title: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

abstract: In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.


title: CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits

abstract: We propose a novel algorithm for optimizing multivariate linear threshold functions as split functions of decision trees to create improved Random Forest classifiers. Standard tree induction methods resort to sampling and exhaustive search to find good univariate split functions. In contrast, our method computes a linear combination of the features at each node, and optimizes the parameters of the linear combination (oblique) split functions by adopting a variant of latent variable SVM formulation. We develop a convex-concave upper bound on the classification loss for a one-level decision tree, and optimize the bound by stochastic gradient descent at each internal node of the tree. Forests of up to 1000 Continuously Optimized Oblique (CO2) decision trees are created, which significantly outperform Random Forest with univariate splits and previous techniques for constructing oblique trees. Experimental results are reported on multi-class classification benchmarks and on Labeled Faces in the Wild (LFW) dataset.


title: Cultural Theories of Postpartum Bleeding in Matlab, Bangladesh: Implications for Community Health Intervention

abstract: Early recognition can reduce maternal disability and deaths due to postpartum haemorrhage. This study identified cultural theories of postpartum bleeding that may lead to inappropriate recognition and delayed care-seeking. Qualitative and quantitative data obtained through structured interviews with 149 participants living in Matlab, Bangladesh, including women aged 18-49 years, women aged 50+ years, traditional birth attendants (TBAs), and skilled birth attendants (SBAs), were subjected to cultural domain. General consensus existed among the TBAs and lay women regarding signs, causes, and treatments of postpartum bleeding (eigenvalue ratio 5.9, mean competence 0.59, and standard deviation 0.15). Excessive bleeding appeared to be distinguished by flow characteristics, not colour or quantity. Yet, the TBAs and lay women differed significantly from the SBAs in beliefs about normalcy of blood loss, causal role of the retained placenta and malevolent spirits, and care practices critical to survival. Cultural domain analysis captures variation in theories with specificity and representativeness necessary to inform community health intervention.


title: Privacy and Security in E-Learning *

abstract: For a variety of advantages, universities and other organizations are resorting to e-learning to provide instruction on-line. While many advances have been made in the mechanics of providing online instruction, the needs for privacy and security have to-date been largely ignored. This paper examines privacy and security issues associated with e-learning. It presents the basic principles behind privacy practices and legislation. It investigates the more popular e-learning standards to determine their provisions and limitations for privacy and security. Privacy requirements for e-learning systems are explored with respect to the “Privacy Principles”. The capabilities of a number of existing privacy enhancing technologies, including methods for network privacy, policy-based privacy/security management, and trust systems, are reviewed and assessed.


title: Stability Enhancement Based on Virtual Impedance for DC Microgrids With Constant Power Loads

abstract: In this paper, a converter-based dc microgrid is studied. By considering the impact of each component in dc microgrids on system stability, a multistage configuration is employed, which includes the source stage, interface converter stage between buses, and common load stage. In order to study the overall stability of the above dc microgrid with constant power loads (CPLs), a comprehensive small-signal model is derived by analyzing the interface converters in each stage. The instability issue induced by the CPLs is revealed by using the criteria of impedance matching. Meanwhile, virtual-impedance-based stabilizers are proposed in order to enhance the damping of dc microgrids with CPLs and guarantee the stable operation. Since droop control is commonly used to reach proper load power sharing in dc microgrids, its impact is taken into account when testing the proposed stabilizers. By using the proposed stabilizers, virtual impedances are employed in the output filters of the interface converters in the second stage of the multistage configuration. In particular, one of the virtual impedances is connected in series with the filter capacitor, and the other one is connected at the output path of the converter. It can be seen that by using the proposed stabilizers, the unstable poles induced by the CPLs are forced to move into the stable region. The proposed method is verified by the MATLAB/Simulink model of multistage dc microgrids with three distributed power generation units.


title: A fuzzy-Based Intelligent Traffic Control System for Managing VIP-Induced Chaos at Road Intersections

abstract: In this work, an intelligent traffic light controller that incorporates very important personnel (VIP) movement is presented. VIP movement often cause chaos or in the worst case accident at cross junctions. Traffic quantity, waiting time and siren intensity are the inputs to the system. The fuzzy logic tool box in Matlab 7.0 was used to simulate the system. The results obtained from running the simulation on sample traffic data shows that the system was able to accord the VIP movement its desired priority at the cross junctions thereby reducing the possibility of VIP movement causing accident at the junction. Keyword: Intelligent system, traffic control, VIP movement, fuzzy logic, simulation.


title: Interactive Document Clustering Revisited: A Visual Analytics Approach

abstract: Document clustering is an efficient way to get insight into large text collections. Due to the personalized nature of document clustering, even the best fully automatic algorithms cannot create clusters that accurately reflect the user»s perspectives. To incorporate the user»s perspective in the clustering process and, at the same time, effectively visualize document collections to enhance user's sense-making of data, we propose a novel visual analytics system for interactive document clustering. We built our system on top of clustering algorithms that can adapt to user's feedback. First, the initial clustering is created based on the user-defined number of clusters and the selected clustering algorithm. Second, the clustering result is visualized to the user. A collection of coordinated visualization modules and document projection is designed to guide the user towards a better insight into the document collection and clusters. The user changes clusters and key-terms iteratively as a feedback to the clustering algorithm until the result is satisfactory. In key-term based interaction, the user assigns a set of key-terms to each target cluster to guide the clustering algorithm. A set of quantitative experiments, a use case, and a user study have been conducted to show the advantages of the approach for document analytics based on clustering.


title: A Survey on Privacy and Security in Online Social Networks

abstract: Online Social Networks (OSN) are a permanent presence in today's personal and professional lives of a huge segment of the population, with direct consequences to offline activities. Built on a foundation of trust-users connect to other users with common interests or overlapping personal trajectories-online social networks and the associated applications extract an unprecedented volume of personal information. Unsurprisingly, serious privacy and security risks emerged, positioning themselves along two main types of attacks: attacks that exploit the implicit trust embedded in declared social relationships; and attacks that harvest user's personal information for ill-intended use. This article provides an overview of the privacy and security issues that emerged so far in OSNs. We introduce a taxonomy of privacy and security attacks in OSNs, we overview existing solutions to mitigate those attacks, and outline challenges still to overcome.


title: Deep Reinforcement Learning for Network Slicing.

abstract: Network slicing means an emerging business to operators and allows them to sell the customized slices to various tenants at different prices. In order to provide better-performing and costefficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforces the tendency actions producing more rewarding consequences, is emerging as a promising solution. In this paper, after briefly reviewing the fundamental concepts and evolution-driving factors of DRL, we investigate the application of DRL in some typical resource management scenarios of network slicing, which include radio resource slicing and priority-based core network slicing, and demonstrate the performance advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.


title: Benchmarks for evaluating socially assistive robotics

abstract: Socially assistive robotics (SAR) is a growing area of research. Evaluating SAR systems presents novel challenges. Using a robot for a socially assistive task can have various benefits and ethical implications. Many questions are important to understanding whether a robot is effective for a given application domain. This paper describes several benchmarks for evaluating SAR systems. There exist numerous methods for evaluating the many factors involved in a robot’s design. Benchmarks from psychology, anthropology, medicine, and human–robot interaction are proposed as measures of success in evaluating a given SAR system and its impact on the user and broader population.


title: LemmaGen: Multilingual Lemmatisation with Induced Ripple-Down Rules

abstract: Lemmatisation is the process of finding the normalised forms of words appearing in text. It is a useful preprocessing step for a number of language engineering and text mining tasks, and especially important for languages with rich inflectional morphology. This paper presents a new lemmatisation system, LemmaGen, which was trained to generate accurate and efficient lemmatisers for twelve different languages. Its evaluation on the corresponding lexicons shows that LemmaGen outperforms the lemmatisers generated by two alternative approaches, RDR and CST, both in terms of accuracy and efficiency. To our knowledge, LemmaGen is the most efficient publicly available lemmatiser trained on large lexicons of multiple languages, whose learning engine can be retrained to effectively generate lemmatisers of other languages.


A study of skew in mapreduce applications


title: Common-mode noise reduction on broadside-coupled delay line

abstract: Conventional edge-coupled differential delay lines are becoming popular choice on high-speed connection, which depend on their balanced property and superior immunity to noise interference. Although edge-coupled differential delay lines with differential signaling have many advantages, such as high noise immunity and low noise radiation, yet common-mode noise are becoming higher and higher resulting data rate approaches GHz level. Hence, broadside-coupled differential delay lines are proposed for common-mode noise reduction in this research. Simulation results show that time-domain common-mode noise on broadside-coupled serpentine and spiral delay lines can reduce 79 % and 84.57 % compared with edge-coupled serpentine and spiral delay lines. Similarly, frequency-domain differential to common-mode conversions have significant reduction at the frequency band of interest.


