title: Answering enumeration queries with the crowd

text: 1.1. 能否全部搞定?枚举查询 在本文中,我们思考了关系数据库管理系统 (RDBMS) 中一种最基本的运算,即利用筛选约束条件或断言 来扫描单一数据库表格;此查询枚举用户感兴趣的一 组特定项目。例如,请思考以下 SQL 查询,它要列 出可以忍耐低光环境的所有室内盆栽植物:SELECT DISTINCT name FROM Plants WHERE lightneeds = ‘low’。在使用传统的 RDBMS 并且给定数 据库状态时,此查询只有一个正确的答案,通过扫描 相关表格、筛选记录,再返回所有匹配的记录即可获 得该答案。这种方法对即便实际上无界的关系也奏效, 因为封闭世界假定指出:在查询执行之时数据库中没 有的任何记录都不存在。 相反,在 CrowdDB 等众包系统中,一旦耗尽已 存储表格中的记录时,我们可以向众人发送作业来索 取更多记录。然后问题演变为:查询结果集何时才算 完整?众包查询天生具有模糊性或拥有无界结果集, 其元组分散于网络或者仅存在于人脑。例如,我们可 以思考这样的查询:列出就业市场上现有的博士学位 应届毕业生,或者列出加利福尼亚州对绿色技术感兴 趣的企业。这种查询是利用众人力量的数据库系统的 主要用例,对发出要执行的查询的用户而言均属于劳 动密集型任务,但由于并不经常需要执行,所以不一 定值得去开发、调节和使用复杂的机器学习解决方案。 在本文中,我们将解决以下问题:“用户在众包 数据库系统的开放世界中应当如何思索枚举查询?” 我们开发的统计学工具可以帮助用户推导时间 / 成本 与完整度的权衡,还可用于推进查询执行和众包策略。


title: VARAN the Unbelievable: An Efficient N-version Execution Framework

text:
With the widespread availability of multi-core processors, running multiple diversified variants or several different versions of an application in parallel is becoming a viable approach for increasing the reliability and security of software systems. The key component of such N-version execution (NVX) systems is a runtime monitor that enables the execution of multiple versions in parallel. Unfortunately, existing monitors impose either a large performance overhead or rely on intrusive kernel-level changes. Moreover, none of the existing solutions scales well with the number of versions, since the runtime monitor acts as a performance bottleneck.
 In this paper, we introduce Varan, an NVX framework that combines selective binary rewriting with a novel event-streaming architecture to significantly reduce performance overhead and scale well with the number of versions, without relying on intrusive kernel modifications.
 Our evaluation shows that Varan can run NVX systems based on popular C10k network servers with only a modest performance overhead, and can be effectively used to increase software reliability using techniques such as transparent failover, live sanitization and multi-revision execution.


title: Identification of influencers - Measuring influence in customer networks

text: Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through self-replicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process. The simple out-degree centrality performed well in all treatments.


title: Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

text: Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).


title: Projection Based Weight Normalization for Deep Neural Networks

text: Optimizing deep neural networks (DNNs) often suffers from the ill-conditioned problem. We observe that the scaling-based weight space symmetry property in rectified nonlinear network will cause this negative effect. Therefore, we propose to constrain the incoming weights of each neuron to be unit-norm, which is formulated as an optimization problem over Oblique manifold. A simple yet efficient method referred to as projection based weight normalization (PBWN) is also developed to solve this problem. PBWN executes standard gradient updates, followed by projecting the updated weight back to Oblique manifold. This proposed method has the property of regularization and collaborates well with the commonly used batch normalization technique. We conduct comprehensive experiments on several widely-used image datasets including CIFAR-10, CIFAR-100, SVHN and ImageNet for supervised learning over the state-of-the-art convolutional neural networks, such as Inception, VGG and residual networks. The results show that our method is able to improve the performance of DNNs with different architectures consistently. We also apply our method to Ladder network for semi-supervised learning on permutation invariant MNIST dataset, and our method outperforms the state-of-the-art methods: we obtain test errors as 2.52%, 1.06%, and 0.91% with only 20, 50, and 100 labeled samples, respectively.


title: Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora

text: We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.


title: Reducing Patient Mortality in Hospitals : The Role of Human Resource

text: Developing effective health care organizations is increasingly complex as a result of demographic changes, globalization and developments in medicine. This study examines the potential contribution of organizational behavior theory and research by investigating the relationship between systems of human resource management (HRM) practices and effectiveness of patient care in hospitals. Relatively little research has been conducted to explore these issues in health care settings. In a sample of 52 hospitals in England, we examine the relationship between the HRM system and health care outcome. Specifically, we study the association between high performance HRM policies and practices and standardized patient mortality rates. The research reveals that, after controlling for prior mortality and other potentially confounding factors such as the ratio of doctors to patients, greater use of a complementary set of HRM practices has a statistically and practically significant relationship with patient mortality. The findings suggest that managers and policy makers should focus sharply on improving the functioning of relevant HR management systems in health care organizations as one important means by which to improve patient care.


title: Digital Calibration Techniques for Pipelined ADC ’ s

text: A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC’s) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDAC’s) commonly used in multi-step or pipelined ADC’s. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multi-step or pipelined ADC’s. The proposed method improves the performance of the inherently fast ADC’s by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, op amp dc gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within 2/3 of the Nyquist bandwidth is recovered with 16-bit accuracy using a 44-th order polynomial interpolation, behaving essentially as an FIR filter. This work was supported by the National Science Foundation under Grant MIP-9312671.


title: Role of Broca's Area in Implicit Motor Skill Learning: Evidence from Continuous Theta-burst Magnetic Stimulation

text: Complex actions can be regarded as a concatenation of simple motor acts, arranged according to specific rules. Because the caudal part of the Broca's region (left Brodmann's area 44, BA 44) is involved in processing hierarchically organized behaviors, we aimed to test the hypothesis that this area may also play a role in learning structured motor sequences. To address this issue, we investigated the inhibitory effects of a continuous theta-burst TMS (cTBS) applied over left BA 44 in healthy subjects, just before they performed a serial RT task (SRTT). SRTT has been widely used to study motor skill learning and is also of interest because, for complex structured sequences, subjects spontaneously organize them into smaller subsequences, referred to as chunks. As a control, cTBS was applied over the vertex in another group, which underwent the same experiment. Control subjects showed both a general practice learning effect, evidenced by a progressive decrease in RT across blocks and a sequence-specific learning effect, demonstrated by a significant RT increase in a pseudorandom sequence. In contrast, when cTBS was applied over left BA 44, subjects lacked both the general practice and sequence-specific learning effects. However, surprisingly, their chunking pattern was preserved and remained indistinguishable from controls. The present study indicates that left BA 44 plays a role in motor sequence learning, but without being involved in elementary chunking. This dissociation between chunking and sequence learning could be explained if we postulate that left BA 44 intervenes in high hierarchical level processing, possibly to integrate elementary chunks together.


