title: Comparing deep neural networks against humans: object recognition when the signal gets weaker

abstract: Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algorithms called deep neural networks (DNNs) that achieve human-level classification performance on object recognition tasks. Furthermore, a growing number of studies report similarities in the way DNNs and the human visual system process objects, suggesting that current DNNs may be good models of human visual object recognition. Yet there clearly exist important architectural and processing differences between state-of-the-art DNNs and the primate visual system. The potential behavioural consequences of these differences are not well understood. We aim to address this issue by comparing human and DNN generalisation abilities towards image degradations. We find the human visual system to be more robust to image manipulations like contrast reduction, additive noise or novel eidolon-distortions. In addition, we find progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition. We envision that our findings as well as our carefully measured and freely available behavioural datasets provide a new useful benchmark for the computer vision community to improve the robustness of DNNs and a motivation for neuroscientists to search for mechanisms in the brain that could facilitate this robustness.


title: Large-Scale Cross-Game Player Behavior Analysis on Steam

abstract: Behavioral game analytics has predominantly been confined to work on single games, which means that the cross-game applicability of current knowledge remains largely unknown. Here four experiments are presented focusing on the relationship between game ownership, time invested in playing games, and the players themselves, across more than 3000 games distributed by the Steam platform and over 6 million players, covering a total playtime of over 5 billion hours. Experiments are targeted at uncovering high-level patterns in the behavior of players focusing on playtime, using frequent itemset mining on game ownership, cluster analysis to develop playtime-dependent player profiles, correlation between user game rankings and, review scores, playtime and game ownership, as well as cluster analysis on Steam games. Within the context of playtime, the analyses presented provide unique insights into the behavior of game players as they occur across games, for example in how players distribute their time across games. Introduction and Contribution Game companies today are able to collect behavioral telemetry data from entire populations of players, and using cloud based storage technologies, it is possible to collect and process every single user event from games. Furthermore, with the help of global game platforms; such as Steam, Good Old Games, or console-based services, as well as social networking platforms like Facebook or Tango, increasingly larger and broader audiences can be reached. However, despite a remarkable growth of interest, fueled by new business models (notably Free-to-Play, F2P) and mobile technologies, publicly available behavioral analytics in digital games has as yet been predominantly confined to single games. Unlike other sectors such as e-commerce, there have been no large-scale cross-game behavioral studies, in part due to the recent, if highly accelerated, introduction of analytics practices in the game industry, but perhaps more importantly due to the confidentiality associated with behavioral telemetry data. This means that while dozens of telemetry-based studies and hundreds of observational studies of behavior in games have been published or presented, there is very Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. little knowledge available in the public domain about how these translate across games (exceptions including (Chambers and Saha 2005; Bauckhage et al. 2012)). This places challenges in the way of establishing behavioral patterns that operate across some or all games, also for applied purposes such as informing game design, for example via improving retention and engagement (Bauckhage et al. 2012; Pittman and GauthierDickey 2010; Feng and Saha 2007). It also limits the ability to develop techniques used in e.g. e-commerce and behavioral economics for understanding and modeling user behavior (Resnick and Varian 1997; Ricci et al. 2011; Bogers 2009). The importance of crossgames behavioral analysis is emphasized when considering the increasing number of available platforms that offer games, and that the same players tend to own multiple games. Understanding how games are played is not a trivial task considering that multiple gameplay profiles can be observed from individual players. In this paper we present four experiments performed on a 6 million player dataset, covering a total playtime of over 5 billion hours of play across more than 3000 games distributed via the Steam platform. Additional data was collected covering game rankings and review scores, as well as information on the genre, type and key game mechanics. The results provide insights into the patterns around playtime in the games bought and played by Steam users, as well as patterns about the users themselves. Playtime is the focus of the experiments conducted because this feature is an indication of player interest or engagement with a game. In a highly competitive global marketplace for games, understanding the connections between the games played by a user, not just within any one game, is vital e.g. for tasks such as cross-game promotions, migrating players between games (Sifa, Ojeda, and Bauckhage 2015) or game recommender systems (Sifa, Bauckhage, and Drachen 2014a). Summarizing the results: 1) Playtime distribution players: Cluster analysis shows that the majority of players are more or less dedicated to one or a few games. Only about a third of the players put similar amounts of time into a variety of games (given a k = 11 solution). Playtime distribution is highly skewed. The number of owned games is distributed in a similar way. The average number of owned games is 22.1 (standard deviation = 35.5). 2) Playtime distribution games: Cluster results run on aggregate playtime Proceedings, The Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-15)


title: Towards the Design of Efficient and Consistent Index Structure with Minimal Write Activities for Non-Volatile Memory

abstract: Index structures can significantly accelerate the data retrieval operations in data intensive systems, such as databases. Tree structures, such as B $^{+}$ -tree alike, are commonly employed as index structures; however, we found that the tree structure may not be appropriate for Non-Volatile Memory (NVM) in terms of the requirements for high-performance and high-endurance. This paper studies what is the best index structure for NVM-based systems and how to design such index structures. The design of an NVM-friendly index structure faces a lot of challenges. First , in order to prolong the lifetime of NVM, the write activities on NVM should be minimized. To this end, the index structure should be as simple as possible. The index proposed in this paper is based on the simplest data structure, i.e., linked list. Second , the simple structure brings challenges to achieve high-performance data retrieval operations. To overcome this challenge, we design a novel technique by explicitly building up a contiguous virtual address space on the linked list, such that efficient search algorithms can be performed. Third , we need to carefully consider data consistency issues in NVM-based systems, because the order of memory writes may be changed and the data content in NVM may be inconsistent due to write-back effects of CPU cache. This paper devises a novel indexing scheme, called “ V irtual L inear A ddressable B uckets” (VLAB). We implement VLAB in a storage engine and plug it into MySQL. Evaluations are conducted on an NVDIMM workstation using YCSB workloads and real-world traces. Results show that write activities of the state-of-the-art indexes are 6.98 times more than ours; meanwhile, VLAB achieves 2.53 times speedup.


title: Protecting location privacy: optimal strategy against localization attacks

abstract: The mainstream approach to protecting the location-privacy of mobile users in location-based services (LBSs) is to alter the users' actual locations in order to reduce the location information exposed to the service provider. The location obfuscation algorithm behind an effective location-privacy preserving mechanism (LPPM) must consider three fundamental elements: the privacy requirements of the users, the adversary's knowledge and capabilities, and the maximal tolerated service quality degradation stemming from the obfuscation of true locations. We propose the first methodology, to the best of our knowledge, that enables a designer to find the optimal LPPM for a LBS given each user's service quality constraints against an adversary implementing the optimal inference algorithm. Such LPPM is the one that maximizes the expected distortion (error) that the optimal adversary incurs in reconstructing the actual location of a user, while fulfilling the user's service-quality requirement. We formalize the mutual optimization of user-adversary objectives (location privacy vs. correctness of localization) by using the framework of Stackelberg Bayesian games. In such setting, we develop two linear programs that output the best LPPM strategy and its corresponding optimal inference attack. Our optimal user-centric LPPM can be easily integrated in the users' mobile devices they use to access LBSs. We validate the efficacy of our game theoretic method against real location traces. Our evaluation confirms that the optimal LPPM strategy is superior to a straightforward obfuscation method, and that the optimal localization attack performs better compared to a Bayesian inference attack.


title: Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection

abstract: In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter that, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and nonspeech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.


title: Convex repeated games and Fenchel duality

abstract: We describe an algorithmic framework for an abstract game which we term a convex repeated game. We show that various online learning and boosting algorithms can be all derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Our algorithmic framework stems from a connection that we build between the notions of regret in game theory and weak duality in convex optimization.


title: Electroanatomical Mapping of the Urinary Bladder

abstract: A noncontact mapping system (EnSite) was used for electroanatomical mapping of the bladder simultaneously with pressure flow study in three women with lower urinary tract symptoms. We selected the periods of obvious detrusor activity. Data were processed to remove baseline drift, and an envelope of electrovesicography (EVG) data was created. The correlation coefficient for the correlation between between the EVG envelope and the detrusor pressure (Pdet) was calculated. Bladder geometry was successfully created in all 3 patients. Simultaneous recording of EVG and pressure flow data was successful in 1 patient. Scatter plots were made of the highest correlation coefficient, showing a positive correlation between the Pdet and the envelope, and negative correlation between abdominal pressure (Pabd) and the envelope. Minimal electrical activity could be observed. Significant weak to moderate correlation coefficients were found for the correlations between Pdet and EVG and between Pabd and EVG.


