title: Gray-box Adversarial Training

abstract: Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples along with clean samples has been introduced to learn robust models. In order to scale adversarial training for large datasets, these perturbations can only be crafted using fast and simple methods (e.g., gradient ascent). However, it is shown that adversarial training converges to a degenerate minimum, where the model appears to be robust by generating weaker adversaries. As a result, the models are vulnerable to simple black-box attacks. In this paper we, (i) demonstrate the shortcomings of existing evaluation policy, (ii) introduce novel variants of white-box and black-box attacks, dubbed gray-box adversarial attacks" based on which we propose novel evaluation method to assess the robustness of the learned models, and (iii) propose a novel variant of adversarial training, named Graybox Adversarial Training" that uses intermediate versions of the models to seed the adversaries. Experimental evaluation demonstrates that the models trained using our method exhibit better robustness compared to both undefended and adversarially trained model


title: A qualitative study of clinical champions in context: Clinical champions across three levels of acute care

abstract: Objectives ::: To compare activities and field descriptions of clinical champions across three levels of stroke centers. ::: ::: ::: Methods ::: A cross-sectional qualitative study using quota sampling was conducted. The setting for this study was 38 acute stroke centers based in US Veterans Affairs Medical Centers with 8 designated as Primary, 24 as Limited Hours, and 6 as Stroke Support Centers. Key informants involved in stroke care were interviewed using a semi-structured approach. A cross-case synthesis approach was used to conduct a qualitative analysis of clinical champions' behaviors and characteristics. Clinical champion behaviors were described and categorized across three dimensions: enthusiasm, persistence, and involving the right people. ::: ::: ::: Results ::: Clinical champions at Primary Stroke Centers represented diverse medical disciplines and departments (education, quality management); directed implementation of acute stroke care processes; coordinated processes across service lines; and benefited from supportive contexts for implementation. Clinical champions at Limited Hours Stroke Centers varied in steering implementation efforts, building collaboration across disciplines, and engaging in other clinical champion activities. Clinical champions at Stroke Support Centers were implementing limited changes to stroke care and exhibited few behaviors fitting the three clinical champion dimensions. Other clinical champion behaviors included educating colleagues, problem-solving, implementing new care pathways, monitoring progress, and standardizing processes. ::: ::: ::: Conclusion ::: These data demonstrate clinical champion behaviors for implementing changes to complex care processes such as acute stroke care. Changes to complex care processes involved coordination among clinicians from multiple services lines, persistence facing obstacles to change, and enthusiasm for targeted practice changes.


AFOSR-TR-82-0562 Local Thresholding Using Convergent Evidence Amar Mitiche


Visual Recognition and Fault Detection for Power Line Insulators


title: Robot Vacuum Cleaner Personality and Behavior

abstract: In this paper we report our study on the user experience of robot vacuum cleaner behavior. How do people want to experience this new type of cleaning appliance? Interviews were conducted to elicit a desired robot vacuum cleaner personality. With this knowledge in mind, behavior was designed for a future robot vacuum cleaner. A video prototype was used to evaluate how people experienced the behavior of this robot vacuum cleaner. The results indicate that people recognized the intended personality in the robot behavior. We recommend using a personality model as a tool for developing robot behavior.


title: Hierarchical Attention Networks for Document Classification

abstract: We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences.


title: Adjustable autonomy for mobile teleoperation of personal service robots

abstract: Controlling personal service robots is an important, complex task which must be accomplished by the users themselves. In this paper, we propose a novel user interface for personal service robots that allows the user to teleoperate the robot using handheld computers. The user can adjust the autonomy of the robot between three levels: body control, skill control, and task control. We design several user interfaces for teleoperation on these levels. On the higher levels, autonomous behavior of the robot relieves the user from significant workload. If the autonomous execution fails, or autonomous functionality is not provided by the robot system, the user can select a lower level of autonomy, e.g., direct body control, to solve a task. In a qualitative user study we evaluate usability aspects of our teleoperation interface with our domestic service robots Cosero and Dynamaid. We demonstrate the benefits of providing adjustable manual and autonomous control.


title: Facebook as a learning tool? A case study on the appropriation of social network sites from mobile phones in developing countries

abstract: This exploratory research investigates how students and professionals use social network sites ( SNSs) in the setting of developing and emerging countries. Data collection included focus groups consisting of medical students and faculty as well as the analysis of a Facebook site centred on medical and clinical topics. The findings show how users, both students and professionals, appropriate SNSs from their mobile phones as rich educational tools in informal learning contexts. First, unlike in previous studies, the analysis revealed explicit forms of educational content embedded in informal learning contexts in Facebook. Quizzes, JOUR presentations and associated deliberate (e-)learning practices which are typically found in (more) formal educational settings were identified. Second, from a sociocultural learning perspective, it is shown how the participation in such virtual professional communities across national boundaries permits the announcement and negotiation of occupational status and professional identities. Practitioner Notes What is already known about this topic Social network sites (SNSs) support education- related learning practices., What this paper adds Learners appropriate SNSs sites from their mobiles as tools for a wide range of educational practices in informal learning contexts in developing/emerging countries., The (e-)learning practices identified include deliberate engagement by users with explicit forms of educational content such as quizzes and JOUR presentations as well as participation in virtual professional communities that allows for the announcement and negotiation of occupational status and professional identities., Such technologies permit the students' educational engagement beyond local communities and facilitate loose connections to professional networks., Implications for practice and/or policy Overhasty claims regarding the more systematic use or the integration of such informal (e-)learning in formal educational settings to support education and health in developing countries should be avoided. Instead, more systematic research is needed. [ABSTRACT FROM AUTHOR]


title: SBVLC: Secure Barcode-Based Visible Light Communication for Smartphones

abstract: As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive applications including payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the screen of a smartphone. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC - a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop two secure data exchange schemes. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and mobile payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.


title: Cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial region: a systematic review of the literature.

abstract: This study reviewed the literature on cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial (OMF) region. A PUBMED search (National Library of Medicine, NCBI; revised 1 December 2007) from 1998 to December 2007 was conducted. This search revealed 375 papers, which were screened in detail. 176 papers were clinically relevant and were analyzed in detail. CBCT is used in OMF surgery and orthodontics for numerous clinical applications, particularly for its low cost, easy accessibility and low radiation compared with multi-slice computerized tomography. The results of this systematic review show that there is a lack of evidence-based data on the radiation dose for CBCT imaging. Terminology and technical device properties and settings were not consistent in the literature. An attempt was made to provide a minimal set of CBCT device-related parameters for dedicated OMF scanners as a guideline for future studies.


Augmented reality at the workplace : a context-aware assistive system using in-situ projection


title: Unsupervised alignment of natural language instructions with video segments

abstract: We propose an unsupervised learning algorithm for automatically inferring the mappings between English nouns and corresponding video objects. Given a sequence of natural language instructions and an unaligned video recording, we simultaneously align each instruction to its corresponding video segment, and also align nouns in each instruction to their corresponding objects in video. While existing grounded language acquisition algorithms rely on pre-aligned supervised data (each sentence paired with corresponding image frame or video segment), our algorithm aims to automatically infer the alignment from the temporal structure of the video and parallel text instructions. We propose two generative models that are closely related to the HMM and IBM 1 word alignment models used in statistical machine translation. We evaluate our algorithm on videos of biological experiments performed in wetlabs, and demonstrate its capability of aligning video segments to text instructions and matching video objects to nouns in the absence of any direct supervision.


title: Lessons learned in ITIL implementation failure

abstract: ITIL is the most widely used IT framework in majority of organizations in the world now. However, implementing such best practice experiences in an organization comes with some implementation challenges such as staff resistance, task conflicts and ambiguous orders. It means that implementing such framework is not easy and it can be caused of the organization destruction. This paper tries to describe overall view of ITIL framework and address major reasons on the failure of this framework’s implementation in the organizations


title: Gesture Recognition: A Survey

abstract: Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted


title: DeepFace: Closing the Gap to Human-Level Performance in Face Verification

abstract: In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.


title: The Relation Between Intelligence and Religiosity

abstract: A meta-analysis of 63 studies showed a significant negative association between intelligence and religiosity. The association was stronger for college students and the general population than for participants younger than college age; it was also stronger for religious beliefs than religious behavior. For college students and the general population, means of weighted and unweighted correlations between intelligence and the strength of religious beliefs ranged from −.20 to −.25 (mean r = −.24). Three possible interpretations were discussed. First, intelligent people are less likely to conform and, thus, are more likely to resist religious dogma. Second, intelligent people tend to adopt an analytic (as opposed to intuitive) thinking style, which has been shown to undermine religious beliefs. Third, several functions of religiosity, including compensatory control, self-regulation, self-enhancement, and secure attachment, are also conferred by intelligence. Intelligent people may therefore have less need for religious beliefs and practices.


title: Analyzing recommender systems and applying a location based approach using tagging

abstract: In the past few decades, due to the increase in the accessibility of the world wide web, data is being generated on a large scale, everyday. When the end user tries to access this data, the available variety of options make it a difficult task to choose amongst various products. In order to simplify this process, Recommender Systems were designed. In this paper, we have analyzed how these Recommender Systems came into existence, their functionalities and how this idea is shaping the world of data science. We analyzed few of the prominent techniques used, such as Content based, Collaborative and Hybrid recommendations. Tagging has become an important tool in Data Science, which can be used to collect implicit data of users and generate recommendations. The paper proposes such an approach which is motivated by observing that many effective recommendations can be generated by analyzing the tagging done by the users. Most of the work is based on the feedback from users having a tendency to use the tagging feature, mostly on social media platforms. The tag based recommendations are also generated by taking into consideration a pattern between users who use location-based services. Counting in the similarities between their Internet browsing preferences and the tag information collected, accurate recommendations can be suggested to the users.


