title: The Esterel Synchronous Programming Language: Design, Semantics, Implementation

abstract: Abstract We present the E sterel programming language which is especially designed to program reactive systems, that is systems which maintain a permanent interaction with their environment: real-time process controllers, communication protocols, man-machine interface drivers, etc. E sterel is a deterministic concurrent programming language. It differs from classical asynchronous languages by its synchrony hypothesis: the outputs of a system are conceptually synchronous with its inputs. The synchrony hypothesis permits a high-level modular programming style simpler and more rigorous than its asynchronous counterpart. We present the imperative primitives of E esterel and the temporal manipulations they permit. We give a small programming example. We present two mathematical semantics of E esterel , which are given by conditional rewrite rules and related by a correctness theorem. The behavioral semantics defines the behavior of programs in an uneffective way as the solution of fixpoint equations. The effective execution semantics computes actions to be performed by a conceptually infinitely fast execution machine. To relate the two semantics, we solve the causality problems that are inherent in synchronous formalisms. We show how the E esterel v2 and E esterel v3 compilers efficiently translate concurrent E esterel programs into efficient equivalent sequential automata that can be implemented in conventional sequential languages. We discuss the quality of this object code and the practical adequacy of the synchrony hypothesis.


title: Hashing for Similarity Search: A Survey

abstract: Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space.


title: Depersonalisation disorder: clinical features of 204 cases

abstract: Background Depersonalisation disorder is a poorly understood and underresearched syndrome. ::: ::: Aims To carry out a large and comprehensive clinical and psychopathological survey of a series of patients who made contact with a research clinic. ::: ::: Method A total of 204 consecutive eligible referrals were included: 124 had a full psychiatric examination using items of the Present State Examination to define depersonalisation/derealisation and 80 had either a telephone interview ( n =22) or filled out a number of self-report questionnaires. Cases assessed were diagnosed according to DSM—IV criteria. ::: ::: Results The mean age of onset was 22.8 years; early onset was associated with greater severity. There was a slight male preponderance. The disorder tended to be chronic and persistent. Seventy-one per cent met DSM—IV criteria for primary depersonalisation disorder. Depersonalisation symptom scores correlated with both anxiety and depression and a past history of these disorders was commonly reported. ‘Dissociative amnesia’ was not prominent. ::: ::: Conclusions Depersonalisation disorder is a recognisable clinical entity but appears to have significant comorbidity with anxiety and depression. Research into its aetiology and treatment is warranted.


title: Eigen-gap of structure transition matrix: A new criterion for Image Quality Assessment

abstract: A new approach to Image Quality Assessment (IQA) is presented. The idea is based on the fact that two images are similar if their structural relationship within their blocks is preserved. To this end, a transition matrix is defined which exploits structural transitions between corresponding blocks of two images. The matrix contains valuable information about differences of two images, which should be transformed to a quality index. Eigen-value analysis over the transition matrix leads to a new distance measure called Eigen-gap. According to simulation results, the Eigen-gap is not only highly correlated to subjective scores but also, its performance is as good as the SSIM, a trustworthy index.


title: APPLICATIONS OF CELLULAR AUTOMATA IN COMPLEX SYSTEM STUDY

abstract: In recent years, cellular automata have been used widely to study complex systems, such as biological systems, tra-c systems, economic systems, environmental systems, engineering systems, social systems, industrial systems, and a lot of remarkable results have been gained. Due to its underlying prop- erties, cellular automata approach has become a new promising method for studying complex systems. First, this paper analyzes the relationship between cellular automata and complex systems based on their own characteristics. Then, some applications of cellular automata in complex systems' modeling, analyzing and controlling are discussed. Finally, some suggestions for future development of cellular automata approach in studying complex systems are given.


title: Agile User Experience Development in a Large Software Organization: Good Expertise but Limited Impact

abstract: While Agile methods were originally introduced for small, tightly coupled teams, leaner ways of working are becoming a practical method to run entire enterprises. As the emphasis of user experience work has inherently been on the early phases before starting the development, it also needs to be adapted to the Agile way of working. To improve the current practices in Agile user experience work, we determined the present state of a multi-continental software development organization that already had a functioning user experience team. In this paper, we describe the most prevalent issues regarding the interaction of user experience design and software development activities, and suggest improvements to fix those. Most of the observed problems were related to communication issues and to the service mode of the user experience team. The user experience team was operating between management and development organizations trying to adapt to the dissimilar practices of both the disciplines.


title: Visually weighted neighbor voting for image tag relevance learning

abstract: The presence of non-relevant tags in image folksonomies hampers the effective organization and retrieval of user-contributed images. In this paper, we propose to learn the relevance of user-supplied tags by means of visually weighted neighbor voting, a variant of the popular baseline neighbor voting algorithm proposed by Li et al. (IEEE Trans Multimedia 11(7):1310---1322, 2009). To gain insight into the effectiveness of baseline and visually weighted neighbor voting, we qualitatively analyze the difference in tag relevance when using a different number of neighbors, for both tags relevant and tags not relevant to the content of a seed image. Our qualitative analysis shows that tag relevance values computed by means of visually weighted neighbor voting are more stable and representative than tag relevance values computed by means of baseline neighbor voting. This is quantitatively confirmed through extensive experimentation with MIRFLICKR-25000, studying the variation of tag relevance values as a function of the number of neighbors used (for both tags relevant and tags not relevant with respect to the content of a seed image), as well as the influence of tag relevance learning on the effectiveness of image tag refinement, tag-based image retrieval, and image tag recommendation.


title: Learning Heuristic Search via Imitation

abstract: Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration. Limited onboard computation and real-time planning constraints impose a limit on how large this search tree can grow. Heuristics play a crucial role in such situations by guiding the search towards potentially good directions and consequently minimizing search effort. Moreover, it must infer such directions in an efficient manner using only the information uncovered by the search up until that time. However, state of the art methods do not address the problem of computing a heuristic that explicitly minimizes search effort. In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand. Unfortunately, naively training such policies leads to slow convergence and poor local minima. We present SaIL, an efficient algorithm that trains heuristic policies by imitating "clairvoyant oracles" - oracles that have full information about the world and demonstrate decisions that minimize search effort. We leverage the fact that such oracles can be efficiently computed using dynamic programming and derive performance guarantees for the learnt heuristic. We validate the approach on a spectrum of environments which show that SaIL consistently outperforms state of the art algorithms. Our approach paves the way forward for learning heuristics that demonstrate an anytime nature - finding feasible solutions quickly and incrementally refining it over time.


title: ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition

abstract: In recent years, thanks to the efforts of individual scientists and research consortiums, a huge amount of chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) experimental data have been accumulated. Instead of investigating them independently, several recent studies have convincingly demonstrated that a wealth of scientific insights can be gained by integrative analysis of these ChIP-seq data. However, when used for the purpose of integrative analysis, a serious drawback of current ChIP-seq technique is that it is still expensive and time-consuming to generate ChIP-seq datasets of high standard. Most researchers are therefore unable to obtain complete ChIP-seq data for several TFs in a wide variety of cell lines, which considerably limits the understanding of transcriptional regulation pattern. In this paper, we propose a novel method called ChIP-PIT to overcome the aforementioned limitation. In ChIP-PIT, ChIP-seq data corresponding to a diverse collection of cell types, TFs and genes are fused together using the three-mode pair-wise interaction tensor (PIT) model, and the prediction of unperformed ChIP-seq experimental results is formulated as a tensor completion problem. Computationally, we propose efficient first-order method based on extensions of coordinate descent method to learn the optimal solution of ChIP-PIT, which makes it particularly suitable for the analysis of massive scale ChIP-seq data. Experimental evaluation the ENCODE data illustrate the usefulness of the proposed model.


title: Sentiment analysis and text summarization of online reviews: A survey

abstract: Sentiment analysis and text summarization has evoke the interest of many scientists and researchers in last few years, since the textual data has become useful for many real world applications and problems. Sentiment analysis is a machine learning approach in which machine learns and analyze the sentiments, emotions etc about some text data like reviews about movies or products. These reviews are increasing day by day, due to which summarization of reviews comes in role where summarized form of text in needed, which provides useful information from the large number of reviews. It is very difficult for a human being to extract useful data or summarize it from the very large document. In Text summarization, importance of sentences is decided based on linguistic features of sentences. This paper provides the comprehensive overview of recent and past research on sentiment analysis and text summarization and provides excellent research queries and approaches for future aspects.


title: Chemically deposited electrochromic cuprous oxide films for solar light modulation

abstract: Abstract Thin cuprous oxide electrochromic films on the transparent conductive electrodes were prepared by chemical electroless method. The films cycled in K + -based electrolyte revealed typical red-ox peaks with higher intensity compared to those in the Li + and the Na + -based electrolytes. The durability of the cuprous oxide due to cycling into LiClO 4 was about 60 cycles. The thermal treatment of the films invoked decrease in red-ox peak intensity, and thus deterioration in the electrochromic properties. The response time of the coloration and bleaching to an abrupt voltage change from −4.5 to +4.5 V and reverse was found to be in the range of about 10 s. The maximum light intensity modulation ability of the films, as the AM1.5 spectrum is taken for an input, was calculated to be about 65%.


