Query: Daily Routine Recognition through Activity Spotting

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Rapid Object Detection using a Boosted Cascade of Simple Features <=SEP=> This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

ADDITIVE LOGISTIC REGRESSION : A STATISTICAL VIEW OF BOOSTING <=SEP=> Boosting is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion. We develop more direct approximations and show that they exhibit nearly identical results to boosting. Direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations, and far superior in some. We suggest a minor modification to boosting that can reduce computation, often by factors of 10 to 50. Finally, we apply these insights to produce an alternative formulation of boosting decision trees. This approach, based on best-first truncated tree induction, often leads to better performance, and can provide interpretable descriptions of the aggregate decision rule. It is also much faster computationally, making it more suitable to large-scale data mining applications.

Sharing Visual Features for Multiclass and Multiview Object Detection <=SEP=> We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection

A Hybrid Discriminative/Generative Approach for Modeling Human Activities <=SEP=> Accurate recognition and tracking of human activities is an important goal of ubiquitous computing. Recent advances in the development of multi-modal wearable sensors enable us to gather rich datasets of human activities. However, the problem of automatically identifying the most useful features for modeling such activities remains largely unsolved. In this paper we present a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities. We tested the activity recognition system using over 12 hours of wearable-sensor data collected by volunteers in natural unconstrained environments. The models succeeded in identifying a small set of maximally informative features, and were able identify ten different human activities with an accuracy of 95%.

Training Support Vector Machines: an Application to Face Detection <=SEP=> Detection (To appear in the Proceedings of CVPR'97, June 17-19, 1997, Puerto Rico.) Edgar Osunay? Robert Freund? Federico Girosiy yCenter for Biological and Computational Learning and ?Operations Research Center Massachusetts Institute of Technology Cambridge, MA, 02139, U.S.A. Abstract We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classi ers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.

New Hash Functions and Their Use in Authentication and Set Equality <=SEP=> In this paper we exhibit several new classes of hash functions with certain desirable properties, and introduce two novel applications for hashing which make use of these functions. One class contains a small number of functions, yet is almost universal,. If the functions hash n-bit long names into m-bit indices, then specifying a member of the class requires only O((m + log, log,(n)) . log,(n)) bits as compared to O(n) bits for earlier techniques. For long names, this is about a factor of m larger than the lower bound of m + log, n -log, m bits. An application of this class is a provably secure authentication technique for sending messages over insecure lines. A second class of functions satisfies a much stronger property than universal,. We present the application of testing sets for equality. The authentication technique allows the receiver to be certain that a message is genuine. An “enemy”-even one with infinite computer resources-cannot forge or modify a message without detection. The set equality technique allows operations including “add member to set,” “delete member from set” and “test two sets for equality” to be performed in expected constant time and with less than a specified probability of error.

The effect of active video games on cognitive functioning in clinical and non-clinical populations: A meta-analysis of randomized controlled trials <=SEP=> Physically-active video games ('exergames') have recently gained popularity for leisure and entertainment purposes. Using exergames to combine physical activity and cognitively-demanding tasks may offer a novel strategy to improve cognitive functioning. Therefore, this systematic review and meta-analysis was performed to establish effects of exergames on overall cognition and specific cognitive domains in clinical and non-clinical populations. We identified 17 eligible RCTs with cognitive outcome data for 926 participants. Random-effects meta-analyses found exergames significantly improved global cognition (g=0.436, 95% CI=0.18-0.69, p=0.001). Significant effects still existed when excluding waitlist-only controlled studies, and when comparing to physical activity interventions. Furthermore, benefits of exergames where observed for both healthy older adults and clinical populations with conditions associated with neurocognitive impairments (all p<0.05). Domain-specific analyses found exergames improved executive functions, attentional processing and visuospatial skills. The findings present the first meta-analytic evidence for effects of exergames on cognition. Future research must establish which patient/treatment factors influence efficacy of exergames, and explore neurobiological mechanisms of action.