title: A Semantic Framework for Scheduling Parallel Programs

abstract: Declarative parallel programs offer deterministic results, allowing the language implementation to schedule parallel tasks in any order. However, program performance hinges crucially on the way that these tasks are scheduled. In this work, we use formal language semantics to express different scheduling policies. These semantics enable us to compare different policies and to understand their effects on the use of space. We offer several example programs to demonstrate that scheduling policy can have a dramatic, and even asymptotic, effect on space usage. To predict performance, programmers require a means to understand the effects of scheduling. We define a cost semantics that allows programmers to reason about how space is used by parallel declarative programs. At the same time, these costs provide a specification for how implementations should behave.


title: Text line extraction for historical document images

abstract: In this paper we present a language independent global method for automatic text line extraction. The proposed approach computes an energy map of a text image and determines the seams that pass across and between text lines. In this work we have developed two algorithms along this novel idea, one for binary images and the other for grayscale images. The first algorithm works on binary document images and assumes it is possible to extract the components along text lines. The seam passes on the middle and along the text line, l, and marks the components that make the letters and words of l. It then assigns the unmarked component to the closest text line. The second algorithm works directly on grayscale document images. It computes the distance transform directly from the grayscale images and generates two types of seams: medial seams and separating seams. The medial seams determine the text lines and the separating seams define the upper and lower boundaries of these text lines. Moreover, we present a new benchmark dataset of historical document images with various types of challenges. The dataset contains a groundtruth for text line extraction and it contains samples with different languages such as: Arabic, English and Spanish. A binary dataset is used to test the binary algorithm. We performed various experimental results using our two algorithms on the mentioned datasets and report segmentation accuracy. We also compare our algorithms with the state-of-the-art text line segmentation methods.


title: Proteome analysis of conditioned medium from mouse embryonic fibroblast feeder layers which support the growth of human embryonic stem cells

abstract: Human embryonic stem (ES) cells are pluripotent cells with the potential to differentiate into a variety of cell types, which could be used for cell transplantation therapies as well as drug discovery studies. However, the large-scale culture of undifferentiated human ES cells is currently limited by their dependency on mouse embryonic fibroblast feeder layers. The proteomics approach was employed to characterize the environment that supports the growth of undifferentiated human ES cells and to identify factors critical for their independent growth. Conditioned medium from mouse embryonic fibroblast feeder layers, STO cell line, was concentrated and subjected to analyses by two-dimensional electrophoresis mass spectrometry. In total, 136 unique protein species were identified which included some that are known to participate in cell growth and differentiation, extracellular matrix formation and remodeling, in addition to the unexpected but interesting finding of many nominally intracellular proteins. This approach has thus revealed the complexity of the environment provided by the feeder cells and provides a useful starting point for future studies. Moreover, candidates from the initial list of identified proteins can be further investigated for their effects on the growth and differentiation of human ES cells in a defined culture environment.


title: Magnetic human body communication

abstract: This paper presents a new human body communication (HBC) technique that employs magnetic resonance for data transfer in wireless body-area networks (BANs). Unlike electric field HBC (eHBC) links, which do not necessarily travel well through many biological tissues, the proposed magnetic HBC (mHBC) link easily travels through tissue, offering significantly reduced path loss and, as a result, reduced transceiver power consumption. In this paper the proposed mHBC concept is validated via finite element method simulations and measurements. It is demonstrated that path loss across the body under various postures varies from 10-20 dB, which is significantly lower than alternative BAN techniques.


title: A Middleware Layer for Flexible and Cost-Efficient Multi-tenant Applications

abstract: Application-level multi-tenancy is an architectural design principle for Software-as-a-Service applications to enable the hosting of multiple customers (or tenants) by a single application instance. Despite the operational cost and maintenance benefits of application-level multi-tenancy, the current middleware component models for multi-tenant application design are inflexible with respect to providing different software variations to different customers. ::: ::: In this paper we show that this limitation can be solved by a multi-tenancy support layer that combines dependency injection with middleware support for tenant data isolation. Dependency injection enables injecting different software variations on a per tenant basis, while dedicated middleware support facilitates the separation of data and configuration metadata between tenants. We implemented a prototype on top of Google App Engine and we evaluated by means of a case study that the improved flexibility of our approach has little impact on operational costs and upfront application engineering costs.


title: Object identification with tactile sensors using bag-of-features

abstract: In this paper, we present a novel approach for identifying objects using touch sensors installed in the finger tips of a manipulation robot. Our approach operates on low-resolution intensity images that are obtained when the robot grasps an object. We apply a bag-of-words approach for object identification. By means of unsupervised clustering on training data, our approach learns a vocabulary from tactile observations which is used to generate a histogram codebook. The histogram codebook models distributions over the vocabulary and is the core identification mechanism. As the objects are larger than the sensor, the robot typically needs multiple grasp actions at different positions to uniquely identify an object. To reduce the number of required grasp actions, we apply a decision-theoretic framework that minimizes the entropy of the probabilistic belief about the type of the object. In our experiments carried out with various industrial and household objects, we demonstrate that our approach is able to discriminate between a large set of objects. We furthermore show that using our approach, a robot is able to distinguish visually similar objects that have different elasticity properties by using only the information from the touch sensor.


title: A source code linearization technique for detecting plagiarized programs

abstract: It is very important to detect plagiarized programs in the field of computer science education. Therefore, many tools and algorithms have been developed for this purpose. Generally, these tools are operated in two phases. In phase 1, a program plagiarism detecting tool generates an intermediate representation from a given program set. The intermediate representation should reflect the structural characterization of the program. Most tools use the parse tree or token sequence by intermediate representation. In phase 2, the program looks for plagiarized material and evaluates the similarity of two programs. It is helpful to announce the plagiarized metarials between two programs to the instructor. In this paper, we present the static tracing method in order to improve program plagiarism detection accuracy. The static tracing method statically executes a program at the syntax-level and then extracts predefined keywords according to the order of the executed functions. The result of experiment proves this method can detect plagiarism more effectively than the previously released plagiarism detecting method.


title: Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning

abstract: The increasing amount of scientific literature in biological and biomedical science research has created a challenge in continuous and reliable curation of the latest knowledge discovered, and automatic biomedical text-mining has been one of the answers to this challenge. In this paper, we aim to further improve the reliability of biomedical text-mining by training the system to directly simulate the human behaviors such as querying the PubMed, selecting articles from queried results, and reading selected articles for knowledge. We take advantage of the efficiency of biomedical text-mining, the exibility of deep reinforcement learning, and the massive amount of knowledge collected in UMLS into an integrative artificial intelligent reader that can automatically identify the authentic articles and effectively acquire the knowledge conveyed in the articles. We construct a system, whose current primary task is to build the genetic association database between genes and complex traits of human. Our contributions in this paper are three-fold: 1) We propose to improve the reliability of text-mining by building a system that can directly simulate the behavior of a researcher, and we develop corresponding methods, such as Bi-directional LSTM for text mining and Deep Q-Network for organizing behaviors. 2) We demonstrate the effectiveness of our system with an example in constructing a genetic association database. 3) We release our implementation as a generic framework for researchers in the community to conveniently construct other databases.


title: A stereophonic acoustic signal extraction scheme for noisy and reverberant environments

abstract: In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation ('directional BSS'), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions.


title: Tactile sensing in intelligent robotic manipulation – a review

abstract: Purpose - When designing hardware and algorithms for robotic manipulation and grasping, sensory information is typically needed to control the grasping process. This paper presents an overview of t ...


title: Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

abstract: Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as shortcut for gradients, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked) dimensions, aiming to enhance the recognition rate. When testing with the Opportunity dataset and the public domain UCI dataset, the accuracy is significantly improved compared with previous results.


