Online Learning of Weighted Relational Rules for Complex Event Recognition

Systems for symbolic complex event recognition detect occurrences of events in time using a set of event definitions in the form of logical rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, connections to techniques for learning such rules from data. We advance the state-of-the-art by combining an existing online algorithm for learning crisp relational structure with an online method for weight learning in Markov Logic Networks (MLN). The result is an algorithm that learns complex event patterns in the form of Event Calculus theories in the MLN semantics. We evaluate our approach on a challenging real-world application for activity recognition and show that it outperforms both its crisp predecessor and competing online MLN learners in terms of predictive performance, at the price of a small increase in training time.


Deep Reinforcement Learning for Robotic Manipulation

Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on offpolicy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.


Gradient and Curvature from Photometric Stereo Including Local Confidence Estimation

Photometric stereo is one technique for 3D shape determination that has been implemented in a variety of experimental settings and that has produced consistently good results. The idea is to use intensity values recorded from multiple images obtained from the same viewpoint but under different conditions of illumination. The resulting radiometric constraint makes it possible to obtain local estimates of both surface orientation and surface curvature without requiring global smoothness assumptions and without requiring prior image segmentation. This paper moves photometric stereo one step closer to practical viability by describing an experimental setting in which surface gradient estimation is achieved on full frame video data at near video frame rates (i.e., 15Hz). The implementation uses commercially available hardware. Reflectance is modeled empirically using measurements obtained from a calibration sphere. Estimation of the gradient, (p, q), requires only simple table lookup. Curvature estimation uses, in addition, the reflectance map, R(p, q). The required lookup table and reflectance maps are derived during calibration. Because reflectance is modeled empirically, no prior physical model of the reflectance characteristics of the objects to be analyzed is assumed. At the same time, if a good physical model is available, it can be retrofit to the method for implementation purposes. Photometric stereo is subject to error in the presence of cast shadows and interreflection. No purely local technique can succeed since these phenomena are inherently non-local. Nevertheless, this paper demonstrates that one can exploit the redundancy in three light source photometric stereo to, in most cases, locally detect the presence of cast shadows and interreflection. Detection is facilitated by explicitly including a local confidence estimate in the lookup table used for gradient estimation.


Anatomisation with slicing: a new privacy preservation approach for multiple sensitive attributes

An enormous quantity of personal health information is available in recent decades and tampering of any part of this information imposes a great risk to the health care field. Existing anonymization methods are only apt for single sensitive and low dimensional data to keep up with privacy specifically like generalization and bucketization. In this paper, an anonymization technique is proposed that is a combination of the benefits of anatomization, and enhanced slicing approach adhering to the principle of k-anonymity and l-diversity for the purpose of dealing with high dimensional data along with multiple sensitive data. The anatomization approach dissociates the correlation observed between the quasi identifier attributes and sensitive attributes (SA) and yields two separate tables with non-overlapping attributes. In the enhanced slicing algorithm, vertical partitioning does the grouping of the correlated SA in ST together and thereby minimizes the dimensionality by employing the advanced clustering algorithm. In order to get the optimal size of buckets, tuple partitioning is conducted by MFA. The experimental outcomes indicate that the proposed method can preserve privacy of data with numerous SA. The anatomization approach minimizes the loss of information and slicing algorithm helps in the preservation of correlation and utility which in turn results in reducing the data dimensionality and information loss. The advanced clustering algorithms prove its efficiency by minimizing the time and complexity. Furthermore, this work sticks to the principle of k-anonymity, l-diversity and thus avoids privacy threats like membership, identity and attributes disclosure.


Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation

Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e.g., Wikipedia). Modeling topical coherence is crucial for this task based on the assumption that information from the same semantic context tends to belong to the same topic. This paper presents a novel deep semantic relatedness model (DSRM) based on deep neural networks (DNN) and semantic knowledge graphs (KGs) to measure entity semantic relatedness for topical coherence modeling. The DSRM is directly trained on large-scale KGs and it maps heterogeneous types of knowledge of an entity from KGs to numerical feature vectors in a latent space such that the distance between two semantically-related entities is minimized. Compared with the state-ofthe-art relatedness approach proposed by (Milne and Witten, 2008a), the DSRM obtains 19.4% and 24.5% reductions in entity disambiguation errors on two publicly available datasets respectively.


Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century.

Dengue fever/dengue hemorrhagic fever is now one of the most important public health problems in tropical developing countries and also has major economic and societal consequences.


AN AUTOMATIC SYSTEM FOR DETECTING AND COUNTING RBC AND WBC USING FUZZY LOGIC

Blood cell detection and counting is the initial process for detecting and diagnosing diseases. Several image processing algorithms are there for the blood cell classification and counting. The processed image helps to detect different blood related diseases. In all those algorithms several pre-processing steps are there for the process of detection and counting. Though all the algorithms give accurate results, the pre-processing steps are complex and time-consuming. This paper discusses about the RBC and WBC detection using fuzzy logic. Fuzzy logic toolbox software in MATLAB is used to develop the model on virtual platform.


Evoked-potential correlates of stimulus uncertainty.

The average evoked-potential waveforms to sound and light stimuli recorded from scalp in awake human subjects show differences as a function of the subject's degree of uncertainty with respect to the sensory modality of the stimulus to be presented. Differences are also found in the evoked potential as a function of whether or not the sensorymodality of the stimulus was anticipated correctly. The major waveform alteration is in the amplitude of a positive-going component which reaches peak amplitude at about 300 milliseconds.


Development of DC to Single-Phase AC Voltage Source Inverter With Active Power Decoupling Based on Flying Capacitor DC/DC Converter

In the present, a power decoupling method without additional component is proposed for a dc to single-phase ac converter, which consists of a flying capacitor dc/dc converter (FCC) and the voltage source inverter (VSI). In particular, a small flying capacitor in the FCC is used for both a boost operation and a double-line-frequency power ripple reduction. Thus, the dc-link capacitor value can be minimized in order to avoid the use of a large electrolytic capacitor. In addition, component design, of, e.g., the boost inductor and the flying capacitor, is clarified when the proposed control is applied. Experiments were carried out using a 1.5-kW prototype in order to verify the validity of the proposed control. The experimental results revealed that the use of the proposed control reduced the dc-link voltage ripple by 74.5%, and the total harmonic distortion (THD) of the inverter output current was less than 5%. Moreover, a maximum system efficiency of 95.4% was achieved at a load of 1.1 kW. Finally, the high power density design is evaluated by the Pareto front optimization. The power densities of three power decoupling topologies, such as a boost topology, a buck topology, and the proposed topology are compared. As a result, the proposed topology achieves the highest power density (5.3 kW/dm3) among the topologies considered herein.


Efficient Exploration in Monte Carlo Tree Search using Human Action Abstractions

Monte Carlo Tree Search (MCTS) is a family of methods for planning in large domains. It focuses on finding a good action for a particular state, making its complexity independent of the size of the state space. However such methods are exponential with respect to the branching factor. Effective application of MCTS requires good heuristics to arbitrate action selection during learning. In this paper we present a policy-guided approach that utilizes action abstractions, derived from human input, with MCTS to facilitate efficient exploration. We draw from existing work in hierarchical reinforcement learning, interactive machine learning and show how multi-step actions, represented as stochastic policies, can serve as good action selection heuristics. We demonstrate the efficacy of our approach in the PacMan domain and highlight its advantages over traditional MCTS.


