title: A Survey of Ontology Learning Procedures

abstract: Ontologies constitute an approach for knowledge representation that can be shared establishing a shared vocabulary for different applications and are also the backbone of the Semantic Web. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. However, ontology development is a difficult and time consuming task. Ontology learning is an approach for the problem of knowledge acquisition bottleneck that aims at reducing the cost of ontology construction through the development of automatic methods for the extraction of knowledge about a specific domain and its representation in an ontology like structure. This paper provides a discussion on existing ontology learning techniques and the state of the art of the field.


title: Large scale global optimization using self-adaptive differential evolution algorithm

abstract: In this paper we present self-adaptive differential evolution algorithm jDElsgo on large scale global optimization. The experimental results obtained by our algorithm on benchmark functions provided for the CEC 2010 competition and special session on Large Scale Global Optimization are presented. The experiments were performed on 20 benchmark functions with high dimension D = 1000. Obtained results show that our algorithm performs highly competitive in comparison with the DECC-G∗, DECC-G and MLCC algorithms.


title: An Improved Magnetic Equivalent Circuit Model for Iron-Core Linear Permanent-Magnet Synchronous Motors

abstract: The aim of this work is to establish an accurate yet simple method for predicting flux density distribution and iron losses in linear permanent-magnet synchronous motors (LPMSMs) for iterative design procedures. For this purpose, an improved magnetic equivalent circuit for calculation of the teeth and yoke flux densities in the LPMSMs is presented. The magnetic saturation of iron core is considered by nonlinear elements and an iterative procedure is used to update these elements. The armature reaction is also taken into account in the modeling by flux sources located on the teeth of motors. These sources are time dependent and can model every winding configuration. The relative motion between the motor primary and secondary is considered by wisely designing air gap elements simplifying the permeance network construction and preventing permeance matrix distortion during primary motion. Flux densities in different load conditions are calculated by means of the proposed model. The effects of saturation and armature reaction on the flux density distribution are shown in detail. Using these flux densities, iron losses in the motor are examined and its variations versus motor parameters are then studied. All results obtained by proposed model are verified by finite-element method based on an extensive analysis.


title: Explicit Knowledge-based Reasoning for Visual Question Answering

abstract: We describe a method for visual question answering which is capable of reasoning about contents of an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can provide an explanation of the reasoning by which it developed its answer. The method is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in the testing. We also provide a dataset and a protocol by which to evaluate such methods, thus addressing one of the key issues in general visual ques- tion answering.


title: High Thymidylate Synthase Expression in Colorectal Cancer with Microsatellite Instability: Implications for Chemotherapeutic Strategies

abstract: Colon cancers displaying microsatellite instability (MSI) are clinically less aggressive. Based on in vitro studies and recent clinical data, cancers displaying MSI do not respond to 5-fluorouracil (5-FU). The reasons why MSI tumors are clinically less aggressive and do not respond to 5-FU–based therapies have not been fully elucidated. ::: ::: Purpose: We investigated biomolecular markers in an attempt to explain the different clinical behavior and chemotherapeutic responses of MSI and non-MSI colon cancers. ::: ::: Experimental Design: One hundred ninety-two sporadic colon cancers were tested for MSI with five mononucleotide markers and methylation of the hMLH1 promoter. Slides were stained for thymidylate synthase (TS), p53, MDM2, p21WAF1/CIP1, β-catenin, vascular endothelial growth factor, hMLH1, hMSH2, and hMSH6. Tumors were regarded as having wild-type, functional p53 (Fp53) if reduced expression of p53 and positive MDM2 and p21WAF1/CIP1 expressions were found. ::: ::: Results: Of the cases, 12.5% were MSI-H (at least two markers mutated). Of MSI-H cases, 83.3% were characterized by a complete loss of at least one of the mismatch repair proteins, in particular loss of hMLH1 by promoter hypermethylation. MSI-H colon cancers showed higher expression of TS compared with MSS (no mutated markers)/MSI-L (one mutated marker) colon cancers (66.6% for MSI-H versus 14.8% MSS/MSI-L; P < 0.0001); 20.8% of MSI-H cases showed high expression of the vascular endothelial growth factor, compared with 45.8% MSS/MSI-L colon cancers ( P = 0.0005); 45.8% MSI-H cases had Fp53 compared 11.9% MSS/MSI-L cases ( P < 0.0001). ::: ::: Conclusions: About 12% of colon cancers display MSI mostly due to lack of hMLH1 resulting from promoter hypermethylation. These tumors have high expression of TS and retain fully functional p53 system. Thus, these data suggest why sporadic hMLH1-defective colon cancers often do not respond to 5-FU.


title: The significance of panic-expectancy for individual patterns of avoidance

abstract: The goal of this study was to examine the role of panic-expectancy in patterns of avoidance. Twenty-eight individuals with Panic Disorder with moderate to severe avoidance underwent individualized behavioral testing. Dependent variables were assessed immediately prior to attempting the behavioral test item. They included, in addition to actual levels of reported fear and avoidance, predicted probability of panic, predicted intensity, discomfort and harm from the physical sensations of panic, predicted negtive consequences of panicking, and predicted level of fear tolerance. Number of months since last attempt to approach the behavioral test item was also recorded. The probability of panic and fear tolerance levels were related significantly to avoidance across individuals. The predicted probability of panic was the variable of most significance for within individual patterns of avoidance, although ratings of the physical symptoms of panic and fear tolerance levels related to some extent. Correlational coefficients suggested that the predicted probability of panic was situationally specific in comparison to the relative stability of predictions relating to negative consequences of panicking. Treatment implications are considered.


title: High resolution stereo imaging of sea waves for validation and optimization of wave modelling

abstract: A passive stereo vision system for high resolution dense 3D imaging of sea surface is developed. The system is designed for the real-time retrieval of the 3D model of sea surface, in order to extract the spectral features of the wave field to be used for validating and optimizing nearshore wave dynamics numerical models. The system is low cost and easily deployable onboard both fixed and mobile platforms, providing dense coverage of the target areas and fast processing time deriving from a Pattern Recognition (PR) approach. The proposed method for extracting the wave field spectral features brings the computational costs down by remapping each 3D point of the sea surface to an elevation map based on the mean water level. Basic PR techniques are applied to this simplified domain in order to identify crests and troughs of waves and to represent them as oriented lines. Wave typical characteristics, such as significant wave height and mean period, are determined by processing in time and space the identified lines. The system was tested in different acquisition sessions in the Ligurian Sea and it proved to be effective in characterizing the observed wave fields. The use of stereo imagery coupled with PR methods is a promising innovation, as commonly arrays of point measurement devices or the virtual point probe approach in 3D-based techniques are used for the wave field characterization, implying high infrastructural and computational costs.


title: A Study of Interspeaker Variability in Speaker Verification

abstract: We propose a new approach to the problem of estimating the hyperparameters which define the interspeaker variability model in joint factor analysis. We tested the proposed estimation technique on the NIST 2006 speaker recognition evaluation data and obtained 10%-15% reductions in error rates on the core condition and the extended data condition (as measured both by equal error rates and the NIST detection cost function). We show that when a large joint factor analysis model is trained in this way and tested on the core condition, the extended data condition and the cross-channel condition, it is capable of performing at least as well as fusions of multiple systems of other types. (The comparisons are based on the best results on these tasks that have been reported in the literature.) In the case of the cross-channel condition, a factor analysis model with 300 speaker factors and 200 channel factors can achieve equal error rates of less than 3.0%. This is a substantial improvement over the best results that have previously been reported on this task.