The evolution of modern texture processing


title: Acquisition of Turkish meronym based on classification of patterns

abstract: The identification of semantic relations from a raw text is an important problem in Natural Language Processing. This paper provides semi-automatic pattern-based extraction of part---whole relations. We utilized and adopted some lexico-syntactic patterns to disclose meronymy relation from a Turkish corpus. We applied two different approaches to prepare patterns; one is based on pre-defined patterns that are taken from the literature, second automatically produces patterns by means of bootstrapping method. While pre-defined patterns are directly applied to corpus, other patterns need to be discovered first by taking manually prepared unambiguous seeds. Then, word pairs are extracted by their occurrence in those patterns. In addition, we used statistical selection on global data that is obtaining from all results of entire patterns. It is a whole-by-part matrix on which several association metrics such as information gain, T-score, etc., are applied. We examined how all these approaches improve the system accuracy especially within corpus-based approach and distributional feature of words. Finally, we conducted a variety of experiments with a comparison analysis and showed advantage and disadvantage of the approaches with promising results.


title: Collaborative Filtering-Based Recommendation of Online Social Voting

abstract: Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath-based NN models outperform computation-intensive MF models in hot-voting recommendation, while users’ interests for nonhot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top- $k$ hit rate.


title: Business ecosystem as the new approach to complex adaptive business environments

abstract: This paper discusses the concept of business ecosystem. Business ecosystem is a relatively new concept in the field of business research, and there is still a lot of work to be done to establish it. First the subject is approached by examining a biological ecosystem, especially how biological ecosystems are defined, how they evolve and how they are classified and structured. Second, different analogies of biological ecosystem are reviewed, including industrial ecosystem, economy as an ecosystem, digital business ecosystem and social ecosystem. Third, business ecosystem concept is outlined by discussing views of main contributors and then bringing authors’ own definition out. Fourth, the emerging research field of complexity in social sciences is brought out due to authors’ attitude to consider ecosystems and business ecosystems as complex, adaptive systems. The focal complexity aspects appearing in business ecosystems are presented; they are self-organization, emergence, co-evolution and adaptation. By connecting business ecosystem concept to complexity research, it is possible to bring new insights to changing business environments.


title: Unsupervised Attention-guided Image to Image Translation

abstract: Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene. Motivated by the important role of attention in human perception, we tackle this limitation by introducing unsupervised attention mechanisms that are jointly adversarialy trained with the generators and discriminators. We demonstrate qualitatively and quantitatively that our approach is able to attend to relevant regions in the image without requiring supervision, and that by doing so it achieves more realistic mappings compared to recent approaches.


title: Bayesian Nonparametric Models

abstract: A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions, and in a density estimation problem the space can consist of all densities. A Bayesian nonparametric model uses only a finite subset of the available parameter dimensions to explain a finite sample of observations, with the set of dimensions chosen depending on the sample, such that the effective complexity of the model (as measured by the number of dimensions used) adapts to the data. Classical adaptive problems, such as nonparametric estimation and model selection, can thus be formulated as Bayesian inference problems. Popular examples of Bayesian nonparametric models include Gaussian process regression, in which the correlation structure is refined with growing sample size, and Dirichlet process mixture models for clustering, which adapt the number of clusters to the complexity of the data. Bayesian nonparametric models have recently been applied to a variety of machine learning problems, including regression, classification, clustering, latent variable modeling, sequential modeling, image segmentation, source separation and grammar induction.


title: Nanofiber Assembly by Rotary Jet-Spinning

abstract: High-voltage electrical fields and low production rate limit electrospinning, the electrical charging of polymer liquids, as a means of nanofiber fabrication. Here, we show a facile method of fabrication of aligned three-dimensional nanofiber structures by utilizing high-speed, rotating polymer solution jets to extrude fibers. Termed rotary jet-spinning, fiber morphology, diameter, and web porosity can be controlled by varying nozzle geometry, rotation speed, and polymer solution properties. We demonstrate the utility of this technique for tissue engineering by building anisotropic arrays of biodegradable polymer fibers and seeding the constructs with neonatal rat ventricular cardiomyocytes. The myocytes used the aligned fibers to orient their contractile cytoskeleton and to self-organize into a beating, multicellular tissue that mimics the laminar, anisotropic architecture of the heart muscle. This technique may prove advantageous for building uniaxially aligned nanofiber structures for polymers which ar...


Image Segmentation Using Fuzzy C-Means


title: ProteusTM: Abstraction Meets Performance in Transactional Memory

abstract: The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no universal implementation fits each and every workload. In fact, the best TM in a given workload can reveal to be disastrous for another one. This forces developers to face the complex task of tuning TM implementations, which significantly hampers their wide adoption. In this paper, we address the challenge of automatically identifying the best TM implementation for a given workload. Our proposed system, ProteusTM, hides behind the TM interface a large library of implementations. Underneath, it leverages a novel multi-dimensional online optimization scheme, combining two popular learning techniques: Collaborative Filtering and Bayesian Optimization. We integrated ProteusTM in GCC and demonstrate its ability to switch between TMs and adapt several configuration parameters (e.g., number of threads). We extensively evaluated ProteusTM, obtaining average performance


title: Temporal-Difference Networks

abstract: We introduce a generalization of temporal-difference (TD) learning to networks of interrelated predictions. Rather than relating a single prediction to itself at a later time, as in conventional TD methods, a TD network relates each prediction in a set of predictions to other predictions in the set at a later time. TD networks can represent and apply TD learning to a much wider class of predictions than has previously been possible. Using a random-walk example, we show that these networks can be used to learn to predict by a fixed interval, which is not possible with conventional TD methods. Secondly, we show that if the inter-predictive relationships are made conditional on action, then the usual learning-efficiency advantage of TD methods over Monte Carlo (supervised learning) methods becomes particularly pronounced. Thirdly, we demonstrate that TD networks can learn predictive state representations that enable exact solution of a non-Markov problem. A very broad range of inter-predictive temporal relationships can be expressed in these networks. Overall we argue that TD networks represent a substantial extension of the abilities of TD methods and bring us closer to the goal of representing world knowledge in entirely predictive, grounded terms.


Inverse Reinforcement Learning via Ranked and Failed Demonstrations


title: Transcranial magnetic stimulation in autism spectrum disorder: Challenges, promise, and roadmap for future research

abstract: Autism Spectrum Disorder (ASD) is a behaviorally defined complex neurodevelopmental syndrome characterized by impairments in social communication, by the presence of restricted and repetitive behaviors, interests and activities, and by abnormalities in sensory reactivity. Transcranial magnetic stimulation (TMS) is a promising, emerging tool for the study and potential treatment of ASD. Recent studies suggest that TMS measures provide rapid and noninvasive pathophysiological ASD biomarkers. Furthermore, repetitive TMS (rTMS) may represent a novel treatment strategy for reducing some of the core and associated ASD symptoms. However, the available literature on the TMS use in ASD is preliminary, composed of studies with methodological limitations. Thus, off-label clinical rTMS use for therapeutic interventions in ASD without an investigational device exemption and outside of an IRB approved research trial is premature pending further, adequately powered and controlled trials. Leaders in this field have gathered annually for a two-day conference (prior to the 2014 and 2015 International Meeting for Autism Research, IMFAR) to share recent progress, promote collaboration across laboratories, and establish consensus on protocols. Here we review the literature in the use of TMS in ASD in the context of the unique challenges required for the study and exploration of treatment strategies in this population. We also suggest future directions for this field of investigations. While its true potential in ASD has yet to be delineated, TMS represents an innovative research tool and a novel, possibly transformative approach to the treatment of neurodevelopmental disorders. Autism Res 2016, 9: 184–203. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.


title: Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice

abstract: Geolocated social media data provides a powerful source of information about place and regional human behavior. Because little social media data is geolocation-annotated, inference techniques serve an essential role for increasing the volume of annotated data. One major class of inference approaches has relied on the social network of Twitter, where the locations of a user's friends serve as evidence for that user's location. While many such inference techniques have been recently proposed, we actually know little about their relative performance, with the amount of ground truth data varying between 5% and 100% of the network, the size of the social network varying by four orders of magnitude, and little standardization in evaluation metrics. We conduct a systematic comparative analysis of nine state-of-the-art network-based methods for performing geolocation inference at the global scale, controlling for the source of ground truth data, dataset size, and temporal recency in test data. Furthermore, we identify a comprehensive set of evaluation metrics that clarify performance differences. Our analysis identifies a large performance disparity between that reported in the literature and that seen in real-world conditions. To aid reproducibility and future comparison, all implementations have been released in an open source geoinference package.


title: AI Benchmark: Running Deep Neural Networks on Android Smartphones

abstract: Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.