A Survey of Music Recommendation Systems and Future Perspectives

Along with the rapid expansion of digital music formats, managing and searching for songs has become significant. Though music information retrieval (MIR) techniques have been made successfully in last ten years, the development of music recommender systems is still at a very early stage. Therefore, this paper surveys a general framework and state-of-art approaches in recommending music. Two popular algorithms: collaborative filtering (CF) and content-based model (CBM), have been found to perform well. Due to the relatively poor experience in finding songs in long tail and the powerful emotional meanings in music, two user-centric approaches: context-based model and emotion-based model, have been paid increasing attention. In this paper, three key components in music recommender user modelling, item profiling, and match algorithms are discussed. Six recommendation models and four potential issues towards user experience, are explained. However, subjective music recommendation system has not been fully investigated. To this end, we propose a motivation-based model using the empirical studies of human behaviour, sports education, music psychology.


Image super-resolution using gradient profile prior

In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.


DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path. In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration. Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1


Adversarial Geometry and Lighting using a Differentiable Renderer

Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm. This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached. Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene. As such, we propose a novel evaluation measure, parametric normballs, by directly perturbing physical parameters that underly image formation. One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry. Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.


A framework for the design and integration of collaborative classroom games

The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom’s revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components. 2011 Elsevier Ltd. All rights reserved.


Effects of growth mode and pyruvate carboxylase on succinic acid production by metabolically engineered strains of Escherichia coli.

Escherichia coli NZN111, which lacks activities for pyruvate-formate lyase and lactate dehydrogenase, and AFP111, a derivative which contains an additional mutation in ptsG (a gene encoding an enzyme of the glucose phophotransferase system), accumulate significant levels of succinic acid (succinate) under anaerobic conditions. Plasmid pTrc99A-pyc, which expresses the Rhizobium etli pyruvate carboxylase enzyme, was introduced into both strains. We compared growth, substrate consumption, product formation, and activities of seven key enzymes (acetate kinase, fumarate reductase, glucokinase, isocitrate dehydrogenase, isocitrate lyase, phosphoenolpyruvate carboxylase, and pyruvate carboxylase) from glucose for NZN111, NZN111/pTrc99A-pyc, AFP111, and AFP111/pTrc99A-pyc under both exclusively anaerobic and dual-phase conditions (an aerobic growth phase followed by an anaerobic production phase). The highest succinate mass yield was attained with AFP111/pTrc99A-pyc under dual-phase conditions with low pyruvate carboxylase activity. Dual-phase conditions led to significant isocitrate lyase activity in both NZN111 and AFP111, while under exclusively anaerobic conditions, an absence of isocitrate lyase activity resulted in significant pyruvate accumulation. Enzyme assays indicated that under dual-phase conditions, carbon flows not only through the reductive arm of the tricarboxylic acid cycle for succinate generation but also through the glyoxylate shunt and thus provides the cells with metabolic flexibility in the formation of succinate. Significant glucokinase activity in AFP111 compared to NZN111 similarly permits increased metabolic flexibility of AFP111. The differences between the strains and the benefit of pyruvate carboxylase under both exclusively anaerobic and dual-phase conditions are discussed in light of the cellular constraint for a redox balance.


Cloning Cryptographic RFID

We develop a new, custom-built hardware for emulating contactless smartcards compliant to ISO 14443. The device is based on a modern low-cost microcontroller and can support basically all relevant (cryptographic) protocols used by contactless smartcards today, e.g., those based on AES or Triple-DES. As a proof of concept, we present a full emulation of Mifare Classic cards on the basis of our highly optimized implementation of the stream cipher Crypto1. The implementation enables the creation of exact clones of such cards, including the UID. We furthermore reverse-engineered the protocol of DESFire EV1 and realize the first emulation of DESFire and DESFire EV1 cards in the literature. We practically demonstrate the capabilities of our emulator by spoofing several real-world systems, e.g., creating a contactless payment card which allows an attacker to set the stored credit balance as desired and hence make an infinite amount of payments.


Spoken Language Recognition: From Fundamentals to Practice

Spoken language recognition refers to the automatic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonological and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.


Entailment above the word level in distributional semantics

We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|=some dogs) to entailment involving unseen quantifiers (all cats|=several cats). Moreover, nominal and quantifier phrase entailment appears to be cued by different distributional correlates, as predicted by the type-based view of entailment in formal semantics.


Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and entailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent property of a single word in the pair: whether that word is a “prototypical hypernym”.


Hierarchical Clustering Algorithms for Document Datasets

Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularity. This paper focuses on document clustering algorithms that build such hierarchical solutions and (i) presents a comprehensive study of partitional and agglomerative algorithms that use different criterion functions and merging schemes, and (ii) presents a new class of clustering algorithms called constrained agglomerative algorithms, which combine features from both partitional and agglomerative approaches that allows them to reduce the early-stage errors made by agglomerative methods and hence improve the quality of clustering solutions. The experimental evaluation shows that, contrary to the common belief, partitional algorithms always lead to better solutions than agglomerative algorithms; making them ideal for clustering large document collections due to not only their relatively low computational requirements, but also higher clustering quality. Furthermore, the constrained agglomerative methods consistently lead to better solutions than agglomerative methods alone and for many cases they outperform partitional methods, as well.


Exploring Various Knowledge in Relation Extraction

Extracting semantic relationships between entities is challenging. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using SVM. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while additional information from full parsing gives limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. We also demonstrate how semantic information such as WordNet and Name List, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE corpus shows that effective incorporation of diverse features enables our system outperform previously best-reported systems on the 24 ACE relation subtypes and significantly outperforms tree kernel-based systems by over 20 in F-measure on the 5 ACE relation types.


Learning concepts and categories: is spacing the "enemy of induction"?

Inductive learning -- that is, learning a new concept or category by observing exemplars -- happens constantly, for example, when a baby learns a new word or a doctor classifies x-rays. What influence does the spacing of exemplars have on induction? Compared with massing, spacing enhances long-term recall, but we expected spacing to hamper induction by making the commonalities that define a concept or category less apparent. We asked participants to study multiple paintings by different artists, with a given artist's paintings presented consecutively (massed) or interleaved with other artists' paintings (spaced). We then tested induction by asking participants to indicate which studied artist (Experiments 1a and 1b) or whether any studied artist (Experiment 2) painted each of a series of new paintings. Surprisingly, induction profited from spacing, even though massing apparently created a sense of fluent learning: Participants rated massing as more effective than spacing, even after their own test performance had demonstrated the opposite.


Using Lexical Resources for Irony and Sarcasm Classification

The paper presents a language dependent model for classification of statements into ironic and non-ironic. The model uses various language resources: morphological dictionaries, sentiment lexicon, lexicon of markers and a WordNet based ontology. This approach uses various features: antonymous pairs obtained using the reasoning rules over the Serbian WordNet ontology (R), antonymous pairs in which one member has positive sentiment polarity (PPR), polarity of positive sentiment words (PSP), ordered sequence of sentiment tags (OSA), Part-of-Speech tags of words (POS) and irony markers (M). The evaluation was performed on two collections of tweets that had been manually annotated according to irony. These collections of tweets as well as the used language resources are in the Serbian language (or one of closely related languages --Bosnian/Croatian/Montenegrin). The best accuracy of the developed classifier was achieved for irony with a set of 5 features -- (PPR, PSP, POS, OSA, M) -- acc = 86.1%, while for sarcasm the best results were achieved with the set (R, PSP, POS, OSA, M) -- acc = 72.8.


