum-Product Algorithm

abstract: Algorithms that must deal with complicated global functions of many variables often exploit the manner in which the given functions factor as a product of "local" functions, each of which depends on a subset of the variables. Such a factorization can be visualized with a bipartite graph that we call a factor graph, In this tutorial paper, we present a generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph. Following a single, simple computational rule, the sum-product algorithm computes-either exactly or approximately-various marginal functions derived from the global function. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can be derived as specific instances of the sum-product algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's (1988) belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform (FFT) algorithms.


title: Off-line programming of robotic system based on DXF files of 3D models

abstract: For the convenience of complex trajectory programming of robot and in order to enrich robotic off-line programming (OLP) types, this paper presents an efficient robotic OLP method based on DXF files of 3D modeling software. By decoding DXF files, the 3D position information of object graphic elements such as points, lines, arcs and splines are first extracted, all the graphic elements are then converted into point elements. A trajectory planning algorithm is proposed to make these points linked into robot trajectories according to the drawing order in CAD software. Except for the position information of the trajectory, the orientation of the trajectory is also planned by an easy and efficient method. Coordinate transformation is fulfilled by calibration,The planned trajectory is verified with OpenGL simulation, the executable program is then generated and downloaded into the robot controlle. Experiment results of the 5-DoFs modular robot show that the presented OLP system is effective and can be used in practice.


title: Motivation and Personality : A Neuropsychological Perspective

abstract: Personality is strongly influenced by motivation systems that organise responses to rewards and punishments and that drive approach and avoidance behavior. Neuropsychological research has identified: (a) two avoidance systems, one related to pure active avoidance and escape, and one to passive avoidance and behavioral inhibition produced by goal-conflict; and (b) two approach systems, one related to the actions of reward seeking and one to experience and behavior related to pleasure on receiving reward. These systems mediate fluid moment-by-moment reactions to changing stimuli, with relatively stable person-specific sensitivities to these stimuli manifested in personality traits. We review what is known about these motivational traits, integrating the theory-driven approach based on animal learning paradigms with the empirical tradition of the Big Five personality model. People differ from one another, and this fact is obvious to everyone. It is common to talk about people’s personalities using lexical terms to describe their characteristic ways of thinking, feeling and behaving (e.g., ‘bold’, ‘lazy’, ‘intelligent’), and we use these descriptors to infer people’s intentions and likely future behavior. Personality psychologists have long analyzed the ratings of large numbers of trait descriptive adjectives to produce the most widely used taxonomy of personality: the Big Five, which includes the dimensions of Extraversion, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience ⁄Intellect (John, Naumann, & Soto, 2008). These Big Five traits also emerge from existing personality questionnaires that were not designed specifically to measure them (e.g., Markon, Krueger, & Watson, 2005), suggesting it is a good candidate for a consensual model in personality psychology. What the Big Five model does not immediately offer, however, is an explanation for the causal sources of personality traits. Why do people think, feel, and act in the ways that they do? People react to situations, of course; but different people react differently to the same situation, suggesting that they have different behavioral propensities. In order to answer this why question, we must discover what drives people’s actions and reactions. Inferring motivation from observed personality has been something of a dark art in psychology. However, one promising approach to this question is based on the biology of motivational control systems, studied by psychologists for over a century in non-human animals, and for somewhat less time in humans. This approach operates on the premise that stable individual differences in behavior (personality traits) must be due to relatively stable individual differences in the operation of brain systems that produce (state) behavior from moment-tomoment. From this perspective, each of our many traits reflects the operations of a set of brain systems that has evolved to respond to a different class of functional requirements


title: Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

abstract: We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 x 10(-7)). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.


title: Biomechatronics design of a robotic arm for rehabilitation

abstract: Rehabilitation is an important process to restore muscle strength and joint's range of motion. This paper proposes a biomechatronic design of a robotic arm that is able to mimic the natural movement of the human shoulder, elbow and wrist joint. In a preliminary experiment, a subject was asked to perform four different arm movements using the developed robotic arm for a period of two weeks. The experimental results were recorded and can be plotted into graphical results using Matlab. Based on the results, the robotic arm shows encouraging effect by increasing the performance of rehabilitation process. This is proven when the result in degree value are accurate when being compared with the flexion of both shoulder and elbow joints. This project can give advantages on research if the input parameter needed in the flexion of elbow and wrist.


