title: A new package of high-voltage cascode gallium nitride device for high-frequency applications

abstract: Lateral gallium nitride (GaN)-based high-electron-mobility transistor (HEMT) power devices have high current density, high switching speed, and low on-resistance in comparison to the established silicon (Si)-based semiconductor devices. Using efficient GaN HEMT devices switched at high frequency in power electronic systems could lead to an increase in power density as well as a reduction in the weight, size, and cost of the system. However, conventional packaging configurations often compromise the benefits provided by high-performance GaN HEMT devices. This undesirable package-induced performance degradation is prominent in the cascode GaN device, where the combination of a high-voltage depletion-mode GaN device and low-voltage enhancement-mode Si device is needed. In this work, a new package is introduced for high-voltage cascode GaN devices and is successfully demonstrated to make the device more suitable for megahertz (MHz) operation. This packaging prototype for cascode GaN devices is fabricated in a power quad flat no-lead (PQFN) format with the new features of a stack-die structure, embedded external capacitor, and flip-chip configuration. The parasitic ringing in hard-switching turn-off and switching losses in soft-switching transitions are both effectively reduced for this newly packaged device compared with a traditional package using the same GaN and Si devices. Improved thermal dissipation capability is also realized using this new package for better reliability.


title: Authorship Attribution Using a Neural Network Language Model

abstract: In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We also consider how the text topics impact performance. Compared with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the proposed method achieves nearly 2:5% reduction in perplexity and increases author classification accuracy by 3:43% on average, given as few as 5 test sentences. The performance is very competitive with the state of the art in terms of accuracy and demand on test data. The source code, preprocessed datasets, a detailed description of the methodology and results are available at this https URL


title: Proofs of Space

abstract: Proofs of work (PoW) have been suggested by Dwork and Naor (Crypto’92) as protection to a shared resource. The basic idea is to ask the service requestor to dedicate some non-trivial amount of computational work to every request. The original applications included prevention of spam and protection against denial of service attacks. More recently, PoWs have been used to prevent double spending in the Bitcoin digital currency system.


title: Variability in findings of anogenital injury in consensual and non- consensual fisting intercourse: A systematic review

abstract: Fisting is an uncommon and potentially dangerous sexual practice. This is usually a homosexual activity, but can also be a heterosexual or an autoerotic practice. A systematic review of the forensic literature yielded 14 published studies from 8 countries between 1968 and 2016 that met the inclusion/exclusion criteria, illustrating that external anogenital (anal and/or genital) trauma due to fisting is observed in 22.2% and 88.8% (reported consensual and non-consensual intercourse, respectively) of the subjects, while internal injuries are observed in the totality of the patients. Establishing the reliability of the conclusions of these studies is difficult due to a lack of uniformity in methodology used to detect and define injuries. Taking this limit into account, the aim of this article is to give a description of the external and internal injuries subsequent to reported consensual and non-consensual fisting practice, and try to find a relation between this sexual practice, the morphology of the injuries, the correlation with the use of drugs, and the relationship with assailant, where possible. The findings reported in this paper could be useful, especially when concerns of sexual assault arise.


title: Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm

abstract: Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. To control these threats, recognition of attacks is critical matter. Probing, Denial of Service (DoS), Remote to User (R2L) Attacks are some of the attacks which affects large number of computers in the world daily. Detection of these attacks and prevention of computers from it is a major research topic for researchers throughout the world. In this paper idea for use of a Genetic Algorithm (GA) based approach for generation of rules to detect Probing, DoS and R2L attacks on the system is proposed. General Terms Network Security, Genetic Algorithms.


title: Lower Order Circle and Ellipse Hough Transform

abstract: In this work we present two new algorithms for the detection of circles and ellipses which use the FHT algorithm as a basis: Fast Circle Hough Transform (FCHT) and Fast Ellipse Hough Transform (FEHT). The first stage of these two algorithms, devoted to obtaining the centers of the figures, is computationally the most costly. With the objective of improving the execution times of this stage it has been implemented using a new focusing algorithm instead of the typical polling process in a parameter space. This new algorithm uses a new strategy that manages to reduce the execution times, especially in the case where multiple figures appear in the image, or when they are of very different sizes. We also perform a labeling of the image points that permits discriminating which of these belong to each figure, saving computations in subsequent stages.


title: Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification

abstract: This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.


title: OPTIMIZING NATURE-BASED FEATURES FOR WAVE DISSIPATION AND LAND-WATER CONNECTIVITY

abstract: Living shorelines integrate structural and natural features to stabilize the shoreline, through reduction of erosion from the wave climate, while keeping the connectivity between land and aquatic ecosystems. This study includes field study for two different types of living shoreline systems to quantify and compare their wave dissipation services and provide physics-based guidance for the design of living shoreline systems that are efficient in wave dissipation yet maximize land-water connectivity.


title: A0C: Alpha Zero in Continuous Action Space

abstract: A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go. These games have a discrete action space. However, many real-world reinforcement learning domains have continuous action spaces, for example in robotic control, navigation and self-driving cars. This paper presents the necessary theoretical extensions of Alpha Zero to deal with continuous action space. We also provide some preliminary experiments on the Pendulum swing-up task, empirically showing the feasibility of our approach. Thereby, this work provides a first step towards the application of iterated search and learning in domains with a continuous action space.


Design of a Fully Differential Two-Stage CMOS Op-Amp for High Gain , High Bandwidth Applications


title: A Low-Cost High-Performance Digital Radar Test Bed

abstract: This paper describes the design of a dual-channel S-band digital radar test bed. The test bed combines stretch processing with a novel and cost-effective hardware architecture that enables it to achieve an in-band dynamic range of 60 dB over 600 MHz of instantaneous bandwidth. The dual digital receiver channels allow for adaptive digital beamforming which can be used to mitigate a directional source of interference. Experimental test and verification results are presented to demonstrate system performance.


title: HF Band Shipboard Antenna Design Using Characteristic Modes

abstract: The large wavelength of decameter wave brings a lot of practical difficulties to the design of shipboard antennas in high-frequency (HF) band. How to design conformal or even platform-embedded HF antennas raises us a new challenging topic. This paper proposes an approach to address such challenging problem through the combination of the characteristic mode (CM) theory with the structural antenna concept. 1) The CMs are solved to understand the resonant behavior of the ship platform. 2) We synthesize the radiating currents for designated radiation pattern by making use of the CMs of the ship platform. It allows the dominant radiating currents to locally distribute on the superstructure of the ship. Consequently, the superstructure becomes the main radiator. The modal solutions in CM theory ensure the efficiency of the synthesis procedure. 3) Nonprotruding slits are proposed to excite the synthesized currents. As the resultant HF shipboard antenna has no protruding elements around the ship, this design can be considered as either platform-conformal or platform-embedded. As an example, HF antenna on a realistic ship with broadside radiation pattern is designed. Simulation and experimental results on a 1:400 scale model demonstrate the effectiveness of the proposed approach in HF shipboard antenna designs.


title: Emotions in the wild : The situated perspective on emotion

abstract: This chapter describes a perspective on emotion, according to which emotions are: 1. Designed to function in a social context: an emotion is often an act of relationship reconfiguration brought about by delivering a social signal; 2. Forms of skillful engagement with the world which need not be mediated by conceptual thought; 3. Scaffolded by the environment, both synchronically in the unfolding of a particular emotional performance and diachronically, in the acquisition of an emotional repertoire; 4. Dynamically coupled to an environment which both influences and is influenced by the unfolding of the emotion We draw heavily on ‘transactional’ accounts of emotion proposed by some contemporary psychologists. Although these authors do not, to our knowledge, conceive their work as a contribution to the ‘situationist’ literature that is the focus of this volume, we contend that their proposals constitute a fairly exact, affective parallel to situationist ideas about cognition. The primary aim of this chapter is to demonstrate that a situated approach to emotion already exists and is backed by a substantial experimental literature.


title: The Web as a graph: How far we are

abstract: In this article we present an experimental study of the properties of webgraphs. We study a large crawl from 2001 of 200M pages and about 1.4 billion edges, made available by the WebBase project at Stanford, as well as several synthetic ones generated according to various models proposed recently. We investigate several topological properties of such graphs, including the number of bipartite cores and strongly connected components, the distribution of degrees and PageRank values and some correlations; we present a comparison study of the models against these measures.Our findings are that (i) the WebBase sample differs slightly from the (older) samples studied in the literature, and (ii) despite the fact that these models do not catch all of its properties, they do exhibit some peculiar behaviors not found, for example, in the models from classical random graph theory.Moreover we developed a software library able to generate and measure massive graphs in secondary memory; this library is publicy available under the GPL licence. We discuss its implementation and some computational issues related to secondary memory graph algorithms.


