title: Catechol-O-methyltransferase, Cognition and Alzheimer's Disease.

abstract: OBJECTIVE ::: Cognition is a complex trait representing a set of all mental abilities and processes related to knowledge. Although diverse brain regions are involved, most cognitive processes appear to engage cortical regions. The activity of dopaminergic neurons in prefrontal cortex represents a biological substrate underlying cognitive functions. Alzheimer's Disease (AD) is the most frequent dementia associated with cognitive impairments. Cognitive impairment in AD starts slowly with discrete deterioration in memory, language, thinking and reasoning, but it progresses into more severe and debilitating cognitive dysfunction. Cognitive function is affected by the complex interactions between various genetic, epigenetic, developmental and environmental factors. One of the most studied genes, associated with cognitive disturbances, is the gene coding for Catechol-O-methyltransferase (COMT), the enzyme with major role in dopamine metabolism and modulation of different brain functions. Therefore, COMT is studied as a target for many neuropsychiatric disorders, including dementias and AD. The COMT Val158/108Met functional polymorphism affects significantly the enzyme activity and consequently cognitive performance associated with altered dopamine function. The association of COMT Val158/108Met polymorphism with some cognitive domains and psychosis in AD was reported in some but not in all studies. Besides COMT Val158/108Met polymorphism, other risk genotypes or haplotypes should be evaluated to determine the association of COMT with cognitive decline in AD. ::: ::: ::: CONCLUSION ::: Better understanding of the role of COMT in cognitive processes in AD, as well as integration of neurobiological, genetic, genomic and epigenetic data, might help in developing new potential therapies of cognitive impairments and psychotic symptoms, characteristic features of AD.


title: The Three R’s of Computer Vision: Recognition, Reconstruction and Reorganization

abstract: Bidirectional interactions between recognition, reconstruction and re-organization are very important.Bottom-up grouping generates object candidates, which can be classified top down, following which the segmentations are refined again.Recognition of 3D objects benefits from a reconstruction of 3D structure.3D reconstruction benefits from object category-specific priors.Reconstruction of 3D structure from video data goes hand in hand with the reorganization of the scene. We argue for the importance of the interaction between recognition, reconstruction and re-organization, and propose that as a unifying framework for computer vision. In this view, recognition of objects is reciprocally linked to re-organization, with bottom-up grouping processes generating candidates, which can be classified using top down knowledge, following which the segmentations can be refined again. Recognition of 3D objects could benefit from a reconstruction of 3D structure, and 3D reconstruction can benefit from object category-specific priors. We also show that reconstruction of 3D structure from video data goes hand in hand with the reorganization of the scene. We demonstrate pipelined versions of two systems, one for RGB-D images, and another for RGB images, which produce rich 3D scene interpretations in this framework.


title: Children's Perspectives on Cyberbullying: Insights Based on Participatory Research

abstract: Cyberbullying is an emerging problem among youngsters. Although the current body of knowledge about cyberbullying is expanding rapidly, it lacks a more in-depth research approach honoring adolescents' perspectives on the problem. Moreover, very few studies have focused on cyberbullying among elementary school children. The purpose of this study therefore, was to explore children's perspectives on the problem of cyberbullying. A participatory research design was used in which 28 children (aged 11–12 from four elementary schools) actively participated for 6 weeks in weekly scheduled group sessions. In these sessions, different aspects of cyberbullying were discussed using various enabling techniques. Between sessions, the children were given preparation assignments. The research revealed several ambiguities that should be addressed in interventions against cyberbullying. First, it appears difficult for all parties involved to distinguish cyberbullying from innocent pranks. Frequency and intention are key variables, but these are ambiguous in the context of cyberbullying. Second, cyberbullies may have very different motives, not all of which have to do with their relationship with the victim. Third, the expectations children have of the way their parents or teachers will react to incidents of cyberbullying are an obstacle for seeking help. Children are particularly afraid of overreaction and the subsequent loss of their Internet privileges. These results confirm earlier insights from research on cyberbullying, and examine the ambiguities in more detail. In addition, the research demonstrates the usefulness of participatory research to investigate cyberbullying among younger children and demonstrates that the research led to mutual learning.


title: Using Bayesian Networks for Probabilistic Identification of Zero-Day Attack Paths

abstract: Enforcing a variety of security measures (such as intrusion detection systems, and so on) can provide a certain level of protection to computer networks. However, such security practices often fall short in face of zero-day attacks. Due to the information asymmetry between attackers and defenders, detecting zero-day attacks remains a challenge. Instead of targeting individual zero-day exploits, revealing them on an attack path is a substantially more feasible strategy. Such attack paths that go through one or more zero-day exploits are called zero-day attack paths. In this paper, we propose a probabilistic approach and implement a prototype system ZePro for zero-day attack path identification. In our approach, a zero-day attack path is essentially a graph. To capture the zero-day attack, a dependency graph named object instance graph is first built as a supergraph by analyzing system calls. To further reveal the zero-day attack paths hidden in the supergraph, our system builds a Bayesian network based upon the instance graph. By taking intrusion evidence as input, the Bayesian network is able to compute the probabilities of object instances being infected. Connecting the high-probability-instances through dependency relations forms a path, which is the zero-day attack path. The experiment results demonstrate the effectiveness of ZePro for zero-day attack path identification.


title: Two-week neurasthenic major depression

abstract: The relationship between the two syndromes neurasthenia and depression is of interest in the context of burnout, which, although not a diagnosis, is often treated in psychiatry. This study defines major depressive episodes according to DSM-5 and neurasthenia by ICD-10 symptom criteria, and both syndromes on the basis of a 2-week minimum duration. The study includes all subjects of the Zurich epidemiological study who had taken part in the last five interviews (1986–2008) and compares three groups, pure depression, pure neurasthenia and their combination (neurasthenic depression), applying nonparametric statistics. The three groups did not differ in common validators: age of onset, course, a family history for depression and anxiety/panic. Psychiatric comorbidity was also very similar, with the exception of suicide attempts and substance abuse, which were less frequent in the pure neurasthenic group. Somatic comorbidity was also highly comparable, except for stomach problems, which were more common in subjects with neurasthenic syndromes. Surprisingly, the well-known preponderance of depression in women was explained by the association with neurasthenic syndromes. The proposed new diagnosis of neurasthenic depression could help diagnose subjects treated for burnout but needs replication by other representative studies.


title: Nonlinear Dimensionality Reduction

abstract: Methods of dimensionality reduction provide a way to understand and visualize the structure of complex data sets. Traditional methods like principal component analysis and classical metric multidimensional scaling suffer from being based on linear models. Until recently, very few methods were able to reduce the data dimensionality in a nonlinear way. However, since the late nineties, many new methods have been developed and nonlinear dimensionality reduction, also called manifold learning, has become a hot topic. New advances that account for this rapid growth are, e.g. the use of graphs to represent the manifold topology, and the use of new metrics like the geodesic distance. In addition, new optimization schemes, based on kernel techniques and spectral decomposition, have lead to spectral embedding, which encompasses many of the recently developed methods. This book describes existing and advanced methods to reduce the dimensionality of numerical databases. For each method, the description starts from intuitive ideas, develops the necessary mathematical details, and ends by outlining the algorithmic implementation. Methods are compared with each other with the help of different illustrative examples. The purpose of the book is to summarize clear facts and ideas about well-known methods as well as recent developments in the topic of nonlinear dimensionality reduction. With this goal in mind, methods are all described from a unifying point of view, in order to highlight their respective strengths and shortcomings. The book is primarily intended for statisticians, computer scientists and data analysts. It is also accessible to other practitioners having a basic background in statistics and/or computational learning, like psychologists (in psychometry) and economists.