title: High-Efficiency Envelope-Tracking W-CDMA Base-Station Amplifier Using GaN HFETs

abstract: A high-efficiency wideband code-division multiple-access (W-CDMA) base-station amplifier is presented using high-performance GaN heterostructure field-effect transistors to achieve high gain and efficiency with good linearity. For high efficiency, class J/E operation was employed, which can attain up to 80% efficiency over a wide range of input powers and power supply voltages. For nonconstant envelope input, the average efficiency is further increased by employing the envelope-tracking architecture using a wide-bandwidth high-efficiency envelope amplifier. The linearity of overall system is enhanced by digital pre-distortion. The measured average power-added efficiency of the amplifier is as high as 50.7% for a W-CDMA modulated signal with peak-to-average power ratio of 7.67 dB at an average output power of 37.2 W and gain of 10.0 dB. We believe that this corresponds to the best efficiency performance among reported base-station power amplifiers for W-CDMA. The measured error vector magnitude is as low as 1.74% with adjacent channel leakage ratio of -51.0 dBc at an offset frequency of 5 MHz


title: Agnostic Online Learning

abstract: We study learnability of hypotheses classes in agnostic online prediction models. The analogous question in the PAC learning model [Valiant, 1984] was addressed by Haussler [1992] and others, who showed that the VC dimension characterization of the sample complexity of learnability extends to the agnostic (or ”unrealizable”) setting. In his influential work, Littlestone [1988] described a combinatorial characterization of hypothesis classes that are learnable in the online model. We extend Littlestone’s results in two aspects. First, while Littlestone only dealt with the realizable case, namely, assuming there exists a hypothesis in the class that perfectly explains the entire data, we derive results for the non-realizable (agnostic) case as well. In particular, we describe several models of non-realizable data and derive upper and lower bounds on the achievable regret. Second, we extend the theory to include margin-based hypothesis classes, in which the prediction of each hypothesis is accompanied by a confidence value. We demonstrate how the newly developed theory seamlessly yields novel online regret bounds for the important class of large margin linear separators.


title: Convolutional-Recursive Deep Learning for 3D Object Classification

abstract: Recent advances in 3D sensing technologies make it possible to easily record color and depth images which together can improve object recognition. Most current methods rely on very well-designed features for this new 3D modality. We introduce a model based on a combination of convolutional and recursive neural networks (CNN and RNN) for learning features and classifying RGB-D images. The CNN layer learns low-level translationally invariant features which are then given as inputs to multiple, fixed-tree RNNs in order to compose higher order features. RNNs can be seen as combining convolution and pooling into one efficient, hierarchical operation. Our main result is that even RNNs with random weights compose powerful features. Our model obtains state of the art performance on a standard RGB-D object dataset while being more accurate and faster during training and testing than comparable architectures such as two-layer CNNs.


2 G-5 G Networks : Evolution of Technologies , Standards , and Deployment


title: Estimation of Miner Hash Rates and Consensus on Blockchains (draft)

abstract: We make several contributions that quantify the real-time hash rate and therefore the consensus of a blockchain. We show that by using only the hash value of blocks, we can estimate and measure the hash rate of all miners or individual miners, with quanti able accuracy. We apply our techniques to the Ethereum and Bitcoin blockchains; our solution applies to any proof-of-work-based blockchain that relies on a numeric target for the validation of blocks. We also show that if miners regularly broadcast status reports of their partial proof-of- work, the hash rate estimates are signi cantly more accurate at a cost of slightly higher bandwidth. Whether using only the blockchain, or the additional information in status reports, merchants can use our techniques to quantify in real-time the threat of double-spend attacks.


title: Practical Defenses for Evil Twin Attacks in 802.11

abstract: Open-access 802.11 wireless networks are commonly deployed in cafes, bookstores, and other public spaces to provide free Internet connectivity. These networks are convenient to deploy, requiring no out-of-band key exchange or prior trust relationships. However, such networks are vulnerable to a variety of threats including the evil twin attack where an adversary clones a client's previously-used access point for a variety of malicious purposes including malware injection or identity theft. We propose defenses that aim to maintain the simplicity, convenience, and usability of open-access networks while offering increased protection from evil twin attacks. First, we present an evil twin detection strategy called context-leashing that constrains access point trust by location. Second, we propose that wireless networks be identified by uncertified public keys and design an SSH-style authentication and session key establishment protocol that fits into the 802.1X standard. Lastly, to mitigate the pitfalls of SSH-style authentication, we present a crowd-sourcing-based reporting protocol that provides historical information for access point public keys while preserving the location privacy of users who contribute reports.


title: Hybrid approach to enhancing fingerprint images using filters in the frequency domain

abstract: Automatic fingerprint recognition has received considerable attention over the past decades as fingerprint is one of the most popular and successful methods used for personal identification. In this paper, a method based on the combination of Fast Fourier transform (FFT) and Gaussian Bandpass Filter is proposed to enhance fingerprint image. Various filters directly generated in the frequency domain have been used and their outcomes are analyzed in order to determine the best filter that can be applied to fingerprint images for enhancement to have better match accuracies. Experiments are conducted on sample fingerprint images that have been collected using an optical fingerprint Reader and its results show the desirable features of the proposed approach.


title: A Robust Method for Detecting Parking Areas in Both Indoor and Outdoor Environments

abstract: Although an automatic parking system has been installed in many vehicles recently, it is still hard for the system to confirm by itself whether a vacant parking area truly exists or not. In this paper, we introduced a robust vision-based vacancy parking area detecting method for both indoor and outdoor environments. The main contribution of this paper is given as follows. First, an automatic image stitching method is proposed. Secondly, the problem of environment illuminating change and line color difference is considered and solved. Thirdly, the proposed algorithm is insensitive to the shadow and scene diversity, which means the detecting result satisfies most of the environment. Finally, a vehicle model is considered for tracking and reconfirming the detecting results to eliminate most of the false positives.


title: Performance isolation and fairness for multi-tenant cloud storage

abstract: Shared storage services enjoy wide adoption in commercial clouds. But most systems today provide weak performance isolation and fairness between tenants, if at all. Misbehaving or high-demand tenants can overload the shared service and disrupt other well-behaved tenants, leading to unpredictable performance and violating SLAs. ::: ::: This paper presents Pisces, a system for achieving datacenter-wide per-tenant performance isolation and fairness in shared key-value storage. Today's approaches for multi-tenant resource allocation are based either on per-VM allocations or hard rate limits that assume uniform workloads to achieve high utilization. Pisces achieves per-tenant weighted fair shares (or minimal rates) of the aggregate resources of the shared service, even when different tenants' partitions are co-located and when demand for different partitions is skewed, time-varying, or bottlenecked by different server resources. Pisces does so by decomposing the fair sharing problem into a combination of four complementary mechanisms--partition placement, weight allocation, replica selection, and weighted fair queuing--that operate on different time-scales and combine to provide system-wide max-min fairness. ::: ::: An evaluation of our Pisces storage prototype achieves nearly ideal (0.99 Min-Max Ratio) weighted fair sharing, strong performance isolation, and robustness to skew and shifts in tenant demand. These properties are achieved with minimal overhead (<3%), even when running at high utilization (more than 400,000 requests/second/server for 10B requests).


title: Best of both worlds: Human-machine collaboration for object annotation

abstract: The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset.


title: A Relation-Based Page Rank Algorithm for Semantic Web Search Engines

abstract: With the tremendous growth of information available to end users through the Web, search engines come to play ever a more critical role. Nevertheless, because of their general-purpose approach, it is always less uncommon that obtained result sets provide a burden of useless pages. The next-generation Web architecture, represented by the Semantic Web, provides the layered architecture possibly allowing overcoming this limitation. Several search engines have been proposed, which allow increasing information retrieval accuracy by exploiting a key content of semantic Web resources, that is, relations. However, in order to rank results, most of the existing solutions need to work on the whole annotated knowledge base. In this paper, we propose a relation-based page rank algorithm to be used in conjunction with semantic Web search engines that simply relies on information that could be extracted from user queries and on annotated resources. Relevance is measured as the probability that a retrieved resource actually contains those relations whose existence was assumed by the user at the time of query definition.


title: A Neural Model for Generating Natural Language Summaries of Program Subroutines

abstract: Source code summarization -- creating natural language descriptions of source code behavior -- is a rapidly-growing research topic with applications to automatic documentation generation, program comprehension, and software maintenance. Traditional techniques relied on heuristics and templates built manually by human experts. Recently, data-driven approaches based on neural machine translation have largely overtaken template-based systems. But nearly all of these techniques rely almost entirely on programs having good internal documentation; without clear identifier names, the models fail to create good summaries. In this paper, we present a neural model that combines words from code with code structure from an AST. Unlike previous approaches, our model processes each data source as a separate input, which allows the model to learn code structure independent of the text in code. This process helps our approach provide coherent summaries in many cases even when zero internal documentation is provided. We evaluate our technique with a dataset we created from 2.1m Java methods. We find improvement over two baseline techniques from SE literature and one from NLP literature.


title: Automated pulmonary nodule detection using 3D deep convolutional neural networks

abstract: Early detection of pulmonary nodules in computed tomography (CT) images is essential for successful outcomes among lung cancer patients. Much attention has been given to deep convolutional neural network (DCNN)-based approaches to this task, but models have relied at least partly on 2D or 2.5D components for inherently 3D data. In this paper, we introduce a novel DCNN approach, consisting of two stages, that is fully three-dimensional end-to-end and utilizes the state-of-the-art in object detection. First, nodule candidates are identified with a U-Net-inspired 3D Faster R-CNN trained using online hard negative mining. Second, false positive reduction is performed by 3D DCNN classifiers trained on difficult examples produced during candidate screening. Finally, we introduce a method to ensemble models from both stages via consensus to give the final predictions. By using this framework, we ranked first of 2887 teams in Season One of Alibaba's 2017 TianChi AI Competition for Healthcare.