title: An Ontology for Insider Threat Indicators: Development and Application

abstract: Abstract : We describe our ongoing development of an insider threat indicator ontology. Our ontology is intended to serve as a standardized expression method for potential indicators of malicious insider activity, as well as a formalization of much of our team's research on insider threat detection, prevention, and mitigation. This ontology bridges the gap between natural language descriptions of malicious insiders, malicious insider activity, and machine-generated data that analysts and investigators use to detect behavioral and technical observables of insider activity. The ontology provides a mechanism for sharing and testing indicators of insider threat across multiple participants without compromising organization-sensitive data, thereby enhancing the data fusion and information sharing capabilities of the insider threat detection domain.


title: Reproductive biology and pollination of southeastern Brazilian Stanhopea Frost ex Hook. (Orchidaceae)

abstract: Reproductive biology and pollination of Stanhopea lietzei and Stanhopea insignis were studied in a semi-deciduous mesophytic forest in the Serra do Japi (SJ), and in the coastal plain of Picinguaba, both in the State of Sao Paulo, Brazil. Floral morphology, pollination, breeding system and fruit set of both species were investigated. S. lietzei and S. insignis are pollinator-specific, being pollinated by male bees of Eufriesea (Apidae, Euglossini), which collect the fragrance produced by pluricellular osmophores at the base of the saccate hypochile. S. lietzei and S. insignis were pollinated by Eufriesea pulchra and Eufriesea purpurata, respectively. Observations using substances present in the floral fragrance of both studied species as chemical baits were also performed. E. purpurata was attracted by benzyl alcohol, the major compound of the perfume of S. insignis, while E. pulchra was attracted by none of the compounds used. Both studied Stanhopea are self-compatible but pollinator dependent. Self-pollination, however, tends to be avoided by floral mechanisms. In experimental self- and cross-pollinations the proportion of fruit abortion was high and related to resource limitation. The reproductive success of S. lietzei and S. insignis was low as a consequence of deficient pollen transference while pollinator scarcity was the main factor.


title: Attenuating Bias in Word Vectors

abstract: Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages. But they are prone to carrying and amplifying bias which can perpetrate discrimination in various applications. In this work, we explore new simple ways to detect the most stereotypically gendered words in an embedding and remove the bias from them. We verify how names are masked carriers of gender bias and then use that as a tool to attenuate bias in embeddings. Further, we extend this property of names to show how names can be used to detect other types of bias in the embeddings such as bias based on race, ethnicity, and age.


title: Performance optimization of induction motors during Voltage-controlled soft starting

abstract: This paper deals with the performance optimization of medium/high-power induction motors during soft starting by eliminating the supply frequency torque pulsations, and by keeping the line current constant at the preset value. Starting torque pulsations are eliminated by triggering back-to-back-connected thyristors at proper points on the first supply voltage cycle. Line current during starting can be kept constant at any preset value by a simple strategy composed of successive cosinusoidal and constant function segments of the triggering angle. These strategies are implemented by the use of an 8-b microcontroller. Transient performance analyses of the system are carried out by means of a hybrid ABC/dq machine model which takes into account the three-phase, two-phase, and disconnected modes of operation in terms of actual stator variables. Experimental results obtained on a custom-design test bed are found to be in good agreement with the theoretical ones.


title: What is the amygdala?

abstract: Abstract `Amygdala' and `amygdalar complex' are terms that now refer to a highly differentiated region near the temporal pole of the mammalian cerebral hemisphere. Cell groups within it appear to be differentiated parts of the traditional cortex, the claustrum, or the striatum, and these parts belong to four obvious functional systems – accessory olfactory, main olfactory, autonomic and frontotemporal cortical. In rats, the central nucleus is a specialized autonomic-projecting motor region of the striatum, whereas the lateral and anterior basolateral nuclei together are a ventromedial extension of the claustrum for major regions of the temporal and frontal lobes. The rest of the amygdala forms association parts of the olfactory system (accessory and main), with cortical, claustral and striatal parts. Terms such as `amygdala' and `lenticular nucleus' combine cell groups arbitrarily rather than according to the structural and functional units to which they now seem to belong. The amygdala is neither a structural nor a functional unit.


title: Polyharmonic smoothing splines and the multidimensional Wiener filtering of fractal-like signals

abstract: Motivated by the fractal-like behavior of natural images, we develop a smoothing technique that uses a regularization functional which is a fractional iterate of the Laplacian. This type of functional was initially introduced by Duchon for the approximation of nonuniformily sampled, multidimensional data. He proved that the general solution is a smoothing spline that is represented by a linear combination of radial basis functions (RBFs). Unfortunately, this is tedious to implement for images because of the poor conditioning of RBFs and their lack of decay. Here, we present a much more efficient method for the special case of a uniform grid. The key idea is to express Duchon's solution in a fractional polyharmonic B-spline basis that spans the same space as the RBFs. This allows us to derive an algorithm where the smoothing is performed by filtering in the Fourier domain. Next, we prove that the above smoothing spline can be optimally tuned to provide the MMSE estimation of a fractional Brownian field corrupted by white noise. This is a strong result that not only yields the best linear filter (Wiener solution), but also the optimal interpolation space, which is not bandlimited. It also suggests a way of using the noisy data to identify the optimal parameters (order of the spline and smoothing strength), which yields a fully automatic smoothing procedure. We evaluate the performance of our algorithm by comparing it against an oracle Wiener filter, which requires the knowledge of the true noiseless power spectrum of the signal. We find that our approach performs almost as well as the oracle solution over a wide range of conditions


title: Exercise prescription for overhead athletes with shoulder pathology: a systematic review with best evidence synthesis

abstract: OBJECTIVE ::: To produce a best evidence synthesis of exercise prescription used when treating shoulder pathology in the overhead athlete. ::: ::: ::: DESIGN ::: A systematic review of exercises used in overhead athletes including case studies and clinical commentaries. ::: ::: ::: DATA SOURCES ::: MEDLINE, PubMed, SPORTDiscus and CINAHL from database inception through July 8, 2016. ::: ::: ::: METHODS ::: We examined data from randomised controlled trials and prospective cohort (level I-IV evidence) studies that addressed exercise intervention in the rehabilitation of the overhead athlete with shoulder pathology. Case studies and clinical commentaries (level V evidence) were examined to account for expert opinion-based research. Data were combined using best evidence synthesis and graded (A-F) recommendations (Centre for Evidence-Based Medicine). ::: ::: ::: RESULTS ::: There were 33 unique exercises in six level I-IV studies that met our inclusion criteria. Most exercises were single-plane, upper extremity exercises performed below 90o of elevation. There were 102 unique exercises in 33 level V studies that met our inclusion criteria. These exercises emphasised plyometrics, kinetic chain and sport-specific training. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Overall, evidence for exercise interventions in overhead athletes with shoulder pathology is dominated by expert opinion (grade D). There is great variability between exercise approaches suggested by experts and those investigated in research studies and the overall level of evidence is low. The strongest available evidence (level B) supports the use of single-plane, open chain upper extremity exercises performed below 90° of elevation and closed chain upper extremity exercises. Clinical expert pieces support a more advanced, global treatment approach consistent with the complex, multidimensional nature of sport.


title: The art of scale-space

abstract: Artists pictures rarely have photo-realistic detail. Tools to create pictures from digital photographs might, therefore, include methods for removing detail. These tools such as Gaussian and anisotropic diffusion filters and connected-set morphological filters (sieves) remove detail whilst maintaining scale-space causality, in other words new detail is not created using these operators. Non-photorealistic rendering is, therefore, a potential application of these vision techniques. Sieves, in particular, preserve the appropriate edges of retained segments of interest. The resulting image has fewer extrema and is perceptually simpler than the original and is a step towards an artistic nonphotorealistic rendering of the origina. By increasing the amount of simpli- fication towards the margins of the image, the picture composition can be modulated to direct attention to centre of interest of the image. A second artistic goal. The process also removes that detail that provides perceptual cues about texture. This allows the 'eye' to readily accept alternative, artistic, textures introduced to further create an artistic impression. Moreover, the edges bounding segments accurately represent shapes in the original image and so provide a starting point for sketches.