Extending the road beyond CMOS - IEEE Circuits and Devices Magazine


title: Finding the Best Feature Detector-Descriptor Combination

abstract: Addressing the image correspondence problem by feature matching is a central part of computer vision and 3D inference from images. Consequently, there is a substantial amount of work on evaluating feature detection and feature description methodology. However, the performance of the feature matching is an interplay of both detector and descriptor methodology. Our main contribution is to evaluate the performance of some of the most popular descriptor and detector combinations on the DTU Robot dataset, which is a very large dataset with massive amounts of systematic data aimed at two view matching. The size of the dataset implies that we can also reasonably make deductions about the statistical significance of our results. We conclude, that the MSER and Difference of Gaussian (DoG) detectors with a SIFT or DAISY descriptor are the top performers. This performance is, however, not statistically significantly better than some other methods. As a byproduct of this investigation, we have also tested various DAISY type descriptors, and found that the difference among their performance is statistically insignificant using this dataset. Furthermore, we have not been able to produce results collaborating that using affine invariant feature detectors carries a statistical significant advantage on general scene types.


title: Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection

abstract: Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation. ::: ::: The presented experiments show that i more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, ii lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and iii paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.


title: The pupil as a measure of emotional arousal and autonomic activation

abstract: Pupil diameter was monitored during picture viewing to assess effects of hedonic valence and emotional arousal on pupillary responses. Autonomic activity (heart rate and skin conductance) was concurrently measured to determine whether pupillary changes are mediated by parasympathetic or sympathetic activation. Following an initial light reflex, pupillary changes were larger when viewing emotionally arousing pictures, regardless of whether these were pleasant or unpleasant. Pupillary changes during picture viewing covaried with skin conductance change, supporting the interpretation that sympathetic nervous systemactivity modulates these changes in the context of affective picture viewing. Taken together, the data provide strong support for the hypothesis that the pupil’s response during affective picture viewing reflects emotional arousal associated with increased sympathetic activity.


TAXONOMY OF THE FUNDAMENTAL CONCEPTS OF METAPROGRAMMING


title: A random walk method for alleviating the sparsity problem in collaborative filtering

abstract: Collaborative Filtering is one of the most widely used approaches in recommendation systems which predicts user preferences by learning past user-item relationships. In recent years, item-oriented collaborative filtering methods came into prominence as they are more scalable compared to user-oriented methods. Item-oriented methods discover item-item relationships from the training data and use these relations to compute predictions. In this paper, we propose a novel item-oriented algorithm, Random Walk Recommender, that first infers transition probabilities between items based on their similarities and models finite length random walks on the item space to compute predictions. This method is especially useful when training data is less than plentiful, namely when typical similarity measures fail to capture actual relationships between items. Aside from the proposed prediction algorithm, the final transition probability matrix computed in one of the intermediate steps can be used as an item similarity matrix in typical item-oriented approaches. Thus, this paper suggests a method to enhance similarity matrices under sparse data as well. Experiments on MovieLens data show that Random Walk Recommender algorithm outperforms two other item-oriented methods in different sparsity levels while having the best performance difference in sparse datasets.


title: Linguistic Regularities in Sparse and Explicit Word Representations

abstract: Recent work has shown that neuralembedded word representations capture many relational similarities, which can be recovered by means of vector arithmetic in the embedded space. We show that Mikolov et al.’s method of first adding and subtracting word vectors, and then searching for a word similar to the result, is equivalent to searching for a word that maximizes a linear combination of three pairwise word similarities. Based on this observation, we suggest an improved method of recovering relational similarities, improving the state-of-the-art results on two recent word-analogy datasets. Moreover, we demonstrate that analogy recovery is not restricted to neural word embeddings, and that a similar amount of relational similarities can be recovered from traditional distributional word representations.


title: High-quality reflections, refractions, and caustics in Augmented Reality and their contribution to visual coherence

abstract: In this paper we present a novel high-quality rendering system for Augmented Reality (AR). We study ray-tracing based rendering techniques in AR with the goal of achieving real-time performance and improving visual quality as well as visual coherence between real and virtual objects in a final composited image. A number of realistic and physically correct rendering effects are demonstrated, that have not been presented in real-time AR environments before. Examples are high-quality specular effects such as caustics, refraction, reflection, together with a depth of field effect and anti-aliasing.


title: AN OPTIMIZATION TOOL FOR THE SELECTION OF ENERGY SAVINGS ALTERNATIVES IN EXISTING HOUSEHOLDS: COMBINING BUILDING PERFORMANCE, RENEWABLE ENERGY, AND ELECTRIC VEHICLES

abstract: This study presents an innovative simulation-based optimization tool to select the minimum cost mix of building technologies, in a typical existing household with at least one vehicle, under different energy market scenarios. The proposed approach considers in its solution scheme the integration of building performance improvements, local renewable energy generation, and possible Electric Vehicle (EV) adoption with vehicle-tohouse (V2H) technology. The method was applied to a prototype single-family building and resulted in relevant minimum cost solutions for a cycle of 20 years. The findings make the proposed methodology useful for the research realm, for the market, and for policy makers.


title: Polarization Reconfigurable Aperture-Fed Patch Antenna and Array

abstract: This paper presents a reconfigurable aperture-fed patch antenna array for ±45° polarizations. First, a new method to realize the reconfigurable ±45° polarizations is proposed. It introduces controllable RF switches on a cross-aperture to excite a square patch for two orthogonal polarizations. The RF switches are controlled by two sets of DC biases, which could select the polarization through the reconfigurable aperture. Second, two patch antennas based on cross-aperture excitation are discussed. The first structure uses a split ground plane with four switches, while the other one employs a united ground plane with eight switches. Both antennas operate well as the single element. However, only the antenna with a united ground is suitable for developing a reconfigurable dual-polarized antenna array, owning to its simple DC biasing lines and the reconfigurable-controlling network. Both simulation and experiment studies were examined to verify the proposed design. The measured 10-dB impedance bandwidth of the array is 9.3%, which can cover the 2.4-GHz WLAN band. The desired radiation patterns were observed with a maximum gain of 13.5 dBi. The proposed antenna array has low cross-polarization and stable gain across the entire operating bandwidth.


title: Deep Sparse Tensor Filtering Network for Synthetic Aperture Radar Images Classification

abstract: Recognizing scenes from synthetic aperture radar (SAR) images has been a challenging task due to the increasing resolution of SAR data. Extracting discriminative features from SAR images is extremely difficult for their sensitivity to target aspect. Considering the intractability of the available deep neural networks in practical implementations, in this brief, we propose a simple and efficient deep sparse tensor filtering network (DSTFN) for SAR image classification. An SAR image is first organized into a data tensor by an overlapped partition. Then, a set of dimension-inseparable geometric filters is developed from a least squares support vector machine, followed by a learned sparse filtering of tensors. Finally, the constructed sparse tensor filters are cascaded to a deep network to automatically extract the discriminative features of the image for accurate classification. Simulations are carried out to verify the effectiveness of the proposed DSTFN.


