title: Efficient Scheduling of Arbitrary TAsk Graphs to Multiprocessors Using a Parallel Genetic Algorithm

text: Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptions and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinatorial optimization problems. While a few GAs for scheduling have already been suggested, in this paper we propose a novel GA-based algorithm with an objective to simultaneously meet the goals of high performance, scalability, and fast running time. The proposed parallel genetic scheduling (PGS) algorithm itself is a parallel algorithm which generates high quality solutions in a short time. By encoding the scheduling list as a chromosome, the PGS algorithm can potentially generate an optimal scheduling list which in turn leads to an optimal schedule. The major strength of the PGS algorithm lies in its two efficient genetic operators: the order crossover and mutation. These operators effectively combine the building-blocks of good scheduling lists to construct better lists. The proposed algorithm is evaluated through a robust comparison with two heuristics best known in terms of performance and time complexity. It outperforms both heuristics while taking considerably less running time. When evaluated with random task graphs for which optimal solutions are known, the PGS algorithm generates optimal solutions for more than half of the test cases and close-to-optimal for the other half. © 1997 Academic Press


title: Learning a Variational Network for Reconstruction of Accelerated MRI Data

text:
PURPOSE
To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.

THEORY AND METHODS
Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data.

RESULTS
The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4.

CONCLUSION
Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.


title: Empowerment-driven Exploration using Mutual Information Estimation

text: Exploration is a difficult challenge in reinforcement learning and is of prime importance in sparse reward environments. However, many of the state of the art deep reinforcement learning algorithms, that reply on epsilon-greedy exploration, fail on these environments. In such environments, empowerment can serve as an intrinsic reward signal to enable the agent to explore by maximizing the influence it has over the near future. We formulate empowerment as the channel capacity between actions and states and is calculated by estimating the mutual information between the actions and the following states. The mutual information is estimated using a Mutual Information Neural Estimator and a forward dynamics model. We demonstrate that an empowerment driven agent is able to improve significantly the score of a baseline DQN agent on the game of Montezuma’s Revenge.


title: Multilevel Inverter Topologies With Reduced Device Count: A Review

text: Multilevel inverters have created a new wave of interest in industry and research. While the classical topologies have proved to be a viable alternative in a wide range of high-power medium-voltage applications, there has been an active interest in the evolution of newer topologies. Reduction in overall part count as compared to the classical topologies has been an important objective in the recently introduced topologies. In this paper, some of the recently proposed multilevel inverter topologies with reduced power switch count are reviewed and analyzed. The paper will serve as an introduction and an update to these topologies, both in terms of the qualitative and quantitative parameters. Also, it takes into account the challenges which arise when an attempt is made to reduce the device count. Based on a detailed comparison of these topologies as presented in this paper, appropriate multilevel solution can be arrived at for a given application.


title: DIZK: A Distributed Zero Knowledge Proof System

text: Recently there has been much academic and industrial interest in practical implementations of zero knowledge proofs. These techniques allow a party to prove to another party that a given statement is true without revealing any additional information. In a Bitcoin-like system, this allows a payer to prove validity of a payment without disclosing the payment’s details. Unfortunately, the existing systems for generating such proofs are very expensive, especially in terms of memory overhead. Worse yet, these systems are “monolithic”, so they are limited by the memory resources of a single machine. This severely limits their practical applicability. We describe DIZK, a system that distributes the generation of a zero knowledge proof across machines in a compute cluster. Using a set of new techniques, we show that DIZK scales to computations of up to billions of logical gates (100× larger than prior art) at a cost of 10 μs per gate (100× faster than prior art). We then use DIZK to study various security applications.


title: Framework for a Digital Forensic Investigation

text: Computer Forensics is essential for the successful prosecution of computer criminals. For a forensic investigation to be performed successfully there are a number of important steps that have to be considered and taken. The aim of this paper is to define a clear, step-by-step framework for the collection of evidence suitable for presentation in a court of law. Existing forensic models will be surveyed and then adapted to create a specific application framework for single computer, entry point forensics.


title: Turn-Taking Estimation Model Based on Joint Embedding of Lexical and Prosodic Contents

text: A natural conversation involves rapid exchanges of turns while talking. Taking turns at appropriate timing or intervals is a requisite feature for a dialog system as a conversation partner. This paper proposes a model that estimates the timing of turn-taking during verbal interactions. Unlike previous studies, our proposed model does not rely on a silence region between sentences since a dialog system must respond without large gaps or overlaps. We propose a Recurrent Neural Network (RNN) based model that takes the joint embedding of lexical and prosodic contents as its input to classify utterances into turn-taking related classes and estimates the turn-taking timing. To this end, we trained a neural network to embed the lexical contents, the fundamental frequencies, and the speech power into a joint embedding space. To learn meaningful embedding spaces, the prosodic features from each single utterance are pretrained using RNN and combined with utterance lexical embedding as the input of our proposed model. We tested this model on a spontaneous conversation dataset and confirmed that it outperformed the use of word embedding-based features.


title: Tutorial on Answering Questions about Images with Deep Learning

text: Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task. 1 Preface In this tutorial1 we build a few architectures that can answer questions about images. The architectures are based on our two papers on this topic: Malinowski et al. [2015] and Malinowski et al. [2016]; and more broadly, on our project towards a Visual Turing Test2. In particular, an encoder-decoder perspective of Malinowski et al. [2016] allows us to effectively experiment with various design choices. For the sake of simplicity, we only consider a classification-based approach to answer questions about images, although an approach that generate answers word-by-word is also studied in the community [Malinowski et al., 2015]. In the tutorial, we mainly focus on the DAQUAR dataset [Malinowski and Fritz, 2014a], but a few possible directions to apply learnt techniques to VQA [Antol et al., 2015] are also pointed. First, we will get familiar with the task of answering questions about images, and a dataset that implements the task (due to a small size, we mainly use DAQUAR as it better serves an educational purpose that we aim at this tutorial). Next, we build a few blind models that answer questions about images without actually seeing such images. Such models already exhibit a reasonable performance as they can effectively learn various biases that exist in a dataset, which we also interpret as learning a common sense knowledge [Malinowski et al., 2015, 2016]. Subsequently, we build a few language+vision models that answer questions based on both a textual and a visual inputs. Finally, we leave the tutorial with a few possible research directions. Technical aspects The tutorial is originally written using Python Notebook, which the reader is welcome to download and use through the tutorial. Instructions necessary to run the Notebook version of this tutorial are provided in the following: https://github.com/mateuszmalinowski/visual_turing_test-tutorial. In this tutorial, we heavily use a Python code, and therefore it is expected the reader either already knows this language, or can quickly learn it. However, we made an effort to make this tutorial approachable to a wider audience. We use Kraino that is a framework prepared for this tutorial in order to simplify the development of the question answering architectures. Under the hood, it uses Theano4 [Bastien et al., 2012] and Keras5 [Chollet, 2015] – two frameworks to build Deep Learning models. We also use various CNNs representations extracted from images that can be downloaded as explained at the beginning of our Notebook tutorial. We also highlight exercises that a curious reader may attempt to solve. This tutorial was presented for the first time during the 2nd Summer School on Integrating Vision and Language: Deep Learning. http://mpii.de/visual_turing_test https://github.com/mateuszmalinowski/visual_turing_test-tutorial/blob/master/visual_ turing_test.ipynb http://deeplearning.net/software/theano/ https://keras.io 1 ar X iv :1 61 0. 01 07 6v 1 [ cs .C V ] 4 O ct 2 01 6


title: Accurate and Efficient On-Chip Spectral Analysis for Built-In Testing and Calibration Approaches