title: A comprehensive study of failure mode in IGBT applications due to freewheeling diode snappy recovery

abstract: In modern power converter circuits, freewheeling diode snappy recovery phenomenon (voltage snap-off) can ultimately destroy the insulated gate bipolar transistor (IGBT) during turn-on and cause a subsequent circuit failure. In this paper, snappy recovery of modern fast power diodes is investigated with the aid of semiconductor device simulation tools, and experimental test results. The work presented here confirms that the reverse recovery process can by expressed by means of diode capacitive effects which influence the reverse recovery characteristics and determine if the diode exhibits soft or snappy recovery behavior. From the experimental and simulation results, a clear view is obtained for the physical process, causes and device/circuit conditions at which snap-off occurs. The analysis is based on the effect of both device and external operating parameters on the excess minority carrier distributions before and during the reverse recovery transient period.


title: Intrusion detection system using data mining a review

abstract: Everyday huge amount of information are transferred from one network to another, the information may be exposed to attacks. The information and information system should be protected from unauthorized users. To provide and maintain the Confidentiality and Integrity of the information is a very tedious job so Intrusion Detection plays a very important role. Although various methods are used to protect the information, loopholes exist. Data mining methods is used to analyze different attack patterns in the network. Various Classification, Clustering and Classification via Clustering (CvC) algorithms are reviewed. From the review it has been concluded that CvC would be the best suitable for Intrusion detection. In Intrusion Detection field the Cyber Security and Technology Group Contributed significantly by providing KDDcup 99 dataset and to motivate the researchers by eliminating security and privacy concern. NSL_KDD, GureKDD and Kyoto 2006+ dataset is discussed with their advantages and disadvantages.


Maximizing Resection of Diffused Low-Grade Glioma Functional Outcome


title: Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

abstract: This paper develops an unsupervised discriminant projection (UDP) technique for dimensionality reduction of high-dimensional data in small sample size cases. UDP can be seen as a linear approximation of a multimanifolds-based learning framework which takes into account both the local and nonlocal quantities. UDP characterizes the local scatter as well as the nonlocal scatter, seeking to find a projection that simultaneously maximizes the nonlocal scatter and minimizes the local scatter. This characteristic makes UDP more intuitive and more powerful than the most up-to-date method, Locality Preserving Projection (LPP), which considers only the local scatter for clustering or classification tasks. The proposed method is applied to face and palm biometrics and is examined using the Yale, FERET, and AR face image databases and the PolyU palmprint database. The experimental results show that UDP consistently outperforms LPP and PCA and outperforms LDA when the training sample size per class is small. This demonstrates that UDP is a good choice for real-world biometrics applications.


title: Impact of Node Mobility on MANET Routing Protocols Models

abstract: A Mobile Ad-Hoc Network (MANET) is a self- configuring network of mobile nodes connected by wire- less links to form an arbitrary topology without the use of existing infrastructure. In this paper, we have studied the effects of various mobility models on the performance of two routing protocols Dynamic Source Routing (DSR- Reactive Protocol) and Destination-Sequenced Distance- Vector (DSDV-Proactive Protocol). For experiment pur- poses, we have considered four mobility scenarios: Ran- dom Waypoint, Group Mobility, Freeway and Manhattan models. These four Mobility Models are selected to rep- resent possibility of practical application in future. Per- formance comparison has also been conducted across varying node densities and number of hops. Experiment results illustrate that performance of the routing protocol varies across different mobility models, node densities and length of data paths.


title: Exploiting approximate computing for deep learning acceleration

abstract: Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of techniques to address challenging artificial intelligence (AI) problems. Applications in domains such as image/video processing, natural language processing, speech synthesis and recognition, genomics and many others have embraced deep learning as the foundational technique. DNNs achieve superior accuracy for these applications using very large models which require 100s of MBs of data storage, ExaOps of computation and high bandwidth for data movement. Despite advances in computing systems, training state-of-the-art DNNs on large datasets takes several days/weeks, directly limiting the pace of innovation and adoption. In this paper, we discuss how these challenges can be addressed via approximate computing. Based on our earlier studies demonstrating that DNNs are resilient to numerical errors from approximate computing, we present techniques to reduce communication overhead of distributed deep learning training via adaptive residual gradient compression (AdaComp), and computation cost for deep learning inference via Prameterized clipping ACTivation (PACT) based network quantization. Experimental evaluation demonstrates order of magnitude savings in communication overhead for training and computational cost for inference while not compromising application accuracy.


title: Environmental impact assessment based on D numbers

abstract: Environmental impact assessment (EIA) is a complex problem influenced by many aspects, such as environmental, social, economic, etc. Due to the involvement of human judgment, various uncertainties are introduced in the EIA process. One critical issue of EIA is the representation and handling of uncertain information. Many different theories are available to deal with uncertainty, however, deficiencies exist in these theories. In this paper, based on a more effective representation of uncertainty, called D numbers, a new method is proposed for the EIA problem. In the proposed method, the assessment results of environmental impacts are expressed and modeled by D numbers. An illustrative case is provided to demonstrate the effectiveness of the proposed method.


title: Efficient Non-Maximum Suppression

abstract: In this work we scrutinize a low level computer vision task - non-maximum suppression (NMS) - which is a crucial preprocessing step in many computer vision applications. Especially in real time scenarios, efficient algorithms for such preprocessing algorithms, which operate on the full image resolution, are important. In the case of NMS, it seems that merely the straightforward implementation or slight improvements are known. We show that these are far from being optimal, and derive several algorithms ranging from easy-to-implement to highly-efficient.


title: A study of DRAM failures in the field

abstract: Most modern computer systems use dynamic random access memory (DRAM) as a main memory store. Recent publications have confirmed that DRAM errors are a common source of failures in the field. Therefore, further attention to the faults experienced by DRAM sub-systems is warranted. In this paper, we present a study of 11 months of DRAM errors in a large high-performance computing cluster. Our goal is to understand the failure modes, rates, and fault types experienced by DRAM in production settings. We identify several unique DRAM failure modes, including single-bit, multi-bit, and multi-chip failures. We also provide a deterministic bound on the rate of transient faults in the DRAM array, by exploiting the presence of a hardware scrubber on our nodes. We draw several conclusions from our study. First, DRAM failures are dominated by permanent, rather than transient, faults, although not to the extent found by previous publications. Second, DRAMs are susceptible to large multi-bit failures, such as failures that affect an entire DRAM row or column, indicating faults in shared internal circuitry. Third, we identify a DRAM failure mode that disrupts access to other DRAM devices that share the same board-level circuitry. Finally, we find that chipkill error-correcting codes (ECC) are extremely effective, reducing the node failure rate from uncorrected DRAM errors by 42x compared to single-error correct/double-error detect (SEC-DED) ECC.


Clinical Management of Marasmus Protocol (Case Study)


title: Structure and dynamics of information pathways in online media

abstract: Diffusion of information, spread of rumors and infectious diseases are all instances of stochastic processes that occur over the edges of an underlying network. Many times networks over which contagions spread are unobserved, and such networks are often dynamic and change over time. In this paper, we investigate the problem of inferring dynamic networks based on information diffusion data. We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network. The task then is to infer the edges and the dynamics of the underlying network. We develop an on-line algorithm that relies on stochastic convex optimization to efficiently solve the dynamic network inference problem. We apply our algorithm to information diffusion among 3.3 million mainstream media and blog sites and experiment with more than 179 million different pieces of information spreading over the network in a one year period. We study the evolution of information pathways in the online media space and find interesting insights. Information pathways for general recurrent topics are more stable across time than for on-going news events. Clusters of news media sites and blogs often emerge and vanish in matter of days for on-going news events. Major social movements and events involving civil population, such as the Libyan'{}s civil war or Syria'{}s uprise, lead to an increased amount of information pathways among blogs as well as in the overall increase in the network centrality of blogs and social media sites.


title: Lazy graph processing in Haskell

abstract: This paper presents a Haskell library for graph processing: DeltaGraph. One unique feature of this system is that intentions to perform graph updates can be memoized in-graph in a decentralized fashion, and the propagation of these intentions within the graph can be decoupled from the realization of the updates. As a result, DeltaGraph can respond to updates in constant time and work elegantly with parallelism support. We build a Twitter-like application on top of DeltaGraph to demonstrate its effectiveness and explore parallelism and opportunistic computing optimizations.