Author Masking through Translation


title: Compact Circularly Polarized Antennas Combining Meta-Surfaces and Strong Space-Filling Meta-Resonators

abstract: Reduced-size single-feed circularly-polarized (CP) patch antennas are proposed and investigated based on the strategy of combining meta-surfaces and meta-resonators owning strong space-filling capability. They all comprise a slot-loaded square patch printed over a well designed reactive impedance surface (artificial meta-surface with magnetic response at the resonant frequency) for improved antenna performances and size reduction. The complementary crossbar fractal tree (CCFT) slot and three-turn complementary spiral resonators (TCSRs) with asymmetric gap orientation are employed as meta-resonators to render the antennas to radiate CP waves in single-band or dual-band operation and to facilitate further miniaturization. Numerical and experimental results indicate that these antennas owning a maximum size of 0.262λ0 × 0.262λ0 around 3 GHz exhibit a comparable impedance and axial ratio (AR) bandwidth over 1.05% and high gain of more than 4.15 dBic, predicting promising applications in portable and handheld communication systems.


title: Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces

abstract: Neuromorphic architectures achieve low-power operation by using many simple spiking neurons in lieu of traditional hardware. Here, we develop methods for precise linear computations in spiking neural networks and use these methods to map the evolution of a linear dynamical system (LDS) onto an existing neuromorphic chip: IBM's TrueNorth. We analytically characterize, and numerically validate, the discrepancy between the spiking LDS state sequence and that of its non-spiking counterpart. These analytical results shed light on the multiway tradeoff between time, space, energy, and accuracy in neuromorphic computation. To demonstrate the utility of our work, we implemented a neuromorphic Kalman filter (KF) and used it for offline decoding of human vocal pitch from neural data. The neuromorphic KF could be used for low-power filtering in domains beyond neuroscience, such as navigation or robotics.


title: Characterization, evaluation, and design of noise Separator for conducted EMI noise diagnosis

abstract: In this paper, at first, three requirements for noise separators are specified and the disadvantages of traditional evaluation methods are pointed out. Noise separators are then characterized using scattering parameters (S-parameters). Existing noise separators are evaluated according to the specified requirements. Finally a noise separator is proposed with parasitic controlled design and the prototype is evaluated using the proposed method. An electromagnetic interference (EMI) measurement shows that the proposed noise separator can effectively separate noise and that it is easy to use.


title: Reducing Bile Leak Following Segmental Liver Transplantation: Understanding Biliary Anatomy of the Caudate Lobe

abstract: Bile leak in split and living donor liver transplantation is not an uncommon postoperative complication with significant morbidity to both donor and recipients. Nonanastomotic bile leaks in these transplants are less well characterized and generally described as cut-surface leaks. A proportion of these leaks may derive from biliary radicles draining the caudate lobe. Based on the caudate lobe biliary anatomy the authors describe measures that may help to reduce such complications after segmental liver transplantation.


title: How significant is human error as a cause of privacy breaches? An empirical study and a framework for error management

abstract: Privacy breaches and their regulatory implications have attracted corporate attention in recent times. An often overlooked cause of privacy breaches is human error. In this study, we first apply a model based on the widely accepted GEMS error typology to analyze publicly reported privacy breach incidents within the U.S. Then, based on an examination of the causes of the reported privacy breach incidents, we propose a defense-in-depth solution strategy founded on error avoidance, error interception, and error correction. Finally, we illustrate the application of the proposed strategy to managing human error in the case of the two leading causes of privacy breach incidents. This study finds that mistakes in the information processing stage constitute the most cases of human error-related privacy breach incidents, clearly highlighting the need for effective policies and their enforcement in organizations.


title: Conglomerate Firms, Internal Capital Markets and the Theory of the Firm

abstract: This article reviews the conglomerate literature, with a focus on recent papers that have cast strong doubt on the hypothesis that conglomerate firms destroy value on average when compared to similar stand-alone firms. Recent work has shown that investment decisions by conglomerate firms are consistent with value maximization; conglomerate firms trade at an average premium relative to single-segment firms when value weighting; and the valuation premia and discounts, both for conglomerates and single-segment firms, are driven by differences in the production of unique differentiated products. A profit-maximizing theory of the firm that considers how firms select their organizational structure can explain these recent findings and much of the large variation in findings in the conglomerate literature. We also review the literature showing how market imperfections create additional benefits and costs for internal capital markets and a potential for managerial distortions.


title: Vision based indoor positioning for intelligent buildings

abstract: With the surveillance camera become more and more popular, people need to observe a large number of monitor at one time. In this paper, we propose a vision based indoor positioning for intelligent buildings to solve this problem. We capture a frame and make the Gaussian mixture learning for video background model. Then we do the background subtraction and get the foreground object. After we have the foreground object, we track objects and use the direct linear transform to integrate camera information to a bird's-eye map. finally all the moving objects are traced and displayed on the bird's-eye view.


An Ontological Model for Fire Emergency Situations


title: Finding Nemo: Searching and Resolving Identities of Users Across Online Social Networks

abstract: An online user joins multiple social networks in order to enjoy dierent services. On each joined social network, she creates an identity and constitutes its three major dimensions namely prole, content and connection network. She largely governs her identity formulation on any social network and therefore can manipulate multiple aspects of it. With no global identier to mark her presence uniquely in the online domain, her online identities remain unlinked, isolated and dicult to search. Earlier research has explored the above mentioned dimensions, to search and link her multiple identities with an assumption that the considered dimensions have been least disturbed across her identities. However, majority of the approaches are restricted to exploitation of one or two dimensions. We make a rst attempt to deploy an integrated system Finding Nemo which uses all the three dimensions of an identity to search for a user on multiple social networks. The system exploits a known identity on one social network to search for her identities on other social networks. We test our system on two most popular and distinct social networks { Twitter and Facebook. We show that the integrated system gives better accuracy than the individual algorithms. We report experimental ndings in the paper.


title: Function-Oriented Decomposition for Reactive Embedded Software

abstract: Due to C's overwhelming dominance in industry, reactive embedded applications usually rely on conventional sequential programming. Adopted approaches favor event-driven paradigms which prevent function-oriented code decomposition in particular. This encourages the violation of fundamental software engineering principles. The reactive programming paradigm is proposed as a general solution. However, most reactive languages cannot keep up with C's practical advantages. It appears, that the subfamily of synchronous languages provides promising features but real-world deployments and evaluations are rarely reported in literature. On this account, we make two major contributions in this paper. First, we elaborate how the lack of function-oriented software decomposition manifests in a real-life industrial application. Second, we provide a corresponding re-implementation which illustrates the deployment and discusses the gained engineering benefits provided by the third-party, synchronous-reactive programming language Ceu. We believe that our work generally reveals a practicable way of improving embedded software quality in industrial applications.


title: Effects of background music on concentration of workers.

abstract: OBJECTIVE ::: Background music is a common element in daily living and the workplace. Determination of whether background music affects human work concentration is a relevant concern. Studies have found background music influences human behavior, and this study attempts to understand how background music and listener fondness for types of music affects worker concentration. ::: ::: ::: METHODS ::: This study analyzes how different types of background music--and how listeners' degree of preference for the background music--can affect listener concentration in attention testing through Randomized Controlled Trial (RCT). ::: ::: ::: PARTICIPANTS ::: Data were collected from 89 workers. The participants ranged in age between 19 and 28 years old, with an average age of 24 years old. ::: ::: ::: RESULTS ::: We conclude background music influenced listener attention. This influence has more to do with listener fondness for the music than with type of music. Compared to situations without background music, the likelihood of background music affecting test-taker attention performance is likely to increase with the degree to which the test-taker likes or dislikes the music. ::: ::: ::: CONCLUSIONS ::: It is important not to select music that workers strongly like or dislike when making a selection of background music to avoid negatively affecting worker concentration.


title: Cognitive Functioning and Acute Sedative Effects of Risperidone and Quetiapine in Patients With Stable Bipolar I Disorder: A Randomized, Double-Blind, Crossover Study

abstract: Objective: Antipsychotic medications differ in their sedative potential, which can affect cognitive performance. The primary objective of this double-blind study was to compare the effects of treatment initiation with risperidone and quetiapine on cognitive function in subjects with stable bipolar disorder. Method: Subjects had a DSM-IV diagnosis of bipolar I disorder in partial or full remission and a Young Mania Rating Scale score ≤ 8 at screening. Subjects were randomly assigned to 1 of 2 treatment sequences: risperidone-quetiapine or quetiapine-risperidone. Subjects in the risperidone-quetiapine sequence received 2 mg of risperidone with dinner and placebo with breakfast during period 1 and 100 mg of quetiapine with dinner and 100 mg with breakfast during period 2. Subjects in the quetiapine-risperidone sequence received the same treatments in reverse order. The 2 treatment periods were separated by a 6- to 14-day washout period. Cognitive function, including attention, working memory, declarative memory, processing speed, and executive functions, was measured before and after dosing. The Visual Analog Scale for Fatigue was also completed. The primary endpoint was a neurocognitive composite score (NCS). The study was conducted from November 2004 through August 2005. Results: Thirty subjects were randomly assigned; 28 took all doses of study medication and completed a baseline and at least 1 postbaseline assessment in each treatment. On the NCS, significantly better overall cognitive function was seen after risperidone than after quetiapine at each time point after dosing. Subjects performed significantly better after risperidone than after quetiapine (p <.05) on 9 of the 18 individual cognitive outcome measures and significantly better after quetiapine than after risperidone on 1 measure. Sleeping or the need for sleep during the test days was reported in significantly more patients after receiving quetiapine than risperidone. Conclusions: The results indicate that initiation of quetiapine treatment was associated with more immediate adverse cognitive effects and increased somnolence than risperidone treatment.