title: A 5.8-GHz radar sensor chip in 0.18-μm CMOS for non-contact vital sign detection

text: This paper presents a 5.8-GHz radar sensor chip for non-contact vital sign detection. The sensor chip is designed and fabricated in TSMC 0.18 μm CMOS 1P6M process. Except for the low-noise amplifier, all the active and passive components of the radar system are fully integrated in a single CMOS chip. With packaging on the printed-circuit board and connecting transmitting and receiving antennas, the radar sensor chip has been successfully demonstrated to detect the respiration and heart beat rates of a human adult.


title: Linear scan register allocation

text: We describe a new algorithm for fast global register allocation called linear scan. This algorithm is not based on graph coloring, but allocates registers to variables in a single linear-time scan of the variables' live ranges. The linear scan algorithm is considerably faster than algorithms based on graph coloring, is simple to implement, and results in code that is almost as efficient as that obtained using more complex and time-consuming register allocators based on graph coloring. The algorithm is of interest in applications where compile time is a concern, such as dynamic compilation systems, “just-in-time” compilers, and interactive development environments.


title: A Classification and Comparison Framework for Software Architecture Description Languages

text: ÐSoftware architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation, and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future. Index TermsÐSoftware architecture, architecture description language, component, connector, configuration, definition, classification, comparison.


title: Specifying Distributed Software Architectures

text: There is a real need for clear and sound design speci cations of distributed systems at the architectural level. This is the level of the design which deals with the high-level organisation of computational elements and the interactions between those elements. The paper presents the Darwin notation for specifying this high-level organisation. Darwin is in essence a declarative binding language which can be used to de ne hierarchic compositions of interconnected components. Distribution is dealt with orthogonally to system structuring. The language supports the speci cation of both static structures and dynamic structures which may evolve during execution. The central abstractions managed by Darwin are components and services. Services are the means by which components interact. In addition to its use in specifying the architecture of a distributed system, Darwin has an operational semantics for the elaboration of speci cations such that they may be used at runtime to direct the construction of the desired system. The paper describes the operational semantics of Darwin in terms of the -calculus, Milner's calculus of mobile processes. The correspondence between the treatment of names in the -calculus and the management of services in Darwin leads to an elegant and concise -calculus model of Darwin's operational semantics. The model is used to argue the correctness of the Darwin elaboration process. The overall objective is to provide a soundly based notation for specifying and constructing distributed software architectures. This paper will appear in the Fifth European Software Engineering Conference, ESEC '95 on 26 September 1995 in Barcelona.


title: An Introduction to the Aesop System

text: As the design of software architectures emerges as a discipline within software engineering, it becomes increasingly important to support architectural description and analysis with tools and environments. This paper provides a brief introduction to Aesop, a set of tools for developing architectural design environments that exploit architectural styles to guide software architects in producing speci c systems.


title: ADLs and dynamic architecture changes

text: Existing ADLs typically support only static architecture specification and do not provide facilities for the support of dynamically changing architectures. This paper presents a possible solution to this problem: in order to adequately support dynamic architecture changes, ADLs can leverage techniques used in dynamic programming languages. In particular, changes to ADL specifications should be interpreted. To enable interpretation, an ADL should have an architecture construction component that supports explicit and incremental specification of architectural changes, in addition to the traditional architecture description facilities. This will allow software architects to specify the changes to an architecture after it has been built. The paper expands upon the results from an ongoing project -building a development environment for CZstyle architectures.’


title: A two-stage short-term load forecasting approach using temperature daily profiles estimation

text: Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria’s National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.


title: Performance analysis of DCO-OFDM in VLC system

text: The performance of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The impact of nonlinearity of Light Emitting Diode (LED) and its beam angle on the VLC system performance is studied. We later analyze the effect of modulation order, number of subcarriers, signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major issue in OFDM based VLC system. Simulation results show that the bit error rate (BER) decreases as the degree of nonlinearity increases and a better BER can be achieved as the LED behavior approaches linear model. In addition, it is shown that reducing the QAM order or increasing the number of subcarriers may reduce the effect of the LED nonlinearity, thus improving the BER performance of the VLC system. Moreover, it is demonstrated that PAPR is higher for a large number of subcarriers and modulation order. Finally, the PAPR of the visible light OFDM system can significantly be reduced by employing the signal scaling combined with biasing operation.


title: When Does More Money Work? Examining the Role of Perceived Fairness in Pay on the Performance Quality of Crowdworkers

text: This research adds to the rich discussion on whether increases in payment to crowdworkers lead to increases in performance quality by introducing the concept of perceived fairness in pay (PFP). PFP refers to the belief that one is fairly compensated for their work. We examine whether PFP mediates the impact of payment amount on the performance quality of crowdworkers. We conducted a field experiment with 152 crowdworkers performing a buttonclicking (BC) task and an instructional manipulation check (IMC) task. PFP mediated the impact of payment amount on performance quality in the BC task but not in the IMC task. PFP also mediated the impact of payment amount on satisfaction and task time. Results suggest that PFP can help us better understand the relationship between payment and performance quality in crowdsourcing.


title: Efficient frame-sequential label propagation for video object segmentation

text: In this work, we present an approach for segmenting objects in videos taken in complex scenes. It propagates initial object label through the entire video by a frame-sequential manner where the initial label is usually given by the user. The proposed method has several contributions which make the propagation much more robust and accurate than other methods. First, a novel supervised motion estimation algorithm is employed between each pair of neighboring frames, by which a predicted shape model can be warped in order to segment the similar color around object boundary. Second, unlike previous methods with fixed modeling range, we design a novel range-adaptive appearance model to handle the tough problem of occlusion. Last, the paper gives a reasonable framework based on GraphCut algorithm for obtaining the final label of the object by combining the clues from both appearance and motion. In the experiments, the proposed approach is evaluated qualitatively and quantitatively with some recent methods to show it achieves state-of-art results on multiple videos from benchmark data sets.


title: A combined transmembrane topology and signal peptide prediction method.

