title: Development of unmanned surface vehicle for smart water quality inspector

abstract: Effective and efficient management of water resources is becoming unprecedentedly more important nowadays due to the increasing demand from a growing population, increasing standards of living and changing supply due to climate change. Water monitoring technology has directly contributed to the effectiveness of water resource management. Traditional water measurement method, mostly done in laboratory, cannot help so much for making timely and informed decisions. The new method called Water Sensor Network can acquire and serve data in real time, but most of them are a type of fixed location platform that makes them lacking of mobility. In this paper, we present the combination of wireless water sensor technology together with unmanned surface vehicle (USV) to solve the problem. The overall system consists of water quality sensors, wireless communication system and mobility platform. The water sensors include temperature, pH, dissolved oxygen (DO), oxidation reduction potential (ORP) and electrical conductivity (EC). Software was developed using open source technology aiming at providing continuous and autonomous water quality measurement at substantially reduced cost. The integrated sensorized USV was tested to perform an autonomous mission for water quality assessment in a lake. Preliminary laboratory and field test results have demonstrated that the system has a capability to do water quality assessment mission in inland water resource. This USV can be used at various surface water spaces, such as domestic water sources, domestic water intake points and industrial water discharge points.


title: Optimal Sizing and Control Strategies for Hybrid Storage System as Limited by Grid Frequency Deviations

abstract: Frequency deviations of power systems caused by grid-connected wind power fluctuations is one of the key factors which restrains the increase of wind penetration level. This paper examines a combined wind and hybrid energy storage system (HESS, supercapacitor, and battery) to smooth wind power fluctuations. A fuzzy-based wind-HESS system (FWHS) controller is proposed to suppress the wind power fluctuations. The proposed controller takes full advantage of the complimentary characteristics of the supercapacitor and battery with the supercapacitor and battery in charge of high and middle frequency components of wind fluctuations, respectively. A differential evolution (DE)-based optimal sizing method for HESS systems is introduced to evaluate the minimum capacity of HESS as being limited by grid frequency deviation. The efficiency of the proposed scheme in the paper for wind-HESS system is evaluated by a real Chinese power system.


title: A Survey of Device-to-Device Communications: Research Issues and Challenges

abstract: Device-to-Device (D2D) communication has emerged as a promising technology for optimizing spectral efficiency in future cellular networks. D2D takes advantage of the proximity of communicating devices for efficient utilization of available resources, improving data rates, reducing latency, and increasing system capacity. The research community is actively investigating the D2D paradigm to realize its full potential and enable its smooth integration into the future cellular system architecture. Existing surveys on this paradigm largely focus on interference and resource management. We review recently proposed solutions in over explored and under explored areas in D2D. These solutions include protocols, algorithms, and architectures in D2D. Furthermore, we provide new insights on open issues in these areas. Finally, we discuss potential future research directions.


title: Music therapy for people with schizophrenia and schizophrenia-like disorders

abstract: BACKGROUND ::: Music therapy is a therapeutic method that uses musical interaction as a means of communication and expression. The aim of the therapy is to help people with serious mental disorders to develop relationships and to address issues they may not be able to using words alone. ::: ::: ::: OBJECTIVES ::: To review the effects of music therapy, or music therapy added to standard care, compared with 'placebo' therapy, standard care or no treatment for people with serious mental disorders such as schizophrenia. ::: ::: ::: SEARCH METHODS ::: We searched the Cochrane Schizophrenia Group Trials Register (December 2010) and supplemented this by contacting relevant study authors, handsearching of music therapy journals and manual searches of reference lists. ::: ::: ::: SELECTION CRITERIA ::: All randomised controlled trials (RCTs) that compared music therapy with standard care, placebo therapy, or no treatment. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Studies were reliably selected, quality assessed and data extracted. We excluded data where more than 30% of participants in any group were lost to follow-up. We synthesised non-skewed continuous endpoint data from valid scales using a standardised mean difference (SMD). If statistical heterogeneity was found, we examined treatment 'dosage' and treatment approach as possible sources of heterogeneity. ::: ::: ::: MAIN RESULTS ::: We included eight studies (total 483 participants). These examined effects of music therapy over the short- to medium-term (one to four months), with treatment 'dosage' varying from seven to 78 sessions. Music therapy added to standard care was superior to standard care for global state (medium-term, 1 RCT, n = 72, RR 0.10 95% CI 0.03 to 0.31, NNT 2 95% CI 1.2 to 2.2). Continuous data identified good effects on negative symptoms (4 RCTs, n = 240, SMD average endpoint Scale for the Assessment of Negative Symptoms (SANS) -0.74 95% CI -1.00 to -0.47); general mental state (1 RCT, n = 69, SMD average endpoint Positive and Negative Symptoms Scale (PANSS) -0.36 95% CI -0.85 to 0.12; 2 RCTs, n=100, SMD average endpoint Brief Psychiatric Rating Scale (BPRS) -0.73 95% CI -1.16 to -0.31); depression (2 RCTs, n = 90, SMD average endpoint Self-Rating Depression Scale (SDS) -0.63 95% CI -1.06 to -0.21; 1 RCT, n = 30, SMD average endpoint Hamilton Depression Scale (Ham-D) -0.52 95% CI -1.25 to -0.21 ); and anxiety (1 RCT, n = 60, SMD average endpoint SAS -0.61 95% CI -1.13 to -0.09). Positive effects were also found for social functioning (1 RCT, n = 70, SMD average endpoint Social Disability Schedule for Inpatients (SDSI) score -0.78 95% CI -1.27 to -0.28). Furthermore, some aspects of cognitive functioning and behaviour seem to develop positively through music therapy. Effects, however, were inconsistent across studies and depended on the number of music therapy sessions as well as the quality of the music therapy provided. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Music therapy as an addition to standard care helps people with schizophrenia to improve their global state, mental state (including negative symptoms) and social functioning if a sufficient number of music therapy sessions are provided by qualified music therapists. Further research should especially address the long-term effects of music therapy, dose-response relationships, as well as the relevance of outcomes measures in relation to music therapy.


title: Improving the Coverage of Millimeter-Wave (mmWave) Cellular Networks using Device-to-Device Relays

abstract: The susceptibility of millimeter waveform propagation to blockages limits the coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry, we derive expressions for the downlink coverage probability of relay-assisted mmWave cellular networks when the D2D links are implemented in either uplink mmWave or uplink microwave bands. We further investigate the spectral efficiency (SE) improvement in the cellular downlink, and the effect of D2D transmissions on the cellular uplink. For mmWave links, we derive the coverage probability using dominant interferer analysis while accounting for both blockages and beamforming gains. For microwave D2D links, we derive the coverage probability considering both line-of-sight and non-line-of-sight (NLOS) propagation. Numerical results show that downlink coverage and SE can be improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve better coverage because D2D connections can be established under NLOS conditions. However, mmWave D2D relays achieve better coverage when the density of interferers is large because blockages eliminate interference from NLOS interferers. The SE on the downlink depends on the relay mode selection strategy, and mmWave D2D relays use a significantly smaller fraction of uplink resources than microwave D2D relays.


HASBE: A HIERARCHICAL ATTRIBUTE-BASED SOLUTION FOR FLEXIBLE AND SCALABLE ACCESS CONTROL IN CLOUD COMPUTING


title: Analysis, design, and experimentation of a high-voltage power supply for ozone generation based on current-fed parallel-resonant push-pull inverter

abstract: The use of supply frequencies above 50-60 Hz allows for an increase in the power density applied to the ozonizer electrode surface and an increase in ozone production for a given surface area, while decreasing the necessary peak voltage. Parallel-resonant converters are well suited for supplying the high capacitive load of ozonizers. Therefore, in this paper the current-fed parallel-resonant push-pull inverter is proposed as a good option to implement high-voltage high-frequency power supplies for ozone generators. The proposed converter is analyzed and some important characteristics are obtained. The design and implementation of the complete power supply are also shown. The UC3872 integrated circuit is proposed in order to operate the converter at resonance, allowing us to maintain a good response disregarding the changes in electric parameters of the transformer-ozonizer pair. Experimental results for a 50-W prototype are also provided.


title: Character N-Gram Tokenization for European Language Text Retrieval

abstract: The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n e 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval.


