title: Lightweight Risk Management in Agile projects

abstract: Risk management in software engineering has become a recognized project management practice but it seems that not all companies are systematically applying it. At the same time, agile methods have become popular, partly because proponents claim that agile methods implicitly reduce risks due to for example, more frequent and earlier feedback, shorter periods of development time and easier prediction of cost. Therefore, there is a need to investigate how risk management can be usable in iterative and evolutionary software development processes. This paper investigates the gathering of empirical data on risk management from the project environment and presents a novel approach to manage risk in agile projects. Our approach is based on a prototype tool, Agile Risk Tool (ART). This tool reduces human effort in risk management by using software agents to identify, assess and monitor risk, based on input and data collected from the project environment and by applying some designated rules. As validation, groups of student project data were used to provide evidence of the efficacy of this approach. We demonstrate the approach and the feasibility of using a lightweight risk management tool to alert, assess and monitor risk with reduced human effort. Keywordssoftware risk, risk management, agile projects.


title: Cooperative Conceptual Retrieval for Heterogeneous Information

abstract: The potentials of formal concept analysis (FCA) for information retrieval have been highlighted by a number of research studies since its inception. With the advent of the Web along with the unprecedented amount of information coming from sources of heterogeneous data, FCA is more useful and practical than ever, because this technology addresses important limitations of the systems that currently support users in their quest for information. In this paper, we will focus on the unique features of FCA for searching in distributed heterogeneous information. The development of FCA-based applications for distributed heterogeneous information returns a major gain.


abstract: In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are un-labeled and different misclassification errors are associated with unequal costs. This scenario occurs in many real-world applications. For example, in some disease diagnosis, the cost of erroneously diagnosing a patient as healthy is much higher than that of diagnosing a healthy person as a patient. Also, the acquisition of labeled data requires medical diagnosis which is expensive, while the collection of unlabeled data such as basic health information is much cheaper. We propose the CS4VM (Cost-Sensitive Semi-Supervised Support Vector Machine) to address this problem. We show that the CS4VM, when given the label means of the unlabeled data, closely approximates the supervised cost-sensitive SVM that has access to the ground-truth labels of all the unlabeled data. This observation leads to an efficient algorithm which first estimates the label means and then trains the CS4VM with the plug-in label means by an efficient SVM solver. Experiments on a broad range of data sets show that the proposed method is capable of reducing the total cost and is computationally efficient.


title: Polychronicity and Multitasking: A Diary Study at Work

abstract: Polychronicity and multitasking have been described as being indispensible in work today because they enable people to use their time flexibly and effectively. We conducted a diary study among 93 employees during the mornings and evenings of 5 consecutive workdays (n = 418 observations). The study used hierarchical linear modeling with polychronicity and other personal characteristics at the person level, and multitasking behavior along with multitasking opportunities, interruptions, and unplanned work as antecedents, and affective well-being and self-rated performance as outcomes at the day level. We found several relations between antecedents and multitasking, as well as between multitasking and consequences. Polychronicity interacts with these relationships, such that polychronic individuals’ affective well-being and self-rated performance are less affected on days with much multitasking compared to monochronic individuals.


title: Alternative educational models for interdisciplinary student teams.

abstract: Background:Few studies compare instructor-modeled learning with modified debriefing to self-directed learning with facilitated debriefing during team-simulated clinical scenarios.Objective:To determine whether self-directed learning with facilitated debriefing during team-simulated clinical scenario


title: Refocusing on the dynamics of the Earth’s climate

abstract: Our opinion on the properties of the Earth’s climate system does not coincide with that generally accepted. Statistical analysis shows that, if we put aside the most substantial natural perturbing factors, the long-term global temperature dynamics appears as a step function of time. This dynamics is fundamentally different from the climate system’s continuous response to external anthropogenic effects, which is demonstrated in the models of the Intergovernmental Panel on Climate Change. The variants illustrated by a simple model (the system, affected either inadvertently or externally, changes from one quasi-stationary state to another) seem to be more suitable.


title: Fabrication and Characterization of an Ammonia Gas Sensor Based on PEDOT-PSS With N-Doped Graphene Quantum Dots Dopant

abstract: This paper investigated a room temperature resistive ammonia gas sensor based on a conductive polymer and N-doped graphene quantum dots (N-GQDs) dopant made on a transparent substrate with electrodes. The sensor fabricated with conductive polymer showed a good sensing response that increases considerably with the addition of N-GQDs. The sensing response of the poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) to NH3 increased from 30.13% to 212.32% at 1500 ppm with the addition of 50 wt% N-GQDs. The response time of the N-GQDs doped sensor decreased to 6.8 min when compared with the sensor without N-GQDs and the stability of the sensor having combined N-GQDs and PEDOT-PSS was higher than that of the PEDOT-PSS sensor. Meanwhile, the structure and morphology of the sensing film are characterized by Fourier transform infrared spectroscopy and field emissions scanning electron microscopy.


title: End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression

abstract: When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions-especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally-weighted logistic regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. We first evaluate our algorithms against other feature labeling algorithms under idealized conditions using feature labels generated by an oracle. In addition, another of our contributions is an evaluation of feature labeling algorithms under real-world conditions using feature labels harvested from actual end users in our user study. Our user study is the first statistical user study for feature labeling involving a large number of end users (43 participants), all of whom have no background in machine learning. Our supervised and semi-supervised algorithms were among the best performers when compared to other feature labeling algorithms in the idealized setting and they are also robust to poor quality feature labels provided by ordinary end users in our study. We also perform an analysis to investigate the relative gains of incorporating the different sources of knowledge available in the labeled training set, the feature labels and the unlabeled data. Together, our results strongly suggest that feature labeling by end users is both viable and effective for allowing end users to improve the learning algorithm behind their customized applications.


title: Sparse Additive Models

abstract: We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. SpAM is closely related to the COSSO model of Lin and Zhang (2006), but decouples smoothing and sparsity, enabling the use of arbitrary nonparametric smoothers. An analysis of the theoretical properties of SpAM is given. We also study a greedy estimator that is a nonparametric version of forward stepwise regression. Empirical results on synthetic and real data are presented, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.


title: An Expanded Conceptualization and a New Measure of Compulsive Buying

abstract: Drawing on the theoretical foundation of obsessive-compulsive spectrum disorder, this article develops an expanded conceptualization and new measure of consumers' proclivity to buy compulsively. Compulsive buying is defined as a consumer's tendency to be preoccupied with buying that is revealed through repetitive buying and a lack of impulse control over buying. This measure includes dimensions of both obsessive-compulsive and impulse-control disorders. By measuring income-dependent items or consequences of compulsive buying separately from the compulsive-buying scale, we develop a measure that has a strong theoretical foundation, well-documented psychometric properties, and an ability to be applied to general consumer populations. (c) 2008 by JOURNAL OF CONSUMER RESEARCH, Inc..


title: Graph Cut Based Point-Cloud Segmentation for Polygonal Reconstruction

abstract: The reconstruction of 3D objects from a point-cloud is based on sufficient separation of the points representing objects of interest from the points of other, unwanted objects. This operation called segmentation is discussed in this paper. We present an interactive unstructured point-cloud segmentation based on graph cut method where the cost function is derived from euclidean distance of point-cloud points. The graph topology and direct 3D point-cloud segmentation are the novel parts of our work. The segmentation is presented on real application, the terrain reconstruction of a complex miniature paper model, the Langweil model of Prague.


title: Video Game Addiction and College Performance Among Males: Results from a 1 Year Longitudinal Study

abstract: Abstract This study explored the pattern of video game usage and video game addiction among male college students and examined how video game addiction was related to expectations of college engagement, college grade point average (GPA), and on-campus drug and alcohol violations. Participants were 477 male, first year students at a liberal arts college. In the week before the start of classes, participants were given two surveys: one of expected college engagement, and the second of video game usage, including a measure of video game addiction. Results suggested that video game addiction is (a) negatively correlated with expected college engagement, (b) negatively correlated with college GPA, even when controlling for high school GPA, and (c) negatively correlated with drug and alcohol violations that occurred during the first year in college. Results are discussed in terms of implications for male students' engagement and success in college, and in terms of the construct validity of video game addiction.


title: JStill: mostly static detection of obfuscated malicious JavaScript code

abstract: The dynamic features of the JavaScript language not only promote various means for users to interact with websites through Web browsers, but also pose serious security threats to both users and websites. On top of this, obfuscation has become a popular technique among malicious JavaScript code that tries to hide its malicious purpose and to evade the detection of anti-virus software. To defend against obfuscated malicious JavaScript code, in this paper we propose a mostly static approach called JStill. JStill captures some essential characteristics of obfuscated malicious code by function invocation based analysis. It also leverages the combination of static analysis and lightweight runtime inspection so that it can not only detect, but also prevent the execution of the obfuscated malicious JavaScript code in browsers. Our evaluation based on real-world malicious JavaScript samples as well as Alexa top 50,000 websites demonstrates high detection accuracy (all in our experiment) and low false positives of JStill. Meanwhile, JStill only incurs negligible performance overhead, making it a practical solution to preventing obfuscated malicious JavaScript code.