Attitudes -1 The Construction of Attitudes <=SEP=> Attitudes have long been considered a central concept of social psychology. In fact, early writers have defined social psychology as the scientific study of attitudes (e.g., Thomas & Znaniecki, 1918) and in 1954 Gordon Allport noted, "This concept is probably the most distinctive and indispensable concept in contemporary American social psychology" (p. 43). As one may expect of any concept that has received decades of attention, the concept of attitudes has changed over the years (see Allport, 1954, for an early review). The initial definitions were broad and encompassed cognitive, affective, motivational, and behavioral components. For example, Allport (1935) defined an attitude as "a mental and neural state of readiness, organized through experience, exerting a directive and dynamic influence upon the individual's response to all objects and situations with which it is related" (p. 810). A decade later, Krech and Crutchfield (1948) wrote, "An attitude can be defined as an enduring organization of motivational, emotional, perceptual, and cognitive processes with respect to some aspect of the individual's world" (p. 152). These definitions emphasized the enduring nature of attitudes and their close relationship to individuals' defined attitudes simply in terms of the probability that a person will show a specified behavior in a specified situation. In subsequent decades, the attitude concept lost much of its breadth and was largely reduced to its evaluative component. In the succinct words of Daryl Bem, "Attitudes are likes and dislikes" attitudes as "a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor" (p. 1). Along the way, many functions that were initially ascribed to attitudes have been reassigned to other cognitive structures and the accumulating body of empirical findings drew many of the classic assumptions into question. A growing body of literature suggests that attitudes may be much less enduring and stable than has traditionally been assumed. As we review below, self-reports of attitudes are highly context-dependent and can be profoundly influenced by minor changes in question wording, question format or question order. Attitudes-3 their assessment is subject to contextual influences. For other researchers, the same findings indicate that all we assess in attitude measurement are evaluative judgments that respondents construct at the time they are asked, based on whatever information happens to be accessible (e.g., Schwarz & Strack, 1991). From this perspective, the traditional attitude concept may not be particularly useful and we may learn more …

Towards Operational Measures of Computer Security <=SEP=> Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of ‘the ability of the system to resist attack’. That is, it should be operational, reflecting the degree to which the system can be expected to remain free of security breaches under particular conditions of operation (including attack). Instead, current security levels at best merely reflect the extensiveness of safeguards introduced during the design and development of a system. Whilst we might expect a system developed to a higher level than another to exhibit ‘more secure behaviour’ in operation, this cannot be guaranteed; more particularly, we cannot infer what the actual security behaviour will be from knowledge of such a level. In the paper we discuss similarities between reliability and security with the intention of working towards measures of ‘operational security’ similar to those that we have for reliability of systems. Very informally, these measures could involve expressions such as the rate of occurrence of security breaches (cf rate of occurrence of failures in reliability), or the probability that a specified ‘mission’ can be accomplished without a security breach (cf reliability function). This new approach is based on the analogy between system failure and security breach. A number of other analogies to support this view are introduced. We examine this duality critically, and have identified a number of important open questions that need to be answered before this quantitative approach can be taken further. The work described here is therefore somewhat tentative, and one of our major intentions is to invite discussion about the plausibility and feasibility of this new approach. Towards Operational Measures of Computer Security 2 _____________________________________________________________________

Estimation of ECG features using LabVIEW <=SEP=> 320 Abstract—Various methods have been proposed and used for ECG feature extraction with considerable percentage of correct detection. Nevertheless, the problem remains open especially with respect to higher detection accuracy in noisy ECGs. In this work we have developed an algorithm based on wavelet transform approach using LabVIEW 8.6. LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical programming language that uses icons instead of lines of text to create programs. Unlike text based programming language, LabVIEW uses the data flow programming, where the flow of data determines execution. The flexibility, modular nature and ease to use programming possible with labview, makes it less complex. The proposed algorithm is executed in two stages. Firstly, it preprocesses (denoises) the signal to remove the noise from the ECG signal. Then it detects P-wave, T-wave, QRS complex and their onsets and offsets. LabVIEW and the related toolkits, advanced signal processing toolkit and math script are used to build the graphical programme for both the stages. The algorithm is evaluated with ECG data files, with different ECG morphologies and noise content taken from CSE multilead data base which contains 5000 samples per ECG, recorded at a sampling frequency of 500Hz.

Computationally Efficient Bayesian Learning of Gaussian Process State Space Models <=SEP=> Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system identification tools or existing learning methods, we show competitive performance and reliable quantification of uncertainties in the model.

