title: Robust acoustic echo cancellation in the short-time fourier transform domain using adaptive crossband filters

abstract: This paper presents a robust acoustic echo cancellation (AEC) system in the short-time Fourier transform (STFT) domain using adaptive crossband filters. The STFT-domain AEC allows for a simpler system structure compared to the traditional frequency-domain AEC, which normally requires several applications of the discrete Fourier transform (DFT) and the inverse DFT, while the robust AEC (RAEC) allows for continuous and stable filter updates during double talk without freezing the adaptive filter. The RAEC and the STFT-domain AEC have been investigated in the past in separate studies. In this work we propose a novel algorithm that combines the advantages of both approaches for robust update of the adaptive crossband filters even during double talk. Experimental results confirm the benefit of incorporating the robustness constraint for the adaptive crossband filters and show improved performance in terms of the echo reduction and the predicted sound quality.


Long Short-term Memory Network over Rhetorical Structure Theory for Sentence-level Sentiment Analysis


title: Segmentation of multi-sentence questions: towards effective question retrieval in cQA services

abstract: Existing question retrieval models work relatively well in finding similar questions in community-based question answering (cQA) services. However, they are designed for single-sentence queries or bag-of-word representations, and are not sufficient to handle multi-sentence questions complemented with various contexts. Segmenting questions into parts that are topically related could assist the retrieval system to not only better understand the user's different information needs but also fetch the most appropriate fragments of questions and answers in cQA archive that are relevant to user's query. In this paper, we propose a graph based approach to segmenting multi-sentence questions. The results from user studies show that our segmentation model outperforms traditional systems in question segmentation by over 30% in user's satisfaction. We incorporate the segmentation model into existing cQA question retrieval framework for more targeted question matching, and the empirical evaluation results demonstrate that the segmentation boosts the question retrieval performance by up to 12.93% in Mean Average Precision and 11.72% in Top One Precision. Our model comes with a comprehensive question detector equipped with both lexical and syntactic features.


title: IoT based smart parking system

abstract: In recent times the concept of smart cities have gained grate popularity. Thanks to the evolution of Internet of things the idea of smart city now seems to be achievable. Consistent efforts are being made in the field of IoT in order to maximize the productivity and reliability of urban infrastructure. Problems such as, traffic congestion, limited car parking facilities and road safety are being addressed by IoT. In this paper, we present an IoT based cloud integrated smart parking system. The proposed Smart Parking system consists of an on-site deployment of an IoT module that is used to monitor and signalize the state of availability of each single parking space. A mobile application is also provided that allows an end user to check the availability of parking space and book a parking slot accordingly. The paper also describes a high-level view of the system architecture. Towards the end, the paper discusses the working of the system in form of a use case that proves the correctness of the proposed model.


title: DL-PRO: A novel deep learning method for protein model quality assessment

abstract: Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-α atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues’ C-α atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.


title: Continuous-time indefinite linear quadratic optimal control with random input gains

abstract: In this paper, the indefinite linear quadratic (LQ) optimal control of continuous-time linear time-invariant (LTI) systems with random input gains is studied. One main novelty of this work is the use of channel/controller co-design framework which bridges and integrates the design of the channels and controller. The co-design is carried out by the twist of channel resource allocation, i.e., the channel capacities can be allocated among the input channels by the control designer subject to an overall capacity constraint. With the channel/controller co-design, necessary and sufficient conditions for the well-posedness as well as the attainability of the indefinite LQ problem concerned are obtained. The optimal control law is given by a linear state feedback associated with the mean-square stabilizing solution of a modified algebraic Riccati equation.


title: If someone is watching, I'll do what I'm asked: mandatoriness, control, and information security

abstract: AbstractInformation security has become increasingly important to organizations. Despite the prevalence of technical security measures, individual employees remain the key link – and frequently the...


title: Attack detection of distributed denial of service based on Splunk

abstract: This study utilizes the open source testing tool, Hping3, and the network analysis tool, Scapy, to simulate DDoS flood, reflection, and amplification attacks. We used the data generated from the attacks with the Splunk platform to conduct data analysis to quickly identify attacks and predict potential dangers that could arise. The analysis results were used in tests conducted on real network environments to determine the types of DDoS attacks. Visual IP mapping was then used to determine actions that could be taken.


title: Strategic foresight: how planning for the unpredictable can improve environmental decision-making.

abstract: Advanced warning of potential new opportunities and threats related to biodiversity allows decision-makers to act strategically to maximize benefits or minimize costs. Strategic foresight explores possible futures, their consequences for decisions, and the actions that promote more desirable futures. Foresight tools, such as horizon scanning and scenario planning, are increasingly used by governments and business for long-term strategic planning and capacity building. These tools are now being applied in ecology, although generally not as part of a comprehensive foresight strategy. We highlight several ways foresight could play a more significant role in environmental decisions by: monitoring existing problems, highlighting emerging threats, identifying promising new opportunities, testing the resilience of policies, and defining a research agenda.


title: A soft hand model for physically-based manipulation of virtual objects

abstract: We developed a new hand model for increasing the robustness of finger-based manipulations of virtual objects. Each phalanx of our hand model consists of a number of deformable soft bodies, which dynamically adapt to the shape of grasped objects based on the applied forces. Stronger forces directly result in larger contact areas, which increase the friction between hand and object as would occur in reality. For a robust collision-based soft body simulation, we extended the lattice-shape matching algorithm to work with adaptive stiffness values, which are dynamically derived from force and velocity thresholds. Our implementation demonstrates that this approach allows very precise and robust grasping, manipulation and releasing of virtual objects and performs in real-time for a variety of complex scenarios. Additionally, laborious tuning of object and friction parameters is not necessary for the wide range of objects that we typically grasp with our hands.


title: Design and Validation of Polarity-Changeable Magnetizer for Encoding Patterns on Ring-Like Rotary Encoders

abstract: Through numerical simulations and calibrated experiments, the aim of the work presented in this paper is placed on the development of a polarity changeable magnetizing platform for a rotary magnetic encoder using a permanent magnet and thin silicon steel plate. Parameter studies are conducted via finite-element analyses to obtain optimized geometric design parameters for the magnetizer, leading to desirable magnetic flux density and effective magnetized patterns on the magnetic media.


title: Biocompatible, High Precision, Wideband, Improved Howland Current Source With Lead-Lag Compensation

abstract: The Howland current pump is a popular bioelectrical circuit, useful for delivering precise electrical currents. In applications requiring high precision delivery of alternating current to biological loads, the output impedance of the Howland is a critical figure of merit that limits the precision of the delivered current when the load changes. We explain the minimum operational amplifier requirements to meet a target precision over a wide bandwidth. We also discuss effective compensation strategies for achieving stability without sacrificing high frequency output impedance. A current source suitable for Electrical Impedance Tomography (EIT) was simulated using a SPICE model, and built to verify stable operation. This current source design had stable output impedance of 3.3 MΩ up to 200 kHz, which provides 80 dB precision for our EIT application. We conclude by noting the difficulty in measuring the output impedance, and advise verifying the plausibility of measurements against theoretical limitations.


title: Family quality of life and psychological well-being in parents of children with autism spectrum disorders: a double ABCX model

abstract: Background ::: This study examined family quality of life (FQOL) and psychological well-being from a multidimensional perspective. The proposed model was based on the double ABCX model, with severity of the disorder, behaviour problems, social support, sense of coherence (SOC) and coping strategies as components. ::: ::: Method ::: One hundred and eighteen parents (59 mothers and 59 fathers) with a child diagnosed with autism spectrum disorders (ASD) participated in the study. Separate path analyses were performed to evaluate models of FQOL and psychological well-being for mothers and fathers. ::: ::: Results ::: In all models, behaviour problems had a negative indirect effect on adaptation (FQOL and psychological well-being) through SOC. For both mothers and fathers, the severity of the disorder and social support played significant roles in FQOL models. Coping strategies were related with adaptation, active avoidance coping with FQOL for fathers and positive and problem-focused coping with psychological well-being for mothers. ::: ::: Conclusions ::: The results of this study highlight the value of the multidimensional approach. The specific patterns of results for mothers and fathers contribute to comprehension of the psychological adaptation of parents. Findings could be taken into account in interventions with families.


