title: Rapid control prototyping of a quad-tilt-wing unmanned aerial vehicle

abstract: In this paper, an advanced Computer Aided Design (CAD) methodology for Processor-In-the-Loop (PIL) cosimulation and rapid control prototyping of a Quad Tilt Wing (QTW) type of convertible Unmanned Aerial Vehicles (UAVs) is proposed and successfully implemented around an embedded NI myRIO target and a host PC. The developed software (SW) and hardware (HW) CAD solution is based on the Control Design and Simulation (CDSim) module of LabVIEW environment as well as a set-up Network Streams-based data communication protocol. A nonlinear dynamic model of the QTW drone is established using the Newton-Euler formalism. Linear Quadratic Gaussian (LQG) control approach is investigated as an implementation example for the proposed rapid control prototyping platform. A three-dimensional (3D) mechanical model of the QTW, with a H-type structure, is built under LabVIEW environment for the SW/HW real-time co-simulations. All obtained co-simulation results as well as our own developed LabVIEW interfaces are presented and discussed to improve the effectiveness of the given CAD methodology for the rapid flight controllers prototyping.


title: Ontology and trust based data warehouse in new generation of business intelligence: State-of-the-art, challenges, and opportunities

abstract: Business intelligence applications are more focused on structured data and support decision makers by providing meaningful information from extracted data mainly coming from day-to-day operational databases and structured external data sources. However, the volume of unstructured data is growing very fast and data analysts need to consider this kind of data especially when analysing external data such as customers' reviews and posts in social media networks and web blogs. As external data is derived from a variety of sources, it is essential to determine the reputation of the source and provide flexibility to the analysts so that they can take into account the trust value of each source in their analysis. Another important consideration is the semantics of extracted textual data from which meaningful information is derived. Ontology is utilized in order to obtain the semantics of textual data. We do not intend to develop ontology from scratch, but rather use the existing available ontologies which are collected in an ontology repository. By using ontology, entities in the extracted textual data are linked with corresponding concepts. Useful knowledge can be inferred and used in the analysis process. In summary, we propose to use the notion of trust to evaluate data sources, and use ontology to enrich textual data. The trusted external data covered global environment, the Voice of the Market, and the Voice of the Customer can be collected and store it in the current data warehouse.


Automatic spelling correction in scientific and scholarly text


title: Qualitative Data Analysis for Health Services Research: Developing Taxonomy, Themes, and Theory

abstract: OBJECTIVE ::: To provide practical strategies for conducting and evaluating analyses of qualitative data applicable for health services researchers. DATA SOURCES AND DESIGN: We draw on extant qualitative methodological literature to describe practical approaches to qualitative data analysis. Approaches to data analysis vary by discipline and analytic tradition; however, we focus on qualitative data analysis that has as a goal the generation of taxonomy, themes, and theory germane to health services research. ::: ::: ::: PRINCIPLE FINDINGS ::: We describe an approach to qualitative data analysis that applies the principles of inductive reasoning while also employing predetermined code types to guide data analysis and interpretation. These code types (conceptual, relationship, perspective, participant characteristics, and setting codes) define a structure that is appropriate for generation of taxonomy, themes, and theory. Conceptual codes and subcodes facilitate the development of taxonomies. Relationship and perspective codes facilitate the development of themes and theory. Intersectional analyses with data coded for participant characteristics and setting codes can facilitate comparative analyses. ::: ::: ::: CONCLUSIONS ::: Qualitative inquiry can improve the description and explanation of complex, real-world phenomena pertinent to health services research. Greater understanding of the processes of qualitative data analysis can be helpful for health services researchers as they use these methods themselves or collaborate with qualitative researchers from a wide range of disciplines.


title: A Systematic Mapping Study on Software Reuse

abstract: Context: Software reuse is considered as the key to a successful software development because of its potential to reduce the time to market, increase quality and reduce costs. This increase in demand made the software organizations to envision the use of software reusable assets which can also help in solving recurring problems. Till now, software reuse is confined to reuse of source code in the name of code scavenging. Now a day, software organizations are extending the concepts of software reuse to other life cycle objects as they realized that reuse of source code alone does not save money. The academia has put forward some assets as reusable and presented methods or approaches for reusing them. Also, for a successful software reuse the organizations should assess the value of reuse and keep track on their reuse programs. The other area which is vital for software reuse is the maintenance. Maintenance of reusable software has direct impact on the cost of the software. In this regard, academia has presented a number of techniques, methods, metrics and models for assessing the value of reuse and for maintaining the reusable software. Objectives: In our thesis, we investigate on the reusable assets and the methods/ approaches that are put forward by the academia for reusing those assets. Also a systematic mapping study is performed to investigate what techniques, methods, models and metrics for assessing the value of reuse and for maintaining the reused software are proposed and we also investigate their validation status as well. Methods: The databases like IEEE Xplore, ACM digital library, Inspec, Springer and Google scholar were used to search for the relevant studies for our systematic mapping study. We followed basic inclusion criteria along with detailed inclusion/exclusion criteria for selecting the appropriate article. Results: Through our systematic mapping study, we could summarize the list of 14 reusable assets along with the approaches/methods for reusing them. Taxonomy for assessing the value of reuse and taxonomy for maintaining the reusable software are presented. We also presented the methods/metrics/models/techniques for measuring reuse to assess its value and for maintaining the reusable software along with their validation status and areas in focus. Conclusion: We conclude that, there is a need for defining a standard set of reusable assets that are commonly accepted by the researchers in the field of software reuse. Most metrics/models/methods/approaches presented for assessing the value of reuse and for maintaining the reuse software are academically validated. Efforts have to be put on industrially validating them using the real data.


title: Electronic stability control for electric vehicle with four in-wheel motors

abstract: The electric vehicle with four direct-driven in-wheel motors is an over actuated system. A three-level control strategy of electronic stability control (ESC) is proposed to achieve optimal torque distribution for four in-wheel motors. The first level is a gain-scheduled linear quadratic regulator which is designed to generate the desired yaw moment command for ESC. Control allocation is the second level which is used to distribute the desired longitudinal tire forces according to the yaw moment command while satisfying the driver’s intent for acceleration and deceleration. The associated weighting matrix is designed using the work load ratio at each wheel to prevent saturating the tire. The third level is slip ratio control (SRC) which is employed at each wheel to generate the desired longitudinal tire force based on a combined-slip tire model. Simulation results show that the proposed method can enhance the ESC performance for the test maneuvers. Since the tire model is often unknown for practical implementation, the effectiveness of the SRC is studied using the sine with dwell test. It is found that the SRC is not crucial for achieving performance similar to the proposed method with SRC, if the slip ratio can be maintained in the stable region using traction control system/anti-lock braking system.


title: Modeling and understanding students' off-task behavior in intelligent tutoring systems

abstract: We present a machine-learned model that can automatically detect when a student using an intelligent tutoring system is off-task, i.e., engaged in behavior which does not involve the system or a learning task. This model was developed using only log files of system usage (i.e. no screen capture or audio/video data). We show that this model can both accurately identify each student's prevalence of off-task behavior and can distinguish off-task behavior from when the student is talking to the teacher or another student about the subject matter. We use this model in combination with motivational and attitudinal instruments, developing a profile of the attitudes and motivations associated with off-task behavior, and compare this profile to the attitudes and motivations associated with other behaviors in intelligent tutoring systems. We discuss how the model of off-task behavior can be used within interactive learning environments which respond to when students are off-task.


title: A curated mammography data set for use in computer-aided detection and diagnosis research

abstract: Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Our data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data-set size capable of analyzing decision support systems in mammography.


title: Ku-band waveguide band-pass filter with iris radius

abstract: This paper presents the design, simulation, and implementation of band pass filters in rectangular waveguides with radius, having 0.1 dB pass band ripple and 6.3% ripple at the center frequency of 14.2 GHz. A Mician microwave wizard software based on the Mode Matching Method (MMM) was used to simulate the structure of the filter. Simulation results are in good agreement with the measured one which improve the validity of the waveguide band pass filter design method.