title: Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms

abstract: Sampling-based algorithms for path planning, such as the Rapidly-exploring Random Tree (RRT), have achieved great success, thanks to their ability to efficiently solve complex high-dimensional problems. However, standard versions of these algorithms cannot guarantee optimality or even high-quality for the produced paths. In recent years, variants of these methods, such as T-RRT, have been proposed to deal with cost spaces: by taking configuration-cost functions into account during the exploration process, they can produce high-quality (i.e., low-cost) paths. Other novel variants, such as RRT*, can deal with optimal path planning: they ensure convergence toward the optimal path, with respect to a given path-quality criterion. In this paper, we propose to solve a complex problem encompassing this two paradigms: optimal path planning in a cost space. For that, we develop two efficient sampling-based approaches that combine the underlying principles of RRT* and T-RRT. These algorithms, called T-RRT* and AT-RRT, offer the same asymptotic optimality guarantees as RRT*. Results presented on several classes of problems show that they converge faster than RRT* toward the optimal path, especially when the topology of the search space is complex and/or when its dimensionality is high.


title: An Investigation into the Challenges and Opportunities Associated with the Application of Internet of Things (IoT) in the Agricultural Sector-A Review

abstract: Internet of things has acquired attention all over the globe. It has transformed the agricultural field and allowed farmers to compete with massive issues they face. The aim of this paper is to review the various challenges and opportunities associated with the applications of internet of things in agricultural sector. This research makes use of secondary sources that have been gathered from existing academic literature such as journals, books, articles, magazines, internet, newsletter, company publications and whitepapers. Applications reviewed in this research are about crop sensing, mapping and monitoring the croplands pattern, managing and controlling with the help of radio frequency identification and real-time monitoring of environment. Some of the challenges that were taken into consideration for reviewing the applications of internet of things are software complexity, security, lack of supporting infrastructure and technical skill requirement. Complexity in the software has to be rectified in order to support the IoT network. Therefore software must be developed as user-friendly for improving the farming, production and quality of the crop. Security is the major threat in the IoT applications. Security has to be enhanced through proper access control, data confidentiality and user authentication. Technical skill is required for farming to enhance the organizational abilities and to perform the farming functions, solving problems and more. Proper supporting infrastructure can be developed with proper internet availability and connectivity. Some of the opportunities were taken for reviewing the applications of internet of things are low power wireless sensor, better connectivity, operational efficiency and remote management.


title: What motivates organizational citizenship behaviours? Exploring the role of regulatory focus theory

abstract: We develop a theoretical rationale and research propositions describing the relationship between employee self-regulation and the likelihood of performing organizational citizenship behaviours. Regulatory Focus Theory is used to describe the underlying motivation for the exhibition of these behaviours. Specifically, we contend that the exhibition of organizational citizenship behaviours is influenced by the employee's regulatory state, the type of behaviour being considered, and the match or lack thereof between employee regulatory focus and how the behaviour is perceived (i.e., whether or not regulatory focus is consistent with employee perceptions of the behaviour). We close by considering several implications of Regulatory Focus Theory for future research on organizational citizenship behaviours.


title: Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds

abstract: Kernel k-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear k-means clustering algorithm. However, kernel k- means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. ::: ::: Kernel approximation, e.g., the Nystrom method, has been applied in previous works to approximately solve kernel learning problems when both of the above conditions are present. This work analyzes the application of this paradigm to kernel k-means clustering, and shows that applying the linear k-means clustering algorithm to k/e (1 + o(1)) features constructed using a so-called rank-restricted Nystrom approximation results in cluster assignments that satisfy a 1+e approximation ratio in terms of the kernel k-means cost function, relative to the guarantee provided by the same algorithm without the use of the Nystrom method. As part of the analysis, this work establishes a novel 1+e relative-error trace norm guarantee for low-rank approximation using the rank-restricted Nystrom approximation. ::: ::: Empirical evaluations on the 8:1 million instance MNIST8M dataset demonstrate the scalability and usefulness of kernel k-means clustering with Nystrom approximation. This work argues that spectral clustering using Nystrom approximation--a popular and computationally efficient, but theoretically unsound approach to non-linear clustering-- should be replaced with the efficient and theoretically sound combination of kernel k-means clustering with Nystrom approximation. The superior performance of the latter approach is empirically verified.


C ALYPSO : Auditable Sharing of Private Data over Blockchains


title: BotGrep: Finding P2P Bots with Structured Graph Analysis

abstract: A key feature that distinguishes modern botnets from earlier counterparts is their increasing use of structured overlay topologies. This lets them carry out sophisticated coordinated activities while being resilient to churn, but it can also be used as a point of detection. In this work, we devise techniques to localize botnet members based on the unique communication patterns arising from their overlay topologies used for command and control. Experimental results on synthetic topologies embedded within Internet traffic traces from an ISP's backbone network indicate that our techniques (i) can localize the majority of bots with low false positive rate, and (ii) are resilient to incomplete visibility arising from partial deployment of monitoring systems and measurement inaccuracies from dynamics of background traffic.


title: TeKnowbase: Towards Construction of a Knowledge-base of Technical Concepts

abstract: In this paper, we describe the construction of TeKnowbase, a knowledge-base of technical concepts in computer science. Our main information sources are technical websites such as Webopedia and Techtarget as well as Wikipedia and online textbooks. We divide the knowledge-base construction problem into two parts -- the acquisition of entities and the extraction of relationships among these entities. Our knowledge-base consists of approximately 100,000 triples. We conducted an evaluation on a sample of triples and report an accuracy of a little over 90\%. We additionally conducted classification experiments on StackOverflow data with features from TeKnowbase and achieved improved classification accuracy.


title: Interventions in Small Food Stores to Change the Food Environment, Improve Diet, and Reduce Risk of Chronic Disease

abstract: Introduction ::: Many small-store intervention trials have been conducted in the United States and other countries to improve the food environment and dietary behaviors associated with chronic disease risk. However, no systematic reviews of the methods and outcomes of these trials have been published. The objective of this study was to identify small-store interventions and to determine their impact on food availability, dietary behaviors, and psychosocial factors that influence chronic disease risk.


title: Hamshahri: A Standard Persian Text Collection

abstract: The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.


title: Are All Training Examples Created Equal? An Empirical Study

abstract: Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for training. In this paper, we propose a gradient-based importance measure that we use to empirically analyze relative importance of training images in four datasets of varying complexity. We find that in some cases, a small subsample is indeed sufficient for training. For other datasets, however, the relative differences in importance are negligible. These results have important implications for active learning on deep networks. Additionally, our analysis method can be used as a general tool to better understand diversity of training examples in datasets.


title: An Entropy-Based Subspace Clustering Algorithm for Categorical Data

abstract: The interest in attribute weighting for soft subspace clustering have been increasing in the last years. However, most of the proposed approaches are designed for dealing only with numeric data. In this paper, our focus is on soft subspace clustering for categorical data. In soft subspace clustering, the attribute weighting approach plays a crucial role. Due to this, we propose an entropy-based approach for measuring the relevance of each categorical attribute in each cluster. Besides that, we propose the EBK-modes (entropy-based k-modes), an extension of the basic k-modes that uses our approach for attribute weighting. We performed experiments on five real-world datasets, comparing the performance of our algorithms with four state-of-the-art algorithms, using three well-known evaluation metrics: accuracy, f-measure and adjusted Rand index. According to the experiments, the EBK-modes outperforms the algorithms that were considered in the evaluation, regarding the considered metrics.


