Query: Lessons Learned From Previous SSL/TLS Attacks - A Brief Chronology Of Attacks And Weaknesses

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The Galois / Counter Mode of Operation ( GCM ) <=SEP=> 

Lattice Attacks on Digital Signature Schemes <=SEP=> We describe a lattice attack on the Digital Signature Algorithm (DSA) when used to sign many messages, mi, under the assumption that a proportion of the bits of each of the associated ephemeral keys, yi, can be recovered by alternative techniques.

Timing Attacks on Implementations of Diffie-Hellman, RSA, DSS, and Other Systems <=SEP=> By carefully measuring the amount of time required to perform private key operations, attackers may be able to nd xed Di eHellman exponents, factor RSA keys, and break other cryptosystems. Against a vulnerable system, the attack is computationally inexpensive and often requires only known ciphertext. Actual systems are potentially at risk, including cryptographic tokens, network-based cryptosystems, and other applications where attackers can make reasonably accurate timing measurements. Techniques for preventing the attack for RSA and Di e-Hellman are presented. Some cryptosystems will need to be revised to protect against the attack, and new protocols and algorithms may need to incorporate measures to prevent timing attacks.

The RC5 Encryption Algorithm <=SEP=> This document describes the RC encryption algorithm a fast symmetric block cipher suitable for hardware or software imple mentations A novel feature of RC is the heavy use of data dependent rotations RC has a variable word size a variable number of rounds and a variable length secret key The encryption and decryption algorithms are exceptionally simple

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.

Knowledge management , flexibility and firm performance : The effects of family involvement <=SEP=> 

Generation of Test Cases from Software Requirements Using Natural Language Processing <=SEP=> Software testing plays an important role in early verification of software systems and it enforces quality in the system under development. One of the challenging tasks in the software testing is generation of software test cases. There are many existing approaches to generate test cases like using uses case, activity diagrams and sequence diagrams; they have their own limitations such as inability to capture test cases for non functional requirements and etc. Thus these techniques have restricted use in acceptance testing and are not effective for verification & acceptance of large software system. If software requirements are stated using semi-formal or formal methods then it is difficult for the testers and other third party domain experts to test the system. It also requires much expertise in interpreting requirements and only limited number of persons can understand them. This paper proposes an approach to generate test case from software requirements expressed in natural language using natural language processing technique.

Cellular planning for next generation wireless mobile network using novel energy efficient CoMP <=SEP=> Cellular planning also called radio network planning is a crucial stage for the deployment of a wireless network. The enormous increase in data traffic requires augmentation of coverage, capacity, throughput, quality of service and optimized cost of the network. The main goal of cellular planning is to enhance the spectral efficiency and hence the throughput of a network. A novel CoMP algorithm has been discussed with two-tier heterogeneous network. Number of clusters has been obtained using V-R (variance ratio) Criterion. The centroid of a cluster obtained using K-means algorithm provides the deployment of BS position. Application of CoMP in this network using DPS approach with sleep mode of power saving, provides higher energy efficiency, SINR and throughput as compared to nominal CoMP. CoMP basically describes a scheme in which a group of base stations (BS) dynamically co-ordinate and co-operate among themselves to convert interference into a beneficial signal. Network planning using stochastic method and Voronoi Tessellation with two-tier network has been applied to a dense region of Surat city in Gujarat state of India. The results show clear improvement in signal-to-interference plus noise ratio (SINR) by 25% and energy efficiency of the network by 28% using the proposed CoMP transmission.

CardBoardiZer: Creatively Customize, Articulate and Fold 3D Mesh Models <=SEP=> Computer-aided design of flat patterns allows designers to prototype foldable 3D objects made of heterogeneous sheets of material. We found origami designs are often characterized by pre-synthesized patterns and automated algorithms. Furthermore, augmenting articulated features to a desired model requires time-consuming synthesis of interconnected joints. This paper presents CardBoardiZer, a rapid cardboard based prototyping platform that allows everyday sculptural 3D models to be easily customized, articulated and folded. We develop a building platform to allow the designer to 1) import a desired 3D shape, 2) customize articulated partitions into planar or volumetric foldable patterns, and 3) define rotational movements between partitions. The system unfolds the model into 2D crease-cut-slot patterns ready for die-cutting and folding. In this paper, we developed interactive algorithms and validated the usability of CardBoardiZer using various 3D models. Furthermore, comparisons between CardBoardiZer and methods of Autodesk® 123D Make, demonstrated significantly shorter time-to-prototype and ease of fabrication.