title: An efficient brush model for physically-based 3D painting

abstract: This paper presents a novel 3D brush model consisting of a skeleton and a surface, which is deformed through constrained energy minimization. The main advantage of our model over existing ones is in its ability to mimic brush flattening and bristle spreading due to brush bending and lateral friction exerted by the paper surface during the painting process. The ability to recreate such deformations is essential to realistic 3D digital painting simulations, especially in the case of Chinese brush painting and calligraphy. To further increase realism, we also model the plasticity of wetted brushes and the resistance exerted by pores on the paper surface onto the brush tip. Our implementation runs on a consumer-level PC in real-time and produces very realistic results.


title: Distributed fault detection of wireless sensor networks

abstract: Wireless Sensor Networks (WSNs) have become a new information collection and monitoring solution for a variety of applications. Faults occurring to sensor nodes are common due to the sensor device itself and the harsh environment where the sensor nodes are deployed. In order to ensure the network quality of service it is necessary for the WSN to be able to detect the faults and take actions to avoid further degradation of the service. The goal of this paper is to locate the faulty sensors in the wireless sensor networks. We propose and evaluate a localized fault detection algorithm to identify the faulty sensors. The implementation complexity of the algorithm is low and the probability of correct diagnosis is very high even in the existence of large fault sets. Simulation results show the algorithm can clearly identify the faulty sensors with high accuracy.


title: Identification and assessment of risks associated with ERP post-implementation in China

abstract: Purpose – The purpose of this paper is to identify, assess and explore potential risks that Chinese companies may encounter when using, maintaining and enhancing their enterprise resource planning (ERP) systems in the post‐implementation phase.Design/methodology/approach – The study adopts a deductive research design based on a cross‐sectional questionnaire survey. This survey is preceded by a political, economic, social and technological analysis and a set of strength, weakness, opportunity and threat analyses, from which the researchers refine the research context and select state‐owned enterprises (SOEs) in the electronic and telecommunications industry in Guangdong province as target companies to carry out the research. The questionnaire design is based on a theoretical risk ontology drawn from a critical literature review process. The questionnaire is sent to 118 selected Chinese SOEs, from which 42 (84 questionnaires) valid and usable responses are received and analysed.Findings – The findings ident...


title: BINSPECT: Holistic Analysis and Detection of Malicious Web Pages

abstract: Malicious web pages are among the major security threats on the Web. Most of the existing techniques for detecting malicious web pages focus on specific attacks. Unfortunately, attacks are getting more complex whereby attackers use blended techniques to evade existing countermeasures. In this paper, we present a holistic and at the same time lightweight approach, called BINSPECT, that leverages a combination of static analysis and minimalistic emulation to apply supervised learning techniques in detecting malicious web pages pertinent to drive-by-download, phishing, injection, and malware distribution by introducing new features that can effectively discriminate malicious and benign web pages. Large scale experimental evaluation of BINSPECT achieved above 97% accuracy with low false signals. Moreover, the performance overhead of BINSPECT is in the range 3-5 seconds to analyze a single web page, suggesting the effectiveness of our approach for real-life deployment.


Comparative analysis of e–learning and traditional teaching methods in the fields of nursing and physiotherapy in the Medical University of Białystok


Health Monitoring and Fault Diagnosis in Induction Motor-A Review


title: Facial recognition using histogram of gradients and support vector machines

abstract: Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.


title: A RESCUE RADAR SYSTEM FOR THE DETECTION OF VICTIMS TRAPPED UNDER RUBBLE BASED ON THE INDEPENDENT COMPONENT ANALYSIS ALGORITHM

abstract: This work presents a light-weight microwave system for the search and rescue of victims trapped under the rubble of collapsed building during an earthquake or other disasters. The proposed system based on a continuous wave X-band radar is able to detect respiratory and heart ∞uctuations: the information is extracted from the backscattered electromagnetic fleld exploiting independent component analysis (ICA) algorithm which provides an e-cient noise and clutter cleaning. The proposed rescue radar is compact enough to be mounted onboard of a small unmanned aerial vehicle (UAV) in order to reach inaccessible or dangerous areas. The obtained experimental results show that the proposed detection method is able to successfully locate trapped victims with a reasonable degree of accuracy.


title: Improved resolution from subpixel shifted pictures

abstract: Abstract In this paper we consider the problem of obtaining a picture with improved resolution from an ensemble of K spatially shifted low-resolution pictures. We show that it can be done and provide a procedure for its implementations. The framework and scheme utilized in the generalized multichannel sampling theorem of Papoulis and Brown was found to be an appropriate representation for our problem. The linear channels of this scheme correspond to the processes that produce the low-resolution pictures (by blur and shifts) and to the process of reconstruction. The total process is shown to consist of two distinct degradation-restoration pairs: blurring-deblurring and undersampling-merging. This paper discusses merging. Deblurring (deconvolution) can be done by any desired scheme independent of merging. It is shown that the procedure achieves the claims of super resolution schemes but, unlike many of them, is well posed and robust. This procedure holds for a selected ensemble so that K is not too large and no two of the pictures practially coincide. The number of multiplications per pixel of the improved picture can be reduced to O ( K 2 ).


title: Detection of dirt impairments from archived film sequences: survey and evaluations

abstract: Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research.


Identifying the Topic-Specific Influential Users in Twitter


title: SVMs Modeling for Highly Imbalanced Classification

abstract: Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class imbalance has been the application of a sundry of sampling strategies. In this paper, we focus on designing modifications to support vector machines (SVMs) to appropriately tackle the problem of class imbalance. We incorporate different ldquorebalancerdquo heuristics in SVM modeling, including cost-sensitive learning, and over- and undersampling. These SVM-based strategies are compared with various state-of-the-art approaches on a variety of data sets by using various metrics, including G-mean, area under the receiver operating characteristic curve, F-measure, and area under the precision/recall curve. We show that we are able to surpass or match the previously known best algorithms on each data set. In particular, of the four SVM variations considered in this paper, the novel granular SVMs-repetitive undersampling algorithm (GSVM-RU) is the best in terms of both effectiveness and efficiency. GSVM-RU is effective, as it can minimize the negative effect of information loss while maximizing the positive effect of data cleaning in the undersampling process. GSVM-RU is efficient by extracting much less support vectors and, hence, greatly speeding up SVM prediction.


title: Implementing 'Awakened Need of Change' for Applying Ergonomics to Work System with Macroergonomics Approach in an Industrially Developing Country and its Meta-Reflection

abstract: This study aimed to create macroergonomics awareness and wakefulness at the organizational level at a manufacturing company with emphasis on understanding the importance improving “Working Conditions Systems” (Health, Safety, and Ergonomics) and understanding the necessity of applying ergonomics to work system. It sought to develop a vision and ideas as well as drawing up checkpoints for the organization of work when the research could be with company and the participation of participants, not only on people or techniques and tools. The survey approach was used. Data was collected from several steps with different sample participants: 1) Three questionnaires were conducted on managers viewpoint (29 people), on health, safety and environment staff’s viewpoint (5 people) and operating worker’s viewpoint (85 people), 2) Using of the “Future Workshop” for middle managers (15 participants), 3) Using of ILO ergonomics checklist in one of units of the company (29 participants), 4) Evaluating the workshop among middle managers (15 participants), 5) Interview with some participants in “Future Workshop” (10 participants) and, 6) interview with some participants involved in applying the ergonomics checklist (4 participants), 7) Review of the organizational documents was used. In the data collection, the participants were made to study the organizational knowledge with different PDSA cycles of learning and the desire of understanding for changing the improving working condition system when an external and internal facilitator team was formed. Based on the analysis of participants feedbacks on the implementation of different work evaluation approaches and their reflection from learning, key findings were made through intentional learning on PDSA Cycles of learning, based on a systemic pre-macroergonomics intervention work process on how the participants could be ‘empowerment through reflection’ with the different getting learning understanding when there were the different concepts of “learning” and reply on the appreciative inquiries as a meta- reflection.