text: An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at


title: Design of a triangular fractal patch antenna with slit for IRNSS and GAGAN applications

text: In this paper a triangular fractal patch antenna with slit is designed for IRNSS and GAGAN applications using ADS software. India is intended to develop a satellite based navigation systems known as Indian Regional Navigational Satellite System (IRNSS) for positioning applications. Design of IRNSS antenna at user sector is indispensable. GPS Aided and Geo Augmented Navigation (GAGAN), a satellite based augmentation system for India, erect over the GPS system is anticipated to provide the flawless navigation support over the Asia-Pacific regions. The desired antenna has been deliberate on dielectric constant εr = 4.8 and substrate thickness h = 3.05 mm. The feed location of the antenna has been selected to produce the circular polarization. The self-similar property in antenna exhibits multi-band resonant frequencies. These specifications should be satisfied at the frequency L5 (1175 MHz), L1 (1575.42 MHz) and S (2492.08 MHz).


title: Flame detection in video using hidden Markov models

text: This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.


title: Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges

text: In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, a large number of amputees still rejects prostheses or points out a low satisfaction level, due to a sub-optimal interaction between the socket and the residual limb tissues. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) affecting the stump-socket interface and reducing the comfort/stability of limb prostheses. In this review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. The paper then describes the technological solutions available to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.


title: Visual inertial odometry for quadrotors on SE(3)

text: The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.


title: Sleep and autism spectrum disorders.

text: Sleep disorders are common in children with autism spectrum disorders and have a significant effect on daytime function and parental stress. The cornerstone of treatment is to establish the cause of the sleep concern, which is often multifactorial. Identifying and treating sleep disorders may result not only in more consolidated sleep, more rapid time to fall asleep, and avoidance of night waking but also favorably affect daytime behavior and parental stress. Targeting effective treatment strategies is dependent on understanding the underlying causes of sleep problems in children with Autism spectrum disorders, therefore further research is paramount.


title: Optimizing distributions over molecular space . An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry ( ORGANIC )

text: Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design. 1


title: Opportunities and Challenges of Switched Reluctance Motor Drives for Electric Propulsion: A Comparative Study

text: Selection of the proper electric traction drive is an important step in design and performance optimization of electrified powertrains. Due to the use of high energy magnets, permanent magnet synchronous machines (PMSM) have been the primary choice in the electric traction motor market. However, manufacturers are very interested to find a permanent magnet-free alternative as a fallback option due to unstable cost of rare-earth metals and fault tolerance issues related to the constant permanent magnet excitation. In this paper, a new comprehensive review of electric machines (EMs) that includes various new switched reluctance machine topologies in addition to conventional EMs such as PMSM, induction machine, synchronous reluctance machine (SynRel), and PM-assisted SynRel is presented. This paper is based on performances such as power density, efficiency, torque ripple, vibration and noise, and fault tolerance. These systematic examinations prove that recently proposed magnetic configurations such as double-stator switched reluctance machine can be a reasonable substitute for permanent magnet machines in electric traction applications.


title: Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search

text: This paper presents a supervised deep hashing approach that constructs binary hash codes from labeled data for large-scale image search. We assume that semantic labels are governed by a set of latent attributes in which each attribute can be on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semanticspreserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network in which binary codes are learned by the optimization of an objective function defined over classification error and other desirable properties of hash codes. With this design, SSDH has a nice property that classification and retrieval are unified in a single learning model, and the learned binary codes not only preserve the semantic similarity between images but also are efficient for image search. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a pointwised manner and thus is naturally scalable to large-scale datasets. SSDH is simple and can be easily realized by a slight modification of an existing deep architecture for classification; yet it is effective and outperforms other unsupervised and supervised hashing approaches on several benchmarks and one large dataset comprising more than 1 million images.


title: Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example

text: Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.


title: SHOP2: An HTN Planning System

text: The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.


title: Extending the Hierarchical Deep Reinforcement Learning framework

text: iv


title: Unsupervised Image-to-Image Translation Networks

text: Most of the existing image-to-image translation frameworks—mapping an image in one domain to a corresponding image in another—are based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework, which is based on variational autoencoders and generative adversarial networks. The proposed framework can learn the translation function without any corresponding images in two domains. We enable this learning capability by combining a weight-sharing constraint and an adversarial training objective. Through visualization results from various unsupervised image translation tasks, we verify the effectiveness of the proposed framework. An ablation study further reveals the critical design choices. Moreover, we apply the UNIT framework to the unsupervised domain adaptation task and achieve better results than competing algorithms do in benchmark datasets.


title: Process scheduling under uncertainty: Review and challenges

text: Uncertainty is a very important concern in production scheduling since it can cause infeasibilities and production disturbances. Thus scheduling nder uncertainty has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. he purpose of this paper is to review the main methodologies that have been developed to address the problem of uncertainty in production cheduling as well as to identify the main challenges in this area. The uncertainties in process scheduling are first analyzed, and the different athematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, lternative scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of process cheduling under uncertainty are identified and some new ideas are discussed. 2007 Elsevier Ltd. All rights reserved.


title: Diagnosis and compensation of amplitude imbalance, imperfect quadrant and offset in resolver signals

text: In this paper, a new method for detecting some errors in resolver signals is presented. Sin and cosine signals in resolver to digital converter's (RDC's) input might have errors, such as amplitude imbalance, imperfect quadrant or DC offset. Like other sensors some defects in mechanical structure and electrical or magnetic circuits cause to these errors in resolver output signals. The amplitude and phase of resolver output signals and consequently calculated angle are affected by these errors at any moment. Resolver output signals are more affected in their peak points than the other points. These peak points are easier and more accurate than the other points for analyzing, too. It is obvious that any error has unique effects on the signals. Therefore, by analyzing the effect on signals' peak points the error type and the value can be detected. To avoid wrong detection, error signal is defined, which detects the per unit signals deviation from the unit circle. Also, for error detection, there is no need to interrupt converter's operation. The proposed method is simulated in Matlab/Simulink environment, and the obtained results validate its effectiveness. Also, it is simulated on TMS320F2812 DSP using CCS 3.3, and the results are presented. Furthermore, by implementing the present RDC on motor controller DSP, it will be a low-cost approach.


title: A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls.

text: In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.


title: Apposcopy: semantics-based detection of Android malware through static analysis

text: We present Apposcopy, a new semantics-based approach for identifying a prevalent class of Android malware that steals private user information. Apposcopy incorporates (i) a high-level language for specifying signatures that describe semantic characteristics of malware families and (ii) a static analysis for deciding if a given application matches a malware signature. The signature matching algorithm of Apposcopy uses a combination of static taint analysis and a new form of program representation called Inter-Component Call Graph to efficiently detect Android applications that have certain control- and data-flow properties. We have evaluated Apposcopy on a corpus of real-world Android applications and show that it can effectively and reliably pinpoint malicious applications that belong to certain malware families.


title: Towards 3D object maps for autonomous household robots

text: This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.


title: Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation

text: Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children's lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources.