text: The fast Fourier transform (FFT) algorithm is widely used as a standard tool to carry out spectral analysis because of its computational efficiency. However, the presence of multiple tones frequently requires a fine frequency resolution to achieve sufficient accuracy, which imposes the use of a large number of FFT points that results in large area and power overheads. In this paper, an FFT method is proposed for on-chip spectral analysis of multi-tone signals with particular harmonic and intermodulation components. This accurate FFT analysis approach is based on coherent sampling, but it requires a significantly smaller number of points to make the FFT realization more suitable for on-chip built-in testing and calibration applications that require area and power efficiency. The technique was assessed by comparing the simulation results from the proposed method of single and multiple tones with the simulation results obtained from the FFT of coherently sampled tones. The results indicate that the proper selection of test tone frequencies can avoid spectral leakage even with multiple narrowly spaced tones. When low-frequency signals are captured with an analog-to-digital converter (ADC) for on-chip analysis, the overall accuracy is limited by the ADC's resolution, linearity, noise, and bandwidth limitations. Post-layout simulations of a 16-point FFT showed that third-order intermodulation (IM3) testing with two tones can be performed with 1.5-dB accuracy for IM3 levels of up to 50 dB below the fundamental tones that are quantized with a 10-bit resolution. In a 45-nm CMOS technology, the layout area of the 16-point FFT for on-chip built-in testing is 0.073 mm2, and its estimated power consumption is 6.47 mW.


title: Advanced automated SQL injection attacks and defensive mechanisms

text: SQL Injection vulnerabilities still exist even after almost two decades that it first appeared. In spite of numerous prevention methodologies being used today, web applications still tend to be vulnerable to SQL injection attacks. Technology has improved drastically over the past few years and computers have certainly brought a great impact on our lifestyle. The computer applications and their usage over the web are myriad. It is quite evident that the in the near future, the usage of computers would relatively be higher than what we are witnessing today. A wide variety of data such as credit information, military data, human communication data, and countless types of data is shared over the far-flung computer networks. As the usage and reliability on computers increase, the threat to sensitive data likewise increases. The challenges with the cyber security when dealing with sensitive information is now a nightmare. To help understand the threats and the severity of exploits deployed, the paper provides proof of concepts for exploits carried out to compromise web applications and how the databases are exploited using the SQL injection methodologies. The SQL injection vulnerabilities in the web applications are surprisingly very vast and this is definitely is a huge security threat to personal data of people that is stored on web. In this paper, the methods used in information gathering, how the security is breached, and how payloads are used to exploit web applications are explained using the Kali Linux. In addition, an analysis is carried out on how the websites are comprised. Advanced methods on how to defend SQL injections are briefly justified. For the readers to understand better, a real time scenario of a penetration tester and a database server is set up with a few suppositions, and the commands that dodge the security characteristics and manipulate the databases are explicated.


title: Ultrasound for fetal assessment in early pregnancy.

text:
BACKGROUND
Advantages of early pregnancy ultrasound screening are thought to be more accurate calculation of gestational age, earlier identification of multiple pregnancies, and diagnosis of non-viable pregnancies and certain fetal malformations.

OBJECTIVES
The objective of this review was to assess the use of routine (screening) ultrasound compared with the selective use of ultrasound in early pregnancy (ie before 24 weeks).

SEARCH STRATEGY
The Cochrane Pregnancy and Childbirth Group trials register and the Cochrane Controlled Trials Register (up to July 1998) were searched.

SELECTION CRITERIA
Adequately controlled trials of routine ultrasound imaging in early pregnancy.

DATA COLLECTION AND ANALYSIS
One reviewer assessed trial quality and extracted data. Study authors were contacted for additional information.

MAIN RESULTS
Nine trials were included. The quality of the trials was generally good. Routine ultrasound examination was associated with earlier detection of multiple pregnancies (twins undiagnosed at 26 weeks, odds ratio 0.08, 95% confidence interval 0.04 to 0.16) and reduced rates of induction of labour for post-term pregnancy (odds ratio 0. 61, 95% confidence interval 0.52 to 0.72). There were no differences detected for substantive clinical outcomes such as perinatal mortality (odds ratio 0.86, 95% confidence interval 0.67 to 1.12). Where detection of fetal abnormality was a specific aim of the examination, the number of terminations of pregnancy for fetal anomaly increased.

REVIEWER'S CONCLUSIONS
Routine ultrasound in early pregnancy appears to enable better gestational age assessment, earlier detection of multiple pregnancies and earlier detection of clinically unsuspected fetal malformation at a time when termination of pregnancy is possible. However the benefits for other substantive outcomes are less clear.


title: A Mobile-Cloud Collaborative Traffic Lights Detector for Blind Navigation

text: Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application.


title: An inductorless linear optical receiver for 20Gbaud/s (40Gb/s) PAM-4 modulation using 28nm CMOS

text: This paper1 presents a linear optical receiver designed using a 28nm CMOS technology suitable for 20Gbaud/s (40Gb/s) PAM-4 modulation. The optical receiver consists of a transimpedance amplifier (gain adjustable from 40dBΩ to 56dBO) followed by a variable gain amplifier (gain adjustable from 6dB to 17dB). Capacitive peaking is used to achieve a bandwidth of ~10GHz, thus avoiding the use of on-chip inductors which require large die area. A robust automatic gain control loop is used to ensure a constant differential output voltage swing of ~100mV for an input dynamic range of 20μA to 500μA (peak current). Over this same range, high linearity (total harmonic distortion less than 5%, 250MHz sinewave, 10harmonics taken into account) is obtained. The rms input referred noise current (integrated from 10MHz to 20GHz) was 2.5μArms. The linear optical receiver consumes 56mW from a 1.5V supply voltage.


title: Conservation cores: reducing the energy of mature computations

text: Growing transistor counts, limited power budgets, and the breakdown of voltage scaling are currently conspiring to create a utilization wall that limits the fraction of a chip that can run at full speed at one time. In this regime, specialized, energy-efficient processors can increase parallelism by reducing the per-computation power requirements and allowing more computations to execute under the same power budget. To pursue this goal, this paper introduces conservation cores. Conservation cores, or c-cores, are specialized processors that focus on reducing energy and energy-delay instead of increasing performance. This focus on energy makes c-cores an excellent match for many applications that would be poor candidates for hardware acceleration (e.g., irregular integer codes). We present a toolchain for automatically synthesizing c-cores from application source code and demonstrate that they can significantly reduce energy and energy-delay for a wide range of applications. The c-cores support patching, a form of targeted reconfigurability, that allows them to adapt to new versions of the software they target. Our results show that conservation cores can reduce energy consumption by up to 16.0x for functions and by up to 2.1x for whole applications, while patching can extend the useful lifetime of individual c-cores to match that of conventional processors.


title: Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework

text: This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameteri- zation space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.


title: HS2: Active Learning over Hypergraphs

text: We propose a hypergraph-based active learning scheme which we term HS; HS generalizes the previously reported algorithm S originally proposed for graph-based active learning with pointwise queries [1]. Our HS method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS for the above described generalized settings. Both the theoretical and empirical results show that HS requires a significantly fewer number of queries than S when one uses S over a graph obtained from the corresponding hypergraph via clique expansion.