title: Toward Deep Learning Software Repositories

abstract: Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP). Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora. Hence, we motivate deep learning for software language modeling, highlighting fundamental differences between state-of-the-practice software language models and connectionist models. Our deep learning models are applicable to source code files (since they only require lexically analyzed source code written in any programming language) and other types of artifacts. We show how a particular deep learning model can remember its state to effectively model sequential data, e.g., Streaming software tokens, and the state is shown to be much more expressive than discrete tokens in a prefix. Then we instantiate deep learning models and show that deep learning induces high-quality models compared to n-grams and cache-based n-grams on a corpus of Java projects. We experiment with two of the models' hyper parameters, which govern their capacity and the amount of context they use to inform predictions, before building several committees of software language models to aid generalization. Then we apply the deep learning models to code suggestion and demonstrate their effectiveness at a real SE task compared to state-of-the-practice models. Finally, we propose avenues for future work, where deep learning can be brought to bear to support model-based testing, improve software lexicons, and conceptualize software artifacts. Thus, our work serves as the first step toward deep learning software repositories.


title: Managing Supply Chain Risk: A Supply-Side Perspective

abstract: In the past, most research on supply chain man- agement focused on initiatives that made supply chains leaner, faster and more flexible, resulting in highly efficient but more vulnerable operations. Our study draws upon the existing literature to develop a model for upstream supply chain risk management. We collected survey data from 162 companies among several manufacturing industries. In a path analytic model, we link three steps of the supply chain risk management process - risk identification, risk assess- ment and risk mitigation - with upstream supply chain risk performance. We also include the effect of a continuous risk management improvement process on the risk management activities in order to adapt to the requirements of a changing environment. The data provides robust support to all our hypotheses. The study contributes to the literature by elabo- rating some theory aspects in more detail and empirically confirming the contribution of upstream supply chain risk management activities to the actual supply chain risk per- formance. Managers benefit from the insights how risk iden- tification, risk assessment and risk mitigation activities in- fluence risk performance and which role a continuous im- provement process can play.


title: Medium Access Control protocols for ad hoc wireless networks : A survey

abstract: Studies of ad hoc wireless networks are a relatively new field gaining more popularity for various new applications. In these networks, the Medium Access Control (MAC) protocols are responsible for coordinating the access from active nodes. These protocols are of significant importance since the wireless communication channel is inherently prone to errors and unique problems such as the hidden-terminal problem, the exposed-terminal problem, and signal fading effects. Although a lot of research has been conducted on MAC protocols, the various issues involved have mostly been presented in isolation of each other. We therefore make an attempt to present a comprehensive survey of major schemes, integrating various related issues and challenges with a view to providing a big-picture outlook to this vast area. We present a classification of MAC protocols and their brief description, based on their operating principles and underlying features. In conclusion, we present a brief summary of key ideas and a general direction for future work.


title: Example-Based Image Colorization Using Locality Consistent Sparse Representation

abstract: Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.


title: A break in the clouds: towards a cloud definition

abstract: This paper discusses the concept of Cloud Computing to achieve a complete definition of what a Cloud is, using the main characteristics typically associated with this paradigm in the literature. More than 20 definitions have been studied allowing for the extraction of a consensus definition as well as a minimum definition containing the essential characteristics. This paper pays much attention to the Grid paradigm, as it is often confused with Cloud technologies. We also describe the relationships and distinctions between the Grid and Cloud approaches.


title: Duplicate News Story Detection Revisited

abstract: In this paper, we investigate near-duplicate detection, particularly looking at the detection of evolving news stories. These stories often consist primarily of syndicated information, with local replacement of headlines, captions, and the addition of locally-relevant content. By detecting near-duplicates, we can offer users only those stories with content materially different from previously-viewed versions of the story. We expand on previous work and improve the performance of near-duplicate document detection by weighting the phrases in a sliding window based on the term frequency within the document of terms in that window and inverse document frequency of those phrases. We experiment on a subset of a publicly available web collection that is comprised solely of documents from news web sites. News articles are particularly challenging due to the prevalence of syndicated articles, where very similar articles are run with different headlines and surrounded by different HTML markup and site templates. We evaluate these algorithmic weightings using human judgments to determine similarity. We find that our techniques outperform the state of the art with statistical significance and are more discriminating when faced with a diverse collection of documents.


title: Performance evaluation of IEEE 802.15.4e DSME MAC protocol for wireless sensor networks

abstract: In this paper, we evaluate performance of time synchronized multi-channel MAC protocol called DSME for wireless sensor networks. DSME MAC enhances IEEE 802.15.4 MAC specification, which provides backward compatibility and adds new features such as multichannel extension to the current single channel based superframe structure; enhancing peer-to-peer communications. Our numerical results show that throughput of DSME MAC protocol is 12 times higher against IEEE802.15.4-2006 slotted CSMA/CA in a multi-hop network with a 5×5 square grid device deployment. Energy consumption of DSME MAC protocol remains constant as the number of endpoint devices increases, while that of slotted CSMA/CA increases exponentially in star device deployment, thus, life span of a device can be increased substantially by employing DSME MAC protocol for the applications with large number of endpoint devices such as smart metering and infrastructure monitoring.


THE EFFECT OF DARIFENACIN ON NEUROGENIC DETRUSOR OVERACTIVITY IN PATIENTS WITH SPINAL CORD INJURY


title: Temporal Patterning in Speech: The Implications of Temporal Resolution and Signal-Processing

abstract: There are wide variations in behavioural indices of temporal resolution in humans, with degradation of resolution accompanying both brain damage (Lackner and Teuber 1973), and hearing impairment (Tyler et al. 1982). The presence of only minimal abnormalities of temporal processing in animal models of cochlear pathology (Harrison and Evans 1979; Evans 1984, and papers cited therein) raises some doubt as to whether degradation of temporal resolution is a direct consequence of cochlear pathology, or some loosely associated correlate, or the result of abnormal central neural organisation following the lack of afferent stimulation. While physiological answers are being sought to such questions, it is profitable (i) to develop the ecological description of temporal structure in significant auditory events; (ii) to develop an understanding of the perceptual processes and phenomena resulting from temporal structure; and (iii) to develop ways of transforming that structure which are scientifically and technologically useful. This paper is directed to those three ends. We must remember, however, that the contrast between time- and frequency-based structures is more a dichotomy of our explanatory framework than a dichotomy in the reality we seek to describe.


title: Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs

abstract: This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional neural network (called pixel-level CNN) is constructed by modifying the VGGNet architecture to perform multi-scale feature learning, based on which an image-to-image prediction is conducted to accomplish the pixel-level saliency detection. For region-level saliency estimation, an adaptive superpixel based region generation technique is first designed to partition an image into regions, based on which the region-level saliency is estimated by using a CNN model (called region-level CNN). The pixel-level and region-level saliencies are fused to form the final salient map by using another CNN (called fusion CNN). And the pixel-level CNN and fusion CNN are jointly learned. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that the proposed method greatly outperforms the state-of-the-art saliency detection approaches.


title: Revealing stealthy attacks in control systems

abstract: In this paper the problem of revealing stealthy data-injection attacks on control systems is addressed. In particular we consider the scenario where the attacker performs zero-dynamics attacks on the system. First, we characterize and analyze the stealthiness properties of these attacks for linear time-invariant systems. Then we tackle the problem of detecting such attacks by modifying the system's structure. Our results provide necessary and sufficient conditions that the modifications should satisfy in order to detect the zero-dynamics attacks. The results and proposed detection methods are illustrated through numerical examples.


title: Is linguistic information relevant for the classification of legal texts?