Hieroglyphic Hittite Inscriptions of Commagene


Adaptive Binary Search Trees


title: Is Facebook creating "iDisorders"? The link between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety

abstract: This study systematically tested whether the use of specific technologies or media (including certain types of Facebook use), technology-related anxieties, and technology-related attitudes (including multitasking preference) would predict clinical symptoms of six personality disorders (schizoid, narcissistic, antisocial, compulsive, paranoid and histrionic) and three mood disorders (major depression, dysthymia and bipolar-mania). In addition, the study examined the unique contributions of technology uses after factoring out demographics, anxiety and attitudes. Teens, young adults and adults (N=1143) completed an anonymous, online questionnaire that assessed these variables. Each disorder had a unique set of predictors with 17 of the 22 significant predictors being Facebook general use, impression management and friendship. More Facebook friends predicted more clinical symptoms of bipolar-mania, narcissism and histrionic personality disorder but fewer symptoms of dysthymia and schizoid personality disorder. Technology-related attitudes and anxieties significantly predicted clinical symptoms of the disorders. After factoring out attitudes and anxiety, Facebook and selected technology uses predicted clinical symptoms with Facebook use, impression management and friendship being the best predictors. The results showed both positive and negative aspects of technology including social media as well as apparently detrimental effects of a preference for multitasking.


title: Applying persuasive design in a diabetes mellitus application

abstract: This paper describes persuasive design methods and compares this to an application currently under development for diabetes mellitus patients. Various elements of persuasion and a categorization of persuasion types are mentioned. Also discussed are principles of how successful persuasion should be designed, as well as the practical applications and ethics of persuasive design. This paper is not striving for completeness of theories on the topic, but uses the theories to compare it to an application intended for diabetes mellitus patients. The results of this comparison can be used for improvements of the application.


title: Motivation and Cognitive Control: From Behavior to Neural Mechanism

abstract: Research on cognitive control and executive function has long recognized the relevance of motivational factors. Recently, however, the topic has come increasingly to center stage, with a surge of new studies examining the interface of motivation and cognitive control. In the present article we survey research situated at this interface, considering work from cognitive and social psychology and behavioral economics, but with a particular focus on neuroscience research. We organize existing findings into three core areas, considering them in the light of currently vying theoretical perspectives. Based on the accumulated evidence, we advocate for a view of control function that treats it as a domain of reward-based decision making. More broadly, we argue that neuroscientific evidence plays a critical role in understanding the mechanisms by which motivation and cognitive control interact. Opportunities for further cross-fertilization between behavioral and neuroscientific research are highlighted.


title: Engaging Image Chat: Modeling Personality in Grounded Dialogue

abstract: To achieve the long-term goal of machines being able to engage humans in conversation, our models should be engaging. We focus on communication grounded in images, whereby a dialogue is conducted based on a given photo, a setup that is naturally engaging to humans (Hu et al., 2014). We collect a large dataset of grounded human-human conversations, where humans are asked to play the role of a given personality, as the use of personality in conversation has also been shown to be engaging (Shuster et al., 2018). Our dataset, Image-Chat, consists of 202k dialogues and 401k utterances over 202k images using 215 possible personality traits. We then design a set of natural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. Automatic metrics and human evaluations show the efficacy of approach, in particular where our best performing model is preferred over human conversationalists 47.7% of the time


Deconstructing Xen


title: Efficient heart disease prediction system using decision tree

abstract: Cardiovascular disease (CVD) is a big reason of morbidity and mortality in the current living style. Identification of Cardiovascular disease is an important but a complex task that needs to be performed very minutely, efficiently and the correct automation would be very desirable. Every human being can not be equally skillful and so as doctors. All doctors cannot be equally skilled in every sub specialty and at many places we don't have skilled and specialist doctors available easily. An automated system in medical diagnosis would enhance medical care and it can also reduce costs. In this study, we have designed a system that can efficiently discover the rules to predict the risk level of patients based on the given parameter about their health. The rules can be prioritized based on the user's requirement. The performance of the system is evaluated in terms of classification accuracy and the results shows that the system has great potential in predicting the heart disease risk level more accurately.


title: Product-Service Systems Engineering: State of the art and research challenges

abstract: The design and development of a Product-Service System (PSS) raises new issues since the service component introduces further requirements than traditional product engineering. Compared to physical products, services are generally under-designed and inefficiently developed. For this reason, approaches such as New Service Development, Service Design and Service Engineering have emerged during the years to support the design and development of service either as a system itself or as a constituting element of a Product-Service System. However, only Service Engineering investigates service design and development with a systematic perspective and with a seamless integration of product and service contents. The purpose of this paper is to provide a holistic conceptualisation and an up-to-date review of the literature on Service Engineering with a specific focus on its adoption in the PSS context. A critical analysis is also performed with the aim to define a research agenda and the most prominent key actions that could give directions for future research.


title: Stem cell mediated cardiovascular repair.

abstract: Recent increase in the interest in stem and progenitor cells may be attributed to their behavioural characteristics. A consensus has been reached that embryonic or adult stem cells have therapeutic potential. As cardiovascular health issues are still the major culprits in many developed countries, stem and progenitor cell driven approaches may give the clinicians a new arsenal to tackle many significant health issues. However, stem and progenitor cell mediated cardiovascular regeneration can be achieved via complex and dynamic molecular mechanisms involving a variety of cells, growth factors, cytokines, and genes. Functional contributions of transplanted cells on target organs and their survival are still critical problems waiting to be resolved. Moreover, the regeneration of contracting myocardial tissue has controversial results in human trials. Thus, moderately favourable clinical results should be interpreted carefully. Determining the behavioural programs, genetic and transcriptional control of stem cells, mechanisms that determine cell fate, and functional characteristics are the primary targets. In addition, ensuring the long-term follow-up of cells with efficient imaging techniques in human clinical studies may provide a resurgence of the initial enthusiasm, which has faded over time. Here, we provide a brief historical perspective on stem cell driven cardiac regeneration and discuss cardiac and vascular repair in the context of translational science.


title: A single phase T-type inverter operating in boundary conduction mode

abstract: This paper proposes a single phase T-type inverter operating in boundary conduction mode (BCM). In the past, boundary conduction mode has been widely used to achieve valley voltage switching. In this paper, one switching leg of a full-bridge inverter is replaced by a T-type switching leg to further improve the efficiency. Because the T-type switching leg generates three voltage levels, the voltage stress across switches is halved and the switching frequency decreases compared with the conventional two-level full-bridge inverter operating in BCM. As a result, the switching losses and inductor core losses are largely reduced and the efficiency is improved. The characteristics of the proposed inverter are detailed in this paper. A prototype is built and experimental results validate the superiority of the proposed inverter over the conventional two-level full-bridge inverter.


title: Influence of ultraviolet-B radiation on growth, prevalence of deformities, and susceptibility to predation in Cascades frog (Rana cascadae) larvae

abstract: Ambient levels of ultraviolet-B radiation (UVB) have a variety of detrimental effects on aquatic organisms. These include death and effects on growth, development, physiology, and behavior. Amphibians show all of these effects. However, the effects vary with species, life history stage, and ecological context. Little is known about the implications of the detrimental effects of UVB on ecological dynamics. Our study was designed to test how UVB may affect predator–prey interactions, an important ecological dynamic. Specifically, we tested the effect of UVB on the susceptibility of Cascades frog (Rana cascadae) larvae to predation by rough-skinned newts (Taricha granulosa). We also further examined the sublethal effects of UVB on growth and development in Cascades frog larvae. We found no direct effect of UVB exposure on survival. However, UVB-exposed frog larvae displayed decreased growth and increased prevalence of deformities. UVB also caused increased susceptibility to predation, but there was a significant treatment–block interaction. UVB increased susceptibility to predation in two out of five blocks of Cascades frogs. The other three blocks did not show an effect of UVB on susceptibility to predation. Our study suggests that UVB can alter susceptibility to predation in at least one amphibian species. UVB-induced alteration of predator–prey interactions could potentially lead to changes at the population, community, and ecosystem levels.