NEOSHIELD - A Global Approach to Near-earth ObjectNEAR-EARTH OBJECT Impact ThreatIMPACT THREAT Mitigation


title: Strain accommodation by slow slip and dyking in a youthful continental rift, East Africa

abstract: Continental rifts begin and develop through repeated episodes of faulting and magmatism, but strain partitioning between faulting and magmatism during discrete rifting episodes remains poorly documented. In highly evolved rifts, tensile stresses from far-field plate motions accumulate over decades before being released during relatively short time intervals by faulting and magmatic intrusions1, 2, 3. These rifting crises are rarely observed in thick lithosphere during the initial stages of rifting. Here we show that most of the strain during the July–August 2007 seismic crisis in the weakly extended Natron rift, Tanzania, was released aseismically. Deformation was achieved by slow slip on a normal fault that promoted subsequent dyke intrusion by stress unclamping. This event provides compelling evidence for strain accommodation by magma intrusion, in addition to slip along normal faults, during the initial stages of continental rifting and before significant crustal thinning.


title: Continuum robots - a state of the art

abstract: Like the human limbs which inspired them most robots are discrete mechanisms with rigid links connected by single degree of freedom joints. In contrast, 'continuum' and 'serpentine' robot mechanisms move by bending through a series of continuous arcs producing motion which resembles that of biological tentacles or snakes. This paper provides a single reference to the expanding technology of continuum robot mechanisms. It defines the fundamental difference between discrete, serpentine and continuum robot devices, presents the 'state of the art' of continuum robots, outlines their areas of application, and introduces some control issues. Finally, some conclusions regarding the continued development of these devices are made.


title: Factors influencing teachers' intention to use technology: Model development and test

abstract: Among the key players in any effective integration of technology in teaching and learning is the teacher. Despite the research that has been conducted to examine the factors that explain teachers' intention to use technology, few have developed a model to statistically explain the interactions among these factors and how they influence teachers' intention to use technology. Five variables (perceived usefulness, perceived ease of use, subjective norm, facilitating conditions, and attitude towards use) and behavioural intention to use technology were used to build a research model in this study and structural equation modelling was used for parameter estimation and model testing. Self-reported data were gathered from 592 teachers from schools in Singapore. Results revealed a good model fit and of the nine hypotheses formulated in this study, eight were supported. Subjective norm was not found to be a significant influence on teachers' intention to use technology while the other four variables were.


title: SeeNav: Seamless and Energy-Efficient Indoor Navigation using Augmented Reality

abstract: Augmented Reality (AR) based navigation has emerged as an impressive, yet seamless way of guiding users in unknown environments. Its quality of experience depends on many factors, including the accuracy of camera pose estimation, response delay, and energy consumption. In this paper, we present SeeNav - a seamless and energy-efficient AR navigation system for indoor environments. SeeNav combines image-based localization and inertial tracking to provide an accurate and robust camera pose estimation. As vision processing is much more compute intensive than the processing of inertial sensor data, SeeNav offloads the former one from resource-constrained mobile devices to a cloud to improve tracking performance and reduce power consumption. More than that, SeeNav implements a context-aware task scheduling algorithm that further minimizes energy consumption while maintaining the accuracy of camera pose estimation. Our experimental results, including a user study, show that SeeNav provides seamless navigation experience and reduces the overall energy consumption by 21.56% with context-aware task scheduling.


title: Development of PLGA-coated β-TCP scaffolds containing VEGF for bone tissue engineering

abstract: Abstract Bone tissue engineering is sought to apply strategies for bone defects healing without limitations and short-comings of using either bone autografts or allografts and xenografts. The aim of this study was to fabricate a thin layer poly(lactic-co-glycolic) acid (PLGA) coated beta-tricalcium phosphate (β-TCP) scaffold with sustained release of vascular endothelial growth factor (VEGF). PLGA coating increased compressive strength of the β-TCP scaffolds significantly. For in vitro evaluations, canine mesenchymal stem cells (cMSCs) and canine endothelial progenitor cells (cEPCs) were isolated and characterized. Cell proliferation and attachment were demonstrated and the rate of cells proliferation on the VEGF released scaffold was significantly more than compared to the scaffolds with no VEGF loading. A significant increase in expression of COL1 and RUNX2 was indicated in the scaffolds loaded with VEGF and MSCs compared to the other groups. Consequently, PLGA coated β-TCP scaffold with sustained and localized release of VEGF showed favourable results for bone regeneration in vitro, and this scaffold has the potential to use as a drug delivery device in the future.


title: A Large-Scale Empirical Study of Security Patches

abstract: Given how the "patching treadmill" plays a central role for enabling sites to counter emergent security concerns, it behooves the security community to understand the patch development process and characteristics of the resulting fixes. Illumination of the nature of security patch development can inform us of shortcomings in existing remediation processes and provide insights for improving current practices. In this work we conduct a large-scale empirical study of security patches, investigating more than 4,000 bug fixes for over 3,000 vulnerabilities that affected a diverse set of 682 open-source software projects. For our analysis we draw upon the National Vulnerability Database, information scraped from relevant external references, affected software repositories, and their associated security fixes. Leveraging this diverse set of information, we conduct an analysis of various aspects of the patch development life cycle, including investigation into the duration of impact a vulnerability has on a code base, the timeliness of patch development, and the degree to which developers produce safe and reliable fixes. We then characterize the nature of security fixes in comparison to other non-security bug fixes, exploring the complexity of different types of patches and their impact on code bases. Among our findings we identify that: security patches have a lower footprint in code bases than non-security bug patches; a third of all security issues were introduced more than 3 years prior to remediation; attackers who monitor open-source repositories can often get a jump of weeks to months on targeting not-yet-patched systems prior to any public disclosure and patch distribution; nearly 5% of security fixes negatively impacted the associated software; and 7% failed to completely remedy the security hole they targeted.


title: SHTM: A neocortex-inspired algorithm for one-shot text generation

abstract: Text generation is a typical nature language processing task, and is the basis of machine translation and question answering. Deep learning techniques can get good performance on this task under the condition that huge number of parameters and mass of data are available for training. However, human beings do not learn in this way. People combine knowledge learned before and something new with only few samples. This process is called one-shot learning. In this paper, we propose a neocortex based computational model, Semantic Hierarchical Temporal Memory model (SHTM), for one-shot text generation. The model is refined from Hierarchical Temporal Memory model. LSTM is used for comparative study. Results on three public datasets show that SHTM performs much better than LSTM on the measures of mean precision and BLEU score. In addition, we utilize SHTM model to do question answering in the fashion of text generation and verifying its superiority.


title: MapReduce program synthesis

abstract: By abstracting away the complexity of distributed systems, large-scale data processing platforms—MapReduce, Hadoop, Spark, Dryad, etc.—have provided developers with simple means for harnessing the power of the cloud. In this paper, we ask whether we can automatically synthesize MapReduce-style distributed programs from input–output examples. Our ultimate goal is to enable end users to specify large-scale data analyses through the simple interface of examples. We thus present a new algorithm and tool for synthesizing programs composed of efficient data-parallel operations that can execute on cloud computing infrastructure. We evaluate our tool on a range of real-world big-data analysis tasks and general computations. Our results demonstrate the efficiency of our approach and the small number of examples it requires to synthesize correct, scalable programs.


title: Neural Probabilistic Model for Non-projective MST Parsing

abstract: In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM and CNN. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. We evaluate our model on 17 different datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straight-forward end-to-end model training procedure via back-propagation. Our parser achieves state-of-the-art parsing performance on nine datasets.


title: A product network analysis for extending the market basket analysis

abstract: In this study, we propose a product network analysis, a network-based analysis to analyze a network-leveled relation among all products. Compared to market basket analysis, which focuses on the transaction-leveled relation between products, the suggested product network analysis focuses on extended network-leveled point of view of the relation between all products. For such a purpose, we suggest two kinds of product networks, market basket networks and co-purchased product networks. Two networks are comparatively evaluated to analyze the topological characteristics and the structure of those networks. The extended use of market basket analysis, network-leveled analysis are expected to be more effectively and efficiently used in personalized services, such as cross selling, up selling, and personalized product display utilizing the deep relation between products.


title: Defense Methods Against Adversarial Examples for Recurrent Neural Networks

abstract: Adversarial examples are known to mislead deep learning models to incorrectly classify them, even in domains where such models achieve state-of-the-art performance. Until recently, research on both attack and defense methods focused on image recognition, primarily using convolutional neural networks (CNNs). In recent years, adversarial example generation methods for recurrent neural networks (RNNs) have been published, demonstrating that RNN classifiers are also vulnerable to such attacks. In this paper, we present a novel defense method, termed sequence squeezing, to make RNN classifiers more robust against such attacks. Our method differs from previous defense methods which were designed only for non-sequence based models. We also implement four additional RNN defense methods inspired by recently published CNN defense methods. We evaluate our methods against state-of-the-art attacks in the cyber security domain where real adversaries (malware developers) exist, but our methods can be applied against other discrete sequence based adversarial attacks, e.g., in the NLP domain. Using our methods we were able to decrease the effectiveness of such attack from 99.9% to 15%.