Robust Non-Rigid Motion Tracking and Surface Reconstruction Using $L_0$ Regularization

We present a new motion tracking technique to robustly reconstruct non-rigid geometries and motions from a single view depth input recorded by a consumer depth sensor. The idea is based on the observation that most non-rigid motions (especially human-related motions) are intrinsically involved in articulate motion subspace. To take this advantage, we propose a novel <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math><alternatives> <inline-graphic xlink:href="xu-ieq2-2688331.gif"/></alternatives></inline-formula> based motion regularizer with an iterative solver that implicitly constrains local deformations with articulate structures, leading to reduced solution space and physical plausible deformations. The <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math> <alternatives><inline-graphic xlink:href="xu-ieq3-2688331.gif"/></alternatives></inline-formula> strategy is integrated into the available non-rigid motion tracking pipeline, and gradually extracts articulate joints information online with the tracking, which corrects the tracking errors in the results. The information of the articulate joints is used in the following tracking procedure to further improve the tracking accuracy and prevent tracking failures. Extensive experiments over complex human body motions with occlusions, facial and hand motions demonstrate that our approach substantially improves the robustness and accuracy in motion tracking.


Grid interfaced solar PV based water pumping using brushless DC motor drive

This paper proposes a solar photovoltaic (PV) fed water pumping system driven by a brushless DC (BLDC) motor. A promising case of interruption in the water pumping due to the intermittency of PV power generation is resolved by using a power grid as an external power backup. The power is drawn from the grid in case the PV array is unable to meet the required power demand, otherwise the PV array is preferably used. A unidirectional power flow control for the same is developed and realized through a power factor corrected (PFC) boost converter. The proposed system and control enable a consumer to get a full volume of water delivery throughout the day and night regardless of the climatic condition. The utilization of motor-pump at best is thus made possible in addition to an enhanced reliability of the pumping system. The power quality standards required at the utility grid are met by the developed control. The MATLAB/Simulink based simulations and the performance analysis are carried out to demonstrate the applicability of the system.


Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features

Viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. This is likely due to the difficulty in matching two objects with unknown viewpoint and pose. This paper presents a method of performing viewpoint invariant pedestrian recognition using an efficiently and intelligently designed object representation, the ensemble of localized features (ELF). Instead of designing a specific feature by hand to solve the problem, we define a feature space using our intuition about the problem and let a machine learning algorithm find the best representation. We show how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm. This approach allows many different kinds of simple features to be combined into a single similarity function. The method is evaluated using a viewpoint invariant pedestrian recognition dataset and the results are shown to be superior to all previous benchmarks for both recognition and reacquisition of pedestrians.


OpenCV on TI ’ s DSP + ARM ® platforms : Mitigating the challenges of porting OpenCV to embedded platforms Introduction

In today’s advancing market, the growing performance and decreasing price of embedded processors are opening many doors for developers to design highly sophisticated solutions for different end applications. The complexities of these systems can create bottlenecks for developers in the form of longer development times, more complicated development environments and issues with application stability and quality. Developers can address these problems using sophisticated software packages such as OpenCV, but migrating this software to embedded platforms poses its own set of challenges. This paper will review how to mitigate some of these issues, including C++ implementation, memory constraints, floating-point support and opportunities to maximize performance using vendor-optimized libraries and integrated accelerators or co-processors. Finally, we will introduce a new effort by Texas Instruments (TI) to optimize vision systems by running OpenCV on the C6000TM digital signal processor (DSP) architecture. Benchmarks will show the advantage of using the DSP by comparing the performance of a DSP+ARM® system-on-chip (SoC) processor against an ARM-only device. OpenCV on TI’s DSP+ARM platforms: Mitigating the challenges of porting OpenCV to embedded platforms Introduction OpenCV is a free and open-source computer vision library that offers a broad range of functionality under the permissive Berkeley Software Distribution (BSD) license. The library itself is written in C++ and is also usable through C or Python language applications. Thousands of developers use OpenCV to power their own specialized applications, making it the most widely-used library of its kind. The OpenCV project is under active development, with regular updates to eliminate bugs and add new functionality. The mainline development effort targets the x86 architecture and supports acceleration via Intel’s proprietary Integrated Performance Primitives (IPP) library. A recent release also added support for graphics processing unit (GPU) acceleration using NVIDIA’s Compute Unifi ed Device Architecture (CUDA) standard. OpenCV’s greatest asset is the sheer breadth of algorithms included in its standard distribution. Figure 1 on page 2 shows an incomplete list of some of the key function categories included in OpenCV. These range from low-level image fi ltering and transformation to sophisticated feature analysis and machine learning functionality. A complete listing of every function and use case is beyond the scope of this article, but we will consider the unique requirements of developers in the embedded vision space. For these developers, OpenCV represents an attractively comprehensive toolbox of useful, well-tested algorithms that can serve as building blocks for their own specialized applications. The question then becomes whether or not OpenCV can be used directly in their embedded systems. Despite its original development focus for use with PC workstations, OpenCV can also be a useful tool for embedded development. There are vendor-specifi c libraries that offer OpenCV-like capabilities on various embedded systems, but few can match OpenCV’s ubiquity in the computer vision fi eld or the sheer breadth of its included algorithms. It should come as no surprise that OpenCV has already been ported to the ARM® architecture, a popular CPU choice for embedded processors. It’s certainly possible to cross-compile the OpenCV source code as-is and use the result with embedded devices, but memory constraints and other architectural considerations may pose a problem. This white paper will examine some of the specifi c obstacles that must be overcome for OpenCV to achieve acceptable performance on Joseph Coombs, Applications engineering, Texas Instruments Rahul Prabhu, Applications engineering, Texas Instruments W H I T E P A P E R


Model-based control of a 6-dof electrohydraulic Stewart – Gough platform

In this paper, a novel model-based controller for a six Degree-of-Freedom (dof) electrohydraulic Stewart–Gough platform is developed. Dynamic models of low complexity are employed that describe the salient dynamics of the main electrohydraulic components. Rigid body equations of motion and hydraulics dynamics, including friction and servovalve models are used. The developed feedback controller uses the system dynamic and hydraulic model to yield servovalve currents, so that the error dynamics converge asymptotically to zero, independent of load variations. In this approach, force, pressure or acceleration feedback is not required. Simulations with typical desired trajectory inputs are presented and a good performance of the controller is obtained. The proposed methodology can be extended to electrohydraulic serial or closed-chain manipulators and simulators. 2007 Elsevier Ltd. All rights reserved.


Model Selection Management Systems: The Next Frontier of Advanced Analytics

John Boyd recognized in the 1960's the importance of situation awareness for military operations and introduced the notion of the OODA loop (Observe, Orient, Decide, and Act). Today we realize that many applications have to deal with situation awareness: Customer Relationship Management, Human Capital Management, Supply Chain Management, patient care, power grid management, and cloud services management, as well as any IoT (Internet of Things) related application; the list seems to be endless. Situation awareness requires applications to support the management of data, knowledge, processes, and other services such as social networking in an integrated way. These applications additionally require high personalization as well as rapid and continuous evolution. They must provide a wide variety of operational and functional requirements, including real time processing.
 Handcrafting these applications is an almost impossible task requiring exhaustive resources for development and maintenance. Due to the resources and time involved in their development, these applications typically fall way short of the desired functionality, operational characteristics, and ease and speed of evolution. We - the authors - have developed a model enabling the development and maintenance of situation-aware applications in a declarative and therefore economical manner; we call this model KIDS - Knowledge Intensive Data-processing System.