title: A robust and enhanced approach for human detection in crowd

abstract: In this paper we have presented and enhanced methodology for robust detection of human in videos. Our research covers most of the limitations for detection of human in crowded places like detection of non-human objects and large human shadow as humans. The proposed technique is based on hierarchal structure consisting of three phases. Firstly, segmentation of moving objects is done using Gaussian Mixture model. Secondly, shadow removal technique is applied to avoid detection of large human shadows as human. Finally, human detection is achieved by applying human detection algorithm [3] on shadowless segmented images. Experiments are performed on different videos having single and multiple humans in indoor and outdoor scenes and videos under different illumination producing large and small shadows. This paper also presents comparative results of our methodology with existing techniques and the results clearly proved that the proposed technique outperforms the existing techniques and this is proved by producing comparative results.


title: The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries.

abstract: Self-determination theory proposes a multidimensional conceptualization of motivation comprising autonomous and controlled forms. Whereas autonomous motivation relates positively to individuals’ optimal functioning (e.g., well-being, performance), controlled motivation is less beneficial. To be able to use self-determination theory in the field of organizational behaviour, the Multidimensional Work Motivation Scale was developed and tested using data from 3435 workers in seven languages and nine countries. Factorial analyses indicated that the 19-item scale has the same factor structure across the seven languages. Convergent and discriminant validity tests across the countries also indicate that the psychological needs for autonomy, competence, and relatedness as well as the theoretically derived antecedents to work motivation (e.g., leadership and job design) are predictably related to the different forms of motivation, which in turn are predictably related to important work outcomes (e.g., well-being, commitment, performance, and turnover intentions). Implications for the development of organizational research based on self-determination theory are discussed.


title: CardboardSense: Interacting with DIY Cardboard VR Headset by Tapping

abstract: DIY cardboard-based VR headset that enfolds a commodity smartphone is a popular means of achieving low cost immersive stereoscopic viewing experiences. However, existing applications for these devices have limited user-input capability, allowing mainly look-at control by head tilting. CardboardSense is a set of novel tap-gesture-based inputs that take advantage of the motion sensing capabilities already available in most smartphones. The key premise is that finger contacts with the cardboard surface produce incidental but detectable vibrations that can be reliably differentiated from typical user head motion. Moreover, tapping on different surface locations produces differentiable temporal signatures in the sensor signals. We present methods to extract and analyze these temporal patterns, and experimentally determine a set of tapping gestures that can be accurately recognized. Experimental results show promising potential for CardboardSense to extend the interactivity of future cardboard-based VR headset applications.


title: Semi-autonomous flying robot for physical interaction with environment

abstract: This contribution presents the first results of the development of an unmanned aerial vehicle (UAV) which is capable of applying force to a wall while maintaining flight stability. This is a novel idea since UAVs are used so far only for tasks without physical contact to the surrounding objects. The basis for the work presented is a quadrotor system which is stabilized with an inertial measurement unit. As a new approach an additional actuator was added to generate forces in physical contact while the UAV stays horizontal. A control architecture based on ultrasonic distance sensors and a CMOS-camera is proposed. The performance of the system was proved by several flight tests. Potential applications of the system can be physical tasks at high places like cleaning windows or walls as well as rescue or maintenance tasks.


Delaunay Tetrahedralization using an Advancing-Front Approach


title: A Neuromorphic Chip Optimized for Deep Learning and CMOS Technology With Time-Domain Analog and Digital Mixed-Signal Processing

abstract: Demand for highly energy-efficient coprocessor for the inference computation of deep neural networks is increasing. We propose the time-domain neural network (TDNN), which employs time-domain analog and digital mixed-signal processing (TDAMS) that uses delay time as the analog signal. TDNN not only exploits energy-efficient analog computing, but also enables fully spatially unrolled architecture by the hardware-efficient feature of TDAMS. The proposed fully spatially unrolled architecture reduces energy-hungry data moving for weight and activations, thus contributing to significant improvement of energy efficiency. We also propose useful training techniques that mitigate the non-ideal effect of analog circuits, which enables to simplify the circuits and leads to maximizing the energy efficiency. The proof-of-concept chip shows unprecedentedly high energy efficiency of 48.2 TSop/s/W.


title: Numerical Study of p-n-Doped Poly-Silicon Shield Gate Trench MOSFET With Reduced Output Capacitance

abstract: This letter proposes a new shield gate (SG) trench MOSFET structure, which utilizes a p-n-doped polysilicon refill as the SG electrode to minimize the total output capacitance or charge. TCAD simulation is carried out to compare the proposed and conventional device structures. It is shown that the proposed structure offers 30% lower $\text{Q}_{\text {OSS}}$ and 18.2% reduction of inductive switching loss while retaining other attractive characteristics of the SG trench MOSFET.


title: Discriminative Methods for Multi-Labeled Classification

abstract: In this paper we present methods of enhancing existing discriminative classifiers for multi-labeled predictions. Discriminative methods like support vector machines perform very well for uni-labeled text classification tasks. Multi-labeled classification is a harder task subject to relatively less attention. In the multi-labeled setting, classes are often related to each other or part of a is-a hierarchy. We present a new technique for combining text features and features indicating relationships between classes, which can be used with any discriminative algorithm. We also present two enhancements to the margin of SVMs for building better models in the presence of overlapping classes. We present results of experiments on real world text benchmark datasets. Our new methods beat accuracy of existing methods with statistically significant improvements.


title: ACTIVITY RECOGNITION USING K-NEAREST NEIGHBOR ALGORITHM ON SMARTPHONE WITH TRI-AXIAL ACCELEROMETER

abstract: Mobile devices are becoming increasingly sophisticated. These devices are inherently sensors for collection and communication of textual and voice signals. In a broader sense, the latest generation of smart cell phones incorporates many diverse and powerful sensors such as GPS (Global Positioning Systems) sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. So, it is not surprising that modern mobile devices, particularly cell phones of last generations that work on different mobile operating systems, got equipped with quite sensitive sensors. This paper is devoted to one approach that solves human activity classification problem with help of a mobile device carried by user. Current method is based on K-Nearest Neighbor algorithm (K-NN). Using the magnitude of the accelerometer data and K-NN algorithm we could identify general activities performed by user.


Consumer Behavior In The Process Of Purchasing Tourism Product In Social Media


title: Sentiment analysis of movie reviews: A study on feature selection & classification algorithms

abstract: Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured or unstructured textual data. In this paper, we try to focus our task of sentiment analysis on IMDB movie review database. We examine the sentiment expression to classify the polarity of the movie review on a scale of 0(highly disliked) to 4(highly liked) and perform feature extraction and ranking and use these features to train our multi-label classifier to classify the movie review into its correct label. Due to lack of strong grammatical structures in movie reviews which follow the informal jargon, an approach based on structured N-grams has been followed. In addition, a comparative study on different classification approaches has been performed to determine the most suitable classifier to suit our problem domain. We conclude that our proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain. "Our approach using classification techniques has the best accuracy of 88.95%."


title: Reliability and Validity of the Korean Version of the Connor-Davidson Resilience Scale

abstract: ObjectiveaaThe Connor-Davidson Resilience Scale (CD-RISC) measures various aspects of psychological resilience in patients with posttraumatic stress disorder (PTSD) and other psychiatric ailments. This study sought to assess the reliability and validity of the Korean version of the Connor-Davidson Resilience Scale (K-CD-RISC). MethodsaaIn total, 576 participants were enrolled (497 females and 79 males), including hospital nurses, university students, and firefighters. Subjects were evaluated using the K-CD-RISC, the Beck Depression Inventory (BDI), the Impact of Event Scale-Revised (IES-R), the Rosenberg Self-Esteem Scale (RSES), and the Perceived Stress Scale (PSS). Test-retest reliability and internal consistency were examined as a measure of reliability, and convergent validity and factor analysis were also performed to evaluate validity. ResultsaaCronbach’s α coefficient and test-retest reliability were 0.93 and 0.93, respectively. The total score on the K-CD-RISC was positively correlated with the RSES (r=0.56, p<0.01). Conversely, BDI (r=-0.46, p<0.01), PSS (r=-0.32, p<0.01), and IES-R scores (r=-0.26, p<0.01) were negatively correlated with the K-CD-RISC. The K-CD-RISC showed a five-factor structure that explained 57.2% of the variance. ConclusionaaThe K-CD-RISC showed good reliability and validity for measurement of resilience among Korean subjects. Psychiatry Investig 2010;7:109-115


title: The 3dSOBS+ algorithm for moving object detection

abstract: We propose the 3dSOBS+ algorithm, a newly designed approach for moving object detection based on a neural background model automatically generated by a self-organizing method. The algorithm is able to accurately handle scenes containing moving backgrounds, gradual illumination variations, and shadows cast by moving objects, and is robust against false detections for different types of videos taken with stationary cameras. Experimental results and comparisons conducted on the Background Models Challenge benchmark dataset demonstrate the improvements achieved by the proposed algorithm, that compares well with the state-of-the-art methods.