abstract: Text classification is an important task in the legal domain. In fact, most of the legal information is stored as text in a quite unstructured format and it is important to be able to automatically classify these texts into a predefined set of concepts.Support Vector Machines (SVM), a machine learning algorithm, has shown to be a good classifier for text bases [12]. In this paper, SVMs are applied to the classification of European Portuguese legal texts - the Portuguese Attorney General's Office Decisions - and the relevance of linguistic information in this domain, namely lemmatisation and part-of-speech tags, is evaluated.The obtained results show that some linguistic information (namely, lemmatisation and the part-of-speech tags) can be successfully used to improve the classification results and, simultaneously, to decrease the number of features needed by the learning algorithm.


title: Classification of Malware Based on String and Function Feature Selection

abstract: Anti-malware software producers are continually challenged to identify and counter new malware as it is released into the wild. A dramatic increase in malware production in recent years has rendered the conventional method of manually determining a signature for each new malware sample untenable. This paper presents a scalable, automated approach for detecting and classifying malware by using pattern recognition algorithms and statistical methods at various stages of the malware analysis life cycle. Our framework combines the static features of function length and printable string information extracted from malware samples into a single test which gives classification results better than those achieved by using either feature individually. In our testing we input feature information from close to 1400 unpacked malware samples to a number of different classification algorithms. Using k-fold cross validation on the malware, which includes Trojans and viruses, along with 151 clean files, we achieve an overall classification accuracy of over 98%.


title: Alert: An Architecture for Transforming a Passive DBMS into an Active DBMS

abstract: Alert is an extension architecture designed for transforming a passive SQL DBMS into. an active DBMS. The salient features of the design of Alert are reusing, to the extent possible, the passive DBMS technology, and making minimal changes to the language and implementation of the passive DBMS. Alert provides a layered architecture that allows the semantics of a variety of production rule languages to be supported on top. Rules may be specified on userdefined as well as built-in operations. Both synchronous and asynchronous event, monit,oring are possible. This paper presents the design of Alert and its implementation in the Starburst extensible DBMS.


title: Model driven engineering: an emerging technical space

abstract: As an emerging solution to the handling of complex and evolving software systems, Model Driven Engineering (MDE) is still very much in evolution. The industrial demand is quite high while the research answer for a sound set of foundation principles is still far from being stabilized. Therefore it is important to provide a current state of the art in MDE, describing what its origins are, what its present state is, and where it seems to be presently leading. One important question is how MDE relates to other contemporary technologies. This tutorial proposes the ”technical space” concept to this purpose. The two main objectives are to present first the basic MDE principles and second how these principles may be mapped onto modern platform support. Other issues that will be discussed are the applicability of these ideas, concepts, and tools to solve current practical problems. Various organizations and companies (OMG, IBM, Microsoft, etc.) are currently proposing several environments claiming to support MDE. Among these, the OMG MDATM(Model Driven Architecture) has a special place since it was historically one of the original proposals in this area. This work focuses on the identification of basic MDE principles, practical characteristics of MDE (direct representation, automation, and open standards), original MDE scenarios, and discussions of suitable tools and methods.


title: Signal Detection and Parameter Estimation in Massive MIMO Systems

abstract: This book chapter reviews signal detection and parameter estimation techniques for multiuser multiple-antenna wireless systems with a very large number of antennas, known as massive multi-input multi-output (MIMO) systems. We consider both centralized antenna systems (CAS) and distributed antenna systems (DAS) architectures in which a large number of antenna elements are employed and focus on the uplink of a mobile cellular system. In particular, we focus on receive processing techniques that include signal detection and parameter estimation problems and discuss the specific needs of massive MIMO systems. Simulation results illustrate the performance of detection and estimation algorithms under several scenarios of interest. Key problems are discussed and future trends in massive MIMO systems are pointed out.


title: Facebook use and depressive symptomatology: Investigating the role of neuroticism and extraversion in youth

abstract: We examined the impact of Facebook use, personality, and sex on depressive symptoms.We found no direct link between Facebook use and depressive symptoms.Facebook use among high neuroticism females predicted lower depressive symptoms. The popularity of social networking sites, such as Facebook, has increased rapidly over the past decade, especially among youth. Consequently, the impact of Facebook use on mental health problems (e.g., depressive symptomatology) has become a recent area of concern. Yet, evidence for such a link has been mixed and factors that contribute to heterogeneity of findings have not been identified. In this study, we examined whether the association between Facebook use and depressive symptoms is moderated by individual factors (i.e., personality and sex). To this end, we measured Facebook use, depressive symptoms, and personality domains (i.e., extroversion and neuroticism) among 237 young adults. No direct association was found between Facebook use and depressive symptoms. However, for females with high neuroticism, more frequent Facebook use was associated with lower depressive symptoms. Our findings suggest a complex relationship between Facebook use and depressive symptomatology that appears to vary by sex and personality. Facebook use may be protective against depressive symptoms for female users with high levels of neuroticism, while Facebook use may be unrelated to depressive symptoms among males.


title: Min-Hash Fingerprints for Graph Kernels: A Trade-off among Accuracy, Efficiency, and Compression

abstract: Graph databases that emerge from several relevant scenarios (e.g., social networks, the Web) require powerful data management algorithms and techniques. A fundamental operation in graph data management is computing the similarity between two graphs. However, due to the large scale and high dimensionality of real graph databases, computing graph similarity becomes a challenging problem in real settings. Many graph data management tasks, such as graph mining, classification, and retrieval, can be contextualized in the framework of graph kernels. A graph kernel is, roughly speaking, a function that computes the similarity between graph structures as means to enable the application of linear methods to graph data. Nevertheless, large databases usually require the use of compact representations of graphs known as graph fingerprints (or signatures). Graph fingerprinting techniques provide a solution that is a trade-off among accuracy, efficiency, and compression in graph kernels. In this paper, we study the problem of generating fingerprints for graph kernels. We introduce a graph fingerprinting technique based on the min-hashing scheme, which is a powerful strategy for computing the similarity between large sets of items using a small amount of data. An algorithm for the generation of graph fingerprints as vectors of min-hash values is presented and integrated into the framework of graph kernels. Results show that graph fingerprinting achieves efficiency gains of up to one order of magnitude with up to 97% space savings when compared against the complete set of graph substructures. Moreover, the proposed technique is up to 9 times more accurate than a baseline method.


title: HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification

abstract: Analysis of high dimensional data in modern applications, such as neuroscience, text mining, spectral analysis, chemometrices naturally requires tensor decomposition methods. The Tucker decompositions allow us to extract hidden factors (component matrices) with different dimension in each mode, and investigate interactions among various modalities. The alternating least squares (ALS) algorithms have been confirmed effective and efficient in most of tensor decompositions, especially Tucker with orthogonality constraints. However, for nonnegative Tucker decomposition (NTD), standard ALS algorithms suffer from unstable convergence properties, demand high computational cost for large scale problems due to matrix inverse, and often return suboptimal solutions. Moreover they are quite sensitive with respect to noise, and can be relatively slow in the special case when data are nearly collinear. In this paper, we propose a new algorithm for nonnegative Tucker decomposition based on constrained minimization of a set of local cost functions and hierarchical alternating least squares (HALS). The developed NTD-HALS algorithm sequentially updates components, hence avoids matrix inverse, and is suitable for large-scale problems. The proposed algorithm is also regularized with additional constraint terms such as sparseness, orthogonality, smoothness, and especially discriminant. Extensive experiments confirm the validity and higher performance of the developed algorithm in comparison with other existing algorithms.