title: Designing an UHF RFID Reader Antenna

text: In this paper, a circularly polarized (CP) Octagonal shape microstrip patch antenna fed by coaxial feed is designed and analyzed for RFID (Radio Frequency Identification) reader applications. The physical parameters of the structure as well as its partial ground plane are analyzed and optimized using Zealand IE3D simulation software. Return loss (S11), voltage standing wave ratio (VSWR), directivity and gain are carried out. The results show that the proposed antenna has good impedance and radiation characteristics over the required bandwidth, 860-960 MHz (UHF RFID band). The return loss of the optimized Octagonal-shaped microstrip patch antenna is below 10dB over the UHF frequency band. The proposed antenna is very promising for various modern communication applications.


title: Emotion AI , Real-Time Emotion Detection using CNN

text: In this paper, we describe a Convolutional Neural Network (CNN) approach to realtime emotion detection. We utilize data from the Extended Cohn-Kanade dataset, Japanese Female Facial Expression data set, and our own custom images in order to train this model, and apply preprocessing steps to improve performance. We re-train a LeNet and AlexNet implementation, both of which perform with above 97% accuracy. Qualitative analysis of real-time images shows that the above models perform reasonably well at classifying facial expressions, but not as well as the quantitative results would indicate.


title: Identity-Based Broadcast Encryption with Constant Size Ciphertexts and Private Keys

text: This paper describes the first identity-based broadcast encryption scheme (IBBE) with constant size ciphertexts and private keys. In our scheme, the public key is of size linear in the maximal size m of the set of receivers, which is smaller than the number of possible users (identities) in the system. Compared with a recent broadcast encryption system introduced by Boneh, Gentry and Waters (BGW), our system has comparable properties, but with a better efficiency: the public key is shorter than in BGW. Moreover, the total number of possible users in the system does not have to be fixed in the setup.


title: Unobservable Communication over Fully Untrusted Infrastructure

text: Keeping communication private has become increasingly important in an era of mass surveillance and statesponsored attacks. While hiding the contents of a conversation has well-known solutions, hiding the associated metadata (participants, duration, etc.) remains a challenge, especially if one cannot trust ISPs or proxy servers. This paper describes a communication system called Pung that provably hides all content and metadata while withstanding global adversaries. Pung is a key-value store where clients deposit and retrieve messages without anyone— including Pung’s servers—learning of the existence of a conversation. Pung is based on private information retrieval, which we make more practical for our setting with new techniques. These include a private multi-retrieval scheme, an application of the power of two choices, and batch codes. These extensions allow Pung to handle 103× more users than prior systems with a similar threat model.


title: IRON: Functional Encryption using Intel SGX

text: Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.


title: SCONE: Secure Linux Containers with Intel SGX

text: Sergei Arnautov, Bohdan Trach, Franz Gregor, Thomas Knauth, Andre Martin, Christian Priebe, Joshua Lind, Divya Muthukumaran, Dan O’Keeffe, Mark L Stillwell, David Goltzsche, David Eyers, Rüdiger Kapitza, Peter Pietzuch, and Christof Fetzer Fakultät Informatik, TU Dresden, christof.fetzer@tu-dresden.de Dept. of Computing, Imperial College London, prp@imperial.ac.uk Informatik, TU Braunschweig, rrkapitz@ibr.cs.tu-bs.de Dept. of Computer Science, University of Otago, dme@cs.otago.ac.nz


title: Sanctum: Minimal Hardware Extensions for Strong Software Isolation

text: Sanctum offers the same promise as Intel’s Software Guard Extensions (SGX), namely strong provable isolation of software modules running concurrently and sharing resources, but protects against an important class of additional software attacks that infer private information from a program’s memory access patterns. Sanctum shuns unnecessary complexity, leading to a simpler security analysis. We follow a principled approach to eliminating entire attack surfaces through isolation, rather than plugging attack-specific privacy leaks. Most of Sanctum’s logic is implemented in trusted software, which does not perform cryptographic operations using keys, and is easier to analyze than SGX’s opaque microcode, which does. Our prototype targets a Rocket RISC-V core, an open implementation that allows any researcher to reason about its security properties. Sanctum’s extensions can be adapted to other processor cores, because we do not change any major CPU building block. Instead, we add hardware at the interfaces between generic building blocks, without impacting cycle time. Sanctum demonstrates that strong software isolation is achievable with a surprisingly small set of minimally invasive hardware changes, and a very reasonable overhead.


title: Consensus photometric stereo

text: This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.


title: A computer vision-aided motion sensing algorithm for mobile robot's indoor navigation

text: This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robot's indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robot's positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.


title: ANALYTIC PROBLEMS FOR ELLIPTIC CURVES

text: We consider some problems of analytic number theory for elliptic curves which can be considered as analogues of classical questions around the distribution of primes in arithmetic progressions to large moduli, and to the question of twin primes. This leads to some local results on the distribution of the group structures of elliptic curves defined over a prime finite field, exhibiting an interesting dichotomy for the occurence of the possible groups.


title: Classification of text documents supervised by domain ontologies

text: The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.


title: Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

text: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.


title: Function and regulation in MAPK signaling pathways: lessons learned from the yeast Saccharomyces cerevisiae.

text: Signaling pathways that activate different mitogen-activated protein kinases (MAPKs) elicit many of the responses that are evoked in cells by changes in certain environmental conditions and upon exposure to a variety of hormonal and other stimuli. These pathways were first elucidated in the unicellular eukaryote Saccharomyces cerevisiae (budding yeast). Studies of MAPK pathways in this organism continue to be especially informative in revealing the molecular mechanisms by which MAPK cascades operate, propagate signals, modulate cellular processes, and are controlled by regulatory factors both internal to and external to the pathways. Here we highlight recent advances and new insights about MAPK-based signaling that have been made through studies in yeast, which provide lessons directly applicable to, and that enhance our understanding of, MAPK-mediated signaling in mammalian cells.


title: The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function

text: The ALS Functional Rating Scale (ALSFRS) is a validated rating instrument for monitoring the progression of disability in patients with amyotrophic lateral sclerosis (ALS). One weakness of the ALSFRS as originally designed was that it granted disproportionate weighting to limb and bulbar, as compared to respiratory, dysfunction. We have now validated a revised version of the ALSFRS, which incorporates additional assessments of dyspnea, orthopnea, and the need for ventilatory support. The Revised ALSFRS (ALSFRS-R) retains the properties of the original scale and shows strong internal consistency and construct validity. ALSFRS-R scores correlate significantly with quality of life as measured by the Sickness Impact Profile, indicating that the quality of function is a strong determinant of quality of life in ALS.