title: An Analysis of Minutiae Matching Strength

abstract: In recent years there has been exponential growth in the use of biometrics for user authentication applications because biometrics-based authentication offers several advantages over knowledge and possession-based methods such as password/PIN-based systems. However, it is important that biometrics-based authentication systems be designed to withstand different sources of attacks on the system when employed in security-critical applications. This is even more important for unattended remote applications such as e-commerce. In this paper we outline the potential security holes in a biometrics-based authentication scheme, quantify the numerical strength of one method of fingerprint matching, then discuss how to combat some of the remaining weaknesses.


title: Specular-to-Diffuse Translation for Multi-View Reconstruction

abstract: Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively. Our network extends unsupervised image-to-image translation to multi-view "specular to diffuse" translation. To preserve object appearance across multiple views, we introduce a Multi-View Coherence loss (MVC) that evaluates the similarity and faithfulness of local patches after the view-transformation. Our MVC loss ensures that the similarity of local correspondences among multi-view images is preserved under the image-to-image translation. As a result, our network yields significantly better results than several single-view baseline techniques. In addition, we carefully design and generate a large synthetic training data set using physically-based rendering. During testing, our network takes only the raw glossy images as input, without extra information such as segmentation masks or lighting estimation. Results demonstrate that multi-view reconstruction can be significantly improved using the images filtered by our network. We also show promising performance on real world training and testing data.


title: Who Watches the Watchmen: A Security-focused Review on Current State-of-the-art Techniques, Tools, and Methods for Systems and Binary Analysis on Modern Platforms

abstract: Malicious software, a threat users face on a daily basis, have evolved from simple bankers based on social engineering to advanced persistent threats. Recent research and discoveries reveal that malware developers have been using a wide range of anti-analysis and evasion techniques, in-memory attacks, and system subversion, including BIOS and hypervisors. In addition, code-reuse attacks like Returned Oriented Programming emerge as highly potential remote code execution threats. To counteract the broadness of malicious codes, distinct techniques and tools have been proposed, such as transparent malware tracers, system-wide debuggers, live forensics tools, and isolated execution rings. In this work, we present a survey on state-of-the-art techniques that detect, mitigate, and analyze the aforementioned attacks. We show approaches based on Hardware Virtual Machines introspection, System Management Mode instrumentation, Hardware Performance Counters, isolated rings (e.g., Software Guard eXtensions), as well as others based on external hardware. We also discuss upcoming threats based on the very same technologies used for defense. Our main goal is to provide the reader with a broader, more comprehensive understanding of recently surfaced tools and techniques aiming at binary analysis for modern platforms.


title: A framework for remote sensing images processing using deep learning technique

abstract: Deep learning techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, Convolutional Neural Networks and Recurrent Neural Networks based systems achieve state of the art results on satellite and aerial imagery in many applications. While these approaches are subject to scientific interest, there is currently no operational and generic implementation available at user-level for the remote sensing community. In this paper, we presents a framework enabling the use of deep learning techniques with remote sensing images and geospatial data. Our solution takes roots in two extensively used open-source libraries, the remote sensing image processing library Orfeo ToolBox, and the high performance numerical computation library TensorFlow. It can apply deep nets without restriction on images size and is computationally efficient, regardless hardware configuration.


title: Towards polynomial lower bounds for dynamic problems

abstract: We consider a number of dynamic problems with no known poly-logarithmic upper bounds, and show that they require nΩ(1) time per operation, unless 3SUM has strongly subquadratic algorithms. Our result is modular: (1) We describe a carefully-chosen dynamic version of set disjointness (the "multiphase problem"), and conjecture that it requires n^Omega(1) time per operation. All our lower bounds follow by easy reduction. (2) We reduce 3SUM to the multiphase problem. Ours is the first nonalgebraic reduction from 3SUM, and allows 3SUM-hardness results for combinatorial problems. For instance, it implies hardness of reporting all triangles in a graph. (3) It is plausible that an unconditional lower bound for the multiphase problem can be established via a number-on-forehead communication game.


title: The benign online disinhibition effect: Could situational factors induce self-disclosure and prosocial behaviors?

abstract: The current study, which focuses on the benign effects of disinhibition, was designed as a continuation of an earlier study conducted on the toxic effects of online disinhibition (Lapidot-Lefler & Barak, 2012). Using a factorial design, the study examined the effects of three online situational factors—anonymity, invisibility, and lack of eye contact—on inducing self-disclosure and prosocial behaviors as expressions of benign online disinhibition. Random pairs of adult strangers (n = 144) discussed a dilemma and were required to reach a joint solution using online chat. Self-disclosure and prosocial behavior effects were measured using participants’ self-reports, expert judges’ ratings of chat transcripts, and textual analyses of the conversations. Results suggested that the interaction between anonymity and invisibility had a significant effect on the revealing of emotions. Lack of eye contact, the interaction between anonymity and invisibility and the interaction between lack of eye contact and invisibility had a significant effect on the inducement of first-person words. The interaction between anonymity, invisibility and lack of eye contact had significant effects on the total self-disclosure score, yet no significant effects were found for prosocial behaviors. A discussion of the findings with regard to previous research on toxic online disinhibition suggests that different factors play a role in the inducement of benign vs. toxic online disinhibition effects. More research is required to substantiate current findings and determine the nature of the contribution of each situational factor.


title: NICBPM: Non-invasive cuff-less blood pressure monitor

abstract: An ultra-low power, ultra portable, wearable integrated bio-medical device, the Non-Invasive Cuff-less Blood Pressure Monitor (NICBPM) is proposed to measure the Pulse Transit Time (PTT). The relationship between PTT and Blood Pressure (BP) is investigated using linear and non linear regression models. Simultaneous reading obtained from an FDA approved, commercially available Blood Pressure monitor and the (Electrocardiogram) ECG and (photoplethsymogram) PPG signal from the NICBPM are used to calibrate the device for 30 subjects during cardiopulmonary exercise test. Bland-Altman plots for the linear regression model revealed limits of agreement of −10.6 to 10.6 mmHg for systolic BP (sBP) and −10.5 to 10.5 mmHg for distolic BP (dBP). Bland-Altman plots for the non linear regression model revealed marginally better limits of agreement between −8.5 to 8.5 mmHg for sBP and −6.6 to 6.6 mmHg for dBP. sBP can be estimated from the NICBPM with 99% accuracy and 99% sensitivity.


title: Computer Based Testing (CBT): An Assessment of Student Perception of JAMB UTME in Nigeria

abstract: In Nigeria students are assessed using paper and pen on cognitive abilities. This method of assessment is however, characterized by different forms of examination malpractices. The threat of examination malpractices on the validity of examination outcomes has resulted in some examination bodies adopting a different method of administration. One of such is the Joint Admissions and Matriculation Board (JAMB) that has introduced Computer Based Testing (CBT) in the conduct of Unified Tertiary Matriculation Examination (UTME). Using survey research method, this study investigated the perception of student on the use of computer based testing in examinations. Problems encountered by the student and prospective methods of enhancing CBT acceptance in Nigeria were also documented. A total of 300 questionnaires were administered to students who participated in the 2014 UTME at Ahmadu Bello University (ABU), Zaria center and 237 were adequately completed and found usable representing (79%). The finding revealed among others, that majority of the respondents confirmed that CBT can curb examination malpractice. Majority of candidates were also found to prefer CBT than the conventional way of writing examination. The Chisquare and Pearson’s correlation analysis showed that the respondents preferences for CBT was sensitive across gender, age distribution and student faculty. While improving electricity supply was identified as critical in enhancing CBT Exams, poor ICT skill on the part of student and the invigilators were identified as the major problem facing the implementation of JAMB CBT Exam. The paper recommends the need for JAMB to create more awareness on CBT inorder to enhance JAMB CBT examination in Nigeria.