Source Code Analysis: A Road Map <=SEP=> The automated and semi-automated analysis of source code has remained a topic of intense research for more than thirty years. During this period, algorithms and techniques for source-code analysis have changed, sometimes dramatically. The abilities of the tools that implement them have also expanded to meet new and diverse challenges. This paper surveys current work on source-code analysis. It also provides a road map for future work over the next five-year period and speculates on the development of source-code analysis applications, techniques, and challenges over the next 10, 20, and 50 years.

Efficient 3D Object Segmentation from Densely Sampled Light Fields with Applications to 3D Reconstruction <=SEP=> Precise object segmentation in image data is a fundamental problem with various applications, including 3D object reconstruction. We present an efficient algorithm to automatically segment a static foreground object from highly cluttered background in light fields. A key insight and contribution of our article is that a significant increase of the available input data can enable the design of novel, highly efficient approaches. In particular, the central idea of our method is to exploit high spatio-angular sampling on the order of thousands of input frames, for example, captured as a hand-held video, such that new structures are revealed due to the increased coherence in the data. We first show how purely local gradient information contained in slices of such a dense light field can be combined with information about the camera trajectory to make efficient estimates of the foreground and background. These estimates are then propagated to textureless regions using edge-aware filtering in the epipolar volume. Finally, we enforce global consistency in a gathering step to derive a precise object segmentation in both 2D and 3D space, which captures fine geometric details even in very cluttered scenes. The design of each of these steps is motivated by efficiency and scalability, allowing us to handle large, real-world video datasets on a standard desktop computer. We demonstrate how the results of our method can be used for considerably improving the speed and quality of image-based 3D reconstruction algorithms, and we compare our results to state-of-the-art segmentation and multiview stereo methods.

Odin: Team VictorTango's entry in the DARPA Urban Challenge <=SEP=> The DARPA Urban Challenge required robotic vehicles to travel more than 90 km through an urban environment without human intervention and included situations such as stop intersections, traffic merges, parking, and roadblocks. Team VictorTango separated the problem into three parts: base vehicle, perception, and planning. A Ford Escape outfitted with a custom drive-by-wire system and computers formed the basis for Odin. Perception used laser scanners, global positioning system, and a priori knowledge to identify obstacles, cars, and roads. Planning relied on a hybrid deliberative/reactive architecture to

The Skiplist-Based LSM Tree <=SEP=> Log-Structured Merge (LSM) Trees provide a tiered data storage and retrieval paradigm that is aractive for write-optimized data systems. Maintaining an ecient buer in memory and deferring updates past their initial write-time, the structure provides quick operations over hot data. Because each layer of the structure is logically separate from the others, the structure is also conducive to opportunistic and granular optimization. In this paper, we introduce the SkiplistBased LSM Tree (sLSM), a novel system in which the memory buer of the LSM is composed of a sequence of skiplists. We develop theoretical and experimental results that demonstrate that the breadth of tuning parameters inherent to the sLSM allows it broad exibility for excellent performance across a wide variety of workloads.

Computer vision-based registration techniques for augmented reality <=SEP=> Augmented reality is a term used to describe systems in which computer-generated information is superimposed on top of the real world; for example, through the use of a see-through head-mounted display. A human user of such a system could still see and interact with the real world, but have valuable additional information, such as descriptions of important features or instructions for performing physical tasks, superimposed on the world. For example, the computer could identify objects and overlay them with graphic outlines, labels, and schematics. The graphics are registered to the real-world objects and appear to be “painted” onto those objects. Augmented reality systems can be used to make productivity aids for tasks such as inspection, manufacturing, and navigation. One of the most critical requirements for augmented reality is to recognize and locate real-world objects with respect to the person’s head. Accurate registration is necessary in order to overlay graphics accurately on top of the real-world objects. At the Colorado School of Mines, we have developed a prototype augmented reality system that uses head-mounted cameras and computer vision techniques to accurately register the head to the scene. The current system locates and tracks a set of preplaced passive fiducial targets placed on the real-world objects. The system computes the pose of the objects and displays graphics overlays using a see-through head-mounted display. This paper describes the architecture of the system and outlines the computer vision techniques used.

Lecture Notes on Network Information Theory <=SEP=> These lecture notes have been converted to a book titled Network Information Theory published recently by Cambridge University Press. This book provides a significantly expanded exposition of the material in the lecture notes as well as problems and bibliographic notes at the end of each chapter. The authors are currently preparing a set of slides based on the book that will be posted in the second half of 2012. More information about the book can be found at http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/.