Paraphrasing Out-of-Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation.


title: Streaming trend detection in Twitter

abstract: As social media continue to grow, the zeitgeist of society is increasingly found not in the headlines of traditional media institutions, but in the activity of ordinary individuals. The identification of trending topics utilises social media (such as Twitter) to provide an overview of the topics and issues that are currently popular within the online community. In this paper, we outline methodologies of detecting and identifying trending topics from streaming data. Data from Twitter's streaming API was collected and put into documents of equal duration using data collection procedures that allow for analysis over multiple timespans, including those not currently associated with Twitter-identified trending topics. Term frequency-inverse document frequency analysis and relative normalised term frequency analysis were performed on the documents to identify the trending topics. Relative normalised term frequency analysis identified unigrams, bigrams, and trigrams as trending topics, while term frequency-inverse document frequency analysis identified unigrams as trending topics. Application of these methodologies to streaming data resulted in F-measures ranging from 0.1468 to 0.7508.


title: Media Quality Assessment by Perceptual Gaze-Shift Patterns Discovery

abstract: Quality assessment is an indispensable technique in a large body of media applications, i.e., photo retargeting, scenery rendering, and video summarization. In this paper, a fully automatic framework is proposed to mimic how humans subjectively perceive media quality. The key is a locality-preserved sparse encoding algorithm that accurately discovers human gaze shifting paths from each image or video clip. In particular, we first extract local image descriptors from each image/video, and subsequently project them into the so-called perceptual space. Then, a nonnegative matrix factorization (NMF) algorithm is proposed that represents each graphlet by a linear and sparse combination of the basis ones. Since each graphlet is visually/semantically similar to its neighbors, a locality-preserved constraint is encoded into the NMF algorithm. Mathematically, the saliency of each graphlet is quantified by the norm of its sparse codes. Afterward, we sequentially link them into a path to simulate human gaze allocation. Finally, a probabilistic quality model is learned based on such paths extracted from a collection of photos/videos, which are marked as high quality ones via multiple Flickr users. Comprehensive experiments have demonstrated that: 1) our quality model outperforms many of its competitors significantly, and 2) the learned paths are on average 89.5% consistent with real human gaze shifting paths.


title: X-band 11.7-W, 29-dB gain, 42% PAE three-stage pHEMT MMIC power amplifier

abstract: This paper presents an X-band high gain and high power three-stage PHEMT monolithic microwave integrated circuit (MMIC) power amplifier (PA). Based on 0.15-μm GaAs power PHEMT technology, this PA is fabricated on a 2-mil thick wafer. While operating under 7.2 V and 3300 mA dc bias condition, the characteristics of 29.2-dB small signal gain, 11.7-W output power, and 42.2% power added efficiency at 9.2 GHz can be achieved.


title: Global effects of smoking, of quitting, and of taxing tobacco.

abstract: Cigarette smoking is a major cause of illness and death. This article reviews both the magnitude of the disease burden from cigarette smoking worldwide and strategies to limit smoking.


title: Acne Vulgaris A Disease of Western Civilization

abstract: BACKGROUND ::: In westernized societies, acne vulgaris is a nearly universal skin disease afflicting 79% to 95% of the adolescent population. In men and women older than 25 years, 40% to 54% have some degree of facial acne, and clinical facial acne persists into middle age in 12% of women and 3% of men. Epidemiological evidence suggests that acne incidence rates are considerably lower in nonwesternized societies. Herein we report the prevalence of acne in 2 nonwesternized populations: the Kitavan Islanders of Papua New Guinea and the Aché hunter-gatherers of Paraguay. Additionally, we analyze how elements in nonwesternized environments may influence the development of acne. ::: ::: ::: OBSERVATIONS ::: Of 1200 Kitavan subjects examined (including 300 aged 15-25 years), no case of acne (grade 1 with multiple comedones or grades 2-4) was observed. Of 115 Aché subjects examined (including 15 aged 15-25 years) over 843 days, no case of active acne (grades 1-4) was observed. ::: ::: ::: CONCLUSIONS ::: The astonishing difference in acne incidence rates between nonwesternized and fully modernized societies cannot be solely attributed to genetic differences among populations but likely results from differing environmental factors. Identification of these factors may be useful in the treatment of acne in Western populations.


title: An Explanatory Study of MBA Students with Regards to Sustainability and Ethics Commitment

abstract: (1) Background: The consequences of the recent economic crisis have shown the need for promoting certain key skills in future entrepreneurs to create enterprises that are capable of producing added value, generating employment and remaining solvent or growing in a competitive and highly volatile environment. Therefore, the issue is not simply a matter of creating more businesses, but rather, a model based on organizations with strong sustainable values in the long term. (2) Methods: The originality and new approach of this article is to establish, through an empirical approach, the link between the entrepreneurial skills and environmental and social commitment in times of crisis. Based on the analysis of a questionnaire answered by MBA students and using structural equation modelling, the relation between entrepreneurial skills, social and environment commitment are tested. (3) Results: The statistical analysis proved the existence of a positive relation between entrepreneurs’ skills and environmental and social commitment pointing out the importance of sustainability commitment as a source of high added value activities. (4) Conclusion: The paper demonstrates that investing in the promotion of entrepreneurial skills in education systems has positive effects on how future entrepreneurs will manage their firms. Therefore, this sustainably depends on the present education policies.


title: Support Vector Method for Novelty Detection

abstract: Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified ν between 0 and 1. ::: ::: We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. ::: ::: The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.


title: Optimised management strategies for the Biosphere reserve Lobau, Austria - based on a multi criteria decision support system

abstract: Abstract Without sustainable rehabilitation measures, the Lobau, a freshwater Biosphere Reserve, will soon became a primarily terrestrial ecosystem with major implications for its rich aquatic and amphibic biodiversity. An innovative ecosystem management scheme for this Danube floodplain needs to optimally balance between conservation and restoration objectives and to harmonize the partly competing ecologic and socio-economic requirements for the next 40 years. Therefore, we present a project dealing with the development of a multi-criteria Decision Support System (DSS), based on interdisciplinary historical analyses and state-of-the-art ecosystem modelling. The DSS will assists multiple stakeholders in finding coherent and realistic management alternatives, by linking all objectives and measures in a transparent and reproducible way.


ATM Service Analysis Using Predictive Data Mining


title: Powers of Tensors and Fast Matrix Multiplication

abstract: This paper presents a method to analyze the powers of a given trilinear form (a special kind of algebraic constructions also called a tensor) and obtain upper bounds on the asymptotic complexity of matrix multiplication. Compared with existing approaches, this method is based on convex optimization, and thus has polynomial-time complexity. As an application, we use this method to study powers of the construction given by Coppersmith and Winograd [Journal of Symbolic Computation, 1990] and obtain the upper bound $\omega<2.3728639$ on the exponent of square matrix multiplication, which slightly improves the best known upper bound.


title: Choosing an NLP Library for Analyzing Software Documentation: A Systematic Literature Review and a Series of Experiments

abstract: To uncover interesting and actionable information from natural language documents authored by software developers, many researchers rely on "out-of-the-box" NLP libraries. However, software artifacts written in natural language are different from other textual documents due to the technical language used. In this paper, we first analyze the state of the art through a systematic literature review in which we find that only a small minority of papers justify their choice of an NLP library. We then report on a series of experiments in which we applied four state-of-the-art NLP libraries to publicly available software artifacts from three different sources. Our results show low agreement between different libraries (only between 60% and 71% of tokens were assigned the same part-of-speech tag by all four libraries) as well as differences in accuracy depending on source: For example, spaCy achieved the best accuracy on Stack Overflow data with nearly 90% of tokens tagged correctly, while it was clearly outperformed by Google's SyntaxNet when parsing GitHub ReadMe files. Our work implies that researchers should make an informed decision about the particular NLP library they choose and that customizations to libraries might be necessary to achieve good results when analyzing software artifacts written in natural language.


title: Resource Management in Cloud Computing: Classification and Taxonomy

abstract: Cloud Computing is a new era of remote computing / Internet based computing where one can access their personal resources easily from any computer through Internet. Cloud delivers computing as a utility as it is available to the cloud consumers on demand. It is a simple pay-per-use consumer-provider service model. It contains large number of shared resources. So Resource Management is always a major issue in cloud computing like any other computing paradigm. Due to the availability of finite resources it is very challenging for cloud providers to provide all the requested resources. From the cloud providers perspective cloud resources must be allocated in a fair and efficient manner. Research Survey is not available from the perspective of resource management as a process in cloud computing. So this research paper provides a detailed sequential view / steps on resource management in cloud computing. Firstly this research paper classifies various resources in cloud computing. It also gives taxonomy on resource management in cloud computing through which one can do further research. Lastly comparisons on various resource management algorithms has been presented.