title: Space Vector PWM scheme without sector identification for an open-end winding induction motor based 3-level inverter

abstract: In this paper, a Space Vector Pulse Width Modulation (SVPWM) scheme for open-end winding induction motor based, 3 — level inverter is presented. 3-level inversion is achieved with dual 2-level inverters feeding an open-end winding induction motor drive. The proposed PWM signal generation does not use any sector identification. It does not use look- up tables for generating the switching vectors for the two inverters. The individual inverters are directly driven by the switching vectors without using any translation circuitry. The drive is implemented and tested with a 2HP open-end winding induction motor drive and the experimental results are presented.


title: Another Look at Forecast-Accuracy Metrics for Intermittent Demand

abstract: Some traditional measurements of forecast accuracy are unsuitable for intermittent demand data because they can give infinite or undefined values. Rob Hyndman summarizes these forecast accuracy metrics and explains their potential failings. He also introduces a new metric-the mean absolute scaled error (MASE)-which is more appropriate for intermittent-demand data. More generally, he believes that the MASE should become the standard metric for comparing forecast accuracy across multiple time series. Copyright International Institute of Forecasters, 2006


title: Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature

abstract: This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpus. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems.


title: Neural network gradient-based learning of black-box function interfaces

abstract: Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training. In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions. We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions. We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process. At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function. Using this "Estimate and Replace" paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels. We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.


title: F.F.: Generative Portrait Sketching

abstract: We consider the problem of generating art with a computer system, as it relates to the understanding of a drawing style. Our system in its present development is able to sketch faces automatically, starting from a picture, typically a photographic snapshot of a scene with humans. Once a digital image is considered, the system automatically finds where some of the faces (or face-like patterns) are and isolate these. Each face image pattern is then used to produce a stylised portrait. The style which the system aims at using is derived from the one the first author has developed over the years in his artistic practice. The goal is for the final system to mimic the process developed by the artist, rather than aiming at results exactly reproducing his way of drawing a portrait. Nevertheless, the produced sketches are in the style of the artist. Various steps in the process of producing a sketch by the artist were isolated, including: image segmentation, filtering, shape selection and depiction, filling and shading. The implementation of these steps relies on an understanding of human visual perception, of the artist’s work process, and of advances made in computer vision.


title: Decoupled Neural Interfaces using Synthetic Gradients

abstract: Training directed neural networks typically requires forward-propagating data through a computation graph, followed by backpropagating error signal, to produce weight updates. All layers, or more generally, modules, of the network are therefore locked, in the sense that they must wait for the remainder of the network to execute forwards and propagate error backwards before they can be updated. In this work we break this constraint by decoupling modules by introducing a model of the future computation of the network graph. These models predict what the result of the modelled subgraph will produce using only local information. In particular we focus on modelling error gradients: by using the modelled synthetic gradient in place of true backpropagated error gradients we decouple subgraphs, and can update them independently and asynchronously i.e. we realise decoupled neural interfaces. We show results for feed-forward models, where every layer is trained asynchronously, recurrent neural networks (RNNs) where predicting one's future gradient extends the time over which the RNN can effectively model, and also a hierarchical RNN system with ticking at different timescales. Finally, we demonstrate that in addition to predicting gradients, the same framework can be used to predict inputs, resulting in models which are decoupled in both the forward and backwards pass -- amounting to independent networks which co-learn such that they can be composed into a single functioning corporation.


INFORMATION, COMMUNICATION TECHNOLOGY AND WOMEN'S PRODUCTIVITY AT COASTAL AREA


title: The SDN Shuffle: Creating a Moving-Target Defense using Host-based Software-Defined Networking

abstract: Moving target systems can help defenders limit the utility of reconnaissance for adversaries, hindering the effectiveness of attacks. While moving target systems are a topic of robust research, we find that prior work in network-based moving target defenses has limitations in either scalability or the ability to protect public servers accessible to unmodified clients. In this work, we present a new moving target defense using software-defined networking (SDN) that can service unmodified clients while avoiding scalability limitations. We then evaluate this approach according to seven moving-target properties and evaluate its performance. We find that the approach achieves its security goals while introducing low overheads.


title: Architecting phase change memory as a scalable dram alternative

abstract: Memory scaling is in jeopardy as charge storage and sensing mechanisms become less reliable for prevalent memory technologies, such as DRAM. In contrast, phase change memory (PCM) storage relies on scalable current and thermal mechanisms. To exploit PCM's scalability as a DRAM alternative, PCM must be architected to address relatively long latencies, high energy writes, and finite endurance. We propose, crafted from a fundamental understanding of PCM technology parameters, area-neutral architectural enhancements that address these limitations and make PCM competitive with DRAM. A baseline PCM system is 1.6x slower and requires 2.2x more energy than a DRAM system. Buffer reorganizations reduce this delay and energy gap to 1.2x and 1.0x, using narrow rows to mitigate write energy and multiple rows to improve locality and write coalescing. Partial writes enhance memory endurance, providing 5.6 years of lifetime. Process scaling will further reduce PCM energy costs and improve endurance.


title: Multiple relationship types in online communities and social networks

abstract: Online social networking is increasingly popular and is a feature of many websites. Providing multiple types of relationship, such as friend, fan or colleague, can enhance the significance of the networks. We present an empirical study of an online political forum where users engage in content creation, voting, and discussion. The users also make explicit connections via three relationship types, forming three distinct networks. We establish a strong correlation between network participation and site activity and show that users stayed faithful to the relationship semantics, in aggregate. Moreover, we demonstrate significant structural differences among the networks, indicating different uses for each. Social networking features may help spread the word about new content, but we show that the networks played a surprisingly moderate role in this respect. Usage and social networking patterns were typical of many web communities, suggesting that multiple relationship types could be successfully featured in other such communities.


title: Probiotics supplementation for athletes – Clinical and physiological effects

abstract: Probiotic supplementation has traditionally focused on gut health. However, in recent years, the clinical applications of probiotics have broadened to allergic, metabolic, inflammatory, gastrointestinal and respiratory conditions. Gastrointestinal health is important for regulating adaptation to exercise and physical activity. Symptoms such as nausea, bloating, cramping, pain, diarrhoea and bleeding occur in some athletes, particularly during prolonged exhaustive events. Several studies conducted since 2006 examining probiotic supplementation in athletes or highly active individuals indicate modest clinical benefits in terms of reduced frequency, severity and/or duration of respiratory and gastrointestinal illness. The likely mechanisms of action for probiotics include direct interaction with the gut microbiota, interaction with the mucosal immune system and immune signalling to a variety of organs and systems. Practical issues to consider include medical and dietary screening of athletes, sourcing of recommended probiotics and formulations, dose–response requirements for different probiotic strains, storage, handling and transport of supplements and timing of supplementation in relation to travel and competition.


title: Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN

abstract: Cloud computing affords lot of resources and computing facilities through Internet. Cloud systems attract many users with its desirable features. In spite of them, Cloud systems may experience severe security issues. Thus, it is essential to create an Intrusion Detection System (IDS) to detect both insider and outsider attacks with high detection accuracy in cloud environment. This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network (FCM-ANN) to improve the accuracy of the detection system. The proposed system is implemented and compared with Naive Bayes classifier and Classic ANN algorithm. The DARPA's KDD cup dataset 1999 is used for experiments. Based on extensive theoretical and performance analysis, it is evident that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate even for low frequent attacks thereby outperforming Naive Bayes classifier and Classic ANN.


title: Fraud detection and frequent pattern matching in insurance claims using data mining techniques

abstract: Fraudulent insurance claims increase the burden on society. Frauds in health care systems have not only led to additional expenses but also degrade the quality and care which should be provided to patients. Insurance fraud detection is quite subjective in nature and is fettered with societal need. This empirical study aims to identify and gauge the frauds in health insurance data. The contribution of this insurance claim fraud detection experimental study untangle the fraud identification frequent patterns underlying in the insurance claim data using rule based pattern mining. This experiment is an effort to assess the fraudulent patterns in the data on the basis of two criteria-period based claim anomalies and disease based anomalies. Rule based mining results according to both criteria are analysed. Statistical Decision rules and k-means clustering are applied on Period based claim anomalies outliers detection and association rule based mining with Gaussian distribution is applied on disease based anomalies outlier detection. These outliers depict fraud insurance claims in the data. The proposed approach has been evaluated on real-world dataset of a health insurance organization and results show that our proposed approach is efficient in detecting fraud insurance claim using rule based mining.


title: Pulling levers focused deterrence strategies and the prevention of gun homicide

abstract: A number of jurisdictions have been experimenting with new problem-oriented policing frameworks to understand and respond to gun violence among gang-involved offenders. These interventions are based on the "pulling levers" deterrence strategy that focuses criminal justice and social service attention on a small number of chronically offending gang members responsible for the bulk of urban gun violence problems. Unfortunately, there is relatively little rigorous evaluation evidence on the effectiveness of these approaches to violence prevention. In Stockton, California, an interagency task force implemented a pulling levers strategy to prevent gun homicide among gang-involved offenders. A U.S. Department of Justice-sponsored impact evaluation suggests that the pulling levers strategy was associated with a statistically significant decrease in the monthly number of gun homicide incidents in Stockton. A comparative analysis of gun homicide trends in Stockton relative to other midsize California cities also supports a unique program effect associated with the pulling levers intervention.


