title: DrugBank 4.0: shedding new light on drug metabolism

abstract: DrugBank (http://www.drugbank.ca) is a comprehensive online database containing extensive biochemical and pharmacological information about drugs, their mechanisms and their targets. Since it was first described in 2006, DrugBank has rapidly evolved, both in response to user requests and in response to changing trends in drug research and development. Previous versions of DrugBank have been widely used to facilitate drug and in silico drug target discovery. The latest update, DrugBank 4.0, has been further expanded to contain data on drug metabolism, absorption, distribution, metabolism, excretion and toxicity (ADMET) and other kinds of quantitative structure activity relationships (QSAR) information. These enhancements are intended to facilitate research in xenobiotic metabolism (both prediction and characterization), pharmacokinetics, pharmacodynamics and drug design/discovery. For this release, >1200 drug metabolites (including their structures, names, activity, abundance and other detailed data) have been added along with >1300 drug metabolism reactions (including metabolizing enzymes and reaction types) and dozens of drug metabolism pathways. Another 30 predicted or measured ADMET parameters have been added to each DrugCard, bringing the average number of quantitative ADMET values for Food and Drug Administration-approved drugs close to 40. Referential nuclear magnetic resonance and MS spectra have been added for almost 400 drugs as well as spectral and mass matching tools to facilitate compound identification. This expanded collection of drug information is complemented by a number of new or improved search tools, including one that provides a simple analyses of drug–target, –enzyme and –transporter associations to provide insight on drug–drug interactions.


title: Fake tweet buster: a webtool to identify users promoting fake news ontwitter

abstract: We present the "Fake Tweet Buster" (FTB), a web application that identifies tweets with fake images and users that are consistently uploading and/or promoting fake information on Twitter. To do that we mix three techniques: (i) reverse image searching, (ii) user analysis and (iii) a crowd sourcing approach to detected that kind of malicious users on Twitter. Using that information we provide a credibility classification for the tweet and the user.


title: Recent trends in the Internet of Things

abstract: The Internet of Things (IoT) has become a hot topic in the present tech-driven world. A strong framework of cloud computing, backed up by a seamless blending of sensors and actuators with the environment around us, is making this “network of networks of autonomous objects” a reality. From smart wearables to smart cities, from domestic life to industries, the IoT is expanding itself to different areas. According to Gartner Inc., the IoT will include 26 billion units installed by 2020. Smart security solutions, smart home automation, smart health care, smart wearables etc. are in-trend applications of IoT, and by the near future we expect to see its application to a city's transportation system or smart power grids. This paper presents a brief overview on different trends of the IoT and also discusses about the effects of the IoT on our day-to-day life. It also discusses the importance of cloud computing, autonomous control, artificial intelligence in the context of the IoT. Lastly, it's concluded with the need of synchronization of the Internet, wireless sensors and actuators and distributed computing for successfully enabling technologies for the IoT.


title: Consumerization of IT - where is the Theory?

abstract: Consumerization of IT (CoIT) is a key trend affecting society at large, including organizations of all kinds. A consensus about the defining aspects of CoIT has not yet been reached. Some refer to CoIT as employees bringing their own devices and technologies to work, while others highlight different aspects. While the debate about the nature and consequences of CoIT is still ongoing, many definitions have already been proposed. In this paper, we review these definitions and what is known about CoIT thus far. To guide future empirical research in this emerging area, we also review several established theories that have not yet been applied to CoIT but in our opinion have the potential to shed a deeper understanding on CoIT and its consequences. We discuss which elements of the reviewed theories are particularly relevant for understanding CoIT and thereby provide targeted guidance for future empirical research employing these theories. Overall, our paper may provide a useful starting point for addressing the lack of theorization in the emerging CoIT literature stream and stimulate discussion about theorizing CoIT.


title: Color retinal image enhancement using CLAHE

abstract: Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.


title: End-to-end differentiable learning of protein structure

abstract: Predicting protein structure from sequence is a central challenge of biochemistry. Co‐evolution methods show promise, but an explicit sequence‐to‐structure map remains elusive. Advances in deep learning that replace complex, human‐designed pipelines with differentiable models optimized end‐to‐end suggest the potential benefits of similarly reformulating structure prediction. Here we report the first end‐to‐end differentiable model of protein structure. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co‐evolutionary data and predicting known folds without structural templates. In the first task the model achieves state‐of‐the‐art accuracy and in the second it comes within 1‐2Å; competing methods using co‐evolution and experimental templates have been refined over many years and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.


title: Classification of Artistic Styles Using Binarized Features Derived from a Deep Neural Network

abstract: With the vast expansion of digital contemporary painting collections, automatic theme stylization has grown in demand in both academic and commercial fields. The recent interest in deep neural networks has provided powerful visual features that achieve state-of-the-art results in various visual classification tasks. In this work, we examine the perceptiveness of these features in identifying artistic styles in paintings, and suggest a compact binary representation of the paintings. Combined with the PiCoDes descriptors, these features show excellent classification results on a large scale collection of paintings.


Can We Trust Test Outcomes ?


title: The Optimization Research of Geo-Environmental Monitoring Image Fusion Based on Framelet

abstract: As various types of geo-environmental monitoring image instrument are sprouting up, there are more and more materials based on infrared, visible light, dim light, remote sensing image, using various types of image data to improve detection accuracy and efficiency of the environment has become a focus for environmental technical personnels. From the perspective of information fusion, as well as comparing framelet and the traditional wavelet features, the environmental image fusion algorithm based on framelet transform is proposed, by choosing suitable integration rules in different directions, the amount of information is further improved. The experiments show that this algorithm has better effects for small target detection with infrared and visible light images.


title: Can collective sentiment expressed on twitter predict political elections

abstract: Research examining the predictive power of social media (especially Twitter) displays conflicting results, particularly in the domain of political elections. This paper applies methods used in studies that have shown a direct correlation between volume/sentiment of Twitter chatter and future electoral results in a new dataset about political elections. We show that these methods display a series of shortcomings, that make them inadequate for determining whether social media messages can predict the outcome of elections.


title: Software security requirements management as an emerging cloud computing service

abstract: We present concise methods, techniques, and best practice requirements management guidelines on software security.Outlines features of the proposed requirements engineering and management as an emerging cloud service (SSREMaaES).Developed an Integrated-Secure SDLC model (IS-SDLC), which will benefit practitioners, researchers, learners, and educators.Application and process of using Microsoft SDLC for DDoS attacks on cloud services.Illustrates our approach for a large cloud system Amazon EC2 service. Emerging cloud applications are growing rapidly and the need for identifying and managing service requirements is also highly important and critical at present. Software Engineering and Information Systems has established techniques, methods and technology over two decades to help achieve cloud service requirements, design, development, and testing. However, due to the lack of understanding of software security vulnerabilities that should have been identified and managed during the requirements engineering phase, we have not been so successful in applying software engineering, information management, and requirements management principles that have been established for the past at least 25 years, when developing secure software systems. Therefore, software security cannot just be added after a system has been built and delivered to customers as seen in today's software applications. This paper provides concise methods, techniques, and best practice requirements engineering and management as an emerging cloud service (SSREMaaES) and also provides guidelines on software security as a service. This paper also discusses an Integrated-Secure SDLC model (IS-SDLC), which will benefit practitioners, researchers, learners, and educators. This paper illustrates our approach for a large cloud system Amazon EC2 service.


title: Intrinsic Plagiarism Detection Using Character n-gram Profiles

abstract: The task of intrinsic plagiarism detection deals with cases where no reference corpus is available and it is exclusively based on stylistic changes or inconsistencies within a given document. In this paper a new method is presented that attempts to quantify the style variation within a document using character n-gram profiles and a style change function based on an appropriate dissimilarity measure originally proposed for author identification. In addition, we propose a set of heuristic rules that attempt to detect plagiarism-free documents and plagiarized passages, as well as to reduce the effect of irrelevant style changes within a document. The proposed approach is evaluated on the recently-available corpus of the 1 st Int.


title: Personality Traits Recognition on Social Network - Facebook

abstract: For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naive Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.


title: Review of text-to-speech conversion for English.

abstract: The automatic conversion of English text to synthetic speech is presently being performed, remarkably well, by a number of laboratory systems and commercial devices. Progress in this area has been made possible by advances in linguistic theory, acoustic-phonetic characterization of English sound patterns, perceptual psychology, mathematical modeling of speech production, structured programming, and computer hardware design. This review traces the early work on the development of speech synthesizers, discovery of minimal acoustic cues for phonetic contrasts, evolution of phonemic rule programs, incorporation of prosodic rules, and formulation of techniques for text analysis. Examples of rules are used liberally to illustrate the state of the art. Many of the examples are taken from Klattalk, a text-to-speech system developed by the author. A number of scientific problems are identified that prevent current systems from achieving the goal of completely human-sounding speech. While the emphasis is on rule programs that drive a format synthesizer, alternatives such as articulatory synthesis and waveform concatenation are also reviewed. An extensive bibliography has been assembled to show both the breadth of synthesis activity and the wealth of phenomena covered by rules in the best of these programs. A recording of selected examples of the historical development of synthetic speech, enclosed as a 33 1/3-rpm record, is described in the Appendix.