title: Autonomous Farming and Surveillance Agribot in Adjacent Boundary

text: This research strives to implement the agricultural farming robot which can move instinctively, involuntarily for ploughing in different depths depending upon soil, seeding and irrigation in adjacent field. This research also contributes to enhance functionality of agribot in the field for surveillance. The agribot is equipped with a camera and sends the data to pc through Wi-Fi network. The robot has servo motors for ploughing, distributing the seeds and irrigation into the field. It works with ultrasonic sensor and IR sensor. Ultrasonic sensor is for avoiding the obstacles in the field, IR sensor is for sensing the path and field of adjacent boundary. Arduino Uno controller acts as heart and brain of the system, it makes fast, accurate, autonomous movement. This Research features a agribot that uses Wi-Fi 802.11G along with TCP/IP protocol. IP address and Video is received in laptop for further processing. This research intends to reduce human efforts, increase profit and provide an intelligent aid to the farmers.


title: A fast iterative nearest point algorithm for support vector machine classifier design

text: In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Friess is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive.


title: An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text

text: We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of “naturally” occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.


title: Overview of Permanent-Magnet Brushless Drives for Electric and Hybrid Electric Vehicles

text: With ever-increasing concerns on our environment, there is a fast growing interest in electric vehicles (EVs) and hybrid EVs (HEVs) from automakers, governments, and customers. As electric drives are the core of both EVs and HEVs, it is a pressing need for researchers to develop advanced electric-drive systems. In this paper, an overview of permanent-magnet (PM) brushless (BL) drives for EVs and HEVs is presented, with emphasis on machine topologies, drive operations, and control strategies. Then, three major research directions of the PM BL drive systems are elaborated, namely, the magnetic-geared outer-rotor PM BL drive system, the PM BL integrated starter-generator system, and the PM BL electric variable-transmission system.


title: Dual-Rotor Multiphase Permanent Magnet Machine With Harmonic Injection to Enhance Torque Density

text: Torque density of a novel dual-rotor, radial-flux multiphase permanent magnet (PM) machine is enhanced by injecting stator current harmonics without increasing stator yoke thickness. In order to enhance a regular PM machine torque capability by injecting harmonic currents, the no-load air-gap flux density should be designed to be trapezoidal, which increases the flux per pole, and results in a thicker stator yoke. However, this drawback can be avoided in a dual-rotor, radial-flux PM machine. In such a dual-rotor PM machine, the main flux can be designed to go directly from the inner rotor poles, to stator inner and outer teeth, then to the outer rotor poles, or reverse, without through the stator yoke. Therefore, the stator yoke thickness is not affected by air-gap flux at all and can actually be reduced to minimum for mechanical reasons. This paper applies stator current harmonic injection technique to a dual-rotor, radial-flux PM machine. Sizing equations of the multiphase PM machines are deduced and used for initial design and theoretical comparison. The torque performance of the proposed dual-rotor, radial-flux multiphase PM machine with stator harmonic current injection is evaluated using finite element simulation, and compared to a traditional three-phase dual-rotor, radial-flux PM machine. Simulation results validate the feasibility of torque density enhancement. The same technique is proven that it is also suitable in a dual-rotor axial-flux PM machine topology.


title: A general approach to sizing and power density equations for comparison of electrical machines

text: Whenever an electrical machine is meant to be fed by a power converter, the design should be approached as a system optimization, more than a simple machine sizing. A great variety of electrical machines is available to accomplish this goal, and the task of comparing the different options can be very difficult. A general purpose sizing equation, easily adjustable for every topology, that could take into account different waveforms and machine characteristics, would be a very desirable tool. In this paper, a general approach is presented to develop and to discuss such an equation. Sample applications of the sizing and power density equations are utilized to compare the induction machine and the doubly salient permanent magnet (DSPM) machine.


title: Multiphase Electric Machines for Variable-Speed Applications

text: Although the concept of variable-speed drives, based on utilization of multiphase machines, dates back to the late 1960s, it was not until the mid- to late 1990s that multiphase drives became serious contenders for various applications. These include electric ship propulsion, locomotive traction, electric and hybrid electric vehicles, ldquomore-electricrdquo aircraft, and high-power industrial applications. As a consequence, there has been a substantial increase in the interest for such drive systems worldwide, resulting in a huge volume of work published during the last ten years. An attempt is made in this paper to provide a brief review of the current state of the art in the area. After addressing the reasons for potential use of multiphase rather than three-phase drives and the available approaches to multiphase machine designs, various control schemes are surveyed. This is followed by a discussion of the multiphase voltage source inverter control. Various possibilities for the use of additional degrees of freedom that exist in multiphase machines are further elaborated. Finally, multiphase machine applications in electric energy generation are addressed.


title: Hybrid electric vehicle propulsion system architectures of the e-CVT type

text: There is now significant interest in hybrid electric vehicle (HEV) propulsion systems globally. Economics play a major role as evidenced by oil prices in North America pressing upwards of $100/Bbl coupled with a customer preference for full size crossover and sport utility vehicles. The situation in Oceania is milder, but emerging markets such as China are experiencing automotive sector growth rates of 37%/year. Europe remains least affected by hybrids since nearly 47% of all new vehicles sold are diesel fueled and have economy ratings on par with that of gasoline-electric hybrids. In the global economy there are presently some 57 Mil new vehicles manufactured each year. Toyota and Honda have projected that HEVs will be 10 % to 15 % of the U.S. market by 2009, with Toyota raising the bar further by stating they will produce 1 Mil hybrids a year in the 2012 time frame. Hybrid propulsion system types are only vaguely comprehended by the buying public, and to a large measure, even by technical professionals. This paper addresses this latter issue by presenting a summary of the globally accepted standard in hybrid power trains-the power split architecture, or more generically and in common usage, the electronic-continuously variable transmission


title: Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory

text: Firms invest millions of dollars in the introduction of new information systems for long-term benefit. If employees are not willing to accept a new information system, such investments may be wasted. Employee acceptance of a new information system is in part determined by external influences. However, previous research has neglected the paths of persuasive strategies and external social influences on information system acceptance. Linkages between persuasive strategies and external social influences are also scarce. By integrating social influence theory and an elaboration likelihood model, this study explores the influence of persuasive messages (source credibility and argument quality) on social influence, affective response and cognitive response. This study also investigates the interrelationships among affective response, cognitive response and behavior intention. Furthermore, the moderating roles of social influences on the impact of affective response and cognitive response on behavior intention are identified. 2012 Elsevier Ltd. All rights reserved.


title: Differentiating anxiety forms and their role in academic performance from primary to secondary school

text:
INTRODUCTION
Individuals with high levels of mathematics anxiety are more likely to have other forms of anxiety, such as general anxiety and test anxiety, and tend to have some math performance decrement compared to those with low math anxiety. However, it is unclear how the anxiety forms cluster in individuals, or how the presence of other anxiety forms influences the relationship between math anxiety and math performance.

METHOD
We measured math anxiety, test anxiety, general anxiety and mathematics and reading performance in 1720 UK students (year 4, aged 8-9, and years 7 and 8, aged 11-13). We conducted latent profile analysis of students' anxiety scores in order to examine the developmental change in anxiety profiles, the demographics of each anxiety profile and the relationship between profiles and academic performance.

RESULTS
Anxiety profiles appeared to change in specificity between the two age groups studied. Only in the older students did clusters emerge with specifically elevated general anxiety or academic anxiety (test and math anxiety). Our findings suggest that boys are slightly more likely than girls to have elevated academic anxieties relative to their general anxiety. Year 7/8 students with specifically academic anxiety show lower academic performance than those who also have elevated general anxiety.