Agency Selling or Reselling? Channel Structures in Electronic Retailing


title: Planning with Smart Objects

abstract: This paper presents a novel method of employing "smart objects" for problem solving in virtual environments. Smart objects were primarily used for behavioral animation in the past. The paper demonstrates how to use them for AI and planning purposes as well. We formally define which operations can be performed on a smart object in terms of their requirements and their effects. A planner uses this information to determine the correct sequence of actions needed to achieve a goal. This approach enables intelligent agents to solve problems requiring a collaboration of several agents and complex interactions with objects.


title: Opportunistic Computation Offloading in Mobile Edge Cloud Computing Environments

abstract: The dynamic mobility and limitations in computational power, battery resources, and memory availability are main bottlenecks in fully harnessing mobile devices as data mining platforms. Therefore, the mobile devices are augmented with cloud resources in mobile edge cloud computing (MECC) environments to seamlessly execute data mining tasks. The MECC infrastructures provide compute, network, and storage services in one-hop wireless distance from mobile devices to minimize the latency in communication as well as provide localized computations to reduce the burden on federated cloud systems. However, when and how to offload the computation is a hard problem. In this paper, we present an opportunistic computation offloading scheme to efficiently execute data mining tasks in MECC environments. The scheme provides the suitable execution mode after analyzing the amount of unprocessed data, privacy configurations, contextual information, and available on-board local resources (memory, CPU, and battery power). We develop a mobile application for online activity recognition and evaluate the proposed scheme using the event data stream of 5 million activities collected from 12 users for 15 days. The experiments show significant improvement in execution time and battery power consumption resulting in 98% data reduction.


title: How online brand community participation strengthens brand trust and commitment: A relationship marketing perspective

abstract: Advancements in information technology have shaped the way customers and organizations interact with one another. Online brand communities (OBCs), especially have found their way into 21st century relationship marketing. While research embraces these OBCs for their cost-efficiency and ability for faster interactions, it has not thoroughly examined the procedure through which participation in such OBCs affects the major constructs of relationship marketing. Drawing from the trust-commitment theory and its central concepts of brand trust, brand commitment and brand loyalty, this thesis discusses the applicability of this theory in a brand community and in an online context. It also introduces the concept of OBC commitment as a very important outcome of participation in an OBC. Using probability sampling and a self-administered questionnaire, this study employs a deductive logic to investigate if higher levels of participation in an OBC translate to higher levels of member commitment toward that OBC and higher levels of trust, commitment and loyalty toward the brand that the OBC supports. This study underlines the importance of understanding the process through which an OBC member gradually develops strong emotional ties with that OBC, as a result of continuous interaction with other OBC members. Additionally, how participation in an OBC evolves to attachment towards the brand that the OBC supports.


title: Interpreting the Ratio Criterion for Matching SIFT Descriptors

abstract: Matching keypoints by minimizing the Euclidean distance between their SIFT descriptors is an effective and extremely popular technique. Using the ratio between distances, as suggested by Lowe, is even more effective and leads to excellent matching accuracy. Probabilistic approaches that model the distribution of the distances were found effective as well. This work focuses, for the first time, on analyzing Lowe’s ratio criterion using a probabilistic approach. We provide two alternative interpretations of this criterion, which show that it is not only an effective heuristic but can also be formally justified. The first interpretation shows that Lowe’s ratio corresponds to a conditional probability that the match is incorrect. The second shows that the ratio corresponds to the Markov bound on this probability. The interpretations make it possible to slightly increase the effectiveness of the ratio criterion, and to obtain matching performance that exceeds all previous (non-learning based) results.


title: Better Living by Technical Assistance and Mobility in an Aging Society

abstract: The project PASSAge (Personalized Mobility, Assistance and Service Systems in an Aging Society), funded by the German Federal Ministry of Education and Research (BMBF, Bundesministerium fur Bildung und Forschung) aims at the implementation of seamless mobility chains that smoothly connect private and public spaces with a special regard to the needs of an aging society. By extending existing means of mobility with user-oriented components, individual and self-determined mobility for elderly people shall be ensured. Individual physical adaptions for buildings, vehicles, and mobility aids shall facilitate the usage of and changing between means of mobility. For planning trips, navigating, providing contextual mobility-related information, and keeping track of the health status, the so-called ‘HealthPhone’ will be an electronic mobility companion that can provide safety and security for the elderly users. In this paper, we describe how the technical, information architecture and physical components and solutions are planned and evaluated, in order to create innovative solutions meeting the needs of the users, ensuring user acceptance and thereby sustaining a economically feasible solution.


title: Collaborative detection and filtering of shrew DDoS

abstract: This paper presents a new spectral template-matching approach to countering shrew distributed denial-of-service (DDoS) attacks. These attacks are stealthy, periodic, pulsing, and low-rate in attack volume, very different from the flooding type of attacks. They are launched with high narrow spikes in very low frequency, periodically. Thus, shrew attacks may endanger the victim systems for a long time without being detected. In other words, such attacks may reduce the quality of services unnoticeably. Our defense method calls for collaborative detection and filtering (CDF) of shrew DDoS attacks. We detect shrew attack flows hidden in legitimate TCP/UDP streams by spectral analysis against pre-stored template of average attack spectral characteristics. This novel scheme is suitable for either software or hardware implementation. ::: ::: The CDF scheme is implemented with the NS-2 network simulator using real-life Internet background traffic mixed with attack datasets used by established research groups. Our simulated results show high detection accuracy by merging alerts from cooperative routers. Both theoretical modeling and simulation experimental results are reported here. The experiments achieved up to 95% successful detection of network anomalies along with a low 10% false positive alarms. The scheme cuts off malicious flows containing shrew attacks using a newly developed packet-filtering scheme. Our filtering scheme retained 99% of legitimate TCP flows, compared with only 20% TCP flows retained by using the Drop Tail algorithm. The paper also considers DSE FPGA, and network processor implementation issues and discusses limitations and further research challenges.


title: A 0.003 mm2 5.2 mW/tap 20 GBd inductor-less 5-tap analog RX-FFE

abstract: A 0.003 mm2 5.2 mW/tap analog receive-side feedforward equalizer (RX-FFE) is demonstrated in 40 nm CMOS for up to 20 GBd ADC-based links. The FFE is constructed entirely with analog-inverter transconductors and capacitors, avoiding the use of area-intensive inductors. The delay element is implemented as a first-order Pade approximant of an ideal delay. The equalization performance is measured to be sufficient to relax the ADC resolution requirement by 1 bit. The total power consumed is less than 26 mW with less than 9.2 nV/√Hz output noise for all configurations.


title: A comparison of modified Howland circuits as current generators with current mirror type circuits

abstract: Multi-frequency electrical impedance tomography (EIT) systems require stable voltage controlled current generators that will work over a wide frequency range and with a large variation in load impedance. In this paper we compare the performance of two commonly used designs: the first is a modified Howland circuit whilst the second is based on a current mirror. The output current and the output impedance of both circuits were determined through PSPICE simulation and through measurement. Both circuits were stable over the frequency ranges 1 kHz to 1 MHz. The maximum variation of output current with frequency for the modified Howland circuit was 2.0% and for the circuit based on a current mirror 1.6%. The output impedance for both circuits was greater than 100 kohms for frequencies up to 100 kHz. However, neither circuit achieved this output impedance at 1 MHz. Comparing the results from the two circuits suggests that there is little to choose between them in terms of a practical implementation.