Architecture recovery of Apache 1.3 - A case study


title: Word-Entity Duet Representations for Document Ranking

abstract: This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations and entity-based representations. Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph. To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed. With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet. Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems.


title: A Scaling Roadmap and Performance Evaluation of In-Plane and Perpendicular MTJ Based STT-MRAMs for High-Density Cache Memory

abstract: This paper explores the scalability of in-plane and perpendicular MTJ based STT-MRAMs from 65 nm to 8 nm while taking into consideration realistic variability effects. We focus on the read and write performances of a STT-MRAM based cache rather than the obvious advantages such as the denser bit-cell and zero static power. An accurate MTJ macromodel capturing key MTJ properties was adopted for efficient Monte Carlo simulations. For the simulation of access devices and peripheral circuitries, ITRS projected transistor parameters were utilized and calibrated using the MASTAR tool that has been widely used in industry. 6T SRAM and STT-MRAM arrays were implemented with aggressive assist schemes to mimic industrial memory designs. A constant JC0·RA/VDD scaling scenario was used which to the first order gives the optimal balance between read and write margins of STT-MRAMs. The thermal stability factor ensuring a 10 year retention time was obtained by adjusting the free layer thickness as well as assuming improvement in the crystalline anisotropy. Our studies based on the proposed scaling methodology show that in-plane STT-MRAM will outperform SRAM from 15 nm node, while its perpendicular counterpart requires further innovations in MTJ material in order to overcome the poor write performance scaling from 22 nm node onwards.


title: Relevant areas of functioning in patients with adolescent idiopathic scoliosis on the International Classification of Functioning, Disability and Health: The patientsâ€™ perspective

abstract: OBJECTIVE ::: To investigate relevant aspects of functioning and disability, and environmental factors in people with adolescent idiopathic scoliosis according to patients' self-reports based on the International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY). ::: ::: ::: DESIGN ::: Multicentre, empirical, cross-sectional study. ::: ::: ::: SETTING ::: Four departments of orthopaedics in 4 hospitals, and 5 departments of rehabilitation medicine in 5 hospitals. ::: ::: ::: METHODS ::: Semi-structured interviews were conducted with 975 patients with adolescent idiopathic scoliosis from 5 hospitals according to the patients' self-reporting. In addition, patients were divided into 3 groups according to clinical outcome. Participant information included demographic and disease-related characteristics. Three adolescent idiopathic scoliosis groups were then compared with respect to the problems identified. Interviews were transcribed verbatim. Categories identified by qualitative analysis were subsequently mapped to the ICF-CY using established linking rules. In order to enrich these findings, we also translated the Scoliosis Research Society 22 Patient Questionnaire (SRS-22 PQ) into the language of the ICF-CY, based on ICF linking rules. ::: ::: ::: RESULTS ::: A total of 1278 themes that linked to 54 ICF-CY cate-gories from 18 chapters were identified. Twenty-two (41%) categories were identified as Body Functions, 7 (13%) as Body Structures, 15 (27%) as Activities and Participation, and 10 (19%) as Environmental Factors. Of the 54 categories, 45 (83%) were second-level, 5 (9%) were third-level, and 4 (7%) were fourth-level. Differences between the SRS-22 PQ results and our findings were observed for several ICF-CY categories. ::: ::: ::: CONCLUSION ::: Patients with AIS reported activity limitations and participation restrictions combined with impaired body structures and functions. Environmental factors may act as a barrier to, or facilitator of, patient functioning in daily life. The ICF-CY provides a valuable framework for representing the complexity and multifaceted impact of AIS, and for comparing and examining the content of the SRS-22 PQ for AIS in children and adolescents. These results will be used to develop ICF Core Sets for AIS in China.


title: Image Texture Feature Extraction Using GLCM Approach

abstract: Feature Extraction is a method of capturing visual content of images for indexing & retrieval. Primitive or low level image features can be either general features, such as extraction of color, texture and shape or domain specific features. This paper presents an application of gray level co-occurrence matrix (GLCM) to extract second order statistical texture features for motion estimation of images. The Four features namely, Angular Second Moment, Correlation, Inverse Difference Moment, and Entropy are computed using Xilinx FPGA. The results show that these texture features have high discrimination accuracy, requires less computation time and hence efficiently used for real time Pattern recognition applications.


title: The STAPL Parallel Graph Library

abstract: This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraph pViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges.


title: Emulation of Software Defined Networks Using Mininet in Different Simulation Environments

abstract: In this paper an evaluation of an SDN emulation tool called Mininet is conducted. Tests were conducted to study Mininnet limitations related to the simulation environment, resource capabilities. To evaluate the later, the scalability of Mininet in term of creating many topologies is tested with varying number of nodes and two different environment scenarios. Results show that the simulation environment has a remarkable effect on the required time for building a topology, for instance, the powerful resources scenario needed only 0.19 sec, whereas, 5.611 sec were needed when the resources were less. However, the required time were increased in both scenarios when the number of nodes was increased into 242.842 and 3718.117 sec for the powerful and less capabilities resources respectively.


title: An IoT-based smart exam application

abstract: The exam statistics are crucial for instructors to better assess the exam metrics, e.g., exam duration. However, one statistic, time spent on each question by each student, is not available or difficult to obtain by human observation. In this work, we propose a smart exam system that obtains such statistics and sends data to a web application for further processing by using an internet-of-things based technology. The information obtained by the proposed system can even be used to diagnose students with learning difficulties or disabilities, e.g., dyslexia. We provided an experimental setup and conducted experiments with volunteers. The experiments conducted during the study show that the proposed system effectively collect information and process data for the use of instructor.


title: Point Cloud Filtering via Generalized Gaussian Mixture Model

abstract: 3D point cloud is extensively utilized to reconstruct the surface of the object. However, in many real-world situations, the acquired point cloud is corrupted by noise due to the insufficient precision of the measuring instruments. To filter the noise, a typical conventional method showing promising results attempts to capture the probabilistic distribution of the data with Gaussian mixture model (GMM). However, the assumption that the data only complies with Gaussian distribution restricts its performance if the data is non-Gaussian. In this paper we propose to filter the noise by generalized Gaussian mixture model (GGMM). With the shape parameter in GGMM determining the type of probabilistic model of each component, GGMM is able to capture the underlying distribution of the point cloud more accurately regardless of the non-Gaussianity of the data, which leads to a better performance in preserving geometric features while filtering the point cloud. Experiment with synthetic noisy data demonstrates the effectiveness of the proposed method both qualitatively and quantitatively.


title: Exploring Twitter Hashtags

abstract: Twitter messages often contain so-called hashtags to denote keywords related to them. Using a dataset of 29 million messages, I explore relations among these hashtags with respect to co-occurrences. Furthermore, I present an attempt to classify hashtags into five intuitive classes, using a machine-learning approach. The overall outcome is an interactive Web application to explore Twitter hashtags.


title: Ipsative measurement and the analysis of organizational values: an alternative approach for data analysis

abstract: In this paper, the analysis and test of ipsative data will be discussed, and some alternative methods will be suggested. Following a review of the literature about ipsative measurement, the Competing Values Framework will be presented as a major application in the field of organizational culture and values. An alternative approach for the intra-individual analysis and test of ipsative data will be suggested, which consists of: (i) a method that uses closed part-wise geometric means as a descriptive statistic; (ii) a nonparametric bootstrap test to create confidence intervals; and (iii) a permutation test to evaluate equivalence between ipsative scores. All suggested methods satisfy the three basic statistical requirements for the analysis of ipsative data, that is: scale invariance, permutation invariance, and subcompositional coherence. Our suggested approach can correctly compute and compare organizational culture profiles within the same organization, as will be demonstrated with an example. However, the problem of drawing inter-organizational contrasts in ipsative measurement still remains unsolved. Also, our alternative approach only allows for a relative interpretation of the results.


title: Realizing Autonomous Valet Parking with automotive grade sensors

abstract: The availability of several Advanced Driver Assistance Systems has put a correspondingly large number of inexpensive, yet capable sensors on production vehicles. By combining this reality with expertise from the DARPA Grand and Urban Challenges in building autonomous driving platforms, we were able to design and develop an Autonomous Valet Parking (AVP) system on a 2006 Volkwagen Passat Wagon TDI using automotive grade sensors. AVP provides the driver with both convenience and safety benefits - the driver can leave the vehicle at the entrance of a parking garage, allowing the vehicle to navigate the structure, find a spot, and park itself. By leveraging existing software modules from the DARPA Urban Challenge, our efforts focused on developing a parking spot detector, a localization system that did not use GPS, and a back-in parking planner. This paper focuses on describing the design and development of the last two modules.


title: Default-mode brain dysfunction in mental disorders: A systematic review

abstract: In this review we are concerned specifically with the putative role of the default-mode network (DMN) in the pathophysiology of mental disorders. First, we define the DMN concept with regard to its neuro-anatomy, its functional organisation through low frequency neuronal oscillations, its relation to other recently discovered low frequency resting state networks, and the cognitive functions it is thought to serve. Second, we introduce methodological and analytical issues and challenges. Third, we describe putative mechanisms proposed to link DMN abnormalities and mental disorders. These include interference by network activity during task performance, altered patterns of antagonism between task specific and non-specific elements, altered connectively and integrity of the DMN, and altered psychological functions served by the network DMN. Fourth, we review the empirical literature systematically. We relate DMN dysfunction to dementia, schizophrenia, epilepsy, anxiety and depression, autism and attention deficit/hyperactivity disorder drawing out common and unique elements of the disorders. Finally, we provide an integrative overview and highlight important challenges and tasks for future research.


title: CADET: Computer Assisted Discovery Extraction and Translation

abstract: Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.