title: RARE: A Systematic Augmented Router Emulation for Malware Analysis

abstract: How can we analyze and profile the behavior of a router malware? This is the motivating question behind our work focusing on router. Router-specific malware has emerged as a new vector for hackers, but has received relatively little attention compared to malware on other devices. A key challenge in analyzing router malware is getting it to activate, which is hampered by the diversity of firmware of various vendors and a plethora of different platforms. We propose, RARE, a systematic approach to analyze router malware and profile its behavior focusing on home-office routers. The key novelty is the intelligent augmented operation of our emulation that manages to fool malware binaries to activate irrespective of their target platform. This is achieved by leveraging two key capabilities: (a) a static level analysis that informs the dynamic execution, and (b) an iterative feedback loop across a series of dynamic executions, whose output informs the subsequent executions. From a practical point of view, RARE has the ability to: (a) instantiate an emulated router with or without malware, (b) replay arbitrary network traffic, (c) monitor and interact with the malware in a semi-automated way. We evaluate our approach using 221 router-specific malware binaries. First, we show that our method works: we get 94% of the binaries to activate, including obfuscated ones, which is a nine-fold increase compared to the 10% success ratio of the baseline method. Second, we show that our method can extract useful information towards understanding and profiling the botnet behavior: (a) we identify 203 unique IP addresses of C&C servers, and (b) we observe an initial spike and an overall 50% increase in the number of system calls on infected routers.


title: FieldSAFE: Dataset for Obstacle Detection in Agriculture

abstract: In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360 ∘ camera, LiDAR and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles and vegetation. All obstacles have ground truth object labels and geographic coordinates.


title: Recent Advances in VANET Security: A Survey

abstract: Vehicular ad hoc networks (VANET) are emerging as a prominent form of mobile ad hoc networks (MANETs) and as an effective technology for providing a wide range of safety applications for vehicle passengers. Nowadays, VANETs are of an increasing importance as they enable accessing a large variety of ubiquitous services. Such increase is also associated with a similar increase in vulnerabilities in these inter-vehicular services and communications, and consequently, the number of security attacks and threats. It is of paramount importance to ensure VANETs security as their deployment in the future must not compromise the safety and privacy of their users. The successful defending against such VANETs attacks prerequisite deploying efficient and reliable security solutions and services, and the research in this field is still immature and is continuously and rapidly growing. As such, this paper is devoted to provide a structured and comprehensive overview of the recent research advances on VANETS security services, surveying the state-of-the-art on security threats, vulnerabilities and security services, while focusing on important aspects that are not well-surveyed in the literature such as VANET security assessment tools.


title: Robotics for sustainable broad-acre agriculture

abstract: This paper describes the development of small low-cost cooperative robots for sustainable broad-acre agriculture to increase broad-acre crop production and reduce environmental impact. The current focus of the project is to use robotics to deal with resistant weeds, a critical problem for Australian farmers. To keep the overall system affordable our robot uses low-cost cameras and positioning sensors to perform a large scale coverage task while also avoiding obstacles. A multi-robot coordinator assigns parts of a given field to individual robots. The paper describes the modification of an electric vehicle for autonomy and experimental results from one real robot and twelve simulated robots working in coordination for approximately two hours on a 55 hectare field in Emerald Australia. Over this time the real robot ‘sprayed’ 6 hectares missing 2.6% and overlapping 9.7% within its assigned field partition, and successfully avoided three obstacles.


title: Dynamic projection mapping onto a deformable object with occlusion based on high-speed tracking of dot marker array

abstract: In recent years, projection mapping has attracted much attention in a variety of fields. Generally, however, the objects in projection mapping are limited to rigid and static or quasistatic objects. Dynamic projection mapping onto a deformable object could remarkably expand the possibilities. In order to achieve such a projection mapping, it is necessary to recognize the deformation of the object even when it is occluded. However, it is still a challenging problem to achieve this task in real-time with low latency. In this paper, we propose an efficient, high-speed tracking method utilizing high-frame-rate imaging. Our method is able to track an array of dot markers arranged on a deformable object even when there is external occlusion caused by the user interaction and self-occlusion caused by the deformation of the object itself. Additionally, our method can be applied to a stretchable object. Dynamic projection mapping with our method showed robust and consistent display onto a sheet of paper and cloth with a tracking performance of about 0.2 ms per frame, with the result that the projected pattern appeared to be printed on the deformable object.


title: Analyzing Argumentative Discourse Units in Online Interactions

abstract: Argument mining of online interactions is in its infancy. One reason is the lack of annotated corpora in this genre. To make progress, we need to develop a principled and scalable way of determining which portions of texts are argumentative and what is the nature of argumentation. We propose a two-tiered approach to achieve this goal and report on several initial studies to assess its potential.


title: Visualizing web search results using glyphs: Design and evaluation of a flower metaphor

abstract: While the Web provides a lot of useful information to managers and decision makers in organizations for decision support, it requires a lot of time and cognitive effort for users to sift through a search result list returned by search engines to find useful information. Previous research in information visualization has shown that visualization techniques can help users comprehend information and accomplish information tasks more efficiently and effectively. However, only a limited number of such techniques have been applied to Web search result visualization with mixed evaluation results. Using a design science approach, this research designed and implemented a glyph (a graphical object that represents the values of multiple dimensions using multiple visual parameters) and a system for visualizing Web search results. A flower metaphor was adopted in the glyph design to represent the characteristics and metadata of Web documents. Following the cognitive fit theory, an experimental study was conducted to evaluate three displays: a numeric display, a glyph display, and a combined display which showed numbers only, glyphs only, and both, respectively. Experimental results showed that the glyph display and the combined display performed better when task complexity was high, and the numeric display and the combined display performed better when task complexity was low. The combined display also received the best perceived usability from the subjects. Based on the findings, the implications of the study to research and practice are discussed and some future research directions are suggested.


title: Real-time 3D object pose estimation and tracking for natural landmark based visual servo

abstract: A real-time solution for estimating and tracking the 3D pose of a rigid object is presented for image-based visual servo with natural landmarks. The many state-of-the-art technologies that are available for recognizing the 3D pose of an object in a natural setting are not suitable for real-time servo due to their time lags. This paper demonstrates that a real-time solution of 3D pose estimation become feasible by combining a fast tracker such as KLT [7] [8] with a method of determining the 3D coordinates of tracking points on an object at the time of SIFT based tracking point initiation, assuming that a 3D geometric model with SIFT description of an object is known a-priori. Keeping track of tracking points with KLT, removing the tracking point outliers automatically, and reinitiating the tracking points using SIFT once deteriorated, the 3D pose of an object can be estimated and tracked in real-time. This method can be applied to both mono and stereo camera based 3D pose estimation and tracking. The former guarantees higher frame rates with about 1 ms of local pose estimation, while the latter assures of more precise pose results but with about 16 ms of local pose estimation. The experimental investigations have shown the effectiveness of the proposed approach with real-time performance.


title: An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm

abstract: The VAT algorithm is a visual method for determining the possible number of clusters in, or the cluster tendency of a set of objects. The improved VAT (iVAT) algorithm uses a graph-theoretic distance transform to improve the effectiveness of the VAT algorithm for “tough” cases where VAT fails to accurately show the cluster tendency. In this paper, we present an efficient formulation of the iVAT algorithm which reduces the computational complexity of the iVAT algorithm from O(N3) to O(N2). We also prove a direct relationship between the VAT image and the iVAT image produced by our efficient formulation. We conclude with three examples displaying clustering tendencies in three of the Karypis data sets that illustrate the improvement offered by the iVAT transformation. We also provide a comparison of iVAT images to those produced by the Reverse Cuthill-Mckee (RCM) algorithm; our examples suggest that iVAT is superior to the RCM method of display.


title: Android Smartphone Based Visual Object Recognition for Visually Impaired Using Deep Learning

abstract: In this paper, we propose a deep learning based visual object recognition model, to guide a visually impaired person in an outdoor environment using a smartphone equipped with internet connectivity. The deep learning based computing is performed by the high-end server and is to be hosted on a cloud. The boosted optimized convolutional neural network (CNN) based visual object recognition model is proposed by experimenting various CNN models, different optimizers, and varying learning rate. Visually impaired person captures the video and sends frame as input to the server for object segmentation and object recognition using proposed model. He then receives the information about surrounding objects and obstacles as voice input to the hearing device attached to the smartphone. The proposed system provides measurable high accuracy for recognition of 11 outdoor objects, to guide visually impaired person more effectively than traditional guiding cane.


title: Zero-shot Neural Transfer for Cross-lingual Entity Linking

abstract: Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-basedentity linking, which leverages information from a highresource “pivot” language to train character-level neural entity linking models that are transferred to the source lowresource language in a zero-shot manner. With experiments on 9 low-resource languages and transfer through a total of54 languages, we show that our proposed pivot-based framework improves entity linking accuracy 17% (absolute) on average over the baseline systems, for the zero-shot scenario.1 Further, we also investigate the use of language-universal phonological representations which improves average accuracy (absolute) by 36% when transferring between languages that use different scripts.