A Bilingual Interactive Human Avatar Dialogue System


title: Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

abstract: Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.


title: Anatomical Variants in Prostate Artery Embolization: A Pictorial Essay

abstract: Prostate artery embolization (PAE) has emerged as a new treatment option for patients with symptomatic benign prostatic hyperplasia. The main challenges related to this procedure are navigating arteries with atherosclerosis and anatomical variations, and the potential risk of non-target embolization to pelvic structures due to the presence of collateral shunts and reflux of microparticles. Knowledge of classical vascular anatomy and the most common variations is essential for safe embolization, good clinical practice, and optimal outcomes. The aim of this pictorial essay is to illustrate the pelvic vascular anatomy relevant to PAE in order to provide a practical guide that includes the most common anatomical variants as well as to discuss the technical details related to each.


title: Read disturb-free SRAM bit-cell for subthreshold memory applications

abstract: In this work, we present a novel bit-cell which improves data stability in subthreshold SRAM operation. It consists of eight transistors, two of which cut off a positive feedback of cross-coupled inverters during the read access. In addition, the bit-cell keeps the noise-vulnerable data ‘low’ node voltage close to the ground level during the dummy-read operation, and thus producing near-ideal voltage transfer characteristics essential for robust SRAM functionality. In the write access, the boosted wordline facilitates to change the contents of the memory bit. Implementation results in a 180 nm CMOS technology exhibit that the proposed cell remains unaffected by the read disturbance, while achieves 58.7 % higher dummy read stability and 3.68 × better write-ability at 0.4 V supply compared to the standard 6T SRAM cell.


title: Robust Controller for Tremor Suppression at Musculoskeletal Level in Human Wrist

abstract: Tremor is a rhythmical and involuntary oscillatory movement of a body part and it is one of the most common movement disorders. Orthotic devices have been under investigation as a noninvasive tremor suppression alternative to medication or surgery. The challenge in musculoskeletal tremor suppression is estimating and attenuating the tremor motion without impeding the patient's intentional motion. In this research a robust tremor suppression algorithm was derived for patients with pathological tremor in the upper limbs. First the motion in the tremor frequency range is estimated using a high-pass filter. Then, by applying the backstepping method the appropriate amount of torque is calculated to drive the output of the estimator toward zero. This is equivalent to an estimation of the tremor torque. It is shown that the arm/orthotic device control system is stable and the algorithm is robust despite inherent uncertainties in the open-loop human arm joint model. A human arm joint simulator, capable of emulating tremorous motion of a human arm joint was used to evaluate the proposed suppression algorithm experimentally for two types of tremor, Parkinson and essential. Experimental results show 30-42 dB (97.5-99.2%) suppression of tremor with minimal effect on the intentional motion.


Consumers ’ Perception and Willingness to Pay for Eco-Labeled Seafood : A Case-Study


User^|^apos;s Action and Decision Making of Retweet Messages towards Reducing Misinformation Spread during Disaster


title: Near duplicate detection in an academic digital library

abstract: The detection and potential removal of duplicates is desirable for a number of reasons, such as to reduce the need for unnecessary storage and computation, and to provide users with uncluttered search results. This paper describes an investigation into the application of scalable simhash and shingle state of the art duplicate detection algorithms for detecting near duplicate documents in the CiteSeerX digital library. We empirically explored the duplicate detection methods and evaluated their performance and application to academic documents and identified good parameters for the algorithms. We also analyzed the types of near duplicates identified by each algorithm. The highest F-scores achieved were 0.91 and 0.99 for the simhash and shingle-based methods respectively. The shingle-based method also identified a larger variety of duplicate types than the simhash-based method.


title: BFT-Bench: A Framework to Evaluate BFT Protocols

abstract: Byzantine Fault Tolerance (BFT) has been extensively studied and numerous protocols and software prototypes have been proposed. However, most BFT prototypes have been evaluated in an ad-hoc setting, considering different fault types and fault injection scenarios. In this paper, we present BFT-Bench, the first benchmarking framework for evaluating and comparing BFT protocols in practice. BFT-Bench includes different BFT protocols implementations, their automatic deployment in a distributed setting, the ability to define and inject different faulty behaviors, and the online monitoring and reporting of performance and dependability measures. Preliminary results of BFT-Bench show the effectiveness of the framework, easily allowing an empirical comparison of different BFT protocols, in various workload and fault scenarios.


title: A rotational micro biopsy device for the capsule endoscope

abstract: Recently the technology of capsule endoscopy has developed dramatically and many researchers are making efforts to combine surgical function into the capsule type endoscope. In this paper, the micro biopsy module which is a part of the capsule endoscope is proposed. The proposed module is less than 2 mm in thickness and has a diameter of 10 mm. It consists of a trigger with paraffin block, rotational tissue-cutting razor with a torsion spring and controller. This module makes it possible for the capsule endoscope to obtain a sample tissue inside the small intestine which can not be reached by a conventional biopsy device. Through dedicated experiments, tissue samples were successfully extracted using the proposed biopsy module and the cells in samples were extracted and tested by a microscope.


title: Generic decoding of seen and imagined objects using hierarchical visual features

abstract: Object recognition is a key function in both human and machine vision. While recent studies have achieved fMRI decoding of seen and imagined contents, the prediction is limited to training examples. We present a decoding approach for arbitrary objects, using the machine vision principle that an object category is represented by a set of features rendered invariant through hierarchical processing. We show that visual features including those from a convolutional neural network can be predicted from fMRI patterns and that greater accuracy is achieved for low/high-level features with lower/higher-level visual areas, respectively. Predicted features are used to identify seen/imagined object categories (extending beyond decoder training) from a set of computed features for numerous object images. Furthermore, the decoding of imagined objects reveals progressive recruitment of higher to lower visual representations. Our results demonstrate a homology between human and machine vision and its utility for brain-based information retrieval.


title: Social big data mining: A survey focused on opinion mining and sentiments analysis

abstract: The emergence of social media and the huge amount of data generated by them, has lead researchers to study the possibility of their exploitation in order to identify hidden knowledge. Thus, two areas are attracting more and more interest in the research community, the opinion mining and sentiment analysis. The research papers in this field are increasing rapidly. However, few survey papers have been published in this area. The rare survey papers that have been published focusing on a particular aspect (for example, the sentiment classification techniques, the challenges and application of opinion mining and sentiment analysis, etc.) and don't concern the research work proposed this last two years. We propose, in this paper, a study of 60 recent papers (60 % published between 2013 and 2015) treating the social media mining. We compare this work regarding to several criteria. Finally, we present a table summarizing all studied work. Due to the richness of social media opinions, emotions and sentiments. We focus in this paper on opinion mining and sentiment analysis and show the position of this field in social media mining issues. Then, we describe them in detail by studying the research work on each task in these issues. We will finish by identifying a set of open issues that we will try to propose contributions in our future work.


title: Design and modeling of 200kHz 1,5kW LLC power semiconductor resonant converter

abstract: The paper deals with the design and optimization of the LLC power semiconductor resonant converter. First, the design of the power stage of LLC converter is being described, accompanied with mathematical simplification and description of the basic operation. For main circuit optimization the simulation analysis in OrCad PSPICE have been made. Consequently the design strategy for control system and regulators is described. After it, as the best solution, the hysteretic regulator in discrete form is presented and simulation analysis of control system in Matlab SIMULINK has been made.


title: Muscular adaptations in low- versus high-load resistance training: A meta-analysis

abstract: AbstractThere has been much debate as to optimal loading strategies for maximising the adaptive response to resistance exercise. The purpose of this paper therefore was to conduct a meta-analysis of randomised controlled trials to compare the effects of low-load (≤60% 1 repetition maximum [RM]) versus high-load (≥65% 1 RM) training in enhancing post-exercise muscular adaptations. The strength analysis comprised 251 subjects and 32 effect sizes (ESs), nested within 20 treatment groups and 9 studies. The hypertrophy analysis comprised 191 subjects and 34 ESs, nested with 17 treatment groups and 8 studies. There was a trend for strength outcomes to be greater with high loads compared to low loads (difference = 1.07 ± 0.60; CI: −0.18, 2.32; p = 0.09). The mean ES for low loads was 1.23 ± 0.43 (CI: 0.32, 2.13). The mean ES for high loads was 2.30 ± 0.43 (CI: 1.41, 3.19). There was a trend for hypertrophy outcomes to be greater with high loads compared to low loads (difference = 0.43 ± 0.24; CI: −0.05, 0.92; p ...