Global-scale object detection using satellite imagery


title: Security Metrics for the Android Ecosystem

abstract: The security of Android depends on the timely delivery of updates to fix critical vulnerabilities. In this paper we map the complex network of players in the Android ecosystem who must collaborate to provide updates, and determine that inaction by some manufacturers and network operators means many handsets are vulnerable to critical vulnerabilities. We define the FUM security metric to rank the performance of device manufacturers and network operators, based on their provision of updates and exposure to critical vulnerabilities. Using a corpus of 20 400 devices we show that there is significant variability in the timely delivery of security updates across different device manufacturers and network operators. This provides a comparison point for purchasers and regulators to determine which device manufacturers and network operators provide security updates and which do not. We find that on average 87.7% of Android devices are exposed to at least one of 11 known critical vulnerabilities and, across the ecosystem as a whole, assign a FUM security score of 2.87 out of 10. In our data, Nexus devices do considerably better than average with a score of 5.17; and LG is the best manufacturer with a score of 3.97.


title: DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization

abstract: Cloud computing provides the scalable computation capability based on a virtualization technique. The energy conservation for green computing is one of the vital issues while allocating resources. To improve energy efficiency, the dynamic power-saving resource allocation (DPRA) mechanism based on a particle swarm optimization algorithm is proposed. The DPRA mechanism not only considers the energy consumption of physical machine (PM) and virtual machine (VM) but also newly tackles the energy efficiency ratio of air conditioner. Moreover, the least squares regression method is utilized to forecast PM's resource utilization for allocating VM and eliminating VM migrations. In simulation, the proposed DPRA mechanism is compared with three familiar allocation schemes and one previous solution. Simulation results show that in the presence of VM number, DPRA outperforms traditional schemes and previous solution in terms of total energy consumption (includes PMs and air conditioners), total electric bill, VM migration, and the number of shutdown PMs, chosen as objective performance metrics.


Techno-Economic Analysis of 5G Deployment Scenarios involving Massive MIMO HetNets over mmWave: A Case Study on the US State of Texas


title: Wideband dual-polarized magneto-electric miniaturization using capacitive loading

abstract: In this paper, an original method to reduce the height of a dual-polarized unidirectional wideband antenna based on two crossed magneto-electric dipoles is proposed. The miniaturization technique consists in adding a capacitive load between vertical plates. The height of the radiating element is reduced to 0.1λ0, where λ0 is the wavelength at the lowest operation frequency for a Standing Wave Ratio (SWR) <2.5, which corresponds to a reduction factor of 37.5%. The measured input impedance bandwidth is 64% from 1.6 GHz to 3.1 GHz with a SWR <2.5.


title: A Survey on Access Control Mechanisms in E-commerce Environments

abstract: With continuously growing numbers of applications, enterprises face the problem of efficiently managing the assignment of access permissions to their users. Access Control (AC) represents the process of mediating every request to services and data, maintained by a system and determining whether the requests should be granted or denied. The AC decision is enforced by a mechanism implementing regulations established by a security policy. Different AC policies can be applied, corresponding to different criteria for defining what should, and what should not be allowed. Over the past few years AC mechanisms have been deployed in diverse enterprises of all sizes. The aforementioned success has led to an abundance of available access control models corresponding to the special needs of every enterprise. In this paper we firstly attempt to stress the importance for every business that uses information systems to incorporate access control mechanisms in its production line. In the framework of investigating the problem of AC, studies have been held on specific issues relating to the configuration and the management of AC methods. We comprehensively study and classify the problem properly discovering and selecting AC mechanisms by reviewing recent research results and secondly analyze and identify the current AC approaches along with its several variants and the corresponding solution strategies. We highlight the advantages, the methods and techniques involved and the challenges of each approach. Finally, we analyze their influence on designing and implementing these approaches in e-commerce environments, discuss the limitations of existing methods and identify new areas of research that can lead to further enrichment of this field.


title: Markov Random Field-Based Statistical Character Structure Modeling for Handwritten Chinese Character Recognition

abstract: This paper proposes a statistical-structural character modeling method based on Markov random fields (MRFs) for handwritten Chinese character recognition (HCCR). The stroke relationships of a Chinese character reflect its structure, which can be statistically represented by the neighborhood system and clique potentials within the MRF framework. Based on the prior knowledge of character structures, we design the neighborhood system that accounts for the most important stroke relationships. We penalize the structurally mismatched stroke relationships with MRFs using the prior clique potentials and derive the likelihood clique potentials from Gaussian mixture models, which encode the large variations of stroke relationships statistically. In the proposed HCCR system, we use the single-site likelihood clique potentials to extract many candidate strokes from character images and use the pair-site clique potentials to determine the best structural match between the input candidate strokes and the MRF-based character models by relaxation labeling. The experiments on the Korea Advanced Institute of Science and Technology (KAIST) character database demonstrate that MRFs can statistically model character structures, and work well in the HCCR system.


title: Testing error handling code in device drivers using characteristic fault injection

abstract: Device drivers may encounter errors when communicating with OS kernel and hardware. However, error handling code often gets insufficient attention in driver development and testing, because these errors rarely occur in real execution. For this reason, many bugs are hidden in error handling code. Previous approaches for testing error handling code often neglect the characteristics of device drivers, so their efficiency and accuracy are limited. In this paper, we first study the source code of Linux drivers to find useful characteristics of error handling code. Then we use these characteristics in fault injection testing, and propose a novel approach named EH-Test, which can efficiently test error handling code in drivers. To improve the representativeness of injected faults, we design a pattern-based extraction strategy to automatically and accurately extract target functions which can actually fail and trigger error handling code. During execution, we use a monitor to record runtime information and pair checkers to check resource usages. We have evaluated EH-Test on 15 real Linux device drivers and found 50 new bugs in Linux 3.17.2. The code coverage is also effectively increased. Comparison experiments to previous related approaches also show the effectiveness of EH-Test.


title: Towards detection of bad habits by fusing smartphone and smartwatch sensors

abstract: Recently, there has been a growing interest in the research community about using wrist-worn devices, such as smartwatches for human activity recognition, since these devices are equipped with various sensors such as an accelerometer and a gyroscope. Similarly, smartphones are already being used for activity recognition. In this paper, we study the fusion of a wrist-worn device (smartwatch) and a smartphone for human activity recognition. We evaluate these two devices for their strengths and weaknesses in recognizing various daily physical activities. We use three classifiers to recognize 13 different activities, such as smoking, eating, typing, writing, drinking coffee, giving a talk, walking, jogging, biking, walking upstairs, walking downstairs, sitting, and standing. Some complex activities, such as smoking, eating, drinking coffee, giving a talk, writing, and typing cannot be recognized with a smartphone in the pocket position alone. We show that the combination of a smartwatch and a smartphone recognizes such activities with a reasonable accuracy. The recognition of such complex activities can enable well-being applications for detecting bad habits, such as smoking, missing a meal, and drinking too much coffee. We also show how to fuse information from these devices in an energy-efficient way by using low sampling rates. We make our dataset publicly available in order to make our work reproducible.


title: In pursuit of human-friendly interaction with a computational system: Computational humor

abstract: With AI celebrating its 60th anniversary, questions arise of when (not even if) a computational system will be able to understand humor. These questions open up interesting opportunities, but point out areas of research that yet are insufficient for informal human computer communication. This paper looks at computational humor as a way of verifying computational understanding of text (written or verbal). In particular, we treat ontology as a knowledge representation mechanism and natural language as a vehicle delivering this knowledge. A true ontology should provide a world model for the described domain, identifying its main concepts and tying them together with all relevant contentful properties. The question is how to get this model from text accurately? Assuming, as we do, that there is an accurate and unambiguous way of getting explicitly stated information from text, a lot of information is, in fact, implicit and yet crucial to the world model that we are creating. This implicit information has to be made explicit at the reasoning stage if we hope to come up with the results similar to human reasoning or understanding. In this paper, we will look at various ways, requiring optimal human-computer hybrid collaboration, in which ontology helps text understanding for humor processing, and text helps with dynamic ontology development. We hypothesize that such communication will be helpful for interaction with any computational system in a human-friendly way in general, and for robots in particular.