Journal of Dental Science and Therapy Simplified Classification for Dental Ceramics


title: A DSSS superregenerative receiver with tau-dither loop

abstract: This paper describes a direct-sequence spread-spectrum superregenerative receiver using a PN code synchronization loop based on the tan-dither technique. The receiver minimizes overall complexity by using a single signal processing path for data detection and PN code synchronization. An analytical study on the loop dynamics is presented, and the conditions for optimum performance are examined. Experimental results in the 433 MHz European ISM band confirm the receiver ability to perform acquisition and tracking, achieving a sensitivity of -103 dBm and an input dynamic range of 65 dB.


title: Application of Fuzzy Logic Control Approach in a Microcontroller-Based Sun Tracking System

abstract: The efficiency of a solar panel is related to the amount of solar energy it acquired, so it is necessary for the panel to track the sun’s position accurately. A microcontroller-based sun tracking system is proposed in this paper, which is comprised of a sensor, a microcontroller, and two stepper motors. The solar sensor is a four-quadrant type with four photodiodes, and is used for the direction determination of the sun. The fuzzy logic control approach is developed in the microcontroller to drive the stepper motors. By implementation of the tracking system, the solar panel can be perpendicular to the light beam at any moment so as to obtain the maximum solar radiation. The experimental results illustrate that the proposed system can increase the output power of the solar panel significantly.


optimised noise barriers a state-of-the-art report Danish roaD institute report optimised noise barriers a state-of-the-art report Report


title: Social Media Analytics and Intelligence

abstract: In a broad sense, social media refers to a conversational, distributed mode of content generation, dissemination, and communication among communities. Different from broadcast-based traditional and industrial media, social media has torn down the boundaries between authorship and readership, while the information consumption and dissemination process is becoming intrinsically intertwined with the process of generating and sharing information. This special issue samples the state of the art in social media analytics and intelligence research that has direct relevance to the AI subfield from either an methodological or domain perspective.


title: After Arundel but before Luther: The First Half-Century of Print

abstract: The chapter looks at devotional and religious books printed in England in English between 1476 and 1526. Why they are what they are is a consideration throughout, but specifically in the second part of the chapter, which considers some examples of the conduits of religious texts to the printers. As a whole, the chapter is informed by the publication in 1409 of Archbishop Arundel’s Constitutions, and assesses their effect on publication during this period.


Leadership Can Be Learned , But How Is It Measured ?


title: Psychopathy, treatment involvement, and subsequent violence among civil psychiatric patients.

abstract: Individuals with psychopathy typically are viewed as incurable cases that should be diverted from treatment settings to environments where their behavior can be monitored and controlled. The prevailing clinical conviction that psychopaths are untreatable has crucial implications, given the scarcity of mental health care resources, the number of legal contexts that call for assessment of treatability, and the explosion of research on psychopathy and violence risk over recent years. Based on a sample of 871 civil psychiatric patients (including 195 “potentially psychopathic” and 72 “psychopathic” patients), this study explores the relations among psychopathy, receipt of outpatient mental health services in real-world settings, and subsequent violence in the community. The results suggest that psychopathic traits do not moderate the effect of treatment involvement on violence, even after controlling statistically for the treatment assignment process. Psychopathic patients appear as likely as nonpsychopathic patients to benefit from adequate doses of treatment, in terms of violence reduction. We interpret these results in light of prior research with offenders and analyze their implications for future research, policy, and practice.


title: Sartorial branch of the saphenous nerve in relation to a medial knee ligament repair or reconstruction

abstract: Surgical approaches to repair or reconstruct the medial knee structures note caution to avoid the sartorial branch of saphenous nerve. However, the approximate area of potential iatrogenic nerve injury has not been previously described in relation to landmarks for a medial knee reconstruction. The purpose of this study was to define the course of the sartorial branch of the saphenous nerve in relation to the superficial medial collateral ligament. A total of ten non-paired, fresh-frozen cadaveric knees, with no evidence of prior injury or disease were utilized. Dissection to identify the medial knee structures was performed. The sartorial branch of the saphenous nerve was identified in all specimens. The perpendicular distance from the anterior border of the superficial medial collateral ligament 2 cm distal from the joint line to the sartorial branch of the saphenous nerve was 4.8 ± 0.9 cm. The distance from the anterior border of the superficial medial collateral ligament to the sartorial branch of the saphenous nerve decreased as the distance was increased distally with a mean distance of 4 cm (4.1 ± 0.8 cm) distal from the joint line and 6 cm (3.8 ± 0.8 cm) distal from the joint line. We have characterized the surgically relevant landmark anatomy of the sartorial branch of the saphenous nerve in regards to performing a repair or reconstruction of the medial knee structures. Familiarity with these anatomic landmarks and associated distances from the sartorial branch of the saphenous nerve, we can assess the potential area of vulnerability to this nerve branch intraoperatively.


title: Using the opinion leaders in social networks to improve the cold start challenge in recommender systems

abstract: The increasing volume of information about goods and services has been growing confusion for online buyers in cyberspace and this problem still continues. One of the most important ways to deal with the information overload is using a system called recommender system. The task of a recommender system is to offer the most appropriate and the nearest product to the user's demands and needs. In this system, one of the main problems is the cold start challenge. This problem occurs when a new user logs on and because there is no sufficient information available in the system from the user, the system won't be able to provide appropriate recommendation and the system error will rise. In this paper, we propose to use a new measurement called opinion leaders to alleviate this problem. Opinion leader is a person that his opinion has an impact on the target user. As a result, in the case of a new user logging in and the user — item's matrix sparseness, we can use the opinion of opinion leaders to offer the appropriate recommendation for new users and thereby increase the accuracy of the recommender system. The results of several conducted tests showed that opinion leaders combined with recommender systems will effectively reduce the recommendation errors.


title: Mining processes in dentistry

abstract: Business processes in dentistry are quickly evolving towards 'digital dentistry'. This means that many steps in the dental process will increasingly deal with computerized information or computerized half products. A complicating factor in the improvement of process performance in dentistry, however, is the large number of independent dental professionals that are involved in the entire process. In order to reap the benefits of digital dentistry, it is essential to obtain an accurate view on the current processes in practice. In this paper, so called process mining techniques are applied in order to demonstrate that, based on automatically stored data, detailed process knowledge can be obtained on dental processes, e.g. it can be discovered how dental processes are actually executed. To this end, we analyze a real case of a private dental practice, which is responsible for the treatment of patients (diagnosis, placing of implants and the placement of the final restoration), and the dental lab that is responsible for the production of the final restoration. To determine the usefulness of process mining, the entire process has been investigated from three different perspectives: (1) the control-flow perspective, (2) the organizational perspective and (3) the performance perspective. The results clearly show that process mining is useful to gain a deep understanding of dental processes. Also, it becomes clear that dental process are rather complex, which require a considerable amount of flexibility. We argue that the introduction of workflow management technology is needed in order to make digital dentistry a success.


title: Attenuation of Brain Nitrostative and Oxidative Damage by Brain Cooling during Experimental Traumatic Brain Injury

abstract: The aim of the present study was to ascertain whether brain cooling causes attenuation of traumatic brain injury by reducing brain nitrostative and oxidative damage. Brain cooling was accomplished by infusion of 5 mL of 4°C saline over 5 minutes via the external jugular vein. Immediately after the onset of traumatic brain injury, rats were randomized into two groups and given 37°C or 4°C normal saline. Another group of rats were used as sham operated controls. Behavioral and biochemical assessments were conducted on 72 hours after brain injury or sham operation. As compared to those of the sham-operated controls, the 37°C saline-treated brain injured animals displayed motor deficits, higher cerebral contusion volume and incidence, higher oxidative damage (e.g., lower values of cerebral superoxide dismutase, catalase, glutathione peroxidase and glutathione reductase, but higher values of cerebral malondialdehyde), and higher nitrostative damage (e.g., higher values of neuronal nitric oxide synthase and 3-nitrotyrosine). All the motor deficits and brain nitrostative and oxidative damage were significantly reduced by retrograde perfusion of 4°C saline via the jugular vein. Our data suggest that brain cooling may improve the outcomes of traumatic brain injury in rats by reducing brain nitrostative and oxidative damage.


title: On Using the Intelligent Edge for IoT Analytics

abstract: This article presents a flexible architecture for Internet of Things (IoT) data analytics using the concept of fog computing. The authors identify different actors and their roles in order to design adaptive IoT data analytics solutions. The presented approach can be used to effectively design robust IoT applications that require a tradeoff between cloud- and edge-based computing depending on dynamic application requirements. The potential use cases of this technology can be found in scenarios such as smart cities, security surveillance, and smart manufacturing, where the quality of user experience is important.