title: Gaming frequency and academic performance

abstract: There are numerous claims that playing computer and video games may be educationally beneficial, but there has been little formal investigation into whether or not the frequency of exposure to such games actually affects academic performance. This paper explores the issue by analysing the relationships between gaming frequency - measured as the amount of time undergraduate students spend playing games in their free time - and their academic performance as measured by their examination marks. Using a sample of 713 students, correlation analyses between gaming frequency and examination performance were conducted for students of varying gaming frequency, study discipline, gender, and general attitudes towards gaming and study. The results reveal that examination marks are in fact negatively correlated with gaming frequency - i.e. frequent gamers generally achieve lower marks than less frequent gamers.


title: Learning to Learn without Gradient Descent by Gradient Descent

abstract: We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.


title: Multilayered substrate integrated waveguide (MSIW) elliptic filter

abstract: This letter presents the design and experiment of a novel elliptic filter based on the multilayered substrate integrated waveguide (MSIW) technique. A C-band elliptic filter with four folded MSIW cavities is simulated by using high frequency structure simulator software and fabricated with a two-layer printed circuit board process, the measured results show good performance and in agreement with the simulated results.


Perceived Benefit and Risk as Multidimensional Determinants of Bitcoin Use: A Quantitative Exploratory Study


title: Experience and Abstract Reasoning in Learning Backward Induction

abstract: Backward induction is a benchmark of game theoretic rationality, yet surprisingly little is known as to how humans discover and initially learn to apply this abstract solution concept in experimental settings. We use behavioral and functional magnetic resonance imaging (fMRI) data to study the way in which subjects playing in a sequential game of perfect information learn the optimal backward induction strategy for the game. Experimental data from our two studies support two main findings: First, subjects converge to a common process of recursive inference similar to the backward induction procedure for solving the game. The process is recursive because earlier insights and conclusions are used as inputs in later steps of the inference. This process is matched by a similar pattern in brain activation, which also proceeds backward, following the prediction error: brain activity initially codes the responses to losses in final positions; in later trials this activity shifts to the starting position. Second, the learning process is not exclusively cognitive, but instead combines experience-based learning and abstract reasoning. Critical experiences leading to the adoption of an improved solution strategy appear to be stimulated by brain activity in the reward system. This indicates that the negative affect induced by initial failures facilitates the switch to a different method of solving the problem. Abstract reasoning is combined with this response, and is expressed by activation in the ventrolateral prefrontal cortex. Differences in brain activation match differences in performance between subjects who show different learning speeds.


title: An industrial case study on reuse oriented development

abstract: Software reuse can become a key factor for improving and guaranteeing software quality, when adopted systematically all along the software process. The main characteristic of reuse-oriented processes is that they require a common repository for storing, searching and retrieving software modules. Moreover, reuse occurs systematically and is an integrated part of the process. Previous works of the same authors have empirically shown that the full reuse maintenance model (FRM) slows down quality degradation following to maintenance interventions on a software system. This work is a further step in the investigation towards demonstrating how reuse oriented development (ROD) impacts on software quality; how it favors FRM model; and finally, whether reuse-oriented development influences productivity, and as so, is more efficient. This has been done through a case study carried out on two ongoing industrial projects. Results are positive and support our research hypotheses.


title: Dynamic small cell placement strategies for LTE Heterogeneous Networks

abstract: Small cell deployments have proven to be a cost-effective solution to meet the ever growing capacity and coverage requirements of mobile networks. While small cells are commonly deployed indoors, more recently outdoor roll-outs have garnered industry interest to complement existing macrocell infrastructure. However, the problem of where and when to deploy these small cells remains a challenge. In this paper, we investigate the small base station (SBS) placement problem in high demand outdoor environments. First, we propose a dynamic placement strategy (DPS) that optimizes SBS deployment for two different network objectives: 1) minimizing data delivery cost, and 2) minimizing macrocell utilization. We formulate each problem as a mixed integer linear program (MILP) that determines the optimal set of deployment locations among the candidate hot-spots to meet each network objective. Then we develop two greedy algorithms, one for each objective, that achieve close to optimal MILP performance. Our simulation results demonstrate that significant delivery cost and MBS utilization reductions are possible by incorporating the proposed deployment strategies.


title: Training an Agent for FPS Doom Game using Visual Reinforcement Learning and VizDoom

abstract: Because of the recent success and advancements in deep mind technologies, it is now used to train agents using deep learning for first-person shooter games that are often outperforming human players by means of only screen raw pixels to create their decisions. A visual Doom AI Competition is organized each year on two different tracks: limited death-match on a known map and a full death-match on an unknown map for evaluating AI agents, because computer games are the best test-beds for testing and evaluating different AI techniques and approaches. The competition is ranked based on the number of frags each agent achieves. In this paper, training a competitive agent for playing Doom’s (FPS Game) basic scenario(s) in a semi-realistic 3D world ‘VizDoom’ using the combination of convolutional Deep learning and Q-learning by considering only the screen raw pixels in order to exhibit agent’s usefulness in Doom is proposed. Experimental results show that the trained agent outperforms average human player and inbuilt game agents in basic scenario(s) where only move left, right and shoot actions are allowed.


title: Blockstack: Design and Implementation of a Global Naming System with Blockchains

abstract: Cryptocurrency blockchains like Bitcoin and Namecoin and their respective P2P networks have seen significant adoption in the past few years, and show promise as naming systems with no trusted parties. Users can register human-readable names and securely associate data with them; only the owner of a particular private-key can write or update the name/value pair. In theory, many decentralized systems can be built using these cryptocurrency networks, such as new, decentralized versions of DNS or PKI. As the technology is relatively new and evolving rapidly, however, little production data or experience is available to guide design tradeoffs. In this paper, we describe our experience operating a large, real-world deployment of a decentralized PKI service built on top of the Namecoin blockchain. We present various challenges (network reliability, throughput, security issues) that we needed to overcome while registering/updating over 33,000 entries on the blockchain which involved over 200,000 transactions on the Namecoin network. Further, we discuss how our experience informed the design of a new blockchain-based naming system, Blockstack. We detail why we changed from Namecoin to the Bitcoin network for the new system, as well as operational lessons from this migration. Blockstack is released as open source and currently powers a production PKI system for 40,000 users.


Collaborative Tools For Global Design Project Management: Case Study Of An Academic Experience


title: Enhancing SDN security for IoT-related deployments through blockchain

abstract: The majority of business activity of our integrated and connected world takes place in networks based on cloud computing infrastructure that cross national, geographic and jurisdictional boundaries. Such an efficient entity interconnection is made possible through an emerging networking paradigm, Software Defined Networking (SDN) that intends to vastly simplify policy enforcement and network reconfiguration in a dynamic manner. However, despite the obvious advantages this novel networking paradigm introduces, its increased attack surface compared to traditional networking deployments proved to be a thorny issue that creates skepticism when safety-critical applications are considered. Especially when SDN is used to support Internet-of-Things (IoT)-related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. The overall number of connected nodes makes the efficient monitoring of all entities a real challenge, that must be tackled to prevent system degradation and service outage. This position paper provides an overview of common security issues of SDN when linked to IoT clouds, describes the design principals of the recently introduced Blockchain paradigm and advocates the reasons that render Blockchain as a significant security factor for solutions where SDN and IoT are involved.


title: Performance analysis of feature selection algorithm for educational data mining

abstract: Student's academic performance is the main focus of all educational institutions. Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. The obtained results of the different FS algorithms and classifiers will also help the new researchers in finding the best combinations of FS algorithms and classifiers. Selecting relevant features for student prediction model is very sensitive issue for educational stakeholders, as they have to take decisions on the basis of results of prediction models. Furthermore our paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.


title: Induction of parturition in dairy cows with dexamethasone

abstract: Sixty Holstein cows were paired by parity and sire, and one of each pair was allocated at random to treatment or control; 17 cows were injected with 7.5 mg/100 kg dexamethasone trioxa undecanoate 14 days before the predicted date of calving, 13 cows received the same dose five days before term and 30 cows were left untreated. The treatment significantly advanced parturition and 29 of the 30 induced cows calved within 72 hours of the injection. Induction at day 14 before term was safe for calf and dam, the calves were 3.2 kg lighter than control calves and there was a high incidence of retained placenta. Treatment for this condition resulted in increased veterinary costs of 14.50 pounds per cow exclusive of dexamethasone treatment. Treatment at this stage was also associated with low pregnancy rates in the next breeding season. Calves born after induction at five days before term were not significantly lighter than calves from control cows, the problem of retained placenta was less marked and there were no subsequent effects on fertility. There were no significant effects of induction on milk yield or milk quality up to 200 days of lactation.