title: Noise and Sensitivity of Harmonic Radar Architecture for Remote Sensing and Detection of Vital Signs

abstract: This paper addresses noise and sensitivity issues in remote sensing and detection of vital signs based on a continuous wave biomedical radar operating at multiple harmonic carrier frequencies or channels. This Doppler radar makes use of a single mixer, taking advantage of the inherent nonlinearity and harmonic characteristics of the mixer. Other system building elements such as antennas, amplifiers, and circulators can also operate at and comply with multiple harmonic frequencies or channels requirements, which makes the system compact. Noise is one of the most important factors that affect the sensitivity of this type of system. The total noise is the combined contribution of thermal noise, residual phase noise, and flicker noise. Flicker noise is found to be the critical parameter for the baseband detection. Experimental results show that with the use of the harmonic radar technique, the flicker noise can be reduced by 20 dB around 1-Hz baseband frequency compared with the counterpart in a conventional radar operating at single frequency. The noise and sensitivity of a harmonic radar system operating at 12 and 24 GHz for vital signs detection are studied theoretically and experimentally. It is proven that the harmonic radar solution is able to increase detection sensitivity by increasing the signal-to-noise ratio. The performance of the harmonic radar is tested experimentally with a moving plate and also a real patient. For the heartbeat detection, an oximeter giving the oxygen saturation of blood and heart rate is used as the reference.


title: Opponent modeling in real-time strategy games

abstract: Real-time strategy games present an environment in which game AI is expected to behave realistically. One feature of realistic behaviour in game AI is the ability to recognise the strategy of the opponent player. This is known as opponent modeling. In this paper, we propose an approach of opponent modeling based on hierarchically structured models. The top-level of the hierarchy can classify the general play style of the opponent. The bottom-level of the hierarchy can classify specific strategies that further define the opponent’s behaviour. Experiments that test the approach are performed in the RTS game Spring. From our results we may conclude that the approach can be successfully used to classify the strategy of an opponent in the Spring game.


Focal Stack Compositing for Depth of Field Control


title: The common mechanisms of transformation by the small DNA tumor viruses: The inactivation of tumor suppressor gene products: p53

abstract: Abstract The small DNA tumor viruses, Polyoma virus, Simian Vacuolating Virus 40, the Papilloma viruses and the human Adenoviruses, were first described during a period of intense virus discovery (1930–1960s) and shown to produce tumors in animals. In each of these cases the viral DNA was shown to persist (commonly integrated into a host chromosome) and only a selected portion of this DNA was expressed as m-RNA and proteins in these cancers. The viral encoded tumor antigens were identified and shown to be required to both establish the tumor and maintain the transformed cell phenotype. The functions of these viral tumor antigens were explored and shown to have common features and mechanisms even though they appear to have evolved from diverse genes. The SV40 large tumor antigen, the human Papilloma virus E7 protein and the Adenovirus E1A protein were shown to bind to and inactivate the functions of the Retinoblastoma proteins in transformed cells. This resulted in the activation of the E2F and DP transcription factors and the entry of cells into the S-phase of DNA synthesis which was required for viral DNA replication. These events triggered the activation of p53 which promotes apoptosis of these virus infected cells limiting virus replication and tumor formation. These viruses responded by evolving and producing the SV40 large tumor antigen, the human Papilloma virus E6 protein and the Adenovirus E1b-55Kd protein which binds to and inactivates the p53 functions in both the infected cells and transformed cells. Some of the human Papilloma viruses and one of the Polyoma viruses have been shown to cause selected cancers in humans. Both the p53 tumor suppressor gene, which was uncovered in the studies with these viruses, and the retinoblastoma protein, have been shown to play a central role in the origins of human cancers via both somatic and germ line mutations in those genes.


title: The Operator 4.0: Human Cyber-Physical Systems & Adaptive Automation Towards Human-Automation Symbiosis Work Systems

abstract: A vision for the Operator 4.0 is presented in this paper in the context of human cyber-physical systems and adaptive automation towards human-automation symbiosis work systems for a socially sustainable manufacturing workforce. Discussions include base concepts and enabling technologies for the development of human-automation symbiosis work systems in Industry 4.0.


title: The Ring of Gyges: Investigating the Future of Criminal Smart Contracts

abstract: Thanks to their anonymity (pseudonymity) and elimination of trusted intermediaries, cryptocurrencies such as Bitcoin have created or stimulated growth in many businesses and communities. Unfortunately, some of these are criminal, e.g., money laundering, illicit marketplaces, and ransomware. Next-generation cryptocurrencies such as Ethereum will include rich scripting languages in support of smart contracts, programs that autonomously intermediate transactions. In this paper, we explore the risk of smart contracts fueling new criminal ecosystems. Specifically, we show how what we call criminal smart contracts (CSCs) can facilitate leakage of confidential information, theft of cryptographic keys, and various real-world crimes (murder, arson, terrorism). We show that CSCs for leakage of secrets (a la Wikileaks) are efficiently realizable in existing scripting languages such as that in Ethereum. We show that CSCs for theft of cryptographic keys can be achieved using primitives, such as Succinct Non-interactive ARguments of Knowledge (SNARKs), that are already expressible in these languages and for which efficient supporting language extensions are anticipated. We show similarly that authenticated data feeds, an emerging feature of smart contract systems, can facilitate CSCs for real-world crimes (e.g., property crimes). Our results highlight the urgency of creating policy and technical safeguards against CSCs in order to realize the promise of smart contracts for beneficial goals.


Multiplex and homologous recombination–mediated genome editing in Arabidopsis and Nicotiana benthamiana using guide RNA and Cas9


title: Knowledge Generation Model for Visual Analytics

abstract: Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.


title: Ex ante prediction of cascade sizes on networks of agents facing binary outcomes

abstract: We consider in this paper the potential for ex ante prediction of the cascade size in a model of binary choice with externalities (Schelling 1973, Watts 2002). Agents are connected on a network and can be in one of two states of the world, 0 or 1. Initially, all are in state 0 and a small number of seeds are selected at random to switch to state1. A simple threshold rule specifies whether other agents switch subsequently. The cascade size (the percolation) is the proportion of all agents which eventually switches to state 1. We select information on the connectivity of the initial seeds, the connectivity of the agents to which they are connected, the thresholds of these latter agents, and the thresholds of the agents to which these are connected. We obtain results for random, small world and scale -free networks with different network parameters and numbers of initial seeds. The results are robust with respect to these factors. We perform least squares regression of the logit transformation of the cascade size (Hosmer and Lemeshow 1989) on these potential explanatory variables. We find considerable explanatory power for the ex ante prediction of cascade sizes. For the random networks, on average 32 per cent of the variance of the cascade sizes is explained, 40 per cent for the small world and 46 per cent for the scale-free. The connectivity variables are hardly ever significant in the regressions, whether relating to the seeds themselves or to the agents connected to the seeds. In contrast, the information on the thresholds of agents contains much more explanatory power. This supports the conjecture of Watts and Dodds (2007.) that large cascades are driven by a small mass of easily influenced agents.


title: Annotating Object Instances with a Polygon-RNN

abstract: We propose an approach for semi-automatic annotation of object instances. While most current methods treat object segmentation as a pixel-labeling problem, we here cast it as a polygon prediction task, mimicking how most current datasets have been annotated. In particular, our approach takes as input an image crop and sequentially produces vertices of the polygon outlining the object. This allows a human annotator to interfere at any time and correct a vertex if needed, producing as accurate segmentation as desired by the annotator. We show that our approach speeds up the annotation process by a factor of 4.7 across all classes in Cityscapes, while achieving 78:4% agreement in IoU with original ground-truth, matching the typical agreement between human annotators. For cars, our speed-up factor is 7.3 for an agreement of 82:2%. We further show generalization capabilities of our approach to unseen datasets.


title: Persistent Monitoring of Stochastic Spatio-temporal Phenomena with a Small Team of Robots

abstract: This paper presents a solution for persistent monitoring of real-world stochastic phenomena, where the underlying covariance structure changes sharply across time, using a small number of mobile robot sensors. We propose an adaptive solution for the problem where stochastic real-world dynamics are modeled as a Gaussian Process (GP). The belief on the underlying covariance structure is learned from recently observed dynamics as a Gaussian Mixture (GM) in the low-dimensional hyper-parameters space of the GP and adapted across time using Sequential Monte Carlo methods. Each robot samples a belief point from the GM and locally optimizes a set of informative regions by greedy maximization of the submodular entropy function. The key contributions of this paper are threefold: adapting the belief on the covariance using Markov Chain Monte Carlo (MCMC) sampling such that particles survive even under sharp covariance changes across time; exploiting the belief to transform the problem of entropy maximization into a decentralized one; and developing an approximation algorithm to maximize entropy on a set of informative regions in the continuous space. We illustrate the application of the proposed solution through extensive simulations using an artificial dataset and multiple real datasets from fixed sensor deployments, and compare it to three competing state-of-the-art approaches.