Economic modelling using computational intelligence techniques <=SEP=> Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets’ vulnerability to external and non-finance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. In an attempt to solve this engineering problem, the authors of this paper present a rough set theory based predictive model for the financial markets. Rough set theory has, as its base, imperfect data analysis and approximation. The theory is used to extract a set of reducts and a set of trading rules based on trading data of the Johannesburg Stock Exchange (JSE) for the period 1 April 2006 to 1 April 2011. To increase the efficiency of the model four of dicretization algorithms were used on the data set, namely (Equal Frequency Binning (EFB), Boolean Reasoning, Entropy and the Naïve Algorithm. The EFB algorithm gives the least number of rules and highest accuracy. Next, the reducts are extracted using the Genetic Algorithm and finally the set of dependency rules are generated from the set of reducts. A rough set confusion matrix is used to assess the accuracy of the model. The model gave a prediction accuracy of 80.4% using the Standard Voting classifier. Keywords-rough set theory; financial market modelling, neural networks, discretization, classification. INTRODUCTION Around the world, trading in the stock market has gained enormous popularity as a means through which one can reap huge profits. Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. Thus, stock price movement prediction has long been a cherished desire of investors, speculators and industries [1]. For a long time statistical techniques such as Bayesian models, regression and some econometric techniques have dominated research activities in prediction [2]. The primary approach to financial forecasting has been the identification of a stock price trend and continuation of the investment strategy until evidence suggests that the trend has reversed [3]. One of the biggest problems with the use of regression methods is that they fail to give satisfactory forecasting results for some series’ because of their linear structure and other inherent limitations [8, 9]. However the emergence of computational intelligence techniques as a viable alternative to the “traditional” statistical models that have dominated this area since the 1930’s [2,3] has given impetus to the increasing usage of these techniques in fields such as economics and finance [3]. Apart from these, there have been many other successful applications of intelligent systems to decision support and complex automation tasks [4 6]. Since the year of its inception in 1982, rough set theory has been extensively used as an effective data mining and knowledge discovery technique in numerous applications in the finance, investment and banking fields [4, 7, 11, 12, 20]. Data mining is a discipline in computational intelligence that deals with knowledge discovery, data analysis, and full and semi-autonomous decision making [7]. It entails the analysis of data sets such that unsuspected relationships among data objects are found. Predictive modeling is the practice of deriving future inferences based on these relationships. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets’ vulnerability to external and nonfinance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. This paper presents a generic stock price prediction model based on rough set theory. The model is derived on data from the daily movements of the Johannesburg Stock Exchange’s All Share Index. The data used was collected over a five year period from 1 April 2006 to 1 April 2011. The methodology used in this paper is as follows: data preprocessing, data splitting, data discreetization, redundant attribute elimination, reduct generation, rule generation and prediction. 71 The rest of this paper is organized as follows: Section II covers the theoretical foundations of rough set theory, Section III gives the design of the proposed prediction model while an analysis of the results and conclusions are presented in Sections IV and V respectively. THEORETICAL FOUNDATIONS OF ROUGH SETS Rough set theory (RST) was introduced by Pawlak in 1982. The theory can be regarded as a mathematical tool used for imperfect data analysis [10]. Thus RST has proved useful in applications spanning the engineering, financial and decision support domains to mention but a few. It is based on the assumption that “with every object in the universe of discourse, some information (data or knowledge) is associated” [10]. In practical applications, the “universe of discourse” described in [10] is usually a table called the decision table in which the rows are objects or data elements and the columns are attributes and the entries are called the attribute values [3]. The objects or data elements described by the same attributes are said to be indiscernible (indistinguishable) by the attribute set. Any set of indiscernible data elements forms a granule or atom of knowledge about the entire “universe of discourse” (information system framework) [10]. A union of these elementary sets (granules) is said to be a precise or crisp set, other-wise the set is said to be rough [1,2,7,10]. Every rough set will have boundary cases i.e data objects which cannot certainly be classified as belonging to the set or its complement when using the available information [10]. Associated with every rough set is a pair of sets called the lower and upper approximation of the rough set. The lower approximation consists of those objects which one can definitively say belong to the target set. The upper approximation consists of those objects which possibly belong to the target set. The difference between the two sets is the boundary region. The decision rule derived specifies an outcome based on certain conditions. Where the derived rule uniquely identifies outcomes based on some conditions the rule is said to be certain else it is uncertain. Every decision rule has a pair of probabilities associated with it, the certainty and coverage coefficients [3]. These conditional probabilities also satisfy Bayes’ theorem [7,10]. The certainty coefficient is the conditional probability that an object that belongs to the decision class outlined by the rule given that it satisfies the conditions of the rule. The coverage coefficient on other hand expresses the conditional probability of reasons given some decision [10]. Clearly RST can be seen to overlap with many other theories in the realm of imperfect knowledge analysis such as evidence theory, Bayesian inference, fuzzy sets etc [1, 3, 4, 10, 11, 12]. To define rough sets mathematically, we begin by defining an information system S = (U,A), where U and A are finite and non-empty sets that represent the data objects and attributes respectively. Every attribute has a set of possible values Va. Va is called the domain of a. A subset of A say B will determine a binary relation I(B) on U, which is called the indiscerniblity relation. The relation is defined as follows: ( ) ( ) if and only if a(x) = a(y) for every a in B, where a(x) denotes the value of attribute a for data object x [10]. I(B) is an equivalence relation. All equivalence classes of I(B) as U/I(B). An equivalence class of I(B) containing x is denoted as B(x). If (x,y) belong to I(B) they are said to be indiscernible with respect to B. All equivalence classes of the indescernibility relation, I(B), are referred to as B-granules or B-elementary sets [10]. In the information system defined above, we define as in [10]: (1) And, (2) We now define the two operators assigned to every (1) two sets called the upper and lower approximation of X. The two sets are defined as follows [10]: ( ) ⋃ { ( ) ( ) } (3) And, ( ) ⋃ { ( ) ( ) } (4) Thus, the lower approximation is the union of all Belementary sets that are included in the target set, whilst the upper approximation is the union of all B-elementary sets that have a non-empty intersection with the target set. The difference between the two sets is called the boundary of region of X.