Understand Functionality and Dimensionality of Vector Embeddings : the Distributional Hypothesis , the Pairwise Inner Product Loss and Its Bias-Variance Trade


On temporal-spatial realism in the virtual reality environment


title: Tools for Resolving Functional Activity and Connectivity within Intact Neural Circuits

abstract: Mammalian neural circuits are sophisticated biological systems that choreograph behavioral processes vital for survival. While the inherent complexity of discrete neural circuits has proven difficult to decipher, many parallel methodological developments promise to help delineate the function and connectivity of molecularly defined neural circuits. Here, we review recent technological advances designed to precisely monitor and manipulate neural circuit activity. We propose a holistic, multifaceted approach for unraveling how behavioral states are manifested through the cooperative interactions between discrete neurocircuit elements.


title: Enhancing Use Case Points Estimation Method Using Soft Computing Techniques

abstract: Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the details of software have not been revealed yet. Several commercial and non-commercial tools exist to estimate software in the early stages. Most software effort estimation methods require software size as one of the important metric inputs and consequently, software size estimation in the early stages becomes essential. One of the approaches that has been used for about two decades in the early size and effort estimation is called use case points. Use case points method relies on the use case diagram to estimate the size and effort of software projects. Although the use case points method has been widely used, it has some limitations that might adversely affect the accuracy of estimation. This paper presents some techniques using fuzzy logic and neural networks to improve the accuracy of the use case points method. Results showed that an improvement up to 22% can be obtained using the proposed approach.


title: Design of optimum Maximum Power Point Tracking algorithm for solar panel

abstract: It is important to track the maximum power point of the Photovoltaic panel, as the electrical power supplied by solar array depends on insolation, temperature and load. Maximum Power Point Tracking (MPPT) is an Power electronic system that operates the photovoltaic modules to produce maximum power. MPPT varies the electrical operating point of the modules and enables them to deliver maximum available power. Normally Pulse width modulation type dc to dc conveters are used as the impedence matching between the PV arrays and loads. A new modified perturb and observation MPPT control algorithm is proposed to track the maximum power point. Unlike the control of PV voltage or current in normal P&O method the duty ratio of the dc to dc boost converter is directly controlled in the proposed algorithm. The proposed MPPT has the advantages of simplicity and high convergence speed. The algorithm is tested under various operating conditions. The obtained results have proven that the MPP is tracked even under sudden change of irradiation and loading level.


title: GPGPU: general purpose computation on graphics hardware

abstract: The graphics processor (GPU) on today's commodity video cards has evolved into an extremely powerful and flexible processor. The latest graphics architectures provide tremendous memory bandwidth and computational horsepower, with fully programmable vertex and pixel processing units that support vector operations up to full IEEE floating point precision. High level languages have emerged for graphics hardware, making this computational power accessible. Architecturally, GPUs are highly parallel streaming processors optimized for vector operations, with both MIMD (vertex) and SIMD (pixel) pipelines. Not surprisingly, these processors are capable of general-purpose computation beyond the graphics applications for which they were designed. Researchers have found that exploiting the GPU can accelerate some problems by over an order of magnitude over the CPU.However, significant barriers still exist for the developer who wishes to use the inexpensive power of commodity graphics hardware, whether for in-game simulation of physics of for conventional computational science. These chips are designed for and driven by video game development; the programming model is unusual, the programming environment is tightly constrained, and the underlying architectures are largely secret. The GPU developer must be an expert in computer graphics and its computational idioms to make effective use of the hardware, and still pitfalls abound. This course provides a detailed introduction to general purpose computation on graphics hardware (GPGPU). We emphasize core computational building blocks, ranging from linear algebra to database queries, and review the tools, perils, and tricks of the trade in GPU programming. Finally we present some interesting and important case studies on general-purpose applications of graphics hardware.The course presenters are experts on general-purpose GPU computation from academia and industry, and have presented papers and tutorials on the topic at SIGGRAPH, Graphics Hardware, Game Developers Conference, and elsewhere.


title: Profit Analysis and Supply Chain Planning Model for Closed-Loop Supply Chain in Fashion Industry

abstract: In recent decades, due to market growth and use of synthetic fiber, the fashion industry faces a rapid increase of CO 2 emission throughout the production cycle and raises environmental issues in recovery processing. This study proposes a closed-loop supply chain (CLSC) structure in fashion industry and develops its planning model as multi-objective mixed integer linear programming to find an optimal trade-off between CLSC profit and CO 2 emission. The planning model is associated with the profit analysis of each member in CLSC to find the optimal price of products on CLSC network. The model determines optimal production, transportation, and inventory quantities on CLSC network. The proposed models are validated using numerical experiments and sensitivity analyses, and from the results some managerial insights are addressed.


title: A Compact CPW-Fed Slotted Patch Antenna for Dual-Band Operation

abstract: A compact coplanar waveguide (CPW)-fed patch antenna designed by simply embedding two types of shaped slots into a rectangular patch for achieving dual-band operation is presented. The use of embedded slots can effectively excite multiresonant modes together with good dual-impedance bandwidths, especially ultrawide for the upper bandwidth. By fabricating and measuring the prototype of the proposed optimal antenna, dual operating bands with 10-dB return-loss bandwidths of about 230 MHz centered at 2.42 GHz and of about 73% ranging from 4.8 to 9.62 GHz covering the required bandwidths of 2.4/5.2/5.8 GHz WLAN standards and the C-band satellite communication were obtained. Also, a stable monopole-like radiation pattern and an average antenna gain of 1.4 and 5.1 dBi, respectively, across the dual operating bands have been measured.


title: A report on sound event detection with different binaural features

abstract: In this paper, we compare the performance of using binaural audio features in place of single-channel features for sound event detection. Three different binaural features are studied and evaluated on the publicly available TUT Sound Events 2017 dataset of length 70 minutes. Sound event detection is performed separately with single-channel and binaural features using stacked convolutional and recurrent neural network and the evaluation is reported using standard metrics of error rate and F-score. The studied binaural features are seen to consistently perform equal to or better than the single-channel features with respect to error rate metric.


title: Joint Distance Maps Based Action Recognition With Convolutional Neural Networks

abstract: Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.


title: Overview of Web Content Mining Tools

abstract: Nowadays, the Web has become one of the most widespread platforms for information change and retrieval. As it becomes easier to publish documents, as the number of users, and thus publishers, increases and as the number of documents grows, searching for information is turning into a cumbersome and time-consuming operation. Due to heterogeneity and unstructured nature of the data available on the WWW, Web mining uses various data mining techniques to discover useful knowledge from Web hyperlinks, page content and usage log. The main uses of web content mining are to gather, categorize, organize and provide the best possible information available on the Web to the user requesting the information. The mining tools are imperative to scanning the many HTML documents, images, and text. Then, the result is used by the search engines. In this paper, we first introduce the concepts related to web mining; we then present an overview of different Web Content Mining tools. We conclude by presenting a comparative table of these tools based on some pertinent criteria.


title: Considering a new framework for designing public safety 'filler' messages on highway variable-message signs: Applying the behaviour change wheel

abstract: This paper reviews literature regarding 'filler' (particularly safety) messages on variable message signs (VMS), then evaluates the applicability of Michie, van Stralen and West's behaviour change wheel for message generation using examples from Queensland. Although designed for generating health care interventions, the wheel readily extends itself to road safety. The paper concludes with a brief list of techniques for writing persuasive safety messages. This research was conducted because limited research is available on VMS safety messages or models for their generation. The literature review indicated that although controversy exists regarding the use of VMS for safety messages, more drivers would rather have the messages than blank signs; however, certain messages are seen as more useful than others. Further, VMS safety messages should not be expected to change the behaviour of all drivers but rather help a small share. The key benefit of this paper is that it proposes a comprehensive framework for generating VMS safety messages and describes strategies for writing them. Further research should be conducted on driver reactions to these messages.


title: Towards an understanding of the role of business intelligence systems in organisational knowing

abstract: Recent advances in information technology IT, such as the advent of business intelligence BI systems, have increased the ability of organisations to collect and analyse data to support decisions. There is little focus to date, however, on how BI systems might play a role in organisational knowledge creation - in organisational knowing. We develop a conceptual framework of organisational knowing based on a synthesis of the literature, and use this as a framework to investigate how BI systems facilitate knowing in a case organisation. We identify two practices triggered by BI systems that distinguish them from prior applications of IT: the ability to initiate problem articulation and dialogue, and that of data selection e.g. to address information needs of organisational decision makers at different managerial levels. This study provides empirical evidence of the performative outcome of BI systems in relation to organisational knowing through the practices of articulation and data selection. It provides a practice perspective on BI and focuses on the role of BI systems in organisational knowing thereby opening up a new departure for BI research that considers the implications of BI systems in organisations with actual practice in mind.


title: Anomaly Detection in Vessel Tracks using Bayesian networks

abstract: Abstract In recent years electronic tracking has provided voluminous data on vessel movements, leading researchers to try various data mining techniques to find patterns and, especially, deviations from patterns, i.e., for anomaly detection. Here we describe anomaly detection with data mined Bayesian Networks, learning them from real world Automated Identification System (AIS) data, and from supplementary data, producing both dynamic and static Bayesian network models. We find that the learned networks are quite easy to examine and verify despite incorporating a large number of variables. We also demonstrate that combining dynamic and static modelling approaches improves the coverage of the overall model and thereby anomaly detection performance.