title: Flexible alarm calling in meerkats: the role of the social environment and predation urgency

abstract: Flexible vocal production has been demonstrated in several vertebrate species, with much work focusing on the role of the social "audience" in explaining variation in call production. It is, however, likely that the decision to call is an emergent property of both external and internal factors, and the extent to which these factors are integrated has been little investigated. We addressed this question by examining the production of alarm calls in wild male meerkats (Suricata suricatta) in different social environments and different predator-encounter contexts. Males searching for reproductive opportunities (rovers) were followed 1) in their home group and when prospecting either 2) solitarily or 3) in a coalition with other males. Results showed that conspecific presence influenced the production of flee-alarm and recruitment calls. Solitary rovers were less likely to produce flee-alarm calls compared with when they are with conspecifics, whether coalitionary rovers or the rover’s home group. Experimentally elicited recruitment calls were also produced less when males were solitary than when in their home group. Bark vocalizations, emitted when meerkats were safe, were always produced irrespective of conspecific presence, indicating that these calls function to address predators. The probability of producing flee alarms also increased with the urgency of the predation event. Our results indicate that variation in alarm call production depends on whom the call is addressed to and also on the motivational state of the caller. We argue that neglecting to integrate internal and external factors when elucidating mechanisms underlying vocal production can potentially lead to misguided, parsimonious conclusions regarding vocal flexibility in animals.


title: ZigBee technology: Current status and future scope

abstract: ZigBee is an enabling technology providing low data rate, low battery consumption with low cost based on the standard IEEE 802.15.4. It is contributing to wireless personal area network and wireless sensor network. It is mainly used for various wireless sensor and wireless personal area network applications as low power consumption applications like home automation, industry control, remote control, medical assistance and other wireless sensor applications. This paper introduces the ZigBee, its specification, its layered architecture, topologies, types of devices, comparison of ZigBee with other standards, applications of ZigBee and future scope of ZigBee technology.


title: New approach for utilization of cellulose derivatives metal complexes in preparation of durable and permanent colored papers

abstract: Abstract This work focused on studying the used of polymer complexes as a new approach for the preparation of high performance colored paper. In this respect, the paper strength, thermal stability, biological resistance, magnetic properties, as well as the durability of aged paper were evaluated. It was found that, using carboxymethyl cellulose–copper complexes [CMC–Cu(II)], as paper additive, enhances the strength properties of wood pulp paper sheet, and depends on the anion of the copper salt used and the pH-value during the preparation process. The best polymer complex is that produced from using copper sulfate as the origin of copper ion, at pH 5.4. Also, incorporating the CMC–Cu(II) complexes with wood pulp provides thermal stability, fire retardancy, biological resistance, magnetism, as well as durability to the paper sheets obtained.


title: Steel-Sense: Integrating Machine Elements with Sensors by Additive Manufacturing

abstract: Many interactive devices use both machine elements and sensors, simultaneously but redundantly enabling and measuring the same physical function. We present Steel-Sense, an approach to joining these two families of elements to create a new type of HCI design primitive. We leverage recent developments in 3D printing to embed sensing in metal structures that are otherwise difficult to equip with sensors, and present four design principles, implementing (1) an electronic switch integrated within a ball bearing; (2) a voltage divider within a gear; (3) a variable capacitor embedded in a hinge; and (4) a pressure sensor within a screw. Each design demonstrates a different sensing principle, and signals its performance through (1) movement; (2) position; (3) angle (4) or stress. We mirror our elements physical performance in a virtual environment, evaluate our designs electronically and structurally, and discuss future work and implications for HCI research.


Appropriating the Returns from Industrial Research and Development


title: Modeling and simulation of multi coil induction heating system for semiconductor wafer processing

abstract: A quick, uniform and high temperature heating are the main requirements for processing circular semiconductor wafers. With single coil induction heating system which uses longitudinal flux heating, it is rather difficult to uniformly heat the semiconductor wafer. In this paper a multi coil induction heating system is proposed for semiconductor processing. A six coil induction heating system is modeled and simulated using ANSYS MAXWELL 3D Finite Element Analysis (FEA) tool. The excitation currents in the coils have same frequency but differ in phase by 60 in order to generate a travelling wave magnetic field. By controlling the amplitude of the currents in the working coils uniform heating of the graphite susceptor can be achieved.


title: A distributed canny edge detector: Comparative approach

abstract: The aim of the system is to implement the new canny algorithm for parallel preprocessing block with no loss in edge detection results compared with the original frame-level Canny algorithm. The methodology employed here is distributed canny edge detection algorithm on the basis of threshold segmentation as the segmentation has good advantage in noise restraining with wide applicability and computes the thresholds based on the block statistics. The main objective of the proposed segmentation based system includes significant reduction in latency, better edge preservation. The simulation of proposed system with MATLAB version R2012a is carried out. The experimental result indicates the effect of mask size on the latency and effect of PSNR on quality of edges which quantify the overall system performance.


Capturing Deep Correlations with 2-Way Nets.


INTRODUCTION TO CIRCLE PACKING: A REVIEW


title: An enhanced variable selection and Isolation Forest based methodology for anomaly detection with OES data

abstract: The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low, and is an active area of research in semiconductor manufacturing, particularly in the context of using Optical Emission Spectroscopy (OES) data. The high dimension and correlated nature of OES data can limit the performance achievable with anomaly detection systems. In this paper we present a dimensionality reducing variable selection and isolation forest based anomaly detection and diagnosis methodology that addresses these issues. In particular, it takes account of isolated variables that can be overlooked when using conventional approaches such as PCA, and provides greater interpretability than afforded by PCA. The proposed methodology is illustrated with the aid of simulated and industrial plasma etch case studies.


title: MCD: Mutual Clustering across Multiple Social Networks

abstract: Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple "partially aligned networks". In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formally defined as the "Mutual Clustering" problem. The "Mutual Clustering" problem is very challenging as it has two important issues to address: (1) how to preserve the network characteristics in mutual community detection? and (2) how to utilize the information in other aligned networks to refine and disambiguate the community structures of the shared users? To solve these two challenges, a novel community detection method, MCD (Mutual Community Detector), is proposed in this paper. MCD can detect social community structures of users in multiple partially aligned networks at the same time with full considerations of (1) characteristics of each network, and (2) information of the shared users across aligned networks. Extensive experiments conducted on two real-world partially aligned heterogeneous social networks demonstrate that MCD can solve the "Mutual Clustering" problem very well.


title: Analytic Hierarchy Process and Supply Chain Management: A Bibliometric Study

abstract: Abstract A comparative study was used to outline the literature in the research topic. This paper aims to present a bibliometric study ofmulti-criteria decision-making methods most applied in publications from 1990 to 2014. Our research presented relations of papers published in the Web of Science Core Collection, regarding the following keywords Analytic Hierarchy Process and Supply Chain. The research evidenced that the Analytic Hierarchy Process has been the method mostapplied in publications from 1993. It also showed the analysis of the predecessor and successor citation network for the selected publications under topics as supplier selection, supply development, performance measurement and value chain through the CitNetExplore software.


HOW DO NEURAL NETWORKS OVERCOME LABEL NOISE?


2005-38 Generalized Steady-state Analysis of Multiphase Interleaved Boost Converter with Coupled


title: The Globus Pallidus Sends Reward-Related Signals to the Lateral Habenula

abstract: As a major output station of the basal ganglia, the globus pallidus internal segment (GPi) projects to the thalamus and brainstem nuclei thereby controlling motor behavior. A less well known fact is that the GPi also projects to the lateral habenula (LHb) which is often associated with the limbic system. Using the monkey performing a saccade task with positionally biased reward outcomes, we found that antidromically identified LHb-projecting neurons were distributed mainly in the dorsal and ventral borders of the GPi and that their activity was strongly modulated by expected reward outcomes. A majority of them were excited by the no-reward-predicting target and inhibited by the reward-predicting target. These reward-dependent modulations were similar to those in LHb neurons but started earlier than those in LHb neurons. These results suggest that GPi may initiate reward-related signals through its effects on the LHb, which then influences the dopaminergic and serotonergic systems.


title: Fractional Fourier transform estimation of simple randomly sampled signals

abstract: Random sampling as a special kind of nonuniform sampling has been used as a digital alias-free signal processing method in analog-to-digital conversion. This paper presents the fractional Fourier transform estimation of nonstationary signals based on the simple random sampling. The spectrum estimation is proved to be unbiased. The variance of it is calculated to show the estimation effect. The effects of sampling jitters and observation errors on performance of the fractional spectrum estimation are analyzed. Among them, the sampling jitters introduce bias to the estimation. The bias can be compensated by the new defined fractional characteristic function. All of the analysis results are simulated and verified in the last numerical experiments.