CONCLUSIONS
There may be a developmental change in the specificity of anxiety and gender seems to play a strong role in determining one's anxiety profile. The anxiety profiles present in our year 7/8 sample, and their relationships with math performance, suggest a bidirectional relationship between math anxiety and math performance.


title: HOW TO CALCULATE INFORMATION VALUE FOR EFFECTIVE SECURITY RISK ASSESSMENT

text: The actual problem of information security (infosec) risk assessment is determining the value of information property or asset. This is particularly manifested through the use of quantitative methodology in which it is necessary to state the information value in quantitative sizes. The aim of this paper is to describe the evaluation possibilities of business information values, and the criteria needed for determining importance of information. For this purpose, the dimensions of information values will be determined and the ways used to present the importance of information contents will be studied. There are two basic approaches that can be used in evaluation: qualitative and quantitative. Often they are combined to determine forms of information content. The proposed criterion is the three-dimension model, which combines the existing experiences (i.e. possible solutions for information value assessment) with our own criteria. An attempt for structuring information value in a business environment will be made as well.


title: Traffic speed prediction using deep learning method

text: Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time-series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic traffic flow characteristics. Recently, various deep learning models have been introduced to the prediction field. In this paper, a deep learning method, the Deep Belief Network (DBN) model, is proposed for short-term traffic speed information prediction. The DBN model is trained in a greedy unsupervised method and fine-tuned by labeled data. Based on traffic speed data collected from one arterial in Beijing, China, the model is trained and tested for different prediction time horizons. From experiment analysis, it is concluded that the DBN can outperform Back Propagation Neural Network (BPNN) and Auto-Regressive Integrated Moving Average (ARIMA) for all time horizons. The advantages of DBN indicate that deep learning is promising in traffic research area.


title: Classification of benign and malignant tumors in histopathology images

text: Breast cancer leads the list of cancer that act on women worldwide. It starts when cells in the breast begin to build up beyond control. These cells normally create a tumour that can usually be seen on an x-ray or felt as a lump. Analysing and grading the tumour will take up much of a pathologist time. Pathologists have been largely diagnosing disease the same way for the past years, by manually reviewing images under a microscope. Thus, to help the pathologists improve accuracy and significantly change the way breast cancer been diagnosed, this paper presents an automated classification program. BreakHis dataset was used which build of 7909 breast tumor images gathered from 82 patients. This system is developed in order to categorize the cancer cells into two classes of cancer which are benign and malignant. The classification system compared different types of feature extractors using k-nearest neighbours classifier to efficiently observe the performance of the classification system. An extensive set of experiments showed that the overall accuracy rates range from 83% to 86%.


title: Footwear bio-modelling: An industrial approach

text: There is a growing need within the footwear sector to customise the design of the last from which a specific footwear style is to be produced. This customisation is necessary for user comfort and health reasons, as the user needs to wear a suitable shoe. For this purpose, a relationship must be established between the user foot and the last with which the style will be made; up until now, no model has existed that integrates both elements. On the one hand, traditional customised footwear manufacturing techniques are based on purely artisanal procedures which make the process arduous and complex; on the other hand, geometric models proposed by different authors present the impossibility of implementing them in an industrial environment with limited resources for the acquisition of morphometric and structural data for the foot, apart from the fact that they do not prove to be sufficiently accurate given the non similarity of the foot and last. In this paper, two interrelated geometric models are defined, the first, a bio-deformable foot model and the second, a deformable last model. The experiments completed show the goodness of the model, with it obtaining satisfactory results in terms of comfort, efficiency and precision, which make it viable for use in the sector.


title: Humpectomy and spreader flaps.

text: In a primary rhinoplasty that requires a humpectomy, the dorsal aspect of the upper lateral cartilages is commonly discarded. Many of these patients need spreader grafts to reconstruct the middle third of the nose. However, it is possible to reconstruct the upper lateral cartilages into "spreader flaps" that act much like spreader grafts. In the process of making spreader flaps, an incremental humpectomy is performed on the dorsal septum and bony hump. This humpectomy procedure is more accurate than the conventional humpectomy that involves resection of the bone, and septum as a single unit. The open and closed approaches of this technique are discussed in this article.


title: Simulating Human Game Play for Level Difficulty Estimation with Convolutional Neural Networks

text: This thesis presents an approach to predict the difficulty of levels in a game by simulating game play following a policy learned from human game play. Using state-action pairs tracked from players of the game Candy Crush Saga, we train a Convolutional Neural Network to predict an action given a game state. The trained model then acts as a policy. Our goal is to predict the success rate (SR) of players, from the SR obtained by simulating game play. Previous state-ofthe-art was using Monte Carlo tree search (MCTS) or handcrafted heuristics for game play simulation. We benchmark our suggested approach against one using MCTS. The hypothesis is that, using our suggested approach, predicting the players’ SR from the SR obtained through the simulation, leads to better estimations of the players’ SR. Our results show that we could not only significantly improve the predictions of the players’ SR, but also decrease the time for game play simulation by at least 50 times. Referat Simulering av mänskligt spelande för bedömning av spelbanors svårighetsgrad med Convolutional Neural Networks. Den här avhandlingen presenterar ett tillvägagångssätt för att förutse svårighetsgrad av spelbanor genom spelsimulering enligt en strategi lärd från mänskligt spelande. Med användning av tillstånd-handlings par insamlade från spelare av spelet Candy Crush Saga, tränar vi ett Convolutional Neural Network att förutse en handling från ett givet tillstånd. Den tränade modellen agerar sedan som strategi. Vårt mål är att förutse success rate (SR) av spelare, från SR erhållen från spelsimulering. Tidigare state-of-the-art använde Monte Carlo tree search (MCTS) eller handgjorda heuristiker för spelsimulering. Vi jämför vårt tillvägagångssätt med MCTS. Hypotesen är att vårt föreslagna tillvägagångssätt leder till bättre förutsägelser av mänsklig SR från SR erhållen från spelsimulering. Våra resultat visar att vi inte bara signifikant kunde förbättra förutsägelserna av mänsklig SR utan också kunde minska tidsåtgången för spelsimulering med åtminstone en faktor 50.


title: On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV

text: In this work we present a novel approach for evaluating job applicants in online recruitment systems, using machine learning algorithms to solve the candidate ranking problem and performing semantic matching techniques. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and compares them semantically to the job’s prerequisites. It also infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters, thus it can be trusted for the automation of applicant ranking and personality mining.


title: Linear Time Algorithm for Isomorphism of Planar Graphs (Preliminary Report)

text:
The isomorphism problem for graphs G1 and G2 is to determine if there exists a one-to-one mapping of the vertices of G1 onto the vertices of G2 such that two vertices of G1 are adjacent if and only if their images in G2 are adjacent. In addition to determining the existence of such an isomorphism, it is useful to be able to produce an isomorphism-inducing mapping in the case where one exists.
 The isomorphism problem for triconnected planar graphs is particularly simple since a triconnected planar graph has a unique embedding on a sphere [6]. Weinberg [5] exploited this fact in developing an algorithm for testing isomorphism of triconnected planar graphs in O(|V|2) time where V is the set consisting of the vertices of both graphs. The result has been extended to arbitrary planar graphs and improved to O(|V|log|V|) steps by Hopcroft and Tarjan [2,3]. In this paper, the time bound for planar graph isomorphism is improved to O(|V|). In addition to determining the isomorphism of two planar graphs, the algorithm can be easily extended to partition a set of planar graphs into equivalence classes of isomorphic graphs in time linear in the total number of vertices in all graphs in the set. A random access model of computation (see Cook [1]) is assumed. Although the proposed algorithm has a linear asymptotic growth rate, at the present stage of development it appears to be inefficient on account of a rather large constant. This paper is intended only to establish the existence of a linear algorithm which subsequent work might make truly efficient.