title: A Capsule Network-based Embedding Model for Search Personalization

abstract: Search personalization aims to tailor search results to each specific user based on the user's personal interests and preferences (i.e., the user profile). Recent research approaches to search personalization by modelling the potential 3-way relationship between the submitted query, the user and the search results (i.e., documents). That relationship is then used to personalize the search results to that user. In this paper, we introduce a novel embedding model based on capsule network, which recently is a breakthrough in deep learning, to model the 3-way relationships for search personalization. In the model, each user (submitted query or returned document) is embedded by a vector in the same vector space. The 3-way relationship is described as a triple of (query, user, document) which is then modeled as a 3-column matrix containing the three embedding vectors. After that, the 3-column matrix is fed into a deep learning architecture to re-rank the search results returned by a basis ranker. Experimental results on query logs from a commercial web search engine show that our model achieves better performances than the basis ranker as well as strong search personalization baselines.


title: Functional and anatomical cortical underconnectivity in autism: Evidence from an fMRI study of an executive function task and corpus callosum morphometry

abstract: The brain activation of a group of high-functioning autistic participants was measured using functional magnetic resonance imaging during the performance of a Tower of London task, in comparison with a control group matched with respect to intelligent quotient, age, and gender. The 2 groups generally activated the same cortical areas to similar degrees. However, there were 3 indications of underconnectivity in the group with autism. First, the degree of synchronization (i.e., the functional connectivity or the correlation of the time series of the activation) between the frontal and parietal areas of activation was lower for the autistic than the control participants. Second, relevant parts of the corpus callosum, through which many of the bilaterally activated cortical areas communicate, were smaller in cross-sectional area in the autistic participants. Third, within the autism group but not within the control group, the size of the genu of the corpus callosum was correlated with frontal--parietal functional connectivity. These findings suggest that the neural basis of altered cognition in autism entails a lower degree of integration of information across certain cortical areas resulting from reduced intracortical connectivity. The results add support to a new theory of cortical underconnectivity in autism, which posits a deficit in integration of information at the neural and cognitive levels.


title: Cyberbullying and traditional bullying in adolescence: Differential roles of moral disengagement, moral emotions, and moral values

abstract: The aim of this study was to investigate whether different aspects of morality predict traditional bullying and cyberbullying behaviour in a similar way. Students between 12 and 19 years participated in an online study. They reported on the frequency of different traditional and cyberbullying behaviours andcompleted self-report measures on moral emotions and moral values. A scenario approach with open questions was used to assess morally disengaged justifications. Tobit regressions indicated that a lack of moral values and a lack of remorse predicted both traditional and cyberbullying behaviour. Traditional bullying was strongly predictive for cyberbullying. A lack of moral emotions and moral values predicted cyberbullying behaviour even when controlling for traditional bullying. Morally disengaged justifications were only predictive for traditional, but not for cyberbullying behaviour. The findings show that moral standards and moral affect are important to understand individual differences in engagement...


title: iVisClustering: An Interactive Visual Document Clustering via Topic Modeling

abstract: Clustering plays an important role in many large-scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented with interactive visualization strategies. This paper proposes an interactive visual analytics system for document clustering, called iVisClustering, based on a widely-used topic modeling method, latent Dirichlet allocation (LDA). iVisClustering provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates. The main view of the system provides a 2D plot that visualizes cluster similarities and the relation among data items with a graph-based representation. iVisClustering provides several other views, which contain useful interaction methods. With help of these visualization modules, we can interactively refine the clustering results in various ways. Keywords can be adjusted so that they characterize each cluster better. In addition, our system can filter out noisy data and re-cluster the data accordingly. Cluster hierarchy can be constructed using a tree structure and for this purpose, the system supports cluster-level interactions such as sub-clustering, removing unimportant clusters, merging the clusters that have similar meanings, and moving certain clusters to any other node in the tree structure. Furthermore, the system provides document-level interactions such as moving mis-clustered documents to another cluster and removing useless documents. Finally, we present how interactive clustering is performed via iVisClustering by using real-world document data sets. © 2012 Wiley Periodicals, Inc.


title: Active Learning for Recommender Systems

abstract: Recommender systems learn user preferences and provide them personalized recommendations. Evidently, the performance of recommender systems depends on the amount of information that users provide regarding items, most often in the form of ratings. This problem is amplified for new users because they have not provided any rating, which impacts negatively on the quality of generated recommendations. This problem is called new-user problem. A simple and effective way to overcome this problem is posing queries to new users so that they express their preferences about selected items, e.g., by rating them. Nevertheless, the selection of items must take into consideration that users are not willing to answer a lot of such queries. To address this problem, active learning methods have been proposed to acquire the most informative ratings, i.e., ratings from users that will help most in determining their interests. Active learning is a learning algorithm that is able to interactively query the Oracle to obtain labels for data instances. The Oracle is a user or teacher who knows the labels. The aim of this dissertation [8] is to take inspiration from the literature of active learning for classification (regression) problems and develop new methods for the new-user problem in recommender systems. In the recommender system context, new users play the role of the Oracle and provide ratings (labels) to items (data instances). Specifically, the following questions are addressed in this dissertation: (1) which recommendation model is suitable for active-learning purposes? (Sect. 2) (2) how can active learning criteria be adapted and customized for the new-user problem and which one is the best? (Sect. 3) (3) what are the specific requirements and properties of the new-user problem that do not exist in active learning and how can new active learning methods be developed based on these properties? (Sects. 4, 5).


title: Location-Based Influence Maximization in Social Networks

abstract: In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately. Moreover, the existing researches of influence maximization tend to only concern on the online network of relationships but rarely take the offline part into consideration. This paper introduces the location property into the influence maximization to accord with the characteristic of O2O model. Firstly, we propose an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment. Meanwhile, the definition of location-based influence maximization is presented. Then the user mobility pattern is analyzed and the calculation method of offline probability is designed. Considering the influence ability, a location-based influence maximization algorithm named TPH is proposed. Experiments prove TPH algorithm has general advantage. Finally, focusing on the performance of TPH algorithm under special circumstances, MR algorithm is designed as complement and experiments also verify its high effectiveness.


title: Chapter 12 Blindsight, conscious vision, and the role of primary visual cortex

abstract: Abstract What is the role the primary visual cortex (V1) in vision? Is it necessary for conscious sight, as indicated by the cortical blindness that results from V1 destruction? Is it even necessary for blindsight, the nonreflexive visual functions that can be evoked with stimuli presented to cortically blind fields? In the context of this controversial issue, I present evidence indicating that not only is blindsight possible, but that conscious vision may, to a varying degree, return to formerly blind fields with time and practice even in cases where functional neuroimaging reveals no V1 activation.


title: An Evaluation of Aggregation Techniques in Crowdsourcing

abstract: As the volumes of AI problems involving, human knowledge are likely to soar, crowdsourcing has become essential in a wide range of world-wide-web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from the crowd since the workers might have wide-ranging levels of expertise. In order to tackle this challenge, many aggregation techniques have been proposed. These techniques, however, have never been compared and analyzed under the same setting, rendering a ‘right’ choice for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the aggregation techniques. Specifically, we integrated several state-of-the-art methods in a comparable manner, and measured various performance metrics with our benchmark, including computation time, accuracy, robustness to spammers, and adaptivity to multi-labeling. We then provide in-depth analysis of benchmarking results, obtained by simulating the crowdsourcing process with different types of workers. We believe that the findings from the benchmark will be able to serve as a practical guideline for crowdsourcing applications.


title: Combining Reinforcement Learning with a Multi-level Abstraction Method to Design a Powerful Game AI

abstract: This paper investigates the design of a challenging Game AI for a modern strategy game, which can be seen as a large-scale multiagent simulation of an historical military confrontation. As an alternative to the typical script-based approach used in industry, we test an approach where military units and leaders, organized in a hierarchy, learn to improve their collective behavior through playing repeated games. In order to allow the application of a reinforcement learning framework at each level of this complex hierarchical decision-making structure, we propose an abstraction mechanism that adapts semi-automatically the level of detail of the state and action representations to the level of the agent. We also study specifically various reward signals as well as inter-agent communication setups and show their impact on the Game AI performance, distinctively in offensive and defensive modes. The resulting Game AI achieves very good performance when compared with the existing commercial script-based solution.