title: Hipster wars: Discovering elements of fashion styles

abstract: The clothing we wear and our identities are closely tied, revealing to the world clues about our wealth, occupation, and socio-identity. In this paper we examine questions related to what our clothing reveals about our personal style. We first design an online competitive Style Rating Game called Hipster Wars to crowd source reliable human judgments of style. We use this game to collect a new dataset of clothing outfits with associated style ratings for 5 style categories: hipster, bohemian, pinup, preppy, and goth. Next, we train models for between-class and within-class classification of styles. Finally, we explore methods to identify clothing elements that are generally discriminative for a style, and methods for identifying items in a particular outfit that may indicate a style.


title: Caffe2C: A Framework for Easy Implementation of CNN-based Mobile Applications

abstract: In this study, we create "Caffe2C" which converts CNN (Convolutional Neural Network) models trained with the existing CNN framework, Caffe, C-language source codes for mobile devices. Since Caffe2C generates a single C code which includes everything needed to execute the trained CNN, csCaffe2C makes it easy to run CNN-based applications on any kinds of mobile devices and embedding devices without GPUs. Moreover, Caffe2C achieves faster execution speed compared to the existing Caffe for iOS/Android and the OpenCV iOS/Android DNN class. The reasons are as follows: (1) directly converting of trained CNN models to C codes, (2) efficient use of NEON/BLAS with multithreading, and (3) performing pre-computation as much as possible in the computation of CNNs. In addition, in this paper, we demonstrate the availability of Caffe2C by showing four kinds of CNN-base object recognition mobile applications.


title: IntelliCare: An Eclectic, Skills-Based App Suite for the Treatment of Depression and Anxiety

abstract: Background: Digital mental health tools have tended to use psychoeducational strategies based on treatment orientations developed and validated outside of digital health. These features do not map well to the brief but frequent ways that people use mobile phones and mobile phone apps today. To address these challenges, we developed a suite of apps for depression and anxiety called IntelliCare, each developed with a focused goal and interactional style. IntelliCare apps prioritize interactive skills training over education and are designed for frequent but short interactions. Objective: The overall objective of this study was to pilot a coach-assisted version of IntelliCare and evaluate its use and efficacy at reducing symptoms of depression and anxiety. Methods: Participants, recruited through a health care system, Web-based and community advertising, and clinical research registries, were included in this single-arm trial if they had elevated symptoms of depression or anxiety. Participants had access to the 14 IntelliCare apps from Google Play and received 8 weeks of coaching on the use of IntelliCare. Coaching included an initial phone call plus 2 or more texts per week over the 8 weeks, with some participants receiving an additional brief phone call. Primary outcomes included the Patient Health Questionnaire-9 (PHQ-9) for depression and the Generalized Anxiety Disorder-7 (GAD-7) for anxiety. Participants were compensated up to US $90 for completing all assessments; compensation was not for app use or treatment engagement. Results: Of the 99 participants who initiated treatment, 90.1% (90/99) completed 8 weeks. Participants showed substantial reductions in the PHQ-9 and GAD-7 (P<.001). Participants used the apps an average of 195.4 (SD 141) times over the 8 weeks. The average length of use was 1.1 (SD 2.1) minutes, and 95% of participants downloaded 5 or more of the IntelliCare apps. Conclusions: This study supports the IntelliCare framework of providing a suite of skills-focused apps that can be used frequently and briefly to reduce symptoms of depression and anxiety. The IntelliCare system is elemental, allowing individual apps to be used or not used based on their effectiveness and utility, and it is eclectic, viewing treatment strategies as elements that can be applied as needed rather than adhering to a singular, overarching, theoretical model. Trial Registration: Clinicaltrials.gov NCT02176226; http://clinicaltrials.gov/ct2/show/NCT02176226 (Archived by WebCite at http://www.webcitation/6mQZuBGk1) [J Med Internet Res 2017;19(1):e10]


title: Experiential Robot Learning with Accelerated Neuroevolution

abstract: Derivative-based optimization techniques such as Stochastic Gradient Descent has been wildly successful in training deep neural networks. However, it has constraints such as end-to-end network differentiability. As an alternative, we present the Accelerated Neuroevolution algorithm. The new algorithm is aimed towards physical robotic learning tasks following the Experiential Robot Learning method. We test our algorithm first on a simulated task of playing the game Flappy Bird, then on a physical NAO robot in a static Object Centering task. The agents successfully navigate the given tasks, in a relatively low number of generations. Based on our results, we propose to use the algorithm in more complex tasks.


title: Anomalous Payload-Based Network Intrusion Detection

abstract: We present a payload-based anomaly detector, we call PAYL, for intrusion detection. PAYL models the normal application payload of network traffic in a fully automatic, unsupervised and very effecient fashion. We first compute during a training phase a profile byte frequency distribution and their standard deviation of the application payload flowing to a single host and port. We then use Mahalanobis distance during the detection phase to calculate the similarity of new data against the pre-computed profile. The detector compares this measure against a threshold and generates an alert when the distance of the new input exceeds this threshold. We demonstrate the surprising effectiveness of the method on the 1999 DARPA IDS dataset and a live dataset we collected on the Columbia CS department network. In once case nearly 100% accuracy is achieved with 0.1% false positive rate for port 80 traffic.


title: NEZHA: Efficient Domain-Independent Differential Testing

abstract: Differential testing uses similar programs as cross-referencing oracles to find semantic bugs that do not exhibit explicit erroneous behaviors like crashes or assertion failures. Unfortunately, existing differential testing tools are domain-specific and inefficient, requiring large numbers of test inputs to find a single bug. In this paper, we address these issues by designing and implementing NEZHA, an efficient input-format-agnostic differential testing framework. The key insight behind NEZHA's design is that current tools generate inputs by simply borrowing techniques designed for finding crash or memory corruption bugs in individual programs (e.g., maximizing code coverage). By contrast, NEZHA exploits the behavioral asymmetries between multiple test programs to focus on inputs that are more likely to trigger semantic bugs. We introduce the notion of δ-diversity, which summarizes the observed asymmetries between the behaviors of multiple test applications. Based on δ-diversity, we design two efficient domain-independent input generation mechanisms for differential testing, one gray-box and one black-box. We demonstrate that both of these input generation schemes are significantly more efficient than existing tools at finding semantic bugs in real-world, complex software. NEZHA's average rate of finding differences is 52 times and 27 times higher than that of Frankencerts and Mucerts, two popular domain-specific differential testing tools that check SSL/TLS certificate validation implementations, respectively. Moreover, performing differential testing with NEZHA results in 6 times more semantic bugs per tested input, compared to adapting state-of-the-art general-purpose fuzzers like American Fuzzy Lop (AFL) to differential testing by running them on individual test programs for input generation. NEZHA discovered 778 unique, previously unknown discrepancies across a wide variety of applications (ELF and XZ parsers, PDF viewers and SSL/TLS libraries), many of which constitute previously unknown critical security vulnerabilities. In particular, we found two critical evasion attacks against ClamAV, allowing arbitrary malicious ELF/XZ files to evade detection. The discrepancies NEZHA found in the X.509 certificate validation implementations of the tested SSL/TLS libraries range from mishandling certain types of KeyUsage extensions, to incorrect acceptance of specially crafted expired certificates, enabling man-in-the-middle attacks. All of our reported vulnerabilities have been confirmed and fixed within a week from the date of reporting.


title: BOPS, Not FLOPS! A New Metric and Roofline Performance Model For Datacenter Computing

abstract: The past decades witness FLOPS (Floating-point Operations per Second) as an important computation-centric performance metric. However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity, and the average FLOPS efficiency is only 0.1%, while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform). Furthermore, we reveal that the traditional FLOPS based Roofline performance model is not suitable for modern DC workloads, and gives misleading information for system optimization. These observations imply that FLOPS is inappropriate for evaluating DC computer systems. To address the above issue, we propose a new computation-centric metric BOPs (Basic OPerations) that measures the efficient work defined by the source code, includes floating-point operations and the arithmetic, logical, comparing, and array addressing parts of integer operations. We define BOPS as the average number of BOPs per second, and propose replacing FLOPS with BOPS to measure DC computer systems. On the basis of BOPS, we propose a new Roofline performance model for DC computing, which we call DC-Roofline model, with which we optimize DC workloads with the improvement varying from 119% to 325%.