title: SAR ATR by a combination of convolutional neural network and support vector machines

abstract: A combination of a convolutional neural network, which belongs to the deep learning research field, and support vector machines is presented as an efficient automatic target recognition system. Additional training methods that incorporate prior knowledge to the classifier and further improve its robustness against imaging errors and target variations are also presented. These methods generate artificial training data by elastic distortion and affine transformations that represent typical examples of image errors, like a changing range scale dependent on the depression angle or an incorrectly estimated aspect angle. With these examples presented to the classifier during the training, the system should become invariant against these variations and thus more robust. For the classification, the spotlight synthetic aperture radar images of the moving and stationary target acquisition and recognition database are used. Results are shown for the ten class database with a forced decision classification as well as with rejection class.


title: Direct analysis of protein complexes using mass spectrometry

abstract: We describe a rapid, sensitive process for comprehensively identifying proteins in macromolecular complexes that uses multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) to separate and fragment peptides. The SEQUEST algorithm, relying upon translated genomic sequences, infers amino acid sequences from the fragment ions. The method was applied to the Saccharomyces cerevisiae ribosome leading to the identification of a novel protein component of the yeast and human 40S subunit. By offering the ability to identify >100 proteins in a single run, this process enables components in even the largest macromolecular complexes to be analyzed comprehensively.


title: Mining Resource Scheduling Protocols

abstract: In service processes, as found in the telecommunications, financial, or healthcare sector, customers compete for the scarce capacity of service providers. For such processes, performance analysis is important and it often targets the time that customers are delayed prior to service. However, this wait time cannot be fully explained by the load imposed on service providers. Indeed, it also depends on resource scheduling protocols, which determine the order of activities that a service provider decides to follow when serving customers. This work focuses on automatically learning resource decisions from events. We hypothesize that queueing information serves as an essential element in mining such protocols and hence, we utilize the queueing perspective of customers in the mining process. We propose two types of mining techniques: advanced classification methods from data mining that include queueing information in their explanatory features and heuristics that originate in queueing theory. Empirical evaluation shows that incorporating the queueing perspective into mining of scheduling protocols improves predictive power.


title: Infill-Dependent 3-D-Printed Material Based on NinjaFlex Filament for Antenna Applications

abstract: This letter presents one of the first examples of the exploitation of 3-D printing in the fabrication of microwave components and antennas. Additive manufacturing represents an enabling technology for a wide range of electronic devices, thanks to its inherent features of fast prototyping, the reasonable accuracy, fully 3-D topologies, and the low fabrication cost. A novel 3-D printable flexible filament, based on NinjaFlex, has been adopted for manufacturing the substrate of a 3-D printed patch antenna. NinjaFlex is a recently introduced material with extraordinary features in terms of mechanical strain, flexibility, and printability. Initially, the electrical properties of this material are investigated at 2.4 GHz using the ring resonator technique. The capability of selectively changing the dielectric constant by modifying the printed material density by fine-tuning printing infill percentage is verified experimentally. Subsequently, a square patch antenna is prototyped through 3-D printing and measured to validate the manufacturing technology. Finally, exploiting mechanical flexibility properties of NinjaFlex, the antenna is tested under different bending conditions.


title: Post-Editing Error Correction Algorithm for Speech Recognition using Bing Spelling Suggestion

abstract: ASR short for Automatic Speech Recognition is the process of converting a spoken speech into text that can be manipulated by a computer. Although ASR has several applications, it is still erroneous and imprecise especially if used in a harsh surrounding wherein the input speech is of low quality. This paper proposes a post-editing ASR error correction method and algorithm based on Bing's online spelling suggestion. In this approach, the ASR recognized output text is spell-checked using Bing's spelling suggestion technology to detect and correct misrecognized words. More specifically, the proposed algorithm breaks down the ASR output text into several word-tokens that are submitted as search queries to Bing search engine. A returned spelling suggestion implies that a query is misspelled; and thus it is replaced by the suggested correction; otherwise, no correction is performed and the algorithm continues with the next token until all tokens get validated. Experiments carried out on various speeches in different languages indicated a successful decrease in the number of ASR errors and an improvement in the overall error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor computers.


title: Weakly-supervised Convolutional learning for detection of inflammatory gastrointestinal lesions

abstract: Graphic image annotations provide the necessary ground truth information for supervised machine learning in image-based computer-aided medical diagnosis. Performing such annotations is usually a time-consuming and cost-inefficient process requiring knowledge from domain experts. To cope with this problem we propose a novel weakly-supervised learning method based on a Convolutional Neural Network (CNN) architecture. The advantage of the proposed method over conventional supervised approaches is that only image-level semantic annotations are used in the training process, instead of pixel-level graphic annotations. This can drastically reduce the required annotation effort. Its advantage over the few state-of-the-art weakly-supervised CNN architectures is its simplicity. The performance of the proposed method is evaluated in the context of computer-aided detection of inflammatory gastrointestinal lesions in wireless capsule endoscopy videos. This is a broad category of lesions, for which early detection and treatment can be of vital importance. The results show that the proposed weakly-supervised learning method can be more effective than the conventional supervised learning, with an accuracy of 90%.


title: FPTree: A Hybrid SCM-DRAM Persistent and Concurrent B-Tree for Storage Class Memory

abstract: The advent of Storage Class Memory (SCM) is driving a rethink of storage systems towards a single-level architecture where memory and storage are merged. In this context, several works have investigated how to design persistent trees in SCM as a fundamental building block for these novel systems. However, these trees are significantly slower than DRAM-based counterparts since trees are latency-sensitive and SCM exhibits higher latencies than DRAM. In this paper we propose a novel hybrid SCM-DRAM persistent and concurrent B-Tree, named Fingerprinting Persistent Tree (FPTree) that achieves similar performance to DRAM-based counterparts. In this novel design, leaf nodes are persisted in SCM while inner nodes are placed in DRAM and rebuilt upon recovery. The FPTree uses Fingerprinting, a technique that limits the expected number of in-leaf probed keys to one. In addition, we propose a hybrid concurrency scheme for the FPTree that is partially based on Hardware Transactional Memory. We conduct a thorough performance evaluation and show that the FPTree outperforms state-of-the-art persistent trees with different SCM latencies by up to a factor of 8.2. Moreover, we show that the FPTree scales very well on a machine with 88 logical cores. Finally, we integrate the evaluated trees in memcached and a prototype database. We show that the FPTree incurs an almost negligible performance overhead over using fully transient data structures, while significantly outperforming other persistent trees.


title: POSTER: Rethinking Fingerprint Identification on Smartphones

abstract: Modern smartphones popularly adopt a small touch sensor for fingerprint identification of a user, but it captures only a partial limited portion of a fingerprint. Recently we have studied a gap between actual risk and user perception of latent fingerprints remaining on a smartphone, and developed a fake fingerprint attack that exploits the latent fingerprints as actual risk. We successfully reconstructed a fake fingerprint image in good quality for small touch sensors. In this paper, we subsequently conduct post hoc experimental studies on the facts that we have missed or have since learned. First of all, we examine that the presented attack is not conceptual but realistic. We employ the reconstructed image and make its fake fingerprint, using a conductive printing or a silicon-like glue, to pass directly the touch sensor of real smartphones. Our target smartphones are Samsung Galaxy S6, S7 and iPhone 5s, 6, 7. Indeed we have succeeded in passing Galaxy S6, S7, and now work on the remaining smartphones. We also conduct an experimental study for one of our mitigation methods to see how it can reduce actual risk. Finally, we perform a user survey study to understand user perception on the fake fingerprint attacks and the mitigation methods.


Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning


title: A Bee Colony Optimization Algorithm for Fault Coverage Based Regression Test Suite Prioritization

abstract: The process of verifying the modified software in the maintenance phase is called Regression Testing. The size of the regression test suite and its selection process is a complex task for regression testers because of time and budget constraints. In this research paper, the Bee Colony Optimization (BCO) algorithm for the fault coverage regression test suite prioritization has been presented. In the natural bee colony, there are of two types of worker bees; Scout bees and forager bee, who are responsible for the development and maintenance of the colony. The BCO algorithm developed for the fault coverage regression test suite is based on the behavior of these two bees. The BCO algorithm has been formulated for fault coverage to attain maximum fault coverage in minimal units of execution time of each test case, using two examples whose results are comparable to optimal solution. Average Percentage of Fault Detection (APFD) metrics and charts has been used to show the effectiveness of proposed algorithm.


title: Alterations in energy properties of eucalyptus wood and bark subjected to torrefaction: the potential of mass loss as a synthetic indicator.