title: A comparative study of coreless and conventional axial flux permanent magnet synchronous machines for low and high speed operation

abstract: Axial Flux Permanent Magnet (AFPM) motors of two types, coreless and conventional topologies, have been studied in order to identify their merits and drawbacks at different ratings, particularly for in-wheel electric transportation applications. The performance of the coreless with several slotpole combinations and number of disks is evaluated. Design of experiments (DOE) methodology is utilized to determine the correlation between input variables and output parameters and the regression models are used to identify the optimum design. Detailed comparison between coreless and conventional topologies at two operating speeds at extreme ends of the driving cycle are conducted.


title: Attracting students' engagement in programming courses with gamification

abstract: Gamification has the ability to stimulate creativity and active minds. In the process of learning programming, a passive learning style often leads to some level of student disengagement during class time. Retaining the engagement of motivated students does not present much of a problem. However, in the case of problematic students, sometimes an unpleasant experience they encountered during a previous programming class will lead to an unmotivated attitude in the next programming class. Thus, this paper aims to provide a way for institutions to help problematic students who may have difficulty in engaging with the programming subject. This paper presents an example of how the gamification elements with COI framework can help to reach out to timid students who have difficulties engaging with the programming subject. Mapping of top gamification elements will create interdependence among inquiries for better educational experience.


title: Compact coplanar waveguide (CPW)-fed resonant antennas for dual-frequency operation using epsilon negative transmission line

abstract: This paper presents the design and analysis of compact coplanar waveguide (CPW)-fed dual-frequency resonant antenna. It can be analyzed and designed on the concept of the epsilon negative (ENG) meta-structured transmission line (MTL) and on a CPW single layer where vias are not required. The resonant phenomenon is employed to reduce the antenna size. To design and analyze the proposed antenna, the circuit simulation of the ENG MTL unit resonator is executed by the equivalent circuit and the results are compared with those of full wave simulation and experiment. The results show that the presented antenna has such a reasonable radiation characteristics of bandwidth and size which are verified by commercial EM simulation software HFSS11 and is suitable for compact dual-frequency antenna. Then the antenna is fabricated and measured. The realized antenna has a compact size of 22 mm×26.6 mm . Experimental results show that the proposed antenna can operate at 2.5 (2.485 to 2.515) GHz and 4.20 (4.075 to 4.327)GHz bands. Good agreement between the simulated and measured results is obtained.


title: Big Data for Remote Sensing: Challenges and Opportunities

abstract: Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.


title: Solving NP-Hard Problems with Physarum-Based Ant Colony System

abstract: NP-hard problems exist in many real world applications. Ant colony optimization ACO algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a Physarum-based pheromone matrix optimization strategy in ant colony system ACS for solving NP-hard problems such as traveling salesman problem TSP and 0/1 knapsack problem 0/1 KP. In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.


title: Feature Fusion for Scene Text Detection

abstract: A significant challenge in scene text detection is the large variation in text sizes. In particular, small text are usually hard to detect. This paper presents an accurate oriented text detector based on Faster R-CNN. We observe that Faster R-CNN is suitable for general object detection but inadequate for scene text detection due to the large variation in text size. We apply feature fusion both in RPN and Fast R-CNN to alleviate this problem and furthermore, enhance model's ability to detect relatively small text. Our text detector achieves comparable results to those state of the art methods on ICDAR 2015 and MSRA-TD500, showing its advantage and applicability.


title: Malicious URL filtering — A big data application

abstract: Malicious URLs have become a channel for Internet criminal activities such as drive-by-download, spamming and phishing. Applications for the detection of malicious URLs are accurate but slow (because they need to download the content or query some Internet host information). In this paper we present a novel lightweight filter based only on the URL string itself to use before existing processing methods. We run experiments on a large dataset and demonstrate a 75% reduction in workload size while retaining at least 90% of malicious URLs. Existing methods do not scale well with the hundreds of millions of URLs encountered every day as the problem is a heavily-imbalanced, large-scale binary classification problem. Our proposed method is able to handle nearly two million URLs in less than five minutes. We generate two filtering models by using lexical features and descriptive features, and then combine the filtering results. The on-line learning algorithms are applied here not only for dealing with large-scale data sets but also for fitting the very short lifetime characteristics of malicious URLs. Our filter can significantly reduce the volume of URL queries on which further analysis needs to be performed, saving both computing time and bandwidth used for content retrieval.


title: The design and development of a Lie Detection System using facial micro-expressions

abstract: Detecting lies is crucial in many areas, such as airport security, police investigations, counter-terrorism, etc. One technique to detect lies is through the identification of facial micro-expressions, which are brief, involuntary expressions shown on the face of humans when they are trying to conceal or repress emotions. Manual measurement of micro-expressions is hard labor, time consuming, and inaccurate. This paper presents the Design and Development of a Lie Detection System using Facial Micro-Expressions. It is an automated vision system designed and implemented using LabVIEW. An Embedded Vision System (EVS) is used to capture the subject's interview. Then, a LabVIEW program converts the video into series of frames and processes the frames, each at a time, in four consecutive stages. The first two stages deal with color conversion and filtering. The third stage applies geometric-based dynamic templates on each frame to specify key features of the facial structure. The fourth stage extracts the needed measurements in order to detect facial micro-expressions to determine whether the subject is lying or not. Testing results show that this system can be used for interpreting eight facial expressions: happiness, sadness, joy, anger, fear, surprise, disgust, and contempt, and detecting facial micro-expressions. It extracts accurate output that can be employed in other fields of studies such as psychological assessment. The results indicate high precision that allows future development of applications that respond to spontaneous facial expressions in real time.


title: HarmTrace: Improving Harmonic Similarity Estimation Using Functional Harmony Analysis

abstract: Harmony theory has been essential in composing, analysing, and performing music for centuries. Since Western tonal harmony exhibits a considerable amount of structure and regularity, it lends itself to formalisation. In this paper we present HARMTRACE, a system that, given a sequence of symbolic chord labels, automatically derives the harmonic function of a chord in its tonal context. Among other applications, these functional annotations can be used to improve the estimation of harmonic similarity in a local alignment of two annotated chord sequences. We evaluate HARMTRACE and three other harmonic similarity measures on a corpus of 5,028 chord sequences that contains harmonically related pieces. The results show that HARMTRACE outperforms all three other similarity measures, and that information about the harmonic function of a chord improves the estimation of harmonic similarity between two chord sequences.


How does Internet recruitment have effect on recruitment performance ?


title: Potential astronauts' Speech and Language disorders. Case study: Astronaut's interviews analysis before and after space mission

abstract: In the context of lengthy human spaceflight missions and in the need for non-invasive, non-contact and non-obtrusive solutions for astronauts' psychological problems, the study expressed herein presents a distinctive and interdisciplinary perspective through a case study which analyses variations of certain Speech and Language (SL) characteristics residing to rhythm and fluency, oral language and voice disorders on an astronaut's SL within two separate interviews corresponding to different moments in time as before- and after-long duration space mission. Additionally, this case study proposes an approach for detecting syllable repetitions and SL hesitations by making the differences between the number of syllables detected on one side through an existent syllable detection algorithm, and on the other side through a speech-to-text analysis which has the capability to eliminate the deficits of morphology and syntax.


title: Shape-Preserving Half-Projective Warps for Image Stitching

abstract: This paper proposes a novel parametric warp which is a spatial combination of a projective transformation and a similarity transformation. Given the projective transformation relating two input images, based on an analysis of the projective transformation, our method smoothly extrapolates the projective transformation of the overlapping regions into the non-overlapping regions and the resultant warp gradually changes from projective to similarity across the image. The proposed warp has the strengths of both projective and similarity warps. It provides good alignment accuracy as projective warps while preserving the perspective of individual image as similarity warps. It can also be combined with more advanced local-warp-based alignment methods such as the as-projective-as-possible warp for better alignment accuracy. With the proposed warp, the field of view can be extended by stitching images with less projective distortion (stretched shapes and enlarged sizes).