title: Numerical Simulation and Fracture Test of Structural Steel Notched Tubes Based on Generalized Strength Model

abstract: Fracture tests were performed on 10 structural steel tubes with circular hole. Test results indicate that crack initiate at the edge of the hole and propagate perpendicularly to loading direction. The initial crack come into being earlier with the magnification of the radius of the hole. Tubes with two holes behave more ductile than those with one hole. The numerical procedure implemented the generalized hydrostatic yield model through the parametric design language (APDL) together with the User Programmable Features (UPFs) available in the general-purpose finite element package ANSYS. Numerical simulation results verify the precision of the generalized yield model and the validity of the subroutine. Numerical analysis results of the structural steel tubes with hole show that metal ellipsoidal fracture model could successfully predict the first crack of the steel tubes with circular holes.


title: A Higher-Order CRF Model for Road Network Extraction

abstract: The aim of this work is to extract the road network from aerial images. What makes the problem challenging is the complex structure of the prior: roads form a connected network of smooth, thin segments which meet at junctions and crossings. This type of a-priori knowledge is more difficult to turn into a tractable model than standard smoothness or co-occurrence assumptions. We develop a novel CRF formulation for road labeling, in which the prior is represented by higher-order cliques that connect sets of super pixels along straight line segments. These long-range cliques have asymmetric PN-potentials, which express a preference to assign all rather than just some of their constituent super pixels to the road class. Thus, the road likelihood is amplified for thin chains of super pixels, while the CRF is still amenable to optimization with graph cuts. Since the number of such cliques of arbitrary length is huge, we furthermore propose a sampling scheme which concentrates on those cliques which are most relevant for the optimization. In experiments on two different databases the model significantly improves both the per-pixel accuracy and the topological correctness of the extracted roads, and outperforms both a simple smoothness prior and heuristic rule-based road completion.


title: Low Profile Wideband Antenna Array With Hybrid Microstrip and Waveguide Feed Network for Ku Band Satellite Reception Systems

abstract: Wideband, planar and low profile antenna array is proposed and designed for Ku band direct broadcast from satellite reception for mobile systems. Contrary to earlier designs, the array antenna covers entire downlink Ku-band frequencies (10.8-12.75 GHz) for simultaneous reception from multiple transponders. The array antenna exhibits 16.5% bandwidth and has 30 dBi average gain for both vertical and horizontal polarizations. To achieve high efficiency, hybrid waveguide microstrip feed network is proposed. H-plane waveguide T-junctions with reduced waveguide height and disk loaded coaxial probe for microstrip to waveguide transition are designed. Loss in the array is formulated as an optimization problem and it is minimized for subarray and array configuration. Overall height of the planar antenna with waveguide feed network is under 25 mm. The antenna is built and tested for reception quality. Carrier-to-noise ratio of 9.5 dB was observed for the target transponder (42 °E) at 11.9 GHz using IF loop-out of the receiver.


title: Ambient belonging: How stereotypical cues impact gender participation in computer science.

abstract: People can make decisions to join a group based solely on exposure to that group’s physical environment. Four studies demonstrate that the gender difference in interest in computer science is influenced by exposure to environments associated with computer scientists. In Study 1, simply changing the objects in a computer science classroom from those considered stereotypical of computer science (e.g., Star Trek poster, video games) to objects not considered stereotypical of computer science (e.g., nature poster, phone books) was sufficient to boost female undergraduates’ interest in computer science to the level of their male peers. Further investigation revealed that the stereotypical broadcast a masculine stereotype that discouraged women’s sense of ambient belonging and subsequent interest in the environment (Studies 2, 3, and 4) but had no similar effect on men (Studies 3, 4). This masculine stereotype prevented women’s interest from developing even in environments entirely populated by other women (Study 2). Objects can thus come to broadcast stereotypes of a group, which in turn can deter people who do not identify with these stereotypes from joining that group.


title: Real-time 3D visualization of accurate specular reflections in curved mirrors a GPU implementation

abstract: This paper presents a vertex-based solution for rendering real-time accurate reflections in quadric mirrors in dynamic scenes using CUDA and OpenGL. Our method, based on forward projection, exploits the global information of the vertices and textures as they are computed from its original positions, to their reflections points in the mirror, and finally, to the eye. This solution does not suffer from parallax or visibility issues, neither does it needs to deal with ray intersection. As viewers navigate through the scene, the reflection points are instantly recalculated, depending on the position of the camera. Thus, given a 3D scene, this method gathers all vertex, light, and texture information and computes them at every instance, finding the reflection points and rendering the reflections on the mirror surface. We also demonstrate the accuracy and performance of our method by rendering two sample scenes.


title: Usability Dimensions for Mobile Applications-A Review

abstract: Usability has been increasingly recognized as a significant quality dimension to determine the success of mobile applications. Due to its importance, a number of usability guidelines have been proposed to direct the design of usable applications. The guidelines are intended particularly for desktop and web-based applications. Mobile applications on the other hand are different in many ways from those applications due to the mobility nature of mobile devices. To date, the usability guidelines for mobile applications are very limited. They in fact are isolated, which makes usability evaluation for mobile devices more difficult. This study aims to address this issue by proposing a set of usability dimensions that should be considered for designing and evaluating mobile applications. The dimensions are illustrated as a model that considers four contextual factors: user, environment, technology and task/activity. The model was proposed based on the reviews of previous related studies, which were analyzed by using content analysis approach. Twenty-five dimensions were found from the analysis. The dimensions however were synthesized and prioritized based on their importance towards designing usable mobile applications. As a result, ten most important dimensions were outlined in the model. The model can be used by practitioners and researchers as a guideline to design usable mobile applications and further research can be conducted in the near future.


title: Neurobiology of dyslexia

abstract: Dyslexia is one of the most common learning disabilities, yet its brain basis and core causes are not yet fully understood. Neuroimaging methods, including structural and functional magnetic resonance imaging, diffusion tensor imaging, and electrophysiology, have significantly contributed to knowledge about the neurobiology of dyslexia. Recent studies have discovered brain differences before formal instruction that likely encourage or discourage learning to read effectively, distinguished between brain differences that likely reflect the etiology of dyslexia versus brain differences that are the consequences of variation in reading experience, and identified distinct neural networks associated with specific psychological factors that are associated with dyslexia.


title: An information fusion approach to recognizing microphone array speech in the CHiME-3 challenge based on a deep learning framework

abstract: We present an information fusion approach to robust recognition of microphone array speech for the recently launched 3rd CHiME Challenge. It is based on a deep learning framework with a large neural network consisting of subnets with different architectures. Multiple knowledge sources are integrated via an early fusion of normalized noisy features with different beamforming techniques, speech enhanced features, speaker related features, and other auxiliary features concatenated as the input to each subnet, and a late fusion by combining the outputs of all subnets to produce one single output set. Our experiments demonstrate that all information sources are complementary in our proposed framework. Our best system achieves an average word error rate reduction of 68% from the officially released baseline results on the test set of real data.


title: Robust face recognition based on weighted DeepFace

abstract: In this paper, we present a new face recognition algorithm based on weighted deep face learning. Our proposed method composes of two steps: face detection and face feature extraction. The aim of face detection is to find an accurate face position. The face alignment is then applied by finding the facial landmarks in the face rectangle. With the help of face alignment the error rate of face recognition can be reduced. Deep learning is employed to extract distinctive features of the face components. We create the weights for each face feature by optimizing the within class variations with respect to between class similarity measures. We achieved the lowest total error rate of 0.01429% on XM2VTS database. We also accomplished the 98.61% of accuracy on LFW database. For realtime face recognition we achieved 99.17% for our own video database.


Microfluidic Organ/Body-on-a-Chip Devices at the Convergence of Biology and Microengineering


title: Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

abstract: Recovering a low-rank tensor from incomplete information is a recurring problem in signal processing and machine learning. The most popular convex relaxation of this problem minimizes the sum of the nuclear norms of the unfoldings of the tensor. We show that this approach can be substantially suboptimal: reliably recovering a $K$-way tensor of length $n$ and Tucker rank $r$ from Gaussian measurements requires $\Omega(r n^{K-1})$ observations. In contrast, a certain (intractable) nonconvex formulation needs only $O(r^K + nrK)$ observations. We introduce a very simple, new convex relaxation, which partially bridges this gap. Our new formulation succeeds with $O(r^{\lfloor K/2 \rfloor}n^{\lceil K/2 \rceil})$ observations. While these results pertain to Gaussian measurements, simulations strongly suggest that the new norm also outperforms the sum of nuclear norms for tensor completion from a random subset of entries. Our lower bound for the sum-of-nuclear-norms model follows from a new result on recovering signals with multiple sparse structures (e.g. sparse, low rank), which perhaps surprisingly demonstrates the significant suboptimality of the commonly used recovery approach via minimizing the sum of individual sparsity inducing norms (e.g. $l_1$, nuclear norm). Our new formulation for low-rank tensor recovery however opens the possibility in reducing the sample complexity by exploiting several structures jointly.