title: Design and PID control of two wheeled autonomous balance robot

text: In this study, a two-wheeled autonomous balance robot has been designed and implemented practically. A visual computer interface based on Qt-Creator has been created. Thanks to the computer interface, different control algorithms can be performed on the robot easily, control parameters can be set up online, filter algorithms in various structures can be tried and the reaction of these changeable values to the system can be observed. The effects of some controllers such as Proportional (P), Proportional-Integral (PI), Proportional-Integral-Derivative (PID) on developed robot have been viewed successfully. Kalman Filters have been used for a stable control of the system and it has seen that the system can balance itself for a long time with optimum PID control parameters obtained.


title: Classifying racist texts using a support vector machine

text: In this poster we present an overview of the techniques we used to develop and evaluate a text categorisation system to automatically classify racist texts. Detecting racism is difficult because the presence of indicator words is insufficient to indicate racist texts, unlike some other text classification tasks. Support Vector Machines (SVM) are used to automatically categorise web pages based on whether or not they are racist. Different interpretations of what constitutes a term are taken, and in this poster we look at three representations of a web page within an SVM -- bag-of-words, bigrams and part-of-speech tags.


title: Randomized Block Coordinate Descent for Online and Stochastic Optimization

text: Two types of low cost-per-iteration gradient descent metho ds have been extensively studied in parallel. One is online or stochastic gradient descent ( OGD/SG ), and the other is randomzied coordinate descent (RBCD). In this paper, we combine the two types o f methods together and propose online randomized block coordinate descent (ORBCD). At each itera t on, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem where one function is the average of th e losses of a large number of samples and the other is a simple regularizer defined on high dimensional variables. We show that the iteration complexity of ORBCD has the same order as OGD or SGD. For strongly convex functions, by reducing the variance of stochastic gradients, we show that ORBCD can con verge at a geometric rate in expectation, matching the convergence rate of SGD with variance reductio n and RBCD.


title: Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory

text: On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.


title: Anatomy and physiology of a color system in the primate visual cortex.

text: Staining for the mitochondrial enzyme cytochrome oxidase reveals an array of dense regions (blobs) in the primate primary visual cortex. They are most obvious in the upper layers, 2 and 3, but can also be seen in layers 4B, 5, and 6, in register with the blobs in layers 2 and 3. We compared cells inside and outside blobs in macaque and squirrel monkeys, looking at their physiological responses and anatomical connections. Cells within blobs did not show orientation selectivity, whereas cells between blobs were highly orientation selective. Receptive fields of blob cells had circular symmetry and were of three main types, Broad-Band Center-Surround, Red-Green Double-Opponent, and Yellow-Blue Double-Opponent. Double-Opponent cells responded poorly or not at all to white light in any form, or to diffuse light at any wavelength. In contrast to blob cells, none of the cells recorded in layer 4C beta were Double-Opponent: like the majority of cells in the parvocellular geniculate layers, they were either Broad-Band or Color-Opponent Center-Surround, e.g., red-on-center green-off-surround. To our surprise cells in layer 4C alpha were orientation selective. In tangential penetrations throughout layers 2 and 3, optium orientation, when plotted against electrode position, formed long, regular, usually linear sequences, which were interrupted but not perturbed by the blobs. Staining area 18 for cytochrome oxidase reveals a series of alternating wide and narrow dense stripes, separated by paler interstripes. After small injections of horseradish peroxidase into area 18, we saw a precise set of connections from the blobs in area 17 to thin stripes in area 18, and from the interblob regions in area 17 to interstripes in area 18. Specific reciprocal connections also ran from thin stripes to blobs and from interstripes to interblobs. We have not yet determined the area 17 connections to thick stripes in area 18. In addition, within area 18 there are stripe-to-stripe and interstripe-to-interstripe intrinsic connections. These results suggest that a system involved in the processing of color information, especially color-spatial interactions, runs parallel to and separate from the orientation-specific system. Color, encoded in three coordinates by the major blob cell types, red-green, yellow-blue, and black-white, can be transformed into the three coordinates, red, green, and blue, of the Retinex algorithm of Land.


title: Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

text: Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying sparsity, it is necessary to locally adapt the dictionary learning process and the sparsity-regularization parameters. However, spatial adaptation alone runs into the risk of over-fitting the data because variation and invariance are two sides of the same coin. In this work, we propose two sets of complementary ideas for regularizing image reconstruction process: (1) the sparsity regularization parameters are locally estimated for each coefficient and updated along with adaptive learning of PCA-based dictionaries; (2) a nonlocal self-similarity constraint is introduced into the overall cost functional to improve the robustness of the model. An efficient alternative minimization algorithm is present to solve the proposed objective function and then an effective image reconstruction algorithm is presented. The experimental results on image deblurring, super-resolution and compressive sensing demonstrate that the proposed image reconstruct method outperforms many existing image reconstruction methods in both PSNR and visual quality assessment. & 2012 Elsevier B.V. All rights reserved.


title: A model for high school computer science education: the four key elements that make it!

text: This paper presents a model program for high school computer science education. It is based on an analysis of the structure of the Israeli high school computer science curriculum considered to be one of the leading curricula worldwide. The model consists of four key elements as well as interconnections between these elements. It is proposed that such a model be considered and/or adapted when a country wishes to implement a nation-wide program for high school computer science education.


title: Electromagnetic Two-Dimensional Scanner Using Radial Magnetic Field

text: In this paper, we present the design, fabrication, and measurement results of a two-dimensional electromagnetic scanning micromirror actuated by radial magnetic field. The scanner is realized by combining a gimbaled single-crystal-silicon micromirror with a single turn electroplated metal coil, with a concentric permanent magnet assembly composed of two concentric permanent magnets and an iron yoke. The proposed scanner utilizes the radial magnetic field rather than using a lateral magnetic field oriented 45deg to the horizontal and vertical scan axes to achieve a biaxial magnetic actuation. The single turn coil fabricated with electroplated copper achieves a nominal resistance of 1.2 Omega. A two-dimensional scanner with mirror size of 1.5 mm in diameter was fabricated. Maximum optical scan angle of 8.8deg in horizontal direction and 8.3deg in vertical direction were achieved. Forced actuation of the gimbal at 60 Hz and resonant actuation of the micromirror at 19.1-19.7 kHz provide slow vertical scan and fast horizontal scan, respectively. The proposed scanner can be used in raster scanning laser display systems and other scanner applications.


title: Survey of multi-objective optimization methods for engineering

text: Asurveyofcurrentcontinuousnonlinearmultiobjective optimization (MOO) concepts and methods is presented. It consolidates and relates seemingly different terminology and methods. The methods are divided into three major categories: methods with a priori articulation of preferences, methods with a posteriori articulation of preferences, and methods with no articulation of preferences. Genetic algorithms are surveyed as well. Commentary is provided on three fronts, concerning the advantages and pitfalls of individual methods, the different classes of methods, and the field of MOO as a whole. The Characteristics of the most significant methods are summarized. Conclusions are drawn that reflect often-neglected ideas and applicability to engineering problems. It is found that no single approach is superior. Rather, the selection of a specific method depends on the type of information that is provided in the problem, the user’s preferences, the solution requirements, and the availability of software.


title: A data mining approach for diagnosis of coronary artery disease

text: Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.