title: A Network Protection Framework for DNP3 over TCP/IP protocol

abstract: the pervasive deployment of intelligent devices in the critical infrastructures sector and the high dependency of these devices on the Internet motivated attackers to target the communication and control protocols of these devices. DNP3 over TCP/IP is among those protocols that are widely used as communication and control protocols in critical infrastructures. Due to the facts that security was not part of the goals for designing the DNP3 and the incompetent of current protection systems, adversary can easily succeed in attacking DNP3 devices and network. In this paper, we present an Autonomic Network Protection Framework for DNP3 over TCP/IP that detects old attacks that cannot be prevented by the legacy DNP3 security devices as well as new attacks. The system's detection module is based on rule-based anomaly intrusion detection. We evaluated the effectiveness of the generated rules in detecting anomalies through both offline and online testing. Both the false positive and the false negative rates of our approach are quite low. In addition, we present a classification technique and an access control mechanism to provide autonomic network protection.


title: Analyzing and improving Linux kernel memory protection: a model checking approach

abstract: Code injection continues to pose a serious threat to computer systems. Among existing solutions, W ⊕ X is a notable approach to prevent the execution of injected code. In this paper, we focus on the Linux kernel memory protection and systematically check for possible W ⊕ X violations in the Linux kernel design and implementation. In particular, we have developed a Murphi-based abstract model and used it to discover several serious shortcomings in the current Linux kernel that violate the W ⊕ X property. We have confirmed with the Linux community the presence of these problems and accordingly developed five Linux kernel patches. (Four of them are in the process of being integrated into the mainline Linux kernel.) Our evaluation with these patches indicate that they involve only minimal changes to the existing code base and incur negligible performance overhead.


title: Pattern Division Multiple Access—A Novel Nonorthogonal Multiple Access for Fifth-Generation Radio Networks

abstract: In this paper, pattern division multiple access (PDMA), which is a novel nonorthogonal multiple access scheme, is proposed for fifth-generation (5G) radio networks. The PDMA pattern defines the mapping of transmitted data to a resource group that can consist of time, frequency, and spatial resources or any combination of these resources. The pattern is introduced to differentiate signals of users sharing the same resources, and the pattern is designed with disparate diversity order and sparsity so that PDMA can take the advantage of the joint design of transmitter and receiver to improve system performance while maintaining detection complexity to a reasonable level. System level simulation results show that PDMA can support $\text{six}$ times simultaneous connections than that of conventional and at least $\text{30}\%$ improvement in spectrum efficiency over orthogonal frequency division multiple access.


title: A model for evaluating the effectiveness of middle managers' training courses: evidence from a major banking organization in Greece

abstract: Contemporary management thinking embraces the organizational training theory that sustainable success rests, to a great extent, upon a systematic evaluation of training interventions. However, the evidence indicates that few organizations take adequate steps to assess and analyse the quality and outcomes of their training. The authors seek to develop the existing literature on training evaluation by proposing a new model, specific to management training, which might encourage more and better evaluation by practitioners. Their thesis is that training evaluation is best if it can be based on criteria derived from the objectives of the training and they draw on the management effectiveness literature to inform their proposed model. The study seeks to examine the effect of six evaluation levels – reactions, learning, job behaviour, job performance, organizational team performance and some wider, societal effects – in measuring training interventions with regard to the alterations to learning, transfer and organizational impact. The model was tested with data obtained from 190 middle managers employed by a large banking organization in Greece and the results suggest that there is considerable consistency in the evaluation framework specified. The paper discusses these results and draws conclusions about their practical implications. The study's limitations are considered and some future research needs identified.


title: Learning Non-taxonomic Relationships of Financial Ontology

abstract: Finance ontology is, in most cases, manually addressed. This results in a tedious development process and error prone that delay their applicability. This is why there is a need of domain ontology learning methods that built the ontology automatically and without human intervention. However, in this learning process, the discovery of non-taxonomic relationships has been recognized as one of the most difficult problems. In this paper, we propose a new methodology for learning non-taxonomic relationships and building financial ontology from scratch. Our new technique is based on using and adapting Open Information Extraction algorithms to extract and label domain relations between concepts. To evaluate our new method effectiveness, we compare the extracted non-taxonomic relations of our algorithm with related works in the same finance corpus. The results showed that our system is more accurate and more effective.


title: Summary of Modeling and Simulation for NIST RoboCrane ® Applications

abstract: A fuel injection control method for an internal combustion engine in which asynchronous fuel injection can be carried out in accordance with the change in the number of engine rotations and throttle valve opening and asynchronously with the rotations of a crank shaft within a predetermined range of the number of engine rotations while inhibiting the asynchronous fuel injection when the number of engine rotations is not within the range, whereby the lean and over rich conditions of the air/fuel ratio which often occurred in the conventional method for controlling fuel injection can be prevented and acceleration with a good response can be realized.


title: Learning Determinantal Point Processes

abstract: We present a novel probabilistic model for distributions over sets of structures— for example, sets of sequences, trees, or graphs. The critical characteristic of our model is a preference for diversity: sets containing dissimilar structures are more likely. Our model is a marriage of structured probabilistic models, like Markov random fields and context free grammars, with determinantal point processes, which arise in quantum physics as models of particles with repulsive interactions. We extend the determinantal point process model to handle an exponentially-sized set of particles (structures) via a natural factorization of the model into parts. We show how this factorization leads to tractable algorithms for exact inference, including computing marginals, computing conditional probabilities, and sampling. Our algorithms exploit a novel polynomially-sized dual representation of determinantal point processes, and use message passing over a special semiring to compute relevant quantities. We illustrate the advantages of the model on tracking and articulated pose estimation problems.


Sentiment Analysis Using Support Vector Machines With Diverse Information Sources


title: Region-based facial representation for real-time Action Units intensity detection across datasets

abstract: Most research on facial expressions recognition has focused on binary Action Units (AUs) detection, while graded changes in their intensity have rarely been considered. ::: This paper proposes a method for the real-time detection of AUs intensity in terms of the Facial Action Coding System scale. It is grounded on a novel and robust anatomically based facial representation strategy, for which features are registered from a different region of interest depending on the AU considered. Real-time processing is achieved by combining Histogram of Gradients descriptors with linear kernel Support Vector Machines. Following this method, AU intensity detection models are built and validated through the DISFA database, outperforming previous approaches without real-time capabilities. An in-depth evaluation through three different databases (DISFA, BP4D and UNBC Shoulder-Pain) further demonstrates that the proposed method generalizes well across datasets. This study also brings insights about existing public corpora and their impact on AU intensity prediction.


MINING OF SENSOR DATA IN HEALTHCARE: A SURVEY


title: The Effect of a Haptic Guidance Steering System on Fatigue-Related Driver Behavior

abstract: Prolonged driving on monotonous roads often leads to a reduction in task load that causes drivers passive fatigue. Passive fatigue results in loss of driver alertness and is detrimental to driver safety. This paper focuses on the effect of a haptic guidance steering system on improving behaviors of passively fatigued drivers. By continuously exerting active torque on a steering wheel, the haptic system guides drivers to follow the centerline of a lane; meanwhile, the drivers sense the torque and interact with it while operating the steering wheel. An experiment was conducted with 12 healthy participants in a high-fidelity driving simulator. A monotonous driving course was designed, and vehicle speed was fixed in order to induce drivers’ passive fatigue. A treatment session was arranged with the haptic guidance steering system, and a control session was conducted as a comparison. Driving performance, assessed by standard deviation of lane position, mean absolute lateral error, and time-to-lane crossing, was significantly improved when haptic guidance was activated. Results of physiological measures, including heart rate variability and percentage of eye closure, revealed that passively fatigued drivers were aroused when they were aware of the active torque on the steering wheel. In conclusion, the activation of haptic guidance can be regarded as an effective countermeasure for the passively fatigued drivers who have performed a prolonged monotonous driving task.


title: A fractal analogy approach to the Raven's test of intelligence

abstract: We present a fractal technique for addressing geometric analogy problems from the Raven's Standard Progressive Matrices test of general intelligence. In this method, an image is represented fractally, capturing its inherent self-similarity. We apply these fractal representations to problems from the Raven's test, and show how these representations afford a new method for solving complex geometric analogy problems. We present results using the fractal algorithm on all 60 problems from the Standard Progressive Matrices version of the Raven's test.