title: Ray tracing and volume rendering large molecular data on multi-core and many-core architectures

abstract: Visualizing large molecular data requires efficient means of rendering millions of data elements that combine glyphs, geometry and volumetric techniques. The geometric and volumetric loads challenge traditional rasterization-based vis methods. Ray casting presents a scalable and memory- efficient alternative, but modern techniques typically rely on GPU-based acceleration to achieve interactive rendering rates. In this paper, we present bnsView, a molecular visualization ray tracing framework that delivers fast volume rendering and ball-and-stick ray casting on both multi-core CPUs and many-core Intel® Xeon Phi™ co-processors, implemented in a SPMD language that generates efficient SIMD vector code for multiple platforms without source modification. We show that our approach running on co- processors is competitive with similar techniques running on GPU accelerators, and we demonstrate large-scale parallel remote visualization from TACC's Stampede supercomputer to large-format display walls using this system.


title: Phonological Awareness: Explicit Instruction for Young Deaf and Hard-of-Hearing Children.

abstract: The goal of this study was to explore the development of spoken phonological awareness for deaf and hard-ofhearing children (DHH) with functional hearing (i.e., the ability to access spoken language through hearing). Teachers explicitly taught five preschoolers the phonological awareness skills of syllable segmentation, initial phoneme isolation, and rhyme discrimination in the context of a multifaceted emergent literacy intervention. Instruction occurred in settings where teachers used simultaneous communication or spoken language only. A multiple-baseline across skills design documented a functional relation between instruction and skill acquisition for those children who did not have the skills at baseline with one exception; one child did not meet criteria for syllable segmentation. These results were confirmed by changes on phonological awareness tests that were administered at the beginning and end of the school year. We found that DHH children who varied in primary communication mode, chronological age, and language ability all benefited from explicit instruction in phonological awareness.


title: Design of a Closed-Loop, Bidirectional Brain Machine Interface System With Energy Efficient Neural Feature Extraction and PID Control

abstract: This paper presents a bidirectional brain machine interface (BMI) microsystem designed for closed-loop neuroscience research, especially experiments in freely behaving animals. The system-on-chip (SoC) consists of 16-channel neural recording front-ends, neural feature extraction units, 16-channel programmable neural stimulator back-ends, in-channel programmable closed-loop controllers, global analog-digital converters (ADC), and peripheral circuits. The proposed neural feature extraction units includes 1) an ultra low-power neural energy extraction unit enabling a 64-step natural logarithmic domain frequency tuning, and 2) a current-mode action potential (AP) detection unit with time-amplitude window discriminator. A programmable proportional-integral-derivative (PID) controller has been integrated in each channel enabling a various of closed-loop operations. The implemented ADCs include a 10-bit voltage-mode successive approximation register (SAR) ADC for the digitization of the neural feature outputs and/or local field potential (LFP) outputs, and an 8-bit current-mode SAR ADC for the digitization of the action potential outputs. The multi-mode stimulator can be programmed to perform monopolar or bipolar, symmetrical or asymmetrical charge balanced stimulation with a maximum current of 4 mA in an arbitrary channel configuration. The chip has been fabricated in 0.18 $\mu$ m CMOS technology, occupying a silicon area of 3.7 mm $^2$ . The chip dissipates 56 $\mu$ W/ch on average. General purpose low-power microcontroller with Bluetooth module are integrated in the system to provide wireless link and SoC configuration. Methods, circuit techniques and system topology proposed in this work can be used in a wide range of relevant neurophysiology research, especially closed-loop BMI experiments.


title: A graph-based model for malware detection and classification using system-call groups

abstract: In this paper we present a graph-based model that, utilizing relations between groups of System-calls, detects whether an unknown software sample is malicious or benign, and classifies a malicious software to one of a set of known malware families. More precisely, we utilize the System-call Dependency Graphs (or, for short, ScD-graphs), obtained by traces captured through dynamic taint analysis. We design our model to be resistant against strong mutations applying our detection and classification techniques on a weighted directed graph, namely Group Relation Graph, or Gr-graph for short, resulting from ScD-graph after grouping disjoint subsets of its vertices. For the detection process, we propose the \(\Delta \)-similarity metric, and for the process of classification, we propose the SaMe-similarity and NP-similarity metrics consisting the SaMe-NP similarity. Finally, we evaluate our model for malware detection and classification showing its potentials against malicious software measuring its detection rates and classification accuracy.


title: Device-to-Device Communication Underlaying a Finite Cellular Network Region

abstract: Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this work. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. Additionally, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease while the D2D users' average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks..


title: Ensemble of Deep Models for Event Recognition

abstract: In this article, we address the problem of recognizing an event from a single related picture. Given the large number of event classes and the limited information contained in a single shot, the problem is known to be particularly hard. To achieve a reliable detection, we propose a combination of multiple classifiers, and we compare three alternative strategies to fuse the results of each classifier, namely: (i) induced order weighted averaging operators, (ii) genetic algorithms, and (iii) particle swarm optimization. Each method is aimed at determining the optimal weights to be assigned to the decision scores yielded by different deep models, according to the relevant optimization strategy. Experimental tests have been performed on three event recognition datasets, evaluating the performance of various deep models, both alone and selectively combined. Experimental results demonstrate that the proposed approach outperforms traditional multiple classifier solutions based on uniform weighting, and outperforms recent state-of-the-art approaches.


title: Direct torque control of brushless DC drives with reduced torque ripple

abstract: The application of direct torque control (DTC) to brushless AC drives (BLAC) has been investigated extensively. This work describes its application to brushless DC drives (BLDC), and highlights the essential differences in its implementation, in relation to the torque estimation and the representation of the inverter voltage space vectors. Simulated and experimental results are presented, and it is shown that, compared with conventional current control, instantaneous torque control results in reduced torque ripple and a faster torque response.


title: AirDraw: Leveraging smart watch motion sensors for mobile human computer interactions

abstract: Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have some features, such as consistent user interaction and hands-free, heads-up operations, which pave the way for gesture recognition methods of text entry. This paper proposes a new text input method for smart watches, which utilizes motion sensor data and machine learning approaches to detect letters written in the air by a user. This method is less computationally intensive and less expensive when compared to computer vision approaches. It is also not affected by lighting factors, which limit computer vision solutions. The AirDraw system prototype developed to test this approach is presented. Additionally, experimental results close to 71% accuracy are presented.


title: Observability analysis of SLAM using fisher information matrix

abstract: This paper presents a new technique for evaluating the observability of the simultaneous localization and mapping (SLAM) problem. The state vector of an estimation theoretic formulation of the SLAM problem is recast to include all robot poses from which the measurements are made. This converts SLAM to a problem of estimating a set of unknown, constant random variables. Fisher Information Matrix of the resulting static estimation problem is derived and analyzed to examine the observability of SLAM. Outcomes of the analysis and comparisons to the observability analysis presented in recent literature are presented. Proposed technique makes it possible to analyze the observability of a range of SLAM problems with ease.


title: Detecting fraud, corruption, and collusion in international development contracts: The design of a proof-of-concept automated system

abstract: International development banks provide low-interest loans to developing countries in an effort to stimulate social and economic development. These loans support key infrastructure projects including the building of roads, schools, and hospitals. However, despite the best efforts of development banks, these loan funds are often lost to fraud, corruption, and collusion. In an effort to sanction and deter this wrongdoing and to ensure proper use of funds, development banks conduct extensive, costly investigations that can take over a year to complete. This paper describes a proof-of-concept of a fully automated fraud, corruption, and collusion classification system for identifying risk in international development contracts. We developed this system in conjunction with the World Bank Group — the largest international development bank — to improve the time and cost efficiency of their investigation process. Using historical monetary award data and past investigation outcomes, our classifier assigns a “risk score” to World Bank contracts. This risk score is designed to enable World Bank investigators to identify the contracts most likely to lead to a substantiated investigation. If implemented, our automated system is predicted to successfully identify fraud, corruption, and collusion in 70% of cases.


title: Subspace clustering for high dimensional data: a review

abstract: Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. Often in high dimensional data, many dimensions are irrelevant and can mask existing clusters in noisy data. Feature selection removes irrelevant and redundant dimensions by analyzing the entire dataset. Subspace clustering algorithms localize the search for relevant dimensions allowing them to find clusters that exist in multiple, possibly overlapping subspaces. There are two major branches of subspace clustering based on their search strategy. Top-down algorithms find an initial clustering in the full set of dimensions and evaluate the subspaces of each cluster, iteratively improving the results. Bottom-up approaches find dense regions in low dimensional spaces and combine them to form clusters. This paper presents a survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics. We then compare the two main approaches to subspace clustering using empirical scalability and accuracy tests and discuss some potential applications where subspace clustering could be particularly useful.