Data Processing on FPGAs


title: Material-Efficient Permanent-Magnet Shape for Torque Pulsation Minimization in SPM Motors for Automotive Applications

abstract: This paper focuses on the design and analysis of a novel material-efficient permanent-magnet (PM) shape for surface-mounted PM (SPM) motors used in automotive actuators. Most of such applications require smooth torque with minimum pulsation for an accurate position control. The proposed PM shape is designed to be sinusoidal and symmetrical in the axial direction for minimizing the amount of rare earth magnets as well as for providing balanced axial electromagnetic force, which turns out to obtain better sinusoidal electromotive force, less cogging torque, and, consequently, smooth electromagnetic torque. The contribution of the novel PM shape to motor characteristics is first estimated by 3-D finite-element method, and all of the simulation results are compared with those of SPM motors with two conventional arched PM shapes: one previously reported sinusoidal PM shape and one step skewed PM shape. Finally, some finite-element analysis results are confirmed by experimental results.


title: Reinforcement Learning for Build-Order Production in StarCraft II

abstract: StarCraft II is one of the most popular real-time strategy games and has become an important benchmark for AI research as it provides a complex environment with numerous challenges. The build order problem is one of the key challenges which concern the order and type of buildings and units to produce based on current game situation. In contrast to existing hand-craft methods, we propose two reinforcement learning based models: Neural Network Fitted Q-Learning (NNFQ) and Convolutional Neural Network Fitted Q-Learning (CNNFQ). NNFQ and CNNFQ have been applied into a simple bot for fighting against the enemy race. Experimental results show that both these two models are capable of finding the most effective production sequence to defeat the opponent.


title: Active Learning for Parameter Estimation in Bayesian Networks

abstract: Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with a data set consisting of randomly generated instances from the underlying distribution. In many situations, however, we also have the option of active learning, where we have the possibility of guiding the sampling process by querying for certain types of samples. This paper addresses the problem of estimating the parameters of Bayesian networks in an active learning setting. We provide a theoretical framework for this problem, and an algorithm that chooses which active learning queries to generate based on the model learned so far. We present experimental results showing that our active learning algorithm can significantly reduce the need for training data in many situations.


A Neural Attention Model for Disfluency Detection


title: High-Efficiency, Broadband Converter From A Rectangular Waveguide TE10 Mode to A Circular Waveguide TM01 Mode for Overmoded Device Measurement

abstract: To accurately measure the performance of overmoded devices used in high-power microwave systems, a novel structure that effectively converts the TE10 mode in a rectangular waveguide to a high-purity TM01 mode in a circular waveguide is proposed. The circular TM01 mode is excited by a hybrid rectangular E-T waveguide structure, which uses a circular waveguide as the E element. Convex slots loaded at the rectangular waveguide, axially symmetric about the cylindrical waveguide, are introduced to suppress the competing lower order TE11 mode. A tuning stub loaded at the rectangular short wall is used to increase the frequency tuning range. The scattering matrix technique was used to study the mode converter along with the microwave network analysis method using some generalizations of the input port reflection and conversion efficiency, which were verified by the numerical full-wave simulation. Based on the theoretical analysis, an X-band TM01 mode generator is designed and measured. Low-power tests show that, when the length of the tuner increases from 5.7 to 11.7 mm, a wide frequency tuning range of ~500 MHz is obtained with a conversion efficiency of ~99%. Measured results and the theoretical analysis are in good agreement.


title: Evaluating Blockchains for IoT

abstract: As proof-of-work blockchains are inherently energy greedy and offer probabilistic guarantees, blockchains based on Byzantine consensus appear as a promising technology to track billions of connected devices. In this paper, we evaluate the performance of prominent blockchains that solve the classic Byzantine consensus problem. Our results show that while offering reasonable throughput their performance usually do not scale to tens of devices and drops dramatically as the number of devices increases. This study motivates the need for solutions that solves the Blockchain consensus problem, a scalable variant of the classic Byzantine consensus problem but dedicated to blockchains.


title: RoboWarfare: can robots be more ethical than humans on the battlefield?

abstract: Telerobotically operated and semiautonomous machines have become a major component in the arsenals of industrial nations around the world. By the year 2015 the United States military plans to have one-third of their combat aircraft and ground vehicles robotically controlled. Although there are many reasons for the use of robots on the battlefield, perhaps one of the most interesting assertions are that these machines, if properly designed and used, will result in a more just and ethical implementation of warfare. This paper will focus on these claims by looking at what has been discovered about the capability of humans to behave ethically on the battlefield, and then comparing those findings with the claims made by robotics researchers that their machines are able to behave more ethically on the battlefield than human soldiers. Throughout the paper we will explore the philosophical critique of this claim and also look at how the robots of today are impacting our ability to fight wars in a just manner.


Getting the Most out of AMR Parsing


title: Procedural Generation of 3D Caves for Games on the GPU

abstract: Procedural Content Generation in Games (PCG) is a thriv- ing field of research and application. Recent presented ex- amples range from levels, stories and race tracks to complete rulesets for games. However, there is not much research to date on procedural 3D modeling of caves, and similar en- closed natural spaces. In this paper, we present a modular pipeline to procedurally generate underground caves in real- time, to be used as part of larger landscapes in game worlds. We propose a three step approach, which can be fully im- plemented using General-Purpose Computing on Graphics Processing (GPGPU) technology: 1) an L-System to em- ulate the expanded cracks and passages which form cave structures in nature, 2) a noise-perturbed metaball approach for virtual 3D carving, and 3) a rendering component for isosurface extraction of the modeled voxel data, and fur- ther mesh enhancement through shader programming. We demonstrate how the interaction between these components produce results comparable to real world caves, and show that the solution is viable for video game environments. For this, we present the findings of a user study we conducted among indie-game developers and players, using our results. (Less)


title: Branching and merging: an investigation into current version control practices

abstract: The use of version control has become ubiquitous in software development projects. Version control systems facilitate parallel development and maintenance through branching, the creation of isolated codelines. Merging is a consequence of branching and is the process of integrating codelines. However, there are unanswered questions about the use of version control to support parallel development; in particular, how are branching and merging used in practice? What defines a successful branching and merging strategy? As a first step towards answering these questions, we recruited a diverse sample of 140 version control users to participate in an online survey. In this paper, we present the survey results and 4 key observations about branching and merging practices in software development projects.


title: Zeta: A Global Method for Discretization of Continuous Variables

abstract: This paper introduces a new technique for discretization of continuous variables based on zeta, a measure of strength of association between nominal variables developed for this purpose. Zeta is defined as the maximal accuracy achievable if each value of an independent variable must predict a different value of a dependent variable. We describe both how a continuous variable may be dichotomised by searching for a maximum value of zeta, and how a heuristic extension of this method can partition a continuous variable into more than two categories. Experimental comparisons with other published methods, show that zeta-discretization runs considerably faster than other techniques without any loss of accuracy.


title: Efficacy of acupuncture for migraine prophylaxis: A single-blinded, double-dummy, randomized controlled trial

abstract: Insufficient clinical trial data were available to prove the efficacy of acupuncture for migraine prophylaxis. A multicenter, double-dummy, single-blinded, randomized controlled clinical trial was conducted at the outpatient departments of acupuncture at 5 hospitals in China to evaluate the effectiveness of acupuncture. A total of 140 patients with migraine without aura were recruited and assigned randomly to 2 different groups: the acupuncture group treated with verum acupuncture plus placebo and the control group treated with sham acupuncture plus flunarizine. Treated by acupuncture 3 times per week and drugs every night, patients from both groups were evaluated at week 0 (baseline), week 4, and week 16. The primary outcome was measured by the proportion of responders (defined as the proportion of patients with a reduction of migraine days by at least 50%). The secondary outcome measures included the number of migraine days, visual analogue scale (VAS, 0 to 10 cm) for pain, as well as the physical and mental component summary scores of the 36-item short-form health survey (SF-36). The patients in the acupuncture group had better responder rates and fewer migraine days compared with the control group (P .05). The results suggested that acupuncture was more effective than flunarizine in decreasing days of migraine attacks, whereas no significantly differences were found between acupuncture and flunarizine in reduction of pain intensity and improvement of the quality of life.


title: A Perspective View and Survey of Meta-Learning

abstract: Different researchers hold different views of what the term meta-learning exactly means. The first part of this paper provides our own perspective view in which the goal is to build self-adaptive learners (i.e. learning algorithms that improve their bias dynamically through experience by accumulating meta-knowledge). The second part provides a survey of meta-learning as reported by the machine-learning literature. We find that, despite different views and research lines, a question remains constant: how can we exploit knowledge about learning (i.e. meta-knowledge) to improve the performance of learning algorithms? Clearly the answer to this question is key to the advancement of the field and continues being the subject of intensive research.


title: Finding Consensus Among Words: Lattice-Based Word Error Minimization

abstract: We describe a new algorithm for finding the hypothesis in a recognition lattice that is expected to minimize the word error rate (WER). Our approach thus overcomes the mismatch between the word-based performance metric and the standard MAP scoring paradigm that is sentence-based, and that can lead to sub-optimal recognition results. To this end we first find a complete alignment of all words in the recognition lattice, identifying mutually supporting and competing word hypotheses. Finally, a new sentence hypothesis is formed by concatenating the words with maximal posterior probabilities. Experimentally, this approach leads to a significant WER reduction in a large vocabulary recognition task.