title: The management of testicular masses and acute scrotal pain.

abstract: Within military primary care patients may present with testicular masses or acute scrotal pain. The aim of this article is to examine, by means of case studies, the differential diagnosis, treatment and clinical considerations in managing patients in the military environment.


title: BioDWH: A Data Warehouse Kit for Life Science Data Integration

abstract: This paper presents a novel bioinformatics data warehouse software kit that integrates biological information from multiple public life science data sources into a local database management system. It stands out from other approaches by providing up-to-date integrated knowledge, platform and database independence as well as high usability and customization. This open source software can be used as a general infrastructure for integrative bioinformatics research and development. The advantages of the approach are realized by using a Java-based system architecture and object-relational mapping (ORM) technology. Finally, a practical application of the system is presented within the emerging area of medical bioinformatics to show the usefulness of the approach. The BioDWH data warehouse software is available for the scientific community at http://sourceforge.net/projects/biodwh/.


title: High Performance Mixed Signal and RF Circuits Enabled by the Direct Monolithic Heterogeneous Integration of GaN HEMTs and Si CMOS on a Silicon Substrate

abstract: We present recent results on the direct heterogeneous integration of InP HBTs and Si CMOS on a silicon template wafer or SOLES (Silicon On Lattice Engineered Substrate). InP HBTs whose performance are comparable to HBTs on the native InP substrates have been repeatedly achieved. 100% heterogeneous interconnect yield has been achieved on daisy chain test structures with CMOS-InP HBT spacing (interconnect length) as small as 2.5um. In DARPA COSMOS Phase 1 we designed and fabricated a differential amplifier that met the program Go/NoGo metrics with first pass design success. As the COSMOS Phase 2 demonstration vehicle we designed and fabricated a low power dissipation, high resolution, 500MHz bandwidth digital-to-analog converter (DAC).


title: Acute idiopathic polyradiculoneuritis concurrent with acquired myasthenia gravis in a West Highland white terrier dog

abstract: Acute Idiopathic Polyradiculoneuritis, an animal model for the axonal form of the Guillain – Barre Syndrome in humans and the acquired myasthenia gravis are different autoimmune disorders affecting the peripheral nerves and the neuromuscular junction, respectively. Both lead to muscle weakness and possible respiratory failure. The coexistence of these two entities combined in the same patient is rare in humans and, to our knowledge, the present case is the first reported in dogs. An 11-year-old West Highland WhiteTerrier female dog was referred to our clinic with a history of symmetrical weakness beginning with the pelvic limbs and evolving cranially, progressing to non-ambulatory flaccid tetraparesis over the preceding week. The history did not reveal signs of a recent other illness, trauma or exposure to a neurotoxin or raccoon bite. The last vaccination was carried out 5 months before presentation. Upon clinical examination, spinal reflexes and postural reactions were decreased in all four limbs and became absent within the following 24 h; perineal reflex was normal, and loss of voice was observed. The patient maintained its ability to urinate and defecate and it had no difficulty to eat or to drink. There were no cerebellar or sensory deficits. The electrophysiological findings revealed positive sharp waves and complex repetitive discharges on the electromyogram, temporal dispersion of compound muscle action potentials associated with polyphasia and a slow motor nerve conduction velocity as signs of demyelination, and an increased latency of F-waves. The cerebrospinal fluid had a normal cellularity with increased protein content. A reduction by 18 % in the amplitudes of the third compound muscle action potential as compared to the first one was observed during a repetitive nerve stimulation, indicating a postsynaptic disturbance. Only the motor electrophysiology was considered in this study. The diagnosis was based on clinical and electrophysiological findings associated with a positive titer for acetylcholine receptor antibodies. The diagnosis of MG was based on the typical clinical findings such as dysphonia and dysphagia, decremental response of RNS and positive AChRs antibody titre. Flaccid tetraparesis associated with diminished reflexes, increase of the distal latencies and temporal dispersion which caused lower MNCV, along with the increase of the F wave latencies supported the diagnosis of AIP. A cerebrospinal fluid tap indicated an albuminocytologic dissociation sustaining the radicular implication of AIP. As such, a diagnosis of MG and AIP co-occurrence syndrome was established.


title: Analysis of large-scale scalar data using hixels

abstract: One of the greatest challenges for today's visualization and analysis communities is the massive amounts of data generated from state of the art simulations. Traditionally, the increase in spatial resolution has driven most of the data explosion, but more recently ensembles of simulations with multiple results per data point and stochastic simulations storing individual probability distributions are increasingly common. This paper introduces a new data representation for scalar data, called hixels, that stores a histogram of values for each sample point of a domain. The histograms may be created by spatial down-sampling, binning ensemble values, or polling values from a given distribution. In this manner, hixels form a compact yet information rich approximation of large scale data. In essence, hixels trade off data size and complexity for scalar-value “uncertainty”. Based on this new representation we propose new feature detection algorithms using a combination of topological and statistical methods. In particular, we show how to approximate topological structures from hixel data, extract structures from multi-modal distributions, and render uncertain isosurfaces. In all three cases we demonstrate how using hixels compares to traditional techniques and provide new capabilities to recover prominent features that would otherwise be either infeasible to compute or ambiguous to infer. We use a collection of computer tomography data and large scale combustion simulations to illustrate our techniques.


A G ] 2 N ov 2 01 6 YANO ’ S CONJECTURE FOR 2-PUISEUX PAIRS IRREDUCIBLE PLANE CURVE SINGULARITIES


Integration of data sources on traffic accidents.


title: An empirical study of global software development: distance and speed

abstract: Global software development is rapidly becoming the norm for technology companies. Previous qualitative research suggests that multi-site development may increase the development cycle time. We use both survey data and data from the source code change management system to model the extent of delay in a multi-site software development organization, and explore several possible mechanisms for this delay. We also measure differences in same-site and cross-site communication patterns, and analyze the relationship of these variables to delay. Our results show that, compared to same-site work, cross-site work takes much longer and requires more people for work of equal size and complexity. We also report a strong relationship between delay in cross-site work and the degree to which remote colleagues are perceived to help out when workloads are heavy. We discuss the implications of our findings for collaboration technology for distributed software development.


title: Towards a Mathematical Understanding of the Difficulty in Learning with Feedforward Neural Networks

abstract: Training deep neural networks for solving machine learning problems is one great challenge in the field, mainly due to its associated optimisation problem being highly non-convex. Recent developments have suggested that many training algorithms do not suffer from undesired local minima under certain scenario, and consequently led to great efforts in pursuing mathematical explanations for such observations. This work provides an alternative mathematical understanding of the challenge from a smooth optimisation perspective. By assuming exact learning of finite samples, sufficient conditions are identified via a critical point analysis to ensure any local minimum to be globally minimal as well. Furthermore, a state of the art algorithm, known as the Generalised Gauss-Newton (GGN) algorithm, is rigorously revisited as an approximate Newton's algorithm, which shares the property of being locally quadratically convergent to a global minimum under the condition of exact learning.


title: A bioinspired soft manipulator for minimally invasive surgery

abstract: This paper introduces a novel, bioinspired manipulator for minimally invasive surgery (MIS). The manipulator is entirely composed of soft materials, and it has been designed to provide similar motion capabilities as the octopus's arm in order to reach the surgical target while exploiting its whole length to actively interact with the biological structures. The manipulator is composed of two identical modules (each of them can be controlled independently) with multi-directional bending and stiffening capabilities, like an octopus arm. In the authors' previous works, the design of the single module has been addressed. Here a two-module manipulator is presented, with the final aim of demonstrating the enhanced capabilities that such a structure can have in comparison with rigid surgical tools currently employed in MIS. The performances in terms of workspace, stiffening capabilities, and generated forces are characterized through experimental tests. The combination of stiffening capabilities and manipulation tasks is also addressed to confirm the manipulator potential employment in a real surgical scenario.


title: Studying the History of Ideas Using Topic Models

abstract: How can the development of ideas in a scientific field be studied over time? We apply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006. We induce topic clusters using Latent Dirichlet Allocation, and examine the strength of each topic over time. Our methods find trends in the field including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp decline of research in semantics and understanding between 1978 and 2001, possibly rising again after 2001. We also introduce a model of the diversity of ideas, topic entropy, using it to show that COLING is a more diverse conference than ACL, but that both conferences as well as EMNLP are becoming broader over time. Finally, we apply Jensen-Shannon divergence of topic distributions to show that all three conferences are converging in the topics they cover.