title: Pupil detection and tracking using multiple light sources

abstract: Abstract We present a fast, robust, and low cost pupil detection technique that uses two near-infrared time multiplexed light sources synchronized with the camera frame rate. The two light sources generate bright and dark pupil images, which are used for pupil segmentation. To reduce artifacts caused mostly by head motion, a larger temporal support is used. This method can be applied to detect and track several pupils (or several people). Experimental results from a real-time implementation of the system show that this technique is very robust, and able to detect pupils using wide field of view low cost cameras under different illumination conditions, even for people with glasses, from considerable long distances.


title: Victims of cybercrime in Europe: a review of victim surveys

abstract: Review the evidence provided by victim surveys in order to provide a rough estimate of the personal crime prevalence of the main types of cybercrime. We performed a search in databases, searched online, and contacted several Offices for National Statistics in Europe and selected surveys that provided information about individual victims of crime which were representative for a general population. Six types of cybercrime have been distinguished, namely online shopping fraud, online fraud banking/payment, other cyber fraud (such as advanced fee fraud), cyber threats/harassment, malware, and hacking. For every survey the questions on cybercrime are presented and the crime prevalence estimates are compared. Nine surveys were included. Annual crime prevalence rates ranged from 1 to 3% for online shopping fraud, from less than 1 to 2% for online banking/payment fraud. Less than 1% of the population is a victim of other types of fraud and a maximum of 3% of the population experiences some sort of online bullying such as stalking (1%) or threatening (1%). 1–6% is a victim of hacking. The estimates for being a victim of malware range from 2 to 15%. For all offences it cannot be estimated how much of the differences are due to variation in methods and questioning between the studies or real differences between countries or change over time. As yet there have been very few well performed randomised sampled studies on cybercrime amongst the general population. Cybercrime prevalence (and its trend) can only be well measured if the questions are frequently updated and adequately address new aspects of cybercrime. To adequately monitor cybercrime in the future it is advisable to develop some fairly abstract main categories that are of durable validity, whilst allowing for up-to-date illustrations. Furthermore, ideally the questioning in the ongoing surveys in the different countries should be standardised and there should be a uniform categorisation of the different cyber offences. A screening question in order to permit more accurate dating is essential to reduce telescoping bias. Surveys should ask about the impact on or damage to the victims.


Structured Aspect Extraction


title: A framework for detection and measurement of phishing attacks

abstract: Phishing is form of identity theft that combines social engineering techniques and sophisticated attack vectors to harvest financial information from unsuspecting consumers. Often a phisher tries to lure her victim into clicking a URL pointing to a rogue page. In this paper, we focus on studying the structure of URLs employed in various phishing attacks. We find that it is often possible to tell whether or not a URL belongs to a phishing attack without requiring any knowledge of the corresponding page data. We describe several features that can be used to distinguish a phishing URL from a benign one. These features are used to model a logistic regression filter that is efficient and has a high accuracy. We use this filter to perform thorough measurements on several million URLs and quantify the prevalence of phishing on the Internet today


title: Group CRM: a new telecom CRM framework from social network perspective

abstract: The structure of customer communication network provides us a natural way to understand customers' relationships. Traditional customer relationship management (CRM) methods focus on various customer profitability models, and they are short of ways to understand the social interactions. Graph mining and social network analysis provide ways to understand the relationships between customers, and there are already a few applications in CRM using these methods. To transform the traditional CRM methods from individuals to social groups, we propose a novel technical framework (GCRM) to manage the social groups in massive telecom call graphs. Our framework is based on a series of newly emerged methods for social network analysis, such as group detecting, group evolution tracking and group life-cycle modeling in telecom applications. We analyze the relationships between social groups and propose a method to find potential customers in these groups. To evaluate GCRM, we present a comprehensive study to explore the group evolutions in real-world massive telecom call graphs. Empirical results show that by taking this framework, analysts can gain deeper insights into the communication patterns of social groups and their evolutionary patterns which makes the management of these social groups much easier in real-world telecom applications.


title: Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation

abstract: In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e.g. from short-axis MR images of the left-ventricle. In this work we propose a recurrent fully-convolutional network (RFCN) that learns image representations from the full stack of 2D slices and has the ability to leverage inter-slice spatial dependences through internal memory units. RFCN combines anatomical detection and segmentation into a single architecture that is trained end-to-end thus significantly reducing computational time, simplifying the segmentation pipeline, and potentially enabling real-time applications. We report on an investigation of RFCN using two datasets, including the publicly available MICCAI 2009 Challenge dataset. Comparisons have been carried out between fully convolutional networks and deep restricted Boltzmann machines, including a recurrent version that leverages inter-slice spatial correlation. Our studies suggest that RFCN produces state-of-the-art results and can substantially improve the delineation of contours near the apex of the heart.


title: Floating-Point DSP Block Architecture for FPGAs

abstract: This work describes the architecture of a new FPGA DSP block supporting both fixed and floating point arithmetic. Each DSP block can be configured to provide one single precision IEEE-754 floating multiplier and one IEEE-754 floating point adder, or when configured in fixed point mode, the block is completely backwards compatible with current FPGA DSP blocks. The DSP block operating frequency is similar in both modes, in the region of 500MHz, offering up to 2 GMACs fixed point and 1 GFLOPs performance per block. In floating point mode, support for multi-block vector modes are provided, where multiple blocks can be seamlessly assembled into any size real or complex dot products. By efficient reuse of the fixed point arithmetic modules, as well as the fixed point routing, the floating point features have only minimal power and area impact. We show how these blocks are implemented in a modern Arria 10 FPGA family, offering over 1 TFLOPs using only embedded structures, and how scaling to multiple TFLOPs densities is possible for planned devices.


title: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models

abstract: Summary: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Γ yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets ≥4000 taxa it also runs 2--3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25 057 (1463 bp) and 2182 (51 089 bp) taxa, respectively. ::: ::: Availability: icwww.epfl.ch/~stamatak ::: ::: Contact: Alexandros.Stamatakis@epfl.ch ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.


title: Mergeable summaries

abstract: We study the mergeability of data summaries. Informally speaking, mergeability requires that, given two summaries on two data sets, there is a way to merge the two summaries into a single summary on the union of the two data sets, while preserving the error and size guarantees. This property means that the summaries can be merged in a way like other algebraic operators such as sum and max, which is especially useful for computing summaries on massive distributed data. Several data summaries are trivially mergeable by construction, most notably all the sketches that are linear functions of the data sets. But some other fundamental ones like those for heavy hitters and quantiles, are not (known to be) mergeable. In this paper, we demonstrate that these summaries are indeed mergeable or can be made mergeable after appropriate modifications. Specifically, we show that for e-approximate heavy hitters, there is a deterministic mergeable summary of size O(1/e) for e-approximate quantiles, there is a deterministic summary of size O(1 over e log(en))that has a restricted form of mergeability, and a randomized one of size O(1 over e log 3/21 over e) with full mergeability. We also extend our results to geometric summaries such as e-approximations and ekernels. We also achieve two results of independent interest: (1) we provide the best known randomized streaming bound for e-approximate quantiles that depends only on e, of size O(1 over e log 3/21 over e, and (2) we demonstrate that the MG and the SpaceSaving summaries for heavy hitters are isomorphic.


title: Research on the Performances and Parameters of Interior PMSM Used for Electric Vehicles

abstract: As a kind of traction device, interior permanent-magnet synchronous machines (IPMSMs) are widely used in modern electric vehicles. This paper performs a design and comparative study of IPMSMs with different rotor topologies (spoke-type PMs, tangential-type PMs, U-shape PMs, and V-shape PMs). The research results indicate that the IPMSM with V-shape PMs is more satisfying with comprehensive consideration. Furthermore, the IPMSM with V-shape PMs is investigated in detail. The influences of geometrical parameters (magnetic bridge and angle between the two V-shape PMs under each pole, etc.) on the performances of V-shape motor are evaluated based on finite-element method (FEM). For accurate research, the effects of saturation, cross-magnetization, and the change in PM flux linkage on d - and q -axis inductances are considered. The back-electromotive force (EMF), flux leakage coefficient, average torque, torque ripple, cogging torque, power per unit volume, power factor, and flux-weakening ability are investigated, respectively. The experimental results verify the validity and accuracy of the process presented in this paper.


title: Unitary Evolution Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) are notoriously difficult to train. When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies. To circumvent this problem, we propose a new architecture that learns a unitary weight matrix, with eigenvalues of absolute value exactly 1. The challenge we address is that of parametrizing unitary matrices in a way that does not require expensive computations (such as eigendecomposition) after each weight update. We construct an expressive unitary weight matrix by composing several structured matrices that act as building blocks with parameters to be learned. Optimization with this parameterization becomes feasible only when considering hidden states in the complex domain. We demonstrate the potential of this architecture by achieving state of the art results in several hard tasks involving very long-term dependencies.