Deep Learning without Poor Local Minima <=SEP=> In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, we prove the following statements under the independence assumption adopted from recent work: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) the property of saddle points differs for shallow networks (with three layers) and deeper networks (with more than three layers). Moreover, we prove that the same four statements hold for deep linear neural networks with any depth, any widths and no unrealistic assumptions. As a result, we present an instance, for which we can answer to the following question: how difficult to directly train a deep model in theory? It is more difficult than the classical machine learning models (because of the non-convexity), but not too difficult (because of the nonexistence of poor local minima and the property of the saddle points). We note that even though we have advanced the theoretical foundations of deep learning, there is still a gap between theory and practice.

Aggregating User Input in Ecology Citizen Science Projects <=SEP=> Camera traps (remote, automatic cameras) are revolutionizing large-scale studies in ecology. The Serengeti Lion Project has used camera traps to produce over 1.5 million pictures of animals in the Serengeti. To analyze these pictures, the Project created Snapshot Serengeti, a citizen science website where volunteers can help classify animals. To increase accuracy, each photo is shown to multiple users and a critical step is aggregating individual classifications. In this paper, we present a new aggregation algorithm which achieves an accuracy of 98.6%, better than many human experts. Our algorithm also requires fewer users per photo than existing methods. The algorithm is intuitive and designed so that nonexperts can understand the end results. Ecology seeks to understand the interrelationships of species with one another and with their environment. Monitoring many species of animals simultaneously has traditionally been very difficult. Camera traps (remote, automatic cameras) are revolutionizing ecological research by providing a non-invasive, cost-effective approach for large-scale monitoring. Ecologists are currently using these traps in the Serengeti National Park, one of the world’s last large intact natural areas, to understand the dynamics of its dozens of large mammals (Swanson et al. 2014). As of November 2013, the ecologists have spent 3 years using more than 200 cameras spread over 1,125 square kilometers to take more than 1.5 million photos. In order to process so many images, the ecologists, along with Zooniverse (a citizen science platform), created Snapshot Serengeti, a web site where over 35,000 volunteers helped classify the species in the photos (Zooniverse 2014a). Since volunteers can make mistakes, each photo is shown to multiple users. A critical step is to combine these classifications into one aggregate classification: e.g., if 4 out of 5 users classify a photo as containing a zebra, we might ⇤These authors are part of the Zooniverse project, funded in part by MICO. Copyright c 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. decide that the photo does indeed contain a zebra. In this paper, we develop an aggregation algorithm for Snapshot Serengeti. Classification aggregation is an active area in machine learning; however, we show that much of the existing literature is based on assumptions which do not apply to Snapshot Serengeti, and must therefore develop a novel approach. 1 In addition, current machine learning work on classification aggregation often draws on ideas such as expectation maximization and Bayesian reasoning. While powerful, these methods obscure the connection between input and results, making it hard for non-machine learning experts to understand the end results. Thus, our algorithm must be both accurate and intuitive. Our paper proceeds as follows. We begin by discussing Snapshot Serengeti and previous machine learning literature on classifier aggregation. We then discuss why much of this existing work is not applicable to Snapshot Serengeti. We next introduce a new classifier aggregation algorithm for Snapshot Serengeti and compare it against the current algorithm. Finally, we conclude and discuss possible future work.

Amplified Boomerang Attacks Against Reduced-Round MARS and Serpent <=SEP=> We introduce a new cryptanalytic technique based on Wagner’s boomerang and inside-out attacks. We first describe this new attack in terms of the original boomerang attack, and then demonstrate its use on reduced-round variants of the MARS core and Serpent. Our attack breaks eleven rounds of the MARS core with 2 chosen plaintexts, 2 memory, and 2 partial decryptions. Our attack breaks eight rounds of Serpent with 2 chosen plaintexts, 2 memory, and 2 partial decryptions.