Training Sources 3 D Normalized Pose Space 2 D Normalized Pose Space KD-Tree Input Image 2 D Pose Estimation 3 D Pose Reconstruction

In this work we address the challenging problem of 3D human pose estimation from single images. Recent approaches learn deep neural networks to regress 3D pose directly from images. One major challenge for such methods, however, is the collection of training data. Specifically, collecting large amounts of training data containing unconstrained images annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of accurate 3D motion capture data, and the second source consists of unconstrained images with annotated 2D poses. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient 3D pose retrieval. To this end, we first convert the motion capture data into a normalized 2D pose space, and separately learn a 2D pose estimation model from the image data. During inference, we estimate the 2D pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a mapping from the 3D pose space to the image and reconstruct the 3D pose. We provide a comprehensive evaluation of the proposed method and experimentally demonstrate the effectiveness of our approach, even when the skeleton structures of the two sources differ substantially.


Accurate and dense wide-baseline stereo matching using SW-POC

This paper proposes an accurate and dense wide-baseline stereo matching method using Scaled Window Phase-Only Correlation (SW-POC). The wide-baseline setting of the stereo camera can improve the accuracy of the 3D reconstruction compared with the short-baseline setting. However, it is difficult to find accurate and dense correspondence from wide-baseline stereo images due to its large perspective distortion. Addressing this problem, we employ the SW-POC, which is a correspondence matching method using 1D POC with the concept of Scale Window Matching (SWM). The use of SW-POC makes it possible to find the accurate and dense correspondence from a wide-baseline stereo image pair with low computational cost. We also apply the proposed method to 3D reconstruction using a moving and uncalibrated consumer digital camera.


Assessment of chronic pain coping strategies

Introduction. We made an adaptation of the Coping Strategies Questionnaire (CSQ) to the Spanish population. This measure, the most used in its scope, was developed by Rosenstiel and Keefe in 1983. Method. 205 participants coming from Primary Health Care and pain clinics made up the sample. More than half suffered migraine and chronic tension-type headache; the rest, fibromyalgia, low back pain, arthrosis or arthritis. Results. Factor analyses explained 59% of the total variance, on an 8-factor structure that converged into a 2-factor structure. In the 8-factor solution the novelty was the diversification of mental-non-mental distraction strategies, and religious-non-religious hope strategies. In the 2-factor solution the novelty was the grouping according to the efficacy of the coping. All the CSQ factors showed inner consistency and construct validity. Thus, unadaptive coping strategies were related to negative, anxious and depressed self-talk, related to lack of control and perceived self-efficacy, and related to many pain behaviors. On the contrary it happened with adaptive coping strategies. In addition, the diagnosis of pain was related to the utilization and effectiveness of coping strategies. Conclussions. CSQ is shown to be a reliable and valid measure of coping strategies in chronic pain in the Spanish population, showing the difference between theoretical and empirical factor structures again.


Heart rate variability is associated with emotion recognition: direct evidence for a relationship between the autonomic nervous system and social cognition.

It is well established that heart rate variability (HRV) plays an important role in social communication. Polyvagal theory suggests that HRV may provide a sensitive marker of one's ability to respond and recognize social cues. The aim of the present study was to directly test this hypothesis. Resting-state HRV was collected and performance on the Reading the Mind in the Eyes Test was assessed in 65 volunteers. HRV was positively associated with performance on this emotion recognition task confirming our hypothesis and these findings were retained after controlling for a variety of confounding variables known to influence HRV - sex, BMI, smoking habits, physical activity levels, depression, anxiety, and stress. Our data suggests that increased HRV may provide a novel marker of one's ability to recognize emotions in humans. Implications for understanding the biological basis of emotion recognition, and social impairment in humans are discussed.


Medusa: a programming framework for crowd-sensing applications

The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.


The Cultural Evolution of Religion

After almost a century of dwelling in two “non-overlapping magisteria,” as Steven Jay Gould once put it, religion and science are coming together again. Long the exclusive province of the humanities and left outside of the mainstream of psychology and the behavioural sciences, religion is gaining scientific attention at a rapid pace. The dismantling of the taboos that have kept religion out of the scientific spotlight will take time (Dennett, 2006). Nevertheless, these are exciting times, and we can now safely say that religion—to paraphrase Chomsky about language—has been upgraded from scientific mystery to scientific puzzle (Boyer, 2001). This growing scientific interest promises to offer a naturalistic account for a deeply affecting aspect of human lives that is widespread in all known cultures in the world.


Probabilistic routing in intermittently connected networks

We consider the problem of routing in intermittently connected networks. In such networks there is no guarantee that a fully connected path between source and destination exist at any time, rendering traditional routing protocols unable to deliver messages between hosts. We propose a probabilistic routing protocol for such networks.


DIAC+: a Professional Diacritics Recovering System

In languages that use diacritical characters, if these special signs are stripped-off from a word, the resulted string of characters may not exist in the language, and therefore its normative form is, in general, easy to recover. However, this is not always the case, as presence or absence of a diacritical sign attached to a base letter of a word which exists in both variants, may change its grammatical properties or even the meaning, making the recovery of the missing diacritics a difficult task, not only for a program but sometimes even for a human reader. We describe and evaluate an accurate knowledge-based system for automatic recovering the missing diacritics in MSOffice documents written in Romanian. For the rare cases when the system is not able to reliably make a decision, it either provides the user a list of words with their recovery suggestions, or probabilistically choose one of the possible changes, but leaves a trace (a highlighted comment) on each word the modification of which was uncertain.


A Performance Comparison of Open-Source Stream Processing Platforms

Distributed stream processing platforms is a new class of real-time monitoring systems that analyze and extracts knowledge from large continuous streams of data. This type of systems is crucial for providing high throughput and low latency required by Big Data or Internet of Things monitoring applications. This paper describes and analyzes three main open-source distributed stream- processing platforms: Storm Flink, and Spark Streaming. We analyze the system architectures and we compare their main features. We carry out two experiments concerning anomaly detection on network traffic to evaluate the throughput efficiency and the resilience to node failures. Results show that the performance of native stream processing systems, Storm and Flink, is up to 15 times higher than the micro-batch processing system, Spark Streaming. On the other hand, Spark Streaming is more robust to node failures and provides recovery without losses.


Challenges during the transition to Agile Methodologies : A Holistic Overview


An Introduction to Active Shape Models ∗

Biomedical images usually contain complex objects, which will vary in appearance significantly from one image to another. Attempting to measure or detect the presence of particular structures in such images can be a daunting task. The inherent variability will thwart naive schemes. However, by using models which can cope with the variability it is possible to successfully analyse complex images. Here we will consider a number of methods where the model represents the expected shape and local grey-level structure of a target object in an image. Model-based methods make use of a prior model of what is expected in the image, and typically attempt to find the best match of the model to the data in a new image. Having matched the model, one can then make measurements or test whether the target is actually present. This approach is a ‘top-down’ strategy, and differs significantly from ‘bottomup’ methods. In the latter the image data is examined at a low level, looking for local structures such as edges or regions, which are assembled into groups in an attempt to identify objects of interest. Without a global model of what to expect, this approach is difficult and prone to failure. A wide variety of model based approaches have been explored (see the review below). This chapter will concentrate on a statistical approach, in which a model is built from analysing the appearance of a set of labelled examples. Where structures vary in shape or texture, it is possible to learn what are plausible variations and what are not. A new image can be interpreted by finding the best plausible match of the model to the image data. The advantages of such a method are that


Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech

Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech. We capitalize on the current breakthrough of Recurrent Neural Network (RNN) and seek to investigate its performance for emotion classification from noisy speech. We particularly focus on the recently proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion recognition from speech. Experiments conducted with speech compounded with eight different types of noises reveal that GRU incurs an 18.16% smaller run-time while performing quite comparably to the Long Short-Term Memory (LSTM), which is the most popular Recurrent Neural Network proposed to date. This result is promising for any embedded platform in general and will initiate further studies to utilize GRU to its full potential for emotion recognition on smartphones.