title: Blockchain-based Data Management and Analytics for Micro-insurance Applications

abstract: In this paper, we demonstrate a blockchain-based solution for transparently managing and analyzing data in a pay-as-you-go car insurance application. This application allows drivers who rarely use cars to only pay insurance premium for particular trips they would like to travel. One of the key challenges from database perspective is how to ensure all the data pertaining to the actual trip and premium payment made by the users are transparently recorded so that every party in the insurance contract including the driver, the insurance company, and the financial institution is confident that the data are tamper-proof and traceable. Another challenge from information retrieval perspective is how to perform entity matching and pattern matching on customer data as well as their trip and claim history recorded on the blockchain for intelligent fraud detection. Last but not least, the drivers' trip history, once have been collected sufficiently, can be much valuable for the insurance company to do offline analysis and build statistics on past driving behaviour and past vehicle runtime. These statistics enable the insurance company to offer the users with transparent and individualized insurance quotes. Towards this end, we develop a blockchain-based solution for micro-insurance applications that transparently keeps records and executes smart contracts depending on runtime conditions while also connecting with off-chain analytic databases.


title: DUAL-BAND DUAL-POLARIZED SPIRAL ANTENNA FOR CHINESE COMPASS NAVIGATION SATELLITE SYSTEM

abstract: In Chinese Compass Navigation Satellite System (CNSS for short), dual-band antennas are more attractive, because they can provide both navigation and communication services. In this paper, we present a dual-band dual-circular-polarized planar spiral-slot CNSS antenna. This antenna works at L band (1616 § 5MHz, left-handed circular polarization, LHCP) and S band (2492 § 5MHz, right-handed circular polarization, RHCP). Numerical results show that the impedance bandwidth (S11 < i10dB), 3dB axial ratio bandwidth and antenna gain at L band are about 242MHz, 79MHz and 4.92dB, respectively, while the simulated impedance bandwidth (S11 < i10dB), 3dB axial ratio bandwidth and antenna gain at S band are about 180MHz, 58MHz and 5.25dB, respectively. An experiment was carried out to verify our design. Measured results show that impedance bandwidth (S11 < i10dB) and 3dB axial ratio bandwidth L band are about 300MHz and 14MHz, respectively, while the measured impedance bandwidth (S11 < i10 dB) and 3dB axial ratio bandwidth at S band are about 210MHz, 10MHz, respectively. The measured results basically agree with the simulated ones and meet the requirement of CNSS terminal antennas.


title: A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation

abstract: Abstract Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation.


title: Taking Advantage of Semantics in Recommendation Systems

abstract: Recommendation systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommendation systems, content-based recommendation systems and a few hybrid systems. We propose a semantic framework to overcome common limitations of current systems. We present a system whose representations of items and user-profiles are based on concept taxonomies in order to provide personalized recommendation and services. The recommender incorporates semantics to enhance (1) user modeling by applying a domain-based inference method, and (2) recommendation by applying a semantic-similarity method. We show that semantics can often be used to overcome information scarcity. Experiments on movie-data from Netflix show that systems incorporating semantics produce significantly better quality recommendations than content-based ones.


title: Mechanisms Linking Glucose Homeostasis and Iron Metabolism Toward the Onset and Progression of Type 2 Diabetes

abstract: OBJECTIVE The bidirectional relationship between iron metabolism and glucose homeostasis is increasingly recognized. Several pathways of iron metabolism are modified according to systemic glucose levels, whereas insulin action and secretion are influenced by changes in relative iron excess. We aimed to update the possible influence of iron on insulin action and secretion and vice versa. ::: ::: RESEARCH DESIGN AND METHODS The mechanisms that link iron metabolism and glucose homeostasis in the main insulin-sensitive tissues and insulin-producing β-cells were revised according to their possible influence on the development of type 2 diabetes (T2D). ::: ::: RESULTS The mechanisms leading to dysmetabolic hyperferritinemia and hepatic overload syndrome were diverse, including diet-induced alterations in iron absorption, modulation of gluconeogenesis, heme-mediated disruption of circadian glucose rhythm, impaired hepcidin secretion and action, and reduced copper availability. Glucose metabolism in adipose tissue seems to be affected by both iron deficiency and excess through interaction with adipocyte differentiation, tissue hyperplasia and hypertrophy, release of adipokines, lipid synthesis, and lipolysis. Reduced heme synthesis and dysregulated iron uptake or export could also be contributing factors affecting glucose metabolism in the senescent muscle, whereas exercise is known to affect iron and glucose status. Finally, iron also seems to modulate β-cells and insulin secretion, although this has been scarcely studied. ::: ::: CONCLUSIONS Iron is increasingly recognized to influence glucose metabolism at multiple levels. Body iron stores should be considered as a potential target for therapy in subjects with T2D or those at risk for developing T2D. Further research is warranted.


title: Chapter 2 A General Survey of Privacy-Preserving Data Mining Models and Algorithms

abstract: In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.


A COMPARISON-FREE SORTING ALGORITHM ON CPUs


title: Skin distribution of fipronil by microautoradiography following topical administration to the beagle dog

abstract: To investigate the localisation of fipronil in dog skin, [14C]-fipronil was topically applied to a male beagle dog (spot-on administration) at the therapeutic dose of 10 mg/kg. By means of autohistoradiography, the radioactivity was precisely detected in the skin and appendages at various intervals after application. Radioactivity was predominantly observed within the stratum corneum, the viable epidermis, and in the pilo-sebaceous units (mainly in the sebaceous glands and epithelial layers). [14C]-fipronil was significantly detected in these structures up to 56 days post-treatment, in the application zone (neck) but also in the lumbar zone, thus indicating the mechanical displacement of fipronil. No radioactivity was detected in either the dermal or the hypodermal layers, confirming the low percutaneous passage of fipronil.


title: Model-Based performance prediction with the palladio component model

abstract: One aim of component-based software engineering (CBSE) is to enable the prediction of extra-functional properties, such as performance and reliability, utilising a well-defined composition theory. Nowadays, such theories and their accompanying prediction methods are still in a maturation stage. Several factors influencing extra-functional properties need additional research to be understood. A special problem in CBSE stems from its specific development process: Software components should be specified and implemented independent from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influence factors like the hardware platform or the usage profile into account. In our approach, we use the Palladio Component Model (PCM) to specify component-based software architectures in a parametric way. This model offers direct support of the CBSE development process by dividing the model creation among the developer roles. In this paper, we present our model and a simulation tool based on it, which is capable of making performance predictions. Within a case study, we show that the resulting prediction accuracy can be sufficient to support the evaluation of architectural design decisions.


title: Supporting Exploration of Eye Tracking Data: Identifying Changing Behaviour Over Long Durations

abstract: Visual analytics of eye tracking data is a common tool for evaluation studies across diverse fields. In this position paper we propose a novel user-driven interactive data exploration tool for understanding the characteristics of eye gaze movements and the changes in these behaviours over time. Eye tracking experiments generate multidimensional scan path data with sequential information. Many mathematical methods in the past have analysed one or a few of the attributes of the scan path data and derived attributes such as Area of Interest (AoI), statistical measures, geometry, domain specific features etc. In our work we are interested in visual analytics of one of the derived attributes of sequential data-the: AoI and the sequences of visits to these AoIs over time. In the case of static stimuli, such as images, or dynamic stimuli, like videos, having predefined or fixed AoIs is not an efficient way of analysing scan path patterns. The AoI of a user over a stimulus may evolve over time and hence determining the AoIs dynamically through temporal clustering could be a better method for analysing the eye gaze patterns. In this work we primarily focus on the challenges in analysis and visualization of the temporal evolution of AoIs. This paper discusses the existing methods, their shortcomings and scope for improvement by adopting visual analytics methods for event-based temporal data to the analysis of eye tracking data.


title: A Survey of General-Purpose Crowdsourcing Techniques

abstract: Since Jeff Howe introduced the term Crowdsourcing in 2006, this human-powered problem-solving paradigm has gained a lot of attention and has been a hot research topic in the field of computer science. Even though a lot of work has been conducted on this topic, so far we do not have a comprehensive survey on most relevant work done in the crowdsourcing field. In this paper, we aim to offer an overall picture of the current state of the art techniques in general-purpose crowdsourcing. According to their focus, we divide this work into three parts, which are: incentive design, task assignment, and quality control. For each part, we start with different problems faced in that area followed by a brief description of existing work and a discussion of pros and cons. In addition, we also present a real scenario on how the different techniques are used in implementing a location-based crowdsourcing platform, gMission. Finally, we highlight the limitations of the current general-purpose crowdsourcing techniques and present some open problems in this area.