An approach for offensive text detection and prevention in Social Networks <=SEP=> Social Network has become a place where people from every corner of the world has established a virtual civilization. In this virtual community, people used to share their views, express their feelings, photos, videos, blogs, etc. Social Networking Sites like Facebook, Twitters, etc. has given a platform to share innumerable contents with just a click of a button. However, there is no restriction applied by them for the uploaded content. These uploaded content may contains abusive words, explicit images which may be unsuitable for social platforms. As such there is no defined mechanism for restricting offensive contents from publishing on social sites. To solve this problem we have used our proposed approach. In our approach we are developing a social network prototype for implementing our approach for automatic filtering of offensive content in social network. Many popular social networking sites today don't have proper mechanism for restricting offensive contents. They use reporting methods in which user report if the content is abuse. This requires substantial human efforts and time. In this paper, we applied pattern matching algorithm for offensive keyword detection from social networking comments and prevent it from publishing on social platform. Apart from conventional method of reporting abusive contents by users our approach does not requires any human intervention and thus restrict offensive words by detecting and preventing it automatically.

A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank Property <=SEP=> This paper presents a content-adaptively sparse reconstruction method for abnormal events detection by exploiting the low-rank property of video sequences. In dictionary learning phase, the bases which describe more important characteristics of the normal behavior patterns are assigned with lower reconstruction costs. Based on the low-rank property of the bases captured by the low-rank approximation, a weighted sparse reconstruction method is proposed to measure the abnormality of testing samples. Multiscale 3-D gradient features, which encode the spatiotemporal information, are adopted as the low level descriptors. The benefits of the proposed method are threefold: first, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; second, the sparsity-based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for representing the dynamic scene semantics; and third, based on the weighted sparse reconstruction method, the proposed method is more efficient for detecting the abnormal events. Experimental results on the public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.

Major League Soccer Scheduling Problem <=SEP=> Major League Soccer is the highest level mens professional soccer league in United States and Canada, consisting of 20 teams, with 17 in United States and 3 in Canada. The League is divided by two Conferences: Western and Eastern with 10 teams each. The regular season of the League runs from early March to late October each year. It is important to look at MLS mainly for three reasons. Firstly, it is a fast-growing league, with only 10 teams competed in 1996 when the League started. Secondly, the popularity of the game has increased dramatically, with the average per-game attendance in MLS increased by 40% in the past decade, which exceeds that of NHL and NBA. Lastly, the big TV broadcasting contract signed with ESPN, Fox Sports, and Univision has demonstrated the huge commercial value of the League.

Information Security and Privacy: Rethinking Governance Models <=SEP=> Concerns about information security and privacy continue to make headlines in the media and rate as serious issues in business and public surveys. Conventional thinking suggests that enhancements to governance models and business practices improve the performance of individual businesses. However, is this enough? Good governance is based on clear rights and responsibilities, and this panel will address the contention that such clarity is lacking in the treatment of personal information. As a result, certain types of business innovation may be constrained and good practices may remain tick box exercises, disconnected from wider business objectives. More radical thinking would go beyond incremental improvements to the status quo and recognize the profound challenges to privacy and information security created by digital technology. This panel will bring together a range of research disciplines and senior business representatives to critique current practice and develop a future research agenda.

Software Test Automation practices in agile development environment: An industry experience report <=SEP=> The increased importance of Test Automation in software engineering is very evident considering the number of companies investing in automated testing tools nowadays, with the main aim of preventing defects during the development process. Test Automation is considered an essential activity for agile methodologies being the key to speed up the quality assurance process. This paper presents empirical observations and the challenges of a test team new to agile practices and Test Automation using open source testing tools integrated in software projects that use the Scrum methodology. The results obtained showed some important issues to be discussed and the Test Automation practices collected based on the experiences and lessons learned.