title: Dynamic Pricing by Multiagent Reinforcement Learning

abstract: Dynamic pricing in electronic marketplaces is a basic problem in electronic commercial. In multiagent environments, the optimal pricing policy of agent depends on the pricing policies of other agents. This makes the learning problem more problematic. This paper proposes an efficient online learning algorithm, which integrates the observed objective actions as well as the subjective inferential intention of the opponents. By establishing the decision model of other agents and predicting their proposed price in advance, agent becomes adaptive to its opponents and can make good decisions in long terms. The algorithm is proven to be effective when coming to the problem of seller's pricing in electronic marketplaces.


title: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding

abstract: We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making. Our contribution is a practical system which is able to predict pixel-wise class labels with a measure of model uncertainty. We achieve this by Monte Carlo sampling with dropout at test time to generate a posterior distribution of pixel class labels. In addition, we show that modelling uncertainty improves segmentation performance by 2-3% across a number of state of the art architectures such as SegNet, FCN and Dilation Network, with no additional parametrisation. We also observe a significant improvement in performance for smaller datasets where modelling uncertainty is more effective. We benchmark Bayesian SegNet on the indoor SUN Scene Understanding and outdoor CamVid driving scenes datasets.


title: HPS: High precision stemmer

abstract: Abstract Research into unsupervised ways of stemming has resulted, in the past few years, in the development of methods that are reliable and perform well. Our approach further shifts the boundaries of the state of the art by providing more accurate stemming results. The idea of the approach consists in building a stemmer in two stages. In the first stage, a stemming algorithm based upon clustering, which exploits the lexical and semantic information of words, is used to prepare large-scale training data for the second-stage algorithm. The second-stage algorithm uses a maximum entropy classifier. The stemming-specific features help the classifier decide when and how to stem a particular word. In our research, we have pursued the goal of creating a multi-purpose stemming tool. Its design opens up possibilities of solving non-traditional tasks such as approximating lemmas or improving language modeling. However, we still aim at very good results in the traditional task of information retrieval. The conducted tests reveal exceptional performance in all the above mentioned tasks. Our stemming method is compared with three state-of-the-art statistical algorithms and one rule-based algorithm. We used corpora in the Czech, Slovak, Polish, Hungarian, Spanish and English languages. In the tests, our algorithm excels in stemming previously unseen words (the words that are not present in the training set). Moreover, it was discovered that our approach demands very little text data for training when compared with competing unsupervised algorithms.


title: Max-value Entropy Search for Efficient Bayesian Optimization

abstract: Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the $\arg\max$ of the unknown function; yet, both are plagued by the expensive computation for estimating entropies. We propose a new criterion, Max-value Entropy Search (MES), that instead uses the information about the maximum function value. We show relations of MES to other Bayesian optimization methods, and establish a regret bound. We observe that MES maintains or improves the good empirical performance of ES/PES, while tremendously lightening the computational burden. In particular, MES is much more robust to the number of samples used for computing the entropy, and hence more efficient for higher dimensional problems.


title: Uncertainty and Exploration in a Restless Bandit Problem

abstract: Decision making in noisy and changing environments requires a fine balance between exploiting knowledge about good courses of action and exploring the environment in order to improve upon this knowledge. We present an experiment on a restless bandit task in which participants made repeated choices between options for which the average rewards changed over time. Comparing a number of computational models of participants' behavior in this task, we find evidence that a substantial number of them balanced exploration and exploitation by considering the probability that an option offers the maximum reward out of all the available options. Language: en


title: Prosocial Behavior during Adolescence

abstract: Prosocial behaviors are an aspect of adolescents' positive development that has gained greater attention in the developmental literature since the 1990s. In this article, the authors review the literature pertaining to prosocial behaviors during adolescence. The authors begin by defining prosocial behaviors as prior theory and empirical studies have done. They describe antecedents to adolescents' prosocial behaviors with a focus on two primary factors: socialization and cultural orientations . Accordingly, the authors review prior literature on prosocial behaviors among different ethnic/cultural groups throughout this article. As limited studies have examined prosocial behaviors among some specific ethnic groups, the authors conclude with recommendations for future research.


title: cite2vec: Citation-Driven Document Exploration via Word Embeddings

abstract: Effectively exploring and browsing document collections is a fundamental problem in visualization. Traditionally, document visualization is based on a data model that represents each document as the set of its comprised words, effectively characterizing what the document is. In this paper we take an alternative perspective: motivated by the manner in which users search documents in the research process, we aim to visualize documents via their usage, or how documents tend to be used. We present a new visualization scheme — cite2vec — that allows the user to dynamically explore and browse documents via how other documents use them, information that we capture through citation contexts in a document collection. Starting from a usage-oriented word-document 2D projection, the user can dynamically steer document projections by prescribing semantic concepts, both in the form of phrase/document compositions and document:phrase analogies, enabling the exploration and comparison of documents by their use. The user interactions are enabled by a joint representation of words and documents in a common high-dimensional embedding space where user-specified concepts correspond to linear operations of word and document vectors. Our case studies, centered around a large document corpus of computer vision research papers, highlight the potential for usage-based document visualization.


title: Query Expansion in Information Retrieval Systems using a Bayesian Network-Based Thesaurus

abstract: Information Retrieval (IR) is concerned with the identification of documents in a collection that are relevant to a given information need, usually represented as a query containing terms or keywords, which are supposed to be a good description of what the user is looking for. IR systems may improve their effectiveness (i.e., increasing the number of relevant documents retrieved) by using a process of query expansion, which automatically adds new terms to the original query posed by an user. In this paper we develop a method of query expansion based on Bayesian networks. IJsing a learning algorithm, we construct a Bayesian network that represents some of the relationships among the terms appearing in a given document collection; this network is then used as a thesaurus (specific for that collection). We also report the results obtained by our method on three standard test collections.


title: UMDFaces: An annotated face dataset for training deep networks

abstract: Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress. ::: In this paper we introduce a new face dataset, called UMDFaces, which has 367,888 annotated faces of 8,277 subjects. We also introduce a new face recognition evaluation protocol which will help advance the state-of-the-art in this area. We discuss how a large dataset can be collected and annotated using human annotators and deep networks. We provide human curated bounding boxes for faces. We also provide estimated pose (roll, pitch and yaw), locations of twenty-one key-points and gender information generated by a pre-trained neural network. In addition, the quality of keypoint annotations has been verified by humans for about 115,000 images. Finally, we compare the quality of the dataset with other publicly available face datasets at similar scales.


title: Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison

abstract: Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRQ global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data.


[Evidence for a non-specific immunostimulating effect on anti-rabies vaccine of the Fermi type on the Klebsiella pneumoniae system - mice].


title: A recurrent neural network based health indicator for remaining useful life prediction of bearings

abstract: In data-driven prognostic methods, prediction accuracy of bearing remaining useful life (RUL) mainly depends on the performance of bearing health indicators, which are usually fused from some statistical features extracted from vibration signals. However, many existing bearing health indicators have the following two shortcomings: (1) many statistical features do not have equal contribution to construction of health indicators since the ranges of these statistical features are different; (2) it is difficult to determine a failure threshold since health indicators of different machines are generally different at a failure time. To overcome these drawbacks, a recurrent neural network based health indicator (RNN-HI) for RUL prediction of bearings is proposed in this paper. Firstly, six related-similarity features are proposed to be combined with eight classical time-frequency features so as to form an original feature set. Then, with monotonicity and correlation metrics, the most sensitive features are selected from the original feature set. Finally, these selected features are fed into a recurrent neural network to construct the RNN-HI. The performance of the RNN-HI is verified by two bearing data sets collected from experiments and an industrial field. The results show that the RNN-HI obtains fairly high monotonicity and correlation values and it is beneficial to bearing RUL prediction. In addition, it is experimentally demonstrated that the proposed RNN-HI is able to achieve better performance than a self organization map based method.


title: Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels

abstract: We address the problem of novelty detection in multiclass scenarios where some class labels are missing from the training set. Our method is based on the initial assignment of confidence values, which measure the affinity between a new test point and each known class. We first compare the values of the two top elements in this vector of confidence values. In the heart of our method lies the training of an ensemble of classifiers, each trained to discriminate known from novel classes based on some partition of the training data into presumed-known and presumednovel classes. Our final novelty score is derived from the output of this ensemble of classifiers. We evaluated our method on two datasets of images containing a relatively large number of classes the Caltech-256 and Cifar-100 datasets. We compared our method to 3 alternative methods which represent commonly used approaches, including the one-class SVM, novelty based on k-NN, novelty based on maximal confidence, and the recent KNFST method. The results show a very clear and marked advantage for our method over all alternative methods, in an experimental setup where class labels are missing during training.