title: Developing a bioinspired pole climbing robot

text: This paper proposes a pole climbing robot that has an ability to climb pipes. The novelty of this design is that it uses no motors to climb on the pole. Till now many robots have been fabricated with the ability to climb pipes but most of them solely depend on DC motors. The use of DC motor induces the risk of loosening the grip of the robot in case of power failure that may lead to disastrous situations if robot is working on high altitudes. This could be easily avoidable by the use of electro-pneumatics and by using self-locking circuits. We used two pairs of pneumatic cylinders as linear actuators for gripping the pole i.e. one pair for one claw. Besides this two more cylinders are used for climbing purpose. Unlike other mechanisms, installation is extremely easy. It is like controlling a radio controlled car. This mechanism can be easily modified for different payload capacities by simply using the appropriate diameter of the pneumatic cylinders. The robot can be controlled in tele-operated mode as well as autonomous mode which is a closed loop system. It has a high accuracy with 4mm error per cycle that has a cycle time period of 5 seconds. This robot can be very useful in oil industries for inspection of pipes and power poles in electric industries. It can serve of great help in cases of emergency situation caused during fire accidents.


title: A framework of changing image emotion using emotion prediction

text: Most works about affective image classification in computer vision treat each emotion category independently and predict hard labels, ignoring the correlation between emotion categories. In this work, inspired by psychological theories, we adopt a dimensional emotion model to model the correlation among certain emotion categories. We also propose a framework of changing image emotion by using our emotion predictor. Easily extendable to other feature transformations, our framework changes image emotion by color histogram specification, relaxing the limitation of the previous method that each emotion is associated with a monotonic palette. Effective and comparable to the previous work of changing image emotion shown by user study, our proposed framework provides users with more flexible control in changing image emotion compared with the previous work.


title: Problematizing cultural appropriation

text: Cultural appropriation in games entails the taking of knowledge, artifacts or expression from a culture and recontextualizing it within game structures. While cultural appropriation is a pervasive practice in games, little attention has been given to the ethical issues that emerge from such practices with regards to how culture is portrayed. This paper problematizes cultural appropriation in the context of a serious game for children inspired by Día de los Muertos, a Mexican festival focused on remembrance of the dead. Taking a research through design approach, we demonstrate that recontextualised cultural elements can retain their basic, original meaning. However, we also find that cultural appropriation is inevitable and its ethical implications can be far reaching. In our context, ethical concerns arose as a result of children's beliefs that death affects prominent others and their destructive ways of coping with death. We argue that revealing emergent ethical concerns is imperative before deciding how and in what way to encourage culturally authentic narratives.


title: An Efficient Power Scheduling Scheme for Residential Load Management in Smart Homes

text: In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modelled in a simple way, which considers appliance class, user preferences and weather conditions. The wind-driven optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, min-max regret-based knapsack problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with a fast convergence rate, as compared to the existing techniques. Appl. Sci. 2015, 5 1135


title: Demand Response as a Load Shaping Tool in an Intelligent Grid With Electric Vehicles

text: As electric vehicles (EVs) take a greater share in the personal automobile market, their penetration will cause overload conditions at the distribution transformer. This paper focuses on the impacts of charging EVs on residential distribution networks including the transformer. The cost to accommodate a large-scale EV penetration by upgrading distribution transformers can be prohibitive. To alleviate the potential new load peaks with minimal infrastructure investments, a demand response strategy is proposed as a load shaping tool that allows improvement in distribution transformer usage. With the proposed strategy, consumers' preferences, load priorities, and privacy can be taken into account.


title: No free lunch theorems for optimization

text: A framework is developed to explore the connection between e ective optimization algorithms and the problems they are solving A number of no free lunch NFL theorems are presented that establish that for any algorithm any elevated performance over one class of problems is exactly paid for in performance over another class These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem Applications of the NFL theorems to information theoretic aspects of optimization and benchmark measures of performance are also presented Other issues addressed are time varying optimization problems and a priori head to head minimax distinctions between optimization algorithms distinctions that can obtain despite the NFL theorems enforcing of a type of uniformity over all algorithms


title: Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

text: We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.


title: Computer vision based road traffic accident and anomaly detection in the context of Bangladesh

text: Road traffic accident is one of the major causes of death in Bangladesh. In this article, we propose a method that uses road side video data to learn the traffic pattern and uses vision based techniques to track and determine various kinetic features of vehicles. Finally, the proposed method detects anomaly (possibility of accident) and accidents on the road. The proposed method shows approximately 85% accuracy level in detecting special situations.


title: Writing for synthesis of evidence in empirical software engineering

text:
Context: Systematic literature reviews have become common in software engineering in the last decade, but challenges remain.
 Goal: Given the challenges, the objective is to describe improvement areas in writing primary studies, and hence provide a good basis for researchers aiming at synthesizing research evidence in a specific area.
 Method: The results presented are based on a literature review with respect to synthesis of research results in software engineering with a particular focus on empirical software engineering. The literature review is complemented and exemplified with experiences from conducting systematic literature reviews and working with research methodologies in empirical software engineering.
 Results: The paper presents three areas where improvements are needed to become more successful in synthesizing empirical evidence. These three areas are: terminology, paper content and reviewing.
 Conclusion: It is concluded that it must be possible to improve the primary studies, but it requires that researchers start having synthesis in mind when writing their research papers.


title: On-line Trend Analysis with Topic Models: \#twitter Trends Detection Topic Model Online

text: We present a novel topic modelling-based methodology to track emerging events in microblogs such as Twitter. Our topic model has an in-built update mechanism based on time slices and implements a dynamic vocabulary. We first show that the method is robust in detecting events using a range of datasets with injected novel events, and then demonstrate its application in identifying trending topics in Twitter.


title: A hybrid recommendation approach for a tourism system

text: Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.


title: Automatic White Balance for Digital Still Cameras

text: In recent years digital cameras have captured the camera market. Although the factors that consumer consider is the quality of the photographs produced. White balance is one of the methods used to improve the quality of the photographs. The basic white balance methods are, however powerless to handle all possible situations in the captured scene. In order to improve the white balance we divide this problem in three steps–white object purification, white point detection, and white point adjustment. The proposed method basically solves the problem of detecting the white point very well. The experimental results show that the proposed method can achieve superior result in both objective and subjective evaluative terms. The complexity of the proposed system is acceptable. The propose method can be easily applied to digital cameras to obtain good results.


title: Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP

text: When people search for a target in a novel image they often make use of eye movements to bring the relatively high acuity fovea to bear on areas of interest. The strategies that control these eye movements for visual search have been of substantial scientific interest. In the current article we report a new computational model that shows how strategies for visual search are an emergent consequence of perceptual/motor constraints and approximately optimal strategies. The model solves a Partially Observable Markov Decision Process (POMDP) using deep Q-learning to acquire strategies that optimise the tradeoff between speed and accuracy. Results are reported for the Distractor-ratio task.


title: Learning with Taxonomies: Classifying Documents and Words

text: Automatically extracting semantic information about word meaning and document topic from text typically involves an extensive number of classes. Such classes may represent predefined word senses, topics or document categories and are often organized in a taxonomy. The latter encodes important information, which should be exploited in learning classifiers from labeled training data. To that extent, this paper presents an extension of multiclass Support Vector Machine learning which can incorporate prior knowledge about class relationships. The latter can be encoded in the form of class attributes, similarities between classes or even a kernel function defined over the set of classes. The paper also discusses how to specify and optimize meaningful loss functions based on the relative position of classes in the taxonomy. We include experimental results for text categorization and for word sense classification.


title: Learning scale-variant and scale-invariant features for deep image classification

text: Convolutional Neural Networks (CNNs) require large image corpora to be trained on classification tasks. The variation in image resolutions, sizes of objects and patterns depicted, and image scales, hampers CNN training and performance, because the task-relevant information varies over spatial scales. Previous work attempting to deal with such scale variations focused on encouraging scale-invariant CNN representations. However, scale-invariant representations are incomplete representations of images, because images contain scale-variant information as well. This paper addresses the combined development of scaleinvariant and scale-variant representations. We propose a multiscale CNN method to encourage the recognition of both types of features and evaluate it on a challenging image classification task involving task-relevant characteristics at multiple scales. The results show that our multi-scale CNN outperforms single-scale CNN. This leads to the conclusion that encouraging the combined development of a scale-invariant and scale-variant representation in CNNs is beneficial to image recognition performance.