title: Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

abstract: BackgroundActivities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data.MethodsA wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL.ResultsOut of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB.ConclusionsThe performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.


title: Smart Contract Negotiation in Cloud Computing

abstract: A smart contract is the formalisation of an agreement, whose terms are automatically enforced by relying on a transaction protocol, while minimising the need of intermediaries. Such contracts not only specify the service and its quality but also the possible changes at runtime of the terms of agreement. Although smart contracts provide a great deal of flexibility, analysing their compatibility and reaching agreements with this level of dynamism is considerably more challenging, due to the freedom of clients and providers in formulating needs/offers. We introduce a formal language to specify interactions between offers and requests and present a methodology for the autonomous negotiation of smart contracts, which analyses the cost and the necessary changes for reaching an agreement. Moreover, we describe a set of experiments that provides insights on the relative cost of dynamism in negotiating smart contracts and compare the request/offer matching rates of our solution with related works.


title: [POSTER] Prevention of Visually Induced Motion Sickness Based on Dynamic Real-Time Content-Aware Non-salient Area Blurring

abstract: This paper proposes an innovative method for reducing the visually induced motion sickness (MS) occurred in a 3D immersive virtual environment (VE) by utilizing a flexible dynamic scene smoothing approach based on saliency analysis. A saliency model based on fully convolutional network (FCN) is first trained to establish the saliency map, then the probability maps representing the salient information and the non-salient information are combined to alter the field of view (FOV) by smoothing the non-salient area. An experiment is conducted to evaluate the performance of the proposed approach. The experimental data demonstrate that participants experiencing dynamic blurring VE reports a 50% reduction of the severity of MS symptom on average during the VR experience than the participants experiencing the control condition, which show that the proposed approach can be used to effectively prevent the visually induced MS in VR and support longer duration of users in VE.


title: A microstrip periodic leaky-wave antenna optimized for broadside scanning

abstract: In this paper a microstrip periodic leaky-wave antenna optimized for radiation scanning through broadside is presented. This antenna is based on the excitation of a leaky mode that propagates along the structure, with radiation occurring from the -1 space harmonic. The specific geometry of the metalization inside the unit cell permits a continuous scanning from the backward to the forward quadrant without any degradation of the beam, avoiding the presence of open-stopband effects. This new design provides a valid alternative to metamaterial leaky-wave antennas already presented in the literature and demonstrates the possibility of obtaining a conventional microstrip periodic LWA operating in the -1 harmonic with a backfire-to-endfire frequency-scanning capability.


title: The Autism Impact Measure (AIM): Initial Development of a New Tool for Treatment Outcome Measurement.

abstract: The current study describes the development and psychometric properties of a new measure targeting sensitivity to change of core autism spectrum disorder (ASD) symptoms, the Autism Impact Measure (AIM). The AIM uses a 2-week recall period with items rated on two corresponding 5-point scales (frequency and impact). Psychometric properties were examined using a large sample (n = 440) of children with ASD enrolled in the Autism Treatment Network. The exploratory factor analysis indicated four factors and resulted in a 25-item questionnaire with excellent overall model fit. Test–retest reliability, cross-informant reliability, and convergent validity with other measures of ASD symptoms and overall functioning were strong. The AIM is a reliable and valid measure of frequency and impact of core ASD symptoms.


Ninja: Towards Transparent Tracing and Debugging on ARM.


title: Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings

abstract: AbstractExtracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent the semantic information. Joint inference with dual decomposition and novel word embeddings are adopted to address the two problems, respectively, in this work. Word embeddings are learnt from large scale unlabeled texts and integrated as an unsupervised feature into other rich features based on dependency parse graphs to detect triggers and arguments. The proposed system consists of four components: trigger detector, argument detector, jointly inference with dual decomposition, and rule-based semantic post-processing, and outperforms the state-of-the-art systems. On the development set of BioNLP'09, the F-score is 59.77 percent on the primary task, which is 0.96 percent higher than the best system. On the test set of BioNLP'11, the F-score is 56.09 and 0.89 percent higher than the best published result that do not adopt additional techniques. On the test set of BioNLP'13, the F-score reaches 53.19 percent which is 2.22 percent higher than the best result.


title: The calculi of emergence: Computation, dynamics, and induction

abstract: Abstract Defining structure and detecting the emergence of complexity in nature are inherently subjective, though essential, scientific activities. Despite the difficulties, these problems can be analyzed in terms of how model-building observers infer from measurements the computational capabilities embedded in nonlinear processes. An observer's notion of what is ordered, what is random, and what is complex in its environment depends directly on its computational resources: the amount of raw measurement data, of memory, and of time available for estimation and inference. The discovery of structure in an environment depends more critically and subtlely though on how those resources are organized. The descriptive power of the observer's chosen (or implicit) computational model class, for example, can be an overwhelming determinant in finding regularity in data. This paper presents an overview of an inductive framework-hierarchical ϵ-machine reconstruction—in which the emergence of complexity is associated with the innovation of new computational model classes. Complexity metrics for detecting structure and quantifying emergence, along with an analysis of the constraints on the dynamics of innovation, are outlined. Illustrative examples are drawn from the onset of unpredictability in nonlinear systems, finitary nondeterministic processes, and cellular automata pattern recognition. They demonstrate how finite inference resources drive the innovation of new structures and so lead to the emergence of complexity.


title: UBIRIS: A Noisy Iris Image Database

abstract: This paper presents a new iris database that contains images with noise. This is in contrast with the existing databases, that are noise free. UBIRIS is a tool for the development of robust iris recognition algorithms for biometric proposes. ::: ::: We present a detailed description of the many characteristics of UBIRIS and a comparison of several image segmentation approaches used in the current iris segmentation methods where it is evident their small tolerance to noisy images.


title: Extracting Events and Event Descriptions from Twitter

abstract: This paper describes methods for automatically detecting events involving known entities from Twitter and understanding both the events as well as the audience reaction to them. We show that NLP techniques can be used to extract events, their main actors and the audience reactions with encouraging results.


title: Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation

abstract: Foreword. Introduction. Signal Processing Fundamentals. Pulse Compression. Synthetic Aperture Concepts. SAR Signal Properties. The Range Doppler Algorithm. The Chirp Scaling Algorithm. The Omega-K Algorithm. The SPECAN Algorithm. Processing ScanSAR Data. Doppler Parameter Estimation. Comparison of Algorithms. References.


title: Construction of an ontology-based common criteria review tool

abstract: The Common Criteria (CC) provides comprehensive guidelines for the evaluation and certification of IT security. Due to the complexity of CC, CC-based certification process is quite time-consuming. The research aims to develop a CC Ontology and then construct an ontology-based tool supporting CC knowledge query, markup, review, and report functions. Results of this research can improve the understandability of CC, and enhance the efficiency and effectiveness of CC-certification process.


title: Towards Energy-Efficient 5G Mm-Wave links: Exploiting broadband Mm-Wave doherty power amplifier and multi-feed antenna with direct on-antenna power combining

abstract: It is envisioned that future 5G wireless links will extensively employ multiple mm-Wave spectra (e.g., 28GHz, 37GHz, 39GHz, and 70GHz). A major bottom neck for silicon-based mm-Wave electronics lies in the generation of mm-Wave signals with high energy efficiency, high linearity, and large modulation bandwidth, all of which are indispensable to support Gbit/s complex modulations with large peak-to-average-power ratio (PAPR) in 5G systems. In this paper, we focus on two technical aspects that recently attract increasing attention and may together address these challenges. We will first present Doherty power amplifier (PA) architecture that offers large back-off efficiency enhancement, linear operation, and low overhead on signal processing. A recent multi-band (28/37/39GHz) mm-Wave Doherty PA in a 130nm SiGe process will be presented as a design example. Next, we will present a new concept of antenna-electronics co-design at mm-Wave. A new “Multi-Feed Antenna” (MFA) is recently proposed and demonstrated by the authors to achieve direct on-antenna low-loss power combining of multiple mm-Wave PAs. A 60GHz all-silicon transmitter design employs on-chip MFA to combine power from 16 PAs and achieves state-of-the-art total radiated output power per transmitter element.