title: Viterbi algorithm with sparse transitions (VAST) for nonintrusive load monitoring

abstract: Implementation of smart grid provides an opportunity for concurrent implementation of nonintrusive appliance load monitoring (NIALM), which disaggregates the total household electricity data into data on individual appliances. This paper introduces a new disaggregation algorithm for NIALM based on a modified Viterbi algorithm. This modification takes advantage of the sparsity of transitions between appliances' states to decompose the main algorithm, thus making the algorithm complexity linearly proportional to the number of appliances. By consideration of a series of data and integrating a priori information, such as the frequency of use and time on/time off statistics, the algorithm dramatically improves NIALM accuracy as compared to the accuracy of established NIALM algorithms.


title: Human information behavior: integrating diverse approaches and information use

abstract: For millennia humans have sought, organized, and used information as they learned and evolved patterns of human information behaviors to resolve their human problems and survive. However, despite the current focus on living in an "information age," we have a limited evolutionary understanding of human information behavior. In this article the authors examine the current three interdisciplinary approaches to conceptualizing how humans have sought information including (a) the everyday life information seeking-sense-making approach, (b) the information foraging approach, and (c) the problem-solution perspective on information seeking approach. In addition, due to the lack of clarity regarding the role of information use in information behavior, a fourth information approach is provided based on a theory of information use. The use theory proposed starts from an evolutionary psychology notion that humans are able to adapt to their environment and survive because of our modular cognitive architecture. Finally, the authors begin the process of conceptualizing these diverse approaches, and the various aspects or elements of these approaches, within an integrated model with consideration of information use. An initial integrated model of these different approaches with information use is proposed.


title: Unsupervised Feature Learning for RGB-D Image Classification

abstract: Motivated by the success of Deep Neural Networks in computer vision, we propose a deep Regularized Reconstruction Independent Component Analysis network (R\(^2\)ICA) for RGB-D image classification. In each layer of this network, we include a R\(^2\)ICA as the basic building block to determine the relationship between the gray-scale and depth images corresponding to the same object or scene. Implementing commonly used local contrast normalization and spatial pooling, we gradually enhance our network to be resilient to local variance resulting in a robust image representation for RGB-D image classification. Moreover, compared with conventional handcrafted feature-based RGB-D image representation, the proposed deep R\(^2\)ICA is a feedforward network. Hence, it is more efficient for image representation. Experimental results on three publicly available RGB-D datasets demonstrate that the proposed method consistently outperforms the state-of-the-art conventional, manually designed RGB-D image representation confirming its effectiveness for RGB-D image classification.


title: Designing hybrid flexure systems and elements using Freedom and Constraint Topologies

abstract: In this paper we introduce the principles necessary to synthesize hybrid flexure systems and elements. Flexure systems consist of rigid bodies that are joined together by flexure elements that elastically deform to guide the system's rigid bodies with desired degrees of freedom (DOFs). The principles introduced here for synthesizing hybrid flexure systems and elements are extensions of the Freedom and Constraint Topologies (FACT) synthesis approach. FACT utilizes a comprehensive library of geometric shapes from which designers can rapidly consider and compare a multiplicity of flexure concepts that achieve any desired set of DOFs. Prior to this paper, designers primarily used these shapes to synthesize parallel and serial flexure systems and elements. With this paper, designers may now use these same shapes to synthesize more general flexures that consist of various combinations of parallel and serial systems and elements (i.e., hybrid configurations). As such, designers can access a larger body of flexure solutions that satisfy demanding design requirements. Instructions for helping designers utilize or avoid the advantages and challenges of over-, under-, and exact-constraint are also provided. Hybrid systems and elements are analysed and designed as case studies.


title: Scheduling internet of things applications in cloud computing

abstract: Internet of Things (IoT) is one of the greatest technology revolutions in the history. Due to IoT potential, daily objects will be consciously worked in harmony with optimized performances. However, today, technology is not ready to fully bring its power to our daily life because of huge data analysis requirements in instant time. On the other hand, the powerful data management of cloud computing gives IoT an opportunity to make the revolution in our life. However, the traditional cloud computing server schedulers are not ready to provide services to IoT because IoT consists of a number of heterogeneous devices and applications which are far away from standardization. Therefore, to meet the expectations of users, the traditional cloud computing server schedulers should be improved to efficiently schedule and allocate IoT requests. There are several proposed scheduling algorithms for cloud computing in the literature. However, these scheduling algorithms are limited because of considering neither heterogeneous servers nor dynamic scheduling approach for different priority requests. Our objective is to propose dynamic dedicated server scheduling for heterogeneous and homogeneous systems to efficiently provide desired services by considering priorities of requests. Results show that the proposed scheduling algorithm improves throughput up to 40 % in heterogeneous and homogeneous cloud computing systems for IoT requests. Our proposed scheduling algorithm and related analysis will help cloud service providers build efficient server schedulers which are adaptable to homogeneous and heterogeneous environments by considering system performance metrics, such as drop rate, throughput, and utilization in IoT.


Scalable Detection of Concept Drifts on Data Streams with Parallel Adaptive Windowing


title: EEG seizure detection and prediction algorithms: a survey

abstract: Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.


title: Use of geosynthetics in piggy-back landfills : development of an iterative methodology for the design of the lining system over old unlined waste

abstract: Given the difficulties in identifying suitable sites for the establishment of new sanitary landfills, the extension of existing sites is considered more and more often as a solution. This increasing interest is also linked to the possibility of increasing the waste storage capacity and using the operating in-frastructures over a longer period, thus allowing savings. Beyond the benefits associated to this rather new landfill concept, it is necessary to carefully consider the integrity of the liner system considering the im-portant settlements (reactivated under the new cell load) that can develop. Indeed, the settlements can alter the sealing and the drainage functions of the liner system. An iterative method inspired mainly from the Incremental Settlement Prediction Model (Olivier, 2003) was developed to look deeper into this issue. Applicable to various geometric configurations, this method helps designing the barrier reinforcement us-ing high-performance geogrids. In addition to this aspect, the proposed methodology also helps designing bottom liner system slopes to guarantee free drainage and avoid leachate accumulation.


title: Learning a General-Purpose Confidence Measure Based on O(1) Features and a Smarter Aggregation Strategy for Semi Global Matching

abstract: Inferring dense depth from stereo is crucial for several computer vision applications and Semi Global Matching (SGM) is often the preferred choice due to its good tradeoff between accuracy and computation requirements. Nevertheless, it suffers of two major issues: streaking artifacts caused by the Scanline Optimization (SO) approach, at the core of this algorithm, may lead to inaccurate results and the high memory footprint that may become prohibitive with high resolution images or devices with constrained resources. In this paper, we propose a smart scanline aggregation approach for SGM aimed at dealing with both issues. In particular, the contribution of this paper is threefold: i) leveraging on machine learning, proposes a novel generalpurpose confidence measure suited for any for stereo algorithm, based on O(1) features, that outperforms state of-the-art ii) taking advantage of this confidence measure proposes a smart aggregation strategy for SGM enabling significant improvements with a very small overhead iii) the overall strategy drastically reduces the memory footprint of SGM and, at the same time, improves its effectiveness and execution time. We provide extensive experimental results, including a cross-validation with multiple datasets (KITTI 2012, KITTI 2015 and Middlebury 2014).


title: Optimized Memristor-Based Multipliers

abstract: Since memristors came to the forefront of research, minimal work has explored their application to computer arithmetic. This paper proposes two memristor-based implementations of an N-bit shift-and-add multiplier, one using IMPLY operations and a second using MAD operations. The optimized IMPLY-based implementation reduces the baseline delay from 2N ::: 2 ::: + 29N steps and 17N+3 memristors to 2N ::: 2 ::: + 21N steps and 7N+1 memristors. A second implementation is proposed that is constructed from MAD gates, a lower-area, lower-delay alternative to IMPLY logic. This design performs an N-bit multiplication in N ::: 2 ::: + N steps with 5N memristors and 3N+2 drivers. Both designs require fewer steps and less than 1/6 of the number of components of a traditional CMOS design. Finally, both of the implementations are extended to implement radix-2 Booth multipliers. The IMPLY design only increases by 1 step per iteration and 2N memristors and drivers. The MAD design increases by N memristors and 6N switches but maintains the same delay as the shift-and-add multiplier. Both designs maintain a lower area and lower delay than the CMOS equivalent.


title: Astrophysical Journal, in press Photon Bubbles in the Circumstellar Envelopes of Young Massive Stars

abstract: We show that the optically thick dusty envelopes surrounding young high-mass stars are subject to the photon bubble instability. The infrared radiation passing through the envelope amplifies magnetosonic disturbances, with growth rates in our local numerical radiation MHD calculations that are consistent with a linear analysis. Modes with wavelengths comparable to the gas pressure scale height grow by more than 2 orders of magnitude in 1000 yr, reaching nonlinear amplitudes within the envelope lifetime. If the magnetic pressure in the envelope exceeds the gas pressure, the instability develops into trains of propagating shocks. Radiation escapes readily through the low-density material between the shocks, enabling accretion to continue despite the Eddington limit imposed by the dust opacity. The supersonic motions arising from the photon bubble instability can help explain the large velocity dispersions of hot molecular cores, while conditions in the shocked gas are suitable for maser emission. We conclude that the photon bubble instability may play a key role in the formation of massive stars.