title: Enriched Diagrammatic Representation for Resolution

abstract: Abstract Computers can help in teaching logic; however, this ability is not particularly useful since it is reported that students lack understanding of deduction methods. Current logical methods are based on a conceptualization formed from a language for formulas that can be structured as in abstracted trees or sets, and from semantics in terms of truth values formulated as tables or drawn as marks on trees and sets. This paper proposes a unifying description methodology based on the notion of flow progressing through a system of six stages. Truth values and formulas are visualized as “things that flow”, i.e., things that can be created, received, processed, released and transferred. Accordingly, a conceptual map can be drafted for the propagation of these flow things during the process of truth-value evaluation or the chain of formula constructions. The new methodology presents a viable contribution for building a deeper understanding of proofs. This aid to understanding is demonstrated in examples adapted from the resolution method


title: Design of Wideband Circularly Polarized Antenna Using Coupled Rotated Vertical Metallic Plates

abstract: A simple structure composed of four rotated metallic plates is proposed to enhance the bandwidth of circularly polarized (CP) crossed-dipole antenna. The metallic plates are vertically added on the ground plane in a sequential rotation way, surrounding the primary radiator of the crossed-dipole antenna. Owing to the coupling with the radiator, orthogonal currents can be induced on the metallic plates. Additional impedance and axial ratio passbands can, therefore, be generated beside the original operating band, enhancing its bandwidth substantially. As the metallic plates are mounted on the ground plane, no extra footprint is needed, making the area of the wideband antenna very compact. Simulation and measured results show that by using this simple structure, the operating bandwidth of CP crossed-dipole antenna can be enhanced significantly from ~30% to 106.1%. The structure is also applicable to the intrinsically narrowband patch antennas.


title: FPGA implementation of an embedded face detection system based on LEON3

abstract: Comunicacion presentada al: "2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV'12)" celebrado dentro del WORLDCOMP'12 en Las Vegas (USA) del 16 al 19 de julio del 2012. Mas informacion en http://www.worldacademyofscience.org/worldcomp12/ws/conferences/ipcv12.


title: Effect of changing climate on rice water requirement in Guilan, north of Iran

abstract: In this study, the effectiveness of 15 global climate models (GCMs) for simulating weather data of Rasht synoptic station in the north of Iran was evaluated using a statistical downscaling approach. Downscaling of GCMs was performed using a stochastic weather generator model (LARS-WG5.5) and the best GCM (INCM3) was selected. The parameters such as precipitation, radiation, temperature and reference evapotranspiration were simulated using the selected GCMs for two periods of 2013–2042 and 2043–2072, and accordingly, the rice water requirement was estimated for the coming periods. Then, simulated results were compared with data in the baseline period (1981–2010). The results showed that reference evapotranspiration ( ET o ) for all the seasons will increase in the coming periods. The highest ET o increase (18.5–23.7 mm month –1 ) will occur in the spring. Also, the average rice water requirement will increase between 178 and 572 m 3 ha –1 depending on the emission scenarios and future studied periods. The incremental changes in ET o and, consequently, in rice water requirement for the coming periods will occur as a result of the significant increase in temperature. The results of this study can be used by local planners as a correct view of water demand in the future.


title: Deep context-aware descreening and rescreening of halftone images

abstract: A fully automatic method for descreening halftone images is presented based on convolutional neural networks with end-to-end learning. Incorporating context level information, the proposed method not only removes halftone artifacts but also synthesizes the fine details lost during halftone. The method consists of two main stages. In the first stage, intrinsic features of the scene are extracted, the low-frequency reconstruction of the image is estimated, and halftone patterns are removed. For the intrinsic features, the edges and object-categories are estimated and fed to the next stage as strong visual and contextual cues. In the second stage, fine details are synthesized on top of the low-frequency output based on an adversarial generative model. In addition, the novel problem of rescreening is addressed, where a natural input image is halftoned so as to be similar to a separately given reference halftone image. To this end, a two-stage convolutional neural network is also presented. Both networks are trained with millions of before-and-after example image pairs of various halftone styles. Qualitative and quantitative evaluations are provided, which demonstrates the effectiveness of the proposed methods.


title: News Article Teaser Tweets and How to Generate Them

abstract: In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. Teasers are one of the main vehicles for transmitting news to social media users. We compile a novel dataset of teasers by systematically accumulating tweets and selecting those that conform to the teaser definition. We have compared a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al.(2017)'s seq2seq with pointer network.


title: A review on intelligent process for smart home applications based on IoT: coherent taxonomy, motivation, open challenges, and recommendations

abstract: Innovative technology on intelligent processes for smart home applications that utilize Internet of Things (IoT) is mainly limited and dispersed. The available trends and gaps were investigated in this study to provide valued visions for technical environments and researchers. Thus, a survey was conducted to create a coherent taxonomy on the research landscape. An extensive search was conducted for articles on (a) smart homes, (b) IoT and (c) applications. Three databases, namely, IEEE Explore, ScienceDirect and Web of Science, were used in the article search. These databases comprised comprehensive literature that concentrate on IoT-based smart home applications. Subsequently, filtering process was achieved on the basis of intelligent processes. The final classification scheme outcome of the dataset contained 40 articles that were classified into four classes. The first class includes the knowledge engineering process that examines data representation to identify the means of accomplishing a task for IoT applications and their utilisation in smart homes. The second class includes papers on the detection process that uses artificial intelligence (AI) techniques to capture the possible changes in IoT-based smart home applications. The third class comprises the analytical process that refers to the use of AI techniques to understand the underlying problems in smart homes by inferring new knowledge and suggesting appropriate solutions for the problem. The fourth class comprises the control process that describes the process of measuring and instructing the performance of IoT-based smart home applications against the specifications with the involvement of intelligent techniques. The basic features of this evolving approach were then identified in the aspects of motivation of intelligent process utilisation for IoT-based smart home applications and open-issue restriction utilisation. The recommendations for the approval and utilisation of intelligent process for IoT-based smart home applications were also determined from the literature.


title: Enabling search over encrypted multimedia databases

abstract: Performing information retrieval tasks while preserving data confidentiality is a desirable capability when a ::: database is stored on a server maintained by a third-party service provider. This paper addresses the problem ::: of enabling content-based retrieval over encrypted multimedia databases. Search indexes, along with multimedia ::: documents, are first encrypted by the content owner and then stored onto the server. Through jointly applying ::: cryptographic techniques, such as order preserving encryption and randomized hash functions, with image ::: processing and information retrieval techniques, secure indexing schemes are designed to provide both privacy ::: protection and rank-ordered search capability. Retrieval results on an encrypted color image database and security ::: analysis of the secure indexing schemes under different attack models show that data confidentiality can ::: be preserved while retaining very good retrieval performance. This work has promising applications in secure ::: multimedia management.


title: Identification and quantification of ageing mechanisms in Lithium-ion batteries using the EIS technique

abstract: Ageing diagnosis in Lithium-ion batteries is essential to ensure their reliability and optimum performance over time. The Battery Management System (BMS) usually monitors battery ageing with the aid of two metrics: capacity and power fade. However, these metrics do not identify the main root causes of battery ageing. Using the Electrochemical Impedance Spectroscopy technique, this work proposes a novel method to identify and quantify ageing mechanisms over time. The method is applied to four parallelised Lithium-ion cells cycled with a constant driving profile for 500 cycles. As a result, Loss of Active Material (LAM) and Loss of Lithium Ions (LLI) were found to be the most pertinent ageing mechanisms over time for the four cells. Identification and quantification of ageing mechanisms will support novel battery lifetime control strategies within the BMS, so that potential failures during normal operation are prevented.


title: The IR Task at the CLEF eHealth Evaluation Lab 2016: User-centred Health Information Retrieval

abstract: This paper details the collection, systems and evaluation methods used in the IR Task of the CLEF 2016 eHealth Evaluation Lab. This task investigates the e↵ectiveness of web search engines in providing access to medical information for common people that have no or little medical knowledge. The task aims to foster advances in the development of search technologies for consumer health search by providing resources and evaluation methods to test and validate search systems. The problem considered in this year’s task was to retrieve web pages to support the information needs of health consumers that are faced by a medical condition and that want to seek relevant health information online through a search engine. As part of the evaluation exercise, we gathered 300 queries users posed with respect to 50 search task scenarios. The scenarios were developed from real cases of people seeking health information through posting requests of help on a web forum. The presence of query variations for a single scenario helped us capturing the variable quality at which queries are posed. Queries were created in English and then translated into other languages. A total of 49 runs by 10 different teams were submitted for the English query topics; 2 teams submitted 29 runs for the multilingual topics.


title: WordPress security: an analysis based on publicly available exploits

abstract: The danger of SQL injections has been known for more than a decade but injection attacks have led the OWASP top 10 for years and still are one of the major reasons for devastating attacks on web sites. As about 24% percent of the top 10 million web sites are built upon the content management system WordPress, it's no surprise that content management systems in general and WordPress in particular are frequently targeted. To understand how the underlying security bugs can be discovered and exploited by attackers, 199 publicly disclosed SQL injection exploits for WordPress and its plugins have been analyzed. The steps an attacker would take to uncover and utilize these bugs are followed in order to gain access to the underlying database through automated, dynamic vulnerability scanning with well-known, freely available tools. Previous studies have shown that the majority of the security bugs are caused by the same programming errors as 10 years ago and state that the complexity of finding and exploiting them has not increased significantly. Furthermore, they claim that although the complexity has not increased, automated tools still do not detect the majority of bugs. The results of this paper show that tools for automated, dynamic vulnerability scanning only play a subordinate role for developing exploits. The reason for this is that only a small percentage of attack vectors can be found during the detection phase. So even if the complexity of exploiting an attack vector has not increased, this attack vector has to be found in the first place, which is the major challenge for this kind of tools. Therefore, from today's perspective, a combination with manual and/or static analysis is essential when testing for security vulnerabilities.