title: Edge Detection by Helmholtz Principle

abstract: We apply to edge detection a recently introduced method for computing geometric structures in a digital image, without any a priori information. According to a basic principle of perception due to Helmholtz, an observed geometric structure is perceptually “meaningful” if its number of occurences would be very small in a random situation: in this context, geometric structures are characterized as large deviations from randomness. This leads us to define and compute edges and boundaries (closed edges) in an image by a parameter-free method. Maximal detectable boundaries and edges are defined, computed, and the results compared with the ones obtained by classical algorithms.


title: P2V and V2P communication for Pedestrian warning on the basis of Autonomous Vehicles

abstract: The use of smartphones in a road context by drivers and Vulnerable Road Users (VRU) is rapidly increasing. To reduce the risks related to the influence of smartphone usage in a situation where traffic needs to be considered, a collision prediction algorithm is proposed based on Pedestrian to Vehicle (P2V) and Vehicle to Pedestrian (V2P) communication technologies, which increases the visual situational awareness of VRU regarding the nearby location of both autonomous and manually-controlled vehicles in a user-friendly form. The proposed application broadcasts the device's position to the vehicles nearby, and reciprocally, the vehicles nearby broadcast their position to the device in use, supporting pedestrians and other VRU to minimize potential dangers and increase the acceptance of autonomous vehicles on our roads. Results regarding the evaluation of the proposed approach showed a good performance and high detection rate, as well as a high user satisfaction derived from the interaction with the system.


title: Arabic NLP tools for ontology construction from Arabic text: An overview

abstract: Natural Language Processing (NLP) is a technique used to extract data in natural language text that is used in many applications. In the last years, NLP techniques have been adapted for ontology construction and population. For example, Part-of-Speech (POS) tagging, filtering, lexical semantics tagging or tagged named entities used to extract concepts and relationships among entities. The effect of the NLP tools on ontology construction from Arabic text is an area of research. However, to the best of our knowledge, there are no comparative studies that show the effectiveness of these preprocessing techniques. The main goal of this paper is to present a brief review for the existing Arabic NLP tools and assess their capabilities on Arabic ontology construction.


title: The challenge problem for automated detection of 101 semantic concepts in multimedia

abstract: We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously offering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that inuence video indexing performance, the challenge offers to the research community a manually annotated concept lexicon, pre-computed low-level multimedia features, trained classifier models, and five experiments together with baseline performance, which are all available at http://www.mediamill.nl/challenge/.


title: Multiple-Attribute Text Style Transfer

abstract: The dominant approach to unsupervised"style transfer"in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its"style". In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.


title: Character Recognition Systems: A Guide for Students and Practitioners

abstract: Figures. List of Tables. Preface. Acknowledgments. Acronyms. 1. Introduction: Character Recognition, Evolution and Development. 1.1 Generation and Recognition of Characters. 1.2 History of OCR. 1.3 Development of New Techniques. 1.4 Recent Trends and Movements. 1.5 Organization of the Remaining Chapters. References. 2. Tools for Image Pre-Processing. 2.1 Generic Form Processing System. 2.2 A Stroke Model for Complex Background Elimination. 2.2.1 Global Gray Level Thresholding. 2.2.2 Local Gray Level Thresholding. 2.2.3 Local Feature Thresholding-Stroke Based Model. 2.2.4 Choosing the Most Efficient Character Extraction Method. 2.2.5 Cleaning up Form Items Using Stroke Based Model. 2.3 A Scale-Space Approach for Visual Data Extraction. 2.3.1 Image Regularization. 2.3.2 Data Extraction. 2.3.3 Concluding Remarks. 2.4 Data Pre-Processing. 2.4.1 Smoothing and Noise Removal. 2.4.2 Skew Detection and Correction. 2.4.3 Slant Correction. 2.4.4 Character Normalization. 2.4.5 Contour Tracing/Analysis. 2.4.6 Thinning. 2.5 Chapter Summary. References 72. 3. Feature Extraction, Selection and Creation. 3.1 Feature Extraction. 3.1.1 Moments. 3.1.2 Histogram. 3.1.3 Direction Features. 3.1.4 Image Registration. 3.1.5 Hough Transform. 3.1.6 Line-Based Representation. 3.1.7 Fourier Descriptors. 3.1.8 Shape Approximation. 3.1.9 Topological Features. 3.1.10 Linear Transforms. 3.1.11 Kernels. 3.2 Feature Selection for Pattern Classification. 3.2.1 Review of Feature Selection Methods. 3.3 Feature Creation for Pattern Classification. 3.3.1 Categories of Feature Creation. 3.3.2 Review of Feature Creation Methods. 3.3.3 Future Trends. 3.4 Chapter Summary. References. 4. Pattern Classification Methods. 4.1 Overview of Classification Methods. 4.2 Statistical Methods. 4.2.1 Bayes Decision Theory. 4.2.2 Parametric Methods. 4.2.3 Non-ParametricMethods. 4.3 Artificial Neural Networks. 4.3.1 Single-Layer Neural Network. 4.3.2 Multilayer Perceptron. 4.3.3 Radial Basis Function Network. 4.3.4 Polynomial Network. 4.3.5 Unsupervised Learning. 4.3.6 Learning Vector Quantization. 4.4 Support Vector Machines. 4.4.1 Maximal Margin Classifier. 4.4.2 Soft Margin and Kernels. 4.4.3 Implementation Issues. 4.5 Structural Pattern Recognition. 4.5.1 Attributed String Matching. 4.5.2 Attributed Graph Matching. 4.6 Combining Multiple Classifiers. 4.6.1 Problem Formulation. 4.6.2 Combining Discrete Outputs. 4.6.3 Combining Continuous Outputs. 4.6.4 Dynamic Classifier Selection. 4.6.5 Ensemble Generation. 4.7 A Concrete Example. 4.8 Chapter Summary. References. 5. Word and String Recognition. 5.1 Introduction. 5.2 Character Segmentation. 5.2.1 Overview of Dissection Techniques. 5.2.2 Segmentation of Handwritten Digits. 5.3 Classification-Based String Recognition. 5.3.1 String Classification Model. 5.3.2 Classifier Design for String Recognition. 5.3.3 Search Strategies. 5.3.4 Strategies for Large Vocabulary. 5.4 HMM-Based Recognition. 5.4.1 Introduction to HMMs. 5.4.2 Theory and Implementation. 5.4.3 Application of HMMs to Text Recognition. 5.4.4 Implementation Issues. 5.4.5 Techniques for Improving HMMs' Performance. 5.4.6 Summary to HMM-Based Recognition. 5.5 Holistic Methods For Handwritten Word Recognition. 5.5.1 Introduction to Holistic Methods. 5.5.2 Overview of Holistic Methods. 5.5.3 Summary to Holistic Methods. 5.6 Chapter Summary. References. 6. Case Studies. 6.1 Automatically Generating Pattern Recognizers with Evolutionary Computation. 6.1.1 Motivation. 6.1.2 Introduction. 6.1.3 Hunters and Prey. 6.1.4 Genetic Algorithm. 6.1.5 Experiments. 6.1.6 Analysis. 6.1.7 Future Directions. 6.2 Offline Handwritten Chinese Character Recognition. 6.2.1 Related Works. 6.2.2 System Overview. 6.2.3 Character Normalization. 6.2.4 Direction Feature Extraction. 6.2.5 Classification Methods. 6.2.6 Experiments. 6.2.7 Concluding Remarks. 6.3 Segmentation and Recognition of Handwritten Dates on Canadian Bank Cheques. 6.3.1 Introduction. 6.3.2 System Architecture. 6.3.3 Date Image Segmentation. 6.3.4 Date Image Recognition. 6.3.5 Experimental Results. 6.3.6 Concluding Remarks. References.


title: Detecting DDoS attacks with Hadoop

abstract: Recent distributed denial-of-service (DDoS) attacks have demonstrated horrible destructive power by paralyzing web servers within short time. As the volume of Internet traffic rapidly grows up, the current DDoS detection technologies have met a new challenge that should efficiently deal with a huge amount of traffic within the affordable response time. In this work, we propose a novel DDoS detection method based on Hadoop that implements a HTTP GET flooding detection algorithm in MapReduce on the distributed computing platform.


title: The transformative potential of social innovation. The case of wheat and bread value chain in Tuscany

abstract: The complexity of the transition to more sustainable food systems has taken multiple trajectories, differently driven and oriented. In this scenario, innovation pathways promoted at the grassroots level and based on collective action driven by social purposes appear to carry a strong transformative capacity. Considered as expressions of social innovation, their study has been approached through different theoretical frameworks. By referring to some of these, and in particular to the developments of transition theories, we analyse the innovation pathways involving the wheat-bread value chain in Tuscany (Italy). The analysis sheds light on the relevance of the nature of social innovation carried out by grassroots initiatives in their pursuing radical change aimed at deeply redefining production-consumption practices through social interaction, to meet socially shared needs and achieving several social benefits. The paper also analyses the factors and mechanisms underlying the change processes this innovation has triggered in the mainstream system, focusing on the so-called “anchoring” process. Through this analysis, the work aims at improving the understanding of the transformative potential of social innovation.