title: A meta-analysis of the relationship between socioeconomic status and executive function performance among children

abstract: The relationship between childhood socioeconomic status (SES) and executive function (EF) has recently attracted attention within psychology, following reports of substantial SES disparities in children's EF. Adding to the importance of this relationship, EF has been proposed as a mediator of socioeconomic disparities in lifelong achievement and health. However, evidence about the relationship between childhood SES and EF is mixed, and there has been no systematic attempt to evaluate this relationship across studies. This meta-analysis systematically reviewed the literature for studies in which samples of children varying in SES were evaluated on EF, including studies with and without primary hypotheses about SES. The analysis included 8760 children between the ages of 2 and 18 gathered from 25 independent samples. Analyses showed a small but statistically significant correlation between SES and EF across all studies (rrandom = .16, 95% CI [.12, .21]) without correcting for attenuation owing to range restriction or measurement unreliability. Substantial heterogeneity was observed among studies, and a number of factors, including the amount of SES variability in the sample and the number of EF measures used, emerged as moderators. Using only the 15 studies with meaningful SES variability in the sample, the average correlation between SES and EF was small-to-medium in size (rrandom = .22, 95% CI [.17, .27]). Using only the six studies with multiple measures of EF, the relationship was medium in size (rrandom = .28, 95% CI [.18, .37]). In sum, this meta-analysis supports the presence of SES disparities in EF and suggests that they are between small and medium in size, depending on the methods used to measure them.


title: Graph based Representation and Analysis of Text Document: A Survey of Techniques

abstract: A common and standard approach to model text document is bag-of-words. This model is suitable for capturing word frequency, however structural and semantic information is ignored. Graph representation is mathematical constructs and can model relationship and structural information effectively. A text can appropriately represented as Graph using vertex as feature term and edge relation can be significant relation between the feature terms. Text representation using Graph model provides computations related to various operations like term weight, ranking which is helpful in many applications in information retrieval. This paper presents a systematic survey of existing work on Graph based representation of text and also focused on Graph based analysis of text document for different operations in information retrieval. In this process taxonomy of Graph based representation and analysis of text document is derived and result of different methods of Graph based text representation and analysis are discussed. The survey results shows that Graph based representation is appropriate way of representing text document and improved result of analysis over traditional model for different text applications.


title: An evaluation study of driver profiling fuzzy algorithms using smartphones

abstract: Profiling driving behavior has become a relevant aspect in fleet management, automotive insurance and eco-driving. Detecting inefficient or aggressive drivers can help reducing fleet degradation, insurance policy cost and fuel consumption. In this paper, we present a Fuzzy-Logic based driver scoring mechanism that uses smartphone sensing data, including accelerometers and GPS. In order to evaluate the proposed mechanism, we have collected traces from a testbed consisting in 20 vehicles equipped with an Android sensing application we have developed to this end. The results show that the proposed sensing variables using smartphones can be merged to provide each driver with a single score.


title: Extended Self in a Digital World: Table 1.

abstract: The extended self was proposed in 1988. Since it was formulated, many technological changes have dramatically affected the way we consume, present ourselves, and communicate. This conceptual update seeks to revitalize the concept, incorporate the impacts of digitization, and provide an understanding of consumer sense of self in today's technological environment. It is necessarily a work in progress, for the digital environment and our behavior within it continue to evolve. But some important changes are already clear. Five changes with digital consumption are considered that impact the nature of self and the nature of possessions. Needed modifications and additions to the extended self are outlined, and directions for future research are suggested. The digital world opens a host of new means for self-extension, using many new consumption objects to reach a vastly broader audience. Even though this calls for certain reformulations, the basic concept of the extended self remains vital.


title: Living With Seal Robots—Its Sociopsychological and Physiological Influences on the Elderly at a Care House

abstract: Robot therapy for elderly residents in a care house has been conducted since June 2005. Two therapeutic robots were introduced into a care facility, and activated for over 9 h each day to interact with the residents. This paper presents the results of the first month of this experiment. To investigate the psychological and social effects of the robots, each subject was interviewed, and their social network was analyzed. In addition, the activities of the residents in public areas were recorded by video cameras during daytime hours (8:30-18:00). For physiological analysis, residents' urine was obtained and analyzed for hormones 17-ketosteroid sulfate and 17-hydroxycorticosteroids. The experimental protocol was reviewed and approved by the ethical committee of the National Institute of Advanced Industrial Science and Technology. The results indicate that interaction with the seal robots increased their social interaction. Furthermore, the urinary tests showed that the reactions of the subjects' vital organs to stress improved after the introduction of the robots.


title: Automatic Generation of 3D Caricatures Based on Artistic Deformation Styles

abstract: Caricatures are a form of humorous visual art, usually created by skilled artists for the intention of amusement and entertainment. In this paper, we present a novel approach for automatic generation of digital caricatures from facial photographs, which capture artistic deformation styles from hand-drawn caricatures. We introduced a pseudo stress-strain model to encode the parameters of an artistic deformation style using “virtual” physical and material properties. We have also developed a software system for performing the caricaturistic deformation in 3D which eliminates the undesirable artifacts in 2D caricaturization. We employed a Multilevel Free-Form Deformation (MFFD) technique to optimize a 3D head model reconstructed from an input facial photograph, and for controlling the caricaturistic deformation. Our results demonstrated the effectiveness and usability of the proposed approach, which allows ordinary users to apply the captured and stored deformation styles to a variety of facial photographs.


title: Subagging for credit scoring models

abstract: The logistic regression framework has been for long time the most used statistical method when assessing customer credit risk. Recently, a more pragmatic approach has been adopted, where the first issue is credit risk prediction, instead of explanation. In this context, several classification techniques have been shown to perform well on credit scoring, such as support vector machines among others. While the investigation of better classifiers is an important research topic, the specific methodology chosen in real world applications has to deal with the challenges arising from the real world data collected in the industry. Such data are often highly unbalanced, part of the information can be missing and some common hypotheses, such as the i.i.d. one, can be violated. In this paper we present a case study based on a sample of IBM Italian customers, which presents all the challenges mentioned above. The main objective is to build and validate robust models, able to handle missing information, class unbalancedness and non-iid data points. We define a missing data imputation method and propose the use of an ensemble classification technique, subagging, particularly suitable for highly unbalanced data, such as credit scoring data. Both the imputation and subagging steps are embedded in a customized cross-validation loop, which handles dependencies between different credit requests. The methodology has been applied using several classifiers (kernel support vector machines, nearest neighbors, decision trees, Adaboost) and their subagged versions. The use of subagging improves the performance of the base classifier and we will show that subagging decision trees achieve better performance, still keeping the model simple and reasonably interpretable.


title: Classification of crops and weeds from digital images: A support vector machine approach

abstract: Abstract In most agricultural systems, one of the major concerns is to reduce the growth of weeds. In most cases, removal of the weed population in agricultural fields involves the application of chemical herbicides, which has had successes in increasing both crop productivity and quality. However, concerns regarding the environmental and economic impacts of excessive herbicide applications have prompted increasing interests in seeking alternative weed control approaches. An automated machine vision system that can distinguish crops and weeds in digital images can be a potentially cost-effective alternative to reduce the excessive use of herbicides. In other words, instead of applying herbicides uniformly on the field, a real-time system can be used by identifying and spraying only the weeds. This paper investigates the use of a machine-learning algorithm called support vector machine (SVM) for the effective classification of crops and weeds in digital images. Our objective is to evaluate if a satisfactory classification rate can be obtained when SVM is used as the classification model in an automated weed control system. In our experiments, a total of fourteen features that characterize crops and weeds in images were tested to find the optimal combination of features that provides the highest classification rate. Analysis of the results reveals that SVM achieves above 97% accuracy over a set of 224 test images. Importantly, there is no misclassification of crops as weeds and vice versa.


title: A Novel Embedding Method for Information Diffusion Prediction in Social Network Big Data

abstract: With the increase of social networking websites and the interaction frequency among users, the prediction of information diffusion is required to support effective generalization and efficient inference in the context of social big data era. However, the existing models either rely on expensive probabilistic modeling of information diffusion based on partially known network structures, or discover the implicit structures of diffusion from users’ behaviors without considering the impacts of different diffused contents. To address the issues, in this paper, we propose a novel information-dependent embedding-based diffusion prediction (IEDP) model to map the users in observed diffusion process into a latent embedding space, then the temporal order of users with the timestamps in the cascade can be preserved by the embedding distance of users. Our proposed model further learns the propagation probability of information in the cascade as a function of the relative positions of information-specific user embeddings in the information-dependent subspace. Then, the problem of temporal propagation prediction can be converted into the task of spatial probability learning in the embedding space. Moreover, we present an efficient margin-based optimization algorithm with a fast computation to make the inference of the information diffusion in the latent embedding space. When applying our proposed method to several social network datasets, the experimental results show the effectiveness of our proposed approach for the information diffusion prediction and the efficiency with respect to the inference speed compared with the state-of-the-art methods.