title: - BasedMusic Information Retrieval : Current Directions and Future Challenges Current retrieval systems can handle tens-of-thousands of music tracks but new systems need to aim at huge online music collections that contain tens-of-millions of tracks

abstract: The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web. Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content. Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engi- neering, and computer science engaged in truly interdisciplin- ary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.


title: Shadow walking: An unencumbered locomotion technique for systems with under-floor projection

abstract: When viewed from below, a user's feet cast shadows onto the floor screen of an under-floor projection system, such as a six-sided CAVE. Tracking those shadows with a camera provides enough information for calculating a user's ground-plane location, foot orientation, and footstep events. We present Shadow Walking, an unencumbered locomotion technique that uses shadow tracking to sense a user's walking direction and step speed. Shadow Walking affords virtual locomotion by detecting if a user is walking in place. In addition, Shadow Walking supports a sidestep gesture, similar to the iPhone's pinch gesture. In this paper, we describe how we implemented Shadow Walking and present a preliminary assessment of our new locomotion technique. We have found Shadow Walking provides advantages of being unencumbered, inexpensive, and easy to implement compared to other walking-in-place approaches. It also has potential for extended gestures and multi-user locomotion.


Spatial Predictive Control for Agile Semi-Autonomous Ground Vehicles


title: Reducing network overhead of IoTDTLS protocol employing ChaCha20 and Poly1305

abstract: IP based communication protocols will play a key role enabling the pervasive connectivity among the devices within IoT network. Such a resource-restricted platform specific protocol is Datagram Transport Layer Security (DTLS) protocol. Applicability of DTLS to constrained environments is still a controversial issue because of its network overhead, memory management, and latency. This work presents a security scheme with two-way verification for wireless sensor nodes employing established standards with rather insignificant data transfer and handshaking latency. The recommended security scheme is built with a new cipher suite incorporated with an authentication algorithm “Poly1305” and an encryption algorithm “ChaCha20”.


title: USING WEB 2.0 TO SHARE KNOWLEDGE OF CONSTRUCTION SAFETY: THE FABLE OF ECONOMIC ANIMALS

abstract: Gone are the days when knowledge sharing depended solely on face-to-face meetings. With the help of Web 2.0, rapid and dynamic cross-border knowledge sharing among construction safety researchers has become possible. Nevertheless, empirical observation has shown that researchers are falling behind the curve. Generally speaking, there are two major types of motivations: intrinsic and extrinsic. Introducing extrinsic motivators on top of intrinsic motivators can produce better results than relying solely on intrinsic motivation.


title: Construction of the Literature Graph in Semantic Scholar

abstract: We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting literature graph consists of more than 280M nodes, representing papers, authors, entities and various interactions between them (e.g., authorships, citations, entity mentions). We reduce literature graph construction into familiar NLP tasks (e.g., entity extraction and linking), point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task. The methods described in this paper are used to enable semantic features in www.semanticscholar.org


title: An eigenvalue-based similarity measure and its application in defect detection

abstract: In this paper, we propose an eigenvalue-based similarity measure between two gray-level images and, in particular, aim at the application in defect detection. The pair-wise gray levels at coincident pixel locations in two compared images are used as the coordinates to plot the correspondence map. If two compared images are identical, the plot in the correspondence map is a diagonal straight line. Otherwise, it results in a non-linear shape in the correspondence map. The smaller eigenvalue of the covariance matrix of the data points in the correspondence map is used as the similarity measure. It will be approximately zero for two resembled images, and distinctly large for dissimilar images. Experimental results from a number of assembled PCBs (printed circuit boards) have shown the effectiveness of the proposed similarity measure for detecting local defects in complicated images.


title: Examination of a New Defense Mechanism:

abstract: Past experiences show us that password breach is still one of the main methods of attackers to obtain personal or sensitive user data. Basically, assuming they have access to list of hashed passwords, they apply guessing attacks, i.e., attempt to guess a password by trying a large number of possibilities. We certainly need to change our way of thinking and use a novel and creative approach in order to protect our passwords. In fact, there are already novel attempts to provide password protection. The Honeywords system of Juels and Rivest is one of them which provides a detection mechanism for password breaches. Roughly speaking, they propose a method for password-based authentication systems where fake passwords, i.e., “honeywords” are added into a password file, in order to detect impersonation. Their solution includes an auxiliary secure server called “honeychecker” which can distinguish a user’s real password among her honeywords and immediately sets off an alarm whenever a honeyword is used. However, they also pointed out that their system needs to be improved in various ways by highlighting some open problems. In this paper, after revisiting the security of their proposal, we specifically focus on and aim to solve a highlighted open problem, i.e., active attacks where the adversary modifies the code running on either the login server or the honeychecker.


title: An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis

abstract: In this work, we propose an effective neural network architecture GABi-LSTM to address fine-grained financial target-dependent sentiment analysis from financial microblogs and news. We first adopt a gated mechanism to adaptively integrate character level and word level embeddings for word representation, then present an attention-based Bi-LSTM component to embed target-dependent information into sentence representation, and finally use a linear regression layer to predict sentiment score with respect to target company. Comparative experiments on financial benchmark datasets show that our proposed GABi-LSTM model outperforms baselines and previous top systems by a large margin and achieves the state-of-the-art performance.


title: [External plastic surgery of the pre-ostial valve in sapheno-femoral junction insufficiency. Experimental studies and clinical cases].

abstract: External plasty of the pre-ostial valve is a surgical technique which has the aim of correcting reflux resulting from incompetence of the sapheno-femoral valve and at the time retaining the anatomical structure of the region. The artificial stenosis produced by external valvuloplasty of the pre-terminal valve complex has been studied in physical experiments, the first results of which already appear to be significant. The authors have operated upon 18 patients, with follow-up too short for the presentation of final results, but already sufficient to permit discussion of findings after a short period of time.


title: Fusion of depth maps with multiple scales

abstract: Multi-view stereo systems can produce depth maps with large variations in viewing parameters, yielding vastly different sampling rates of the observed surface. We present a new method for surface reconstruction by integrating a set of registered depth maps with dramatically varying sampling rate. The method is based on the construction of a hierarchical signed distance field represented in an incomplete primal octree by incrementally adding triangulated depth maps. Due to the adaptive data structure, our algorithm is able to handle depth maps with varying scale and to consistently represent coarse, low-resolution regions as well as small details contained in high-resolution depth maps. A final surface mesh is extracted from the distance field by construction of a tetrahedral complex from the scattered signed distance values and applying the Marching Tetra-hedra algorithm on the partition. The output is an adaptive triangle mesh that seamlessly connects coarse and highly detailed regions while avoiding filling areas without suitable input data.


title: Treatment Algorithm of Complications after Filler Injection: Based on Wound Healing Process

abstract: Soft tissue filler injection has been a very common procedure worldwide since filler injection was first introduced for soft tissue augmentation. Currently, filler is used in various medical fields with satisfactory results, but the number of complications is increasing due to the increased use of filler. The complications after filler injection can occur at any time after the procedure, early and delayed, and they range from minor to severe. In this review, based on our experience and previously published other articles, we suggest a treatment algorithm to help wound healing and tissue regeneration and generate good aesthetic results with early treatment in response to the side effects of filler. Familiarity with the treatment of these rare complications is essential for achieving the best possible outcome. ::: ::: Graphical Abstract ::: ::: ::: ::: ::: ::: ::: Keywords: Soft Tissue Filler, Injectable, Stem Cell, Filler Complications, Treatment Algorithm ::: ::: ::: INTRODUCTION ::: Various managements for restoring volume to the body or rejuvenation of facial wrinkles with injectable materials have been described over the last 100 yr. Autogenous fat graft was firstly performed for the correction of facial soft tissue defect at 1893 (1). In the beginning of 20th century, liquid paraffin was injected for the facial contouring in Vienna (1). In spite of substantial complications and public backlash, alternative injectable substances were explored. ::: ::: Liquid silicone was used to improve body contouring in Switzerland and Japan during the 1940s and facial injections of liquid silicone was generalized and common procedure for rejuvenation in the 1960s, particularly with the microdroplet injection technique (2). ::: ::: There was a great development of injectable fillers in 1981 with initial U.S. Food and Drug Administration (FDA)'s approval of bovine collagen fillers (Zyderm, Zyplast; Inamed Aesthetics, Santa Barbara, California) for cosmetic use (1). After that, bovine collagen became the standard for injectable materials. The new era of injection with synthetic selective bioactive fillers began in 2003, when a Restylane (Medicis, Scottsdale, Arizona) became FDA approved product. Since then, the FDA has approved a variety of fillers for cosmetic use, mostly in the European market. This has led to a boom in the use of injectable fillers (1). Many filler types have been developed, resulting in expectations for reduced complications. However, variable complications have been reported.