title: P3: Joint optimization of charger placement and power allocation for wireless power transfer

abstract: Wireless power transfer is a promising technology to extend the lifetime of, and thus enhance the usability of, the energy-hungry battery-powered devices. It enables energy to be wirelessly transmitted from power chargers to energy receiving devices. Existing studies have mainly focused on maximizing network lifetime, optimizing charging efficiency, minimizing charging delay, etc. Different from these works, our objective is to optimize charging quality in a 2-D target area. Specifically, we consider the following charger Placement and Power allocation Problem (P3): Given a set of candidate locations for placing chargers, find a charger placement and a corresponding power allocation to maximize the charging quality, subject to a power budget. We prove that P3 is NP-complete. We first study P3 with fixed power levels, for which we propose a (1−1/e)-approximation algorithm; we then design an approximation algorithm of factor 1−1/e / 2L for P3, where e is the base of the natural logarithm, and L is the maximum power level of a charger. We also show how to extend P3 in a cycle. Extensive simulations demonstrate that, the gap between our design and the optimal algorithm is within 4.5%, validating our theoretical results.


title: A formal ontological perspective on the behaviors and functions of technical artifacts

abstract: In this paper we present a formal characterization of the engineering concepts of behavior and function of technical artifacts. We capture the meanings that engineers attach to these concepts by formalizing, within the formal ontology DOLCE, the five meanings of artifact behavior and the two meanings of function that Chandrasekaran and Josephson identified in 2000 within the functional representation approach. We begin our formalization by reserving the term “behavior” of a technical artifact as “the specific way in which the artifact occurs in an event.” This general notion is characterized formally, and used to provide definitions of actual behaviors of artifacts, and the physically possible and physically impossible behaviors that rational agents believe that artifacts have. We also define several other notions, for example, input and output behaviors of artifacts, and then show that these ontologically characterized concepts give a general framework in which Chandrasekaran and Josephson's meanings of behavior can be explicitly formalized. Finally we show how Chandrasekaran and Josephson's two meanings of artifact functions, namely, device-centric and environment-centric functions, can be captured in DOLCE via the concepts of behavioral constraint and mode of deployment of an artifact. A more general goal of this work is to show that foundational ontologies are suited to the engineering domain: they can facilitate information sharing and exchange in the various engineering domains by providing concept structures and clarifications that make explicit and precise important engineering notions. The meanings of the terms “behavior” and “function” in domains like designing, redesigning, reverse engineering, product architecture, and engineering knowledge bases are often ambiguous or overloaded. Our results show that foundational ontologies can accommodate the variety of denotations these terms have and can explain their relationships.


title: Measuring, analyzing and predicting security vulnerabilities in software systems

abstract: In this work we examine the feasibility of quantitatively characterizing some aspects of security. In particular, we investigate if it is possible to predict the number of vulnerabilities that can potentially be present in a software system but may not have been found yet. We use several major operating systems as representatives of complex software systems. The data on vulnerabilities discovered in these systems are analyzed. We examine the results to determine if the density of vulnerabilities in a program is a useful measure. We also address the question about what fraction of software defects are security related, i.e., are vulnerabilities. We examine the dynamics of vulnerability discovery hypothesizing that it may lead us to an estimate of the magnitude of the undiscovered vulnerabilities still present in the system. We consider the vulnerability discovery rate to see if models can be developed to project future trends. Finally, we use the data for both commercial and open-source systems to determine whether the key observations are generally applicable. Our results indicate that the values of vulnerability densities fall within a range of values, just like the commonly used measure of defect density for general defects. Our examination also reveals that it is possible to model the vulnerability discovery using a logistic model that can sometimes be approximated by a linear model.


Data Security and Privacy in the IoT.


title: CanSpeak: a customizable speech interface for people with dysarthric speech

abstract: Current Automatic Speech Recognition (ASR) systems designed to recognize dysarthric speech require an investment in training that involves considerable effort and must be repeated if speech patterns change. We present CanSpeak, a customizable speech recognition interface that does not require automatic training and uses a list of keywords customized for each user. We conducted a preliminary user study with four subjects with dysarthric speech. Customizing the keyword lists doubled the accuracy rate of the system for two of the subjects whose parents and caregivers participated in the customizing task. For the other two subjects only small improvements were observed.


title: Design and Validation of a CT- and MRI-Guided Robot for Percutaneous Needle Procedures

abstract: This paper describes the design and evaluation of the second generation of a robotic system called the light puncture robot for thoracic and abdominopelvic interventional radiology procedures under CT and MRI guidance. It is mounted on the patient's body and positions and inserts the needle according to the trajectory and target chosen by the radiologist in the image. The mechanical design is described in detail along with its forward and inverse kinematics. The robot can be segmented and registered fully automatically in both imaging modalities. Phantom experiments in the CT scanner and preliminary feasibility experiments in the MRI are described, showing a targeting accuracy of 3.3 ± 1.7 mm in gelatin for depths ranging from 30 to 90 mm and needle orientations of –13˚ to +15˚ about normal from the patient's skin. A detailed error analysis is discussed along with potential improvements in the system. The potential clinical advantages of the system include higher targeting accuracy for complex dual obliquity trajectories, the need for fewer images, and improved accessibility to MRI-guided interventions.


title: Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data

abstract: The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of recommender systems. In this paper we feed recommender systems with features coming from the Linked Open Data (LOD) cloud - a huge amount of machine-readable knowledge encoded as RDF statements - with the aim of improving recommender systems effectiveness. In order to exploit the natural graph-based structure of RDF data, we study the impact of the knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation algorithm. In more detail, we investigate whether the integration of LOD-based features improves the effectiveness of the algorithm and to what extent the choice of different feature selection techniques influences its performance in terms of accuracy and diversity. The experimental evaluation on two state of the art datasets shows a clear correlation between the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, the graph-based algorithm leveraging LOD-based features is able to overcome several state of the art baselines, such as collaborative filtering and matrix factorization, thus confirming the effectiveness of the proposed approach.


title: Performance Analysis and Optimization of Clang's OpenMP 4.5 GPU Support

abstract: The Clang implementation of OpenMP® 4.5 now provides full support for the specification, offering the only open source option for targeting NVIDIA® GPUs. While using OpenMP allows portability across different architectures, matching native CUDA® performance without major code restructuring is an open research issue.In order to analyze the current performance, we port a suite of representative benchmarks, and the mature mini-apps TeaLeaf, CloverLeaf, and SNAP to the Clang OpenMP 4.5 compiler. We then collect performance results for those ports, and their equivalent CUDA ports, on an NVIDIA Kepler GPU. Through manual analysis of the generated code, we are able to discover the root cause of the performance differences between OpenMP and CUDA.A number of improvements can be made to the existing compiler implementation to enable performance that approaches that of hand-optimized CUDA. Our first observation was that the generated code did not use fused-multiply-add instructions, which was resolved using an existing flag. Next we saw that the compiler was not passing any loads through non-coherent cache, and added a new flag to the compiler to assist with this problem.We then observed that the compiler partitioning of threads and teams could be improved upon for the majority of kernels, which guided work to ensure that the compiler can pick more optimal defaults. We uncovered a register allocation issue with the existing implementation that, when fixed alongside the other issues, enables performance that is close to CUDA.Finally, we use some different kernels to emphasize that support for managing memory hierarchies needs to be introduced into the specification, and propose a simple option for programming shared caches.