title: Hierarchy-based Image Embeddings for Semantic Image Retrieval

abstract: Deep neural networks trained for classification have been found to learn powerful image representations, which are also often used for other tasks such as comparing images w.r.t. their visual similarity. However, visual similarity does not imply semantic similarity. In order to learn semantically discriminative features, we propose to map images onto class embeddings whose pair-wise dot products correspond to a measure of semantic similarity between classes. Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e.g., novelty detection or few-shot learning. We introduce a deterministic algorithm for computing the class centroids directly based on prior world-knowledge encoded in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds, and ImageNet show that our learned semantic image embeddings improve the semantic consistency of image retrieval results by a large margin.


title: A Case Study in Understanding OSPF and BGP Interactions Using Efficient Experiment Design

abstract: In this paper, we analyze the two dominant inter- and intradomain routing protocols in the Internet: Open Shortest Path Forwarding (OSPFv2) and Border Gateway Protocol (BGP4). Specifically, we investigate interactions between these two routing protocols as well as overall (i.e. both OSPF and BGP) stability and dynamics. Our analysis is based on large-scale simulations of OSPF and BGP, and careful design of experiments (DoE) to perform an efficient search for the best parameter settings of these two routing protocols


Balancing Renewable Energy Source with Vehicle to Grid Services from a Large Fleet of Plug-In Hybrid Electric Vehicles controlled in a Metropolitan Area Distribution Network Towards the Power System of the Future Modelling / New tools for technical performance assessment


title: Accountable Anonymous Group Messaging

abstract: Users often wish to participate in online groups anonymously, but misbehaving users may abuse this anonymity to spam or disrupt the group. Messaging protocols such as Mix-nets and DC-nets leave online groups vulnerable to denial-of-service and Sybil attacks, while accountable voting protocols are unusable or inefficient for general anonymous messaging. ::: We present the first general messaging protocol that offers provable anonymity with accountability for moderate-size groups, and efficiently handles unbalanced loads where few members have much data to transmit in a given round. The N group members first cooperatively shuffle an NxN matrix of pseudorandom seeds, then use these seeds in N "pre-planned" DC-nets protocol runs. Each DC-nets run transmits the variable-length bulk data comprising one member's message, using the minimum number of bits required for anonymity under our attack model. The protocol preserves message integrity and one-to-one correspondence between members and messages, makes denial-of-service attacks by members traceable to the culprit, and efficiently handles large and unbalanced message loads. A working prototype demonstrates the protocol's practicality for anonymous messaging in groups of 40+ member nodes.


On Trade-offs of Applying Block Chains for Electronic Voting Bulletin Boards.


title: Financial modeling using Gaussian process models

abstract: In the 1960s E. Fama developed the efficient market hypothesis (EMH) which asserts that the financial market is efficient if its prices are formed on the basis of all publicly available information. That means technical analysis cannot be used to predict and beat the market. Since then, it was widely examined and was mostly accepted by mathematicians and financial engineers. However, the predictability of financial-market returns remains an open problem and is discussed in many publications. Usually, it is concluded that a model able to predict financial returns should adapt to market changes quickly and catch local dependencies in price movements. The Bayesian vector autoregression (BVAR) models, support vector machines (SVM) and some other were already applied to financial data quite succesfully. Gaussian process (GP) models are emerging non-parametric Bayesian models and in this paper we test their applicability to financial data. GP model is fitted to daily data from U.S. commodity markets. For a comparison BVAR model and benchmark model that is commonly used in todays financial mathematics are chosen. The results indicate that GP models are applicable to financial data as well as BVAR models.


title: Psychiatric disorders among emotionally distressed disaster victims attending primary mental health clinics in Ecuador.

abstract: Previous studies in developing countries have demonstrated post-disaster stress disorders in a substantial share of the people living through a natural calamity, but questions have remained as to the severity of these mental health problems. This article reports information derived from a 1987 study of Ecuadorian earthquake victims that shows many of the victims had diagnosable psychiatric disorders and provides insight into the nature of those disorders. Language: en


title: WiFi can be the weakest link of round trip network latency in the wild

abstract: As mobile Internet is now indispensable in our daily lives, WiFi's latency performance has become critical to mobile applications' quality of experience. Unfortunately, WiFi hop latency in the wild remains largely unknown. In this paper, we first propose an effective approach to break down the round trip network latency. Then we provide the first systematic study on WiFi hop latency in the wild based on the latency and WiFi factors collected from 47 APs on T university campus for two months. We observe that WiFi hop can be the weakest link in the round trip network latency: more than 50% (10%) of TCP packets suffer from WiFi hop latency larger than 20ms (100ms), and WiFi hop latency occupies more than 60% in more than half of the round trip network latency. To help understand, troubleshoot, and optimize WiFi hop latency for WiFi APs in general, we train a decision tree model. Based on the model's output, we are able to reduce the median latency by 80% from 50ms to 10ms in one real case, and reduce the maximum latency from 250ms to 50ms in another real case.


title: Challenges in Enterprise Search

abstract: Concerted research effort since the nineteen fifties has lead to effective methods for retrieval of relevant documents from homogeneous collections of text, such as newspaper archives, scientific abstracts and CD-ROM encyclopaedias. However, the triumph of the Web in the nineteen nineties forced a significant paradigm shift in the Information Retrieval field because of the need to address the issues of enormous scale, fluid collection definition, great heterogeneity, unfettered interlinking, democratic publishing, the presence of adversaries and most of all the diversity of purposes for which Web search may be used. Now, the IR field is confronted with a challenge of similarly daunting dimensions -- how to bring highly effective search to the complex information spaces within enterprises. Overcoming the challenge would bring massive economic benefit, but victory is far from assured. The present work characterises enterprise search, hints at its economic magnitude, states some of the unsolved research questions in the domain of enterprise search need, proposes an enterprise search test collection and presents results for a small but interesting sub-problem.


2016) Proposal to conserve the name Physalis (Solanaceae) with a conserved type


title: A general factor of personality: evidence from the HEXACO model and a measure of trait emotional intelligence.

abstract: The purpose of the present study was to determine if a general factor of personality (GFP) could be extracted from the six dimensions of the HEXACO model and four factors of trait emotional intelligence. Participants were 1,192 pairs of twins (666 MZ pairs, 526 DZ pairs) between the ages of 19 to 86 years, who completed the Trait Emotional Intelligence Questionnaire - Short Form and the HEXACO Personality Inventory - Revised. Principal components analysis yielded a strong GFP accounting for 33% of the variance, on which all variables with the exception of honesty-humility from the HEXACO showed moderate to large loadings. Behavioral genetic (BG) analyses revealed that individual differences in the GFP were entirely attributable to additive genetic and non-shared environmental factors - results that are in accord with previous BG analyses of a GFP. The present study adds to the body of evidence in support of a heritable GFP but an alternative perspective is also discussed.


title: Example-based hinting of true type fonts

abstract: Hinting in TrueType is a time-consuming manual process in which a typographer creates a sequence of instructions for better fitting the characters of a font to a grid of pixels. In this paper, we propose a new method for automatically hinting TrueType fonts by transferring hints of one font to another. Given a hinted source font and a target font without hints, our method matches the outlines of corresponding glyphs in each font, and then translates all of the individual hints for each glyph from the source to the target font. It also translates the control value table (CVT) entries, which are used to unify feature sizes across a font. The resulting hinted font already provides a great improvement over the unhinted version. More importantly, the translated hints, which preserve the sound, hand-designed hinting structure of the original font, provide a very good starting point for a professional typographer to complete and fine-tune, saving time and increasing productivity. We demonstrate our approach with examples of automatically hinted fonts at typical display sizes and screen resolutions. We also provide estimates of the time saved by a professional typographer in hinting new fonts using this semi-automatic approach.


title: Approximation and Estimation Bounds for Artificial Neural Networks

abstract: For a common class of artificial neural networks, the mean integrated squared error between the estimated network and a target function f is shown to be bounded by O \left ( {{C^2_f}\over n}\right ) + O\left ({nd\over N}{\rm log}\ N\right), where n is the number of nodes, d is the input dimension of the function, N is the number of training observations, and Cf is the first absolute moment of the Fourier magnitude distribution of f. The two contributions to this total risk are the approximation error and the estimation error. Approximation error refers to the distance between the target function and the closest neural network function of a given architecture and estimation error refers to the distance between this ideal network function and an estimated network function. With n ˜ Cf(N/(dlog N))1/2 nodes, the order of the bound on the mean integrated squared error is optimized to be O(Cf((d/N)log N)1/2). The bound demonstrates surprisingly favorable properties of network estimation compared to traditional series and nonparametric curve estimation techniques in the case that d is moderately large. Similar bounds are obtained when the number of nodes n is not preselected as a function of Cf (which is generally not known a priori), but rather the number of nodes is optimized from the observed data by the use of a complexity regularization or minimum description length criterion. The analysis involves Fourier techniques for the approximation error, metric entropy considerations for the estimation error, and a calculation of the index of resolvability of minimum complexity estimation of the family of networks.