title: Deep Component Analysis via Alternating Direction Neural Networks

abstract: Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation learning with component analysis is associated with rich intuition and theory, but smaller capacity often limits its usefulness. To bridge this gap, we introduce Deep Component Analysis (DeepCA), an expressive multilayer model formulation that enforces hierarchical structure through constraints on latent variables in each layer. For inference, we propose a differentiable optimization algorithm implemented using recurrent Alternating Direction Neural Networks (ADNNs) that enable parameter learning using standard backpropagation. By interpreting feed-forward networks as single-iteration approximations of inference in our model, we provide both a novel theoretical perspective for understanding them and a practical technique for constraining predictions with prior knowledge. Experimentally, we demonstrate performance improvements on a variety of tasks, including single-image depth prediction with sparse output constraints.


abstract: Semantic concept hierarchy is still under-explored for semantic segmentation due to the inefficiency and complicated optimization of incorporating structural inference into dense prediction. This lack of modeling semantic correlations also makes prior works must tune highly-specified models for each task due to the label discrepancy across datasets. It severely limits the generalization capability of segmentation models for open set concept vocabulary and annotation utilization. In this paper, we propose a Dynamic-Structured Semantic Propagation Network (DSSPN) that builds a semantic neuron graph by explicitly incorporating the semantic concept hierarchy into network construction. Each neuron represents the instantiated module for recognizing a specific type of entity such as a super-class (e.g. food) or a specific concept (e.g. pizza). During training, DSSPN performs the dynamic-structured neuron computation graph by only activating a sub-graph of neurons for each image in a principled way. A dense semantic-enhanced neural block is proposed to propagate the learned knowledge of all ancestor neurons into each fine-grained child neuron for feature evolving. Another merit of such semantic explainable structure is the ability of learning a unified model concurrently on diverse datasets by selectively activating different neuron sub-graphs for each annotation at each step. Extensive experiments on four public semantic segmentation datasets (i.e. ADE20K, COCO-Stuff, Cityscape and Mapillary) demonstrate the superiority of our DSSPN over state-of-the-art segmentation models. Moreoever, we demonstrate a universal segmentation model that is jointly trained on diverse datasets can surpass the performance of the common fine-tuning scheme for exploiting multiple domain knowledge.


title: Empathy for Pain and Touch in the Human Somatosensory Cortex

abstract: Dipartimento di Scienze della Formazione per le Attivita`Motorie e dello Sport, Istituto Universitario di ScienzeMotorie, I-00194 Rome, ItalyAlthough feeling pain and touch has long been consideredinherently private, recent neuroimaging and neurophysiologicalstudies hint at the social implications of this experience. Here weused somatosensory-evoked potentials (SEPs) to investigatewhether mere observation of painful and tactile stimuli deliveredto a model would modulate neural activity in the somatic system ofan onlooker. Viewing video clips showing pain and tactile stimulidelivered to others, respectively, increased and decreased theamplitude of the P45 SEP component that reﬂects the activity of theprimary somatosensory cortex (S1). These modulations correlatedwith the intensity but not with the unpleasantness of the pain andtouch ascribed to the model or the aversion induced in the onlookerby the video clips. Thus, modulation of S1 activity contingent uponobservation of others’ pain and touch may reﬂect the mapping ofsensory qualities of observed painful and tactile stimuli. Resultsindicate that the S1 is not only involved in the actual perception ofpain and touch but also plays an important role in extractingsomatic features from social interactions.Keywords: mirror systems, primary somatosensory cortex, simulationtheory, social cognition, somatosensory-evoked potentialsIntroductionCurrent neuroscientiﬁc models of empathy postulate thata given motor, perceptual, or emotional state of an individualactivates corresponding representations and neural processesin another individual observing that state (Preston and de Waal2002; Gallese 2003; Decety and Jackson 2004). Thus, theclassical notion of empathy, based on the ability to understandthe emotional experiences of others, has been extended toinclude also the ability of an onlooker to share feelings andsensations of a model (Preston and de Waal 2002; Gallese 2003,DecetyandJackson2004;AvenantiandAglioti2006;DecetyandGrezes 2006). Until the last decade, no plausible mechanismexplained how this resonant mapping between self and othermight occur. However, the discovery of neurons in the monkeypremotor and parietal cortex that become active duringexecution of a given action and observation of the same actionperformed by another human or monkey agent (mirror neu-rons) suggests that the primate nervous system is capable ofmapping observed actions of others onto its own motor system(Gallese etal. 1996; Fogassi et al.2005). In humans, evidenceforshared neural representations between self and others was ﬁrstreported in the domains of action (Rizzolatti et al. 2001) andemotion (Carr et al. 2003; Wicker et al. 2003). In the past fewyears, however, research has shown the role of shared repre-sentations in the domains of pain (Morrison et al. 2004; Singeret al. 2004, 2006; Avenanti et al. 2005, 2006; Botvinick et al.2005;Jacksonetal.2005; Minio-Paluello etal.2006;Saarelaet al.2007) and touch (Keysers et al. 2004; Blakemore et al. 2005;Schaefer et al. 2006) processing.It is widely held that the actual experience of pain involvessensory discriminative (e.g., intensity, duration, localization ofnoxious stimuli) and affective-motivational (e.g., unpleasant-ness)componentsthataremappedin2distinct butoverlappingnodes of a complex neural network referred to as the ‘‘painmatrix’’(Melzack1999).Whereasthesensorydimensionofpainis mainly coded in parietal sensorimotor neural structures,including the somatosensory cortices (Porro et al. 1998;Bushnell et al. 1999; Peyron et al. 2000), the affective compo-nentofthepainexperiencemainlyreliesuponneuralactivityofthe anterior cingulate cortex (ACC) and anterior insula (AI)(Peyron et al. 2000). The neural segregation of sensory andaffective components makes pain an interesting model fortesting theories of empathy based on the notion of sharedneural representations.Most of the neuroimaging studies carried out so far indicatethat only the affective component of the pain matrix is calledinto play during empathy for pain. For example, Singer et al.(2004) showed that knowing via symbolic visual cues that thebeloved person was going to receive an impending painfulstimulus elicited neural activity mainly in ACC and AI, whichcorrelated with emotional empathy scores. Affective neuralresponses to others’ pain were also found in observationalsituationsinwhichsubjectswerepresentedwithstaticpicturesimplying pain (Jackson et al. 2005, 2006; Ogino et al. 2006),videos showing facial pain-related behavior (Botvinick et al.2005; Saarela et al. 2007), or light pinpricks (Morrison et al.2004).Empathy, however, is a complex construct consisting notonly of emotional but also of cognitive and somatomotorcomponents (Davis 1996; Preston and De Waal 2002; Gallese2003; Decety and Jackson 2004; Avenanti and Aglioti 2006).Thus, it is entirely possible that empathy may also rely on basicresonant mechanisms that allow to map others’ sensation ontoone’s own sensorimotor system.By using single-pulse transcranial magnetic stimulation(TMS), we demonstrated that viewing needles deeply penetrat-ing speciﬁc body parts of a stranger model brought abouta selective corticospinal inhibition of the model’s penetratedmuscle.Importantly,theinhibitioncorrelatedwiththeintensitybut not the unpleasantness of the pain ascribed to the model(Avenantietal.2005,2006;Minio-Paluello etal.2006).Althoughtheseresultssupporttheviewthatothers’painelicitsaresonantmapping of sensory qualities of pain (intensity, localization),single-pulse TMS alone cannot provide direct information aboutthe sensorimotor structures involved in this mapping. Theoret-ically, parietal sensorimotor cortices may participate in the


title: Search snippet evaluation at yandex: lessons learned and future directions

abstract: This papers surveys different approaches to evaluation of web search summaries and describes experiments conducted at Yandex. We hypothesize that the complex task of snippet evaluation is best solved with a range of different methods. Automation of evaluation based on available manual assessments and clickthrough analysis is a promising direction.


title: Compensated broadband five-section directional coupler for application in butler matrices

abstract: A broadband five-section symmetrical 3 dB directional coupler has been designed. In order to improve the frequency characteristic of the coupler a recently proposed compensation technique has been used. The developed coupler operates in 2–12 GHz frequency band and can be applied in a broadband 4×4 Butler matrix.


title: Migrating code with statistical machine translation

abstract: In the era of mobile computing, developers often need to migrate code written for one platform in a programming language to another language for a different platform, e.g., from Java for Android to C# for Windows Phone. The migration process is often performed manually or semi-automatically, in which developers are required to manually define translation rules and API mappings. This paper presents semSMT, an automatic tool to migrate code written in Java to C#. semSMT utilizes statistical machine translation to automatically infer translation rules from existing migrated code, thus, requires no manual defining of rules. The video demonstration on semSMT can be found on YouTube at http://www.youtube.com/watch?v=aRSnl5-7vNo.