title: Robust, self-timed power-on reset circuit for low-voltage applications

abstract: The purpose of the Power-on Reset (POR) circuit is to reset the latches and flip-flops in an SOC to a known state when the supply is ramping up. During power-up, supply is not stable, and the ramp-up time can vary depending on the applications. A common approach is to generate a POR signal by comparing the supply voltage with a reference voltage. Conventional POR circuits also use a resistor divider circuit along with band gap reference. Low-power applications, like Internet of Things (IoT) devices, comprise of SRAM arrays, sensors, and logic operating at sub-threshold or extremely low voltages. At these low operating voltages, generating a stable reference voltage is difficult because of band gap reference limitations and process variations. In this paper, we present a POR circuit that operates without using a reference voltage, making it robust against different sources of variation. The proposed circuit is self-timed, meaning the reset signal pulse-width varies according to the time needed to reset the latch. The designed circuit has been fabricated in 16nm FINFET technology. Silicon validation shows that the proposed POR circuit works at a minimum supply voltage of 400mv. Simulation verifies that the POR circuit is operational in sub-threshold region, but is limited to 400mV on silicon due to the operational voltage of additional testchip logic. Also, the POR circuit does not consume any dynamic power during normal operation of the SOC and has minimal area overhead of 21.3pm2.


title: The Role of Supply Chain Agility in Achieving Supply Chain Fit

abstract: The main purpose of this research was to build on Fisher's (1997) seminal article. First, it expands on the Fisher framework by empirically establishing the link between the firm's environmental characteristics and the firm's level of supply chain fit (SCF). The results indicate a negative relationship between environmental uncertainty and SCF indicating that, as the level of environmental uncertainty increases it becomes increasingly challenging to match a product's supply and demand characteristics with its supply chain design characteristics. Second, the key contribution of this research is the introduction of supply chain agility as a capability that helps mitigate the negative relationship between aspects of environmental uncertainty and SCF. Finally, this research contributes to theory development and managerial practice by exploring the complex relationship between SCF, supply chain agility, and financial performance. Developing high levels of supply chain agility and SCF requires deployment of resources for the focal firm. This manuscript provides a better understanding of how such expenditures can generate financial benefits for organizations.


title: State-space model with deep learning for functional dynamics estimation in resting-state fMRI

abstract: Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach.


title: Automated Tag Clustering: Improving search and exploration in the tag space

abstract: In this paper we discuss the use of clustering techniques to enhance the user experience and thus the success of collaborative tagging services. We show that clustering techniques can improve the user experience of current tagging services. We first describe current limitations of tagging services, second, we give an overview of existing approaches. We then describe the algorithms we used for tag clustering and give experimental results. Finally, we explore the use of several techniques to identify semantically related tags.


title: Attribute Discovery via Predictable Discriminative Binary Codes

abstract: We present images with binary codes in a way that balances discrimination and learnability of the codes. In our method, each image claims its own code in a way that maintains discrimination while being predictable from visual data. Category memberships are usually good proxies for visual similarity but should not be enforced as a hard constraint. Our method learns codes that maximize separability of categories unless there is strong visual evidence against it. Simple linear SVMs can achieve state-of-the-art results with our short codes. In fact, our method produces state-of-the-art results on Caltech256 with only 128-dimensional bit vectors and outperforms state of the art by using longer codes. We also evaluate our method on ImageNet and show that our method outperforms state-of-the-art binary code methods on this large scale dataset. Lastly, our codes can discover a discriminative set of attributes.


title: SecuRank: Starving Permission-Hungry Apps Using Contextual Permission Analysis

abstract: Competition among app developers has caused app stores to be permeated with many groups of general-purpose apps that are functionally-similar. Examples are the many flashlight or alarm clock apps to choose from. Within groups of functionally-similar apps, however, permission usage by individual apps sometimes varies widely. Although (run-time) permission warnings inform users of the sensitive access required by apps, many users continue to ignore these warnings due to conditioning or a lack of understanding. Thus, users may inadvertently expose themselves to additional privacy and security risks by installing a more permission-hungry app when there was a functionally-similar alternative that used less permissions. We study the variation in permission usage across 50,000 Google Play Store search results for 2500 searches each yielding a group of 20 functionally-similar apps. Using fine-grained contextual analysis of permission usage within groups of apps, we identified over 3400 (potentially) over-privileged apps, approximately 7% of the studied dataset. We implement our contextual permission analysis framework as a tool, called SecuRank, and release it to the general public in the form of an Android app and website. SecuRank allows users to audit their list of installed apps to determine whether any of them can be replaced with a functionally-similar alternative that requires less sensitive access to their device. By running SecuRank on the entire Google Play Store, we discovered that up to 50% of apps can be replaced with preferable alternative, with free apps and very popular apps more likely to have such alternatives.


title: Residential Demand Management Using Individualized Demand Aware Price Policies

abstract: This paper presents a novel approach to demand side management (DSM), using an “individualized” price policy, where each end user receives a separate electricity pricing scheme designed to incentivize demand management in order to optimally manage flexible demands. These pricing schemes have the objective of reducing the peaks in overall system demand in such a way that the average electricity price each individual user receives is non-discriminatory. It is shown in this paper that this approach has a number of advantages and benefits compared to traditional DSM approaches. The “demand aware price policy” approach outlined in this paper exploits the knowledge, or demand-awareness, obtained from advanced metering infrastructure. The presented analysis includes a detailed case study of an existing European distribution network where DSM trial data was available from the residential end-users.


Diachronic degradation of language models: Insights from social media


Parallel Link Resolution of Connection Graph Refutation and Its Implementation.


title: Multi-attribute Residual Network (MAResNet) for Soft-Biometrics Recognition in Surveillance Scenarios

abstract: In surveillance images, soft biometric attributes have been demonstrated to be quite effective in the problem of person re-identification. Many of these attributes can vary greatly, which has motivated a number of hand crafted features designed to recognize individual attributes. Although deep learning is generally useful in learning features appropriate for classification, it usually requires more data to train than what is available in most person re-identification databases. In this paper, we propose a residual network (MAResNet) deeper than current pedestrian attribute recognition, which we use to recognize multiple attributes simultaneously. The proposed network is both efficient and accurate. We recognize attributes at a rate of 271 FPS while simultaneously outperforming state of the art in attribute recognition on the PETA dataset.


title: Fog Computing Potentials, Applications, and Challenges

abstract: The emergence of Internet of Things (IoT) and the technological advancements of smart devices and wearable gadgets, are empowering new real-time applications such as health monitoring, traffic monitoring, tele-surveillance, online multi-player gaming and live video streaming. These applications require a low latency response in order to ensure the user's quality of service (QoS). Fog computing is a promising solution to the high latency drawback of cloud computing, it allows provisioning resources and services at the proximity of end users. In this paper we give a detailed overview about the definition of fog computing, its architecture and potentials as opposed to cloud computing. State of the art surveys published in the years 2017 and 2018 are presented to guide the readers towards the current trends. Moreover, this survey highlights the most significant applications of fog computing, and reviews twelve recently proposed works for applications in smart cities, smart transportation, tele-surveillance, health care and multimedia. Finally, fog computing challenges and future research opportunities are discussed.


Highway Driving Dataset for Semantic Video Segmentation


title: A Conceptual Framework Representing the User Experience for Business Intelligence Front-Ends

abstract: Business intelligence (BI) has been a top priority of information technology (IT) executives for several years; the wider academic research community has gradually embraced the topic, but little research has been published on the user experience (UX) of the front-end of BI applications. Although numerous UX frameworks have been proposed by literature, none of these are specific to BI front-end applications. This article describes a systematic review of the literature on UX frameworks for BI front-end applications. Using a systematic literature review (2003–2017) as methodology, this article presents an overview of the UX frameworks identified in the literature, the main elements comprising these frameworks, and future research opportunities in the field. Also presented is a literature-based conceptual UX framework for BI front-end applications as a point of departure in representing UX in the BI landscape.


title: Involvement of long non-coding RNA in colorectal cancer: From benchtop to bedside (Review)

abstract: Colorectal cancer (CRC) is one of the greatest threats to public health. Recent advances in whole-genome transcriptome analyses have enabled the identification of numerous members of a novel class of non-coding (nc)RNA, long ncRNA (lncRNA), which is broadly defined as RNA molecules that are >200 nt in length and lacking an open reading frame. In the present review, all lncRNAs associated with CRC are briefly summarized, with a particular focus on their potential roles as clinical biomarkers. CRC-associated lncRNAs involved in the underlying mechanisms of CRC progression are also initially included. This should benefit the development of novel markers and effective therapeutic targets for patients with CRC.


title: COMPARATIVE STUDY OF AEROBIC POWER BETWEEN SPORTSMEN AND SEDENTARY CONTROL GROUP

abstract: Introduction: Maximal oxygen uptake is the fundamental measure of exercise physiology.VO 2 max is widely recognized as a depiction of functional limitation of the cardiopulmonary system as well as a degree of aerobic fitness. VO 2 max is the maximum capacity of an individual’s body to transport and utilize oxygen during exercise, which reflects the physical fitness of the individual. Persons with higher cardiovascular efficiency like sportsmen, have the capacity to yield more amount of energy and perform better. Aim and objective : To find out and compare aerobic power in sportsmen and control group. Material and methods : It was calculated using Harward’s step technique and Astrands-Rhyming nomogram. The present study was a cross sectional study. 50 sedentary medical students as control group were selected from a medical college. 50 sportsmen in the age group of 18-25 years, who were playing that particular game for 3-5 years, were selected from a local sports institute. Result: VO 2 max in sportsmen (4.48±0.31) lit/min was significantly higher than in control group (3.29±0.29) lit/min. Conclusion : Regular physical exercise like sporting activity improves cardio-respiratory fitness (Vo 2 max).