title: Has Actuarial Aging “Slowed” Over the Past 250 Years? A Comparison of Small-Scale Subsistence Populations and European Cohorts

abstract: G.C. Williams's 1957 hypothesis famously argues that higher age-independent, or "extrinsic," mortality should select for faster rates of senescence. Long-lived species should therefore show relatively few deaths from extrinsic causes such as predation and starvation. Theoretical explorations and empirical tests of Williams's hypothesis have flourished in the past decade but it has not yet been tested empirically among humans. We test Williams's hypothesis using mortality data from subsistence populations and from historical cohorts from Sweden and England/Wales, and examine whether rates of actuarial aging declined over the past two centuries. We employ three aging measures: mortality rate doubling time (MRDT), Ricklefs's omega, and the slope of mortality hazard from ages 60-70, m'(60-70), and model mortality using both Weibull and Gompertz-Makeham hazard models. We find that (1) actuarial aging in subsistence societies is similar to that of early Europe, (2) actuarial senescence has slowed in later European cohorts, (3) reductions in extrinsic mortality associate with slower actuarial aging in longitudinal samples, and (4) men senesce more rapidly than women, especially in later cohorts. To interpret these results, we attempt to bridge population-based evolutionary analysis with individual-level proximate mechanisms.


title: Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs

abstract: A multi-layer graph consists of multiple layers of weighted graphs, where the multiple layers represent the different aspects of relationships. Considering multiple aspects (i.e., layers) together is essential to achieve a comprehensive and consolidated view. In this article, we propose a novel framework of differential flattening, which facilitates the analysis of multi-layer graphs, and apply this framework to community detection. Differential flattening merges multiple graphs into a single graph such that the graph structure with the maximum clustering coefficient is obtained from the single graph. It has two distinct features compared with existing approaches. First, dealing with multiple layers is done independently of a specific community detection algorithm, whereas previous approaches rely on a specific algorithm. Thus, any algorithm for a single graph becomes applicable to multi-layer graphs. Second, the contribution of each layer to the single graph is determined automatically for the maximum clustering coefficient. Since differential flattening is formulated by an optimization problem, the optimal solution is easily obtained by well-known algorithms such as interior point methods. Extensive experiments were conducted using the Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks as well as the DBLP, 20 Newsgroups, and MIT Reality Mining networks. The results show that our approach of differential flattening leads to discovery of higher-quality communities than baseline approaches and the state-of-the-art algorithms.


title: Irradiation and PV array energy output, cost, and optimal positioning estimation for South Africa

abstract: Simulation results of an irradiation and PV ar ray performance software package (SunSim) are pr esented. South African irradiation data availability is discussed, an ir radiation data classification system proposed, and the estimation of diffuse irradiation on tilted surfaces analysed. Estimation of PV array energy output using King’s performance model is explained, and the influence of the irradiation and temperature data set measurement interval on PV energy output estimation investigated. Simulation results are pr esented, aimed at identif ying optimal fixed PV panel tilt angles and solar-tracking configurations for different locations in South Africa. Lastly, the cost of PV -based generation in South Africa is investigated.


title: The Testing Effect Is Alive and Well with Complex Materials

abstract: Van Gog and Sweller (2015) claim that there is no testing effect—no benefit of practicing retrieval—for complex materials. We show that this claim is incorrect on several grounds. First, Van Gog and Sweller’s idea of “element interactivity” is not defined in a quantitative, measurable way. As a consequence, the idea is applied inconsistently in their literature review. Second, none of the experiments on retrieval practice with worked-example materials manipulated element interactivity. Third, Van Gog and Sweller’s literature review omitted several studies that have shown retrieval practice effects with complex materials, including studies that directly manipulated the complexity of the materials. Fourth, the experiments that did not show retrieval practice effects, which were emphasized by Van Gog and Sweller, either involved retrieval of isolated words in individual sentences or required immediate, massed retrieval practice. The experiments failed to observe retrieval practice effects because of the retrieval tasks, not because of the complexity of the materials. Finally, even though the worked-example experiments emphasized by Van Gog and Sweller have methodological problems, they do not show strong evidence favoring the null. Instead, the data provide evidence that there is indeed a small positive effect of retrieval practice with worked examples. Retrieval practice remains an effective way to improve meaningful learning of complex materials.


title: Modeling Reservation-Based Autonomous Intersection Control in VISSIM

abstract: The use of autonomous vehicles is attracting more and more attention as a promising approach to improving both highway safety and efficiency. Most previous studies on autonomous intersection management relied heavily on custom-built simulation tools to implement and evaluate their control algorithms, but the use of nonstandard simulation platforms makes the comparison of systems almost impossible. Furthermore, without support from standard simulation platforms, reliable and trustworthy simulation results are hard to obtain. In this context, this paper explores a way to model autonomous intersections through the use of VISSIM, a standard microscopic simulation platform. A reservation-based intersection control system named autonomous control of urban traffic (ACUTA) was introduced and implemented in VISSIM through the use of VISSIM's external driver model. The operational and safety performance characteristics of ACUTA were evaluated with VISSIM's easy-to-use evaluation tools. In comparison with the result...


Trust in the Sharing Economy: An Experimental Framework


Network Applications of Bloom Filters: A Survey


title: Getting it right by getting it wrong : When learners change languages

abstract: When natural language input contains grammatical forms that are used probabilistically and inconsistently, learners will sometimes reproduce the inconsistencies; but sometimes they will instead regularize the use of these forms, introducing consistency in the language that was not present in the input. In this paper we ask what produces such regularization. We conducted three artificial language experiments, varying the use of determiners in the types of inconsistency with which they are used, and also comparing adult and child learners. In Experiment 1 we presented adult learners with scattered inconsistency – the use of multiple determiners varying in frequency in the same context – and found that adults will reproduce these inconsistencies at low levels of scatter, but at very high levels of scatter will regularize the determiner system, producing the most frequent determiner form almost all the time. In Experiment 2 we showed that this is not merely the result of frequency: when determiners are used with low frequencies but in consistent contexts, adults will learn all of the determiners veridically. In Experiment 3 we compared adult and child learners, finding that children will almost always regularize inconsistent forms, whereas adult learners will only regularize the most complex inconsistencies. Taken together, these results suggest that regularization processes in natural language learning, such as those seen in the acquisition of language from non-native speakers or in the formation of young languages, may depend crucially on the nature of language learning by young children.


title: Preliminary investigation of electromagnetic and thermal performance of closed stator switched reluctance machines

abstract: This paper presents the preliminary investigation results of a novel closed stator slot structure in Switched Reluctance Machines (SRM) to form direct cooling channels inside the stator slots in electromagnetic and thermal aspects. The investigation starts by identifying the current challenges faced by various cooling arrangements for electric machines. Two sets of baseline SRMs, with and without the closed slot structure, are then obtained via the combined optimization process based on FEA and Genetic Algorithm (GA). Coupled simulation between Matlab/Simulink and FEA is conducted to obtain the transient electromagnetic characteristics of the target SRM under Average Torque Control (ATC) scheme. The incurred iron and copper loss are then fed to Lumped Parameter Circuit (LPC) for thermal investigations. The results show that in spite of the increased leakage flux due to the added flux bridges, the proposed closed stator slot structure can improve the thermal performance of SRM and allow for higher stator current densities without exceeding the winding insulation limits. As a result, the power level and power density of the studied SRM is still increased.


title: Video Time: Properties, Encoders and Evaluation

abstract: Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying video time is missing. To fill this lacuna, we aim to evaluate video time explicitly. We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality. Based on each we formulate a task able to quantify the associated property. This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time. Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM. We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.


title: Overview of generalized monopulse estimation

abstract: Monopulse is an established technique for radar angle estimation. One can show that monopulse estimation is based on a general approximation derived from maximum likelihood (ML) estimation. This tutorial provides a derivation of this relation and presents extensions of this monopulse principle to multi-dimensional array and parameter estimation problems, in particular to space-time adaptive processing (STAP) with reduced dimension, subarrays and generalized sidelobe canceller (GSLC) configurations. The performance of these monopulse applications can be predicted by exploiting the distribution of the monopulse ratio. It is demonstrated that this distribution is more realistic than the Cramer-Rao bound (CRB). Several examples of performance of monopulse estimators are given for thinned and fully filled planar arrays, adaptive beamforming with and without low sidelobes, GSLC, and STAP. Finally, conditions for estimates with low variance are discussed


title: MongoDB-Based Repository Design for IoT-Generated RFID/Sensor Big Data

abstract: Internet of Things (IoT)-generated data are characterized by its continuous generation, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such IoT-generated data due to the limited processing speed and the significant storage-expansion cost. Thus, big data processing technologies, which are normally based on distributed file systems, distributed database management, and parallel processing technologies, have arisen as a core technology to implement IoT-generated data repositories. In this paper, we propose a sensor-integrated radio frequency identification (RFID) data repository-implementation model using MongoDB, the most popular big data-savvy document-oriented database system now. First, we devise a data repository schema that can effectively integrate and store the heterogeneous IoT data sources, such as RFID, sensor, and GPS, by extending the event data types in electronic product code information services standard, a de facto standard for the information exchange services for RFID-based traceability. Second, we propose an effective shard key to maximize query speed and uniform data distribution over data servers. Last, through a series of experiments measuring query speed and the level of data distribution, we show that the proposed design strategy, which is based on horizontal data partitioning and a compound shard key, is effective and efficient for the IoT-generated RFID/sensor big data.