Class-Conditional Superresolution with GANs

● Super resolution enhances the resolution of a low-res image. ● Many deep learning models today work fairly well with an upscaling factor of 4x but use only the downscaled image as input. ● We demonstrate two class-conditional GAN models that outperform state-of-the-art GANs for superresolution: ○ Explicitly passes in a known class parameter as input ○ Uses a separately trained classifier to inform a ResNet about character attributes ● Both improve the quality of the super-resolved output.


Learning Structured Output Representation using Deep Conditional Generative Models

Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset.


Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition

Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk.
 In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.


Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition

In this paper we show that misclassification attacks against face-recognition systems based on deep neural networks (DNNs) are more dangerous than previously demonstrated, even in contexts where the adversary can manipulate only her physical appearance (versus directly manipulating the image input to the DNN). Specifically, we show how to create eyeglasses that, when worn, can succeed in targeted (impersonation) or untargeted (dodging) attacks while improving on previous work in one or more of three facets: (i) inconspicuousness to onlooking observers, which we test through a user study; (ii) robustness of the attack against proposed defenses; and (iii) scalability in the sense of decoupling eyeglass creation from the subject who will wear them, i.e., by creating “universal” sets of eyeglasses that facilitate misclassification. Central to these improvements are adversarial generative nets, a method we propose to generate physically realizable attack artifacts (here, eyeglasses) automatically.


Learning with a Strong Adversary

The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, learning with a strong adversary, that learns robust classifiers from supervised data by generating adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented that is empirically stronger than existing approaches in terms of the accuracy reduction as a function of perturbation magnitude. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.


Deep learning from temporal coherence in video

This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.


Software Re-Modularization Based on Structural and Semantic Metrics

The structure of a software system has a major impact on its maintainability. To improve maintainability, software systems are usually organized into subsystems using the constructs of packages or modules. However, during software evolution the structure of the system undergoes continuous modifications, drifting away from its original design, often reducing its quality. In this paper we propose an approach for helping maintainers to improve the quality of software modularization. The proposed approach analyzes the (structural and semantic) relationships between classes in a package identifying chains of strongly related classes. The identified chains are used to define new packages with higher cohesion than the original package. The proposed approach has been empirical evaluated through a case study. The context of the study is represented by an open source system, JHotDraw, and two software systems developed by teams of students at the University of Salerno. The analysis of the results reveals that the proposed approach generates meaningful re-modularization of the studied systems, which can lead to higher quality.


Towards a Welsh Semantic Annotation System

Automatic semantic annotation of natural language data is an important task in Natural Language Processing, and a variety of semantic taggers have been developed for this task, particularly for English. However, for many languages, particularly for low-resource languages, such tools are yet to be developed. In this paper, we report on the development of an automatic Welsh semantic annotation tool (named CySemTagger) in the CorCenCC Project, which will facilitate semantic-level analysis of Welsh language data on a large scale. Based on Lancaster’s USAS semantic tagger framework, this tool tags words in Welsh texts with semantic tags from a semantic classification scheme, and is designed to be compatible with multiple Welsh POS taggers and POS tagsets by mapping different tagsets into a core shared POS tagset that is used internally by CySemTagger. Our initial evaluation shows that the tagger can cover up to 91.78% of words in Welsh text. This tagger is under continuous development, and will provide a critical tool for Welsh language corpus and information processing at semantic level.


Two (Note) Heads Are Better Than One: Pen-Based Multimodal Interaction with Music Scores

Digitizing early music sources requires new ways of dealing with musical documents. Assuming that current technologies cannot guarantee a perfect automatic transcription, our intention is to develop an interactive system in which user and software collaborate to complete the task. Since conventional score post-editing might be tedious, the user is allowed to interact using an electronic pen. Although this provides a more ergonomic interface, this interaction must be decoded as well. In our framework, the user traces the symbols using the electronic pen over a digital surface, which provides both the underlying image (offline data) and the drawing made by the e-pen (online data) to improve classification. Applying this methodology over 70 scores of the target musical archive, a dataset of 10 230 bimodal samples of 30 different symbols was obtained and made available for research purposes. This paper presents experimental results on classification over this dataset, in which symbols are recognized by combining the two modalities. This combination of modes has demonstrated its good performance, decreasing the error rate of using each modality separately and achieving an almost error-free performance.


Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses

A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven. The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6 more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm. We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.


The Human Brain in Numbers: A Linearly Scaled-up Primate Brain

The human brain has often been viewed as outstanding among mammalian brains: the most cognitively able, the largest-than-expected from body size, endowed with an overdeveloped cerebral cortex that represents over 80% of brain mass, and purportedly containing 100 billion neurons and 10x more glial cells. Such uniqueness was seemingly necessary to justify the superior cognitive abilities of humans over larger-brained mammals such as elephants and whales. However, our recent studies using a novel method to determine the cellular composition of the brain of humans and other primates as well as of rodents and insectivores show that, since different cellular scaling rules apply to the brains within these orders, brain size can no longer be considered a proxy for the number of neurons in the brain. These studies also showed that the human brain is not exceptional in its cellular composition, as it was found to contain as many neuronal and non-neuronal cells as would be expected of a primate brain of its size. Additionally, the so-called overdeveloped human cerebral cortex holds only 19% of all brain neurons, a fraction that is similar to that found in other mammals. In what regards absolute numbers of neurons, however, the human brain does have two advantages compared to other mammalian brains: compared to rodents, and probably to whales and elephants as well, it is built according to the very economical, space-saving scaling rules that apply to other primates; and, among economically built primate brains, it is the largest, hence containing the most neurons. These findings argue in favor of a view of cognitive abilities that is centered on absolute numbers of neurons, rather than on body size or encephalization, and call for a re-examination of several concepts related to the exceptionality of the human brain.


Detecting Review Spammer Groups via Bipartite Graph Projection

Online product reviews play an important role in E-commerce websites because most customers read and rely on them when making purchases. For the sake of profit or reputation, review spammers deliberately write fake reviews to promote or demote target products, some even fraudulently work in groups to try and control the sentiment about a product. To detect such spammer groups, previous work exploits frequent itemset mining (FIM) to generate candidate spammer groups, which can only find tightly coupled groups, i.e. each reviewer in the group reviews every target product. In this paper, we present the loose spammer group detection problem, i.e. each group member is not required to review every target product. We solve this problem using bipartite graph projection. We propose a set of group spam indicators to measure the spamicity of a loose spammer group, and design a novel algorithm to identify highly suspicious loose spammer groups in a divide and conquer manner. Experimental results show that our method not only can find loose spammer groups with high precision and recall, but also can generate more meaningful candidate spammer groups than FIM, thus it can also be used as an alternative preprocessing tool for existing FIM-based approaches.


WAIS-IV visual puzzles in a mixed clinical sample.