Race, Poverty and SAT Scores: Modeling the Influences of Family Income on Black and White High School Students' SAT Performance.


title: Sector-Based Radio Resource Allocation (SBRRA) Algorithm for Better Quality of Service and Experience in Device-to-Device (D2D) Communication

abstract: The mounting content sharing among users has resulted in a considerable rise in wireless data traffic, pressurizing the cellular networks to undergo a suitable upheaval. A competent technology of the fifth-generation (5G) networks for efficiently supporting proximity-based applications is device-to-device (D2D) communication, underlaying cellular networks. Significant advances have been made till date, for allocating resources to D2D users in cellular networks, such that sharing of spectral resources between cellular and D2D users is carried out in a coordinated manner. In this paper, a sector-based radio resource allocation (SBRRA) algorithm for resource block (RB) allocation to D2D pairs has been proposed, where the number of resource blocks (RBs) is allocated to each D2D pair in an adaptive manner, based on the demanded application by each pair. Different applications demand a varying number of RBs, in accordance with their priority. This algorithm focuses on the use of sectored antennas at the base station, for a better performance and low complexity. Extensive simulations are carried out, considering real-time scenario, for ensuring satisfactory quality of service (QoS) and quality of experience (QoE) by the users. The efficiency of the proposed scheme is proved by comparing it with the RB allocation using hidden Markov model.


title: Benchmarking Optimization Software with Performance Profiles

abstract: We propose performance profiles — distribution functions for a performance metric — as a tool for benchmarking and comparing optimization software. We show that performance profiles combine the best features of other tools for performance evaluation.


title: WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making

abstract: A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter , a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.


title: Supporting Semantic Annotation of Educational Content by Automatic Extraction of Hierarchical Domain Relationships

abstract: The domain model is an essential part of an adaptive learning system. For each educational course, it involves educational content and semantics, which is also viewed as a form of conceptual metadata about educational content. Due to the size of a domain model, manual domain model creation is a challenging and demanding task for teachers or content (and metadata) authors. We propose a method for the automated acquisition of hierarchical relationships between relevant domain terms from educational content, which constitutes a fundamental step in the semantic composition of an educational course. The method is based on existing text mining methods and applied to educational content. We evaluate our approach by performing several experiments. The evaluation shows that the method's performance is very promising. A study in a real-user scenario reveals that despite the fact that utilization of our method does not necessarily improve the speed of the domain model creation nor does it reduce the overall difficulty of the task, a significant improvement in the quality of resulting domain models has been observed. Our work is a promising contribution to the growing field of automated domain model acquisition.


title: Frequency control in competitive electricity market dispatch

abstract: The dynamic and inertial characteristics of electricity systems are evolving with consequences for system frequency control. This issue is particularly important for smaller, more isolated electricity systems, where simple heuristic reserve targets may no longer be sufficient to ensure the security of the system. In a market environment, this issue needs to be dealt with in a systematic way that is compatible with the method of market dispatch to ensure least-cost operation of the system. This paper describes how frequency control constraints can be derived and included into a market dispatch algorithm. Comparisons are made with a conventional reserve target dispatch approach for a real isolated power system. The impact on market prices, cost, and the number and size of problem contingencies is assessed. The effect of increasing amounts of wind capacity is also examined.


title: Virtual reality and augmented reality as a training tool for assembly tasks

abstract: In this paper we investigate whether virtual reality (VR) and augmented reality (AR) offer potential for the training of manual skills, such as for assembly tasks, in comparison to conventional media. We present results from experiments that compare assembly completion times for a number of different conditions. We firstly investigate completion times for a task where participants can study an engineering drawing and an assembly plan and then conduct the task. We then investigate the task under various VR conditions and context-free AR. We discuss the relative advantages and limitations of using VR and AR as training media for investigating assembly operations, and we present the results of our experimental work.


title: Machine Ethics and Automated Vehicles

abstract: Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver’s oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research.


title: Consciousness and Anesthesia

abstract: When we are anesthetized, we expect consciousness to vanish. But does it always? Although anesthesia undoubtedly induces unresponsiveness and amnesia, the extent to which it causes unconsciousness is harder to establish. For instance, certain anesthetics act on areas of the brain’s cortex near the midline and abolish behavioral responsiveness, but not necessarily consciousness. Unconsciousness is likely to ensue when a complex of brain regions in the posterior parietal area is inactivated. Consciousness vanishes when anesthetics produce functional disconnection in this posterior complex, interrupting cortical communication and causing a loss of integration; or when they lead to bistable, stereotypic responses, causing a loss of information capacity. Thus, anesthetics seem to cause unconsciousness when they block the brain’s ability to integrate information.


title: Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios

abstract: Emergency scenarios may necessitate autonomous vehicle maneuvers up to their handling limits in order to avoid collisions. In these scenarios, vehicle stabilization becomes important to ensure that the vehicle does not lose control. However, stabilization actions may conflict with those necessary for collision avoidance, potentially leading to a collision. This paper presents a new control structure that integrates path tracking, vehicle stabilization, and collision avoidance and mediates among these sometimes conflicting objectives by prioritizing collision avoidance. It can even temporarily violate vehicle stabilization criteria if needed to avoid a collision. The framework is implemented using model predictive and feedback controllers. Incorporating tire nonlinearities into the model allows the controller to use all of the vehicle’s performance capability to meet the objectives. A prediction horizon comprised of variable length time steps integrates the different time scales associated with stabilization and collision avoidance. Experimental data from an autonomous vehicle demonstrate the controller safely driving at the vehicle’s handling limits and avoiding an obstacle suddenly introduced in the middle of a turn.


Development of Mobile Robot SLAM Based on ROS


title: Sleep and aging

abstract: This review describes normal and disordered sleep in the older adult population. Although several distinctive sleep changes across the lifespan are normative, including changes in the amount of time spent in different stages of sleep and a shift in the timing of circadian rhythms, sleep disorders are also common in late life. Sleep-disordered breathing, insomnia, circadian rhythm sleep-wake disorders, and parasomnias occur frequently in older adults and contribute to overall higher rates of poor sleep with advanced age. Assessment and treatment of sleep disorders has been shown to improve functioning and quality of life in older adults; however, the process of diagnosis and intervention is often complicated due to the presence of multiple medical comorbidities, medication side effects, and specific age-related risk factors for sleep disruption. Additional challenges to recognizing, diagnosing, and treating sleep disorders in older adults with dementia and those in long-term care facilities also exist, further complicating the clinical management of sleep disorders in these patients.


title: Beyond Cannabis: Plants and the Endocannabinoid System

abstract: Plants have been the predominant source of medicines throughout the vast majority of human history, and remain so today outside of industrialized societies. One of the most versatile in terms of its phytochemistry is cannabis, whose investigation has led directly to the discovery of a unique and widespread homeostatic physiological regulator, the endocannabinoid system. While it had been the conventional wisdom until recently that only cannabis harbored active agents affecting the endocannabinoid system, in recent decades the search has widened and identified numerous additional plants whose components stimulate, antagonize, or modulate different aspects of this system. These include common foodstuffs, herbs, spices, and more exotic ingredients: kava, chocolate, black pepper, and many others that are examined in this review.


title: A review of technologies for MVDC circuit breakers

abstract: Medium voltage direct current (MVDC) distribution networks have been considered for various applications, such as offshore wind farm collector systems, all-electric naval vessels, and aircraft. MVDC circuit breakers are a critical technology to directly manage faults in multi-terminal DC (MTDC) networks. However, DC current breaking is much more challenging than in AC systems because there is no natural zero-crossing of the current waveform to aid fault isolation. This paper reviews existing MVDC circuit breaker technologies and also discusses their advantages and disadvantages. This paper also introduces new topologies that can be applied in MVDC applications. The operation of several hybrid DC circuit breaker topologies with aided commutation is included. The paper illustrates that a hybrid DC circuit breaker with aided commutation can clear a fault within 2–5 msecs with low losses, this shows great potential for future MVDC applications. The implications for the practical design of commercial MVDC circuit breakers are also discussed.