Comparative study of Hough Transform methods for circle finding <=SEP=> A variety of circle detection methods which are based on variations of the Hough Transform are investigated. The ,five methods considered are the standard Hough Trans,form, the Fast Hough Transform of Li et al.‘, a two stage Hough method, and two space saving approaches based on the method devised by Gerig and Klein2. The performance of each of the methods has been compared on synthetic imagery and real images from a metallurgical application. Figures and comments are presented concerning the accuracy, reliability, computational efficiency and storage requirements of each of the methods.

User Identity Linkage by Latent User Space Modelling <=SEP=> User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of difficulty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space. We propose two effective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet effective optimization methods are used for optimizing objective function: the first one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the first work to propose a unified framework to address the following two important aspects of the multi-platform user identity linkage problem --- (I) the platform multiplicity and (II) online data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.

Electricity Smart Meters Interfacing the Households <=SEP=> The recent worldwide measures for energy savings call for a larger awareness of the household energy consumption, given the relevant contribution of domestic load to the national energy balance. On the other hand, electricity smart meters together with gas, heat, and water meters can be interconnected in a large network offering a potential value to implement energy savings and other energy-related services, as long as an efficient interface with the final user is implemented. Unfortunately, so far, the interface of such devices is mostly designed and addressed at the utilities supervising the system, giving them relevant advantages, while the communication with the household is often underestimated. This paper addresses this topic by proposing the definition of a local interface for smart meters, by looking at the actual European Union and international regulations, at the technological solutions available on the market, and at those implemented in different countries, and, finally, by proposing specific architectures for a proper consumer-oriented implementation of a smart meter network.

Multi-target tracking by online learning of non-linear motion patterns and robust appearance models <=SEP=> We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance models for tracking; positive sample pairs are collected from different track-lets so that training samples have high diversity. Finally, using online learned moving groups, a tracklet completion process is introduced to deal with tracklets not reaching entry/exit points. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.

Is a Positive Review Always Effective? Advertising Appeal Effect in the Persuasion of Online Customer Reviews <=SEP=> Despite the expected value of online customer reviews as an emerging advertising medium, the manner of enforcing its effectiveness on customers’ purchase behavior is still a question not well answered. To address the question, we propose a new central route cue called “advertising appeal” to examine attitude and intention to purchase a product or service based on the reasoning of the elaboration likelihood model. We also propose that consumers’ consumption goal and attitude certainty separately moderate the advertising appeal effect on purchase intention through a degree of favorable attitude. We test these hypotheses by conducting a laboratory experiment with 50 participants. In this experiment, we manipulate two kinds of advertising appeals and consumption goals. This study demonstrates that attribute-based appeal reviews are more influential than emotion-based appeal reviews in the persuasion process, regardless of the individuals’ consumption goals. However, the advertising appeal effect on purchase intention is more pronounced for hedonic consumers than for utilitarian consumers.