Deep Reinforcement Learning for Flappy Bird


title: On label dependence and loss minimization in multi-label classification

abstract: Most of the multi-label classification (MLC) methods proposed in recent years intended to exploit, in one way or the other, dependencies between the class labels. Comparing to simple binary relevance learning as a baseline, any gain in performance is normally explained by the fact that this method is ignoring such dependencies. Without questioning the correctness of such studies, one has to admit that a blanket explanation of that kind is hiding many subtle details, and indeed, the underlying mechanisms and true reasons for the improvements reported in experimental studies are rarely laid bare. Rather than proposing yet another MLC algorithm, the aim of this paper is to elaborate more closely on the idea of exploiting label dependence, thereby contributing to a better understanding of MLC. Adopting a statistical perspective, we claim that two types of label dependence should be distinguished, namely conditional and marginal dependence. Subsequently, we present three scenarios in which the exploitation of one of these types of dependence may boost the predictive performance of a classifier. In this regard, a close connection with loss minimization is established, showing that the benefit of exploiting label dependence does also depend on the type of loss to be minimized. Concrete theoretical results are presented for two representative loss functions, namely the Hamming loss and the subset 0/1 loss. In addition, we give an overview of state-of-the-art decomposition algorithms for MLC and we try to reveal the reasons for their effectiveness. Our conclusions are supported by carefully designed experiments on synthetic and benchmark data.


Impact of Organizational Culture on Employee Performance


title: A Survey on Recent Advances in Transport Layer Protocols

abstract: Over the years, the Internet has been enriched with new available communication technologies, for both fixed and mobile networks and devices, exhibiting an impressive growth in terms of performance, with steadily increasing available data rates. The Internet research community has kept trying to evolve the transport layer protocols to match the capabilities of modern networks, in order to fully reap the benefits of the new communication technologies. This paper surveys the main novelties related to transport protocols that have been recently proposed, identifying three main research trends: (i) the evolution of congestion control algorithms, to target optimal performance in challenging scenarios, possibly with the application of machine learning techniques; (ii) the proposal of brand new transport protocols, alternative to the Transmission Control Protocol (TCP) and implemented in the user-space; and (iii) the introduction of multipath capabilities at the transport layer.


title: QUARTERLY Structure and dynamics of a DNA duplex containing single �-anomeric deoxyadenosine residue ��

abstract: Structure and dynamics of an undecamer DNA duplex containing a single alpha-anomeric deoxyadenosine residue placed in opposition to a thymidine unit have been studied using simulation of molecular dynamics in aqueous solution. Despite several noticeable deviations from the B-DNA duplex structure caused by the anomerisation, such as: West type puckering of the alpha-anomeric sugar, disrupted base stacking pattern and unstable duplex bending, the formation of a non-classical alpha-dA-T pair was observed. A novel way of visual presentation of trajectory data allowing high throughput screening of the conformational parameters is presented.


title: The 5C categorization of social media tools

abstract: Social media can offer potential business benefits in the company context. However, successful social media initiative calls for careful strategic planning and approaching social media tools as enablers for reaching a purpose. A categorization of social media tools is called for to help the strategic choices and evaluation between the tools. To date, no coherent classification exists, and those suggested approach social media tools from a substantival perspective of what a tool is. From strategical planning viewpoint it is more practical to take the functional perspective of what a tool does. In this paper, we present a framework for categorizing social media tools by the actions they enable. The 5C categorization is based on the action s enabled by the social media tools: communicating, collaborating, connecting, completing, and combining. In order to demonstrate how the 5C categorization can be used as a framework to assess social media tools we conducted an empirical study of social media based crowdsourcing platforms in business-to-business setting. The 5C categorization can be utilized in planning a social media strategy as it forwards the understanding of which tools are suitable for certain purpose and provides a scheme to evaluate and compare different social media tools and applications.


title: A methodology towards automatic performance analysis of parallel applications

abstract: Tuning and debugging the performance of parallel applications is an iterative process consisting of several steps dealing with identification and localization of inefficiencies, repair, and verification of the achieved performance. In this paper, we address the analysis of the performance of parallel applications from a methodological viewpoint with the aim of identifying and localizing inefficiencies. Our methodology is based on performance metrics and criteria that highlight the properties of the applications and the load imbalance and dissimilarities in the behavior of the processors. A few case studies illustrate the application of the methodology.


title: Maintenance of a minimum spanning forest in a dynamic planar graph

abstract: Abstract : We give efficient algorithms for maintaining a minimum spanning forest of a planar graph subject to on-line modifications. The modifications supported include changes in the edge weights, and insertion and deletion of edges and vertices. To implement the algorithms, we develop a data structure called an edge-ordered dynamic tree, which is a variant of the dynamic tree data structure of Sleator and Tarjan. Using this data structure, our algorithms run in O(log n) time per operation and O(n) space. The algorithms can be used to maintain the connected components of a dynamic planar graph in O(log n) time per operation.


title: EEG Signal Classification Using Power Spectral Features and linear Discriminant Analysis: A Brain Computer Interface Application

abstract: Biological signal processing offers an alternative to improve life quality in handicapped patients. In this sense is possible, to control devices as wheel chairs or computer systems. The signals that are usually used are EMG, EOG and EEG. When the lost of ability is severe the use of EMG signals is not possible because the person had lost, as in the case of ALS patients, the ability to control his body. EOG offers low resolution because the technique depends of many external and uncontrollable variables of the environment. This work shows the design of a set of algorithms capable to classify brain signals related to imaginary motor activities ( left and right hand imaginary). First, digital signal processing is used to select and extract discriminant features, using parametrical methods for the estimation of the power spectral density and the Fisher criterion for separability. The signal is then classified, using linear discriminant analysis. The results show that is possible to obtain good performance with error rates as low as 13% and that the use of parametrical methods for Spectral Power Density estimation can improve the accuracy of the Brain Computer Interface.


Linux containers and the future cloud


title: Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity

abstract: Word embedding, specially with its recent developments, promises a quantification of the similarity between terms. However, it is not clear to which extent this similarity value can be genuinely meaningful and useful for subsequent tasks. We explore how the similarity score obtained from the models is really indicative of term relatedness. We first observe and quantify the uncertainty factor of the word embedding models regarding to the similarity value. Based on this factor, we introduce a general threshold on various dimensions which effectively filters the highly related terms. Our evaluation on four information retrieval collections supports the effectiveness of our approach as the results of the introduced threshold are significantly better than the baseline while being equal to or statistically indistinguishable from the optimal results.


title: Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning

abstract: We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance.


title: Package embedded decoupling capacitor impact on core power delivery network for ARM SoC application

abstract: In this paper, a BGA package having a ARM SoC chip is introduced, which has component-type embedded decoupling capacitors (decaps) for good power integrity performance of core power. To evaluate and confirm the impact of embedded decap on core PDN (power distribution network), two different packages were manufactured with and without the embedded decaps. The self impedances of system-level core PDN were simulated in frequency-domain and On-chip DvD (Dynamic Voltage Drop) simulations were performed in time-domain in order to verify the system-level impact of package embedded decap. There was clear improvement of system-level core PDN performance in middle frequency range when package embedded decaps were employed. In conclusion, the overall system-level core PDN for ARM SoC could meet the target impedance in frequency-domain as well as the target On-chip DvD level by having package embedded decaps.