title: Robust Horizon Detection Using Segmentation for UAV Applications

abstract: A critical step in navigation of unmanned aerial vehicles is the detection of the horizon line. This information can be used for adjusting flight parameters as well as obstacle avoidance. In this paper, a fast and robust technique for precise detection of the horizon path is proposed. The method is based on existence of a unique light field that occurs in imagery where the horizon is viewed. This light field exists in different scenes including sea-sky, soil-sky, and forest-sky horizon lines. Our proposed approach employs segmentation of the scene and subsequent analysis of the image segments for extraction of the mentioned field and thus the horizon path. Through various experiments carried out on our own dataset and that of another previously published paper, we illustrate the significance and accuracy of this technique for various types of terrains from water to ground, and even snow-covered ground. Finally, it is shown that robust performance and accuracy, speed, and extraction of the path as curves (as opposed to a straight line which is resulted from many other approaches) are the benefits of our method.


title: Memory forensics

abstract: Traditionally, digital forensics focused on artifacts located on the storage devices of computer systems, mobile phones, digital cameras, and other electronic devices. In the past decade, however, researchers have created a number of powerful memory forensics tools that expand the scope of digital forensics to include the examination of volatile memory as well. While memory forensic techniques have evolved from simple string searches to deep, structured analysis of application and kernel data structures for a number of platforms and operating systems, much research remains to be done. This paper surveys the state-of-the-art in memory forensics, provide critical analysis of current-generation techniques, describe important changes in operating systems design that impact memory forensics, and sketches important areas for further research.


title: Robust Tube-based Predictive Control for Mobile Robots in Off-Road Conditions

abstract: This paper focuses on the design of a tube-based Model Predictive Control law for the control of constrained mobile robots in off-road conditions with longitudinal slip while ensuring robustness and stability. A time-varying trajectory tracking error model is used, where uncertainties are assumed to be bounded and additive. The robust tube-based MPC is compared with other motion control techniques through simulation and physical experiments. These tests show the satisfactory behavior of the presented control strategy.


title: Interactive collision detection

abstract: Collision detection and response can make a virtual-reality application seem more believable. Unfortunately, existing collision-detection algorithms are too slow for interactive use. The authors present a new algorithm that is not only fast but also interruptible, allowing an application to trade quality for more speed. The algorithm uses simple four-dimensional geometry to approximate motion, and sets of spheres to approximate three-dimensional surfaces. The algorithm allows a sample application to run five to seven times faster than it runs with existing algorithms. >


title: Input-Series-Output-Parallel Phase-Shift Full-Bridge Derived DC–DC Converters With Auxiliary LC Networks to Achieve Wide Zero-Voltage Switching Range

abstract: This letter proposes an input-series-output-parallel derived dc–dc converter composed of two phase-shift full-bridge (PSFB) dc–dc converters and two auxiliary LC networks. Each LC network is connected between the leading-leg midpoint of a module and the lagging-leg midpoint of the other one. For conventional PSFB converters, the lagging-leg switches may lose zero-voltage switching (ZVS) conditions at light loads. In the proposed converter, ZVS for lagging-leg switches can be achieved with the aid of the LC networks even at light loads. The efficiency of the converter at the light load is improved. Therefore, the proposed converter is suitable for the wide load range. The feature of the converter is analyzed. The performance of the proposed converter is verified by a 900 W experimental prototype.


title: Who Will Share My Image?: Predicting the Content Diffusion Path in Online Social Networks

abstract: Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our model's capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.


title: BigDebug: debugging primitives for interactive big data processing in spark

abstract: Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's datacenters is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires rethinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user. First, B ig D ebug 's simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that B ig D ebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that B ig D ebug supports debugging at interactive speeds with minimal performance impact.


title: Algorithms for two bottleneck optimization problems

abstract: A bottleneck optimization problem on a graph with edge costs is the problem of finding a subgraph of a certain kind that minimizes the maximum edge cost in the subgraph. The bottleneck objective contrasts with the more common objective of minimizing the sum of edge costs. We propose fast algorithms for two bottleneck optimization problems. For the problem of finding a bottleneck spanning tree in a directed graph of n vertices and m edges, we propose an O(min{n log n + m, m log∗ n})-timer algorithm. For the bottleneck maximum cardinality matching problem, we propose an O((n log n)12m)-time algorithm.


Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification


title: Domain transfer for person re-identification

abstract: Automatic person re-identification in is a crucial capability underpinning many applications in public space video surveillance. It is challenging due to intra-class variation in person appearance when observed in different views, together with limited inter-class variability. Various recent approaches have made great progress in re-identification performance using discriminative learning techniques. However, these approaches are fundamentally limited by the requirement of extensive annotated training data for every pair of views. For practical re-identification, this is an unreasonable assumption, as annotating extensive volumes of data for every pair of cameras to be re-identified may be impossible or prohibitively expensive. In this paper we move toward relaxing this strong assumption by investigating flexible multi-source transfer of re-identification models across camera pairs. Specifically, we show how to leverage prior re-identification models learned for a set of source view pairs (domains), and flexibly combine these to obtain good re-identification performance in a target view pair (domain) with greatly reduced training data requirements in the target domain.


title: Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.

abstract: PURPOSE ::: To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation. ::: ::: ::: MATERIALS AND METHODS ::: A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis. ::: ::: ::: RESULTS ::: At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section. ::: ::: ::: CONCLUSION ::: With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.


title: Fast rule mining in ontological knowledge bases with AMIE $$+$$ +

abstract: Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive logic programming (ILP) can be used to mine logical rules from these KBs, such as "If two persons are married, then they (usually) live in the same city." While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open-world assumption. This means that absent information cannot be taken as counterexamples. Our approach AMIE (Galarraga et al. in WWW, 2013) has shown how rules can be mined effectively from KBs even in the absence of counterexamples. In this paper, we show how this approach can be optimized to mine even larger KBs with more than 12M statements. Extensive experiments show how our new approach, AMIE$$+$$+, extends to areas of mining that were previously beyond reach.


title: Realising the Right to Data Portability for the Domestic Internet of Things

abstract: There is an increasing role for the IT design community to play in regulation of emerging IT. Article 25 of the EU General Data Protection Regulation (GDPR) 2016 puts this on a strict legal basis by establishing the need for information privacy by design and default (PbD) for personal data-driven technologies. Against this backdrop, we examine legal, commercial and technical perspectives around the newly created legal right to data portability (RTDP) in GDPR. We are motivated by a pressing need to address regulatory challenges stemming from the Internet of Things (IoT). We need to find channels to support the protection of these new legal rights for users in practice. ::: In Part I we introduce the internet of things and information PbD in more detail. We briefly consider regulatory challenges posed by the IoT and the nature and practical challenges surrounding the regulatory response of information privacy by design. ::: In Part II, we look in depth at the legal nature of the RTDP, determining what it requires from IT designers in practice but also limitations on the right and how it relates to IoT. ::: In Part III we focus on technical approaches that can support the realisation of the right. We consider the state of the art in data management architectures, tools and platforms that can provide portability, increased transparency and user control over the data flows. ::: In Part IV, we bring our perspectives together to reflect on the technical, legal and business barriers and opportunities that will shape the implementation of the RTDP in practice, and how the relationships may shape emerging IoT innovation and business models. We finish with brief conclusions about the future for the RTDP and PbD in the IoT. ::: NOTE - Updated v.2 added 30 May 2017.


title: A genome-wide RNAi screen identifies proteins modulating aberrant FLT3-ITD signaling

abstract: Fms-like tyrosine kinase-3 is a commonly mutated gene in acute myeloid leukemia, with about one-third of patients carrying an internal-tandem duplication of the juxtamembrane domain in the receptor (FLT3-ITD). FLT3-ITD exhibits altered signaling quality, including aberrant activation of STAT5. To identify genes affecting FLT3-ITD-mediated STAT5 signaling, we performed an esiRNA-based RNAi screen utilizing a STAT5-driven reporter assay. Knockdowns that caused reduced FLT3-ITD-mediated STAT5 signaling were enriched for genes encoding proteins involved in protein secretion and intracellular protein transport, indicating that modulation of protein transport processes could potentially be used to reduce constitutive STAT5 signaling in FLT3-ITD-positive cells. The relevance of KDELR1, a component involved in the Golgi-ER retrograde transport, was further analyzed. In FLT3-ITD-expressing leukemic MV4-11 cells, downregulation of KDELR1 resulted in reduced STAT5 activation, proliferation and colony-forming capacity. Stable shRNA-mediated depletion of KDELR1 in FLT3-ITD-expressing 32D cells likewise resulted in reduced STAT5 signaling and cell proliferation. Importantly, these cells also showed a reduced capacity to generate a leukemia-like disease in syngeneic C3H/HeJ mice. Together our data suggest intracellular protein transport as a potential target for FLT3-ITD driven leukemias, with KDELR1 emerging as a positive modulator of oncogenic FLT3-ITD activity.