title: Action-depedent Control Variates for Policy Optimization via Stein's Identity

abstract: Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control variate method to effectively reduce variance for policy gradient methods. Motivated by the Stein's identity, our method extends the previous control variate methods used in REINFORCE and advantage actor-critic by introducing more general action-dependent baseline functions. Empirical studies show that our method significantly improves the sample efficiency of the state-of-the-art policy gradient approaches.


title: Flicker study on variable speed wind turbines with doubly fed induction generators

abstract: Grid connected wind turbines may produce flicker during continuous operation. This paper presents a simulation model of a MW-level variable speed wind turbine with a doubly fed induction generator developed in the simulation tool of PSCAD/EMTDC. Flicker emission of variable speed wind turbines with doubly fed induction generators is investigated during continuous operation, and the dependence of flicker emission on mean wind speed, wind turbulence intensity, short circuit capacity of grid and grid impedance angle are analyzed. A comparison is done with the fixed speed wind turbine, which leads to a conclusion that the factors mentioned above have different influences on flicker emission compared with that in the case of the fixed speed wind turbine. Flicker mitigation is realized by output reactive power control of the variable speed wind turbine with doubly fed induction generator. Simulation results show the wind turbine output reactive power control provides an effective means for flicker mitigation regardless of mean wind speed, turbulence intensity and short circuit capacity ratio.


title: Balanced Band-Notched UWB Filtering Circular Patch Antenna With Common-Mode Suppression

abstract: This letter presents a balanced band-notched ultra-wideband (UWB) filtering circular patch antenna fed by a microstrip line. The proposed circuit is composed of a λ/4 slotline-resonator-based balanced band-notched UWB bandpass filter (BPF) and a stub-loaded circular radiating patch. The compact balanced BPF is new in that the slotline resonator is fed judiciously at its open end so that differential-mode fields can be effectively excited in the slotline resonator. Moreover, it can achieve an all-stop-like common-mode rejection because the slotline resonator is incomplete in structure when in common-mode operation. The way the BPF is coupled to the circular radiating patch is novel in that a good impedance match can be easily obtained with the aid of two tuning open stubs. A pair of spur-lines was implemented in the microstrip feedline near the plane of symmetry to produce a notch band around 5.5 GHz. Measurement shows that the differential-mode operating band with return loss greater than 10 dB covers 2.95-10.75 GHz except for the notch band of 5.01-6.19 GHz. Very good common-mode suppression is observed with the measured common-mode reflection coefficient greater than -1.78 dB in 1-12 GHz.


title: Extending the Rocchio Relevance Feedback Algorithm to Provide Contextual Retrieval

abstract: Contextual retrieval supports differences amongst users in their information seeking requests. The Web, which is very dynamic and nearly universally accessible, is an environment in which it is increasingly difficult for users to find documents that satisfy their specific information needs. This problem is amplified as users tend to use short queries. Contextual retrieval attempts to address this problem by incorporating knowledge about the user and past retrieval results in the search process. In this paper we explore a feedback technique based on the Rocchio algorithm that significantly reduces demands on the user while maintaining comparable performance on the Reuters-21578 corpus.


title: High-Resolution Light Field Capture With Coded Aperture

abstract: Acquiring light field with larger angular resolution and higher spatial resolution in low cost is the goal of light field capture. Combining or modifying traditional optical cameras is a usual method for designing light field capture equipment, among which most models should deliberate trade-off between angular and spatial resolution, but augmenting coded aperture avoids this consideration by multiplexing information from different views. On the basis of coded aperture, this paper suggests an improved light field camera model that has double measurements and one mask. The two compressive measurements are respectively realized by a coded aperture and a random convolution CMOS imager, the latter is used as imaging sensor of the camera. The single mask design permits high light efficiency, which enables the sampling images to have high clarity. The double measurement design keeps more correlation information, which is conductive to enhancing the reconstructed light field. The higher clarity and more correlation of samplings mean higher quality of rebuilt light field, which also means higher resolution under condition of a lower PSNR requirement for rebuilt light field. Experimental results have verified advantage of the proposed design: compared with the representative mask-based light field camera models, the proposed model has the highest reconstruction quality and a higher light efficiency.


title: The Optimum Is Not the Goal: Capturing the Decision Space for the Planning of New Neighborhoods

abstract: This article presents the development and application of a methodology that employs optimization not to seek the single or few optimal plan(s) but to provide planners with a systematic overview of their decision space. As urban development projects are not only subject to decisions of planners but to those of many actors, the insight about how different actors would decide based on the decisions of the planners, should enable planners to already adapt their decisions to ensure that final project targets are reached. The existence of different decision makers is considered via a multiparametric mixed-integer linear pro- gramming (mpMILP) approach. Along with the methodology a model was developed, which incorporates multiple domains and scales. Those domains include social, environmental, form, energy and economic aspects. The considered scales range from single floors up to a neighborhood. Model and methodology were applied to a greenfield development project. Two practical questions are answered, which address the impact of planning decisions about (a) the building density and the sustainability of the energy supply on costs of different actors, and (b) the building density and the share of parks on the view on a landmark. The capturing of the decision space revealed trade-offs in terms of chosen energy supply system and urban form, respectively. The presented computational method forms part of the decision support tool URB io which shall assist urban planning.


title: [Published as Journal of the Optical Society of America, A (11)3050–3068, 1994.] Gradient and Curvature from Photometric Stereo Including Local Confidence Estimation

abstract: Photometric stereo is one technique for 3D shape determination that has been implemented in a variety of experimental settings and that has produced consistently good results. The idea is to use intensity values recorded from multiple images obtained from the same viewpoint but under dieren t conditions of illumination. The resulting radiometric constraint makes it possible to obtain local estimates of both surface orientation and surface curvature without requiring global smoothness assumptions and without requiring prior image segmentation. This paper moves photometric stereo one step closer to practical viability by describing an experimental setting in which surface gradient estimation is achieved on full frame video data at near video frame rates (i.e., 15Hz). The implementation uses commercially available hardware. Reectance is modeled empirically using measurements obtained from a calibration sphere. Estimation of the gradient, (p; q), requires only simple table lookup. Curvature estimation uses, in addition, the reectance map, R(p; q). The required lookup table and reectance maps are derived during calibration. Because reectance is modeled empirically, no prior physical model of the reectance characteristics of the objects to be analyzed is assumed. At the same time, if a good physical model is available, it can be retrot to the method for implementation purposes. Photometric stereo is subject to error in the presence of cast shadows and interreection. No purely local technique can succeed since these phenomena are inherently non-local. Nevertheless, this paper demonstrates that one can exploit the redundancy in three light source photometric stereo to, in most cases, locally detect the presence of cast shadows and interreection. Detection is facilitated by explicitly including a local condence estimate in the lookup table used for gradient estimation.


title: A High speed, Low Voltage to High Voltage Level Shifter in Standard 1.2V 0.13μm CMOS

abstract: The design of a high speed, low voltage to high voltage level shifter in a digital 1.2 V, 0.13 mum CMOS technology is presented. The topology uses two differentially switched cascoded transistor ladders. The output signal has an offset of two times the nominal supply voltage of the used technology with respect to the input signal. Oxide stress and hot carrier degradation is minimized since all transistors of the level shifter operate within the voltage limits imposed by the design rules of a mainstream CMOS technology.


title: DeepFish: Accurate underwater live fish recognition with a deep architecture

abstract: Abstract Underwater object recognition is in great demand, while the research is far from enough. The unrestricted natural environment makes it a challenging task. We propose a framework to recognize fish from videos captured by underwater cameras deployed in the ocean observation network. First, we extract the foreground via sparse and low-rank matrix decomposition. Then, a deep architecture is used to extract features of the foreground fish images. In this architecture, principal component analysis (PCA) is used in two convolutional layers, followed by binary hashing in the non-linear layer and block-wise histograms in the feature pooling layer. Then spatial pyramid pooling (SPP) is used to extract information invariant to large poses. Finally, a linear SVM classifier is used for the classification. This deep network model can be trained efficiently. On a real-world fish recognition dataset, we achieve the state-of-the-art accuracy of 98.64%.