title: Properties of Latent Variable Network Models

abstract: We derive properties of Latent Variable Models for networks, a broad class of models that includes the widely-used Latent Position Models. These include the average degree distribution, clustering coefficient, average path length and degree correlations. We introduce the Gaussian Latent Position Model, and derive analytic expressions and asymptotic approximations for its network properties. We pay particular attention to one special case, the Gaussian Latent Position Models with Random Effects, and show that it can represent the heavy-tailed degree distributions, positive asymptotic clustering coefficients and small-world behaviours that are often observed in social networks. Several real and simulated examples illustrate the ability of the models to capture important features of observed networks.


title: Adapting the Large Neighborhood Search to Effectively Solve Pickup and Delivery Problems with Time Windows

abstract: The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints. ::: ::: This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search. ::: ::: The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems. ::: ::: The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.


title: Giveme5W: Main Event Retrieval from News Articles by Extraction of the Five Journalistic W Questions

abstract: Extraction of event descriptors from news articles is a commonly required task for various tasks, such as clustering related articles, summarization, and news aggregation. Due to the lack of generally usable and publicly available methods optimized for news, many researchers must redundantly implement such methods for their project. Answers to the five journalistic W questions (5Ws) describe the main event of a news article, i.e., who did what, when, where, and why. The main contribution of this paper is Giveme5W, the first open-source, syntax-based 5W extraction system for news articles. The system retrieves an article’s main event by extracting phrases that answer the journalistic 5Ws. In an evaluation with three assessors and 60 articles, we find that the extraction precision of 5W phrases is \( p = 0.7 \).


title: Object Ordering with Bidirectional Matchings for Visual Reasoning

abstract: Visual reasoning with compositional natural language instructions, e.g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image. Further, this mapping needs to be processed to answer the question in the statement given the ordering and relationship of the objects across three similar images. In this paper, we propose a novel end-to-end neural model for the NLVR task, where we first use joint bidirectional attention to build a two-way conditioning between the visual information and the language phrases. Next, we use an RL-based pointer network to sort and process the varying number of unordered objects (so as to match the order of the statement phrases) in each of the three images and then pool over the three decisions. Our model achieves strong improvements (of 4-6% absolute) over the state-of-the-art on both the structured representation and raw image versions of the dataset.


title: Evolving Mobile Media: Uses and Conceptualizations of the Mobile Internet

abstract: Technological convergence has led to the ability to access the internet from a variety of mobile devices. Drawing on the Mobile Phone Appropriation Model (Wirth, von Pape & Karnowski, 2008), we sought to understand how people conceptualize and use the mobile internet by conducting semistructured interviews with 21 mobile internet users, half American and half German in order to explore cross-cultural differences. Findings suggest little cross-cultural difference in use and understanding of the mobile Internet. Users do not perceive the act of “going online” as a significant step, even if it is on a mobile device. They do, however, distinguish between different ways of consuming information online (extractive and immersive), relating them to different situations and devices.


title: Fast Global Image Smoothing Based on Weighted Least Squares

abstract: This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory- and computation-intensive large linear system, defined over a d -dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ~10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 <; γ <;2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.


title: Unsupervised document classification using sequential information maximization

abstract: We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential (sIB) approach is guaranteed to converge to a local maximum of the information with time and space complexity typically linear in the data size. information, as required by the original IB principle. Moreover, the time and space complexity are significantly improved. We apply this algorithm to unsupervised document classification. In our evaluation, on small and medium size corpora, the sIB is found to be consistently superior to all the other clustering methods we examine, typically by a significant margin. Moreover, the sIB results are comparable to those obtained by a supervised Naive Bayes classifier. Finally, we propose a simple procedure for trading cluster's recall to gain higher precision, and show how this approach can extract clusters which match the existing topics of the corpus almost perfectly.


title: An Evolutionary Algorithm for the Multi-objective Multiple Knapsack Problem

abstract: In this study, we consider the multi-objective multiple knapsack problem (MMKP) and we adapt our favorable weight based evolutionary algorithm (FWEA) to approximate the efficient frontier of MMKP. The algorithm assigns fitness to solutions based on their relative strengths as well as their non-dominated frontiers. The relative strength is measured based on a weighted Tchebycheff distance from the ideal point where each solution chooses its own weights that minimize its distance from the ideal point. We carry out experiments on test data for MMKP given in the literature and compare the performance of the algorithm with several leading algorithms.


title: Extending Recommender Systems for Disjoint User/Item Sets: The Conference Recommendation Problem

abstract: In this paper, we describe the problem of recommending conference sessions to attendees and show how novel extensions to traditional model-based recommender systems, as suggested in Adomavicius and Tuzhilin [CHECK END OF SENTENCE], can address this problem. We introduce Recommendation Engine by CONjoint Decomposition of ITems and USers (RECONDITUS)-a technique that is an extension of preference-based recommender systems to recommend items from a new disjoint set to users from a new disjoint set. The assumption being that preferences exhibited by users with known usage behavior (e.g., past conference session attendance), which can be abstracted by projections of user and item matrices, will be similar to ones of new (different) users where the basic environment and item domain are the same (e.g., new conference). RECONDITUS requires no item ratings, but operates on observed user behavior such as past conference session attendance. The RECONDITUS paradigm consists of projections of both user and item data, and the learning of relationships in projected space. Once established, the relationships enable predicting new relationships and provide associated recommendations. The approach can encompass several traditional data mining problems where both clustering and prediction are necessary. RECONDITUS has been evaluated using data from the Oracle OpenWorld conference.


Machine-vision-based control of zinc flotation — A case study


title: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

abstract: The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.


title: Software testing: a research travelogue (2000–2014)

abstract: Despite decades of work by researchers and practitioners on numerous software quality assurance techniques, testing remains one of the most widely practiced and studied approaches for assessing and improving software quality. Our goal, in this paper, is to provide an accounting of some of the most successful research performed in software testing since the year 2000, and to present what appear to be some of the most significant challenges and opportunities in this area. To be more inclusive in this effort, and to go beyond our own personal opinions and biases, we began by contacting over 50 of our colleagues who are active in the testing research area, and asked them what they believed were (1) the most significant contributions to software testing since 2000 and (2) the greatest open challenges and opportunities for future research in this area. While our colleagues’ input (consisting of about 30 responses) helped guide our choice of topics to cover and ultimately the writing of this paper, we by no means claim that our paper represents all the relevant and noteworthy research performed in the area of software testing in the time period considered—a task that would require far more space and time than we have available. Nevertheless, we hope that the approach we followed helps this paper better reflect not only our views, but also those of the software testing community in general.


title: Generating Natural Language Questions to Support Learning On-Line

abstract: When instructors prepare learning materials for students, they frequently develop accompanying questions to guide learning. Natural language processing technology can be used to automatically generate such questions but techniques used have not fully leveraged semantic information contained in the learning materials or the full context in which the question generation task occurs. We introduce a sophisticated template-based approach that incorporates semantic role labels into a system that automatically generates natural language questions to support online learning. While we have not yet incorporated the full learning context into our approach, our preliminary evaluation and evaluation methodology indicate our approach is a promising one for supporting learning.


title: Enabling Sparse Winograd Convolution by Native Pruning

abstract: Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs. Sparse Winograd merges these two and thus has the potential to offer a combined performance benefit. Nevertheless, training convolution layers so that the resulting Winograd kernels are sparse has not hitherto been very successful. By introducing a Winograd layer in place of a standard convolution layer, we can learn and prune Winograd coefficients"natively"and obtain sparsity level beyond 90% with only 0.1% accuracy loss with AlexNet on ImageNet dataset. Furthermore, we present a sparse Winograd convolution algorithm and implementation that exploits the sparsity, achieving up to 31.7 effective TFLOP/s in 32-bit precision on a latest Intel Xeon CPU, which corresponds to a 5.4x speedup over a state-of-the-art dense convolution implementation.