Model of Customer Churn Prediction on Support Vector Machine <=SEP=> To improve the prediction abilities of machine learning methods, a support vector machine (SVM) on structural risk minimization was applied to customer churn prediction. Researching customer churn prediction cases both in home and foreign carries, the method was compared with artifical neural network, decision tree, logistic regression, and naive bayesian classifier. It is found that the method enjoys the best accuracy rate, hit rate, covering rate, and lift coefficient, and therefore, provides an effective measurement for customer churn prediction.

Enhancing Windows Firewall Security Using Fuzzy Reasoning <=SEP=> Firewall is a standard security utility within the Microsoft Windows operating system. Most Windows users adopt it as the default security option due to its free availability. Moreover, Windows Firewall is a widely used security tool because of the large market share of the Microsoft Windows operating system. It can be customised for filtering of network traffic based on user-defined inbound and outbound rules. It is supplied with only basic functionality. As a result it cannot be considered as an effective tool for monitoring and analysing of inbound and outbound traffic. Nonetheless, as a freely available and conventional end user security tool, with some enhancement it could perform as a useful security tool for millions of Windows users. Therefore, this paper presents an enhanced Windows Firewall for a more effective monitoring and analysis of network traffic, based upon an intuitive fuzzy reasoning approach. Consequently, it can be used to prevent a greater range of attacks beyond the simple filtering of inbound and outbound network traffic. In this paper, a simulation of ICMP flooding is demonstrated, where the created firewall inbound and outbound rules are insufficient to monitor ICMP flooding. However, the addition of fuzzy reasoning system monitored it successfully and enhanced the standard Windows Firewall functionality to prevent ICMP flooding. The use of this Windows Fuzzy-Firewall can also be extended to prevent TCP flooding, UDP flooding and some other types of denial of service attacks.

Deep Model-Based 6D Pose Refinement in RGB <=SEP=> We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to predict a translational and rotational update. At the core, we propose a new visual loss that drives the pose update by aligning object contours, thus avoiding the definition of any explicit appearance model. In contrast to previous work our method is correspondence-free, segmentation-free, can handle occlusion and is agnostic to geometrical symmetry as well as visual ambiguities. Additionally, we observe a strong robustness towards rough initialization. The approach can run in real-time and produces pose accuracies that come close to 3D ICP without the need for depth data. Furthermore, our networks are trained from purely synthetic data and will be published together with the refinement code at http://campar.in.tum. de/Main/FabianManhardt to ensure reproducibility.

The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models <=SEP=> Defect prediction models help software organizations to anticipate where defects will appear in the future. When training a defect prediction model, historical defect data is often mined from a Version Control System (VCS, e.g., Subversion), which records software changes at the file-level. Software metrics, on the other hand, are often calculated at the class- or method-level (e.g., McCabe's Cyclomatic Complexity). To address the disagreement in granularity, the class- and method-level software metrics are aggregated to file-level, often using summation (i.e., McCabe of a file is the sum of the McCabe of all methods within the file). A recent study shows that summation significantly inflates the correlation between lines of code (Sloc) and cyclomatic complexity (Cc) in Java projects. While there are many other aggregation schemes (e.g., central tendency, dispersion), they have remained unexplored in the scope of defect prediction. In this study, we set out to investigate how different aggregation schemes impact defect prediction models. Through an analysis of 11 aggregation schemes using data collected from 255 open source projects, we find that: (1) aggregation schemes can significantly alter correlations among metrics, as well as the correlations between metrics and the defect count; (2) when constructing models to predict defect proneness, applying only the summation scheme (i.e., the most commonly used aggregation scheme in the literature) only achieves the best performance (the best among the 12 studied configurations) in 11 percent of the studied projects, while applying all of the studied aggregation schemes achieves the best performance in 40 percent of the studied projects; (3) when constructing models to predict defect rank or count, either applying only the summation or applying all of the studied aggregation schemes achieves similar performance, with both achieving the closest to the best performance more often than the other studied aggregation schemes; and (4) when constructing models for effort-aware defect prediction, the mean or median aggregation schemes yield performance values that are significantly closer to the best performance than any of the other studied aggregation schemes. Broadly speaking, the performance of defect prediction models are often underestimated due to our community's tendency to only use the summation aggregation scheme. Given the potential benefit of applying additional aggregation schemes, we advise that future defect prediction models should explore a variety of aggregation schemes.