title: Fully automated facial symmetry axis detection in frontal color images

abstract: In this paper, we consider the problem of automatically detecting a facial symmetry axis in what we will call a standard human face image (acquired when the subject is looking directly into the camera, in front of a clean gray background under controlled illumination). Images of this kind are encountered in face recognition scenarios; this detection should facilitate more sophisticated facial image processing. The proposed method is based on GLDH (gray level difference histogram) analysis and consists of three components: (1) the face region detection stage crops an approximate face region out of the background, (2) symmetry detection discovers a vertical axis to optimally bisect the region of interest, assuming it is bilaterally symmetric, and (3) orientation adjustment aligns the angle of the symmetry axis with the orientation of the face. An implementation of the method is described, and results are presented. This detector's robust performance is evidenced by its success finding symmetry axes in more than 7,500 images collected from 600 distinct subjects. One of our method's most noteworthy contributions is that, according to our experimental results, many of the automatically detected axes are more accurate than the reference axes. Our automated detector is a powerful tool because it is not as susceptible to human error as its manual counterpart and, as the first application of its kind, it could potentially serve as a new biometric.


title: Co-designing Scientific Software: Hackathons for Participatory Interface Design

abstract: Taxonomy is the branch of biology concerned with classifying organisms. Taxonomic work entails a range of complex human-computer and human-information interactions, which are under-supported by current software environments, partially because taxonomic software is largely built through ad hoc collaborations by taxonomists themselves. This results in poor user experience and difficult-to-use tools. Here we describe an interface design Hackathon held as part of the NSF-funded Transforming Taxonomic Interfaces project. We brought taxonomists, software developers, and information scientists together to rapidly prototype new interfaces. Taxonomists quickly took to paper prototyping, and produced a number of promising designs. Emergent themes and findings relevant to the HCI community are described herein.


title: Benefits of Bias: Towards Better Characterization of Network Sampling

abstract: From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural representativeness. We show that certain biases are, in fact, beneficial for many applications, as they"push"the sampling process towards inclusion of desired properties. Finally, we describe how these sampling biases can be exploited in several, real-world applications including disease outbreak detection and market research.


title: Robust Face Recognition via Multimodal Deep Face Representation

abstract: Face images appearing in multimedia applications, e.g., social networks and digital entertainment, usually exhibit dramatic pose, illumination, and expression variations, resulting in considerable performance degradation for traditional face recognition algorithms. This paper proposes a comprehensive deep learning framework to jointly learn face representation using multimodal information. The proposed deep learning structure is composed of a set of elaborately designed convolutional neural networks (CNNs) and a three-layer stacked auto-encoder (SAE). The set of CNNs extracts complementary facial features from multimodal data. Then, the extracted features are concatenated to form a high-dimensional feature vector, whose dimension is compressed by SAE. All of the CNNs are trained using a subset of 9,000 subjects from the publicly available CASIA-WebFace database, which ensures the reproducibility of this work. Using the proposed single CNN architecture and limited training data, 98.43% verification rate is achieved on the LFW database. Benefitting from the complementary information contained in multimodal data, our small ensemble system achieves higher than 99.0% recognition rate on LFW using publicly available training set.


title: Deep background subtraction with scene-specific convolutional neural networks

abstract: Background subtraction is usually based on low-level or hand-crafted features such as raw color components, gradients, or local binary patterns. As an improvement, we present a background subtraction algorithm based on spatial features learned with convolutional neural networks (ConvNets). Our algorithm uses a background model reduced to a single background image and a scene-specific training dataset to feed ConvNets that prove able to learn how to subtract the background from an input image patch. Experiments led on 2014 ChangeDetection.net dataset show that our ConvNet based algorithm at least reproduces the performance of state-of-the-art methods, and that it even outperforms them significantly when scene-specific knowledge is considered.


title: A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

abstract: In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications.


title: Image Classification to Support Emergency Situation Awareness

abstract: Recent advances in image classification methods, along with the availability of associated tools, has seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of emergency situation awareness. We discuss image classification based on low level features as well as methods built on top of pre-trained classifiers. The performance of the classifiers are assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire related classes with an accuracy of 86%.


title: Automatic parking lot mapping for available parking space detection

abstract: This paper presents a method to estimate map of parking lot for automatic system of available parking space detection. The parking space detection is important module for the parking guidance system (PGS) that can help drivers to find the parking space efficiently. Instead of masked parking slots by human, we try to mask them by using histograms of spatial features. However, under poor image quality, we face dynamic change of background. Although we have applied adaptive background model for background subtraction, the problem still remains. To overcome this problem, we propose an automatic thresholding for tuning object (car) detection in adaptive background model method. The fine-tuning technique can improve accuracy of the histogram for the parking lot mapping. The experimental results were shown both simulation on the parking model and real parking lot correctly.


title: Scientific Basis for Polygraph Testing

abstract: Published scientific literature is reviewed for comparison question polygraph testing and its application to diagnostic and screening contexts. The review summarizes the literature for all aspects of the testing procedure including the pretest interview, test data collection, test data analysis, and a proffer of the physiological and psychological basis for polygraph testing. Polygraph accuracy information is summarized for diagnostic and screening exams. Evidence is reviewed for threats to polygraph accuracy and the contribution of polygraph results to incremental validity or increased decision accuracy by professional consumers of polygraph test results. The polygraph is described as a probabilistic and non-deterministic test, involving both physiological recording and statistical methods. Probabilistic tests, statistical models, and scientific tests in general are needed when neither deterministic observation nor physical measurement are possible. Event-specific diagnostic polygraphs have been shown to provide mean accuracy of .89 with a 95% confidence range from .83 to .95. Multi-issue screening polygraphs have been shown to provide accuracy rates, with a mean of .85 and a 95% confidence range of .77 to .93.


title: Virtual Notepad: handwriting in immersive VR

abstract: We present Virtual Notepad, a collection of interface tools that allows the user to take notes, annotate documents and input text using a pen, while still immersed in virtual environments (VEs). Using a spatially-tracked, pressure-sensitive graphics tablet, pen and handwriting recognition software, Virtual Notepad explores handwriting as a new modality for interaction in immersive VEs. This paper reports details of the Virtual Notepad interface and interaction techniques, discusses implementation and design issues, reports the results of initial evaluation and overviews possible applications of virtual handwriting.


title: Learning from examples as an inverse problem

abstract: Many works related learning from examples to regularization techniques for inverse problems, emphasizing the strong algorithmic and conceptual analogy of certain learning algorithms with regularization algorithms. In particular it is well known that regularization schemes such as Tikhonov regularization can be effectively used in the context of learning and are closely related to algorithms such as support vector machines. Nevertheless the connection with inverse problem was considered only for the discrete (finite sample) problem and the probabilistic aspects of learning from examples were not taken into account. In this paper we provide a natural extension of such analysis to the continuous (population) case and study the interplay between the discrete and continuous problems. From a theoretical point of view, this allows to draw a clear connection between the consistency approach in learning theory and the stability convergence property in ill-posed inverse problems. The main mathematical result of the paper is a new probabilistic bound for the regularized least-squares algorithm. By means of standard results on the approximation term, the consistency of the algorithm easily follows.


title: Unsupervised metric learning for face identification in TV video

abstract: The goal of face identification is to decide whether two faces depict the same person or not. This paper addresses the identification problem for face-tracks that are automatically collected from uncontrolled TV video data. Face-track identification is an important component in systems that automatically label characters in TV series or movies based on subtitles and/or scripts: it enables effective transfer of the sparse text-based supervision to other faces. We show that, without manually labeling any examples, metric learning can be effectively used to address this problem. This is possible by using pairs of faces within a track as positive examples, while negative training examples can be generated from pairs of face tracks of different people that appear together in a video frame. In this manner we can learn a cast-specific metric, adapted to the people appearing in a particular video, without using any supervision. Identification performance can be further improved using semi-supervised learning where we also include labels for some of the face tracks. We show that our cast-specific metrics not only improve identification, but also recognition and clustering.


title: Coping with IT Carve-out Projects - Towards a Maturity Model

abstract: The management of IT carve-out projects is very challenging due to the strict time frame, the severe contractual penalties, the huge number of stakeholders and the various as well as unique IT tasks that have to be conduct-ed. Up to now, there is no instrument for evaluating the readiness of the IT for a carve-out and also not for managing such a project. In order to address this, we develop a maturity model based on expert interviews and a literature review on success factors of IT carve-outs. The elements as well as the usage of the IT carve-out maturity model are explained. The maturity model has been evaluated theoretically based on design principles and during a case study in the financial services industry. The developed maturity model can be used by practitioners for the management of IT carve-outs and also by researchers to examine IT carve-outs in empirical research.


title: Deep Learning for the Segmentation of Vessels in Retinal Fundus images and its Interpretation

abstract: The main goal of this dissertation is to study and analyze different approaches based on deep learning techniques for the segmentation of retinal blood vessels. In order to do so, different design and architectures of CNN's will be studied and analysed, as their results and performance are evaluated and compared with the available algorithms. One other important objective of this work is to study and evaluate the different techniques that have been used for vessel segmentation, such as machine learning, and how these can be combined with the deep learning approaches. By Analyzing the features that the learned models are using to perform classification and combining them with different machine learning techniques (such as Random Forest and SVM Classifiers), another goal is to proposed a solution or set of solutions to perform the retinal vessel segmentation.