title: Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals

abstract: Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.


title: Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo

abstract: This paper presents an extension of the OpenAI Gym for robotics using the Robot Operating System (ROS) and the Gazebo simulator. The content discusses the software architecture proposed and the results obtained by using two Reinforcement Learning techniques: Q-Learning and Sarsa. Ultimately, the output of this work presents a benchmarking system for robotics that allows different techniques and algorithms to be compared using the same virtual conditions.


title: Modeling Image Virality with Pairwise Spatial Transformer Networks

abstract: The study of virality and information diffusion is a topic gaining traction rapidly in the computational social sciences. Computer vision and social network analysis research have also focused on understanding the impact of content and information diffusion in making content viral, with prior approaches not performing significantly well as other traditional classification tasks. In this paper, we present a novel pairwise reformulation of the virality prediction problem as an attribute prediction task and develop a novel algorithm to model image virality on online media using a pairwise neural network. Our model provides significant insights into the features that are responsible for promoting virality and surpasses the existing state-of-the-art by a 12% average improvement in prediction. We also investigate the effect of external category supervision on relative attribute prediction and observe an increase in prediction accuracy for the same across several attribute learning datasets.


A Comparison of Static Analysis Tools for Vulnerability Detection in C / C + + Code


title: PERFORMANCE ANALYSIS OF CHAIN CODE DESCRIPTOR FOR HAND SHAPE CLASSIFICATION

abstract: Feature Extraction is an important task for any Image processing application. The visual properties of any image are its shape, texture and colour. Out of these shape description plays important role in any image classification. The shape description method classified into two types, contour base and region based. The contour base method concentrated on the shape boundary line and the region based method considers whole area. In this paper, contour based, the chain code description method was experimented for different hand shape. The chain code descriptor of various hand shapes was calculated and tested with different classifier such as k-nearest- neighbour (k-NN), Support vector machine (SVM) and Naive Bayes. Principal component analysis (PCA) was applied after the chain code description. The performance of SVM was found better than k-NN and Naive Bayes with recognition rate 93%.


title: Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma

abstract: We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous sounds. However, the ideal property of a set of acoustic features that maximizes ASD performance has not been clarified. By considering outlier-detection-based ASD as a statistical hypothesis test, we defined optimality as an objective function that adopts Neyman-Pearson lemma; the acoustic feature extractor is optimized to extract a set of acoustic features which maximize the true positive rate under an arbitrary false positive rate. The variational auto-encoder is applied as an acoustic feature extractor and optimized to maximize the objective function. We confirmed that the proposed method improved the F-measure score from 0.02 to 0.06 points compared to those of conventional methods, and ASD results of a stereolithography 3D-printer in a real-environment show that the proposed method is effective in identifying anomalous sounds.


title: From UI design image to GUI skeleton: a neural machine translator to bootstrap mobile GUI implementation

abstract: A GUI skeleton is the starting point for implementing a UI design image. To obtain a GUI skeleton from a UI design image, developers have to visually understand UI elements and their spatial layout in the image, and then translate this understanding into proper GUI components and their compositions. Automating this visual understanding and translation would be beneficial for bootstraping mobile GUI implementation, but it is a challenging task due to the diversity of UI designs and the complexity of GUI skeletons to generate. Existing tools are rigid as they depend on heuristically-designed visual understanding and GUI generation rules. In this paper, we present a neural machine translator that combines recent advances in computer vision and machine translation for translating a UI design image into a GUI skeleton. Our translator learns to extract visual features in UI images, encode these features' spatial layouts, and generate GUI skeletons in a unified neural network framework, without requiring manual rule development. For training our translator, we develop an automated GUI exploration method to automatically collect large-scale UI data from real-world applications. We carry out extensive experiments to evaluate the accuracy, generality and usefulness of our approach.


title: Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS

abstract: The multiple criteria decision making (MCDM) methods VIKOR and TOPSIS are based on an aggregating function representing ‘‘closeness to the ideal’’, which originated in the compromise programming method. In VIKOR linear normalization and in TOPSIS vector normalization is used to eliminate the units of criterion functions. The VIKOR method of compromise ranking determines a compromise solution, providing a maximum ‘‘group utility’’ for the ‘‘majority’’ and a minimum of an individual regret for the ‘‘opponent’’. The TOPSIS method determines a solution with the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution, but it does not consider the relative importance of these distances. A comparative analysis of these two methods is illustrated with a numerical example, showing their similarity and some differences. � 2003 Elsevier B.V. All rights reserved.


title: Efficient Solution Algorithms for Factored MDPs

abstract: This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition technique, analogous to variable elimination in Bayesian networks, which reduces an exponentially large LP to a provably equivalent, polynomial-sized one. One algorithm uses approximate linear programming, and the second approximate dynamic programming. Our dynamic programming algorithm is novel in that it uses an approximation based on max-norm, a technique that more directly minimizes the terms that appear in error bounds for approximate MDP algorithms. We provide experimental results on problems with over 1040 states, demonstrating a promising indication of the scalability of our approach, and compare our algorithm to an existing state-of-the-art approach, showing, in some problems, exponential gains in computation time.


title: Steering user behavior with badges

abstract: An increasingly common feature of online communities and social media sites is a mechanism for rewarding user achievements based on a system of badges. Badges are given to users for particular contributions to a site, such as performing a certain number of actions of a given type. They have been employed in many domains, including news sites like the Huffington Post, educational sites like Khan Academy, and knowledge-creation sites like Wikipedia and Stack Overflow. At the most basic level, badges serve as a summary of a user's key accomplishments; however, experience with these sites also shows that users will put in non-trivial amounts of work to achieve particular badges, and as such, badges can act as powerful incentives. Thus far, however, the incentive structures created by badges have not been well understood, making it difficult to deploy badges with an eye toward the incentives they are likely to create. In this paper, we study how badges can influence and steer user behavior on a site---leading both to increased participation and to changes in the mix of activities a user pursues on the site. We introduce a formal model for reasoning about user behavior in the presence of badges, and in particular for analyzing the ways in which badges can steer users to change their behavior. To evaluate the main predictions of our model, we study the use of badges and their effects on the widely used Stack Overflow question-answering site, and find evidence that their badges steer behavior in ways closely consistent with the predictions of our model. Finally, we investigate the problem of how to optimally place badges in order to induce particular user behaviors. Several robust design principles emerge from our framework that could potentially aid in the design of incentives for a broad range of sites.


title: Sparse PCA: Convex Relaxations, Algorithms and Applications

abstract: Given a sample covariance matrix, we examine the problem of maximizing the variance explained by a linear combination of the input variables while constraining the number of nonzero coefficients in this combination. This is known as sparse principal component analysis and has a wide array of applications in machine learning and engineering. Unfortunately, this problem is also combinatorially hard and we discuss convex relaxation techniques that efficiently produce good approximate solutions. We then describe several algorithms solving these relaxations as well as greedy algorithms that iteratively improve the solution quality. Finally, we illustrate sparse PCA in several applications, ranging from senate voting and finance to news data.


title: Texture-based fruit detection via images using the smooth patterns on the fruit

abstract: This paper describes a keypoint detection algorithm to accurately detect round fruits in high resolution imagery. The significant challenge associated with round fruits such as grapes and apples is that the surface is smooth and lacks definition and contrasting features, the contours of the fruit may be partially occluded, and the color of the fruit often blends with background foliage. We propose a fruit detection algorithm that utilizes the gradual variation of intensity and gradient orientation on the surface of the fruit. Candidate fruit locations, or “seed points” are tested for both monotonically decreasing intensity and gradient orientation profiles. Candidate fruit locations that pass the initial filter are classified using modified histogram of oriented gradients combined with a pairwise intensity comparison texture descriptor and random forest classifier. We analyse the performance of the fruit detection algorithm on image datasets of grapes and apples using human labeled images as ground truth. Our method to detect candidate fruit locations is scale invariant, robust to partial occlusions and more accurate than existing methods. We achieve overall F1 accuracy score of 0.82 for grapes and 0.80 for apples. We demonstrate our method is more accurate than existing methods.