abstract: Torrefaction is a mild pyrolysis process (usually up to 300 degrees C) that changes the chemical and physical properties of biomass. This process is a possible pre-treatment prior to further processes (transport, grinding, combustion, gasification, etc) to generate energy or biofuels. In this study, three eucalyptus wood species and bark were subjected to different torrefaction conditions to determine the alterations in their structural and energy properties. The most severe treatment (280 degrees C, 5h) causes mass losses of more than 35%, with severe damage to anatomical structure, and an increase of about 27% in the specific energy content. Bark is more sensitive to heat than wood. Energy yields are always higher than mass yields, thereby demonstrating the benefits of torrefaction in concentrating biomass energy. The overall mass loss is proposed as a relevant parameter to synthesize the effect of torrefaction conditions (temperature and duration). Accordingly, all results are summarised by analytical expressions able to predict the energy properties as a function of the overall mass loss. These expressions are intended to be used in any optimization procedure, from production in the field to the final use.


title: Learning Document Similarity Using Natural Language Processing

abstract: The recent considerable growth in the amount of easily available on-line text has brought to the foreground the need for large-scale natural language processing tools for text data mining. In this paper we address the problem of organizing documents into meaningful groups according to their content and to visualize a text collection, providing an overview of the range of documents and of their relationships, so that they can be browsed more easily. We use Self-Organizing Maps (SOMs) (Kohonen 1984). Great efficiency challenges arise in creating these maps. We study linguistically-motivated ways of reducing the representation of a document to increase efficiency and ways to disambiguate the words in the documents.


title: State-frequency memory recurrent neural networks

abstract: Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community. Among those efforts on improving the capability to represent temporal data, the Long Short-Term Memory (LSTM) has achieved great success in many areas. Although the LSTM can capture long-range dependency in the time domain, it does not explicitly model the pattern occurrences in the frequency domain that plays an important role in tracking and predicting data points over various time cycles. We propose the State-Frequency Memory (SFM), a novel recurrent architecture that allows to separate dynamic patterns across different frequency components and their impacts on modeling the temporal contexts of input sequences. By jointly decomposing memorized dynamics into state-frequency components, the SFM is able to offer a fine-grained analysis of temporal sequences by capturing the dependency of uncovered patterns in both time and frequency domains. Evaluations on several temporal modeling tasks demonstrate the SFM can yield competitive performances, in particular as compared with the state-of-the-art LSTM models.


title: Middleware Technologies for Cloud of Things - a survey

abstract: Abstract The next wave of communication and applications will rely on new services provided by the Internet of Things which is becoming an important aspect in human and machines future. IoT services are a key solution for providing smart environments in homes, buildings, and cities. In the era of massive number of connected things and objects with high growth rate, several challenges have been raised, such as management, aggregation, and storage for big produced data. To address some of these issues, cloud computing emerged to the IoT as Cloud of Things (CoT), which provides virtually unlimited cloud services to enhance the large-scale IoT platforms. There are several factors to be considered in the design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying a suitable “middleware” which sits between things and applications as a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next, we study different architecture styles and service domains. Then, we present several middlewares that are suitable for CoT-based platforms and finally, a list of current challenges and issues in the design of CoT-based middlewares is discussed.


title: United States Women and Pornography Through Four Decades: Exposure, Attitudes, Behaviors, Individual Differences

abstract: Responding to a call for research on pornography and women’s sexuality made by Weinberg, Williams, Kleiner, and Irizarry (2010), this study assessed pornography consumption, predictors, and correlates using nationally representative data gathered from U.S. women between 1973 and 2010 (N = 18,225). Women who were younger, less religious, and non-White were more likely to consume pornography. Women who consumed pornography had more positive attitudes toward extramarital sex, adult premarital sex, and teenage sex. Women who consumed pornography also had more sexual partners in the prior year, prior 5 years, and were more likely to have engaged in extramarital sex and paid sex. Consistent with Wright’s (2011a) acquisition, activation, application model of mass media sexual socialization and the theorizing of Linz and Malamuth (1993), liberal–conservative ideology moderated the association between pornography exposure and sexual behavior. Specifically, the positive association between pornography exposure and women’s recent sexual behavior was strongest for the most liberal women and weakest for the most conservative women. Cultural commentators and some academics argue that technological advances have resulted in a steady increase in the percentage of individuals who consume pornography. Little support was found for this assertion among U.S. women.


Graph Classification via Topological and Label Attributes


title: Learning to learn by gradient descent by gradient descent

abstract: The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned algorithms, implemented by LSTMs, outperform generic, hand-designed competitors on the tasks for which they are trained, and also generalize well to new tasks with similar structure. We demonstrate this on a number of tasks, including simple convex problems, training neural networks, and styling images with neural art.


title: Guidelines for Artificial Intelligence Containment

abstract: With almost daily improvements in capabilities of artificial intelligence it is more important than ever to develop safety software for use by the AI research community. Building on our previous work on AI Containment Problem we propose a number of guidelines which should help AI safety researchers to develop reliable sandboxing software for intelligent programs of all levels. Such safety container software will make it possible to study and analyze intelligent artificial agent while maintaining certain level of safety against information leakage, social engineering attacks and cyberattacks from within the container.


title: Towards Safer Roads through Cooperative Hazard Awareness and Avoidance in Connected Vehicles

abstract: Roadway safety is a serious public health issue. Vehicle crashes on the roads and highways cost loss of lives and damages to properties. Technology exists today to help identify and respond quickly to crashes, which is specially critical in rural areas. A feature that enables a car to warn its driver against an eminent crash or to recommend the proper speed based on traffic and weather conditions is much needed in today's Internet of Things (IoT) era. Connected Vehicle (CV) technologies are contributing towards fulfilling this need. In this paper, we present a safety driving system through a cooperative hazard awareness and avoidance (CHAA) system based on V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications. Our approach is based on DSRC-based V2V communications. It aims to alert drivers and recommend the proper speeds for vehicles that are approaching a hazardous zone due, for instance, to low visibility conditions. We also validate the proposed hazard alert and speed recommender system under various scenarios through simulations using the iTetris platform and SUMO simulator and we demonstrate its outperformance, compared to a regular environment, in terms of risk reduction and road safety efficiency. To this end, we have adapted iTetris by updating some of its existing features (like the Geo-broadcast routing capability) which is another contribution of this paper.


title: Overview of Dual-Active-Bridge Isolated Bidirectional DC–DC Converter for High-Frequency-Link Power-Conversion System

abstract: High-frequency-link (HFL) power conversion systems (PCSs) are attracting more and more attentions in academia and industry for high power density, reduced weight, and low noise without compromising efficiency, cost, and reliability. In HFL PCSs, dual-active-bridge (DAB) isolated bidirectional dc-dc converter (IBDC) serves as the core circuit. This paper gives an overview of DAB-IBDC for HFL PCSs. First, the research necessity and development history are introduced. Second, the research subjects about basic characterization, control strategy, soft-switching solution and variant, as well as hardware design and optimization are reviewed and analyzed. On this basis, several typical application schemes of DAB-IBDC for HPL PCSs are presented in a worldwide scope. Finally, design recommendations and future trends are presented. As the core circuit of HFL PCSs, DAB-IBDC has wide prospects. The large-scale practical application of DAB-IBDC for HFL PCSs is expected with the recent advances in solid-state semiconductors, magnetic and capacitive materials, and microelectronic technologies.


title: Frequency discrimination at different frequency levels as indexed by electrophysiological and behavioral measures

abstract: Abstract The present study systematically compared the neural and behavioral accuracy of discriminating a frequency change (“deviant”) in a repetitive tone (“standard”) across a frequency range of 250–4000 Hz. The sound structure (pure sinusoidal vs. harmonically rich tones) and the magnitude of frequency change (2.5%, 5%, 10%, 20%) were also varied. The accuracy of neural frequency-change detector was determined by comparing the auditory event-related potentials (ERP) elicited by deviant and standard stimuli in the absence of attention. In a separate behavioral task, subjects were to indicate when they noticed a frequency change. The ranges of the across-subject means of ERP parameters across the conditions were: the mismatch negativity (MMN) amplitude −0.9 to −4.9 μV, latency 125–218 ms, the P3a amplitude 0.3–3.2 μV, latency 239–304 ms. The ERP latency was shortest for the standard-stimulus frequency from 1000 to 2000 Hz suggesting that automatic frequency discrimination was the most accurate in that range. The ERP latencies and amplitudes correlated with the hit rate (HR) and reaction time (RT), with highest correlation found between the MMN amplitude and the HR (r=0.8). The harmonical tones elicited MMN and P3a with shorter latencies and larger amplitudes, than did pure sinusoidal tones in all frequency bands. The results may have implication to pitch-perception theories.


title: A Fault Detection Method for Hard Disk Drives Based on Mixture of Gaussians and Nonparametric Statistics

abstract: Hard Disk Drives (HDD) failure prediction is a challenging topic that has attracted much attention in recent years. Predicting failures in HDD may avoid losing data thus improving data reliability. Previous works on failure prediction are based on parametric approaches that model healthy drives with a Gaussian distribution. Although they achieved good results, the Gaussianity assumption may not hold true. The following work proposes a method for fault detection in HDD based on a Gaussian Mixture Model. A self-monitoring, analysis, and reporting technology dataset is used to evaluate the proposed method. Results show that the method outperforms previous works in both fault detection and time before failure.