Radial-Flux Permanent-Magnet Hub Drives: A Comparison Based on Stator and Rotor Topologies <=SEP=> In this paper, the performances of electric vehicle (EV) in-wheel (hub) nonoverlap-winding permanent-magnet motors with different stator and rotor topologies are compared. The calculation of the frequency losses in the design optimization of the hub motors is specifically considered. Also, the effect of the slot and pole number choice that determines the winding factor of the machine is investigated. It is shown that the torque-per-copper-loss performance of the machine cannot be judged solely on the winding factor of the machine. Wide open stator slot machines with rectangular preformed coils and, hence, low manufacturing costs are found to perform better than expected. The design detail and test results of a prototype 10-kW water-cooled EV hub drive are presented. The test results confirm the finite-element-calculated results, specifically in the high-speed region of the drive where the rotor topology affects the constant power speed range.

Fast and robust Earth Mover's Distances <=SEP=> We present a new algorithm for a robust family of Earth Mover's Distances - EMDs with thresholded ground distances. The algorithm transforms the flow-network of the EMD so that the number of edges is reduced by an order of magnitude. As a result, we compute the EMD by an order of magnitude faster than the original algorithm, which makes it possible to compute the EMD on large histograms and databases. In addition, we show that EMDs with thresholded ground distances have many desirable properties. First, they correspond to the way humans perceive distances. Second, they are robust to outlier noise and quantization effects. Third, they are metrics. Finally, experimental results on image retrieval show that thresholding the ground distance of the EMD improves both accuracy and speed.