Development and Validation of an Occupational Skills Assessment Instrument


title: A Robust Bayesian Truth Serum for Small Populations

abstract: Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n ≥ 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n ≥ 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n ≥ 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.


title: Managing risk in software process improvement: an action research approach

abstract: Many software organizations engage in software process improvement (SPI) initiatives to increase their capability to develop quality solutions at a competitive level. Such efforts, however, are complex and very demanding. A variety of risks makes it difficult to develop and implement new processes. ::: ::: We studied SPI in its organizational context through collaborative practice research (CPR), a particular form of action research. The CPR program involved close collaboration between practitioners and researchers over a three-year period to understand and improve SPI initiatives in four Danish software organizations. The problem of understanding and managing risks in SPI teams emerged in one of the participating organizations and led to this research. We draw upon insights from the literature on SPI and software risk management as well as practical lessons learned from managing SPI risks in the participating software organizations. ::: ::: Our research offers two contributions. First, we contribute to knowledge on SPI by proposing an approach to understand and manage risks in SPI teams. This risk management approach consists of a framework for understanding risk areas and risk resolution strategies within SPI and a related process for managing SPI risks. Second, we contribute to knowledge on risk management within the information systems and software engineering disciplines. We propose an approach to tailor risk management to specific contexts. This approach consists of a framework for understanding and choosing between different forms of risk management and a process to tailor risk management to specific contexts.


title: Question Classification using Head Words and their Hypernyms

abstract: Question classification plays an important role in question answering. Features are the key to obtain an accurate question classifier. In contrast to Li and Roth (2002)'s approach which makes use of very rich feature space, we propose a compact yet effective feature set. In particular, we propose head word feature and present two approaches to augment semantic features of such head words using WordNet. In addition, Lesk's word sense disambiguation (WSD) algorithm is adapted and the depth of hypernym feature is optimized. With further augment of other standard features such as unigrams, our linear SVM and Maximum Entropy (ME) models reach the accuracy of 89.2% and 89.0% respectively over a standard benchmark dataset, which outperform the best previously reported accuracy of 86.2%.


title: IoT Security Development Framework for building trustworthy Smart car services

abstract: The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect Smart cars, buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. However, with the introduction of IoT, we will be experiencing grand challenges to secure and protect its advanced information services due to the significant increase of the attack surface, complexity, heterogeneity and number of interconnected resources. In this paper, we present an IoT Security Development Framework (ISDF) to build trustworthy and highly secure applications and services. The ISDF enables developers to consider security issues at all IoT layers and integrate security algorithms with the functions and services offered in each layer rather than considering security in an ad-hoc and after thought manner. We also show how this framework can be used to develop highly secure and trustworthy Vehicle Information and management Portal (VIMP) services and how to apply our Anomaly Behavior Analysis (ABA) methodology to secure and protect these services against any type of attacks.


title: A survey: Attacks on RPL and 6LoWPAN in IoT

abstract: 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard allows heavily constrained devices to connect to IPv6 networks. 6LoWPAN is novel IPv6 header compression protocol, it may go easily under attack. Internet of Things consist of devices which are limited in resource like battery powered, memory and processing capability etc. for this a new network layer routing protocol is designed called RPL (Routing Protocol for low power Lossy network). RPL is light weight protocol and doesn't have the functionality like of traditional routing protocols. This rank based routing protocol may goes under attack. Providing security in Internet of Things is challenging as the devices are connected to the unsecured Internet, limited resources, the communication links are lossy and set of novel technologies used such as RPL, 6LoWPAN etc. This paper focus on possible attacks on RPL and 6LoWPAN network, counter measure against them and consequences on network parameters. Along with comparative analysis of methods to mitigate these attacks are done and finally the research opportunities in network layer security are discussed.


title: Flexible Construction of Executable Service Compositions from Reusable Semantic Knowledge

abstract: Most service composition approaches rely on top-down decomposition of a problem and AI-style planning to assemble service components into a meaningful whole, impeding reuse and flexibility. In this article, we propose an approach that starts from declarative knowledge about the semantics of individual service components and algorithmically constructs a full-blown service orchestration process that supports sequence, choice, and parallelism. The output of our algorithm can be mapped directly into a number of service orchestration languages such as OWL-S and BPEL. The approach consists of two steps. First, semantic links specifying data dependencies among the services are derived and organized in a flexible network. Second, based on a user request indicating the desired outcomes from the composition, an executable composition is constructed from the network that satisfies the dependencies. The approach is unique in producing complex compositions out of semantic links between services in a flexible way. It also allows reusing knowledge about semantic dependencies in the network to generate new compositions through new requests and modification of services at runtime. The approach has been implemented in a prototype that outperforms related composition prototypes in experiments.


title: Offender profiling and criminal differentiation.

abstract: Purpose. The psychological hypotheses that form the foundations for ‘Offender Profiling’ are identified and the research that has tested them is reviewed. Argument. ‘Offender profiling’ is taken to be the derivation of inferences about a criminal from aspects of the crime(s) s/he has committed. For this process to move beyond deduction based on personal opinion and anecdote to an empirically based science a number of aspects of criminal activity need to be distinguished and examined. The notion of a hierarchy of criminal differentiation is introduced to highlight the need to search for consistencies and variations at many levels of that hierarchy. However, current research indicates that the key distinctions are those that differentiate, within classes of crime, between offences and between offenders,. This also leads to the hypothesis of a circular ordering of criminal actions, analogous to the colour circle, a ‘radex’. The radex model, tested using Multi-Dimensional Scaling (MDS) procedures, allows specific hypotheses to be developed about important constituents of criminal differentiation: Salience; MDS analyses reveal the importance of the frequency of criminal actions as the basis on which the significance of those actions can be established. Models of Differentiation; the research reviewed mainly supports distinctions between criminals in terms of the forms of their transactions with their explicit or implicit victims, Consistency; offenders have been shown to exhibit similar patterns of action on different occasions. The most reliable examples of this currently are in studies of the spatial behaviour of criminals. Inference; under limited conditions it is possible to show associations between the characteristics of offenders and the thematic focus of their crimes.


title: Estimation of Mental Fatigue During EEG Based Motor Imagery

abstract: Mental Fatigue is a cognitive state which is an outcome of labour or protracted exercise finally leading to downgrading of mental performance. This leads to reduction in efficiency and disinclination of motor skills. Analysis of mental fatigue thus becomes momentous for assessing one’s capability. The aim here is to analyse whether motor imagery is fatiguing. There are many parameters to evaluate fatigue. This study analyses parietal alpha and frontal theta in motor imagery tasks. Decrement of arousal level, working memory and information encoding have been proven to be associated with increased theta power. Increase in alpha power indicates increase in mental effort to maintain vigilance level. When a person experiences fatigue, their concentration, attention, focus and vigilance level decreases for which they need to put more attention which leads to increase in alpha power. We exploit these EEG oscillatory rhythm fluctuations to model EEG-fatigue relationships. A statistical classifier is used to model EEG-fatigue relationship accurately. With Kernel Partial Least Square output we track the growth of mental fatigue with time.


title: Recessed-gate structure approach toward normally off high-Voltage AlGaN/GaN HEMT for power electronics applications

abstract: A recessed-gate structure has been studied with a view to realizing normally off operation of high-voltage AlGaN/GaN high-electron mobility transistors (HEMTs) for power electronics applications. The recessed-gate structure is very attractive for realizing normally off high-voltage AlGaN/GaN HEMTs because the gate threshold voltage can be controlled by the etching depth of the recess without significant increase in on-resistance characteristics. With this structure the threshold voltage can be increased with the reduction of two-dimensional electron gas (2DEG) density only under the gate electrode without reduction of 2DEG density in the other channel regions such as the channel between drain and gate. The threshold-voltage increase was experimentally demonstrated. The threshold voltage of fabricated recessed-gate device increased to -0.14 V while the threshold voltage without the recessed-gate structure was about -4 V. The specific on-resistance of the device was maintained as low as 4 m/spl Omega//spl middot/cm/sup 2/ and the breakdown voltage was 435 V. The on-resistance and the breakdown voltage tradeoff characteristics were the same as those of normally on devices. From the viewpoint of device design, the on-resistance for the normally off device was modeled using the relationship between the AlGaN layer thickness under the gate electrode and the 2DEG density. It is found that the MIS gate structure and the recess etching without the offset region between recess edge and gate electrode will further improve the on-resistance. The simulation results show the possibility of the on-resistance below 1 m/spl Omega//spl middot/cm/sup 2/ for normally off AlGaN/GaN HEMTs operating at several hundred volts with threshold voltage up to +1 V.