System-level fault-tolerance in large-scale parallel machines with buffered coscheduling <=SEP=> Summary form only given. As the number of processors for multiteraflop systems grows to tens of thousands, with proposed petaflops systems likely to contain hundreds of thousands of processors, the assumption of fully reliable hardware has been abandoned. Although the mean time between failures for the individual components can be very high, the large total component count will inevitably lead to frequent failures. It is therefore of paramount importance to develop new software solutions to deal with the unavoidable reality of hardware faults. We will first describe the nature of the failures of current large-scale machines, and extrapolate these results to future machines. Based on this preliminary analysis we will present a new technology that we are currently developing, buffered coscheduling, which seeks to implement fault tolerance at the operating system level. Major design goals include dynamic reallocation of resources to allow continuing execution in the presence of hardware failures, very high scalability, high efficiency (low overhead), and transparency - requiring no changes to user applications. Preliminary results show that this is attainable with current hardware.

A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems <=SEP=> Recent online services rely heavily on automatic personalization to recommend relevant content to a large number of users. This requires systems to scale promptly to accommodate the stream of new users visiting the online services for the first time. In this work, we propose a content-based recommendation system to address both the recommendation quality and the system scalability. We propose to use a rich feature set to represent users, according to their web browsing history and search queries. We use a Deep Learning approach to map users and items to a latent space where the similarity between users and their preferred items is maximized. We extend the model to jointly learn from features of items from different domains and user features by introducing a multi-view Deep Learning model. We show how to make this rich-feature based user representation scalable by reducing the dimension of the inputs and the amount of training data. The rich user feature representation allows the model to learn relevant user behavior patterns and give useful recommendations for users who do not have any interaction with the service, given that they have adequate search and browsing history. The combination of different domains into a single model for learning helps improve the recommendation quality across all the domains, as well as having a more compact and a semantically richer user latent feature vector. We experiment with our approach on three real-world recommendation systems acquired from different sources of Microsoft products: Windows Apps recommendation, News recommendation, and Movie/TV recommendation. Results indicate that our approach is significantly better than the state-of-the-art algorithms (up to 49% enhancement on existing users and 115% enhancement on new users). In addition, experiments on a publicly open data set also indicate the superiority of our method in comparison with transitional generative topic models, for modeling cross-domain recommender systems. Scalability analysis show that our multi-view DNN model can easily scale to encompass millions of users and billions of item entries. Experimental results also confirm that combining features from all domains produces much better performance than building separate models for each domain.

Millimeter Wave MIMO With Lens Antenna Array: A New Path Division Multiplexing Paradigm <=SEP=> Millimeter wave (mmWave) communication is a promising technology for future wireless systems, while one practical challenge is to achieve its large-antenna gains with only limited radio frequency (RF) chains for cost-effective implementation. To this end, we study in this paper a new lens antenna array enabled mmWave multiple-input multiple-output (MIMO) communication system. We first show that the array response of lens antenna arrays follows a “sinc” function, where the antenna element with the peak response is determined by the angle of arrival (AoA)/departure (AoD) of the received/transmitted signal. By exploiting this unique property along with the multi-path sparsity of mmWave channels, we propose a novel low-cost and capacity-achieving spatial multiplexing scheme for both narrow-band and wide-band mmWave communications, termed path division multiplexing (PDM), where parallel data streams are transmitted over different propagation paths with simple per-path processing. We further propose a simple path grouping technique with group-based small-scale MIMO processing to effectively mitigate the inter-stream interference due to similar AoAs/AoDs. Numerical results are provided to compare the performance of the proposed mmWave lens MIMO against the conventional MIMO with uniform planar arrays (UPAs) and hybrid analog/digital processing. It is shown that the proposed design achieves significant throughput gains as well as complexity and cost reductions, thus leading to a promising new paradigm for mmWave MIMO communications.