OBJECTIVE SELF-AWARENESS THEORY : RECENT PROGRESS AND ENDURING PROBLEMS By : <=SEP=> Objective self-awareness theory has undergone fundamental changes in the 3 decades since Duval and Wicklund's (1972) original formulation. We review new evidence that bears on the basic tenets of the theory. Many of the assumptions of self-awareness theory require revision, particularly how expectancies influence approach and avoidance of self-standard discrepancies; the nature of standards, especially when they are changed; and the role of causal attribution in directing discrepancy reduction. However, several unresolved conceptual issues remain; future theoretical and empirical directions are discussed. Article: The human dilemma is that which arises out of a man's capacity to experience himself as both subject and object at the same time. Both are necessary--for the science of psychology, for therapy, and for gratifying living. (May, 1967, p. 8) Although psychological perspectives on the self have a long history (e.g., Cooley, 1902; James, 1890; Mead, 1934), experimental research on the self has emerged only within the last 40 years. One of the earliest "self theories" was objective self-awareness (OSA) theory (Duval & Wicklund, 1972). OSA theory was concerned with the self-reflexive quality of the consciousness. Just as people can apprehend the existence of environmental stimuli, they can be aware of their own existence: "When attention is directed inward and the individual's consciousness is focused on himself, he is the object of his own consciousness--hence 'objective' self awareness" (Duval & Wicklund, 1972, p. 2). This is contrasted with "subjective self-awareness" that results when attention is directed away from the self and the person "experiences himself as the source of perception and action" (Duval & Wicklund, 1972, p. 3). By this, Duval and Wicklund (1972,chap. 3) meant consciousness of one's existence on an organismic level, in which such existence is undifferentiated as a separate and distinct object in the world. OSA theory has stimulated a lot of research and informed basic issues in social psychology, such as emotion (Scheier & Carver, 1977), attribution (Duval & Wicklund, 1973), attitude--behavior consistency (Gibbons, 1983), self-standard comparison (Duval & Lalwani, 1999), prosocial behavior (Froming, Nasby, & McManus, 1998), deindividuation (Diener, 1979), stereotyping (Macrae, Bodenhausen, & Milne, 1998), self-assessment (Silvia & Gendolla, in press), terror management (Arndt, Greenberg, Simon, Pyszczynski, & Solomon, 1998; Silvia, 2001), and group dynamics (Duval, 1976; Mullen, 1983). Self-focused attention is also fundamental to a host of clinical and health phenomena (Hull, 1981; Ingram, 1990; Pyszczynski, Hamilton, Greenberg, & Becker, 1991; Wells & Matthews, 1994). The study of self-focused attention continues to be a dynamic and active research area. A lot of research relevant to basic theoretical issues has been conducted since the last maj or review (Gibbons, 1990). Recent research has made progress in understanding links between self-awareness and causal attribution, the effects of expectancies on self-standard discrepancy reduction, and the nature of standards--the dynamics of selfawareness are now viewed quite differently. We review these recent developments[1] and hope that a conceptual integration of new findings will further stimulate research on self-focused attention. However, there is still much conceptual work left to be done, and many basic issues remain murky and controversial. We discuss these unresolved issues and sketch the beginnings of some possible solutions. Original Theory The original statement of OSA theory (Duval & Wicklund, 1972) employed only a few constructs, relations, and processes. The theory assumed that the orientation of conscious attention was the essence of selfevaluation. Focusing attention on the self brought about objective self-awareness, which initiated an automatic comparison of the self against standards. The self was defined very broadly as the person's knowledge of the person. A standard was "defined as a mental representation of correct behavior, attitudes, and traits ... All of the standards of correctness taken together define what a 'correct' person is" (Duval & Wicklund, 1972, pp. 3, 4). This simple system consisting of self, standards, and attentional focus was assumed to operate according to gestalt consistency principles (Heider, 1960). If a discrepancy was found between self and standards, negative affect was said to arise. This aversive state then motivated the restoration of consistency. Two behavioral routes were proposed. People could either actively change their actions, attitudes, or traits to be more congruent with the representations of the standard or could avoid the self-focusing stimuli and circumstances. Avoidance effectively terminates the comparison process and hence all self-evaluation. Early research found solid support for these basic ideas (Carver, 1975; Gibbons & Wicklund, 1976; Wicklund & Duval, 1971). Duval and Wicklund (1972) also assumed that objective self-awareness would generally be an aversive state--the probability that at least one self-standard discrepancy exists is quite high. This was the first assumption to be revised. Later work found that self-awareness can be a positive state when people are congruent with their standards (Greenberg & Musham, 1981; Ickes, Wicklund, & Ferris, 1973). New Developments OSA theory has grown considerably from the original statement (Duval & Wicklund, 1972). Our review focuses primarily on core theoretical developments since the last review (Gibbons, 1990). Other interesting aspects, such as interpersonal processes and interoceptive accuracy, have not changed significantly since previous reviews (Gibbons, 1990; Silvia & Gendolla, in press). We will also overlook the many clinical consequences of self-awareness; these have been exhaustively reviewed elsewhere (Pyszczynski et al., 1991; Wells & Matthews, 1994). Expectancies and Avoiding Self-Awareness Reducing a discrepancy or avoiding self-focus are equally effective ways of reducing the negative affect resulting from a discrepancy. When do people do one or the other? The original theory was not very specific about when approach versus avoidance would occur. Duval and Wicklund (1972) did, however, speculate that two factors should be relevant. The first was whether people felt they could effectively reduce the discrepancy; the second was whether the discrepancy was small or large. In their translation of OSA theory into a "test--operate--test--exit" (TOTE) feedback system, Carver, Blaney, and Scheier (1979a, 1979b) suggested that expectancies regarding outcome favorability determine approach versus avoidance behavior. When a self-standard discrepancy is recognized, people implicitly appraise their likelihood of reducing the discrepancy (cf. Bandura, 1977; Lazarus, 1966). If eventual discrepancy reduction is perceived as likely, people will try to achieve the standard. When expectations regarding improvement are unfavorable, however, people will try to avoid self-focus. Later research and theory (Duval, Duval, & Mulilis, 1992) refined Duval and Wicklund's (1972) speculations and the notion of outcome favorability. Expectancies are not simply and dichotomously favorable or unfavorable--they connote a person's rate of progress in discrepancy reduction relative to the magnitude of the discrepancy. More specifically, people will try to reduce a discrepancy to the extent they believe that their rate of progress is sufficient relative to the magnitude of the problem. Those who believe their rate of progress to be insufficient will avoid. To test this hypothesis, participants were told they were either highly (90%) or mildly (10%) discrepant from an experimental standard (Duval et al., 1992, Study 1). Participants were then given the opportunity to engage in a remedial task that was guaranteed by the experimenter to totally eliminate their deficiency provided that they worked on the task for 2 hr and 10 min. However, the rate at which working on the task would reduce the discrepancy was varied. In the low rate of progress conditions individuals were shown a performance curve indicating no progress until the last 20 min of working on the remedial task. During the last 20 min discrepancy was reduced to zero. In the constant rate of progress condition, participants were shown a performance curve in which progress toward discrepancy reduction began immediately and continued throughout such efforts with 30% of the deficiency being reduced in the first 30 min of activity and totally eliminated after 2 hr and 10 min. Results indicated that persons who believed the discrepancy to be mild and progress to be constant worked on the remedial task; those who perceived the problem to be mild but the rate of progress to be low avoided this activity. However, participants who thought that the discrepancy was substantial and the rate of progress only constant avoided working on the remedial task relative to those in the mild discrepancy and constant rate of progress condition. These results were conceptually replicated in a second experiment (Duval et al., 1992) using participants' time to complete the total 2 hr and 10 min of remedial work as the dependent measure. This pattern suggests that the rate of progress was sufficient relative to the magnitude of the discrepancy in the mild discrepancy and constant rate of progress condition; this in turn promoted approaching the problem. In the high discrepancy and constant rate of progress condition and the high and mild discrepancy and low rate of progress conditions, rate of progress was insufficient and promoted avoiding the problem. In a third experiment (Duval et al., 1992), people were again led to believe that they were either highly or mildly discrepant from a standard on an intellectual dimension and then given the opportunity to reduce that deficiency by working on a remedial task. A