Active Learning from Critiques via Bayesian Inverse Reinforcement Learning


title: Dimerisation of the Rhodobacter sphaeroides RC–LH1 photosynthetic complex is not facilitated by a GxxxG motif in the PufX polypeptide

abstract: In purple photosynthetic bacteria the initial steps of light energy transduction take place in an RC-LH1 complex formed by the photochemical reaction centre (RC) and the LH1 light harvesting pigment-protein. In Rhodobacter sphaeroides, the RC-LH1 complex assembles in a dimeric form in which two RCs are surrounded by an S-shaped LH1 antenna. There is currently debate over the detailed architecture of this dimeric RC-LH1 complex, with particular emphasis on the location and precise function of a minor polypeptide component termed PufX. It has been hypothesised that the membrane-spanning helical region of PufX contains a GxxxG dimerisation motif that facilitates the formation of a dimer of PufX at the interface of the RC-LH1 dimer, and more specifically that the formation of this PufX dimer seeds assembly of the remaining RC-LH1 dimer (J. Busselez et al., 2007). In the present work this hypothesis was tested by site directed mutagenesis of the glycine residues proposed to form the GxxxG motif. Mutation of these glycines to leucine did not decrease the propensity of the RC-LH1 complex to assemble in a dimeric form, as would be expected from experimental studies of the effect of mutation on GxxxG motifs in other membrane proteins. Indeed increased yields of dimer were seen in two of the glycine-to-leucine mutants constructed. It is concluded that the PufX from Rhodobacter sphaeroides does not contain a genuine GxxxG helix dimerisation motif.


Heuristic for teaching systems thinking Conference or Workshop Item


title: Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

abstract: CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with $L_1$ and $L_{2,1}$ regularizers, leading to a non-convex and non-smooth optimization problem. Then, we propose a novel algorithm to solve it efficiently, based on directional derivatives and quasi-Newton method. In addition, we design a distributed system which can run on hundreds of machines parallel and provides us with the industrial scalability. LS-PLM model can capture nonlinear patterns from massive sparse data, saving us from heavy feature engineering jobs. Since 2012, LS-PLM has become the main CTR prediction model in Alibaba's online display advertising system, serving hundreds of millions users every day.


title: APPLYING CONNECTIONIST MODAL LOGICS TO DISTRIBUTED KNOWLEDGE REPRESENTATION PROBLEMS

abstract: Neural-Symbolic Systems concern the integration of the symbolic and connectionist paradigms of Artificial Intelligence. Distributed knowledge representation is traditionally seen under a symbolic perspective. In this paper, we show how neural networks can represent distributed symbolic knowledge, acting as multi-agent systems with learning capability (a key feature of neural networks). We apply the framework of Connectionist Modal Logics to well-known testbeds for distributed knowledge representation formalisms, namely the muddy children and the wise men puzzles. Finally, we sketch a full solution to these problems by extending our approach to deal with knowledge evolution over time.


Benchmarking Apache Spark with Machine Learning Applications


Multimodal Markers of Persuasive Speech: Designing a Virtual Debate Coach


Minimum jerk motion planning for a prosthetic finger


Learning through Multimodal Interaction


title: Compressed Sensing using Generative Models

abstract: The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain. For almost all results in this literature, the structure is represented by sparsity in a well-chosen basis. We show how to achieve guarantees similar to standard compressed sensing but without employing sparsity at all. Instead, we suppose that vectors lie near the range of a generative model G : ℝk → ℝn. Our main theorem is that, if G is L-Lipschitz, then roughly O(k log L) random Gaussian measurements suffice for an l2/l2 recovery guarantee. We demonstrate our results using generative models from published variational autoencoder and generative adversarial networks. Our method can use 5-10x fewer measurements than Lasso for the same accuracy.


title: Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system

abstract: Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naive Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-? to improve the initial naive Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.


On the Flatness of Loss Surface for Two-layered ReLU Networks


title: Deliberative Discourse Idealized and Realized: Accountable Talk in the Classroom and in Civic Life

abstract: Classroom discussion practices that can lead to reasoned participation by all students are presented and described by the authors. Their research emphasizes the careful orchestration of talk and tasks in academic learning. Parallels are drawn to the philosophical work on deliberative discourse and the fundamental goal of equipping all students to participate in academically productive talk. These practices, termed Accountable TalkSM, emphasize the forms and norms of discourse that support and promote equity and access to rigorous academic learning. They have been shown to result in academic achievement for diverse populations of students. The authors outline Accountable Talk as encompassing three broad dimensions: one, accountability to the learning community, in which participants listen to and build their contributions in response to those of others; two, accountability to accepted standards of reasoning, talk that emphasizes logical connections and the drawing of reasonable conclusions; and, three, accountability to knowledge, talk that is based explicitly on facts, written texts, or other public information. With more than fifteen years research into Accountable Talk applications across a wide range of classrooms and grade levels, the authors detail the challenges and limitations of contexts in which discourse norms are not shared by all members of the classroom community.


title: Factors Affecting Cloud Computing Adoption in Higher Education in Bangladesh: A Case of University of Dhaka

abstract: In the arena of education, Cloud computing is more apparent technology to ensure availability and sharing of resources through virtualization. To acquire benefit from this technology boon, it is important for an educational institution to identify the important factors that contribute on cloud computing adoption. So, this study has been conducted to identify the factors that will affect cloud computing adoption in educational sector. University of Dhaka is chosen as a case for this paper. Both primary and secondary sources are used for data collection. Seventeen potential factors are taken into consideration, on which survey has been conducted focusing primary sources. After performing multiple regression, variance inflation factor and factor analysis on data of primary source, six factors are identified as core factors to cloud computing adoption, these are- service availability, cloud control ability, resource need, usefulness in education, lack of proper infrastructure, lack of training of staff. These findings can be used as a theoretical base and future development of technological adoption in higher education.


title: Optimizing multiple UAV cooperative ground attack missions

abstract: This paper presents an optimization based approach for cooperative multiple UAV attack missions. The objective is to determine the minimum resources required to coordinately attack a target at a given set of directions. We restrict the paths of the munitions to direct Dubins paths to satisfy field of view constraints and to avoid certain undesirable paths. The proposed algorithm derives the feasible positions and headings for each attack angle, and determines intersection regions corresponding to any two attack angles. We pose a set cover problem, the solution of which gives the minimum number of UAVs required to accomplish the mission.


title: Graphene-Wrapped Sulfur Particles as a Rechargeable Lithium–Sulfur Battery Cathode Material with High Capacity and Cycling Stability

abstract: We report the synthesis of a graphene–sulfur composite material by wrapping poly(ethylene glycol) (PEG) coated submicrometer sulfur particles with mildly oxidized graphene oxide sheets decorated by carbon black nanoparticles. The PEG and graphene coating layers are important to accommodating volume expansion of the coated sulfur particles during discharge, trapping soluble polysulfide intermediates, and rendering the sulfur particles electrically conducting. The resulting graphene–sulfur composite showed high and stable specific capacities up to ∼600 mAh/g over more than 100 cycles, representing a promising cathode material for rechargeable lithium batteries with high energy density.


title: Collaborative Augmented Reality for Outdoor Navigation and

abstract: Augmented reality (AR) can provide an excellent user interface for visualization in a mobile computing application. The user’s view is augmented with location based information at the correct spatial location, thus providing an intuitive way of presenting such information. In this work we demonstrate the use of AR for collaborative navigation and information browsing tasks in an urban environment. A navigation function allows one or more users to roam through a city and guides them to selected destinations. Information browsing presents users with information about objects in their surrounding. Both functions feature support for collaboration. The developed system does not only concentrate on the user interface aspects but also provides a scalable infrastructure to support mobile applications. To this end we developed a 3-tier architecture to manage a common data model for a set of applications. It is inspired by current Internet application frameworks and consists of a central storage layer using a common data model, a transformation layer responsible for filtering and adapting the data to the requirements of a particular applications on request, and finally of the applications itself.


title: Using two coefficients modeling of nonsubsampled Shearlet transform for despeckling

abstract: Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise. Two approaches based on modeling the nonsubsampled Shearlet transform (NSST) coefficients are presented. Two-sided generalized Gamma distribution and normal inverse Gaussian probability density function have been used to model the statistics of NSST coefficients. Bayesian maximum a posteriori estimator is applied to the corrupted NSST coefficients in order to estimate the noise-free NSST coefficients. Finally, experimental results, according to objective and subjective criteria, carried out on both artificially speckled images and the true SAR images, demonstrate that the proposed methods outperform other state of art references via two points of view, speckle noise reduction and image quality preservation.