title: Recurrent deep stacking networks for supervised speech separation

abstract: Supervised speech separation algorithms seldom utilize output patterns. This study proposes a novel recurrent deep stacking approach for time-frequency masking based speech separation, where the output context is explicitly employed to improve the accuracy of mask estimation. The key idea is to incorporate the estimated masks of several previous frames as additional inputs to better estimate the mask of the current frame. Rather than formulating it as a recurrent neural network (RNN), which is potentially much harder to train, we propose to train a deep neural network (DNN) with implicit deep stacking. The estimated masks of the previous frames are updated only at the end of each DNN training epoch, and then the updated estimated masks provide additional inputs to train the DNN in the next epoch. At the test stage, the DNN makes predictions sequentially in a recurrent fashion. In addition, we propose to use the L 1 loss for training. Experiments on the CHiME-2 (task-2) dataset demonstrate the effectiveness of our proposed approach.


title: Color Semantics for Image Indexing

abstract: We propose a color-based image descriptor that can be used for image indexing based on high-level semantic concepts. The descriptor is based on Kobayashi’s Color Image Scale, which is a system that includes 130 basic colors combined in 1170 three-color combinations. Each combination is labeled with one of 180 high-level semantic concepts, like ”elegant”, ”romantic”, ”provocative”, etc. Moreover, words are located in a twodimensional semantic space, and arranged into groups based on perceived similarity. From a modified approach for statistical analysis of images, involving transformations of ordinary RGBhistograms, a semantic image descriptor is derived, containing semantic information about both color combinations and single colors in the image. We show how the descriptor can be translated into different levels of semantic information, and used in indexing of multi-colored images. Intended applications are, for instance, image labeling and retrieval.


title: Algorithms for acoustic localization based on microphone array in service robotics

abstract: Abstract This paper deals with the development of acoustic source localization algorithms for service robots working in real conditions. One of the main utilizations of these algorithms in a mobile robot is that the robot can localize a human operator and eventually interact with him/herself by means of verbal commands. The location of a speaking operator is detected with a microphone array based algorithm; localization information is passed to a navigation module which sets up a navigation mission using knowledge of the environment map. In fact, the system we have developed aims at integrating acoustic, odometric and collision sensors with the mobile robot control architecture. Good performance with real acoustic data have been obtained using neural network approach with spectral subtraction and a noise robust voice activity detector. The experiments show that the average absolute localization error is about 40 cm at 0 dB and about 10 cm at 10 dB of SNR for the named localization. Experimental results describing mobile robot performance in a talker following task are reported.


title: Following the leader: A study of individual analysts’ earnings forecasts

abstract: Abstract This paper develops and tests procedures for ranking the performance of security analysts based on the timeliness of their earnings forecasts, the abnormal trading volume associated with these forecasts, and forecast accuracy. Our framework provides an objective assessment of analyst quality that differs from the standard approach, which uses survey evidence to rate analysts. We find that lead analysts identified by our measure of forecast timeliness have a greater impact on stock prices than follower analysts. Further, we find that performance rankings based on forecast timeliness are more informative than rankings based on abnormal trading volume and forecast accuracy. We also present evidence that analyst's forecast revisions are correlated with recent stock price performance, suggesting that security analysts use publicly available information to revise their earnings forecasts.


GravelDB : Introducing Fragmented Log-Structured Merge Trees to RocksDB


bupaR: Business Process Analysis in R


Measurement of critical thinking, clinical reasoning, and clinical judgment in culturally diverse nursing students – A literature review


title: MEDICAL DIAGNOSIS ON PIMA INDIAN DIABETES USING GENERAL REGRESSION NEURAL NETWORKS

abstract: The performance of recently developed neural network structure, general regression neural network (GRNN), is examined on the medical data. Pima Indian Dabetes (PID) data set is chosen to study on that had been examined by more complex neural network structures in the past. The results of early studies and of the GRNN structure presented in this paper is compared. Close classification accuracy to the reference work using ARTMAP-IC structured model, which is the best result obtained since now, is achieved by using GRNN, which has a simpler structure. The performance of the standard multilayer perceptron (MLP) and radial basis function (RBF) feed forward neural networks are also examined for the comparison as they are the most general and commonly used neural network structures. The performance of the MLP was tested for different types of backpropagation training algorithms.


title: Fake Twitter accounts: profile characteristics obtained using an activity-based pattern detection approach

abstract: In Online Social Networks (OSNs), the audience size commanded by an organization or an individual is a critical measure of that entity's popularity. This measure has important economic and/or political implications. Organizations can use information about their audience, such as age, location etc., to tailor their products or their message appropriately. But such tailoring can be biased by the presence of fake profiles on these networks. In this study, analysis of 62 million publicly available Twitter user profiles was conducted and a strategy to retroactively identify automatically generated fake profiles was established. Using a pattern-matching algorithm on screen-names with an analysis of tweet update times, a highly reliable sub-set of fake user accounts were identified. Analysis of profile creation times and URLs of these fake accounts revealed distinct behavior of the fake users relative to a ground truth data set. The combination of this scheme with established social graph analysis will allow for time-efficient detection of fake profiles in OSNs.


title: A novel highly efficient broadband continuous class-F RFPA delivering 74% average efficiency for an octave bandwidth

abstract: A novel, highly efficient and broadband RF power amplifier (PA) operating in “continuous class-F” mode has been realized for first time. The introduction and experimental verification of this new PA mode demonstrates that it is possible to maintain expected output performance, both in terms of efficiency and power, over a very wide bandwidth. Using recently established continuous class-F theory, an output matching network was designed to terminate the first three harmonic impedances. This resulted in a PA delivering an average drain efficiency of 74% and average output power of 10.5W for an octave bandwidth between 0.55GHz and 1.1GHz. A commercially available 10W GaN HEMT transistor has been used for the PA design and realization.


title: Prospects for High-Precision Pulsar Timing with the New Effelsberg PSRIX Backend

abstract: The PSRIX backend is the primary pulsar timing instrument of the Effelsberg 100 m radio telescope since early 2011. This new ROACH-based system enables bandwidths up to 500 MHz to be recorded, significantly more than what was possible with its predecessor, the Effelsberg Berkeley Pulsar Processor (EBPP). We review the first four years of PSRIX timing data for 33 pulsars collected as part of the monthly European Pulsar Timing Array (EPTA) observations. We describe the automated data analysis pipeline, COASTGUARD, that we developed to reduce these observations. We also introduce TOASTER, the EPTA timing data base, used to store timing results, processing information and observation metadata. Using these new tools, we measure the phase-averaged flux densities at 1.4 GHz of all 33 pulsars. For seven of these pulsars, our flux density measurements are the first values ever reported. For the other 26 pulsars, we compare our flux density measurements with previously published values. By comparing PSRIX data with EBPP data, we find an improvement of similar to 2-5 times in signal-to-noise ratio, which translates to an increase of similar to 2-5 times in pulse time-of-arrival (TOA) precision. We show that such an improvement in TOA precision will improve the sensitivity to the stochastic gravitational wave background. Finally, we showcase the flexibility of the new PSRIX backend by observing several millisecond-period pulsars (MSPs) at 5 and 9 GHz. Motivated by our detections, we discuss the potential for complementing existing pulsar timing array data sets with MSP monitoring campaigns at these higher frequencies.


title: Evolving Robust Robot Team Morphologies for Collective Construction

abstract: This research falls within evolutionary robotics and the larger taxonomy of cooperative multi-robot systems. A study of comparative methods to adapt the behaviors and morphologies of simulated robot teams that must solve a collective construction task is presented. Multiple versions of an indirect (developmental) encoding method for the artificial evolution of (team) behaviors and morphologies were tested. The indirect encoding method was able to adapt team morphology (number of sensors) and behavior (ANN controller connections and weights) that out-performed a team with fixed morphology and adaptive behavior. Results also indicated that the developmental method was appropriate for evolving controllers that were able to generalize to a range of team morphologies that solved the collective construction task with a high degree of task performance


title: A 2.4-GHz linear-tuning CMOS LC voltage-controlled oscillator

abstract: This paper presents a voltage-controlled oscillator (VCO) with high linearity in frequency tuning. The VCO has two control inputs. One controls a pair of p/sup +//n-well varactors to realize continuous tuning and the other controls a pair of MOS varactors for band switching. This new circuit topology achieves a good tuning nonlinearity of 1.45% and a wide total tuning range from 2.33 GHz to 2.72 GHz. The simulated phase noise is only -115.7 dBc/Hz at 600kHz offset from 2.4 GHz. Based on a 0.25-/spl mu/m process, the VCO dissipates only 15mW at a 2.5-V supply voltage.