Combination of Neural and Evolutionary Methods for Data Organization


title: A Comparative Study of Classifying Legal Documents with Neural Networks

abstract: In recent years, deep learning has shown promising results when used in the field of natural language processing (NLP). Neural networks (NNs) such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for various NLP tasks including sentiment analysis, information retrieval, and document classification. In this paper, we the present the Supreme Court Classifier (SCC), a system that applies these methods to the problem of document classification of legal court opinions. We compare methods using traditional machine learning with recent NN-based methods. We also present a CNN used with pre-trained word vectors which shows improvements over the state-of-the-art applied to our dataset. We train and evaluate our system using the Washington University School of Law Supreme Court Database (SCDB). Our best system (word2vec + CNN) achieves 72.4% accuracy when classifying the court decisions into 15 broad SCDB categories and 31.9% accuracy when classifying among 279 finer-grained SCDB categories.


title: Learning a High-Precision Robotic Assembly Task Using Pose Estimation from Simulated Depth Images.

abstract: Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new tasks. In this work we aim towards flexible and adaptable robotic assembly by using 3D CAD models for all parts to be assembled. We focus on a generic assembly task - the Siemens Innovation Challenge - in which a robot needs to assemble a gear-like mechanism with high precision into an operating system. To obtain the millimeter-accuracy required for this task and industrial settings alike, we use a depth camera mounted near the robot end-effector. We present a high-accuracy three-stage pose estimation pipeline based on deep convolutional neural networks, which includes detection, pose estimation, refinement, and handling of near- and full symmetries of parts. The networks are trained on simulated depth images by means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.14 millimeters and 1.09 degree leading to 88.6% success rate for robotic assembly of randomly distributed parts. To the best of our knowledge, this is the first time that the Siemens Innovation Challenge is fully solved, opening up new possibilities for automated industrial assembly.


title: Accurate Unlexicalized Parsing

abstract: We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F1) is better than that of early lexicalized PCFG models, and surprisingly close to the current state-of-the-art. This result has potential uses beyond establishing a strong lower bound on the maximum possible accuracy of unlexicalized models: an unlexicalized PCFG is much more compact, easier to replicate, and easier to interpret than more complex lexical models, and the parsing algorithms are simpler, more widely understood, of lower asymptotic complexity, and easier to optimize.


title: Super resolution: an overview

abstract: Abstract : Super-resolution algorithms produce a single high-resolution image from a set of several, low-resolution images of the desired scene. The low-resolution frames are shifted differently with respect to the high resolution frame with subpixel increments. This paper presents first a theoretical overview of super-resolution algorithms. The most important methods, namely, the iterative back-projection, projection onto convex sets, and maximum a posteriori estimation are then compared within the same framework of implementation.


title: Computing the value of a crowd

abstract: This paper proposes that the "value" of a crowd can be defined in terms of the overall engagement of the individuals within the crowd and that engagement is a function of certain characteristics of the crowds such as small world-ness, sparsity and connectedness. Engagement is hypothesized as messages being exchanged over the complex network which represents the crowd and the "value" is calculated from the entropy of message probability distributions. An initial random network is passed through a process of entropy maximization and the values of some structural properties are recorded with the changing topology to study the corresponding behavior. We show that as the small world-ness and connectedness of a crowd increases and the sparsity decreases, the engagement in the crowd increases.


Recent Advances in Google Real-Time HMM-Driven Unit Selection Synthesizer


title: Lacrimal Gland Prolapse: Management During Aesthetic Blepharoplasty: Review of the Literature and Case Reports

abstract: Prolapse of the lacrimal gland is an acquired clinical condition caused mainly by relaxation of the local suspending ligaments. Before an aesthetic blepharoplasty, there should be a preoperative clinical suspicion of lacrimal gland pathology for patients with bulging lateral thirds of the upper eyelids. It should be borne in mind that inadvertent removal of the lacrimal gland can lead to important alterations in ocular lubrication. This report describes two clinical cases of patients with lacrimal gland prolapse associated with dermatochalasis and their treatment.


title: Alternatives to Non-malleability: Definitions, Constructions, and Applications (Extended Abstract)

abstract: We explore whether non-malleability is necessary for the applications typically used to motivate it, and propose two alternatives. The first we call weak non-malleability (wnm) and show that it suffices to achieve secure contract bidding (the application for which non-malleability was initially introduced), despite being strictly weaker than non-malleability. The second we call tag-based non-malleability (tnm), and show that it suffices to construct an efficient universally-composable secure message transmission (SMT) protocol, for which the only previous solution was based on a public key encryption functionality whose security is equivalent to non-malleability. We also demonstrate constructions for wnm and tnm encryption schemes that are simpler than known constructions of non-malleable encryption schemes.


title: A Framework for Evaluating ICT Security Awareness

abstract: ICT resources are important assets of any organization and the protection of these resources are equally important. To be able to protect themselves and their profitability, many organizations have established information security awareness programs. In order for a security awareness program to add value to an organization and at the same time make a contribution to the field of information security it is necessary to have a set of methods to study and measure its effect. This paper gives an overview of a suggested framework for evaluating ICT security awareness. Following a brief description of the framework, a more detailed overview on the identification of areas to be evaluated, using a value focused assessment, will be presented. Comments on possible system generated information, that may be used to assist with the evaluation of security behavior of users, will also be presented.


title: Taxonomic study of the genus Zema Fennah (Hemiptera: Fulgoromorpha: Tropiduchidae) from China

abstract: Zema montana sp. nov. (Hemiptera: Fulgoroidea: Tropiduchidae) is described and illustrated from Yunnan, southwestern China. This represents the second known species of Zema . The type species, Z. gressitti Fennah, unrecorded since its 1956 description, is redescribed and illustrated and a revised generic description provided. New distributional records for Z. gressitti are given for Nepal and China (Yunnan, Xizang).


title: Data Mining of Inputs: Analysing Magnitude and Functional Measures

abstract: The problem of data encoding and feature selection for training back-propagation neural networks is well known. The basic principles are to avoid encrypting the underlying structure of the data, and to avoid using irrelevant inputs. This is not easy in the real world, where we often receive data which has been processed by at least one previous user. The data may contain too many instances of some class, and too few instances of other classes. Real data sets often include many irrelevant or redundant input fields. This paper examines the use of weight matrix analysis techniques and functional measures using two real (and hence noisy) data sets. The first part of this paper examines the use of the weight matrix of the trained neural network itself to determine which inputs are significant. A new technique is introduced and compared with two other techniques from the literature. We present our experience and results on some satellite data augmented by a terrain model. The task was to predict the forest supra-type based on the available information. A brute force technique eliminating randomly selected inputs was used to validate our approach. The second part of this paper examines the use of measures to determine the functional contribution of inputs to outputs. Inputs which include minor but unique information to the network are more significant than inputs with higher magnitude contribution but providing redundant information, which is also provided by another input. A comparison is made to sensitivity analysis, where the sensitivity of outputs to input perturbation is used as a measure of the significance of inputs. This paper presents a novel functional analysis of the weight matrix based on a technique developed for determining the behavioral significance of hidden neurons. This is compared with the application of the same technique to the training and test data. Finally, a novel aggregation technique is introduced.


title: 160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA)

abstract: BackgroundTo infer homology and subsequently gene function, the Smith-Waterman (SW) algorithm is used to find the optimal local alignment between two sequences. When searching sequence databases that may contain hundreds of millions of sequences, this algorithm becomes computationally expensive.ResultsIn this paper, we focused on accelerating the Smith-Waterman algorithm by using FPGA-based hardware that implemented a module for computing the score of a single cell of the SW matrix. Then using a grid of this module, the entire SW matrix was computed at the speed of field propagation through the FPGA circuit. These modifications dramatically accelerated the algorithm's computation time by up to 160 folds compared to a pure software implementation running on the same FPGA with an Altera Nios II softprocessor.ConclusionThis design of FPGA accelerated hardware offers a new promising direction to seeking computation improvement of genomic database searching.


title: How clever is the FiLM model, and how clever can it be?

abstract: The FiLM model achieves close-to-perfect performance on the diagnostic CLEVR dataset and is distinguished from other such models by having a comparatively simple and easily transferable architecture. In this paper, we investigate in more detail the ability of FiLM to learn various linguistic constructions. Our main results show that (a) FiLM is not able to learn relational statements straight away except for very simple instances, (b) training on a broader set of instances as well as pretraining on simpler instance types can help alleviate these learning difficulties, (c) mixing is less robust than pretraining and very sensitive to the compositional structure of the dataset. Overall, our results suggest that the approach of big all-encompassing datasets and the paradigm of "the effectiveness of data" may have fundamental limitations.


title: Sentiment analysis for Arabic language: A brief survey of approaches and techniques

abstract: With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews reviews in an automatic way. In the literature, there are numerous approaches proposed for automatic sentiment analysis for different language contexts. Each language has its own properties that makes the sentiment analysis more challenging. In this regard, this work presents a comprehensive survey of existing Arabic sentiment analysis studies, and covers the various approaches and techniques proposed in the literature. Moreover, we highlight the main difficulties and challenges of Arabic sentiment analysis, and the proposed techniques in literature to overcome these barriers.