Variational Inference with Stein Mixtures


title: Risk Assessment for Cooperative Automated Driving

abstract: Global investment and recent advancements in vehicle automation are making autonomous and cooperative automated driving (AD) a reality. Not only will automated vehicles incorporate more electronics and connectivity than ever before, but also, notably, they will transfer control and responsibility of monitoring the environment from a human driver to a robotic system. While prior work has assessed and provided security solutions for non-automated vehicles, there is much to understand regarding the security implications of AD. In this work, we begin to address this gap in understanding. This paper reports on a risk assessment framework for autonomous and cooperative AD. We aggregate the state of the art in AD research to define a reference architecture for automated vehicles, describing the new attack surfaces and data flow. Employing existing automotive threat models, we propose a novel application-based threat enumeration and analysis approach that is able to address different AD applications across all levels of automation. We demonstrate this framework with an example application assessment and summarize the results and security insights from analyses of other applications. The results of our risk assessment and future assessments with this framework will inform on the design of security solutions and secure architectures for production AD systems.


The Development of the Turtle Carapace " ( 1989 ) , by Ann


title: Enlarging just noticeable differences of visual-proprioceptive conflict in VR using haptic feedback

abstract: Wearable finger-based haptics with cutaneous feedback is promising, as it allows us to duplicate many real-world rich/important tasks that are relying on the dexterity of fingers and hands in the virtual-world. For this system, one of the key challenges is the finger-tracking, which can of course never be perfect, yet, if used in VR (e.g., with HMD), would still be adequate as long as its tracking error is under a certain detection threshold. In this paper, for such wearable finger-based haptics in VR with HMD, via some suitably-designed human subject studies, we aim to quantitatively answer the following questions: 1) what is the detection threshold (i.e., just noticeable difference (JND)) of visual-proprioceptive conflict (i.e., error tolerance of the finger-tracking system in VR); and 2) is it possible to further reduce this visual-proprioceptive conflict by utilizing cutaneous haptic feedback. We believe these results would be useful to determine the design specification of finger-tracking systems for haptic and general VR applications alike.


title: Goals as excuses or guides: The liberating effect of perceived goal progress on choice

abstract: Consumer choices are often driven by multiple goals (e.g., career and family), each of which if viewed in isolation may appear to suggest conflicting choices. This article examines the effect of initial goal pursuit on consumers’ interest in pursuing unrelated or even conflicting goals. Four studies were conducted to test whether perceived goal progress hinders the pursuit of the focal goal. These studies demonstrate that in the course of self-regulation progress along one goal liberates people to pursue inconsistent goals. Furthermore, merely planning to make goal progress in the future may facilitate incongruent choice of immediate action. P eople’s choices are usually driven by multiple underlying goals, each of which if viewed in isolation may appear conflicting. For example, individuals simultaneously believe in saving for retirement as well as taking luxurious vacations, doing well academically and socializing actively with friends, and so forth. Previous research has often portrayed the self-regulation processes as involving setting abstract goals that then motivate consistent choice of actions (Gollwitzer 1999; Higgins 1997; Locke and Latham 1990). The empirical work supporting this basic premise has focused mainly on situations where the individual has set a single goal. If individuals simultaneously hold multiple goals, an account of consumer behavior needs to address the manner in which consumers pursue sequential choices among these potentially conflicting goals. This article examines subsequent consumer choice following initial goal pursuit. We propose that when individuals hold multiple goals, the pursuit or the intention to pursue the initial goal (hereafter referred to as the focal goal) may liberate the individual to subsequently pursue unrelated or even conflicting goals (e.g., succumbing to temptation). For instance, the opening of a new savings account may suggest to the individuals that one’s goal of saving for the future is being actively pursued, and, as a result, new savers might become more willing to spend money on indulgences. Specifically, this article explores the hypothesis that actions that are used to infer goal progress act to liberate the individual and thereby increase the likelihood of pursuing incongruent actions, whereas the same actions interpreted in terms of


title: Isointense infant brain segmentation with a hyper-dense connected convolutional neural network

abstract: Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions. Most existing approaches for this problem are based on multi-atlas label fusion strategies, which are time-consuming and sensitive to registration errors. As alternative to these methods, we propose a hyper-densely connected 3D convolutional neural network that employs MR-T1 and T2 images as input, which are processed independently in two separated paths. An important difference with previous densely connected networks is the use of direct connections between layers from the same and different paths. Adopting such dense connectivity helps the learning process by including deep supervision and improving gradient flow. We evaluated our approach on data from the MICCAI Grand Challenge on 6-month infant Brain MRI Segmentation (iSEG), obtaining very competitive results. Among 21 teams, our approach ranked first or second in most metrics, translating into a state-of-the-art performance.


title: The Off-Switch Game

abstract: It is clear that one of the primary tools we can use to mitigate the potential risk from a misbehaving AI system is the ability to turn the system off. As the capabilities of AI systems improve, it is important to ensure that such systems do not adopt subgoals that prevent a human from switching them off. This is a challenge because many formulations of rational agents create strong incentives for self-preservation. This is not caused by a built-in instinct, but because a rational agent will maximize expected utility and cannot achieve whatever objective it has been given if it is dead. Our goal is to study the incentives an agent has to allow itself to be switched off. We analyze a simple game between a human H and a robot R, where H can press R's off switch but R can disable the off switch. A traditional agent takes its reward function for granted: we show that such agents have an incentive to disable the off switch, except in the special case where H is perfectly rational. Our key insight is that for R to want to preserve its off switch, it needs to be uncertain about the utility associated with the outcome, and to treat H's actions as important observations about that utility. (R also has no incentive to switch itself off in this setting.) We conclude that giving machines an appropriate level of uncertainty about their objectives leads to safer designs, and we argue that this setting is a useful generalization of the classical AI paradigm of rational agents.


title: Real time hand pose estimation for human computer interaction.

abstract: The aim of this thesis is to address the challenge of real-time pose estimation of the hand. Specifically this thesis aims to determine the joint positions of a non-augmented ::: hand. This thesis focuses on the use of depth, performing localisation of the parts of the hand for efficient fitting of a kinematic model and consists of four main contributions. The first contribution presents an approach to Multi-touch(less) tracking, where the objective is to track the fingertips with a high degree of accuracy without sensor contact. ::: Using a graph based approach, the surface of the hand is modelled and extrema of the hand are located. From this, gestures are identified and used for interaction. We briefly discuss one use case for this technology in the context of the Making Sense demonstrator inspired by the film ”The Minority Report”. This demonstration system ::: allows an operator to quickly summarise and explore complex multi-modal multimedia data. The tracking approach allows for collaborative interactions due to its highly efficient tracking, resolving 4 hands simultaneously in real-time. ::: The second contribution applies a Randomised Decision Forest (RDF) to the problem of pose estimation and presents a technique to identify regions of the hand, using ::: features that sample depth. The RDF is an ensemble based classifier that is capable of generalising to unseen data and is capable of modelling expansive datasets, learning ::: from over 70,000 pose examples. The approach is also demonstrated in the challenging application of American Sign Language (ASL) fingerspelling recognition. ::: The third contribution combines a machine learning approach with a model based method to overcome the limitations of either technique in isolation. A RDF provides initial segmentation allowing surface constraints to be derived for a 3D model, which is subsequently fitted to the segmentation. This stage of global optimisation incorporates ::: temporal information and enforces kinematic constraints. Using Rigid Body Dynamics for optimisation, invalid poses due to self-intersection and segmentation noise are resolved. ::: Accuracy of the approach is limited by the natural variance between users and the use of a generic hand model. The final contribution therefore proposes an approach to ::: refine pose via cascaded linear regression which samples the residual error between the depth and the model. This combination of techniques is demonstrated to provide state ::: of the art accuracy in real time, without the use of a GPU and without the requirement for model initialisation.


title: Provision of positive and negative selections in retroviral vectors containing the cytosine deaminase gene

abstract: The E. coli cytosine deaminase (CD) provides a negative selection system for suicide gene therapy as CD transfectants are eliminated following 5-fluorocytosine (5FC) treatment. Here we report a positive selection system for the CD gene using 5-fluorouracil (5FU) and cytosine in selection medium to screen for CD-positive transfectants. It is based on the relief of 5FU toxicity by uracil which is converted from cytosine via CD catalysis, as uracil competes with the toxic 5FU in subsequent pyrimidine metabolism. Hence, a retroviral vector containing the CD gene may pro- vide both positive and negative selections after gene transfer. The CD transfectants selected with the positive selection system showed susceptibility to 5FC in subsequent negative selection in vitro and in vivo. Therefore, this dual selection system is useful not only for combination therapy with transgene and CD gene, but can also act to eliminate selectively transduced cells after the transgene has furnished its effects or upon undesired conditions if 5FC is applied for negative selection in vivo.