On the Development of Name Search Techniques for Arabic


title: Planning and Plan-execution for Human-Robot Cooperative Task achievement

abstract: Human Robot cooperation brings several challenges to autonomous robotics such as adoption of a pro-active behavior, situation analysis and goal generation, intention explanation and adaptation and choice of a correct behavior toward the human. In this paper, we describe a decisional architecture for human robot interaction which addresses some of these challenges. The description will be centered on a planner called HATP (Human Aware Task Planner) and its capacity to synthesize plans for human robot teamwork that respect social conventions and that favour acceptable collaborative behaviors. We provide an overall description of HATP and its integration in a complete implemented architecture. We also illustrate the performance of our system on a daily life scenario achieved by a robot in interaction with a human partner in a realistic setup.


title: pClass: An Effective Classifier for Streaming Examples

abstract: In this paper, a novel evolving fuzzy-rule-based classifier, termed parsimonious classifier (pClass), is proposed. pClass can drive its learning engine from scratch with an empty rule base or initially trained fuzzy models. It adopts an open structure and plug and play concept where automatic knowledge building, rule-based simplification, knowledge recall mechanism, and soft feature reduction can be carried out on the fly with limited expert knowledge and without prior assumptions to underlying data distribution. In this paper, three state-of-the-art classifier architectures engaging multi-input–multi-output, multimodel, and round robin architectures are also critically analyzed. The efficacy of the pClass has been numerically validated by means of real-world and synthetic streaming data, possessing various concept drifts, noisy learning environments, and dynamic class attributes. In addition, comparative studies with prominent algorithms using comprehensive statistical tests have confirmed that the pClass delivers more superior performance in terms of classification rate, number of fuzzy rules, and number of rule-base parameters.


title: Recognition of Images Degraded by Gaussian Blur

abstract: In this paper, we propose a new theory of invariants to Gaussian blur. We introduce a notion of a primordial image as a canonical form of all Gaussian blur-equivalent images. The primordial image is defined in spectral domain by means of projection operators. We prove that the moments of the primordial image are invariant to Gaussian blur and we derive recursive formulas for their direct computation without actually constructing the primordial image itself. We show how to extend their invariance also to image rotation. The application of these invariants is in blur-invariant image comparison and recognition. In the experimental part, we perform an exhaustive comparison with two main competitors: 1) the Zhang distance and 2) the local phase quantization.


title: Peer-to-Peer Rental Markets in the Sharing Economy

abstract: To investigate whether peer-to-peer rental markets for durable goods are welfare-improving, we develop a new dynamic model of such markets in which users with heterogeneous utilization rates may also trade in secondary markets. We calibrate our model with US automobile industry data and transaction-level data from Getaround, a large peer-to-peer car rental marketplace. Counterfactual analyses illustrate significant shifts away from asset ownership as marketplace access grows. Used-good prices fall and replacement rates rise, while gains in consumer surplus range from 0.8% to 6.6%. The changes in consumption mix and the surplus increases are significantly more pronounced for below-median income consumers.


title: Polymorphic Malware Detection Using Sequence Classification Methods

abstract: Polymorphic malware detection is challenging due to the continual mutations miscreants introduce to successive instances of a particular virus. Such changes are akin to mutations in biological sequences. Recently, high-throughput methods for gene sequence classification have been developed by the bioinformatics and computational biology communities. In this paper, we argue that these methods can be usefully applied to malware detection. Unfortunately, gene classification tools are usually optimized for and restricted to an alphabet of four letters (nucleic acids). Consequently, we have selected the Strand gene sequence classifier, which offers a robust classification strategy that can easily accommodate unstructured data with any alphabet including source code or compiled machine code. To demonstrate Stand's suitability for classifying malware, we execute it on approximately 500GB of malware data provided by the Kaggle Microsoft Malware Classification Challenge (BIG 2015) used for predicting 9 classes of polymorphic malware. Experiments show that, with minimal adaptation, the method achieves accuracy levels well above 95% requiring only a fraction of the training times used by the winning team's method.


title: Impulsivity in Multiplayer Online Battle Arena Gamers: Preliminary Results on Experimental and Self-Report Measures

abstract: Background and aims Multiplayer Online Battle Arena (MOBA) games have become the most popular type of video games played worldwide, superseding the playing of Massively Multiplayer Online Role-Playing Games and First-Person Shooter games. However, empirical studies focusing on the use and abuse of MOBA games are still very limited, particularly regarding impulsivity, which is an indicator of addictive states but has not yet been explored in MOBA games. In this context, the objective of the present study is to explore the associations between impulsivity and symptoms of addictive use of MOBA games in a sample of highly involved League of Legends (LoL, currently the most popular MOBA game) gamers. Methods Thirty-six LoL gamers were recruited and completed both experimental (Single Key Impulsivity Paradigm) and self-reported impulsivity assessments (s-UPPS-P Impulsive Behavior Scale, Barratt Impulsiveness Scale), in addition to an assessment of problematic video game use (Problematic Online Gaming Questionnaire). Results Results showed links between impulsivity-related constructs and signs of excessive MOBA game involvement. Findings indicated that impaired ability to postpone rewards in an experimental laboratory task was strongly related to problematic patterns of MOBA game involvement. Although less consistent, several associations were also found between self-reported impulsivity traits and signs of excessive MOBA game involvement. Conclusions Despite these results are preliminary and based upon a small (self-selected) sample, the present study highlights potential psychological factors related to the addictive use of MOBA games.


title: High performance polyphase FIR filter structures in VHDL language for Software Defined Radio based on FPGA

abstract: Digital filters are necessary in digital transmitter / receiver side and popularity of Software Defined Radio (SDR) is forcing complex digital signal processing blocks to be implemented in parallel design flow on FPGA or ASIC. The goal of this paper is to develop efficient pipelined polyphase FIR filter structures in VHDL language for RTL synthesis on FPGA. The proposed structures contain fully parallel polyphase decimation and interpolation FIR filter models. The first part of this paper is focused on formulation of distributed arithmetic technique with polyphase decomposition, which represents the core of designed models. The second part describes mentioned polyphase FIR VHDL models. The extensive emphasis will be put on efficient pipelined implementation with excellent registered performance and optimal design size balance. The third part of this paper deals with rapid design and simulation of proposed VHDL models. The result of RTL synthesis is finally discussed. Very good performance and optimal design size are main benefits of proposed polyphase FIR filters. Developed structures are very suitable for multichannel operation in digital I/Q receiver.


title: From Nature to Maths : Improving Forecasting Performance in Subspace – based methods using Genetics Colonial Theory

abstract: Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.


title: Surgical treatment of ventral mesencephalic cavernoma

abstract: The treatment of an intrinsic midbrain tumour has always been controversial due to the precarious nature of its location. We report a rare example of a cavernoma located in the ventral mesencephalon and presented clinically as Holmes’ tremor. In order to access this vital portion of midbrain, we chose a fronto-temporal trans-sylvian route via an orbito-zygomatic craniotomy. The cavernoma was removed en bloc with a longitudinal pial incision between the fronto-pontine fibers and the pyramidal tracts in the peduncle. After the operation, the tremor dramatically disappeared. Through a review and analysis of the literature, we discuss the surgical strategy for management of an anterior midbrain cavernoma. With an optimal surgical approach, fairly safe entry zones on the anterior face of the rostral brainstem may be accessible, which makes it possible for successful resection of a ventral mesencephalic cavernoma without postoperative complications.


title: 3-D Edge Termination Design and ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ -BV Model of A 700-V Triple RESURF LDMOS With N-Type Top Layer

abstract: 3-D edge termination design and analytic model for specific on-resistance ( ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}})$ versus breakdown voltage (BV) of a 700-V triple reduced surface field lateral double-diffused MOSFET (LDMOS) with n-type top layer are presented in this paper. To eliminate the premature avalanche breakdown induced by the electric field crowding and the local charge imbalance in the transition region of the source-centered edge termination structure, 3-D numerical simulations and experiments are performed to investigate two crucial parameters ${L}_{1}$ and ${L}_{2}$ in the layout of the transition region. It is observed that optimal electric field distribution is obtained and breakdown positions are transferred to the active region at ${L}_{1} = 30~\mu \text{m}$ and ${L}_{2} = 1~\mu \text{m}$ . On the other hand, an analytic model is developed to describe the relation between ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ and BV, which is obtained as ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}} = 5.93 \times 10^{-6} \times 22({T}/298)^{1.7} \times \text {BV}^{2}$ and theoretically verifies that the fabricated device has achieved optimized ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ and BV. Compared with our previous paper, the LDMOS experimentally demonstrated improved performance with ${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ of 86.49 $\text{m}\Omega ~\cdot $ cm2 and BV of 805 V, which is leading performance for the 700-V applications in published data.