title: Near Shannon limit error-correcting coding and decoding: Turbo-codes. 1

abstract: A new class of convolutional codes called turbo-codes, whose performances in terms of bit error rate (BER) are close to the Shannon limit, is discussed. The turbo-code encoder is built using a parallel concatenation of two recursive systematic convolutional codes, and the associated decoder, using a feedback decoding rule, is implemented as P pipelined identical elementary decoders. >


title: Deep Learning for Computational Chemistry

abstract: The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc.


title: Comparison of soft-switching voltage-fed and current-fed bi-directional isolated Dc/Dc converters for fuel cell vehicles

abstract: This paper presents a comparison of two potential soft-switching bi-directional high-frequency transformer isolated voltage-fed and current-fed dc/dc converters for fuel cell vehicle application. The given comparison and discussion is suitable for a front-end dc/dc converter for a fuel cell inverter. The circulating and peak currents, components' ratings, size, losses, and efficiency are compared. Current-fed converter has been justified due to its merits over voltage-fed converter for the given applications. Experimental results of the current-fed converter have been illustrated. Switching losses are reduced a lot due to soft-switching of primary and secondary devices and higher efficiency. It allows high switching frequency operation, which results in compact and low cost system.


title: GRASS: generative recursive autoencoders for shape structures

abstract: We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.


title: Procedural generation of villages on arbitrary terrains

abstract: Although procedural modeling of cities has attracted a lot of attention for the past decade, populating arbitrary landscapes with non-urban settlements remains an open problem. In this work, we focus on the modeling of small, European villages that took benefit of terrain features to settle in safe, sunny or simply convenient places. We introduce a three step procedural generation method. First, an iterative process based on interest maps is used to progressively generate settlement seeds and the roads that connect them. The fact that a new road attracts settlers while a new house often leads to some extension of the road network is taken into account. Then, an anisotropic conquest method is introduced to segment the land into parcels around settlement seeds. Finally, we introduce open shape grammar to generate 3D geometry that adapts to the local slope. We demonstrate the effectiveness of our method by generating different kinds of village on arbitrary terrains, from a mountain hamlet to a fisherman village, and validate through comparison with real data.


title: Effectiveness of school-based occupational therapy intervention on handwriting.

abstract: OBJECTIVE ::: This study investigated the effects of school-based occupational therapy services on students' handwriting. ::: ::: ::: METHOD ::: Students 7 to 10 years of age with poor handwriting legibility who received direct occupational therapy services (n = 29) were compared with students who did not receive services (n = 9) on handwriting legibility and speed and associated performance components. Visual-motor, visual-perception, in-hand manipulation, and handwriting legibility and speed were measured at the beginning and end of the academic year. The intervention group received a mean of 16.4 sessions and 528 min of direct occupational therapy services during the school year. According to the therapists, visual-motor skills and handwriting practice were emphasized most in intervention. ::: ::: ::: RESULTS ::: Students in the intervention group showed significant increases in in-hand manipulation and position in space scores. They also improved more in handwriting legibility scores than the students in the comparison group. Fifteen students in the intervention group demonstrated greater than 90% legibility at the end of the school year. On average, legibility increased by 14.2% in the students who received services and by 5.8% in the students who did not receive services. Speed increased slightly more in the students who did not receive services. ::: ::: ::: CONCLUSION ::: Students who received occupational therapy services demonstrated improved letter legibility, but speed and numeral legibility did not demonstrate positive intervention effects.


title: SCADA Systems: Challenges for Forensic Investigators

abstract: When security incidents occur, several challenges exist for conducting an effective forensic investigation of SCADA systems, which run 24/7 to control and monitor industrial and infrastructure processes. The Web extra at http://youtu.be/L0EFnr-famg is an audio interview with Irfan Ahmed about SCADA (supervisory control and data acquisition) systems.


title: Planar UWB MIMO Antenna With Pattern Diversity and Isolation Improvement for Mobile Platform Based on the Theory of Characteristic Modes

abstract: This communication presents a planar ultra-wideband multi-input multi-output antenna with pattern diversity and isolation improvement based on the theory of characteristic modes. The proposed antenna (for new-generation wireless mobile terminals) consists of two ultra-wideband antenna elements—a compact quarter loop and a circular planar monopole located on the opposite edges of a rectangular ground plane. These two antenna elements are able to excite different modes in the ground plane and thus realize the desired diverse radiation patterns and high isolation without any additional decoupling structures. The experimental results show that both antenna elements are ultra-wideband with impedance bandwidths (return losses more than 6 dB for the quarter-loop antenna and more than 10 dB for the circular planar monopole antenna) of 130% (2–9.5 GHz). The isolation is over 20 dB with a peak value of 50 dB. The realized gains are above 1.5 dBi. The total efficiencies are above 70%. The envelope correlation coefficient is less than 0.03 assuming that the antenna operates in an isotropic channel.


Structured Alignment Networks for Matching Sentences


title: Recent Developments in Derivative-Free Multiobjective Optimisation

abstract: In practical applications it is common to have several conflicting objective functions to optimize. Frequently, these functions are nondifferentiable or discontinuous, could be subject to numerical noise and/or be of black-box type, preventing the use of derivative-based techniques. In this paper we give an overview of some recent developments in Derivative-free Multiobjective Optimization. We introduce the basic concepts and ideas commonly considered for the algorithmic development in Multiobjective Optimization and review some recent classes of methods which do not make use of derivatives. In particular, we will focus on Direct Search Methods (DSM) of directional type and Evolutionary Multiobjective Optimization (EMO).


title: Phishing Detection: A Literature Survey

abstract: This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single silver-bullet solution exists to mitigate all the vulnerabilities effectively, thus multiple techniques are often implemented to mitigate specific attacks. This paper aims at surveying many of the recently proposed phishing mitigation techniques. A high-level overview of various categories of phishing mitigation techniques is also presented, such as: detection, offensive defense, correction, and prevention, which we belief is critical to present where the phishing detection techniques fit in the overall mitigation process.


title: The selective α7 nicotinic acetylcholine receptor agonist A-582941 activates immediate early genes in limbic regions of the forebrain: Differential effects in the juvenile and adult rat

abstract: Abstract Due to the cognitive-enhancing properties of α 7 nicotinic acetylcholine receptor (α 7 nAChR) agonists, they have attracted interest for the treatment of cognitive disturbances in schizophrenia. Schizophrenia typically presents in late adolescence or early adulthood. It is therefore important to study whether α 7 nAChR stimulation activates brain regions involved in cognition in juvenile as well as adult individuals. Here, we compared the effects of the novel and selective α 7 nAChR agonist 2-methyl-5-(6-phenyl-pyridazin-3-yl)-octahydro-pyrrolo[3,4-c]pyrrole (A-582941) in the juvenile and adult rat forebrain using two markers, activity-regulated cytoskeleton-associated protein (Arc) and c-Fos, to map neuronal activity. Acute administration of A-582941 (1, 3, 10 mg/kg) induced a dose-dependent increase in Arc mRNA expression in the medial prefrontal cortex (mPFC) and the ventral/lateral orbitofrontal (VO/LO) cortex of juvenile, but not adult rats. This effect was mitigated by the α 7 nAChR antagonist methyllycaconitine. A-582941 also increased c-Fos mRNA expression in the mPFC of juvenile, but not adult rats. Furthermore, A-582941 increased the number of Arc and c-Fos immunopositive cells in the mPFC, VO/LO, and shell of the nucleus accumbens, in both juvenile and adult rats. The A-582941-induced c-Fos protein expression was significantly greater in the mPFC and VO/LO of juvenile compared with adult rats. These data indicate that A-582941-induced α 7 nAChR stimulation activates brain regions critically involved in working memory and attention. Furthermore, this effect is more pronounced in juvenile than adult rats, indicating that the juvenile forebrain is more responsive to α 7 nAChR stimulation. This observation may be relevant in the treatment of juvenile-onset schizophrenia.