title: Flikker: saving DRAM refresh-power through critical data partitioning

abstract: Energy has become a first-class design constraint in computer systems. Memory is a significant contributor to total system power. This paper introduces Flikker, an application-level technique to reduce refresh power in DRAM memories. Flikker enables developers to specify critical and non-critical data in programs and the runtime system allocates this data in separate parts of memory. The portion of memory containing critical data is refreshed at the regular refresh-rate, while the portion containing non-critical data is refreshed at substantially lower rates. This partitioning saves energy at the cost of a modest increase in data corruption in the non-critical data. Flikker thus exposes and leverages an interesting trade-off between energy consumption and hardware correctness. We show that many applications are naturally tolerant to errors in the non-critical data, and in the vast majority of cases, the errors have little or no impact on the application's final outcome. We also find that Flikker can save between 20-25% of the power consumed by the memory sub-system in a mobile device, with negligible impact on application performance. Flikker is implemented almost entirely in software, and requires only modest changes to the hardware.


title: Persistently Increased Serum Concentrations of Cardiac Troponin T in Patients With Idiopathic Dilated Cardiomyopathy Are Predictive of Adverse Outcomes

abstract: Background—The measurement of serum concentrations of cardiac troponin T (TnT) is a simple, useful method to detect myocyte injury that may be repeated multiple times to follow patients without interobserver variability. Methods and Results—Multiple measurements of TnT with a second-generation assay were performed in 60 patients with dilated cardiomyopathy confirmed by coronary angiography and endomyocardial biopsy between April 1996 and December 1999. Three evolutionary patterns of TnT concentrations were identified. Thirty-three patients had concentrations of TnT <0.02 ng/mL throughout the follow-up period (group 1). The remaining 27 patients had high initial serum concentrations of TnT (≥0.02 ng/mL). In 10 of these 27 patients, TnT decreased to <0.02 ng/mL during follow-up (group 2), whereas 17 had persistently high serum TnT concentrations despite being conventionally treated for chronic congestive heart failure (group 3). Although the initial echocardiographic left ventricular diastolic dimension (LV...


title: Reorienting attention across the horizontal and vertical meridians: Evidence in favor of a premotor theory of attention

abstract: Abstract Stimuli presented in a non-attended location are responded to much slower than stimuli presented in an attended one. The hypptheses proposed to explain this effect make reference to covert movement of attention, hemifield inhibition, or attentional gradients. The experiment reported here was aimed at discriminating among these hypotheses. Subjects were cued to attend to one of four possible stimulus locations, which were arranged either horizontally or vertically, above, below, to the right or left of a fixation point. The instructions were to respond manually as fast as possible to the occurence of a visual stimulus, regardless of whether it occurred in a cued or in a non-cued location. In 70% of the cued trials the stimulus was presented in the cued location and in 30% in one of the non-cued locations. In addition there were trials in which a non-directional cue instructed the subject to pay attention to all four locations. The results showed that (a) the correct orienting of attention yielded a small but significant benefit; (b) the incorrect orienting of attention yielded a large and significant cost; (c) the cost tended to increase as a function of the distance between the attended location and the location that was actually stimulated; and (d) an additional cost was incurred when the stimulated and attended locations were on opposite sides of the vertical or horizontal meridian. We concluded that neither the hypothesis postulating hemifield inhibition nor that postulating movement of attention with a constant time can explain the data. The hypothesis of an attention gradient and that of attention movements with a constant speed are tenable in principle, but they fail to account for the effect of crossing the horizontal and vertical meridians. A hypothesis is proposed that postulates a strick link between covert orienting of attention and programming explicit ocular movements. Attention is oriented to a given point when the oculomotor programme for moving the eyes to this point is ready to be executed. Attentional cost is the time required to erase one ocular program and prepare the next one.


A 2 . 4 GHz Low Voltage CMOS Down-Conversion Double-Balanced Mixer


title: Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

abstract: Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, and embodiment. We term this kind of imitation learning as imitation-from-observation and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations and actions in the same environment, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores, acquiring skills such as sweeping from videos of a human demonstrator. Videos can be found at this https URL


title: Rough Information Set and Its Applications in Decision Making

abstract: The decision making in the real world is inevitably characterized with vagueness, and imprecision due to incomplete knowledge. To this end, we combine the information set with the rough set theory to represent both the vagueness and imprecision at the same time. We term the proposed structure as rough information set that has information sets based on fuzzy equivalence relations as its building blocks. The usefulness of the proposed structure is demonstrated through a case study in credit scoring analysis, and a biometrics application on knuckle-based recognition.