title: Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures.

abstract: Many decisions in the lives of animals and humans require a fine balance between the exploration of different options and the exploitation of their rewards. Do you buy the advertised car, or do you test drive different models? Do you continue feeding from the current patch of flowers, or do you fly off to another one? Do you marry your current partner, or try your luck with someone else? The balance required in these situations is commonly referred to as the exploration– exploitation tradeoff. It features prominently in a wide range of research traditions, including learning, foraging, and decision making literatures. Here, we integrate findings from these and other often-isolated literatures in order to gain a better understand- ing of the possible tradeoffs between exploration and exploitation, and we propose new theoretical insights that might guide future research. Specifically, we explore how potential tradeoffs depend on (a) the conceptualization of exploration and exploitation; (b) the influencing environmental, social, and individual factors; (c) the scale at which exploration and exploitation are considered; (d) the relationship and types of transitions between the 2 behaviors; and (e) the goals of the decision maker. We conclude that exploration and exploitation are best conceptualized as points on a continuum, and that the extent to which an agent’s behavior can be interpreted as exploratory or exploitative depends upon the level of abstraction at which it is considered.


title: Classification of rice grain varieties using two artificial neural networks (MLP and neuro-fuzzy).

abstract: Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively.


title: CHERI: A Hybrid Capability-System Architecture for Scalable Software Compartmentalization

abstract: CHERI extends a conventional RISC Instruction-Set Architecture, compiler, and operating system to support fine-grained, capability-based memory protection to mitigate memory-related vulnerabilities in C-language TCBs. We describe how CHERI capabilities can also underpin a hardware-software object-capability model for application compartmentalization that can mitigate broader classes of attack. Prototyped as an extension to the open-source 64-bit BERI RISC FPGA soft-core processor, Free BSD operating system, and LLVM compiler, we demonstrate multiple orders-of-magnitude improvement in scalability, simplified programmability, and resulting tangible security benefits as compared to compartmentalization based on pure Memory-Management Unit (MMU) designs. We evaluate incrementally deployable CHERI-based compartmentalization using several real-world UNIX libraries and applications.


title: Design of rectangular patch antenna array with multiple slots by using mitered bend feed network for multi-band applications

abstract: The design of four element microstrip patch antenna array with rectangular slots is presented in this paper. This is simulated and analysed in detail by using Mitered bend feed network which is used in wireless application in ISM frequency bands. HFSS 13.0 is used to accomplish this work; besides, its fabrication is done by using photolithography technique, devised by “FR4-epoxy” substrate. VNAE507IC is devised to test patch. Improved Gain, Directivity and Bandwidth in contrast with declined Reflection coefficient and VSWR together with multiband frequency responses are the characteristics of slot antenna.


title: Lightning performance of transmission lines partially protected by surge arresters considering typical Brazilian conditions

abstract: This paper assess the efficiency of installing surge arresters only in one or two phases in terms of the lightning performance of TL. Taking as reference a typical 138-kV Brazilian transmission line, simulations are developed in ATP, considering a rigorous representation of the frequency-dependent behavior of tower-footing grounding. Preliminary results shown that considering moderate values of soil resistivity, a good design of grounding system may be sufficient to achieve the desired lightning performance of TL. In case of high-resistivity soils, the use of surge arresters may be necessary, and using surge arresters only in one or two phases does not guarantee the protection of the phase without SA, but decrease the probability of insulation breakdown occurrence in the tower. The methodology presented in this paper can be used to determine the optimal distribution of surge arresters along the line in order to achieve the desired lightning performance.


title: Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes

abstract: The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet. In consequence, we need curious and continuously learning algorithms that actively acquire knowledge about semantic concepts which are present in available unlabeled data. As a step towards this goal, we show how to perform continuous active learning and exploration, where an algorithm actively selects relevant batches of unlabeled examples for annotation. These examples could either belong to already known or to yet undiscovered classes. Our algorithm is based on a new generalization of the Expected Model Output Change principle for deep architectures and is especially tailored to deep neural networks. Furthermore, we show easy-to-implement approximations that yield efficient techniques for active selection. Empirical experiments show that our method outperforms currently used heuristics.


title: Robust lane marking detection under different road conditions

abstract: In this paper a new lane marking detection algorithm in different road conditions for monocular vision was proposed. Traditional detection algorithms implement the same operation for different road conditions. It is difficult to simultaneously satisfy the requirements of timesaving and robustness in different road conditions. Our algorithm divides the road conditions into two classes. One class is for the clean road, and the other one is for the road with disturbances such as shadows, non-lane markings and vehicles. Our algorithm has its advantages in clean road while has a robust detection of lane markings in complex road. On the remapping image obtained from inverse perspective transformation, a search strategy is used to judge whether pixels belong to the same lane marking. When disturbances appear on the road, this paper uses probabilistic Hough transform to detect lines, and finds out the true lane markings by use of their geometrical features. The experimental results have shown the robustness and accuracy of our algorithm with respect to shadows, changing illumination and non-lane markings.


title: ICORD: Intelligent Collection of Redundant Data — A Dynamic System for Crowdsourcing Cell Segmentations Accurately and Efficiently

abstract: Segmentation is a fundamental step in analyzing biological structures in microscopy images. When state-of-the-art automated methods are found to produce inaccurate boundaries, interactive segmentation can be effective. Since the inclusion of domain experts is typically expensive and does not scale, crowdsourcing has been considered. Due to concerns about the quality of crowd work, quality control methods that rely on a fixed number of redundant annotations have been used. We here introduce a collection strategy that dynamically assesses the quality of crowd work. We propose ICORD (Intelligent Collection Of Redundant annotation Data), a system that predicts the accuracy of a segmented region from analysis of (1) its geometric and intensity-based features and (2) the crowd worker's behavioral features. Based on this score, ICORD dynamically determines if the annotation accuracy is satisfactory or if a higher-quality annotation should be sought out in another round of crowdsourcing. We tested ICORD on phase contrast and fluorescence images of 270 cells. We compared the performance of ICORD and a popular baseline method for which we aggregated 1,350 crowd-drawn cell segmentations. Our results show that ICORD collects annotations both accurately and efficiently. Accuracy levels are within 3 percentage points of those of the baseline. More importantly, due to its dynamic nature, ICORD vastly outperforms the baseline method with respect to efficiency. ICORD only uses between 27% and 50% of the resources, i.e., collection time and cost, that the baseline method requires.


Random Acts of Pizza : Success Factors of Online Requests


title: Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder

abstract: We propose a method for using synthetic data to help learning classifiers. Synthetic data, even is generated based on real data, normally results in a shift from the distribution of real data in feature space. To bridge the gap between the real and synthetic data, and jointly learn from synthetic and real data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by suing MCAE, it is possible to learn a better feature representation for classification. To evaluate the proposed approach, we conduct experiments on two types of datasets. Experimental results on two datasets validate the efficiency of our MCAE model and our methodology of generating synthetic data.


title: Centrality in social networks conceptual clarification

abstract: Abstract The intuitive background for measures of structural centrality in social networks is reviewed and existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the centrality of positions in a network, and one reflecting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.


title: Life as we know it

abstract: This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model—and act on—their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.


title: Robust Biomechanical Model-Based 3-D Indoor Localization and Tracking Method Using UWB and IMU

abstract: This paper proposes a robust sensor fusion algorithm to accurately track the spatial location and motion of a human under various dynamic activities, such as walking, running, and jumping. The position accuracy of the indoor wireless positioning systems frequently suffers from non-line-of-sight and multipath effects, resulting in heavy-tailed outliers and signal outages. We address this problem by integrating the estimates from an ultra-wideband (UWB) system and inertial measurement units, but also taking advantage of the estimated velocity and height obtained from an aiding lower body biomechanical model. The proposed method is a cascaded Kalman filter-based algorithm where the orientation filter is cascaded with the robust position/velocity filter. The outliers are detected for individual measurements using the normalized innovation squared, where the measurement noise covariance is softly scaled to reduce its weight. The positioning accuracy is further improved with the Rauch–Tung–Striebel smoother. The proposed algorithm was validated against an optical motion tracking system for both slow (walking) and dynamic (running and jumping) activities performed in laboratory experiments. The results show that the proposed algorithm can maintain high accuracy for tracking the location of a subject in the presence of the outliers and UWB signal outages with a combined 3-D positioning error of less than 13 cm.