In China, the methods of time-control, optical-control and time-optical-control are in common used to control street lamp, particularly in small and medium-sized cities. But due to the backward lighting control and administrative method, the precision is bad, and the result of work is also poor. Through many kinds of sensor combination sense environment's change, the multi-sensor exhibition can combinatory logically control the new intelligent street light controller system. And based on the degree of illumination control fixed time, in the automatic foundation fixed time, according to the multi-sensing exhibition survey data's special combination change, to control the street light nimbly; the system can also realize the automatic timing control, by pre-installing time to control street light switch and ultimately to control the street light time. Simultaneously the system can also realize the automatic sunshine control, which may act according to the actual determination of the sunlight degree of illumination and the degree of illumination control criterion and automatically control street light. On the basis of the merits of the regular control and the optical control, a new street smart controller is designed, with dual functions including timing control and automatic photoelectric control. It allows street lamps automatically lit in the evening, lighting the road for a few hours (adjustable time). After 0:00, when few vehicles or pedestrians go past, it turn off automatically. And terminal controller has wake-up function. After the street light turn out automatically, when the vehicles or pedestrians are going through, street light will be waken up by terminal controller. When the vehicles or pedestrians go past, the lights are off automatically. Design of new intelligent street light control system does not only achieve energy-saving power but also extend the service life of lighting equipment. Moreover, it is controllable, ease of maintenance. At the same time, it is helpful to highlight the festive and other characteristics, and ultimately make street light network, intelligence, humanity and art. An optimal configuration is reported. Finally, the results of experiment are obtained that: when a motor is closed to 15 meters, LED lights are on automatically. While people passed, the distance is proximity 10 meters.


title: Modelling and analysis of convergence of wireless sensor network and passive optical network using queueing theory

text: Wireless sensor networks (WSNs) are being deployed for an ever-widening range of applications, particularly due to their low power consumption and reliability. In most applications, the sensor data must be sent over the Internet or core network, which results in the WSN backhaul problem. Passive optical networks (PONs), a next-generation access network technology, are well suited form a backhaul which interfaces WSNs to the core network. In this paper a new structure is proposed which converges WSNs and PONs. It is modelled and analyzed through queuing theory by employing two M/M/1 queues in tandem. Numerical results show how the WSN and PON dimensions affect the average queue length, thus serving as a guideline for future resource allocation and scheduling of such a converged network.


title: A morphologic two-stage approach for automated optic disk detection in color eye fundus images

text: A new adaptive morphological method for the automatic detection of the optic disk in digital color eye fundus images is presented in this paper. This method has been designed to detect the optic disk center and the optic disk rim. In our experiments with the DRIVE and DIARETDB1 databases, the proposed method was able to detect the optic disk center with 100% and 97.75% of accuracy, respectively. We considered correct all automatically detected optic disk location that is within the borders of the optic disk marked manually. Using our proposed method, the rim of the optic disk was detected in all images of the DRIVE database with average sensitivity and specificity of 83.54% and 99.81%, respectively, and on the DIARETDB1 database with average sensitivity and specificity of 92.51% and 99.76%, respectively. 2013 Published by Elsevier B.V.


title: Relationship between color and emotion : a study of college students

text: Ninety-eight college students were asked to indicate their emotional responses to five principle hues (i.e., red, yellow, green, blue, purple), five intermediate hues (i.e., yellow-red, greenyellow, blue-green, purple-blue, and red-purple), and three achromatic colors (white, gray, and black) and the reasons for their choices. The color stimuli were referenced from the Munsell Color System. The results revealed that the principle hues comprised the highest number of positive emotional responses, followed by the intermediate hues and the achromatic colors. The color green evoked mainly positive emotions such as relaxation and comfort because it reminded most of the respondents of nature. The color green-yellow had the lowest number of positive responses because it was associated with vomit and elicited the feelings of sickness and disgust. For the achromatic colors, white attained a large number of positive responses, followed by the colors black and gray. The reasons for the color-emotion associations are discussed and future research areas are suggested.


title: A stream function solver for liquid simulations

text:
This paper presents a liquid simulation technique that enforces the incompressibility condition using a stream function solve instead of a pressure projection. Previous methods have used stream function techniques for the simulation of detailed single-phase flows, but a formulation for liquid simulation has proved elusive in part due to the free surface boundary conditions. In this paper, we introduce a stream function approach to liquid simulations with novel boundary conditions for free surfaces, solid obstacles, and solid-fluid coupling.
 Although our approach increases the dimension of the linear system necessary to enforce incompressibility, it provides interesting and surprising benefits. First, the resulting flow is guaranteed to be divergence-free regardless of the accuracy of the solve. Second, our free-surface boundary conditions guarantee divergence-free motion even in the un-simulated air phase, which enables two-phase flow simulation by only computing a single phase. We implemented this method using a variant of FLIP simulation which only samples particles within a narrow band of the liquid surface, and we illustrate the effectiveness of our method for detailed two-phase flow simulations with complex boundaries, detailed bubble interactions, and two-way solid-fluid coupling.


title: Location recommendation for out-of-town users in location-based social networks

text: Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for out-of-town users by taking into account user preference, social influence and geographical proximity. Accordingly, we propose a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering} (UPS-CF), to make location recommendation for mobile users in LBSNs. We validate our ideas by comprehensive experiments using real datasets collected from Foursquare and Gowalla. By comparing baseline algorithms and conventional collaborative filtering approach (and its variants), we show that UPS-CF exhibits the best performance. Additionally, we find that preference derived from similar users is important for in-town users while social influence becomes more important for out-of-town users.


title: Unsupervised Pretraining for Sequence to Sequence Learning

text: Sequence to sequence models are successful tools for supervised sequence learning tasks, such as machine translation. Despite their success, these models still require much labeled data and it is unclear how to improve them using unlabeled data, which is much less expensive to obtain. In this paper, we present simple changes that lead to a significant improvement in the accuracy of seq2seq models when the labeled set is small. Our method intializes the encoder and decoder of the seq2seq model with the trained weights of two language models, and then all weights are jointly fine-tuned with labeled data. An additional language modeling loss can be used to regularize the model during fine-tuning. We apply this method to low-resource tasks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main finding is that the pretraining accelerates training and improves generalization of seq2seq models, achieving state-of-the-art results on the WMT English→German task. Our model obtains an improvement of 1.3 BLEU from the previous best models on both WMT’14 and WMT’15 English→German. Our ablation study shows that pretraining helps seq2seq models in different ways depending on the nature of the task: translation benefits from the improved generalization whereas summarization benefits from the improved optimization.


title: BOINC: a system for public-resource computing and storage

text: BOINC (Berkeley Open Infrastructure for Network Computing) is a software system that makes it easy for scientists to create and operate public-resource computing projects. It supports diverse applications, including those with large storage or communication requirements. PC owners can participate in multiple BOINC projects, and can specify how their resources are allocated among these projects. We describe the goals of BOINC, the design issues that we confronted, and our solutions to these problems.