title: Temporal Pyramid Relation Network for Video-Based Gesture Recognition

abstract: Gesture recognition in video is an important application of computer vision. However, there are few works talked about the temporal order or relation of the frames in video, which is important for model gestures. In this paper, we propose Temporal Pyramid Relation Network (TPRN) which can model the temporal relation of video frames effectively and efficiently. First, we use Temporal Pyramid Pooling (TPP) layer to get temporal feature sequences of multiple scale pyramids. Then, a Temporal Relation Network (TRN) is stacked on the feature sequence of each scale respectively to model the temporal relations of video frames at multiple scales. At last, representations of all scales are aggregated to get the final prediction. TPRN can take video clips of various length as input and is scalable for video length. We evaluate TPRN on a recently released very large video-based gesture recognition dataset - 20BN-Jester dataset v1, and TPRN achieves competitive performance.


title: Portrait-Aware Artistic Style Transfer

abstract: The goal of artistic style transfer is to transfer the style of artistic works into photos. However, the performances of existing style transfer algorithms on portraits are not very satisfactory, because the synthetic photo is either not sufficiently stylized or distorted severely in the portrait domain (i.e., foreground), which limits the use of style transfer for portraits. In this paper, we propose a novel portrait-aware artistic style transfer algorithm, which treats foreground and background differently. Particularly, we separate the foreground from the background, and apply fine-grained style transfer to the background and coarse-grained style transfer to the entire image at the same time, so that the artistic style of entire image can be transferred with the details of the portrait well preserved. Extensive experiments demonstrate the effectiveness of our proposed method.


Correlation of serum GFAP , S 100 B and NSE contents with post-traumatic oxidative stress response and insulin resistance in patients with traumatic brain injury


title: Towards Generic Text-Line Extraction

abstract: Text-line extraction is the backbone of document image analysis. Since decades, a large number of text-line finding methods have been proposed, where these methods rely on certain assumptions about a target class of documents with respect to writing styles, digitization methods, intensity values, and scripts. There is no generic text-line finding method that can be robustly applied to a large variety of simple and complex document images. We introduced the ridge-based text-line finding method, and published its initial results for curled text-line detection on camera-captured document images. In this paper, we demonstrates our ridge-based method as a generic text-line finding approach that can be robustly applied on a diverse collection of simple and complex document images. The comprehensive performance evaluation of the ridge-based method and its comparison with several state-of-the-art methods is presented in the paper. For this purpose, diverse categories of publicly available and standard datasets have been selected: UWIII (scanned, printed English script), DFKI-I (camera-captured, printed English script), UMD (handwritten Chinese, Hindi, and Korean scripts), ICDAR2007 handwritten segmentation contest (handwritten English, French, German and Greek scripts), Arabic/Urdu (scanned, printed script), and Fraktur (scanned, calligraphic German script). Experiments on these datasets show that the ridge-based method achieves better text-line extraction results as those of the best performing, domain-specific text-line finding methods. Firstly, these results show that the ridge-based method is a generic text-line extraction method. Secondly, these results are also helpful for the community to assess the advantages of this method.


title: Neuropsychologic Theory and Findings in Attention-Deficit/Hyperactivity Disorder: The State of the Field and Salient Challenges for the Coming Decade

abstract: The past decade has witnessed the establishment of several now well-replicated findings in the neuropsychology of attention-deficit/hyperactivity disorder (ADHD), which have been confirmed by meta-analyses. Progress has been notable from the importing of cognitive science and neuroscience paradigms. Yet these findings point to many neural networks being involved in the syndrome and to modest effect sizes suggesting that any one neuropsychologic deficit will not be able to explain the disorder. In this article, leading theories and key findings are briefly reviewed in four key domains: attention, executive functions, state regulation and motivation, and temporal information processing. Key issues facing the field of neuropsychologic research and theory in ADHD include 1) the need for more integrative developmental accounts that address both multiple neural systems and the socialization processes that assure their development; 2) consideration of multiple models/measures in the same study so as to examine relative contributions, within-group heterogeneity, and differential deficit; and 3) better integration of cognitive process models with affective and temperament theories so that early precursors to ADHD can be better understood. Overall, the field has witnessed notable progress as it converges on an understanding of ADHD in relation to disruption of a multicomponent self-regulatory system. The next era must articulate multipathway, multilevel developmental accounts of ADHD that incorporate neuropsychologic effects.


title: Evolution of a Subsumption Architecture Neurocontroller

abstract: An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. This approach is used to construct a layered controller for a simulated robot that learns which light source to approach in an environment with obstacles. The evolvability and performance of layered evolution on this task is compared to (standard) monolithic evolution, incremental and modularised evolution. To corroborate the hypothesis that a layered controller performs at least as well as an integrated one, the evolved layers are merged back into a single network. On the grounds of the test results, it is argued that layered evolution provides a superior approach for many tasks, and it is suggested that this approach may be the key to scaling up evolutionary robotics.


title: Protocol state fuzzing of TLS implementations

abstract: We describe a largely automated and systematic analysis of TLS implementations by what we call 'protocol state fuzzing': we use state machine learning to infer state machines from protocol implementations, using only blackbox testing, and then inspect the inferred state machines to look for spurious behaviour which might be an indication of flaws in the program logic. For detecting the presence of spurious behaviour the approach is almost fully automatic: we automatically obtain state machines and any spurious behaviour is then trivial to see. Detecting whether the spurious behaviour introduces exploitable security weaknesses does require manual investigation. Still, we take the point of view that any spurious functionality in a security protocol implementation is dangerous and should be removed. ::: ::: We analysed both server- and client-side implementations with a test harness that supports several key exchange algorithms and the option of client certificate authentication. We show that this approach can catch an interesting class of implementation flaws that is apparently common in security protocol implementations: in three of the TLS implementations analysed new security flaws were found (in GnuTLS, the Java Secure Socket Extension, and OpenSSL). This shows that protocol state fuzzing is a useful technique to systematically analyse security protocol implementations. As our analysis of different TLS implementations resulted in different and unique state machines for each one, the technique can also be used for fingerprinting TLS implementations.


title: Deep Portfolio Theory

abstract: We construct a deep portfolio theory. By building on Markowitz's classic risk-return trade-off, we develop a self-contained four-step routine of encode, calibrate, validate and verify to formulate an automated and general portfolio selection process. At the heart of our algorithm are deep hierarchical compositions of portfolios constructed in the encoding step. The calibration step then provides multivariate payouts in the form of deep hierarchical portfolios that are designed to target a variety of objective functions. The validate step trades-off the amount of regularization used in the encode and calibrate steps. The verification step uses a cross validation approach to trace out an ex post deep portfolio efficient frontier. We demonstrate all four steps of our portfolio theory numerically.


title: A Structural Model For Binaural Sound Synthesis

abstract: A structural model is presented for synthesizing binaural sound from a monaural source. The model produces well-controlled vertical as well as horizontal effects. The model is based on a simplified time-domain description of the physics of wave propagation and diffraction. The components of the model have a one-to-one correspondence with the physical sources of sound diffraction, delay, and reflection. The simplicity of the model permits efficient implementation in DSP hardware, and thus facilitates real-time operation. Additionally, the parameters in the model can be adjusted to fit a particular individual's characteristics, thereby producing individualized head-related transfer functions. Experimental tests verify the perceptual effectiveness of the approach.