title: Earthquake Prediction Based on Spatio-Temporal Data Mining: An LSTM Network Approach

abstract: Earthquake prediction is a very important problem in seismology, the success of which can potentially save many human lives. Various kinds of technologies have been proposed to address this problem, such as mathematical analysis, machine learning algorithms like decision trees and support vector machines, and precursors signal study. Unfortunately, they usually do not have very good results due to the seemingly dynamic and unpredictable nature of earthquakes. In contrast, we notice that earthquakes are spatially and temporally correlated because of the crust movement. Therefore, earthquake prediction for a particular location should not be conducted only based on the history data in that location, but according to the history data in a larger area. In this paper, we employ a deep learning technique called long short-term memory (LSTM) networks to learn the spatio-temporal relationship among earthquakes in different locations and make predictions by taking advantage of that relationship. Simulation results show that the LSTM network with two-dimensional input developed in this paper is able to discover and exploit the spatio-temporal correlations among earthquakes to make better predictions than before.


title: Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/spe.2180 Analysis of operating system diversity for intrusion tolerance

abstract: One of the key benefits of using intrusion-tolerant systems is the possibility of ensuring correct behavior in the presence of attacks and intrusions. These security gains are directly dependent on the components exhibiting failure diversity. To what extent failure diversity is observed in practical deployment depends on how diverse are the components that constitute the system. In this paper, we present a study with operating system's OS's vulnerability data from the NIST National Vulnerability Database NVD. We have analyzed the vulnerabilities of 11 different OSs over a period of 18years, to check how many of these vulnerabilities occur in more than one OS. We found this number to be low for several combinations of OSs. Hence, although there are a few caveats on the use of NVD data to support definitive conclusions, our analysis shows that by selecting appropriate OSs, one can preclude or reduce substantially common vulnerabilities from occurring in the replicas of the intrusion-tolerant system. Copyright © 2013 John Wiley & Sons, Ltd.


Mini-batch Block-coordinate based Stochastic Average Adjusted Gradient Methods to Solve Big Data Problems


title: Video2GIF: Automatic Generation of Animated GIFs from Video

abstract: We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video that really capture our attention. GIFs tell a story, express emotion, turn events into humorous moments, and are the new wave of photojournalism. We pose the question: Can we automate the entirely manual and elaborate process of GIF creation by leveraging the plethora of user generated GIF content? We propose a Robust Deep RankNet that, given a video, generates a ranked list of its segments according to their suitability as GIF. We train our model to learn what visual content is often selected for GIFs by using over 100K user generated GIFs and their corresponding video sources. We effectively deal with the noisy web data by proposing a novel adaptive Huber loss in the ranking formulation. We show that our approach is robust to outliers and picks up several patterns that are frequently present in popular animated GIFs. On our new large-scale benchmark dataset, we show the advantage of our approach over several state-of-the-art methods.


title: A robust digital image watermarking technique using auto encoder based convolutional neural networks

abstract: Watermarking plays a very important role in providing authentication, ownership and transmission of secret information. The existing techniques of watermarking in literature are based on either spatial domain or transformation domain. Human brain consists of large number of neurons which are capable of doing paralleling tasking accurately. This resulted in the evaluation of neural network architectures, providing wide range of well connected features representing the input of the network. Convolutional Neural Networks(CNN) which were evolved in early 90s became popular and are being in use in wide range of tasks like classification, detection, recognition, patch matching. In this paper, we propose a digital image watermarking technique using auto-encoder based CNN which is robust to different noises and attacks like salt & pepper, Gaussian and JPEG effect. The proposed method, to the best of our knowledge, is the very first attempt of CNN in the domain of watermarking. We compare the performance of the proposed technique with the existing techniques which are based on spatial domain and transform domain. We show that the proposed method of watermarking using auto encoder based CNN (ACNNWM) gives better or on par results with the existing methods.


title: A case of asymmetrical monocephalus dipygus (tetrapus dibrachius) in a male Holstein calf in Iran

abstract: Dipygus is a teratological fetus with a double pelvis, genitals, and extremities. Congenital duplications in cattle are rare. Caudal duplication is more common in sheep and pigs while cranial duplications seem to be predominant in cattle. Asymmetric or parasitic conjoined twins consisting of an incomplete twin (parasite) attached to the body of a fully-developed twin (autosite). This report deals with a male Holstein calf with two extra limbs, in the pelvic region which were directed ventrally between the two normal hind limbs. The extra limbs were completely developed in one side and in other side just a bony mass were observed. So classification has been made as asymmetrical attached twins. The genital system was not affected and just one extra kidney-like structure was found. To the authors’ best knowledge, this is the first report of asymmetrical monocephalus dipygus (tetrapus dibrachius) in a male Holstein calf in Iran.


title: Intelligent Blind Cane System

abstract: Around the world, visually impaired individuals struggle with knowing where they are and how to move inside rooms and buildings safely. We have conducted a survey of more than 65 visually impaired individuals to confirm our proposed solution, which has been developed using an Android application and some sensors. This paper presents the methodology behind the Intelligent Blind Cane and demonstrates how the cane helps its user to navigate and recognize locations. After this, the survey, which was conducted with the visually impaired participants, is analysed to identify navigation issues and develop solutions.


title: A content-based recommendation algorithm for learning resources

abstract: Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language model is used. For its output, we propose the latent factor model, which is regularized by L1-norm. Furthermore, the split Bregman iteration method is introduced to solve the model. The major novelty of the proposed recommendation algorithm is that the text information is used directly to make the content-based recommendation without tagging. Experimental results on public databases in terms of quantitative assessment show significant improvements over conventional methods. In addition, the split Bregman iteration method which is introduced to solve the model can greatly improve the training efficiency.


title: Counting plants using deep learning

abstract: In this paper we address the task of counting crop plants in a field using Convolutional Neural Networks (CNN). The number of plants in an Unmanned Aerial Vehicle (UAV) image of the field is estimated using regression instead of classification. This avoids to need to know (or guess) the maximum expected number of plants. We also describe a method to extract images of sections or “plots” from an orthorectified image of the entire crop field. These images will be used for training and evaluation of the CNN. Our experiments show that we can obtain a Mean Absolute Percentage Error (MAPE) as low as 6.7% with the Inception-v3 CNN architecture.


title: Speaker Recognition Using e–Vectors

abstract: Systems based on i–vectors represent the current state–of–the–art in text-independent speaker recognition. Unlike joint factor analysis (JFA), which models both speaker and intersession subspaces separately, in the i–vector approach all the important variability is modeled in a single low-dimensional subspace. This paper is based on the observation that JFA estimates a more informative speaker subspace than the “total variability” i–vector subspace, because the latter is obtained by considering each training segment as belonging to a different speaker. We propose a speaker modeling approach that extracts a compact representation of a speech segment, similar to the speaker factors of JFA and to i–vectors, referred to as “e–vector.” Estimating the e–vector subspace follows a procedure similar to i–vector training, but produces a more accurate speaker subspace, as confirmed by the results of a set of tests performed on the NIST 2012 and 2010 Speaker Recognition Evaluations. Simply replacing the i–vectors with e–vectors we get approximately 10% average improvement of the C $_{\text{primary}}$ cost function, using different systems and classifiers. It is worth noting that these performance gains come without any additional memory or computational costs with respect to the standard i–vector systems.


title: Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning

abstract: Abstract In any non‐deterministic environment, unexpected events can indicate true changes in the world (and require behavioural adaptation) or reflect chance occurrence (and must be discounted). Adaptive behaviour requires distinguishing these possibilities. We investigated how humans achieve this by integrating high‐level information from instruction and experience. In a series of EEG experiments, instructions modulated the perceived informativeness of feedback: Participants performed a novel probabilistic reinforcement learning task, receiving instructions about reliability of feedback or volatility of the environment. Importantly, our designs de‐confound informativeness from surprise, which typically co‐vary. Behavioural results indicate that participants used instructions to adapt their behaviour faster to changes in the environment when instructions indicated that negative feedback was more informative, even if it was simultaneously less surprising. This study is the first to show that neural markers of feedback anticipation (stimulus‐preceding negativity) and of feedback processing (feedback‐related negativity; FRN) reflect informativeness of unexpected feedback. Meanwhile, changes in P3 amplitude indicated imminent adjustments in behaviour. Collectively, our findings provide new evidence that high‐level information interacts with experience‐driven learning in a flexible manner, enabling human learners to make informed decisions about whether to persevere or explore new options, a pivotal ability in our complex environment. HighlightsStudy used instructions to modulate beliefs about informativeness of feedback.Reversal learning performance improved with perceived informativeness.Instruction‐sensitive Hidden Markov Model provides good fit of behaviour.EEG recordings of feedback‐related negativity (FRN) show modulation by instructions.Findings suggest reinforcement learning integrates experience with high‐level beliefs.