Evaluation methods and decision theory for classification of streaming data with temporal dependence <=SEP=> Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data.

Learning Background-Aware Correlation Filters for Visual Tracking <=SEP=> Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this drawback by either learning CFs from more discriminative deep features (e.g. DeepSRDCF [9] and CCOT [11]) or learning complex deep trackers (e.g. MDNet [28] and FCNT [33]). While such methods have been shown to work well, they suffer from high complexity: extracting deep features or applying deep tracking frameworks is very computationally expensive. This limits the real-time performance of such methods, even on high-end GPUs. This work proposes a Background-Aware CF based on hand-crafted features (HOG [6]) that can efficiently model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient- and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers.

Broadband Radial Waveguide Power Combiner with Improved Isolation among Adjacent Output Ports <=SEP=> An eight-way waveguide-based power combiner/divider is presented and investigated in the frequency range 7.5–10.5GHz. A simple approach is proposed for design purposes. The measured combiner shows a good agreement between the simulated and measured results. Insertion loss is about −0.3 dB, return loss is less than −15 dB and isolation between adjacent output ports is better than −11 dB at 8.5GHz and reaches about −14 dB at 9.5GHz.

Telomere measurement by quantitative PCR. <=SEP=> It has long been presumed impossible to measure telomeres in vertebrate DNA by PCR amplification with oligonucleotide primers designed to hybridize to the TTAGGG and CCCTAA repeats, because only primer dimer-derived products are expected. Here we present a primer pair that eliminates this problem, allowing simple and rapid measurement of telomeres in a closed tube, fluorescence-based assay. This assay will facilitate investigations of the biology of telomeres and the roles they play in the molecular pathophysiology of diseases and aging.

Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability <=SEP=> Due to enormous progress in the areas of auto-stereoscopic 3D displays, digital video broadcast and computer vision algorithms, 3D television (3DTV) has reached a high technical maturity and many people now believe in its readiness for marketing. Experimental prototypes of entire 3DTV processing chains have been demonstrated successfully during the last few years, and the motion picture experts group (MPEG) of ISO/IEC has launched related ad hoc groups and standardization efforts envisaging the emerging market segment of 3DTV. In this context the paper discusses an advanced approach for a 3DTV service, which is based on the concept of video-plus-depth data representations. It particularly considers aspects of interoperability and multi-view adaptation for the case that different multi-baseline geometries are used for multi-view capturing and 3D display. Furthermore it presents algorithmic solutions for the creation of depth maps and depth image-based rendering related to this framework of multi-view adaptation. In contrast to other proposals, which are more focused on specialized configurations, the underlying approach provides a modular and flexible system architecture supporting a wide range of multi-view structures. r 2007 Elsevier B.V. All rights reserved.