title: Ray tracing via GPU rasterization

abstract: Ray tracing is a dominant method for generating a wide variety of global illumination effects, such as reflections/refractions, shadows, etc. In this paper, we propose an efficient technique to perform nearly accurate ray tracing using the programmable graphics processor units (GPUs). With the aid of the linked-list A-buffer and the uniform voxel grid to represent scene geometry, the ray-scene intersection can be efficiently computed via the built-in rasterization on GPUs. Based on this novel ray-scene intersection technique, a new ray-tracing framework which supports various light transport algorithms is introduced, including Ray Casting, Whitted Ray tracing, Ambient Occlusion, Path Tracing, and so on. The experimental results demonstrate the accuracy and efficiency of our approach.


title: Get in Step With Synchronization

abstract: This paper presents a review of powersystem synchronization. When two sources are paralleled, it is crucial to close the interconnecting circuit breaker when both sources are in voltage, frequency, and phase coincidence. Operators can synchronize manually, or use the latest, state-of-the-art autosynchronizers (ANSI/IEEE device 25A) and sync-check relays (ANSI/IEEE device 25) to automate closing. Generator and bus synchronization share most principles, with some important differences for each type of synchronization. For generation plants, closing manually or applying an automatic synchronizer depends a lot on the plant configuration and operating mode. For bus-line applications, synchronizing depends on power-system stiffness, motor loads, and whether a wye-delta transformer is between the line and bus. Methods for attaining proper synch-check and fast synchronization are discussed.


title: Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus

abstract: A method is described for selecting the optimal focus measure with respect to gray-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been defined for estimating the noise-sensitivity of different focus measures. The first metric-the autofocusing uncertainty measure (AUM)-is useful in understanding the relation between gray-level noise and the resulting error in lens position for autofocusing. The second metric-autofocusing root-mean-square error (ARMS error)-is an improved metric closely related to AUM. AUM and ARMS error metrics are based on a theoretical noise sensitivity analysis of focus measures, and they are related by a monotonic expression. The theoretical results are validated by actual and simulation experiments. For a given camera, the optimally accurate focus measure may change from one object to the other depending on their focused images. Therefore, selecting the optimal focus measure from a given set involves computing all focus measures in the set.


title: Efficient Second-Order Gradient Boosting for Conditional Random Fields

abstract: Conditional random elds (CRFs) are an important class of models for accurate structured prediction, but eective design of the feature functions is a major challenge when applying CRF models to real world data. Gradient boosting, which is used to automatically induce and select feature functions, is a natural candidate solution to the problem. However, it is non-trivial to derive gradient boosting algorithms for CRFs due to the dense Hessian matrices introduced by variable dependencies. Existing approaches thus use only rst-order information when optimizing likelihood, and hence face convergence issues. We incorporate second-order information by deriving a Markov Chain mixing rate bound to quantify the dependencies, and introduce a gradient boosting algorithm that iteratively optimizes an adaptive upper bound of the objective function. The resulting algorithm induces and selects features for CRFs via functional space optimization, with provable convergence guarantees. Experimental results on three real world datasets demonstrate that the mixing rate based upper bound is eective for learning CRFs with non-linear potentials.


title: Some asymptotic results for the periodogram of a stationary time series

abstract: Clocking signals are recovered from an incoming signal train by a master clock oscillator phase locked to the incoming signals, and, in the event of a failure or malfunction involving the master clock, by a standby clock similarly phase locked to the incoming signals. One of the malfunctions occurs when the phase of the clock output slips a cycle with respect to the phase of the signal train. It is determined that a cycle slippage condition exists when phase comparisons of the signals indicate the clock output is passing from a phase lag error to a phase lead error, or vice versa, without passing through a region of no phase error.


title: Charcoal rendering and shading with reflections

abstract: In this work, we have developed an approach to include global illumination effects into charcoal drawing (see Figure 1). Our charcoal shader provides a robust computation to obtain charcoal effect for a wide variety of diffuse and specular materials. Our contributions can be summarized as follows: (1) A Barrycentric shader that is based on degree zero B-spline basis functions; (2) A set of hand-drawn charcoal control texture images that naturally provide desired charcoal look-and-feel; and (3) A painter's hierarchy for handling a high number of shading parameters consistent with charcoal drawing.


Vertical and horizontal elasticity for dynamic virtual machine reconfiguration


title: An integrated tactile-thermal robot sensor with capacitive tactile array

abstract: The paper discusses a tactile sensor system with high sampling resolution integrated with a thermal sensor. The integration is performed by using the column armature of the matrix of a capacitive tactile sensor as thermal resistor having positive temperature coefficient. A prototype of the sensor has been designed and constructed. From test results on this prototype when used as a tactile sensor, with spatial resolution of 2 mm, has been detected a sensitivity of about 20 N/mm/sup 2/ per picofarad or 0.05 pF per N/mm/sup 2/ with linearity of about 0.4%. As thermal sensor, with spatial resolution of 2 mm, the temperature coefficient has been in about the 0.4% with sensitivity of about 0.4 m/spl Omega///spl deg/C.


title: Tennis Winner Prediction based on Time-Series History with Neural Modeling

abstract: Tennis is one of the most popular sports in the world. Many researchers have studied in tennis model to find out whose player will be the winner of the match by using the statistical data. This paper proposes a powerful technique to predict the winner of the tennis match. The proposed method provides more accurate prediction results by using the statisti- cal data and environmental data based on Multi-Layer Perceptron (MLP) with back-propagation learning al- gorithm. Due to the growth of sport betting, predictions are widely used in many kinds of sports, especially tennis. The ten- nis prediction model is created to evaluate the chance of winning and the expected length of the match that players will face. Most people believe that the first serve person in the set has more advantage than another be- cause most of the games often go like that so the first serve affect to the games' score (2). Additionally, lots of players always make fault in the first serve and do bet- ter in the second serve so second serve might affect to the games' score too. Nevertheless, the first serve and the second serve affect to the games' score but there is another thing, that might be refuting an advantage of serves, it is strongly returns of serve. Moreover, the sur- face characteristics also affect to the players, e.g., some players perform better on grass but they may get worse on clay.


Solar cell efficiency tables ( version 37 )


Panoramic Image Stitching


title: A data-centric design methodology for business processes

abstract: This chapter describes a design methodology for business processes and workflows that focuses first on “business artifacts”, which represent key (real or conceptual) business entities, including both the business-relevant data about them and their macro-level lifecycles. Individual workflow services (a.k.a. tasks) are then incorporated, by specifying how they operate on the artifacts and fit into their lifecycles. The resulting workflow is specified in a particular artifact-centric workflow model, which is introduced using an extended example. At the logical level this workflow model is largely declarative, in contrast with most traditional workflow models which are procedural and/or graph-based. The chapter includes a discussion of how the declarative, artifact-centric workflow specification can be mapped into an optimized physical realization.


title: A Broadband GaN pHEMT Power Amplifier Using Non-Foster Matching

abstract: Non-Foster matching is applied to design a multi- octave broadband GaN power amplifier (PA) in this paper. The bandwidth limitation from high-Q interstage matching is overcome through the use of negative capacitor, which is realized with a negative impedance converter (NIC) using the cross-coupled GaN FETs. For high power operation over the entire bandwidth, the natural interstage matching is optimized for the upper subfrequency band and the lower subfrequency band is compensated for by the negative capacitance presented by non-Foster circuit (NFC). Detailed analysis is presented to understand the frequency and power limits of NIC circuits for PA applications. Two negative impedance matched PAs (NMPAs) are fabricated with 0.25- $\mu$ m GaN pHEMT process. The implemented PA with $2{\times}$ combining shows the output powers of 35.7–37.5 dBm with the power added efficiencies of 13–21% from 6 to 18 GHz. The $4{\times}$ combining PA achieves over 5 W output power from 7 to 17 GHz. The NFC boosts the efficiencies and power below 12 GHz to achieve broadband performance without using any lossy matching or negative feedback. To our knowledge, this is the first demonstration of NIC-based broadband amplifiers with multi-watt-level output power.