title: Designing and implementing an integrated technological pedagogical science knowledge framework for science teachers professional development

text: This paper reports on the design and the implementation of the Technological Pedagogical Science Knowledge (TPASK), a new model for science teachers professional development built on an integrated framework determined by the Technological Pedagogical Content Knowledge (TPACK) model and the authentic learning approach. The TPASK curriculum dimensions and the related course sessions are also elaborated and applied in the context of a teacher trainers’ preparation program aiming at ICT integration in science classroom practice. A brief description of the project, its accomplishments, and perceptions of the participants, through the lens of TPASK professional development model, are presented. This is followed by the presentation of the evaluation results on the impact of the program which demonstrates that science teachers reported meaningful TPASK knowledge and increased willingness to adopt and apply this framework in their instruction. Finally, we draw on the need to expand TPACK by incorporating a fourth dimension, the Educational Context within Pedagogy, Content and Technology mutually interact, in order to address future policy models concerning teacher preparation to integrate ICT in education. 2010 Elsevier Ltd. All rights reserved.


title: Improv Chat: Second Response Generation for Chatbot

text: Existing research on response generation for chatbot focuses on First Response Generation which aims to teach the chatbot to say the first response (e.g. a sentence) appropriate to the conversation context (e.g. the user’s query). In this paper, we introduce a new task Second Response Generation, termed as Improv chat, which aims to teach the chatbot to say the second response after saying the first response with respect the conversation context, so as to lighten the burden on the user to keep the conversation going. Specifically, we propose a general learning based framework and develop a retrieval based system which can generate the second responses with the users’ query and the chatbot’s first response as input. We present the approach to building the conversation corpus for Improv chat from public forums and social networks, as well as the neural networks based models for response matching and ranking. We include the preliminary experiments and results in this paper. This work could be further advanced with better deep matching models for retrieval base systems or generative models for generation based systems as well as extensive evaluations in real-life applications.


title: Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork

text: Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.


title: Robust Appointment Scheduling

text: We consider the problem of appointment scheduling in a robus t optimization framework. The appointment scheduling problem arises in many service operations, for e xample health care. For each job, we are given its minimum and maximum possible execution times. The objective is to find an appointment schedule for which the cost in the worst case scenario of the realization of the processi ng times of the jobs is minimized. We present a global balancing heuristic, which gives an easy to compute closed f orm optimal schedule when the underage costs of the jobs are non-decreasing. In addition, for the case where we h av the flexibility of changing the order of execution of the jobs, we give simple heuristics to find a near-optimal seq uence of the jobs. Working paper, Operations Research Center, MIT. Last updat ed March 3, 2012. Amazon.com, 333 Boren Avenue N, Seattle WA 98101, USA. Email : mshashi@alum.mit.edu. TU Berlin, Matheon, Berlin, Germany. Email: sebastian.stiller@tu-berlin.de.


title: The ASTOOT Approach to Testing Object-Oriented Programs

text: This article describes a new approach to the unit testing of object-oriented programs, a set of tools based on this approach, and two case studies. In this approach, each test case consists of a tuple of sequences of messages, along with tags indicating whether these sequences should put objects of the class under test into equivalent states and/or return objects that are in equivalent states. Tests are executed by sending the sequences to objects of the class under test, then invoking a user-supplied equivalence-checking mechanism. This approach allows for substantial automation of many aspects of testing, including test case generation, test driver generation, test execution, and test checking. Experimental prototypes of tools for test generation and test execution are described. The test generation tool requires the availability of an algebraic specification of the abstract data type being tested, but the test execution tool can be used when no formal specification is available. Using the test execution tools, case studies involving execution of tens of thousands of test cases, with various sequence lengths, parameters, and combinations of operations were performed. The relationships among likelihood of detecting an error and sequence length, range of parameters, and relative frequency of various operations were investigated for priority queue and sorted-list implementations having subtle errors. In each case, long sequences tended to be more likely to detect the error, provided that the range of parameters was sufficiently large and likelihood of detecting an error tended to increase up to a threshold value as the parameter range increased.


title: Confirmatory factor analysis of the WAIS-IV/WMS-IV.

text: The Wechsler Adult Intelligence Scale-fourth edition (WAIS-IV) and the Wechsler Memory Scale-fourth edition (WMS-IV) were co-developed to be used individually or as a combined battery of tests. The independent factor structure of each of the tests has been identified; however, the combined factor structure has yet to be determined. Confirmatory factor analysis was applied to the WAIS-IV/WMS-IV Adult battery (i.e., age 16-69 years) co-norming sample (n = 900) to test 13 measurement models. The results indicated that two models fit the data equally well. One model is a seven-factor solution without a hierarchical general ability factor: Verbal Comprehension, Perceptual Reasoning, Processing Speed, Auditory Working Memory, Visual Working Memory, Auditory Memory, and Visual Memory. The second model is a five-factor model composed of Verbal Comprehension, Perceptual Reasoning, Processing Speed, Working Memory, and Memory with a hierarchical general ability factor. Interpretative implications for each model are discussed.


title: A compact planar feed structure for Ka-band satcom-on-the-move tracking antennas

text: The increasing interest in bi-directional mobile high data rate satellite communications in Ka-band necessitates the development of dedicated antenna tracking systems and feeds. In this paper we describe a compact feed structure based on printed circuit boards for a mobile satellite communications ground terminal with a Cassegrain reflector antenna. The novel structure provides a dual circular polarisation communication mode as well as the TM01 mode for multimode monopulse tracking. This coupler, based on carefully matched transitions from grounded coplanar lines to circular waveguides, is operational at 20GHz and 30 GHz, to cover the downlink and the uplink frequency ranges in Ka-band. This work contributes to the development of a satellite terminal for land-mobile communications in disaster scenarios.


title: Dysbiosis of the gut microbiota in disease

text: There is growing evidence that dysbiosis of the gut microbiota is associated with the pathogenesis of both intestinal and extra-intestinal disorders. Intestinal disorders include inflammatory bowel disease, irritable bowel syndrome (IBS), and coeliac disease, while extra-intestinal disorders include allergy, asthma, metabolic syndrome, cardiovascular disease, and obesity.