title: Slip compensation for a Mars rover

abstract: A system that enables continuous slip compensation for a Mars rover has been designed, implemented, and field-tested. This system is composed of several components that allow the rover to accurately and continuously follow a designated path, compensate for slippage, and reach intended goals in high-slip environments. These components include: visual odometry, vehicle kinematics, a Kalman filter pose estimator, and a slip compensation/path follower. Visual odometry tracks distinctive scene features in stereo imagery to estimate rover motion between successively acquired stereo image pairs. The vehicle kinematics for a rocker-bogie suspension system estimates motion by measuring wheel rates, and rocker, bogie, and steering angles. The Kalman filter merges data from an inertial measurement unit (IMU) and visual odometry. This merged estimate is then compared to the kinematic estimate to determine how much slippage has occurred, taking into account estimate uncertainties. If slippage has occurred then a slip vector is calculated by differencing the current Kalman filter estimate from the kinematic estimate. This slip vector is then used to determine the necessary wheel velocities and steering angles to compensate for slip and follow the desired path.


title: Near-eye varifocal augmented reality display using see-through screens

abstract: We present a new optical design for see-through near-eye displays that is simple, compact, varifocal, and provides a wide field of view with clear peripheral vision and large eyebox. Key to this effort is a novel see-through rear-projection screen. We project an image to the see-through screen using an off-axis path, which is then relayed to the user's eyes through an on-axis partially-reflective magnifying surface. Converting the off-axis path to a compact on-axis imaging path simplifies the optical design. We establish fundamental trade-offs between the quantitative parameters of resolution, field of view, and the form-factor of our design. We demonstrate a wearable binocular near-eye display using off-the-shelf projection displays, custom-designed see-through spherical concave mirrors, and see-through screen designs using either custom holographic optical elements or polarization-selective diffusers.


title: Metamaterial based K band substrate integrated waveguide filter for advanced communication systems

abstract: The next generation communication system demands for high data rate transfer leading towards exploring a higher level of frequency spectrum. In view of this demand, design of substrate integrated waveguide filters has been presented here in conjunction with metamaterial technology to increase the performance. A metamaterial based substrate integrated waveguide filter operating in the K band (18 – 26.5 GHz) has been demonstrated in this paper with the insertion loss of −0.57 dB in passband and provides a rejection band of 4.1 GHz.


title: Fast Keypoint Features From Laser Scanner for Robot Localization and Mapping

abstract: Detecting features in sensor measurements and distinguishing among them is an important capability for robot localization and navigation. Despite the wide diffusion of range finders, there are few works on keypoint features for 2-D LIDAR and there is potential for improvement over the existing methods. This letter proposes two novel keypoint detectors for the stable detection of interest points in laser measurements and two descriptors for robust associations. The features defined by combining keypoints and descriptors allow stable and efficient place recognition. Experiments with standard benchmark datasets assess the performance of the detectors and descriptors investigated. One of the proposed features, termed FALKO-BSC, achieves higher repeatability score and similar descriptor performance compared with the FLIRT state-of-the-art feature. FALKO-BSC is also shown to enable effective localization.


Comparison of Edge Detectors


title: Sum rate maximization for uplink distributed massive MIMO systems with limited backhaul capacity

abstract: We investigated the uplink sum rate maximization for Distributed Massive MIMO (DM-MIMO) system with limited backhaul capacity. In this system, a number of single antenna autonomous terminals simultaneously transmit data streams to a group of distributed Remote Radio Heads (RRH). These RRHs are connected to a Central Unit (CU) via links with limited capacity. Each RRH quantizes the signal received by each of its antennas and forwards the bits to the backhaul. In this paper we first derive uplink achievable sum rate of the DM-MIMO system under limited backhaul capacity. Due to the complexity of the derived sum rate, an estimation is presented considering the massive MIMO context. This estimated sum rate is verified numerically. Then we show that even if backhaul capacity is limited, DM-MIMO can outperform the centralized case. Finally the sum rate maximization problem for DM-MIMO is formulated and simplified version of the problem is solved.


title: Towards an intelligent control system for district heating plants: Design and implementation of a fuzzy logic based control loop

abstract: In this paper, we lay the foundation for a specialized Intelligent Control System (ICS) for district heating plants and investigate the problem of integrating particular knowledge into the developed system. In particular, we consider the fuzzy logic based control module. Based on control system knowledge, a particular control loop is developed and successfully implemented on a live industrial heating plant. The resulting automatic control system improves the performance of the existing control loop, allows to prolong the lifespan of industrial equipment by allowing to maintain it in a nominal operating condition, and alleviates the necessity for frequent manual control override by heating plant operators. The obtained knowledge forms a valuable asset to the ICS as a part of the corresponding module.


title: Big Data Sensors of Organic Advocacy: The Case of Leonardo DiCaprio and Climate Change

abstract: The strategies that experts have used to share information about social causes have historically been top-down, meaning the most influential messages are believed to come from planned events and campaigns. However, more people are independently engaging with social causes today than ever before, in part because online platforms allow them to instantaneously seek, create, and share information. In some cases this “organic advocacy” may rival or even eclipse top-down strategies. Big data analytics make it possible to rapidly detect public engagement with social causes by analyzing the same platforms from which organic advocacy spreads. To demonstrate this claim we evaluated how Leonardo DiCaprio’s 2016 Oscar acceptance speech citing climate change motivated global English language news (Bloomberg Terminal news archives), social media (Twitter postings) and information seeking (Google searches) about climate change. Despite an insignificant increase in traditional news coverage (54%; 95%CI: -144 to 247), tweets including the terms “climate change” or “global warming” reached record highs, increasing 636% (95%CI: 573–699) with more than 250,000 tweets the day DiCaprio spoke. In practical terms the “DiCaprio effect” surpassed the daily average effect of the 2015 Conference of the Parties (COP) and the Earth Day effect by a factor of 3.2 and 5.3, respectively. At the same time, Google searches for “climate change” or “global warming” increased 261% (95%CI, 186–335) and 210% (95%CI 149–272) the day DiCaprio spoke and remained higher for 4 more days, representing 104,190 and 216,490 searches. This increase was 3.8 and 4.3 times larger than the increases observed during COP’s daily average or on Earth Day. Searches were closely linked to content from Dicaprio’s speech (e.g., “hottest year”), as unmentioned content did not have search increases (e.g., “electric car”). Because these data are freely available in real time our analytical strategy provides substantial lead time for experts to detect and participate in organic advocacy while an issue is salient. Our study demonstrates new opportunities to detect and aid agents of change and advances our understanding of communication in the 21st century media landscape.


title: Service recovery's influence on consumer satisfaction, positive word-of-mouth, and purchase intentions

abstract: Abstract This paper reports two studies (i.e., an experimental design and a field study) that examine the effects that different levels of service recovery have on satisfaction, purchase intentions, and one's propensity to spread positive word-of-mouth (WOM). The results indicate that moderate to high service recovery efforts significantly increase post-failure levels of satisfaction, purchase intent, and positive WOM. Alternatively, poor service recoveries seemingly exacerbate the discontent attributed to a service failure. The results do not support a recovery paradox, whereby post-recovery satisfaction is greater than that satisfaction prior to the service failure. In addition, the studies suggest that firms may not always benefit (in terms of consumer perceptions) from service recovery efforts over and above a moderate level.


title: A passive auxiliary circuit achieves zero-voltage-switching in full-bridge converter over entire conversion range

abstract: A passive auxiliary circuit is proposed to achieve zero-voltage-switching (ZVS) over the entire conversion range in a full-bridge (FB) pulse-width modulated (PWM) converter (FBZVS converter) with minimum conduction loss penalty. The stored energy in the auxiliary circuit is minimal under the full-load condition. It increases progressively as the load current decreases. The proposed auxiliary circuit is passive, simple and can be viewed as an add-on to the conventional FBZVS converter. The principle of operation is described and the performance is demonstrated on a 100 kHz, 500 W prototype.


title: N-VERSION PROGRAMMINC: A FAULT-TOLERANCE APPROACH TO RELlABlLlTY OF SOFTWARE OPERATlON

abstract: N-version programing is defined as the independent generation of N2 2 functionally e quivalent prograns fran the sme initial specification. A methodology of N-version programing has been devised and three types of special mechanisms have been identified t hat are needed to coordinate the execution of an N-version software unit and to canpare the correspondent results generated by each version. Two experiments have been conducted to test the feasibility of N-version programing. The results o f these experiments are discussed. In addition, constraints are identified that must be met for effective application of N-version programing.