Generalizing Skills with Semi-Supervised Reinforcement Learning <=SEP=> Deep reinforcement learning (RL) can acquire complex behaviors from low-level inputs, such as images. However, real-world applications of such methods require generalizing to the vast variability of the real world. Deep networks are known to achieve remarkable generalization when provided with massive amounts of labeled data, but can we provide this breadth of experience to an RL agent, such as a robot? The robot might continuously learn as it explores the world around it, even while it is deployed and performing useful tasks. However, this learning requires access to a reward function, to tell the agent whether it is succeeding or failing at its task. Such reward functions are often hard to measure in the real world, especially in domains such as robotics and dialog systems, where the reward could depend on the unknown positions of objects or the emotional state of the user. On the other hand, it is often quite practical to provide the agent with reward functions in a limited set of situations, such as when a human supervisor is present, or in a controlled laboratory setting. Can we make use of this limited supervision, and still benefit from the breadth of experience an agent might collect in the unstructured real world? In this paper, we formalize this problem setting as semisupervised reinforcement learning (SSRL), where the reward function can only be evaluated in a set of “labeled” MDPs, and the agent must generalize its behavior to the wide range of states it might encounter in a set of “unlabeled” MDPs, by using experience from both settings. Our proposed method infers the task objective in the unlabeled MDPs through an algorithm that resembles inverse RL, using the agent’s own prior experience in the labeled MDPs as a kind of demonstration of optimal behavior. We evaluate our method on challenging, continuous control tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available. We also show that our method outperforms direct supervised learning of the reward.

Virtual InfiniBand clusters for HPC clouds <=SEP=> High Performance Computing (HPC) employs fast interconnect technologies to provide low communication and synchronization latencies for tightly coupled parallel compute jobs. Contemporary HPC clusters have a fixed capacity and static runtime environments; they cannot elastically adapt to dynamic workloads, and provide a limited selection of applications, libraries, and system software. In contrast, a cloud model for HPC clusters promises more flexibility, as it provides elastic virtual clusters to be available on-demand. This is not possible with physically owned clusters.
 In this paper, we present an approach that makes it possible to use InfiniBand clusters for HPC cloud computing. We propose a performance-driven design of an HPC IaaS layer for InfiniBand, which provides throughput and latency-aware virtualization of nodes, networks, and network topologies, as well as an approach to an HPC-aware, multi-tenant cloud management system for elastic virtualized HPC compute clusters.

Smart Cities' Data: Challenges and Opportunities for Semantic Technologies <=SEP=> How can we innovate smart systems for smart cities, to make data available homogeneously, inexpensively, and flexibly while supporting an array of applications that have yet to exist or be specified?

cocor: A Comprehensive Solution for the Statistical Comparison of Correlations <=SEP=> A valid comparison of the magnitude of two correlations requires researchers to directly contrast the correlations using an appropriate statistical test. In many popular statistics packages, however, tests for the significance of the difference between correlations are missing. To close this gap, we introduce cocor, a free software package for the R programming language. The cocor package covers a broad range of tests including the comparisons of independent and dependent correlations with either overlapping or nonoverlapping variables. The package also includes an implementation of Zou's confidence interval for all of these comparisons. The platform independent cocor package enhances the R statistical computing environment and is available for scripting. Two different graphical user interfaces-a plugin for RKWard and a web interface-make cocor a convenient and user-friendly tool.

Eye localization from thermal infrared images <=SEP=> By using the knowledge of facial structure and temperature distribution, this paper proposes an automatic eye localization method from long wave infrared thermal images both with eyeglasses and without eyeglasses. First, with the help of support vector machine classifier, three gray-projection features are defined to determine whether a subject is with eyeglasses. For subjects with eyeglasses, the locations of valleys in the projection curve are used to perform eye localization. For subjects without eyeglasses, a facial structure consisting of 15 sub-regions is proposed to extract Haar-like features. Eight classifiers are learned from the features selected by Adaboost algorithm for left and right eye, respectively. A vote strategy is employed to find the most likely eyes. To evaluate the effectiveness of our approach, experiments are performed on NVIE and Equinox databases. The eyeglass detection results on NVIE database and Equinox database are 99.36% and 95%, respectively, which demonstrate the effectiveness and robustness of our eyeglass detection method. Eye localization results of withindatabase experiments and cross-database experiments on these two databases are very comparable with the previous results in this field, verifying the effectiveness and the generalization ability of our approach. & 2013 Elsevier Ltd. All rights reserved.