title: A review of recent advances in visual speech decoding

abstract: Abstract Visual speech information plays an important role in automatic speech recognition (ASR) especially when audio is corrupted or even inaccessible. Despite the success of audio-based ASR, the problem of visual speech decoding remains widely open. This paper provides a detailed review of recent advances in this research area. In comparison with the previous survey [97] which covers the whole ASR system that uses visual speech information, we focus on the important questions asked by researchers and summarize the recent studies that attempt to answer them. In particular, there are three questions related to the extraction of visual features, concerning speaker dependency, pose variation and temporal information, respectively. Another question is about audio-visual speech fusion, considering the dynamic changes of modality reliabilities encountered in practice. In addition, the state-of-the-art on facial landmark localization is briefly introduced in this paper. Those advanced techniques can be used to improve the region-of-interest detection, but have been largely ignored when building a visual-based ASR system. We also provide details of audio-visual speech databases. Finally, we discuss the remaining challenges and offer our insights into the future research on visual speech decoding.


title: Low-loss balun design and compact modeling for RF/millimeter-wave circuit application

abstract: On-chip baluns are widely used in RF and Millimeter-Wave circuit. How to reduce the insertion loss is the key issue for balun designs. Meanwhile, an accurate compact model for balun is highly demanded. In this work, the Patterned Ground Shields and Parallel Metal Layer techniques are used to reduce the insertion loss of the balun. The compact model and corresponding extraction techniques are also presented. Experimental results show that the insertion loss of the fabricated balun is about 1.4db and the proposed compact model can describe it up to 30GHz accurately.


title: Model predictive control of a two-link robot arm

abstract: This paper presents a new approach for a model predictive control dynamics of a two-link manipulator robot. This technique consists of linearizing a nonlinear dynamic model of the robot by using a feedback linearization control. Once, the linear model has been obtained, a predictive control approach will be developed. We have introduced a quadratic criterion and these parameters are calculated to have a specific behavior of the closed loop system. Here, the objective is to control the arm robot from an initial configuration to the final configuration using a predictive control approach and it is obtained by minimizing a quadratic criterion. In order to show the efficiency of the proposed method, some simulation results are given.


title: Dictionary Methods for Cross-Lingual Information Retrieval

abstract: Multi-lingual information retrieval (IR) has largely been limited to the development of systems for use with a specific foreign language. The explosion in the availability of electronic media in languages other than English makes the development of IR systems that can cross language boundaries increasingly important. In this paper, we present experiments that analyze the factors that affect dictionary based methods for cross-lingual retrieval and present methods that dramatically reduce the errors such an approach usually makes.


title: Gut microbial metabolism defines host metabolism: an emerging perspective in obesity and allergic inflammation

abstract: Summary ::: The presence of >100 trillion microorganisms (collectively called gut microbiota) in our large intestine is essential for the maintenance of health. The gut microbiota starts to develop before birth and matures within first three years of life. The Western diet and lifestyle have been implicated in causing an imbalance of gut microbial communities and their metabolites that consequence in disease states, such as obesity and asthma. With more than 13% of the world population currently living with obesity and one out of 10 children diagnosed with asthma, we explore here the recent developments in the biosynthesis and mode of action of the key metabolites in relation to these two chronic inflammatory conditions.


title: Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network

abstract: Cascaded Regression (CR) based methods have been proposed to solve facial landmarks detection problem, which learn a series of descent directions by multiple cascaded regressors separately trained in coarse and fine stages. They outperform the traditional gradient descent based methods in both accuracy and running speed. However, cascaded regression is not robust enough because each regressor's training data comes from the output of previous regressor. Moreover, training multiple regressors requires lots of computing resources, especially for deep learning based methods. In this paper, we develop a Self-Iterative Regression (SIR) framework to improve the model efficiency. Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor. Specifically, we proposed Landmarks-Attention Network (LAN) as our regressor, which concurrently learns features around each landmark and obtains the holistic location increment. By doing so, not only the rest of regressors are removed to simplify the training process, but the number of model parameters is significantly decreased. The experiments demonstrate that with only 3.72M model parameters, our proposed method achieves the state-of-the-art performance.


title: The Relation of Ego Integrity and Despair to Personality Traits and Mental Health

abstract: Objectives:Existing studies in the Eriksonian tradition found that ego integrity and despair are important indicators of life-span development. The present study relates ego integrity and despair to contemporary theories of personality and mental health.Method:A cross-sectional study of Dutch adults aged between 50 and 95 years (N = 218) was carried out, using the Northwestern Ego Integrity Scale, the subscales for neuroticism, extraversion, and openness to experience of the NEO-FFI, the Mental Health Continuum-Short Form, and the Center for Epidemiological Studies Depression Scale-Short Form.Results:Extraversion and openness to experience have an indirect relation to ego integrity that is mediated by well-being. Neuroticism was related to despair and explained the relationship of depressive symptoms to despair. Chronological age did not moderate these findings.Discussion:Ego integrity appears to be related to fluctuating states of mental health, whereas despair is more an expression of a general trait-like disposition of neuroticism. Implications for further research are discussed.


title: Implementing the Free Water Protocol does not Result in Aspiration Pneumonia in Carefully Selected Patients with Dysphagia: A Systematic Review

abstract: The Frazier Free Water Protocol was developed with the aim of providing patients with dysphagia an option to consume thin (i.e. unthickened) water in-between mealtimes. A systematic review was conducted of research published in peer-reviewed journals. An electronic search of the EMBASE, CINAHL and MEDLINE databases was completed up to July 2016. A total of 8 studies were identified for inclusion: 5 randomised controlled trials, 2 cohort studies with matched cases and 1 single group pre-post intervention prospective study. A total of 215 rehabilitation inpatients and 30 acute patients with oropharyngeal dysphagia who required thickened fluids or were to remain 'nil by mouth', as determined by bedside swallow assessment and/or videofluoroscopy/fiberoptic endoscopic evaluation of swallowing, were included. Meta-analyses of the data from the rehabilitation studies revealed (1) low-quality evidence that implementing the protocol did not result in increased odds of having lung complications and (2) low-quality evidence that fluid intake may increase. Patients' perceptions of swallow-related quality of life appeared to improve. This review has found that when the protocol is closely adhered to and patients are carefully selected using strict exclusion criteria, including an evaluation of their cognition and mobility, adult rehabilitation inpatients with dysphagia to thin fluids can be offered the choice of implementing the Free Water Protocol. Further research is required to determine if the Free Water Protocol can be implemented in settings other than inpatient rehabilitation.


title: Diagnosis of Breast Tumor Using SVM-KNN Classifier

abstract: support vector machine (SVM) and K-Nearest Neighbor (KNN) classifier is a combined classifying method, which has excellent performance for various applications. The purpose of this study is to examine the performance of the SVM-KNN classifier on the diagnosis of breast cancer using tumor dataset. The objective is to classify a tumor as either benign or malignant based on cell descriptions gathered by microscopic examination. The classification performance of SVM-KNN classifier is evaluated and compared to the one that obtained by support vector machine. Experimental results show that SVM-KNN model has achieved a remarkable performance with 98.06% classification accuracy on testing subset.


title: From Topologies to Shapes: Parametric Shape Grammars Implemented by Graphs:

abstract: A method for implementing parametric shape grammars is presented. Subgraph detection is used to find subshapes. Parametric shapes are described by restricting topologies.


title: Simultaneous reconstruction and calibration for multi-view structured light scanning

abstract: A self-recalibration method for multi-view structured light systems is proposed.The method allows simultaneous reconstruction and calibration.It is not restricted to special equipment, number of devices or specific arrangements.The proposal achieves the same accuracy obtained by a chessboard pattern calibration.Due to its versatility, it can adapt to a wide range of scenes. Structured light 3D scanning from a single viewpoint requires multiple scans and a registration process for a complete description of the scanned object. Instead, using multiple cameras and projectors simultaneously can reduce the scanning time and increase the visibility. However, a precise estimation of the extrinsic parameters of all the components is a time consuming process prone to errors. This paper proposes a method to automatically reconstruct and self-calibrate multi-view structured light systems with an arbitrary number of devices. The experimentation shows that the proposed method is precise and robust, surpassing other current state of the art approaches. The achieved calibration accuracy is similar to that obtained by a traditional chessboard pattern calibration, but being able to adapt to a wider range of situations.


Robust Face Recognition Using Symmetric Shape-from-Shading