title: Adaptive mesh subdivision for efficient light baking

abstract: Light baking has long been a popular technique for real-time rendering. It usually precomputes and bakes the global lighting effects as vertex attributes or textures. Vertex baking requires less memory but can cause artifacts for large triangles. Texture baking can avoid this and generate a high-quality visual effect in real-time rendering. However, it requires significant memory consumption, which may limit the real-time performance and usage. To address this problem, we propose an adaptive mesh subdivision algorithm for memory-efficient light baking, including a fast triangle subdivision level determination method and an optimized solution to calculate vertex colors. Only the subdivided mesh is required during the real-time rendering. Therefore, memory requirements can be significantly reduced while keeping the visual effect. Besides, the subdivision level is allowed to be intuitively controlled by users with a specified parameter. Our algorithm can be easily implemented on commodity graphics hardware and integrated in existing real-time applications such as online preview systems.


title: Alphasort: A cache-sensitive parallel external sort

abstract: A new sort algorithm, called AlphaSort, demonstrates that commodity processors and disks can handle commercial batch workloads. Using commodity processors, memory, and arrays of SCSI disks, AlphaSort runs the industry-standard sort benchmark in seven seconds. This beats the best published record on a 32-CPU 32-disk Hypercube by 8:1. On another benchmark, AlphaSort sorted more than a gigabyte in one minute. AlphaSort is a cache-sensitive, memory-intensive sort algorithm. We argue that modern architectures require algorithm designers to re-examine their use of the memory hierarchy. AlphaSort uses clustered data structures to get good cache locality, file striping to get high disk bandwidth, QuickSort to generate runs, and replacement-selection to merge the runs. It uses shared memory multiprocessors to break the sort into subsort chores. Because startup times are becoming a significant part of the total time, we propose two new benchmarks: (1) MinuteSort: how much can you sort in one minute, and (2) PennySort: how much can you sort for one penny.


title: Racial and Ethnic Differences in a Linkage With the National Death Index

abstract: OBJECTIVES ::: Differences in the availability of a Social Security Number (SSN) by race/ethnicity could affect the ability to link with death certificate data in passive follow-up studies and possibly bias mortality disparities reported with linked data. Using 1989-2009 National Health Interview Survey (NHIS) data linked with the National Death Index (NDI) through 2011, we compared the availability of a SSN by race/ethnicity, estimated the percent of links likely missed due to lack of SSNs, and assessed if these estimated missed links affect race/ethnicity disparities reported in the NHIS-linked mortality data. ::: ::: ::: METHODS ::: We used preventive fraction methods based on race/ethnicity-specific Cox proportional hazards models of the relationship between availability of SSN and mortality based on observed links, adjusted for survey year, sex, age, respondent-rated health, education, and US nativity. ::: ::: ::: RESULTS ::: Availability of a SSN and observed percent linked were significantly lower for Hispanic and Asian/Pacific Islander (PI) participants compared with White non-Hispanic participants. We estimated that more than 18% of expected links were missed due to lack of SSNs among Hispanic and Asian/PI participants compared with about 10% among White non-Hispanic participants. However, correcting the observed links for expected missed links appeared to only have a modest impact on mortality disparities by race/ethnicity. ::: ::: ::: CONCLUSIONS ::: Researchers conducting analyses of mortality disparities using the NDI or other linked death records, need to be cognizant of the potential for differential linkage to contribute to their results.


title: A Flame Detection Method Based on Novel Gradient Features

abstract: Abstract In this study, we present a novel approach to efficiently detect the flame in multiple scenes in an image. The method uses a set of parametric representation named as Gradient Features (GF), to learn the features of flame color changes in the image. Different from the traditional color features of the flame, GF represents the color changes in RGB channels for further consideration. In this study, support vector machine was applied to generate a set of candidate regions and the decision tree model was used to judge flame regions based on GF. Some exclusive experiments were conducted to verify the validity and effectiveness of the proposed method. The results showed that the proposed method can accurately differentiate between yellow color light and sunrise scenes. A comparison with the state-of-the-art preceding methods showed that this method can utilize the symmetry of flame regions and achieve a better result.


title: A Game Informatical Comparison of Chess and Association Football (Soccer)

abstract: Games which persist across cultures and eras are not static institutions; they evolve when they do not go extinct. Changes of rules, styles and strategies contribute to the evolution of games. Western chess and football continue to grow in popularity after more than a century of codification and periodic changes. We apply the game refinement measure to football, and compare the results to that of chess. Also we consider the results of the sub-game in football and the match game in chess. Using the analogy of the round match in chess applied to the information of the sub-game in football, we found the result of game refinement in championship football to be similar to that of championship chess.


title: Defensive Marketing Strategy by Customer Complaint Management: A Theoretical Analysis:

abstract: On the basis of Hirschman's exit-voice theory, an economic model of defensive marketing strategy is developed for complaint management. Though many firms strive to reduce the number of customer com...


title: Implementing the Service Catalogue Management

abstract: The Service Catalogue is a fundamental need of Information Technology (IT) organizations because it describes in a formal document the available services that these organizations have to provide. The catalogue contains the respective Service Level Agreements (SLA) that should be met, setting expectations between clients and providers of services. The Service Catalogue is integrated with other processes, including Service Level, Financial, Demand and Request Management – all these perfectly described in the ITIL books. However, about 30% of IT Service Management (ITSM) projects do not finish as a result of poorly defined IT services. This research proposes some solutions that try to mitigate the risks of a service catalogue implementation. The proposed solutions include a service definition, its components, the roles involved in its management, an identification process and a lifecycle process. The proposal was implemented in a private company, where we identified the services that composite the service catalogue.


1 Current-Sensing Techniques for DC-DC Converters


Scalable Semantics-Based Detection of Similar Android Applications


title: Asymbiotic and symbiotic seed germination of Eulophia alta (Orchidaceae)—preliminary evidence for the symbiotic culture advantage

abstract: Eulophia alta (Linnaeus) Fawcett & Rendle seeds collected from the Florida Panther National Wildlife Refuge (Collier County, FL; FPNWR) were used in a screen of five asymbiotic orchid seed germination media to determine their effectiveness in promoting germination and protocorm development. In addition, 10 fungal isolates collected from the roots of E. alta at sites in the FPNWR, Highlands County (FL), and Goethe State Forest (Levy County, FL; GSF), and a fungal isolate from the roots of Spiranthes brevilabris collected from GSF were screened for their effectiveness at promoting in vitro symbiotic germination of E. alta seeds. After 18 weeks asymbiotic culture, seeds sown on PhytoTechnology Orchid Seed Sowing Medium germinated to a higher percentage (87.9%) and had a higher percentage of protocorms with developing protomeristems (32.7%) than seeds cultured on Knudson C, Malmgren Modified Terrestrial Orchid Medium, ½-strength Murashige & Skoog, or Vacin & Went Modified Orchid Medium. Significantly more leaf-bearing protocorms were observed on PhytoTechnology Orchid Seed Sowing Medium (0.8%) and Vacin & Went Modified Orchid Medium (1.3%) than other media tested. Of the fungi tested, one fungal isolate (Ealt-396) promoted germination to 69.0%, two isolates promoted germination to less than 0.75% and did not support further protocorm development, and eight isolates did not support germination. Seeds co-cultured in darkness with Ealt-396 grew more rapidly than asymbiotic seedlings following germination. In addition, co-cultured (=symbiotic) seedlings continued to develop more rapidly than asymbiotic seedlings upon transfer to 16/8 h light/dark photoperiod. Symbiotic seed culture of E. alta may be a more desirable method of propagation since protocorms develop more rapidly than seeds sown on asymbiotic media. Symbiotic seedlings may be more appropriate for reintroduction to natural areas than asymbiotic seedlings since symbiotic seedlings could serve to inoculate soils with a germination promoting mycobiont.


title: Obstacle Detection and Collision Avoidance for a UAV With Complementary Low-Cost Sensors

abstract: This paper demonstrates an innovative and simple solution for obstacle detection and collision avoidance of unmanned aerial vehicles (UAVs) optimized for and evaluated with quadrotors. The sensors exploited in this paper are low-cost ultrasonic and infrared range finders, which are much cheaper though noisier than more expensive sensors such as laser scanners. This needs to be taken into consideration for the design, implementation, and parametrization of the signal processing and control algorithm for such a system, which is the topic of this paper. For improved data fusion, inertial and optical flow sensors are used as a distance derivative for reference. As a result, a UAV is capable of distance controlled collision avoidance, which is more complex and powerful than comparable simple solutions. At the same time, the solution remains simple with a low computational burden. Thus, memory and time-consuming simultaneous localization and mapping is not required for collision avoidance.


title: Oral lesions in leprosy.