title: Learning in and about complex systems

text: Change is accelerating, and the complexity of the systems in which we live is growing. Increasingly change is the result of humanity itself. As complexity grows so do the unanticipated side effects of human action, further increasing complexity in a vicious cycle. Many scholars call for the development of 'systems thinking' to improve our ability to manage wisely. But how do people learn in and about complex dynamic systems? Learning is a feedback process in which our decisions alter the real world, we receive information feedback about the world, and using the new information we revise the decisions we make and the mental models that motivate those decisions. Unfortunately, in the world of social action various impediments slow or prevent these learning feedbacks from functioning, allowing erroneous and harmful behaviors and beliefs to persist. The barriers to learning include the dynamic complexity of the systems themselves, inadequate and ambiguous outcome feedback, systematic 'misperceptions of feedback' where our cognitive maps omit important feedback processes, delays, stocks and flows, and nonlinearities that characterize complex systems, inability to simulate mentally the dynamics of our cognitive maps, poor interpersonal and organizational inquiry skills, and poor scientific reasoning skills. To be successful methods to enhance learning about complex systems must address all these impediments. Effective methods for learning in and about complex dynamic systems must include (1) tools to elicit participant knowledge, articulate and reframe perceptions, and create maps of the feedback structure of a problem from those perceptions; (2) simulation tools and management flight simulators to assess the dynamics of those maps and test new policies; and (3) methods to improve scientific reasoning skills, strengthen group process and overcome defensive routines for individuals and teams.


title: Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

text: Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing unsupervised learning. They learn using a simplified and customized “spike timing dependent plasticity” rule. In the network, neurons’ threshold is adjusted following a homeostasis-type rule. We perform system level simulations with an experimentally verified model of the memristive devices’ behavior. They show, on the textbook case of character recognition, that performance can compare with traditional supervised networks of similar complexity. They also show that the system can retain functionality with extreme variations of various memristive devices’ parameters (a relative standard dispersion of more than 50% is tolerated on all device parameters), thanks to the robustness of the scheme, its unsupervised nature, and the capability of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes, is particularly robust to read disturb effects and does not require unrealistic control on the devices’ conductance. These results open the way for a novel design approach for ultraadaptive electronic systems.


title: AlGaN/GaN current aperture vertical electron transistors

text: Describes AlGaN/GaN current aperture vertical electron transistor (CAVET) structures. A CAVET consists of a source region separated from a drain region by an insulating layer containing a narrow aperture which is filled with conducting material. A device mesa is formed by reactive ion etching, and source contacts are deposited on either side of the aperture. The drain metal contacts the n-doped region below the aperture. Electrons flow from the source contacts through the aperture into the n-type base region and are collected at the drain. A Schottky gate, located directly above the aperture, is used to modulate the current passing through the aperture. In a CAVET, because the virtual drain (or pinched off region) is located underneath the gate, charge does not accumulate at the gate edge, so no large fields near the gate edge are present. Instead, our simulations show that the high field region in a CAVET is buried in the bulk. The CAVET therefore has the potential to support large source-drain voltages, since surface related breakdown is eliminated.


title: End-to-End Channel Capacity of a Wireless Sensor Network Under Reachback

text: Many wireless sensor network applications will impose a many-to-one traffic flow pattern. This phenomena in communication networks is referred to as reachback. The higher volume of traffic in traffic hotspots, which usually form around the base station, limits the number of sensors that can participate in the formation of a wireless sensor network with a fixed per sensor data rate. To alleviate the effects of reachback, spatial correlation of sensor data has enabled the use of Slepian-Wolf coding as a compression technique to reduce the volume of traffic in sensor networks. Within a cluster, Slepian-Wolf coding is used to compress the sensor readings. Clusterheads then transmit the compressed sensor readings over the overlay network which is formed by all clusterheads. We develop an expression for the end-to-end (sensor-to-base station) channel capacity of such a wireless sensor network and observe the effect of variations in the number of clusterheads on end-to-end capacity.


title: Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence

text: Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.


title: Central Lip Lift as Aesthetic and Physiognomic Plastic Surgery: The Effect on Lower Facial Profile.

text:
BACKGROUND
A central lip lift was introduced to Westerners in 1980s. However, no studies have been conducted on the facial aesthetic and physiognomic perspectives of a central lip lift in the Asian population.

OBJECTIVES
The authors presented the central lip lift as aesthetic and physiognomic treatment in Asians and explained its effect on lower facial profile.

METHODS
A retrospective chart review was performed in 202 cases of asians. The authors analyzed patient age, cause of long philtrum, purpose of the treatment, and postoperative satisfaction. The authors then performed an anthropometric assessment and a photographic analysis.

RESULTS
The vertical disproportion of the lower face was improved after the treatment, and there was significant shortening of the philtrum length (P < .001) and an increase in a visible upper vermilion (P < .001). In Westerners, a long philtrum was mainly caused by the aging process. Aging patients (range, 40-59 years) underwent the central lip lift for upper lip rejuvenation. In contrast, in Asia, a long philtrum was primarily caused by bone retraction after an orthognathic surgery or orthodontic procedure. Young patients (range, 20-39 years old) underwent the central lip lift to correct a relatively lengthened philtrum after 2-jaw surgery. Furthermore, about half of the patients (52.0%) underwent the central lip lift for facial physiognomic improvement.

CONCLUSIONS
In today's multiracial society, plastic surgeons planning a central lip lift in Asian patients should consider both aesthetic and physiognomic perspectives. Regardless of the aesthetic outcome, the surgeon should strive to maximize patient satisfaction.

LEVEL OF EVIDENCE
4 Therapeutic.


title: A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration

text: We generalize the alternating minimization algorithm recently proposed in [32] to efficiently solve a general, edge-preserving, variational model for recovering multichannel images degraded by withinand cross-channel blurs, as well as additive Gaussian noise. This general model allows the use of localized weights and higher-order derivatives in regularization, and includes a multichannel extension of total variation (MTV) regularization as a special case. In the MTV case, we show that the model can be derived from an extended half-quadratic transform of Geman and Yang [14]. For color images with three channels and when applied to the MTV model (either locally weighted or not), the per-iteration computational complexity of this algorithm is dominated by nine fast Fourier transforms. We establish strong convergence results for the algorithm including finite convergence for some variables and fast q-linear convergence for the others. Numerical results on various types of blurs are presented to demonstrate the performance of our algorithm compared to that of the MATLAB deblurring functions. We also present experimental results on regularization models using weighted MTV and higher-order derivatives to demonstrate improvements in image quality provided by these models over the plain MTV model.


title: Hyperbolic centroidal Voronoi tessellation

text: The centroidal Voronoi tessellation (CVT) has found versatile applications in geometric modeling, computer graphics, and visualization. In this paper, we extend the concept of the CVT from Euclidean space to hyperbolic space. A novel hyperbolic CVT energy is defined, and the relationship between minimizing this energy and the hyperbolic CVT is proved. We also show by our experimental results that the hyperbolic CVT has the similar property as its Euclidean counterpart where the sites are uniformly distributed according to given density values. Two algorithms -- Lloyd's algorithm and the L-BFGS algorithm -- are adopted to compute the hyperbolic CVT, and the convergence of Lloyd's algorithm is proved. As an example of the application, we utilize the hyperbolic CVT to compute uniform partitions and high-quality remeshing results for high-genus (genus>1) surfaces.