title: The effect of balance training on postural control in people with multiple sclerosis using the CAREN virtual reality system: a pilot randomized controlled trial

abstract: BackgroundMultiple sclerosis (MS) is a multi-focal progressive disorder of the central nervous system often resulting in diverse clinical manifestations. Imbalance appears in most people with multiple sclerosis (PwMS). A popular balance training tool is virtual reality (VR) with several advantages including increased compliance and user satisfaction. Therefore, the aim of this pilot RCT (Trial registration number, date: ISRCTN14425615, 21/01/2016) was to examine the efficacy of a 6-week VR balance training program using the computer assisted rehabilitation environment (CAREN) system (Motek Medical BV, Amsterdam, Netherlands) on balance measures in PwMS. Results were compared with those of a conventional balance exercise group. Secondary aims included the impact of this program on the fear of falling.MethodsThirty-two PwMS were equally randomized into the VR intervention group or the control group. Each group received balance training sessions for 6 consecutive weeks, two sessions per week, 30 min sessions. Clinical balance tests and instrumented posturography outcome measures were collected upon initiation of the intervention programs and at termination.ResultsFinal analysis included 30 patients (19 females, 11 males; mean age, (S.D.) = 45.2 (11.6) years; mean EDSS (S.D.) = 4.1 (1.3), mean disease duration (S.D.) = 11.0 (8.9) years). Both groups showed a main effect of time on the center of pressure (CoP) path length with eyes open (F = 5.278, P = .024), sway rate with eyes open (F = 5.852, P = .035), Functional Reach Test (F = 20.841, P = .001), Four Square Step Test (F = 9.011, P = .031) and the Fear of Falls self-reported questionnaire (F = 17.815, P = .023). In addition, significant differences in favor of the VR program were observed for the group x time interactions of the Functional Reach Test (F = 10.173, P = .009) and fear of falling (F = 6.710, P = .021).ConclusionsWe demonstrated that balance training based on the CAREN device is an effective method of balance training for PwMS.


title: An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns

abstract: This paper considers a variety of econometric models for the joint distribution of US stock and bond returns in the presence of regime switching dynamics. While simple two- or three-state models capture the univariate dynamics in bond and stock returns, a more complicated four state model with regimes characterized as crash, slow growth, bull and recovery states is required to capture their joint distribution. The transition probability matrix of this model has a very particular form. Exits from the crash state are almost always to the recovery state and occur with close to 50 percent chance suggesting a bounce-back effect from the crash to the recovery state.


title: Global supply chain design: A literature review and critique

abstract: In this paper, we review decision support models for the design of global supply chains, and assess the fit between the research literature in this area and the practical issues of global supply chain design. The classification scheme for this review is based on ongoing and emerging issues in global supply chain management and includes review dimensions for (1) decisions addressed in the model, (2) performance metrics, (3) the degree to which the model supports integrated decision processes, and (4) globalization considerations. We conclude that although most models resolve a difficult feature associated with globalization, few models address the practical global supply chain design problem in its entirety. We close the paper with recommendations for future research in global supply chain modeling that is both forward-looking and practically oriented.


title: Analysing the privacy policies of Wi-Fi trackers

abstract: Wi-Fi-based tracking systems have recently appeared. By collecting radio signals emitted by Wi-Fi enabled devices, those systems are able to track individuals. They basically rely on the MAC address to uniquely identify each individual. If retailers and business have high expectations for physical tracking, it is also a threat for citizens privacy. We analyse the privacy policies used by the current tracking companies then we show the pitfalls of hash-based anonymization. More particularly we demonstrate that the hash-based anonymization of MAC address used in many Wi-Fi tracking systems can be easily defeated using of-the-shelf software and hardware. Finally we discuss possible solutions for MAC address anonymization in Wi-Fi tracking systems.


title: Intrinsic colorization

abstract: In this paper, we present an example-based colorization technique robust to illumination differences between grayscale target and color reference images. To achieve this goal, our method performs color transfer in an illumination-independent domain that is relatively free of shadows and highlights. It first recovers an illumination-independent intrinsic reflectance image of the target scene from multiple color references obtained by web search. The reference images from the web search may be taken from different vantage points, under different illumination conditions, and with different cameras. Grayscale versions of these reference images are then used in decomposing the grayscale target image into its intrinsic reflectance and illumination components. We transfer color from the color reflectance image to the grayscale reflectance image, and obtain the final result by relighting with the illumination component of the target image. We demonstrate via several examples that our method generates results with excellent color consistency.


title: Development of high torque density axial-gap switched reluctance motor for in-wheel direct-drive EV

abstract: Switched reluctance (SR) motors attract attention as a machine without rare-earth materials. And it is a candidate technology for electric vehicle application. However, torque density of SR motors is smaller than that of Permanent Magnet Synchronous Machines (PMSMs). Recently, an axial-gap structure is attention on possibility of effective utilization of in-wheel flat motor space. In the paper, a new flat volume axial-gap SR motor as high torque density for in-wheel direct-drive EV is designed. The characteristics of the proto-type machines are measured and compared with FEA results. The characteristics agree with simulation results. Finally, the proto-type machines are mounted on an 8-passenger 2,000 kg vehicle like small bus. The test drive of the in-wheel direct-drive axial gap SR motor vehicle is succeeded.


title: Efficient fingerprint search based on database clustering

abstract: Fingerprint identification has been a great challenge due to its complex search of database. This paper proposes an efficient fingerprint search algorithm based on database clustering, which narrows down the search space of fine matching. Fingerprint is non-uniformly partitioned by a circular tessellation to compute a multi-scale orientation field as the main search feature. The average ridge distance is employed as an auxiliary feature. A modified K-means clustering technique is proposed to partition the orientation feature space into clusters. Based on the database clustering, a hierarchical query processing is proposed to facilitate an efficient fingerprint search, which not only greatly speeds up the search process but also improves the retrieval accuracy. The experimental results show the effectiveness and superiority of the proposed fingerprint search algorithm.


title: Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

abstract: Learning is an inherently continuous phenomenon. When humans learn a new task there is no explicit distinction between training and inference. As we learn a task, we keep learning about it while performing the task. What we learn and how we learn it varies during different stages of learning. Learning how to learn and adapt is a key property that enables us to generalize effortlessly to new settings. This is in contrast with conventional settings in machine learning where a trained model is frozen during inference. In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation. A fundamental challenge in navigation is generalization to unseen scenes. In this paper we propose a self-adaptive visual navigation method (SAVN) which learns to adapt to new environments without any explicit supervision. Our solution is a meta-reinforcement learning approach where an agent learns a self-supervised interaction loss that encourages effective navigation. Our experiments, performed in the AI2-THOR framework, show major improvements in both success rate and SPL for visual navigation in novel scenes. Our code and data are available at: https://github.com/allenai/savn.


title: A Dual Path Deep Network for Single Image Super-Resolution Reconstruction

abstract: Super-resolution reconstruction based on deep learning has come a long way since the first proposed method in 2015. Numerous methods have been developed for this task using deep learning approaches. Among these methods, residual deep learning algorithms have shown better performance. Although all early proposed deep learning based super-resolution frameworks used bicubic upsampled versions of low resolution images as the main input, most of the current ones use the low resolution images directly by adding up-sampling layers to their networks. In this work, we propose a new method by using both low resolution and bicubic upsampled images as the inputs to our network. The final results confirm that decreasing the depth of the network in lower resolution space and adding the bicubic path lead to almost similar results to those of the deeper networks in terms of PSNR and SSIM, yet making the network computationally inexpensive and more efficient.


title: BUbiNG: massive crawling for the masses

abstract: Although web crawlers have been around for twenty years by now, there is virtually no freely available, open-source crawling software that guarantees high throughput, overcomes the limits of single-machine systems, and, at the same time, scales linearly with the amount of resources available. This article aims at filling this gap, through the description of BUbiNG, our next-generation web crawler built upon the authors’ experience with UbiCrawler [9] and on the last ten years of research on the topic. BUbiNG is an open-source Java fully distributed crawler; a single BUbiNG agent, using sizeable hardware, can crawl several thousand pages per second respecting strict politeness constraints, both host- and IP-based. Unlike existing open-source distributed crawlers that rely on batch techniques (like MapReduce), BUbiNG job distribution is based on modern high-speed protocols to achieve very high throughput.


title: Does Human Facial Attractiveness Honestly Advertise Health? Longitudinal Data on an Evolutionary Question

abstract: Inspired by the evolutionary conjecture that sexually selected traits function as indicators of pathogen resistance in animals and humans, we examined the notion that human facial attractiveness provides evidence of health. Using photos of 164 males and 169 females in late adolescence and health data on these individuals in adolescence, middle adulthood, and later adulthood, we found that adolescent facial attractiveness was unrelated to adolescent health for either males or females, and was not predictive of health at the later times. We also asked raters to guess the health of each stimulus person from his or her photo. Relatively attractive stimulus persons were mistakenly rated as healthier than their peers. The correlation between perceived health and medically assessed health increased when attractiveness was statistically controlled, which implies that attractiveness suppressed the accurate recognition of health. These findings may have important implications for evolutionary models.


title: Episodic CAMN: Contextual Attention-Based Memory Networks with Iterative Feedback for Scene Labeling

abstract: Scene labeling can be seen as a sequence-sequence prediction task (pixels-labels), and it is quite important to leverage relevant context to enhance the performance of pixel classification. In this paper, we introduce an episodic attention-based memory network to achieve the goal. We present a unified framework that mainly consists of a Convolutional Neural Network (CNN), specifically, Fully Convolutional Network (FCN) and an attention-based memory module with feedback connections to perform context selection and refinement. The full model produces context-aware representation for each target patch by aggregating the activated context and its original local representation produced by the convolution layers. We evaluate our model on PASCAL Context, SIFT Flow and PASCAL VOC 2011 datasets and achieve competitive results to other state-of-the-art methods in scene labeling.