abstract: BACKGROUND ::: Leprotic oral lesions are more common in the lepromatous form of leprosy, indicate a late manifestation, and have a great epidemiological importance as a source of infection. ::: ::: ::: METHODS ::: Patients with leprosy were examined searching for oral lesions. Biopsies of the left buccal mucosa in all patients, and of oral lesions, were performed and were stained with H & E and Wade. ::: ::: ::: RESULTS ::: Oral lesions were found in 26 patients, 11 lepromatous leprosy, 14 borderline leprosy, and one tuberculoid leprosy. Clinically 5 patients had enanthem of the anterior pillars, 3 of the uvula and 3 of the palate. Two had palatal infiltration. Viable bacilli were found in two lepromatous patients. Biopsies of the buccal mucosa showed no change or a nonspecific inflammatory infiltrate. Oral clinical alterations were present in 69% of the patients; of these 50% showed histopathological features in an area without any lesion. ::: ::: ::: DISCUSSION ::: Our clinical and histopathological findings corroborate earlier reports that there is a reduced incidence of oral changes, which is probably due to early treatment. The maintenance of oral infection in this area can also lead to and maintain lepra reactions, while they may also act as possible infection sources. Attention should be given to oral disease in leprosy because detection and treatment of oral lesions can prevent the spread of the disease.


title: Forensic analysis of social networks (case study)

abstract: This paper deals with a new challenge for digital forensic experts - the forensic analysis of social networks. There is a lot of identity theft, theft of personal data, public defamation, cyber stalking and other criminal activities on social network sites. This paper will present a forensic analysis of social networks and cloud forensics in the internet environment. For the purpose of this research one case study is created like - a common practical scenario where the combination of identity theft and public defamation through Facebook activity is explored. Investigators must find the person who stole some others profile, who publish inappropriate and prohibited contents performing act of public defamation and humiliation of profile owner.


title: A3T: Adversarially Augmented Adversarial Training

abstract: Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier. Most classification models, including deep learning models, are highly vulnerable to adversarial attacks. In this work, we investigate a procedure to improve adversarial robustness of deep neural networks through enforcing representation invariance. The idea is to train the classifier jointly with a discriminator attached to one of its hidden layer and trained to filter the adversarial noise. We perform preliminary experiments to test the viability of the approach and to compare it to other standard adversarial training methods.


title: Who Benefits From Humor-Based Positive Psychology Interventions? The Moderating Effects of Personality Traits and Sense of Humor

abstract: The evidence for the effectiveness of humor-based positive psychology interventions (PPIs; i.e., interventions aimed at enhancing happiness and lowering depressive symptoms) is steadily increasing. However, little is known about who benefits most from them. We aim at narrowing this gap by examining whether personality traits and sense of humor moderate the long-term effects of humor-based interventions on happiness and depressive symptoms. We conducted two placebo-controlled online-intervention studies testing for moderation effects. In Study 1 (N = 104) we tested for moderation effects of basic personality traits (i.e., psychoticism, extraversion, and neuroticism) in the three funny things intervention, a humor-based PPI. In Study 2 (N = 632) we tested for moderation effects of the sense of humor in five different humor-based interventions. Happiness and depressive symptoms were assessed before and after the intervention, as well as after one, three, and six months. In Study 2, we assessed sense of humor before and one month after the intervention to investigate if changes in sense of humor go along with changes in happiness and depressive symptoms. We found moderating effects only for extraversion. Extraverts benefitted more from the three funny things intervention than introverts. For neuroticism and psychoticism no moderation effects were found. For sense of humor, no moderating effects were found for the effectiveness of the five humor-based interventions tested in Study 2. However, changes in sense of humor from pretest to the one-month follow-up predicted changes in happiness and depressive symptoms. Taking a closer look, the playful attitude- and sense of humor-subscales predicted changes in happiness and depression for up to six months. Overall, moderating effects for personality (i.e., extraversion) were found, but none for sense of humor at baseline. However, increases in sense of humor during and after the intervention were associated with the interventions’ effectiveness. Thus, we found humor-based interventions to be equally suited for humorous and non-humorous people, but increases in the sense of humor during the intervention phase could serve as an indicator whether it is worth continuing the intervention in the long-term.


title: Computer vision based approach to detect rice leaf diseases using texture and color descriptors

abstract: One of the major reason behind degradation of quality and quantity of rice crop is pest. The lack of technical and scientific knowledge to prevent pest diseases is the main reason for low production of these commodities. This article aims to develop a computer vision based automatic system for the diagnosis of diseases caused by pests in the rice plants. Automatic disease detection using computer vision approach involves three types of feature extraction in this experiment. Diseased area of the leaf, textural descriptors using gray level co-occurrence matrix (GLCM) and color moments are extracted from diseased and non-diseased leaf images resulting in 21-D feature vector. Genetic algorithm based feature selection approach is employed to select relevant features and to discard redundant features, generating a 14-D feature vector that reduces the complexity. Artificial neural network (ANN) and support vector machine (SVM) is used for classification. The proposed algorithm results in classification accuracy of 92.5% using SVM and 87.5% using ANN.


title: Psychometric validation of the Persian Bergen Social Media Addiction Scale using classic test theory and Rasch models

abstract: Background and aims: The Bergen Social Media Addiction Scale (BSMAS), a six-item self-report scale that is a brief and effective psychometric instrument for assessing at-risk social media addiction ...


title: A Measurement Study of the Content Security Policy on Real-World Applications

abstract: Content Security Policy (CSP) is a browser security mechanism that aims to protect websites from content injection attacks. To adopt CSP, website developers need to manually compile a list of allowed content sources. Nearly all websites require modifications to comply with CSP's de- fault behavior, which blocks inline scripts and the use of the ＂eval()＂ function. Alternatively, websites could adopt a policy that allows the use of this unsafe functionality, but this opens up potential attack vectors. In this paper, our measurements on a large corpus of web applications pro-vide a key insight on the amount of efforts web developers required to adapt to CSP. Our results also identified errors in CSP policies that are set by website developers on their websites. To address these issues and make adoption of CSP easier and error free, we implemented UserCSP a tool as a Firefox extension. The UserCSP uses dynamic analysis to automatically infer CSP policies, facilitates testing, and gives savvy users the authority to enforce client-side policies on websites.


title: MPQA 3.0: An Entity/Event-Level Sentiment Corpus

abstract: This paper presents an annotation scheme for adding entity and event target annotations to the MPQA corpus, a rich span-annotated opinion corpus. The new corpus promises to be a valuable new resource for developing systems for entity/event-level sentiment analysis. Such systems, in turn, would be valuable in NLP applications such as Automatic Question Answering. We introduce the idea of entity and event targets (eTargets), describe the annotation scheme, and present the results of an agreement study.


title: Improved Automatic Keyword Extraction Given More Linguistic Knowledge

abstract: In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.


title: Spreadsheet-based interactive design and analysis of mechanisms using Excel and Ch

abstract: A spreadsheet-based mechanism analysis and animation system is presented in this article. The system is developed in Excel using Ch, an embeddable C/C++ interpreter, and Ch Mechanism Toolkit. It allows users to develop and solve complicated planar mechanism problems conveniently using an easy to use Excel spreadsheets as the front end. Users can input the required data to define a mechanism in an Excel spreadsheet and execute script files associated with the work cells. The spreadsheet based system can be used for kinematic and dynamic analysis, graphical plotting, and animation for four bar, crank-slider, five bar, and six bar linkages. The system is open architecture. Users can extend the system for design and analysis of other mechanisms. Examples are provided to illustrate its ease of use. The source code for examples are available for downloading from the internet.


title: A portable near infrared spectroscopy system for bedside monitoring of newborn brain

abstract: BackgroundNewborns with critical health conditions are monitored in neonatal intensive care units (NICU). In NICU, one of the most important problems that they face is the risk of brain injury. There is a need for continuous monitoring of newborn's brain function to prevent any potential brain injury. This type of monitoring should not interfere with intensive care of the newborn. Therefore, it should be non-invasive and portable.MethodsIn this paper, a low-cost, battery operated, dual wavelength, continuous wave near infrared spectroscopy system for continuous bedside hemodynamic monitoring of neonatal brain is presented. The system has been designed to optimize SNR by optimizing the wavelength-multiplexing parameters with special emphasis on safety issues concerning burn injuries. SNR improvement by utilizing the entire dynamic range has been satisfied with modifications in analog circuitry.Results and ConclusionAs a result, a shot-limited SNR of 67 dB has been achieved for 10 Hz temporal resolution. The system can operate more than 30 hours without recharging when an off-the-shelf 1850 mAh-7.2 V battery is used. Laboratory tests with optical phantoms and preliminary data recorded in NICU demonstrate the potential of the system as a reliable clinical tool to be employed in the bedside regional monitoring of newborn brain metabolism under intensive care.