title: Bayes Optimality in Linear Discriminant Analysis

text: We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.


title: Emotional modulation of cognitive control: approach-withdrawal states double-dissociate spatial from verbal two-back task performance.

text: Emotional states might selectively modulate components of cognitive control. To test this hypothesis, the author randomly assigned 152 undergraduates (equal numbers of men and women) to watch short videos intended to induce emotional states (approach, neutral, or withdrawal). Each video was followed by a computerized 2-back working memory task (spatial or verbal, equated for difficulty and appearance). Spatial 2-back performance was enhanced by a withdrawal state and impaired by an approach state; the opposite pattern held for verbal performance. The double dissociation held more strongly for participants who made more errors than average across conditions. The results suggest that approach-withdrawal states can have selective influences on components of cognitive control, possibly on a hemispheric basis. They support and extend several frameworks for conceptualizing emotion-cognition interactions.


title: Smart irrigation using low-cost moisture sensors and XBee-based communication

text: Deficiency in fresh water resources globally has raised serious alarms in the last decade. Efficient management of water resources play an important role in the agriculture sector. Unfortunately, this is not given prime importance in the third world countries because of adhering to traditional practices. This paper presents a smart system that uses a bespoke, low cost soil moisture sensor to control water supply in water deficient areas. The sensor, which works on the principle of moisture dependent resistance change between two points in the soil, is fabricated using affordable materials and methods. Moisture data acquired from a sensor node is sent through XBEE wireless communication modules to a centralized server that controls water supply. A user-friendly interface is developed to visualize the daily moisture data. The low-cost and wireless nature of the sensing hardware presents the possibility to monitor the moisture levels of large agricultural fields. Moreover, the proposed moisture sensing method has the ability to be incorporated into an automated drip-irrigation scheme and perform automated, precision agriculture in conjunction with de-centralized water control.


title: An approach for the economical evaluation of solar irrigation in Bangladesh

text: This paper represents a comparative picture of irrigation cost of different Bangladeshi crops for diesel, grid electricity and solar power based irrigation systems. The study has been conducted on 27 types of crops. All the data were collected about volume of water for those crops. Then three different types of pump (solar, diesel, electric) have been chosen with same power rating i.e. 5hp. Specific area covered for different crops are calculated furthermore from the attained water volumes. Then finally it has been calculated the 10 years cost in taka. The study found for the entire crops grid powered irrigation cost is minimum than solar powered irrigation cost because the later one is associated with huge primary investment.[12] The study also discovered irrigation with solar power for most of the crops such as onion, carrot, chill, tomato, maize, garlic, gourd, ginger, turmeric, pumpkin, cabbage, cauliflower, lady finger, banana, papaya and groundnut are not beneficial at all rather it costs very high among all the three types of irrigation system.[5] It is also evident that irrigation with solar power of certain crops like potato, cotton, soybean, sunflower, strawberry, lentil, mustard are very much lucrative compared to diesel powered irrigation.


title: Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm

text: Feeding of the world in the 21st century is the biggest challenge, especially for smart farm business. The smart farm has used agriculture automation system instead of traditional agriculture. Traditional agricultural methods employed by the local people are highly sustainable, although the all inclusive cost is not cheap. Our research goal is to provide long term sustainable solution for automation of agriculture. Agriculture automation has several methods to getting data from vegetable crop like sensor for environmental measurement. Therefore, we developed a portable measurement technology including soil moisture sensor, air humidity sensor and air temperature sensor. Moreover, irrigation system using wireless sensor network has installed these sensors, with the purpose for collecting the environment data and controlling the irrigation system via smart phone. The purpose of the experiment is to find better ways of controlling an irrigation system with automatic system and manual control by smart phone. In order to control an irrigation system, we have developed the communication methodology of the wireless sensor network for collected environment data and sending control command to turn on/off irrigation system. It is successful for controlling the irrigation system and controlling the water near the vegetable roots. In this paper, we have attempted to demonstrate the automation of the irrigation system that is useful for farm business which make it comfortable than using traditional agriculture by using smart phone for monitoring and controlling the system. Accordingly, in the long-term has reduced cost as well. The experimental result shows that the accuracy of sending and receiving command control for irrigation system is 96 percent and accuracy of environment collection is 98 percent.


title: Microcontroller based drip irrigation system using smart sensor

text: In the past couple of decades, there is rapid growth in terms of technology in the field of agriculture. Different monitoring and controlled systems are installed in order to increase the yield. The yield rate may deceases due to numerous factors. Disease is one of the key factors that cause the degradation of yield. So the developed monitoring system mainly focuses on predicting the start of germination of the disease. Sensor module is used to detect different environmental condition across the farm and the sensed data is displayed on Liquid crystal display using microcontroller. Microcontroller wirelessly transmits different environment conditions across the farm to central unit where data is stored, and analysed. Central unit checks the present data with disease condition and if matches then it commands microcontroller to operate relay. Sensor module is tested for different temperature range and it is found that there are little variations in recorded values. Wireless data transfer is tested with the introduction of various obstacles like wall, metal body, magnet, etc. and it is found that same data is transferred to central unit but with some amount of delay in it. The developed system nearly predicts the start of germination of disease.


title: Design of Electronic Throttle Valve Position Control System using Nonlinear PID Controller

text: In recent years, the use of electronic throttle valve systems has been very popular in the automotive industry. It is used to regulate the amount of air flow into the engine. Due to the existence of multiple nonsmooth nonlinearities, the controller design to the electronic throttle valve becomes difficult task. These nonlinearities including stick–slip friction, backlash, and a discontinuous nonlinear spring involved in the system. In the first part of this paper the electronic throttle valve system is presented first, and then the model is derived for each components of the throttle valve system. Later, the system dimension is reduced to two by ignoring the motor inductance at the end of this part of work. Actually this step enables us to design a nonlinear PID controller electronic throttle valve system. The simulation results, of applying a nonlinear PID controller to the electronic throttle valve system, show the effectiveness of the proposed controller in forcing the angle of the throttle valve to the desired opening angle in presence of nonlinearities and disturbances in throttle valve system model.


title: Mining time-changing data streams

text: Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.