title: Secure embedded system hardware design - A flexible security and trust enhanced approach

abstract: Embedded systems have found a very strong foothold in global Information Technology (IT) market since they can provide very specialized and intricate functionality to a wide range of products. On the other hand, the migration of IT functionality to a plethora of new smart devices (like mobile phones, cars, aviation, game or households machines) has enabled the collection of a considerable number of data that can be characterized sensitive. Therefore, there is a need for protecting that data through IT security means. However, eare usually dployed in hostile environments where they can be easily subject of physical attacks. In this paper, we provide an overview from ES hardware perspective of methods and mechanisms for providing strong security and trust. The various categories of physical attacks on security related embedded systems are presented along with countermeasures to thwart them and the importance of reconfigurable logic flexibility, adaptability and scalability along with trust protection mechanisms is highlighted. We adopt those mechanisms in order to propose a FPGA based embedded system hardware architecture capable of providing security and trust along with physical attack protection using trust zone separation. The benefits of such approach are discussed and a subsystem of the proposed architecture is implemented in FPGA technology as a proof of concept case study. From the performed analysis and implementation, it is concluded that flexibility, security and trust are fully realistic options for embedded system security enhancement.


title: Understanding PubMed® user search behavior through log analysis

abstract: This article reports on a detailed investigation of PubMed users’ needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users’ needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users’ interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users’ decisions. Analysis of characteristics such as these plays a critical role in identifying users’ information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval. ::: ::: Database URL: http://www.ncbi.nlm.nih.gov/PubMed


title: Fabricating spatially-varying subsurface scattering

abstract: Many real world surfaces exhibit translucent appearance due to subsurface scattering. Although various methods exists to measure, edit and render subsurface scattering effects, no solution exists for manufacturing physical objects with desired translucent appearance. In this paper, we present a complete solution for fabricating a material volume with a desired surface BSSRDF. We stack layers from a fixed set of manufacturing materials whose thickness is varied spatially to reproduce the heterogeneity of the input BSSRDF. Given an input BSSRDF and the optical properties of the manufacturing materials, our system efficiently determines the optimal order and thickness of the layers. We demonstrate our approach by printing a variety of homogenous and heterogenous BSSRDFs using two hardware setups: a milling machine and a 3D printer.


title: Artificial Intelligence in the 21st Century

abstract: The field of artificial intelligence (AI) has shown an upward trend of growth in the 21st century (from 2000 to 2015). The evolution in AI has advanced the development of human society in our own time, with dramatic revolutions shaped by both theories and techniques. However, the multidisciplinary and fast-growing features make AI a field in which it is difficult to be well understood. In this paper, we study the evolution of AI at the beginning of the 21st century using publication metadata extracted from 9 top-tier journals and 12 top-tier conferences of this discipline. We find that the area is in the sustainable development and its impact continues to grow. From the perspective of reference behavior, the decrease in self-references indicates that the AI is becoming more and more open-minded. The influential papers/researchers/institutions we identified outline landmarks in the development of this field. Last but not least, we explore the inner structure in terms of topics’ evolution over time. We have quantified the temporal trends at the topic level and discovered the inner connection among these topics. These findings provide deep insights into the current scientific innovations, as well as shedding light on funding policies.


title: 2007 Schemas and memory consolidation

abstract: Memory encoding occurs rapidly, but the consolidation of memory in the neocortex has long been held to be a more gradual process. We now report, however, that systems consolidation can occur extremely quickly if an associative “schema” into which new information is incorporated has previously been created. In experiments using a hippocampal-dependent paired-associate task for rats, the memory of flavor-place associations became persistent over time as a putative neocortical schema gradually developed. New traces, trained for only one trial, then became assimilated and rapidly hippocampal-independent. Schemas also played a causal role in the creation of lasting associative memory representations during one-trial learning. The concept of neocortical schemas may unite psychological accounts of knowledge structures with neurobiological theories of systems memory consolidation.


Classification of the Pigmented Skin lesions in Dermoscopic Images by Shape Features Extraction


title: Ransomware and the GDPR

abstract: Ransomware is a unique form of hacking that leaves data intact but still disrupts enterprises around the globe. This special malware encrypts computer files, network file shares and even databases, thereby preventing user access. To release the files, the victim is asked to pay a ransom to the cyberthieves. It is completely diabolical and you would think with such brazen criminal activity, there would be relevant data security laws that would kick in. And there are.


title: The Promise of Premise: Harnessing Question Premises in Visual Question Answering

abstract: In this paper, we make a simple observation that questions about images often contain premises - objects and relationships implied by the question - and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented with a question that is irrelevant to an image, state-of-the-art VQA models will still answer purely based on learned language biases, resulting in non-sensical or even misleading answers. We note that a visual question is irrelevant to an image if at least one of its premises is false (i.e. not depicted in the image). We leverage this observation to construct a dataset for Question Relevance Prediction and Explanation (QRPE) by searching for false premises. We train novel question relevance detection models and show that models that reason about premises consistently outperform models that do not. We also find that forcing standard VQA models to reason about premises during training can lead to improvements on tasks requiring compositional reasoning.


title: Semantic Segmentation from Limited Training Data

abstract: We present our approach for robotic perception in cluttered scenes that led to winning the recent Amazon Robotics Challenge (ARC) 2017. Next to small objects with shiny and transparent surfaces, the biggest challenge of the 2017 competition was the introduction of unseen categories. In contrast to traditional approaches which require large collections of annotated data and many hours of training, the task here was to obtain a robust perception pipeline with only few minutes of data acquisition and training time. To that end, we present two strategies that we explored. One is a deep metric learning approach that works in three separate steps: semantic-agnostic boundary detection, patch classification and pixel-wise voting. The other is a fully-supervised semantic segmentation approach with efficient dataset collection. We conduct an extensive analysis of the two methods on our ARC 2017 dataset. Interestingly, only few examples of each class are sufficient to fine-tune even very deep convolutional neural networks for this specific task.


title: Estimation of respiratory pattern from video using selective ensemble aggregation

abstract: Noncontact estimation of respiratory pattern (RP) and respiratory rate (RR) has multiple applications. Existing methods for RP and RR measurement fall into one of the three categories—1) estimation through nasal air flow measurement, 2) estimation from video-based remote photoplethysmography, and 2) estimation by measurement of motion induced by respiration using motion detectors. However, these methods require specialized sensors, are computationally expensive, and/or critically depend on selection of a region of interest (ROI) for processing. In this paper, a general framework is described for estimating a periodic signal driving noisy linear time-invariant (LTI) channels connected in parallel with unknown dynamics. The method is then applied to derive a computationally inexpensive method for estimating RP using two-dimensional cameras that does not critically depend on ROI. Specifically, RP is estimated by imaging changes in the reflected light caused by respiration-induced motion. Each spatial location in the field of view of the camera is modeled as a noise-corrupted LTI measurement channel with unknown system dynamics, driven by a single generating respiratory signal. Estimation of RP is cast as a blind deconvolution problem and is solved through a method comprising subspace projection and statistical aggregation. Experiments are carried out on 31 healthy human subjects by generating multiple RPs and comparing the proposed estimates with simultaneously acquired ground truth from an impedance pneumograph device. The proposed estimator agrees well with the ground truth in terms of correlation measures, despite variability in clothing pattern, camera angle, and ROI.


title: Learning Computer Networking on Open Paravirtual Laboratories

abstract: Learning practical information communication technology skills such as network configuration and security planning requires hands-on experience with a number of different devices which may be unavailable or too costly to provide, especially for institutions under tight budget constraints. This paper describes how a specific open software technology, paravirtualization, can be used to set up open source virtual networking labs (VNLs) easily and at virtually no cost. The paper highlights how paravirtual labs can be adopted jointly by partner organizations, e.g., when the institution hosting the virtual lab provides hands-on training and students' skill evaluation as a service to partner institutions overseas. A practical VNL implementation, the open virtual lab (OVL), is used to describe the added value that open source VNLs can give to e-Learning frameworks, achieving a level of students' performance comparable or better than the one obtained when students directly interact with physical networking equipment.