Little is known about which cognitive functions underlie the new Visual Puzzles subtest of the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV). The purpose of this study was to investigate relationships between Visual Puzzles and common neuropsychological measures in a mixed clinical sample. A total of 44 veterans (75% men) were administered the WAIS-IV as part of a neuropsychological evaluation. Average age was 47.4 years (SD = 11.8), and average education was 13.8 years (SD = 2.3). Correlations were conducted to examine relationships between Visual Puzzles, demographic variables, and neuropsychological measures. Hierarchical regression analysis was used to determine which measures contributed the most variance to Visual Puzzles. Visual Puzzles correlated significantly with measures of visuospatial reasoning, verbal learning and recall, mental flexibility, processing speed, and naming, which accounted for 50% of the variance in Visual Puzzles performance. The results indicate that Visual Puzzles is not a pure measure of visuoperceptual reasoning, at least in a mixed clinical sample, because memory, mental flexibility, processing speed, and language abilities also contribute to successful performance of the task. Thus it may be important to consider other aspects of cognitive functioning when interpreting Visual Puzzles performance.


The Great Grevy ’ s Rally : The Need , Methods , Findings , Implications and Next Steps


Recent and robust query auto-completion

Query auto-completion (QAC) is a common interactive feature that assists users in formulating queries by providing completion suggestions as they type. In order for QAC to minimise the user's cognitive and physical effort, it must: (i) suggest the user's intended query after minimal input keystrokes, and (ii) rank the user's intended query highly in completion suggestions. Typically, QAC approaches rank completion suggestions by their past popularity. Accordingly, QAC is usually very effective for previously seen and consistently popular queries. Users are increasingly turning to search engines to find out about unpredictable emerging and ongoing events and phenomena, often using previously unseen or unpopular queries. Consequently, QAC must be both robust and time-sensitive -- that is, able to sufficiently rank both consistently and recently popular queries in completion suggestions. To address this trade-off, we propose several practical completion suggestion ranking approaches, including: (i) a sliding window of query popularity evidence from the past 2-28 days, (ii) the query popularity distribution in the last N queries observed with a given prefix, and (iii) short-range query popularity prediction based on recently observed trends. Using real-time simulation experiments, we extensively investigated the parameters necessary to maximise QAC effectiveness for three openly available query log datasets with prefixes of 2-5 characters: MSN and AOL (both English), and Sogou 2008 (Chinese). Optimal parameters vary for each query log, capturing the differing temporal dynamics and querying distributions. Results demonstrate consistent and language-independent improvements of up to 9.2% over a non-temporal QAC baseline for all query logs with prefix lengths of 2-3 characters. This work is an important step towards more effective QAC approaches.


How Does Disagreement Benefit Co-teaching?

Learning with noisy labels is one of the most important question in weakly-supervised learning domain. Classical approaches focus on adding the regularization or estimating the noise transition matrix. However, either a regularization bias is permanently introduced, or the noise transition matrix is hard to be estimated accurately. In this paper, following a novel path to train on small-loss samples, we propose a robust learning paradigm called Co-teaching+. This paradigm naturally bridges “Update by Disagreement” strategy with Co-teaching that trains two deep neural networks, thus consists of disagreement-update step and cross-update step. In disagreement-update step, two networks predicts all data first, and feeds forward prediction disagreement data only. Then, in cross-update step, each network selects its small-loss data from such disagreement data, but back propagates the small-loss data by its peer network and updates itself parameters. Empirical results on noisy versions of MNIST, CIFAR10 and NEWS demonstrate that Co-teaching+ is much superior to the state-of-the-art methods in the robustness of trained deep models.


Reinforcement Learning for Humanoid Robotics

Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, ‘vanilla’ policy gradient methods, and natural gradient methods. We discuss that greedy methods are not likely to scale into the domain humanoid robotics as they are problematic when used with function approximation. ‘Vanilla’ policy gradient methods on the other hand have been successfully applied on real-world robots including at least one humanoid robot [3]. We demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. A derivation of the natural policy gradient is provided, proving that the average policy gradient of Kakade [10] is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges to the nearest local minimum of the cost function with respect to the Fisher information metric under suitable conditions. The algorithm outperforms non-natural policy gradients by far in a cart-pole balancing evaluation, and for learning nonlinear dynamic motor primitives for humanoid robot control. It offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems.


Experimental Analysis of a UAV-Based Wireless Power Transfer Localization System

Sensors deployed in remote locations provide unprecedented amounts of data, but powering these sensors over long periods remains a challenge. In this paper, we develop and present a UAV-based wireless power transfer system. We discuss design considerations and present our system that allows a UAV to fly to remote locations to charge hard to access sensors. We analyze the impact of different materials on the wireless power transfer system. Since GPS does not provide sufficient accuracy, we develop and experimentally characterize a relative localization algorithm based on sensing the magnetic field of the power transfer system and optical flow that allows the UAV to localize the sensor with an average error of 15 cm to enable the transfer of on average 4.2 W . These results overcome some of the practical challenges associated with wirelessly charging sensors with a UAV and show that UAVs with wireless power transfer systems can greatly extend the life of remotely deployed sensors.


Cruciform: Solving Crosswords with Natural Language Processing

Crossword puzzles are popular word games that require not only a large vocabulary, but also a broad knowledge of topics. Answering each clue is a natural language task on its own as many clues contain nuances, puns, or counter-intuitive word definitions. Additionally, it can be extremely difficult to ascertain definitive answers without the constraints of the crossword grid itself. This task is challenging for both humans and computers. We describe here a new crossword solving system, Cruciform. We employ a group of natural language components, each of which returns a list of candidate words with scores when given a clue. These lists are used in conjunction with the fill intersections in the puzzle grid to formulate a constraint satisfaction problem, in a manner similar to the one used in the Dr. Fill system[4]. We describe the results of several of our experiments with the system.


A Survey on Application of Bio-Inspired Algorithms

The algorithms that are inspired by the principles of natural biological evolution and distributed collective behaviour of social colonies have shown excellence in dealing with complex optimization problems and are becoming more popular nowadays. This paper surveys the recent advances in biologically inspired swarm optimization methods, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in various fields. KeywordsBiologically inspired algorithms, Swarm Intelligence, application of Bio-inspired algorithms.


View-based and modular eigenspaces for face recognition

In this work we describe experiments with eigenfaces for recognition and interactive search in a large-scale face database. Accurate visual recognition is demonstrated using a database of O(10) faces. The problem of recognition under general viewing orientation is also examined. A view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose. In addition, a modular eigenspace description technique is used which incorporates salient features such as the eyes, nose and mouth, in an eigenfeature layer. This modular representation yields higher recognition rates as well as a more robust framework for face recognition. An automatic feature extraction technique using feature eigentemplates is also demonstrated.


Efficient mapping of quantum circuits to the IBM QX architectures

In March 2017, IBM launched the project IBM Q with the goal to provide access to quantum computers for a broad audience. This allowed users to conduct quantum experiments on a 5-qubit and, since June 2017, also on a 16-qubit quantum computer (called IBM QX2 and IBM QX3, respectively). In order to use these, the desired quantum functionality (e.g. provided in terms of a quantum circuit) has to properly be mapped so that the underlying physical constraints are satisfied — a complex task. This demands for solutions to automatically and efficiently conduct this mapping process. In this paper, we propose such an approach which satisfies all constraints given by the architecture and, at the same time, aims to keep the overhead in terms of additionally required quantum gates minimal. The proposed approach is generic and can easily be configured for future architectures. Experimental evaluations show that the proposed approach clearly outperforms IBM's own mapping solution with respect to runtime as well as resulting costs.


Variational methods for the Dirichlet process

Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While often less accurate than MCMC, variational methods provide a fast deterministic approximation to marginal and conditional probabilities. Such approximations can be particularly useful in high dimensional problems where sampling methods are too slow to be effective. A limitation of current methods, however, is that they are restricted to parametric probabilistic models. MCMC does not have such a limitation; indeed, MCMC samplers have been developed for the Dirichlet process (DP), a nonparametric distribution on distributions (Ferguson, 1973) that is the cornerstone of Bayesian nonparametric statistics (Escobar & West, 1995; Neal, 2000). In this paper, we develop a mean-field variational approach to approximate inference for the Dirichlet process, where the approximate posterior is based on the truncated stick-breaking construction (Ishwaran & James, 2001). We compare our approach to DP samplers for Gaussian DP mixture models.