Evolutionary Music Composer


title: Reconfigurable RF MEMS Phased Array Antenna Integrated Within a Liquid Crystal Polymer (LCP) System-on-Package

abstract: For the first time, a fully integrated phased array antenna with radio frequency microelectromechanical systems (RF MEMS) switches on a flexible, organic substrate is demonstrated above 10 GHz. A low noise amplifier (LNA), MEMS phase shifter, and 2 times 2 patch antenna array are integrated into a system-on-package (SOP) on a liquid crystal polymer substrate. Two antenna arrays are compared; one implemented using a single-layer SOP and the second with a multilayer SOP. Both implementations are low-loss and capable of 12deg of beam steering. The design frequency is 14 GHz and the measured return loss is greater than 12 dB for both implementations. The use of an LNA allows for a much higher radiated power level. These antennas can be customized to meet almost any size, frequency, and performance needed. This research furthers the state-of-the-art for organic SOP devices.


title: Modeling Biological Processes for Reading Comprehension

abstract: Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a biological process, and the goal is to answer questions that require an understanding of the relations between entities and events in the process. To answer the questions, we first predict a rich structure representing the process in the paragraph. Then, we map the question to a formal query, which is executed against the predicted structure. We demonstrate that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.


title: Operating system profiling via latency analysis

abstract: Operating systems are complex and their behavior depends on many factors. Source code, if available, does not directly help one to understand the OS's behavior, as the behavior depends on actual workloads and external inputs. Runtime profiling is a key technique to prove new concepts, debug problems, and optimize performance. Unfortunately, existing profiling methods are lacking in important areas---they do not provide enough information about the OS's behavior, they require OS modification and therefore are not portable, or they incur high overheads thus perturbing the profiled OS. We developed OSprof: a versatile, portable, and efficient OS profiling method based on latency distributions analysis. OSprof automatically selects important profiles for subsequent visual analysis. We have demonstrated that a suitable workload can be used to profile virtually any OS component. OSprof is portable because it can intercept operations and measure OS behavior from user-level or from inside the kernel without requiring source code. OSprof has typical CPU time overheads below 4%. In this paper we describe our techniques and demonstrate their usefulness through a series of profiles conducted on Linux, FreeBSD, and Windows, including client/server scenarios. We discovered and investigated a number of interesting interactions, including scheduler behavior, multi-modal I/O distributions, and a previously unknown lock contention, which we fixed.


title: Data Management for Journalism

abstract: We describe the power and potential of data journalism, where news stories are reported and published with data and dynamic visualizations. We discuss the challenges facing data journalism today and how recent data management tools such as Google Fusion Tables have helped in the newsroom. We then describe some of the challenges that need to be addressed in order for data journalism to reach its full


Deep Learning versus Gist Descriptors for Image-based Malware Classification


Internet privacy concerns confirm the case for intervention


title: Control of a Quadrotor Mini-Helicopter via Full State Backstepping Technique

abstract: In this paper, we present a new control approach for a quadrotor mini-helicopter using the full state backstepping technique. The controller can set the helicopter track three Cartesian position and the yaw angle to their desired trajectories and stabilize the pitch and roll angles by varying the input signals of DC-motors. The quadrotor has been presented into three interconnected subsystems. The first one representing the under-actuated subsystem, gives the dynamic relation of the horizontal positions with the pitch and roll angles. The second fully-actuated subsystem represents the dynamic behavior of the vertical position and the yaw angle. The last subsystem gives the dynamic of the four rotors propeller system. The design controller methodology is based on the Lyapunov stability theory. Various simulations of a quadrotor show the good performance of the proposed control law. Finally, we present initial flight experiments where the mini-helicopter is restricted to vertical and yaw motions.


title: Sparse Semantic Hashing for Efficient Large Scale Similarity Search

abstract: Similarity search, or finding approximate nearest neighbors, is an important technique in various large scale information retrieval applications such as document retrieval. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency. However, most existing hashing methods ignore the hidden semantic structure of documents but only use the keyword features (e.g., tf-idf) in hashing codes learning. This paper proposes a novel sparse semantic hashing (SpSH) approach that explores the hidden semantic representation of documents in learning their corresponding hashing codes. In particular, a unified framework is designed for ensuring the hidden semantic structure among the documents by a sparse coding model, while at the same time preserving the document similarity via graph Laplacian. An iterative coordinate descent procedure is then proposed for solving the optimization problem. Extensive experiments on two large scale datasets demonstrate the superior performance of the proposed research over several state-of-the-art hashing methods.


title: Scalable graph hashing with feature transformation

abstract: Hashing has been widely used for approximate nearest neighbor (ANN) search in big data applications because of its low storage cost and fast retrieval speed. The goal of hashing is to map the data points from the original space into a binary-code space where the similarity (neighborhood structure) in the original space is preserved. By directly exploiting the similarity to guide the hashing code learning procedure, graph hashing has attracted much attention. However, most existing graph hashing methods cannot achieve satisfactory performance in real applications due to the high complexity for graph modeling. In this paper, we propose a novel method, called scalable graph hashing with feature transformation (SGH), for large-scale graph hashing. Through feature transformation, we can effectively approximate the whole graph without explicitly computing the similarity graph matrix, based on which a sequential learning method is proposed to learn the hash functions in a bitwise manner. Experiments on two datasets with one million data points show that our SGH method can outperform the state-of-the-art methods in terms of both accuracy and scalability.


title: The Effects of Task Difficulty and Multitasking on Performance

abstract: Multitasking is prevalent during computer-mediated work. Users tend to switch between multiple ongoing computer-based tasks either due to a personal decision to break from the current task (self-interruption) or due to an external interruption, such as an electronic notification. To examine how different types of multitasking, along with subjective task difficulty, influence performance, we conducted a controlled experiment using a custom-developed multitasking environment. A total of 636 subjects were randomly assigned into one of the three conditions: discretionary, where they were allowed to decide when and how often to switch tasks; mandatory, where they were forced to switch tasks at specific times; and sequential, where they had to perform tasks in sequence, without switching. The experimental environment featured a primary problem-solving task and five secondary tasks. The results show that when the primary task was considered difficult, subjects forced to multitask had significantly lower performance compared with not only the subjects who did not multitask but also the subjects who were able to multitask at their discretion. Conversely, when the primary task was considered easy, subjects forced to multitask had significantly higher performance than both the subjects who did not multitask and the subjects who multitasked at their discretion.


Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists.


title: Exploring the Universe of Egregious Conversations in Chatbots

abstract: As part of the raging commercial development of chatbot systems, the ability to improve the quality of conversations quickly and consistently is crucial. In this work, we focus on egregious conversations and the dialog failure points that lead to these extremely bad dialogs. We present a tool that helps chatbot designers exploring and prioritizing failure points that need to be handled. Each failure is accompanied with an explanation and a suggested remedy.


title: Deep Ground Truth Analysis of Current Android Malware

abstract: To build effective malware analysis techniques and to evaluate new detection tools, up-to-date datasets reflecting the current Android malware landscape are essential. For such datasets to be maximally useful, they need to contain reliable and complete information on malware’s behaviors and techniques used in the malicious activities. Such a dataset shall also provide a comprehensive coverage of a large number of types of malware. The Android Malware Genome created circa 2011 has been the only well-labeled and widely studied dataset the research community had easy access to (As of 12/21/2015 the Genome authors have stopped supporting the dataset sharing due to resource limitation). But not only is it outdated and no longer represents the current Android malware landscape, it also does not provide as detailed information on malware’s behaviors as needed for research. Thus it is urgent to create a high-quality dataset for Android malware. While existing information sources such as VirusTotal are useful, to obtain the accurate and detailed information for malware behaviors, deep manual analysis is indispensable. In this work we present our approach to preparing a large Android malware dataset for the research community. We leverage existing anti-virus scan results and automation techniques in categorizing our large dataset (containing 24,650 malware app samples) into 135 varieties (based on malware behavioral semantics) which belong to 71 malware families. For each variety, we select three samples as representatives, for a total of 405 malware samples, to conduct in-depth manual analysis. Based on the manual analysis result we generate detailed descriptions of each malware variety’s behaviors and include them in our dataset. We also report our observations on the current landscape of Android malware as depicted in the dataset. Furthermore, we present detailed documentation of the process used in creating the dataset, including the guidelines for the manual analysis. We make our Android malware dataset available to the research community.


title: A Ultra Wideband Low Loss CBCPW-to-Microstrip Transition With Multiple Via Holes

abstract: A wideband (0 to 26.5 GHz) transition from conductor backed coplanar waveguide (CBCPW) to microstrip line has been developed. Field and impedance transformation have been used in the design; also, via holes were placed on the side ground planes of the CBCPW to suppress the parallel plate resonant mode thereby reducing radiation loss. A back-to-back transition board has been fabricated and tested. The test result shows good agreement with simulation. Under test, the back-to-back design insertion loss is less than 2 dB and the return loss is less than 15 dB from 0 to 26.5 GHz.


title: Beats: Tapping Gestures for Smart Watches

abstract: Interacting with smartwatches poses new challenges. Although capable of displaying complex content, their extremely small screens poorly match many of the touchscreen interaction techniques dominant on larger mobile devices. Addressing this problem, this paper presents beating gestures, a novel form of input based on pairs of simultaneous or rapidly sequential and overlapping screen taps made by the index and middle finger of one hand. Distinguished simply by their temporal sequence and relative left/right position these gestures are designed explicitly for the very small screens (approx. 40mm square) of smartwatches and to operate without interfering with regular single touch input. This paper presents the design of beating gestures and a rigorous empirical study that characterizes how users perform them -- in a mean of 355ms and with an error rate of 5.5%. We also derive thresholds for reliably distinguishing between simultaneous (under 30ms) and sequential (under 400ms) pairs of screen touches or releases. We then present five interface designs and evaluate them in a qualitative study in which users report valuing the speed and ready availability of beating gestures.