title: Wearable Medical Systems for p-Health

text: Driven by the growing aging population, prevalence of chronic diseases, and continuously rising healthcare costs, the healthcare system is undergoing a fundamental transformation, from the conventional hospital-centered system to an individual-centered system. Current and emerging developments in wearable medical systems will have a radical impact on this paradigm shift. Advances in wearable medical systems will enable the accessibility and affordability of healthcare, so that physiological conditions can be monitored not only at sporadic snapshots but also continuously for extended periods of time, making early disease detection and timely response to health threats possible. This paper reviews recent developments in the area of wearable medical systems for p-Health. Enabling technologies for continuous and noninvasive measurements of vital signs and biochemical variables, advances in intelligent biomedical clothing and body area networks, approaches for motion artifact reduction, strategies for wearable energy harvesting, and the establishment of standard protocols for the evaluation of wearable medical devices are presented in this paper with examples of clinical applications of these technologies.


title: 3D Human Pose Estimation via Deep Learning from 2D Annotations

text: We propose a deep convolutional neural network for 3D human pose and camera estimation from monocular images that learns from 2D joint annotations. The proposed network follows the typical architecture, but contains an additional output layer which projects predicted 3D joints onto 2D, and enforces constraints on body part lengths in 3D. We further enforce pose constraints using an independently trained network that learns a prior distribution over 3D poses. We evaluate our approach on several benchmark datasets and compare against state-of-the-art approaches for 3D human pose estimation, achieving comparable performance. Additionally, we show that our approach significantly outperforms other methods in cases where 3D ground truth data is unavailable, and that our network exhibits good generalization properties.


title: Wireless underground sensor networks: Research challenges

text: This work introduces the concept of a Wireless Underground Sensor Network (WUSN). WUSNs can be used to monitor a variety of conditions, such as soil properties for agricultural applications and toxic substances for environmental monitoring. Unlike existing methods of monitoring underground conditions, which rely on buried sensors connected via wire to the surface, WUSN devices are deployed completely belowground and do not require any wired connections. Each device contains all necessary sensors, memory, a processor, a radio, an antenna, and a power source. This makes their deployment much simpler than existing underground sensing solutions. Wireless communication within a dense substance such as soil or rock is, however, significantly more challenging than through air. This factor, combined with the necessity to conserve energy due to the difficulty of unearthing and recharging WUSN devices, requires that communication protocols be redesigned to be as efficient as possible. This work provides an extensive overview of applications and design challenges for WUSNs, challenges for the underground communication channel including methods for predicting path losses in an underground link, and challenges at each layer of the communication protocol stack. 2006 Elsevier B.V. All rights reserved.


title: Converting an unstructured quadrilateral / hexahedral mesh to a rational T-spline

text: This paper presents a novel method for converting any unstructured quadrilateral or hexahedral mesh to a generalized T-spline surface or solid T-spline, based on the rational T-spline basis functions. Our conversion algorithm consists of two stages: the topology stage and the geometry stage. In the topology stage, the input quadrilateral or hexahedral mesh is taken as the initial T-mesh. To construct a gap-free T-spline, templates are designed for each type of node and applied to elements in the input mesh. In the geometry stage, an efficient surface fitting technique is developed to improve the surface accuracy with sharp feature preservation. The constructed T-spline surface and solid T-spline interpolate every boundary node in the input mesh, with C2-continuity everywhere except the local region around irregular nodes. Finally, a Bézier extraction technique is developed and linear independence of the constructed T-splines is studied to facilitate T-spline based isogeometric analysis.


title: Quantifying social group evolution

text: The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency—the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community’s lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.


title: HUMAN-AUTOMATION INTERACTION : TAXONOMIES AND QUALITATIVE MODELS The Meaning of Human-Automation Interaction

text: Automation does not mean humans are replaced; quite the opposite. Increasingly, humans are asked to interact with automation in complex and typically large-scale systems, including aircraft and air traffic control, nuclear power, manufacturing plants, military systems, homes, and hospitals. This is not an easy or error-free task for either the system designer or the human operator/automation supervisor, especially as computer technology becomes ever more sophisticated. This review outlines recent research and challenges in the area, including taxonomies and qualitative models of human-automation interaction; descriptions of automation-related accidents and studies of adaptive automation; and social, political, and ethical issues.


title: Assisting Users in a World Full of Cameras: A Privacy-Aware Infrastructure for Computer Vision Applications

text: Computer vision based technologies have seen widespread adoption over the recent years. This use is not limited to the rapid adoption of facial recognition technology but extends to facial expression recognition, scene recognition and more. These developments raise privacy concerns and call for novel solutions to ensure adequate user awareness, and ideally, control over the resulting collection and use of potentially sensitive data. While cameras have become ubiquitous, most of the time users are not even aware of their presence. In this paper we introduce a novel distributed privacy infrastructure for the Internet-of-Things and discuss in particular how it can help enhance user's awareness of and control over the collection and use of video data about them. The infrastructure, which has undergone early deployment and evaluation on two campuses, supports the automated discovery of IoT resources and the selective notification of users. This includes the presence of computer vision applications that collect data about users. In particular, we describe an implementation of functionality that helps users discover nearby cameras and choose whether or not they want their faces to be denatured in the video streams.


title: New insights into churn prediction in the telecommunication sector: A profit driven data mining approach

text: Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical mea-


title: The Challenge of Optical Music Recognition

text: This article describes the challenges posed by optical music recognition – a topic in computer science that aims to convert scanned pages of music into an on-line format. First, the problem is described; then a generalised framework for software is presented that emphasises key stages that must be solved: staff line identification, musical object location, musical feature classification, and musical semantics. Next, significant research projects in the area are reviewed, showing how each fits the generalised framework. The article concludes by discussing perhaps the most open question in the field: how to compare the accuracy and success of rival systems, highlighting certain steps that help ease the task.


title: A Neural Click Model for Web Search

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title: Inf2vec: Latent Representation Model for Social Influence Embedding

text: As a fundamental problem in social influence propagation analysis, learning influence parameters has been extensively investigated. Most of the existing methods are proposed to estimate the propagation probability for each edge in social networks. However, they cannot effectively learn propagation parameters of all edges due to data sparsity, especially for the edges without sufficient observed propagation. Different from the conventional methods, we introduce a novel social influence embedding problem, which is to learn parameters for nodes rather than edges. Nodes are represented as vectors in a low-dimensional space, and thus social influence information can be reflected by these vectors. We develop a new model Inf2vec, which combines both the local influence neighborhood and global user similarity to learn the representations. We conduct extensive experiments on two real-world datasets, and the results indicate that Inf2vec significantly outperforms state-of-the-art baseline algorithms.


title: BiCoS: A Bi-level co-segmentation method for image classification

text: The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. To this end we introduce a new scalable, alternation-based algorithm for co-segmentation, BiCoS, which is simpler than many of its predecessors, and yet has superior performance on standard benchmark image datasets.