title: Sphinx-4: A flexible open source framework for speech recognition

abstract: Sphinx-4 is a flexible, modular and pluggable framework to help foster new innovations in the core research of hidden Markov model (HMM) speech recognition systems. The design of Sphinx-4 is based on patterns that have emerged from the design of past systems as well as new requirements based on areas that researchers currently want to explore. To exercise this framework, and to provide researchers with a "researchready" system, Sphinx-4 also includes several implementations of both simple and state-of-the-art techniques. The framework and the implementations are all freely available via open source.


title: Wideband millimeter wave waveguide to double-sided parallel-strip line transition

abstract: In this paper, a novel millimeter wave transition between waveguide and double-sided parallel-strip line (DSPSL) is proposed. The energy is coupled to the DSPSL from waveguide through the double-sided taper slot on the substrate which inserted into the waveguide. The back-to-back transition configuration is investigated with 0.5dB insertion loss from 31GHz to 39GHz. The proposed transition has more than 22% fractional bandwidth for which the return loss is better than 15dB.


title: Emotional Intelligence: A Critical Evaluation of the Literature with Implications for Mental Health Nursing Leadership

abstract: Emotional intelligence (EI) is necessary for the development of interpersonal and professional competence in nurses. We argue that the concept of emotional intelligence has particular relevance for mental health nursing leadership. In this critique, we examine the recent empirical evidence (2010–2014) related to emotional intelligence, in general, and nursing, specifically. Correlations between emotional intelligence and better overall health, increased work satisfaction, higher spiritual well-being, and decreased risk of job burnout are noted. We offer suggestions for mental health nurse leaders in developing successful project management teams and improving retention of current leaders. We also provide suggestions for future research.


title: Energy Dissipation In General Purpose Microprocessors

abstract: In this paper we investigate possible ways to improve the energy efficiency of a general purpose microprocessor. We show that the energy of a processor depends on its performance, so we chose the energy-delay product to compare different processors. To improve the energy-delay product we explore methods of reducing energy consumption that do not lead to performance loss (i.e. wasted energy), and explore methods to reduce delay by exploiting instruction level parallelism. We found that careful design reduced the energy dissipation by almost 25%. Pipelining can give approximately a 2/spl times/ improvement in energy-delay product. Superscalar issue, however, does not improve the energy-delay product any further since the overhead required offsets the gains in performance. Further improvements will be hard to come by since a large fraction of the energy (50-80%) is dissipated in the clock network and the on-chip memories. Thus, the efficiency of processors will depend more on the technology being used and the algorithm chosen by the programmer than the micro-architecture.


title: Ranking community answers by modeling question-answer relationships via analogical reasoning

abstract: The method of finding high-quality answers has significant impact on user satisfaction in community question answering systems. However, due to the lexical gap between questions and answers as well as spam typically existing in user-generated content, filtering and ranking answers is very challenging. Previous solutions mainly focus on generating redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead model them as independent information. Moreover, they only consider the answers of the current question, and ignore any previous knowledge that would be helpful to bridge the lexical and semantic gap. We assume that answers are connected to their questions with various types of latent links, i.e. positive indicating high-quality answers, negative links indicating incorrect answers or user-generated spam, and propose an analogical reasoning-based approach which measures the analogy between the new question-answer linkages and those of relevant knowledge which contains only positive links; the candidate answer which has the most analogous link is assumed to be the best answer. We conducted experiments based on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of our approach.


title: Accurate Calibration of Multi-LiDAR-Multi-Camera Systems

abstract: As autonomous driving attracts more and more attention these days, the algorithms and sensors used for machine perception become popular in research, as well. This paper investigates the extrinsic calibration of two frequently-applied sensors: the camera and Light Detection and Ranging (LiDAR). The calibration can be done with the help of ordinary boxes. It contains an iterative refinement step, which is proven to converge to the box in the LiDAR point cloud, and can be used for system calibration containing multiple LiDARs and cameras. For that purpose, a bundle adjustment-like minimization is also presented. The accuracy of the method is evaluated on both synthetic and real-world data, outperforming the state-of-the-art techniques. The method is general in the sense that it is both LiDAR and camera-type independent, and only the intrinsic camera parameters have to be known. Finally, a method for determining the 2D bounding box of the car chassis from LiDAR point clouds is also presented in order to determine the car body border with respect to the calibrated sensors.


title: Determinants of melanocyte density in adult human skin

abstract: The distribution of melanocytes in human skin has been observed to vary within and among individuals, yet little is known of the factors that determine the density of these pigment cells. These factors were explored in a molecular epidemiological study conducted among a population-based sample of 97 male subjects aged over 50 years in Queensland, Australia. Information relating to environmental and phenotypic factors was collected through face-to-face interviews and physical examination of all participants. In addition, 2-mm biopsies of representative skin were taken from the dorsum of the hand and another anatomical site. Melanocytes were identified by cytoplasmic staining with the B8G3 (anti-TRP1) monoclonal antibody using standard immunohistochemical techniques. Melanocyte counts were performed blind by two observers. On crude analysis, melanocyte density decreased with advancing age (P = 0.0002), and increased with increasing number of naevi (P = 0.01). Other pigmentary characteristics (such as hair and eye colour and depth of tan) were not associated with epidermal melanocyte density. Melanocyte density varied significantly by anatomical site (P = 0.02), with highest densities observed on the back/shoulders (n = 50, 17.1 ± 8.8 cells/mm, mean ± SD) followed by the upper limbs (n = 11, 12.6 ± 8.8 cells/mm) and lower limbs (n = 14, 14.4 ± 5.9 cells/mm). Lowest melanocyte densities were recorded on the anterior trunk (n = 3, 3.2 ± 2.4 cells/mm). These findings confirm the results of earlier studies in which site-specific differences in melanocyte density have been found. We speculate that the unequal distribution of melanocytes may partially explain the site-specific incidence of melanoma, offering fresh perspectives on the aetiology of this cancer.


title: Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding

abstract: The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.


title: Modulation and coding for linear Gaussian channels

abstract: Shannon's determination of the capacity of the linear Gaussian channel has posed a magnificent challenge to succeeding generations of researchers. This paper surveys how this challenge has been met during the past half century. Orthogonal minimum-bandwidth modulation techniques and channel capacity are discussed. Binary coding techniques for low-signal-to-noise ratio (SNR) channels and nonbinary coding techniques for high-SNR channels are reviewed. Recent developments, which now allow capacity to be approached on any linear Gaussian channel, are surveyed. These new capacity-approaching techniques include turbo coding and decoding, multilevel coding, and combined coding/precoding for intersymbol-interference channels.


title: Novel High Gain Low Noise CMOS Instrumentation Amplifier for Biomedical Applications

abstract: This work describes a novel technique of designing a high gain low noise CMOS instrumentation amplifier for biomedical applications like ECG signal processing. A three opamp instrumentation amplifier have been designed by using two simple op-amps at the two input stages and a folded cascode opamp at the output stage. Both op-amps at the input and output are 2-stage. Most of the previous or earlier designed op-amp in literature uses same type of op-amp at the input and output stages of instrumentation amplifier. By using folded cascode op-amp at the output, we had achieved significant improvement in gain and CMRR. Transistors sizing plays a major role in achieving high gain and CMRR. To achieve a desirable common mode rejection ratio (CMRR), Gain and other performance metrics, selection of most appropriable op-amp circuit topologies & optimum transistor sizing was the main criteria for designing of instrumentation amplifier for biomedical applications. The complete instrumentation amplifier design is simulated using Cadence Spectre tool and layout is designed and simulated in Cadence Layout editor at 0.18μm CMOS technology. Each of the input two stage op-amp provides a gain and CMRR of 45dB and 72dB respectively. The output two stage folded cascode amplifier provides a CMRR of 92dB and a gain of 82dB. The design achieves an overall CMRR and gain of 92dB and 67db respectively. The overall power consumed by instrumentation amplifier is 263μW which is suitable for biomedical signal processing applications.


title: Supporting Social Engagement for Young Audiences with Serious Games and Virtual Environments in Museums

abstract: Considering the shift of museums toward digital experiences that can satiate the interests of their young audiences, we suggest an integrated schema for socially engaging large visitor groups. As a means to present our position, we propose a framework for audience involvement with complex educational material, combining serious games and virtual environments along with a theory of contextual learning in museums. We suggest that effective learning with school groups visiting museums can occur through the facilitation of coordinated activities of young participants with appropriately designed technological mediation. We use the term orchestrated learning to present our rationale on how such activities can lead to enhanced learning through increased motivation and social interactions. In order to validate our framework, we built a testbed application that supports collaborative gameplay of small and large student groups. The application, named C-OLiVE: Collaborative Orchestrated Learning in Virtual Environments, is a 3D simulation game that allows participants to learn the process of olive oil production. The game has been used so far in three studies with 710 students in schools, summer camps, and a museum. We describe the modes of visitor involvement that were designed and tested with middle school students in the different learning settings. Finally, we present our most important findings and discuss their implications for the design of interactive digital experiences for young audiences visiting museums. These findings serve both as evidence for the applicability of our framework and as a guidepost for the direction we should move to foster richer technology-mediated social engagement of young crowds in museums.