title: Organization, development and function of complex brain networks

abstract: Recent research has revealed general principles in the structural and functional organization of complex networks which are shared by various natural, social and technological systems. This review examines these principles as applied to the organization, development and function of complex brain networks. Specifically, we examine the structural properties of large-scale anatomical and functional brain networks and discuss how they might arise in the course of network growth and rewiring. Moreover, we examine the relationship between the structural substrate of neuroanatomy and more dynamic functional and effective connectivity patterns that underlie human cognition. We suggest that network analysis offers new fundamental insights into global and integrative aspects of brain function, including the origin of flexible and coherent cognitive states within the neural architecture.


title: A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows

abstract: This paper describes a tabu search heuristic for the vehicle routing problem with soft time windows. In this problem, lateness at customer locations is allowed although a penalty is incurred and added to the objective value. By adding large penalty values, the vehicle routing problem with hard time windows can be addressed as well. In the tabu search, a neighborhood of the current solution is created through an exchange procedure that swaps sequences of consecutive customers (or segments) between two routes. The tabu search also exploits an adaptive memory that contains the routes of the best previously visited solutions. New starting points for the tabu search are produced through a combination of routes taken from different solutions found in this memory. Many best-known solutions are reported on classical test problems.


title: A Maximum Power Point Tracking Technique for Partially Shaded Photovoltaic Systems in Microgrids

abstract: A modified fuzzy-logic controller for maximum power point (MPP) tracking is proposed to increase photovoltaic (PV) system performance during partially shaded conditions. Instead of perturbing and observing the PV system MPP, the controller scans and stores the maximum power during the perturbing and observing procedures. The controller offers accurate convergence to the global maximum operating point under different partial shadowing conditions. A mathematical model of the PV system under partial shadowing conditions is derived. To validate the proposed modified fuzzy-logic-based controller, simulation and experimentation results are provided.


title: Which demand systems can be generated by discrete choice?

abstract: We provide a simple necessary and sufficient condition for when a multiproduct demand system can be generated from a discrete choice model with unit demands.


title: Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view

abstract: Small and medium enterprises (SMEs) in the manufacturing sector make a significant contribution to economic growth, yet most of the research into innovation management in the manufacturing sector has focused on large organizations. This article, however, identifies innovation drivers and their performance implications in manufacturing SMEs. Its study gathered survey data from a sample of 600 Australian SMEs and found that SMEs are similar to large firms with respect to the way that innovation strategy and formal structure are the key drivers of their performance, but do not appear to utilize innovation culture in a strategic and structured manner. This study therefore concludes that SMEs' performance is likely to improve as they increase the degree to which they mirror large manufacturing firms with respect to formal strategy and structure, and to which they recognize that innovation culture and strategy are closely aligned throughout the innovation process. Copyright © 2010 John Wiley & Sons, Ltd.


title: Caregivers' descriptions of patients with advanced breast cancer in connection with supervision sessions in a surgical ward.

abstract: The purpose of this study was to describe the content of caregivers' (health care professionals') presentation of care situations as told at supervision sessions. For 1 year, 21 caregivers in a surgical ward of a county hospital in the middle of Sweden participated in a clinical supervision session 2 hours every third week. The participants in the supervision sessions were divided so as to form three mixed groups composed of registered nurses, practical nurses, physiotherapists, and physicians. The purpose of the supervision sessions was to give caregivers the opportunity to reflect on different care situations in a way that contributed to the development of patient care. The 38 supervision sessions were tape-recorded and transcribed verbatim. The transcripts were analyzed using a qualitative content analysis. The caregivers described difficult care situations focusing on the feelings of patients, relatives, and caregivers, with an emphasis on the caregivers' being dominant. Difficult care situations were described as giving caregivers feelings of discomfort, powerlessness, and reduced self-esteem. These feelings were described as arising in connection with caring for women with advanced breast cancer and other seriously ill patients in an organization lacking clear goals and rules. This study found that supervision sessions offering an opportunity to reflect on the difficult care situations are important for caregivers. These sessions seem therefore to be of vital importance for the future development of cancer care on the surgical ward.


OpenCV based disease identification of mango leaves


title: The treatment of giant rhinophyma - Case Report

abstract: The aim of the article is to present an update on the pathophysiology, clinical features and treatment of rhinophyma. A 56 years old patient, living in urban area, presented with a giant rhinophyma which caused him not only upper airways obstruction and difficulty in eating, but also aesthetic and psycho-social disadvantages.The treatment of the patient was a surgical intervention consisting in removal of the nasal tumor and split-thickness skin grafting of the defect. The aesthetic result after surgical intervention was very good, there were no postoperative complications or recurrences.Rhinophyma represents the most advanced form of acne rosacea. The diagnosis is easy to establish based on the clinical features of the disease. In advanced forms of rhinophyma, when the tumor is giant, the main method of treatment is surgery.


title: New Adjustable Slot Meander Patch Antenna for 4G Handheld Devices

abstract: Two different antennas constructed using a new concept, the slot meander patch (SMP) design, are presented in this study. SMP antennas are designed for fourth-generation long-term evolution (4G LTE) handheld devices. These antennas are used for different target specifications: LTE-Time Division Duplex and LTE-Frequency Division Duplex (LTE TDD and LTE FDD). The first antenna is designed to operate in a wideband of 1.68-3.88 GHz to cover eight LTE TDD application frequency bands. Investigations have shown that the antenna designed with unequal meander widths has a higher efficiency compared to its equivalent antenna design with equal meander widths. The second antenna was configured as a multiband SMP antenna, which operates at three distinct frequency bands (0.5-0.75, 1.1-2.7, and 3.3-3.9 GHz), to cover eight LTE FDD application bands including the lowest and the highest bands. There is a good agreement between the measurement and simulation results for both antennas. Moreover, parametric studies have been carried out to investigate the flexible multiband antenna. Results have shown that the bandwidths can be improved through adjusting the meander widths without changing the SMP length and all other parameters.


title: Robot operating system: A modular software framework for automated driving

abstract: Automated vehicles are complex systems with a high degree of interdependencies between its components. This complexity sets increasing demands for the underlying software framework. This paper firstly analyzes the requirements for software frameworks. Afterwards an overview on existing software frameworks, that have been used for automated driving projects, is provided with an in-depth introduction into an emerging open-source software framework, the Robot Operating System (ROS). After discussing the main features, advantages and disadvantages of ROS, the communication overhead of ROS is analyzed quantitatively in various configurations showing its applicability for systems with a high data load.


title: dCompaction: Delayed Compaction for the LSM-Tree

abstract: Key-value (KV) stores have become a backbone of large-scale applications in today’s data centers. Write-optimized data structures like the Log-Structured Merge-tree (LSM-tree) and their variants are widely used in KV storage systems like BigTable and RocksDB. Conventional LSM-tree organizes KV items into multiple, successively larger components, and uses compaction to push KV items from one smaller component to another adjacent larger component until the KV items reach the largest component. Unfortunately, current compaction scheme incurs significant write amplification due to repeated KV item reads and writes, and then results in poor throughput. We propose a new compaction scheme, delayed compaction (dCompaction), that decreases write amplification. dCompaction postpones some compactions and gather them into the following compaction. In this way, it avoids KV item reads and writes during compaction, and consequently improves the throughput of LSM-tree based KV stores. We implement dCompaction on RocksDB, and conduct extensive experiments. Validation using YCSB framework shows that compared with RocksDB dCompaction has about 30% write performance improvements and also comparable read performance.


title: [Magnetic resonance imaging of supratentorial and parafalcial empyema].

abstract: The authors report the MR imaging of two patients with multiple subdural empyemas, including one in the interhemispheric fissure. MRI demonstrated convexity and interhemispheric collections which were mild hyperintense relative to CSF, hypointense relative to gray and white matter on T1W1, and marked hyperintense relative to CSF, and brain on T2W1. On the basis of signal intensity differences, MRI can distinguish subdural empyemas from most sterile effusions and chronic subdural hematomas with similar CT appearances. MRI was found to be clearly more sensitive to subdural empyemas than CT, though such lesions missed on CT were considered to be relevant. MR was superior to CT in demonstrating the nature, presence, and extent of these lesions. In both cases, the capsule of the lesions demonstrated enhancement, and connection between each lesion was obvious on contrast-enhanced MRI. It seems that contrast-enhanced MR image may detect encapsulation of an abscess which can not be detected from contrast-enhanced CT. We emphasized that the most significant factor in the successful surgical management of multiple subdural empyema, particularly including interhemispheric collections is the accurate location of pus. This can be reliably achieved with MR imaging.