title: Singularity analysis of closed-loop kinematic chains

abstract: The different kinds of singularities encountered in closed-loop kinematics chains are analyzed. A general classification of these singularities in three main groups, which is based on the properties of the Jacobian matrices of the chain, is described. The identification of the singular configurations is particularly relevant for hard automation modules or robotic devices based on closed kinematic chains, such as linkages and parallel manipulators. Examples are given to illustrate the application of the method to these mechanical systems. >


title: A semi-supervised learning method for remote sensing data mining

abstract: New approaches are needed to extract useful patterns from increasingly large multi-spectral remote sensing image databases in order to understand global climatic changes, vegetation dynamics, ocean processes, etc. Supervised learning, which is often used in land cover (thematic) classification of remote sensing imagery, requires large amounts of accurate training data. However, in many situations it is very difficult to collect labels for all training samples. In this paper we explore methods that utilize unlabeled samples in supervised learning for thematic information extraction from remote sensing imagery. Our objectives are to understand the impact of parameter estimation with small learning samples on classification accuracy, and to augment the parameter estimation with unlabeled training samples to improve land cover predictions. We have developed a semi-supervised learning method based on the expectation-maximization (EM) algorithm, and maximum likelihood and maximum a posteriori classifiers. This scheme utilizes a small set of labeled and a large number of unlabeled training samples. We have conducted several experiments on multi-spectral images to understand the impact of unlabeled samples on the classification performance. Our study shows that though in general classification accuracy improves with the addition of unlabeled training samples, it is not guaranteed to get consistently higher accuracies unless sufficient care is exercised when designing a semi-supervised classifier


title: Early Maternal Influences on Stress Circuitry: Implications for Resilience and Susceptibility to Physical and Mental Disorders

abstract: Experiences in early life result in enduring and sometimes permanent differences in how the central nervous and endocrine systems function. These changes are referred to as developmental programing, and have numerous consequences for susceptibility to physical and mental disorders, which involve central nervous and endocrine systems. These effects may depend on genetic factors, but more importantly, involve epigenetic mechanisms, which can change the function of the gene independently of genotype. Environmentally induced epigenetic changes alter gene expression and function, and their outcome leads to adaptive or maladaptive expressions, depending on the specific environmental context of the individual (1). ::: ::: In this Frontiers Research Topic “Programing the hypothalamic pituitary adrenal (HPA) axis by early life experience: mechanisms of stress susceptibility and adaptation,” we have assembled a comprehensive selection of original research, mini reviews, and review articles that describe prenatal and postnatal maternal influences on stress response systems in offspring. This is an emerging and critical area in neuroendocrine science as it is becoming increasingly clear that much of the variance associated with biological alterations in physical and mental disorder may be accounted for by maternal history and experiences in the womb or in early postnatal life. While such influences have been broadly acknowledged, until recently, it has been difficult to measure the effects of these maternal influences on offspring. Epigenetic marks can be measured and provide a quantification of relevant early environmental influences. Carefully designed studies can then parse out the origin of early influences. ::: ::: In the opening paper of the volume, Daskalakis and Yehuda systematically review the methodologies employed to study epigenetic effects of early adversity (2). They focus on studies of the most studied genomic region in human stress-related diseases, the exon 1F promoter of the glucocorticoid receptor (GR) gene. The discussion is meant to present a series of issues that influence quantitative methylation analyses (method, tissue type, region of interest). It is important in an emerging field to discuss such issues since they can result in variable results from laboratory to laboratory. The authors make the important point that there may be site-specific accommodations of different adversity factors in different CpG sites within a promoter-based CpG island, leading to a greater complexity of data analysis and interpretation than is currently the standard. Finally, this review paper also includes unpublished data that extend the transgenerational effects previously observed in this region (3) by demonstrating site-specific effects based on maternal and/or paternal PTSD. ::: ::: After this focused review, two review papers provide an overview first of the effects of early adversity on major depression, as an example of a critical psychiatric outcome, and second on the effects of early adversity on metabolic outcomes. Hornung and Heim (4) summarize recent human studies implicating gene by early life stress interactions in vulnerability and resilience for depression. The authors chose the term “intermediate phenotype” throughout the paper in order to emphasize the mediational character of some phenotypic features, which result from gene by environment interactions and may lead to depressive symptoms. Maniam et al. (5) using animal and human data support that early adversity induced maladaptation of the HPA axis will have negative impact on energy metabolism in the presence of chronic stress or unfavorable metabolic conditions in later-life leading to vulnerability to metabolic diseases. To this end, authors have attempted to review the relevant literature, with attention paid to findings dealing with metabolic/body weight measures. They further propose a mechanism whereby the cross-talk between HPA axis and altered glucocorticoid (GC) metabolism in the liver (by 11-beta hydroxysteroid dehydrogenase Type 1) may likely mediate the metabolic outcomes of early life stress. ::: ::: Following these overviews, papers were divided according to the developmental time window in which adversity was present. Two original research articles describe the effects of prenatal maternal exposures to alcohol and GCs on stress-related neurocircuits. Raineki et al. (6) reveal complex sexual dimorphisms by providing a comprehensive (constrained principal component) analysis of the neural activity (by c-fos expression) in amygdala, hippocampus, medial prefrontal cortex, and paraventricular nucleus of hypothalamus in response to acute stress in male and female offspring of dams exposed to alcohol or two control conditions in gestation (gestation day 1–21), which where additionally exposed to a 10-day chronic mild stress paradigm in the peripubertal period. Borges et al. (7) find that prenatal maternal exposure to dexamethasone (gestation days 18 and 19) increased the activity of two regions that belong to the mesopontine cholinergic system (laterodorsal and pedunculopontine tegmental nuclei) associated with enhanced behavioral stress vulnerability (anxiety behavior including fear-associated ultrasonic calls). The review by de Kloet et al. (8) complements the Borges paper by focusing on the impact of perinatal GC exposure on the developing HPA axis and the balance between GR and mineralocorticoid receptor. The authors review animal and also clinical research on perinatal disturbances by GC administration and possible intervention strategies. ::: ::: Postnatal stress in rodents also exerts lasting effects on the stress system as well as on other brain systems. Daskalakis et al. (9) provide original data on strain-dependent differences in the immediate effects of maternal deprivation on the neonatal HPA axis response. While maternal deprivation causes a disruption of the neonatal stress hyporesponsive period in both mice strains studied, one strain was more affected calling for future studies on later-life consequences of early postnatal stress based on genetic background. Loi et al. (10) describe how hippocampal neurogenesis is affected by early life stress in rodents in an age- and sex-dependent manner, and provide novel data showing that normalization may be possible through GC-based interventions during critical stages of brain development. Brief treatment with mifepristone, a GR antagonist, on postnatal days 26–28 protected female rats against the effects of maternal deprivation (postnatal day 3) on neurogenesis markers in dentate gyrus. This finding is interesting because mifepristone is currently being tested in a clinical trial for post-traumatic stress disorder (11), a condition, which has also been linked with maternal PTSD (12), and childhood trauma (13). Loi et al. also speculate about the implications of findings in animal models of perinatal stress for the study of human brain development and vulnerability to psychopathology. Cinini et al. explore the effects of adversity in an early post-weaning period on hippocampal neurogenesis (14). The authors showed that social isolation in young (8–10 months old) non-human primates (marmosets) produced anxiety-like behaviors, elevated cortisol levels, and reduced neurogenesis. ::: ::: Long-term effects of perinatal stress experiences are partially mediated by mother–pup interactions (15). How these interactions influence offspring development is summarized in the mini-review by Lucion and Bortolini (16) where the special role of genes, epigenetics, imprinting, neurohormones, and learning is outlined. The review by Rincon-Cortes and Sullivan continues on the same theme (17). It is explained that the development of rat odor aversion develops after postnatal day 10, when maternal absence can increase GCs and subsequently activate a fear-related amygdala-dependent neurocircuit. The effects of maternal contact are also explored in the mini-review by Stamatakis et al., who summarize findings from their new model of neonatal learning (18). In order to investigate the effects of the early maternal environment, rat pups either receive the expected reward of maternal contact or are denied this reward on postnatal days 10–13. The two training conditions have differential consequences on brain activity, HPA axis function, cognition, and stress-related behavior. ::: ::: The final paper, a human study, by Bader et al. (19) explores transgenerational effects of parental trauma in offspring. The findings replicate and extend previous findings regarding urinary cortisol levels in Holocaust survivor offspring (20–22). Authors observed that the low cortisol levels in association with maternal PTSD are additionally associated with maternal age at Holocaust exposure (a parallel effect on cortisol in offspring that is not related to maternal PTSD). ::: ::: To conclude, the articles of this Research Topic have highlighted the importance of investigating the biological effects of early stressful factors of maternal origin on stress circuitry in order to comprehend resilience and susceptibility to physical and mental disorders. The introduction of modern methodologies (e.g., epigenetic analyses) can give traditional neurobiological findings (e.g., neurogenesis markers) a molecular understanding.