Analytical Techniques for Non-Linear Control Systems


title: VITAL: VIsual Tracking via Adversarial Learning

abstract: The tracking-by-detection framework consists of two stages, i.e., drawing samples around the target object in the first stage and classifying each sample as the target object or as background in the second stage. The performance of existing trackers using deep classification networks is limited by two aspects. First, the positive samples in each frame are highly spatially overlapped, and they fail to capture rich appearance variations. Second, there exists extreme class imbalance between positive and negative samples. This paper presents the VITAL algorithm to address these two problems via adversarial learning. To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes. With the use of adversarial learning, our network identifies the mask that maintains the most robust features of the target objects over a long temporal span. In addition, to handle the issue of class imbalance, we propose a high-order cost sensitive loss to decrease the effect of easy negative samples to facilitate training the classification network. Extensive experiments on benchmark datasets demonstrate that the proposed tracker performs favorably against state-of-the-art approaches.


title: Background Subtraction Using Multiscale Fully Convolutional Network

abstract: Background modeling and subtraction based on change detection are the first step in many high-level computer vision applications. Many background subtraction methods have been proposed in the recent past and their efforts mainly focus on two aspects: more advanced background models and more complex feature representations. Recently, hierarchical features learned from deep convolutional neural networks have been shown to be effective for many computer vision tasks, such as classification and recognition. However, few researchers try to learn the deep features to address the background subtraction problem. Therefore, in this paper, we propose a novel multiscale fully convolutional network (MFCN) architecture which takes advantage of different layer features for background subtraction. We show that the foreground detection accuracy can be greatly improved by using the deep features learned from the MFCN and instead of building highly complex background models, and the complexity of the background subtraction process can be easily solved during the subtraction operation itself. Experimental results on CDnet 2014 data set and SBM-RGBD data set show that the proposed MFCN-based method achieves state-of-the-art performance while operating at real time.


title: NetworkProfiler: Towards automatic fingerprinting of Android apps

abstract: Network operators need to have a clear visibility into the applications running in their network. This is critical for both security and network management. Recent years have seen an exponential growth in the number of smart phone apps which has complicated this task. Traditional methods of traffic classification are no longer sufficient as the majority of this smart phone app traffic is carried over HTTP/HTTPS. Keeping up with the new applications that come up everyday is very challenging and time-consuming. We present a novel technique for automatically generating network profiles for identifying Android apps in the HTTP traffic. A network profile consists of fingerprints, i.e., unique characteristics of network behavior, that can be used to identify an app. To profile an Android app, we run the app automatically in an emulator and collect the network traces. We have developed a novel UI fuzzing technique for running the app such that different execution paths are exercised, which is necessary to build a comprehensive network profile. We have also developed a light-weight technique, for extracting fingerprints, that is based on identifying invariants in the generated traces. We used our technique to generate network profiles for thousands of apps. Using our network profiles we were able to detect the presence of these apps in real-world network traffic logs from a cellular provider.


title: Experimental Investigation of Some Statistical Properties of Monochromatic Speckle Patterns

abstract: Statistical properties of monochromatic, polarized speckle patterns were studied experimentally. The intensity correlation function, the probability density of the spatial derivative of the intensity, and the average number of level crossings of the intensity were measured. The results are compared with theory and a high measuring accuracy obtained using lock-in detection.


title: Mirror neurons and their clinical relevance

abstract: Mirror neurons are motor neurons that discharge both when an individual performs a given motor act and when they observe another individual performing the same act. As Rizzolatti et al. discuss in this Review, the study of the mirror system is providing important insights into the mechanisms that underlie the symptoms of autism, and is also facilitating the development of new neurorehabilitation strategies.


title: Semantics-aware detection of targeted attacks: a survey

abstract: In today’s interconnected digital world, targeted attacks have become a serious threat to conventional computer systems and critical infrastructure alike. Many researchers contribute to the fight against network intrusions or malicious software by proposing novel detection systems or analysis methods. However, few of these solutions have a particular focus on Advanced Persistent Threats or similarly sophisticated multi-stage attacks. This turns finding domain-appropriate methodologies or developing new approaches into a major research challenge. To overcome these obstacles, we present a structured review of semantics-aware works that have a high potential for contributing to the analysis or detection of targeted attacks. We introduce a detailed literature evaluation schema in addition to a highly granular model for article categorization. Out of 123 identified papers, 60 were found to be relevant in the context of this study. The selected articles are comprehensively reviewed and assessed in accordance to Kitchenham’s guidelines for systematic literature reviews. In conclusion, we combine new insights and the status quo of current research into the concept of an ideal systemic approach capable of semantically processing and evaluating information from different observation points.


title: Parsing sewing patterns into 3D garments

abstract: We present techniques for automatically parsing existing sewing patterns and converting them into 3D garment models. Our parser takes a sewing pattern in PDF format as input and starts by extracting the set of panels and styling elements (e.g. darts, pleats and hemlines) contained in the pattern. It then applies a combination of machine learning and integer programming to infer how the panels must be stitched together to form the garment. Our system includes an interactive garment simulator that takes the parsed result and generates the corresponding 3D model. Our fully automatic approach correctly parses 68% of the sewing patterns in our collection. Most of the remaining patterns contain only a few errors that can be quickly corrected within the garment simulator. Finally we present two applications that take advantage of our collection of parsed sewing patterns. Our garment hybrids application lets users smoothly interpolate multiple garments in the 2D space of patterns. Our sketch-based search application allows users to navigate the pattern collection by drawing the shape of panels.


title: Refining Geometry from Depth Sensors using IR Shading Images

abstract: We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.


Assortment Optimization under the Paired Combinatorial Logit Model


title: Real-time multiple vehicle detection and tracking from a moving vehicle

abstract: Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions.


title: Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts

abstract: Majority of the load forecasting literature has been on point forecasting, which provides the expected value for each step throughout the forecast horizon. In the smart grid era, the electricity demand is more active and less predictable than ever before. As a result, probabilistic load forecasting, which provides additional information on the variability and uncertainty of future load values, is becoming of great importance to power systems planning and operations. This paper proposes a practical methodology to generate probabilistic load forecasts by performing Quantile Regression Averaging (QRA) on a set of sister point forecasts. There are two major benefits of the proposed approach: 1) it can leverage the development in the point load forecasting literature over the past several decades; and 2) it does not rely so much on high quality expert forecasts, which are rarely achievable in load forecasting practice. To demonstrate the effectiveness of the proposed approach and make the results reproducible to the load forecasting community, we construct a case study using the publicly available data from the Global Energy Forecasting Competition 2014. Comparing with the benchmark methods that utilize the variability of a selected individual forecast, the proposed approach leads to dominantly better performance as measured by the pinball loss function and the Winkler score.


title: Exploring personalized neural conversational models

abstract: Modeling dialog systems is currently one of the most active problems in Natural Language Processing. Recent advances in Deep Learning have sparked an interest in the use of neural networks in modeling language, particularly for personalized conversational agents that can retain contextual information during dialog exchanges. This work carefully explores and compares several of the recently proposed neural conversation models, and carries out a detailed evaluation on the multiple factors that can significantly affect predictive performance, such as pretraining, embedding training, data cleaning, diversity-based reranking, evaluation setting, etc. Based on the tradeoffs of different models, we propose a new neural generative dialog model conditioned on speakers as well as context history that outperforms previous models on both retrieval and generative metrics. Our findings indicate that pretraining speaker embeddings on larger datasets, as well as bootstrapping word and speaker embeddings, can significantly improve performance (up to 3 points in perplexity), and that promoting diversity in using Mutual Information based techniques has a very strong effect in ranking metrics.


title: A Kinect-Based Wearable Face Recognition System to Aid Visually Impaired Users

abstract: In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.