title: Reliability Prediction of Webpages in the Medical Domain

abstract: In this paper, we study how to automatically predict reliability of web pages in the medical domain. Assessing reliability of online medical information is especially critical as it may potentially influence vulnerable patients seeking help online. Unfortunately, there are no automated systems currently available that can classify a medical webpage as being reliable, while manual assessment cannot scale up to process the large number of medical pages on the Web. We propose a supervised learning approach to automatically predict reliability of medical webpages. We developed a gold standard dataset using the standard reliability criteria defined by the Health on Net Foundation and systematically experimented with different link and content based feature sets. Our experiments show promising results with prediction accuracies of over 80%. We also show that our proposed prediction method is useful in applications such as reliability-based re-ranking and automatic website accreditation.


title: Association between DRD2, 5-HTTLPR, and ALDH2 genes and specific personality traits in alcohol- and opiate-dependent patients

abstract: The vulnerability of developing addictions is associated with genetic factors and personality traits. The predisposing genetic variants and personality traits may be common to all addictions or specific to a particular class of addiction. To investigate the relationship between genetic variances, personality traits, and their interactions in addiction are important. We recruited 175 opiate-dependent patients, 102 alcohol-dependent patients, and 111 healthy controls. All participants were diagnosed using DSM-IV criteria and assessed with Tridimensional Personality Questionnaire (TPQ). The dopamine D2 receptor (DRD2), 5-HTT-linked promoter region (5-HTTLPR), and aldehyde dehydrogenase 2 (ALDH2) genes were genotyped using PCR. The genotype frequency of the 5-HTTLPR and ALDH2 was significantly different between the patients and controls (P = 0.013, P < 0.001, respectively), and borderline significant (P = 0.05) for DRD2 polymorphism. Both Novelty Seeking (NS) and Harm Avoidance (HA) scores were higher for patients (P < 0.001). After stratification by candidate genes, addicts with ALDH2 *1/*1 interacting with the low-functional group of DRD2 and 5-HTTLPR genes have higher HA traits, whereas addicts with ALDH2 *1/*2 or *2/*2 and low-functional group of DRD2 and 5-HTTLPR genes have higher NS traits. We concluded that addicts, both alcohol- and opiate-dependent patients, have common genetic variants in DRD2 and 5-HTTLPR but specific for ALDH2. Higher NS and HA traits were found in both patient groups with the interaction with DRD2, 5-HTTLPR, and ALDH2 genes. The ALDH2 gene variants had different effect in the NS and HA dimension while the DRD2 and 5-HTTLPR genes did not.


title: Medial column foot systems: an innovative tool for improving posture

abstract: Intuitively we know feet affect posture. Engineers use this concept daily: as goes the foundation (foot), so goes the building (posture). Rothbart describes the foot–posture relationship dynamically, e.g. the impact the walking foot has on posture. Morton (1932) asserts a short 1st metatarsal (relative to the 2nd and 3rd metatarsals) prevents it from fully participating in weight bearing. While this concept appears correct, it is only a partial explanation of the pathodynamics engaging hyperpronators. Embryologically the foot goes through a series of torsional changes (Streeter 1945, Lash et al. 1997, Aiton et al. 1995, McLachlan et al. 1997, O’Rahilly et al. 1987, Smith 1999, Jirasek & Keith 2001, Gasser 1975, Patten 1946). If these torsions end prematurely (Tabibzadeh & Pettersson 1995), the 1st metatarsal and big toe (proximal phalanx and hallux) remain structurally elevated and inverted (in elevatus) relative to the lesser metatarsals and phalanges (Straus 1927, Olivier 1962). It is this retained elevatus that forces the walking foot into hyperpronation (Rothbart & Esterbrook 1988, Rothbart & Hansen 1995, Filner 1996, Liley 1996). It is hyperpronation that draws the posture forward (Rothbart et al. 1992, Rothbart & Hansen 1995, Schneider et al. 1995, Filner 1996, Liley 1996). And it is this forward posture and ensuing compensations that lead the patient into chronic pain (Rothbart & Esterbrook 1988, Rothbart et al. 1992, Rothbart & Hansen 1995, Petersen 1995, Schneider 1995, Filner 1996, Liley 1996). The embryology section briefly outlines [1] the normal ontogenetic stages within the lower limb bud and [2] the abnormal ontogenetic events that result in either the Clubfoot deformity (Cfd) or Rothbart foot structure (RFS). (This paper does not deal with the positional or structural deformations that can occur postnatally, e.g. rearfoot varum from massive trauma to the heel bone, leg length discrepancy from sepsis of the femoral growth plate, etc) Section 2 (RFS, clinical significance of PME) discusses RFS impact on posture and gaiting, and provides a methodology for diagnosing RFS in the adult foot. Section 3 (stabilizing RFS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Journal of Bodywork and Movement Therapies (2001) 6(1), 000^000 r 2002 Harcourt Publishers Ltd Brain A. Rothbart DPM,LEd,PhD


title: Developing Robots that Perform Useful Work in Everyday Settings

abstract: The state of the art in and the future of robotics are discussed. The potential paths to the long-term vision of robots that work alongside people in homes and workplaces as useful, capable collaborators are discussed. Robot manipulation in human environments is expected to grow in the coming years as more researchers seek to create robots that actively help in the daily lives of people


title: Similarity encoding for learning with dirty categorical variables

abstract: For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. “Dirty” non-curated data give rise to categorical variables with a very high cardinality but redundancy: several categories reflect the same entity. In databases, this issue is typically solved with a deduplication step. We show that a simple approach that exposes the redundancy to the learning algorithm brings significant gains. We study a generalization of one-hot encoding, similarity encoding, that builds feature vectors from similarities across categories. We perform a thorough empirical validation on non-curated tables, a problem seldom studied in machine learning. Results on seven real-world datasets show that similarity encoding brings significant gains in predictive performance in comparison with known encoding methods for categories or strings, notably one-hot encoding and bag of character n-grams. We draw practical recommendations for encoding dirty categories: 3-gram similarity appears to be a good choice to capture morphological resemblance. For very high-cardinalities, dimensionality reduction significantly reduces the computational cost with little loss in performance: random projections or choosing a subset of prototype categories still outperform classic encoding approaches.


title: Subspace Learning for Facial Age Estimation Via Pairwise Age Ranking

abstract: Age is one of the important biometric traits for reinforcing the identity authentication. The challenge of facial age estimation mainly comes from two difficulties: (1) the wide diversity of visual appearance existing even within the same age group and (2) the limited number of labeled face images in real cases. Motivated by previous research on human cognition, human beings can confidently rank the relative ages of facial images, we postulate that the age rank plays a more important role in the age estimation than visual appearance attributes. In this paper, we assume that the age ranks can be characterized by a set of ranking features lying on a low-dimensional space. We propose a simple and flexible subspace learning method by solving a sequence of constrained optimization problems. With our formulation, both the aging manifold, which relies on exact age labels, and the implicit age ranks are jointly embedded in the proposed subspace. In addition to supervised age estimation, our method also extends to semi-supervised age estimation via automatically approximating the age ranks of unlabeled data. Therefore, we can successfully include more available data to improve the feature discriminability. In the experiments, we adopt the support vector regression on the proposed ranking features to learn our age estimators. The results on the age estimation demonstrate that our method outperforms classic subspace learning approaches, and the semi-supervised learning successfully incorporates the age ranks from unlabeled data under different scales and sources of data set.


title: An automatic extraction method of the domains of competence for learning classifiers using data complexity measures

abstract: The constant appearance of algorithms and problems in data mining makes impossible to know in advance whether the model will perform well or poorly until it is applied, which can be costly. It would be useful to have a procedure that indicates, prior to the application of the learning algorithm and without needing a comparison with other methods, whether the outcome will be good or bad using the information available in the data. In this work, we present an automatic extraction method to determine the domains of competence of a classifier using a set of data complexity measures proposed for the task of classification. These domains codify the characteristics of the problems that are suitable or not for it, relating the concepts of data geometrical structures that may be difficult and the final accuracy obtained by any classifier. In order to do so, this proposal uses 12 metrics of data complexity acting over a large benchmark of datasets in order to analyze the behavior patterns of the method, obtaining intervals of data complexity measures with good or bad performance. As a representative for classifiers to analyze the proposal, three classical but different algorithms are used: C4.5, SVM and K-NN. From these intervals, two simple rules that describe the good or bad behaviors of the classifiers mentioned each are obtained, allowing the user to characterize the response quality of the methods from a dataset's complexity. These two rules have been validated using fresh problems, showing that they are general and accurate. Thus, it can be established when the classifier will perform well or poorly prior to its application.