How does a brain build a cognitive code


title: AUTOMATIC TUNING OF PROPORTIONAL- INTEGRAL-DERIVATIVE (PID) CONTROLLER USING PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM

abstract: The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler- Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler- Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.


title: Analysis of Android Inter-App Security Vulnerabilities Using COVERT

abstract: The state-of-the-art in securing mobile software systems are substantially intended to detect and mitigate vulnerabilities in a single app, but fail to identify vulnerabilities that arise due to the interaction of multiple apps, such as collusion attacks and privilege escalation chaining, shown to be quite common in the apps on the market. This paper demonstrates COVERT, a novel approach and accompanying tool-suite that relies on a hybrid static analysis and lightweight formal analysis technique to enable compositional security assessment of complex software. Through static analysis of Android application packages, it extracts relevant security specifications in an analyzable formal specification language, and checks them as a whole for inter-app vulnerabilities. To our knowledge, COVERT is the first formally-precise analysis tool for automated compositional analysis of Android apps. Our study of hundreds of Android apps revealed dozens of inter-app vulnerabilities, many of which were previously unknown. A video highlighting the main features of the tool can be found at: http://youtu.be/bMKk7OW7dGg.


title: NFC Devices: Security and Privacy

abstract: The aim of this paper is to show security measures for NFC (Near Field Communication) use cases and devices. We give a brief overview over NFC technology and evaluate the implementation of NFC in devices. Out of this technology review we derive different use cases and applications based on NFC technology. Based on the use cases we show assets and interfaces of an NFC device that could be a possible target of an attacker. In the following we apply different attacks against the operation modes to show how applications and devices could be protected against such attacks. The information collected is consolidated in a set of threats giving guidelines on how to improve security and overcome privacy issues. This allows integrating NFC technology in a secure way for the end consumer.


title: Properties of cholera exo-enterotoxin (choleragen) and its natural toxoid (choleragenoid).

abstract: Abstract The cholera enterotoxin is a unique pharmacological agent which both causes the diarrhea of cholera and has potent vascular permeability activity. It is a simple protein of approximately 84,000 mol. wt. and consists of 6 sub-units. The antigenically identical spontaneously formed toxoid, choleragenoid, a protein of approximately 56,000 mol. wt., lacks the toxic activities of the parent toxin. Both proteins have been obtained in pure form and have recently been crystallized. Accumulating evidence indicates that the toxin stimulates adenyl cyclase activity leading to increased levels of the important metabolic mediator, cyclic 3′5′ adenosine monophosphate, but the precise mechanism for the causation of the choleraic diarrhea and an effective means of stopping it remain to be elucidated. Toxin-derived antigens: choleragenoid, formalinized toxoids, and procholeragenoid, the polymeric intermediate in the formation of choleragenoid, may prove useful in control of cholera by stimulating antitoxic immunity. Further study of the cholera problems should yield valuable information applicable to other diarrheal conditions, to an understanding of normal intestinal transport, and to greater comprehension of local immune mechanisms.


title: Intelligent weather forecast

abstract: In recent years, many solutions to intelligent weather forecast have been proposed, especially on temperature and rainfall, however, it is difficult to simulate the meteorological phenomena and the corresponding characters of weather when some complex differential equations and computational algorithms are merely piled up. On the basis of the review of researches on the non-linear characters of meteorology, This work describes a methodology to short-term temperature and rainfall forecasting over the east coast of China based on some necessary data preprocessing technique and the dynamic weighted time-delay neural networks (DWTDNN), in which each neuron in the input layer is scaled by a weighting function that captures the temporal dynamics of the biological task. This network is a simplified version of the focused gamma network and an extension of TDNN as it incorporates a priori knowledge available about the task into the network architecture. As an example, the estimations produced by the methodology were applied on 8 different weather forecasting data provided by the Shanghai Meteorology Centre to make the result more practical. The results confirm that proposed solutions have the potential for successful application to the problem of temperature and rainfall estimation, and the relationships between the factors that contribute to certain weather conditions can be estimated at a certain extent.


title: Time-Frequency Networks for Audio Super-Resolution

abstract: Audio super-resolution (a.k.a. bandwidth extension) is the challenging task of increasing the temporal resolution of audio signals. Recent deep networks approaches achieved promising results by modeling the task as a regression problem in either time or frequency domain. In this paper, we introduced Time-Frequency Network (TFNet), a deep network that utilizes supervision in both the time and frequency domain. We proposed a novel model architecture which allows the two domains to be jointly optimized. Results demonstrate that our method outperforms the state-of-the-art both quantitatively and qualitatively.


title: Comparison of neural networks for solving the travelling salesman problem

abstract: The TSP deals with finding a shortest path through a number of cities. This seemingly simple problem is hard to solve because of the amount of possible solutions. Which is why methods that give a good suboptimal solution in a reasonable time are generally used. In this paper three methods were compared with respect to quality of solution and ease of finding correct parameters: the Integer Linear Programming method, the Hopfield Neural Network, and the Kohonen Self Organizing Feature Map Neural Network.


title: Continuous depth estimation for multi-view stereo

abstract: Depth-map merging approaches have become more and more popular in multi-view stereo (MVS) because of their flexibility and superior performance. The quality of depth map used for merging is vital for accurate 3D reconstruction. While traditional depth map estimation has been performed in a discrete manner, we suggest the use of a continuous counterpart. In this paper, we first integrate silhouette information and epipolar constraint into the variational method for continuous depth map estimation. Then, several depth candidates are generated based on a multiple starting scales (MSS) framework. From these candidates, refined depth maps for each view are synthesized according to path-based NCC (normalized cross correlation) metric. Finally, the multiview depth maps are merged to produce 3D models. Our algorithm excels at detail capture and produces one of the most accurate results among the current algorithms for sparse MVS datasets according to the Middlebury benchmark. Additionally, our approach shows its outstanding robustness and accuracy in free-viewpoint video scenario.


title: Location Systems: An Introduction to the Technology Behind Location Awareness

abstract: Advances in electronic location technology and the coming of age of mobile computing have opened the door for location-aware applications to permeate all aspects of everyday life. Location is at the core of a large number of high-value applications ranging from the life-and-death context of emergency response to serendipitous social meet-ups. For example, the market for GPS products and services alone is expected to grow to US$200 billion by 2015. Unfortunately, there is no single location technology that is good for every situation and exhibits high accuracy, low cost, and universal coverage. In fact, high accuracy and good coverage seldom coexist, and when they do, it comes at an extreme cost. Instead, the modern localization landscape is a kaleidoscope of location systems based on a multitude of different technologies including satellite, mobile telephony, 802.11, ultrasound, and infrared among others. This lecture introduces researchers and developers to the most popular technolog es and systems for location estimation and the challenges and opportunities that accompany their use. For each technology, we discuss the history of its development, the various systems that are based on it, and their trade-offs and their effects on cost and performance. We also describe technology-independent algorithms that are commonly used to smooth streams of location estimates and improve the accuracy of object tracking. Finally, we provide an overview of the wide variety of application domains where location plays a key role, and discuss opportunities and new technologies on the horizon. Table of Contents: Introduction / The Global Positioning System / Infrared and Ultrasonic Systems / Location Esimation with 802.11 / Cellular-Based Systems / Other Approaches / Improving Localization Accuracy / Location-Based Applications and Services / Challenges and Opportunities / References


title: Prosody Contour Prediction with Long Short-Term Memory, Bi-Directional, Deep Recurrent Neural Networks

abstract: Deep Neural Networks (DNNs) have been shown to provide state-of-the-art performance over other baseline models in the task of predicting prosodic targets from text in a speechsynthesis system. However, prosody prediction can be affected by an interaction of short- and long-term contextual factors that a static model that depends on a fixed-size context window can fail to properly capture. In this work, we look at a recurrent formulation of neural networks (RNNs) that are deep in time and can store state information from an arbitrarily large input history when making a prediction. We show that RNNs provide improved performance over DNNs of comparable size in terms of various objective metrics for a variety of prosodic streams (notably, a relative reduction of about 6% in F0 mean-square error accompanied by a relative increase of about 14% in F0 variance), as well as in terms of perceptual quality assessed through mean-opinion-score listening tests. Index Terms: speech synthesis,text-to-speech, prosody prediction, recurrent neural networks, deep learning