A polarimetric 76–79 GHz radar-frontend for target classification in automotive use

Autonomous driving requires precise detection of the environment and correct classification of all targets. W-band radar technology provides high accuracy object detection in all weather conditions, both day and night. Target classification can be improved by evaluating polarimetric scattering information, where it is in particular promising to work with cicularly polarized waves. Both circular polarizations, left-handed circular (LHC) and right-handed circular (RHC), are necessary with a high cross polarization discrimination (XPD). A particularly high XPD can be achieved by hollow waveguide technology. For that purpose, a radar-frontend with corrugated horn antennas has been designed, manufactured and tested. LHC and RHC data is then entered into an algorithm which takes into account switched transmitter technology, fast-ramp frequency-modulated continuous-wave (FMCW) and digital beamforming. Polarimetric radar images are generated, allowing for discrimination and classification of different targets.


Types of minority class examples and their influence on learning classifiers from imbalanced data

Many real-world applications reveal difficulties in learning classifiers from imbalanced data. Although several methods for improving classifiers have been introduced, the identification of conditions for the efficient use of the particular method is still an open research problem. It is also worth to study the nature of imbalanced data, characteristics of the minority class distribution and their influence on classification performance. However, current studies on imbalanced data difficulty factors have been mainly done with artificial datasets and their conclusions are not easily applicable to the real-world problems, also because the methods for their identification are not sufficiently developed. In our paper, we capture difficulties of class distribution in real datasets by considering four types of minority class examples: safe, borderline, rare and outliers. First, we confirm their occurrence in real data by exploring multidimensional visualizations of selected datasets. Then, we introduce a method for an identification of these types of examples, which is based on analyzing a class distribution in a local neighbourhood of the considered example. Two ways of modeling this neighbourhood are presented: with k-nearest examples and with kernel functions. Experiments with artificial datasets show that these methods are able to re-discover simulated types of examples. Next contributions of this paper include carrying out a comprehensive experimental study with 26 real world imbalanced datasets, where (1) we identify new data characteristics basing on the analysis of types of minority examples; (2) we demonstrate that considering the results of this analysis allow to differentiate classification performance of popular classifiers and pre-processing methods and to evaluate their areas of competence. Finally, we highlight directions of exploiting the results of our analysis for developing new algorithms for learning classifiers and pre-processing methods.


Gravitational Search Algorithm and Recent Trends

A R T I C L E I N F O A B S T R A C T Article history: Received 20 March 2017 Received in revised form 22 March 2017 Accepted 28 March 2017 Available online 31March 2017 The inability of conventional optimization algorithms to handle exponential increase in the size of the search space has paved way towards capturing the behavior of natural organisms for solving optimization problems. Nature Inspired Algorithms have mostly been associated with the swarm behavior of organisms. A rather new idea in this respect was proposed by Rashedi et al in 2009 by the name ‘Gravitational Search Algorithm’. The basis of their proposal, as clear from the name, is the Newtonian gravity and interactions between populations of masses. The algorithm has been widely used since then in many applications of science. Since its proposal, researches have been continuously headed in extending GSA for different scenarios and applications. The working of GSA and some such popular and recent proposals in the direction of extending GSA are discussed in this paper.


EFFICIENCY ENHANCEMENT OF BASE STATION POWER AMPLIFIERS USING DOHERTY TECHNIQUE

The power amplifiers are typically the most power-consuming block in wireless communication systems. Spectrum is expensive, and newer technologies demand transmission of maximum amount of data with minimum spectrum usage. This requires sophisticated modulation techniques, leading to wide, dynamic signals that require linear amplification. Although linear amplification is achievable, it always comes at the expense of efficiency. Most of the modern wireless applications such as WCDMA use non-constant envelope modulation techniques with a high peak to average ratio. Linearity being a critical issue, power amplifiers implemented in such applications are forced to operate at a backed off region from saturation. Therefore, in order to overcome the battery lifetime limitation, a design of a high efficiency power amplifier that can maintain the efficiency for a wider range of radio frequency input signal is the obvious solution. A new technique that improves the drain efficiency of a linear power amplifier such as Class A or AB, for a wider range of output power, has been investigated in this research. The Doherty technique consists of two amplifiers in parallel; in such a way that the combination enhances the power added efficiency of the main amplifier at 6dB back off from the maximum output power. The classes of operation of power amplifier (A, AB,B, C etc), and the design techniques are presented. Design of a 2.14 GHz Doherty power amplifier has been provided in chapter 4. This technique shows a 15% increase in power added efficiency at 6 dB back off from the compression point. This PA can be implemented in WCDMA base station transmitter.


Data Mining in Sports: A Systematic Review

Data mining technique has attracted attention in the information industry and society as a whole, because of the big amount of data and the imminent need to transform that data into useful information and knowledge. Recently conducted studies with successfully demarcated results using this technique, to estimate several parameters in a variety of domains. However, the effective use of data in some areas is still developing, as is the case of sports, which has shown moderate growth. In this context, the objective of this article is to present a systematic review of the literature about research involving sports data mining. As systematic searches were made out in five databases, resulting in 21 articles that answered a question that grounded this article.


Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt “pedagogical approaches” (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.


Judgment of information quality and cognitive authority in the Web

In the Web, making judgments of information quality and authority is a difficult task for most users because overall, there is no quality control mechanism. This study examines the problem of the judgment of information quality and cognitive authority by observing people’s searching behavior in the Web. Its purpose is to understand the various factors that influence people’s judgment of quality and authority in the Web, and the effects of those judgments on selection behaviors. Fifteen scholars from diverse disciplines participated, and data were collected combining verbal protocols during the searches, search logs, and postsearch interviews. It was found that the subjects made two distinct kinds of judgment: predictive judgment, and evaluative judgment. The factors influencing each judgment of quality and authority were identified in terms of characteristics of information objects, characteristics of sources, knowledge, situation, ranking in search output, and general assumption. Implications for Web design that will effectively support people’s judgments of quality and authority are also discussed.


Provably secure ciphertext policy ABE

In ciphertext policy attribute-based encryption (CP-ABE), every secret key is associated with a set of attributes, and every ciphertext is associated with an access structure on attributes. Decryption is enabled if and only if the user's attribute set satisfies the ciphertext access structure. This provides fine-grained access control on shared data in many practical settings, e.g., secure database and IP multicast.
 In this paper, we study CP-ABE schemes in which access structures are AND gates on positive and negative attributes. Our basic scheme is proven to be chosen plaintext (CPA) secure under the decisional bilinear Diffie-Hellman (DBDH) assumption. We then apply the Canetti-Halevi-Katz technique to obtain a chosen ciphertext (CCA) secure extension using one-time signatures. The security proof is a reduction to the DBDH assumption and the strong existential unforgeability of the signature primitive.
 In addition, we introduce hierarchical attributes to optimize our basic scheme - reducing both ciphertext size and encryption/decryption time while maintaining CPA security. We conclude with a discussion of practical applications of CP-ABE.