title: The anatomical basis of functional localization in the cortex

abstract: The functions of a cortical area are determined by its extrinsic connections and intrinsic properties. Using the database CoCoMac, we show that each cortical area has a unique pattern of corticocortical connections — a ‘connectional fingerprint’. We present examples of such fingerprints and use statistical analysis to show that no two areas share identical patterns. We suggest that the connectional fingerprint underlies the observed cell-firing differences between areas during different tasks. We refer to this pattern as a ‘functional fingerprint’ and present examples of such fingerprints. In addition to electrophysiological analysis, functional fingerprints can be determined by functional brain imaging. We argue that imaging provides a useful way to define such fingerprints because it is possible to compare activations across many cortical areas and across a wide range of tasks.


title: Security and Privacy in IoT Era

abstract: Trends in miniaturization have resulted in an explosion of small, low-power devices with network connectivity. Welcome to the era of Internet of Things (IoT), wearable devices, and automated home and industrial systems. These devices are loaded with sensors, collect information from their surroundings, process it, and relay it to remote locations for further analysis. Pervasive and seemingly harmless, this new breed of devices raise security and privacy concerns. In this chapter, we evaluate the security of these devices from an industry point of view, concentrating on the design flow, and catalogue the types of vulnerabilities we have found. We also present an in-depth evaluation of the Google Nest Thermostat, the Nike+ Fuelband SE Fitness Tracker, the Haier SmartCare home automation system, and the Itron Centron CL200 electric meter. We study and present an analysis of the effects of these compromised devices in an every day setting. We then finish by discussing design flow enhancements, with security mechanisms that can be efficiently added into a device in a comparative way.


Review of Energy Harvesting Technologies for Sustainable Wireless Sensor Network


title: The effects of rarity and abundance distributions on measurements of local morphological disparity

abstract: Understanding the relationships between morphological disparity and environment, geography, and scale require examination at the local level. Even with disparity metrics that are inherently sample size independent, the nature of rare species and the segregation of common and rare species within morphospace can create substantial sampling issues. Eight well-sampled, Late Ordovician crinoid assemblages were examined for potential biases in the study of local disparity. Disparity is based on the ordination of discrete characters. The rare and common species within these assemblages contributed equally to disparity. In spite of this pattern, rare species in some localities occupy a different area of morphospace, causing disparity to vary greatly with sampling intensity. Morphological rarefaction based on the number of specimens shows that disparity weighted by abundance is constant past a sample size of approximately 30 individuals. This metric is dependent on the evenness within an assemblage as well as the abundance within subgroups in morphospace. Disparity weighted according to abundance gives a view of the functional disparity of an assemblage, which is more applicable in studies of local disparity, though unweighted disparity is still preferred in regional-scale studies and in investigations of morphospace filling through a clade's history.


title: A.: Semi-supervised recognition of sarcastic sentences in Twitter and Amazon

abstract: Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an utterance is sarcastic or not. Recognition of sarcasm can benefit many sentiment analysis NLP applications, such as review summarization, dialogue systems and review ranking systems. ::: ::: In this paper we experiment with semi-supervised sarcasm identification on two very different data sets: a collection of 5.9 million tweets collected from Twitter, and a collection of 66000 product reviews from Amazon. Using the Mechanical Turk we created a gold standard sample in which each sentence was tagged by 3 annotators, obtaining F-scores of 0.78 on the product reviews dataset and 0.83 on the Twitter dataset. We discuss the differences between the datasets and how the algorithm uses them (e.g., for the Amazon dataset the algorithm makes use of structured information). We also discuss the utility of Twitter #sarcasm hashtags for the task.


title: Enhancing network management frameworks with SDN-like control

abstract: Traditional network management tools are too static and inflexible in today's enterprise and campus networks that have to support users with diversity of personal devices. Controller-based Software Defined Networking (SDN) paradigms are gaining popularity because of their promise to reduce the complexity of managing networks by automating the complex process of translating policy definitions to network provisioning and dynamic control. Though this approach provides fine-grained control over end-to-end network flows, they lack mature management and control tools. We propose an integrated network management and control system (i-NMCS) framework that combines legacy network management functions such as discovery, fault detection with the end-to-end flow provisioning and control enabled by SDN. We believe that such hybrid control will be key for incremental deployment of SDN functionality on today's networks.


title: Language Matters in Twitter : A Large Scale Study

abstract: Despite the widespread adoption of Twitter internationally, little research has investigated the differences among users of different languages. In prior research, the natural tendency has been to assume that the behaviors of English users generalize to other language users. We studied 62 million tweets collected over a four-week period and found that more than 100 languages were used. Only half of the tweets were in English (51%). Other popular languages including Japanese, Portuguese, Indonesian, and Spanish together accounted for 39% of the tweets. Examining users of the top 10 languages, we discovered cross-language differences in adoption of features such as URLs, hashtags, mentions, replies, and retweets. We discuss our work’s implications for research on large-scale social systems and design of cross-cultural communication tools.


title: How are campus students using social media to support their studies? An explorative interview study

abstract: Social media hype has created a lot of speculation among educators on how these media can be used to support learning, but there have been rather few studies so far. Our explorative interview study contributes by critically exploring how campus students perceive using social media to support their studies and the perceived benefits and limitations compared with other means. Although the vast majority of the respondents use social media frequently, a "digital dissonance" can be noted, because few of them feel that they use such media to support their studies. The interviewees mainly put forth e-mail and instant messaging, which are used among students to ask questions, coordinate group work and share files. Some of them mention using Wikipedia and YouTube for retrieving content and Facebook to initiate contact with course peers. Students regard social media as one of three key means of the educational experience, alongside face-to-face meetings and using the learning management systems, and are mainly used for brief questions and answers, and to coordinate group work. In conclusion, we argue that teaching strategy plays a key role in supporting students in moving from using social media to support coordination and information retrieval to also using such media for collaborative learning, when appropriate.


title: An intelligent load shedding (ILS) system application in a large industrial facility

abstract: Conventional methods of system load shedding are too slow and do not effectively calculate the correct amount of load to be shed. This results in either excessive or insufficient load reduction. In recent years, load shedding systems have been repackaged using conventional under-frequency relay and/or breaker interlocks schemes integrated with programmable logic controllers to give a new look to an antiquated load preservation methodology. A truly modern and intelligent load shedding system with a computerized power management system should provide fast and optimal load management by utilizing system topology and actual operating conditions tempered with knowledge of past system disturbances. This paper demonstrates the need for a modern load shedding scheme and introduces the new technology of intelligent load shedding. Comparisons of intelligent load shedding with conventional load shedding methods are made from perspectives of system design, system engineering, project implementation, and system operation. A case study of the application of an intelligent load shedding scheme in a large industrial facility is provided.


title: Predictive Control of a Three-Level Boost Converter and an NPC Inverter for High-Power PMSG-Based Medium Voltage Wind Energy Conversion Systems

abstract: In this paper, a new medium voltage power converter topology using a diode rectifier, three-level boost (TLB) converter, and neutral-point-clamped (NPC) inverter is proposed for a high-power permanent magnet synchronous generator-based wind energy conversion system. The generator-side TLB converter performs the maximum power point tracking and balancing of dc-link capacitor voltages, while the grid-side NPC inverter regulates the net dc-bus voltage and reactive power to the grid. A significant improvement in the grid power quality is accomplished as the NPC inverter no longer controls the dc-link neutral point voltage. A model predictive strategy is proposed to control the complete system where the discrete-time models of the proposed power electronic converters are used to predict the future behavior of control variables. These predictions are evaluated using two independent cost functions, and the switching states which minimize these cost functions are selected and applied to the generator- and grid-side converters directly. In order to comply with the high-power application, the switching frequencies of the TLB converter and NPC inverter are minimized and maintained below 1.5 and 1 kHz, respectively. The proposed topology and control strategy are verified through MATLAB simulations on a 3-MW/3000-V/577-A system and dSPACE DS1103-based experiments on 3.6-kW/208-V/10-A prototype.