A PARALLEL APPROACH FOR IMPROVING DATA SECURITY


title: Optimal Control Via Neural Networks: A Convex Approach

abstract: Control of complex systems involves both system identification and controller design. Deep neural networks have proven to be successful in many identification tasks, however, from model-based control perspective, these networks are difficult to work with because they are typically nonlinear and nonconvex. Therefore many systems are still identified and controlled based on simple linear models despite their poor representation capability. In this paper we bridge the gap between model accuracy and control tractability faced by neural networks, by explicitly constructing networks that are convex with respect to their inputs. We show that these input convex networks can be trained to obtain accurate models of complex physical systems. In particular, we design input convex recurrent neural networks to capture temporal behavior of dynamical systems. Then optimal controllers can be achieved via solving a convex model predictive control problem. Experiment results demonstrate the good potential of the proposed input convex neural network based approach in a variety of control applications. In particular we show that in the MuJoCo locomotion tasks, we could achieve over 10% higher performance using 5* less time compared with state-of-the-art model-based reinforcement learning method; and in the building HVAC control example, our method achieved up to 20% energy reduction compared with classic linear models.


title: Interacting with the SOA-Based Internet of Things: Discovery, Query, Selection, and On-Demand Provisioning of Web Services

abstract: The increasing usage of smart embedded devices in business blurs the line between the virtual and real worlds. This creates new opportunities to build applications that better integrate real-time state of the physical world, and hence, provides enterprise services that are highly dynamic, more diverse, and efficient. Service-Oriented Architecture (SOA) approaches traditionally used to couple functionality of heavyweight corporate IT systems, are becoming applicable to embedded real-world devices, i.e., objects of the physical world that feature embedded processing and communication. In such infrastructures, composed of large numbers of networked, resource-limited devices, the discovery of services and on-demand provisioning of missing functionality is a significant challenge. We propose a process and a suitable system architecture that enables developers and business process designers to dynamically query, select, and use running instances of real-world services (i.e., services running on physical devices) or even deploy new ones on-demand, all in the context of composite, real-world business applications.


1 Differential Dynamic Programming Approach for Robust-optimal Low-thrust Trajectory Design Considering Uncertainy


title: Fashion-Gen: The Generative Fashion Dataset and Challenge

abstract: We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists. Each item is photographed from a variety of angles. We provide baseline results on 1) high-resolution image generation, and 2) image generation conditioned on the given text descriptions. We invite the community to improve upon these baselines. In this paper, we also outline the details of a challenge that we are launching based upon this dataset.


title: Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

abstract: As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.


title: Face Aging with Contextual Generative Adversarial Nets

abstract: Face aging, which renders aging faces for an input face, has attracted extensive attention in the multimedia research. Recently, several conditional Generative Adversarial Nets (GANs) based methods have achieved great success. They can generate images fitting the real face distributions conditioned on each individual age group. However, these methods fail to capture the transition patterns, e.g., the gradual shape and texture changes between adjacent age groups. In this paper, we propose a novel Contextual Generative Adversarial Nets (C-GANs) to specifically take it into consideration. The C-GANs consists of a conditional transformation network and two discriminative networks. The conditional transformation network imitates the aging procedure with several specially designed residual blocks. The age discriminative network guides the synthesized face to fit the real conditional distribution. The transition pattern discriminative network is novel, aiming to distinguish the real transition patterns with the fake ones. It serves as an extra regularization term for the conditional transformation network, ensuring the generated image pairs to fit the corresponding real transition pattern distribution. Experimental results demonstrate the proposed framework produces appealing results by comparing with the state-of-the-art and ground truth. We also observe performance gain for cross-age face verification.


title: The medical evaluation of the sexually abused child: lessons from a decade of research.

abstract: The problem of child sexual abuse (CSA) has engendered lively interest among physicians and other health care providers for more than 20 years. The number of cases of sexual abuse known to child protection service agencies in the United States rose sharply from 1977 to 1988, then leveled off and declined between 1988 and 1996 (Fig 1).1 Since the mid-1970s, accumulating clinical experience and research have considerably expanded and refined our understanding of this common problem. ::: ::: ::: ::: Fig. 1. ::: Estimated number of children reported to state child protective service agencies for suspected sexual abuse, 1978–1996. Reprinted with permission from the National Committee to Prevent Child Abuse.1 ::: ::: ::: ::: Several investigators have provided comprehensive reviews of CSA and of the role of the physician in diagnosis and management.2–4A partial list of epidemiologic, psychological, and legal topics in the area of CSA that have been investigated and reviewed during the past decade includes the effectiveness and accuracy of various techniques for obtaining histories of sexual abuse (repeated interviews, standardized questionnaires, drawings, anatomically correct dolls)5–7; the sexual abuse of boys8,,9; the impact of sexual abuse on children's subsequent psychosocial wellbeing10,,11; the relationship of CSA to sexual aggression in children and appropriate treatment for offending children12,,13; the criminal prosecution of CSA cases14; and the effectiveness of CSA prevention programs.2 ::: ::: In this article, we will highlight research published during the past decade that deals particularly with topics in the medical evaluation of sexually abused children and that has advanced our understanding of those topics. We have divided our discussion into three sections: the conduct of the physical examination, the interpretation of physical examination findings, and selected sexually transmitted infections in children. ::: ::: ### Special Examination Techniques ::: ::: The immediate goals of the medical assessment …


title: Tracking-by-Segmentation with Online Gradient Boosting Decision Tree

abstract: We propose an online tracking algorithm that adaptively models target appearances based on an online gradient boosting decision tree. Our algorithm is particularly useful for non-rigid and/or articulated objects since it handles various deformations of the target effectively by integrating a classifier operating on individual patches and provides segmentation masks of the target as final results. The posterior of the target state is propagated over time by particle filtering, where the likelihood is computed based mainly on patch-level confidence map associated with a latent target state corresponding to each sample. Once tracking is completed in each frame, our gradient boosting decision tree is updated to adapt new data in a recursive manner. For effective evaluation of segmentation-based tracking algorithms, we construct a new ground-truth that contains pixel-level annotation of segmentation mask. We evaluate the performance of our tracking algorithm based on the measures for segmentation masks, where our algorithm illustrates superior accuracy compared to the state-of-the-art segmentation-based tracking methods.


title: An Empirical Study of Software Reuse and Quality in an Industrial Setting

abstract: Software reuse is known to be generally effective in reducing development and maintenance time and cost as well as increasing quality. In this paper, the effects of reuse on software quality in an industrial setting are empirically investigated within the framework of three different case studies. Throughout this study, we worked with Turkey’s leading defense industry company Aselsan’s software engineering department. We collected and calculated reuse and quality metrics as well as performance measures of individual embedded software modules and staff productivity rates. By analyzing these measurements, we developed suggestions to further benefit from reuse through systematic improvements to the reuse infrastructure and process.


title: Defending the morality of violent video games

abstract: The effect of violent video games is among the most widely discussed topics in media studies, and for good reason. These games are immensely popular, but many seem morally objectionable. Critics attack them for a number of reasons ranging from their capacity to teach players weapons skills to their ability to directly cause violent actions. This essay shows that many of these criticisms are misguided. Theoretical and empirical arguments against violent video games often suffer from a number of significant shortcomings that make them ineffective. This essay argues that video games are defensible from the perspective of Kantian, Aristotelian, and utilitarian moral theories.


title: What is it?' Categorization flexibility and consumers' responses to really new products

abstract: To understand really new products, consumers face the challenge of constructing new knowledge structures rather simply changing existing ones. Recent research in categorization suggests that one strategy for creating representations for these new products is to use information already contained in familiar product categories. While knowledge from multiple existing categories may be relevant, little research has examined how (and if) consumers process information drawn from more than one domain. We use two experiments to demonstrate how consumers use cues from multiple categories to develop expectations about and preferences for new products. Our findings suggest that the first plausible category label provided to the consumer significantly influences their categorizations, expectations, and preferences. Only when advertisers place limits on the type of information to transfer from each existing category can consumers use information from multiple categories effectively.


title: A low-cost digital control scheme for Brushless DC motor drives in domestic applications

abstract: Electric motors are an integral part of industrial plants with no less than 5 billon motors built worldwide every year. Residential and commercial appliances such as refrigerators and air conditioning systems have been using conventional motor drive technology. The machines in these applications typically have low efficiencies and high maintenance. Brushless DC (BLDC) drives are known for higher efficiency, lower maintenance and higher cost. In a market driven by profit margins, the appliance industry is reluctant to replace the conventional motor drives with the advanced motor drives (BLDC) due to their higher cost. Therefore it is necessary to have a cost-efficient, but effective BLDC motor controller. This paper lays the groundwork for the development of a new low-cost IC for control of BLDC motors. A simple novel digital Pulse Width Modulation (PWM) control has been implemented for a trapezoidal BLDC motor drive system. Due to the simplistic nature of this control, it has the potential to be implemented in a low cost Application Specific Integrated Circuit (ASIC). The novel controller is modeled and verified using simulations. Experimental verification is carried out using Field Programmable Gate Arrays (FPGA) to validate the claims presented.


Identifying Interestingness in Fashion E-commerce using Pinterest Data