title: Controllable Abstractive Summarization

text: Current models for document summarization disregard user preferences such as the desired length, style, the entities that the user might be interested in, or how much of the document the user has already read. We present a neural summarization model with a simple but effective mechanism to enable users to specify these high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, our system can produce high quality summaries that follow user preferences. Without user input, we set the control variables automatically – on the full text CNN-Dailymail dataset, we outperform state of the art abstractive systems (both in terms of F1-ROUGE1 40.38 vs. 39.53 F1-ROUGE and human evaluation).


title: Neuroergonomics: a review of applications to physical and cognitive work

text: Neuroergonomics is an emerging science that is defined as the study of the human brain in relation to performance at work and in everyday settings. This paper provides a critical review of the neuroergonomic approach to evaluating physical and cognitive work, particularly in mobile settings. Neuroergonomics research employing mobile and immobile brain imaging techniques are discussed in the following areas of physical and cognitive work: (1) physical work parameters; (2) physical fatigue; (3) vigilance and mental fatigue; (4) training and neuroadaptive systems; and (5) assessment of concurrent physical and cognitive work. Finally, the integration of brain and body measurements in investigating workload and fatigue, in the context of mobile brain/body imaging ("MoBI"), is discussed.


title: Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes

text: Given a topic of interest, a contrastive theme is a group of opposing pairs of viewpoints. We address the task of summarizing contrastive themes: given a set of opinionated documents, select meaningful sentences to represent contrastive themes present in those documents. Several factors make this a challenging problem: unknown numbers of topics, unknown relationships among topics, and the extraction of comparative sentences. Our approach has three core ingredients: contrastive theme modeling, diverse theme extraction, and contrastive theme summarization. Specifically, we present a hierarchical non-parametric model to describe hierarchical relations among topics; this model is used to infer threads of topics as themes from the nested Chinese restaurant process. We enhance the diversity of themes by using structured determinantal point processes for selecting a set of diverse themes with high quality. Finally, we pair contrastive themes and employ an iterative optimization algorithm to select sentences, explicitly considering contrast, relevance, and diversity. Experiments on three datasets demonstrate the effectiveness of our method.


title: Learning Discriminative and Shareable Features for Scene Classification

text: In this paper, we propose to learn a discriminative and shareable feature transformation filter bank to transform local image patches (represented as raw pixel values) into features for scene image classification. The learned filters are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative and class-specific information. For each category, a subset of the filters are activated in a data-adaptive manner, meanwhile sharing of filters among different categories is also allowed. Discriminative power of the filter bank is further enhanced by enforcing the features from the same category to be close to each other in the feature space, while features from different categories to be far away from each other. The experimental results on three challenging scene image classification datasets indicate that our features can achieve very promising performance. Furthermore, our features also show great complementary effect to the state-of-the-art ConvNets feature.


title: Machine learning in cardiovascular medicine: are we there yet?

text: Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.


title: Intrinsic frames of reference and egocentric viewpoints in scene recognition

text: Three experiments investigated the roles of intrinsic directions of a scene and observer's viewing direction in recognizing the scene. Participants learned the locations of seven objects along an intrinsic direction that was different from their viewing direction and then recognized spatial arrangements of three or six of these objects from different viewpoints. The results showed that triplets with two objects along the intrinsic direction (intrinsic triplets) were easier to recognize than triplets with two objects along the study viewing direction (non-intrinsic triplets), even when the intrinsic triplets were presented at a novel test viewpoint and the non-intrinsic triplets were presented at the familiar test viewpoint. The results also showed that configurations with the same three or six objects were easier to recognize at the familiar test viewpoint than other viewpoints. These results support and develop the model of spatial memory and navigation proposed by Mou, McNamara, Valiquette, and Rump [Mou, W., McNamara, T. P., Valiquiette C. M., & Rump, B. (2004). Allocentric and egocentric updating of spatial memories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30, 142-157].


title: An exact algorithm for solving the vertex separator problem

text: Given G = (V,E) a connected undirected graph and a positive integer β(|V |), the vertex sperator problem is to find a partition of V into noempty three classes A, B, C such that there is no edge between A and B, max{|A|, |B|} ≤ β(|V |) and |C| is minimum. In this paper we consider the vertex separator problem from a polyhedral point of view. We introduce new classes of valid inequalities for the associated polyhedron. Using a natural lower bound for the optimal solution, we present successful computational experiments.


title: Detection and Localization of Curbs and Stairways Using Stereo Vision

text: We present algorithms to detect and precisely localize curbs and stairways for autonomous navigation. These algorithms combine brightness information (in the form of edgels) with 3-D data from a commercial stereo system. The overall system (including stereo computation) runs at about 4 Hz on a 1 GHz laptop. We show experimental results and discuss advantages and shortcomings of our approach.


title: Stereo Processing by Semiglobal Matching and Mutual Information

text: This paper describes the semiglobal matching (SGM) stereo method. It uses a pixelwise, mutual information (Ml)-based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement, and multibaseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments, and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed. A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2 seconds on typical test images. An in depth evaluation of the Ml-based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.


title: Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images

text: In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.


title: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

text: Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.


title: A Compact 90-degree Twist using Novel Ridged Waveguide for Integrated Waveguide Subsystems

text: A compact waveguide twist using a single step of novel ridged structure for integrated waveguide subsystems is proposed. The cross-section of the ridged waveguide has the shape of two squares partially overlapped. The advantages of this configuration are the facilitation of manufacturing and the reduction of overall dimensions. In this paper, the broadband operation principle is described by using equivalent circuit, moreover the its design using HFSS and the experimental results are presented. In the result, the return loss in excess of 30dB could be obtained over 22 percent of frequency band without additional waveguide steps. The twist length is 0.22 times the wavelength of the standard rectangular waveguides


title: An active volumetric model for 3D reconstruction

text: In this paper, we present an active volumetric model (AVM) for 3D reconstruction from multiple calibrated images of a scene. The AVM is a physically motivated 3D deformable model which shrinks actively under the influence of multiple simulated forces towards the real scene by throwing away some of its voxels. It provides a computational framework to integrate several constraints in an intelligible way. In the current work, we use three forces derived respectively from the smooth constraint, the compulsory silhouette constraint, and the color consistency constraint. Based on the composition of the 3 forces, our algorithm can significantly restrain holes and floating voxels, which plague voxel coloring algorithms, and produce precise and smooth models. We test our algorithm by experiments based on both synthetic and real data.


title: Effective Fashion Retrieval Based on Semantic Compositional Networks

text: Typical approaches for fashion retrieval rank clothing images according to the similarity to a user-provided query image. Similarity is usually assessed by encoding images in terms of visual elements such as color, shape and texture. In this work, we proceed differently and consider that the semantics of an outfit is mainly comprised of environmental and cultural concepts such as occasion, style and season. Thus, instead of retrieving outfits using strict visual elements, we find semantically similar outfits that fall into similar clothing styles and are adequate for the same occasions and seasons. We propose a compositional approach for fashion retrieval by arguing that the semantics of an outfit can be recognised by their constituents (i.e., clothing items and accessories). Specifically, we present a semantic compositional network (Comp-Net) in which clothing items are detected from the image and the probability of each item is used to compose a vector representation for the outfit. Comp-Net employs a normalization layer so that weights are updated by taking into consideration the previously known co-occurrence patterns between clothing items. Further, Comp-Net minimizes a cost-sensitive loss function as errors have different costs depending on the clothing item that is misclassified. This results in a space in which semantically related outfits are placed next to each other, enabling to find semantically similar outfits that may not be visually similar. We designed an evaluation setup that takes into account the association between different styles, occasions and seasons, and show that our compositional approach significantly outperforms a variety of recently proposed baselines.