Practical Techniques for Searches on Encrypted Data

It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that on e has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certai n words, it was not previously known how to let the data storage server perform the search and answer the query without loss of data confidentiality. In this paper, we describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Ou r techniques have a number of crucial advantages. They are provably secure : they provideprovable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they providequery isolationfor searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user’s authorization; they also supporthidden queries , so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms we present are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today. We gratefully acknowledge support for this research from se veral US government agencies. This research was suported in part by t he Defense Advanced Research Projects Agency under DARPA contract N66 01-9928913 (under supervision of the Space and Naval Warfare Syst ems Center San Diego), by the National Science foundation under grant F D99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C3513. Views and conclusions contained in this document are t hose of the authors and do not necessarily represent the official opinio n or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS.


Attribute-based encryption for fine-grained access control of encrypted data

As more sensitive data is shared and stored by third-party sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarse-grained level (i.e., giving another party your private key). We develop a new cryptosystem for fine-grained sharing of encrypted data that we call Key-Policy Attribute-Based Encryption (KP-ABE). In our cryptosystem, ciphertexts are labeled with sets of attributes and private keys are associated with access structures that control which ciphertexts a user is able to decrypt. We demonstrate the applicability of our construction to sharing of audit-log information and broadcast encryption. Our construction supports delegation of private keys which subsumesHierarchical Identity-Based Encryption (HIBE).


Plutus: Scalable Secure File Sharing on Untrusted Storage

Plutus is a cryptographic storage system that enables secure file sharing without placing much trust on the file servers. In particular, it makes novel use of cryptographic primitives to protect and share files. Plutus features highly scalable key management while allowing individual users to retain direct control over who gets access to their files. We explain the mechanisms in Plutus to reduce the number of cryptographic keys exchanged between users by using filegroups, distinguish file read and write access, handle user revocation efficiently, and allow an untrusted server to authorize file writes. We have built a prototype of Plutus on OpenAFS. Measurements of this prototype show that Plutus achieves strong security with overhead comparable to systems that encrypt all network traffic.


Diaphragmatic Eventration in Sisters with Asparagine Synthetase Deficiency: A Novel Homozygous ASNS Mutation and Expanded Phenotype.

BACKGROUND
Asparagine Synthetase Deficiency (ASNSD; OMIM #615574) is a newly described rare autosomal recessive neurometabolic disorder, characterised by congenital microcephaly, severe psychomotor delay, encephalopathy and progressive cerebral atrophy. To date, seven families and seven missense mutations in the ASNSD disease causing gene, ASNS, have been published.

METHODS
We report two further affected infant sisters from a consanguineous Indian family, who in addition to the previously described features had diaphragmatic eventration. Both girls died within the first 6 months of life. Whole exome sequencing (WES) was performed for both sisters to identify the pathogenic mutation. The clinical and biochemical parameters of our patient are compared to previous reports.

RESULTS
WES demonstrated a homozygous novel missense ASNS mutation, c.1019G > A, resulting in substitution of the highly conserved arginine residue by histidine (R340H).

CONCLUSION
This report expands the phenotypic and mutation spectrum of ASNSD, which should be considered in neonates with congenital microcephaly, seizures and profound neurodevelopmental delay. The presence of diaphragmatic eventration suggests extracranial involvement of the central nervous system in a disorder that was previously thought to exclusively affect the brain. Like all previously reported patients, these cases were diagnosed with WES, highlighting the clinical utility of next generation sequencing in the diagnosis of rare, difficult to recognise disorders.


Challenges in developing opinion mining tools for social media

While much work has recently focused on the analysis of social media in order to get a feel for what people think about current topics of interest, there are, however, still many challenges to be faced. Text mining systems originally designed for more regular kinds of texts such as news articles may need to be adapted to deal with facebook posts, tweets etc. In this paper, we discuss a variety of issues related to opinion mining from social media, and the challenges they impose on a Natural Language Processing (NLP) system, along with two example applications we have developed in very different domains. In contrast with the majority of opinion mining work which uses machine learning techniques, we have developed a modular rule-based approach which performs shallow linguistic analysis and builds on a number of linguistic subcomponents to generate the final opinion polarity and score.


Background Prior-Based Salient Object Detection via Deep Reconstruction Residual

Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper.


p-Cycle-based node failure protection for survivable virtual network embedding

Network Virtualization offers to Physical Infrastructure Provider (PIP) the possibility to smoothly roll out multiple isolated networks on top of his infrastructure. A major challenge in this respect is the embedding problem which deals with the mapping of Virtual Networks (VNs) resources on PIP network. In literature, a number of research proposals have been focused on providing heuristic approaches to solve this NP-hard problem while most often assuming that PIP infrastructure is operational at all time. In virtualization environment a single physical node failure can result in single/multi logical link failure(s) as it effects all VNs with a mapping that spans over. Setup a dedicated backup for each VN embedding, i.e., no sharing, is inefficient in terms of resource usage. To address these concerns, we propose in this work an approach for VN mapping with combined physical node and multiple logical links protection mechanism (VNM-CNLP) that: (a) uses a large scale optimization technique, developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the effects of a single node failure in VN layer, and (b) proposes in a second stage link p-Cycle based protection techniques that minimize the backup resources while providing a full VN protection scheme against a single physical node failure and a multiple logical links failure. Our simulations show a clear advantage of VNM-CNLP approach over benchmark in terms of VN backup cost and resources utilization.


DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences

Establishing dense correspondences reliably between a pair of images is an important vision task with many applications. Though significant advance has been made towards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building reliable dense correspondence fields for two general images still remains largely unsolved. For instance, two given images sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: DAISY descriptors, filter-based efficient flow inference, and the PatchMatch fast search. Coupling and optimizing these modules seamlessly with image segments as the bridge, the proposed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a generalized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of challenging scenes show that our DFF approach estimates spatially coherent yet discontinuity-preserving image alignment results both robustly and efficiently.


Making TCP/IP Viable for Wireless Sensor Networks

The TCP/IP protocol suite, which has proven itself highly successful in wired networks, is often claimed to be unsuited for wireless micro-sensor networks. In this work, we question this conventional wisdom and present a number of mechanisms that are intended to enable the use of TCP/IP for wireless sensor networks: spatial IP address assignment, shared context header compression, application overlay routing, and distributed TCP caching (DTC). Sensor networks based on TCP/IP have the advantage of being able to directly communicate with an infrastructure consisting either of a wired IP network or of IP-based wireless technology such as GPRS. We have implemented parts of our mechanisms both in a simulator environment and on actual sensor nodes. Our preliminary results are promising.


Introducing the Occupational Balance Questionnaire (OBQ).

OBJECTIVE
The concept of occupational balance is frequently used in occupational therapy but the fact that it has been defined and measured differently is a limitation. This article introduces the Occupational Balance Questionnaire (OBQ), which focuses on satisfaction with the amount and variation of occupations. It consists of 13 items measured on six-step ordinal scales. It has shown good content validity in a sample of 21 occupational therapists but other psychometric properties have not been investigated. The aim was to investigate the OBQ regarding internal consistency, test-retest reliability, and floor/ceiling effects.

METHODS
The OBQ was administered twice to a sample selected through convenience sampling. Internal consistency was investigated by Cronbach's alpha and test-retest reliability analysed with Spearman's Rho correlation for the total score and weighted kappa on each item. Potential floor/ceiling effects were explored by checking for the percentage of participants who scored lowest and highest.

RESULTS
The results demonstrated that the OBQ has good internal consistency (Cronbach's alpha 0.936) and sufficient test-retest reliability (Spearman's Rho for the total score was 0.926) and, thus, seems stable over time. No floor or ceiling effect was detected.

CONCLUSIONS
The OBQ therefore showed promising reliability, although further instrument development studies to examine its construct validity are required.