title: TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment

abstract: A single-valued neutrosophic set is a special case of neutrosophic set. It has been proposed as a generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets in order to deal with incomplete information. In this paper, a new approach for multi-attribute group decision-making problems is proposed by extending the technique for order preference by similarity to ideal solution to single-valued neutrosophic environment. Ratings of alternative with respect to each attribute are considered as single-valued neutrosophic set that reflect the decision makers' opinion based on the provided information. Neutrosophic set characterized by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F) is more capable to catch up incomplete information. Single-valued neutrosophic set-based weighted averaging operator is used to aggregate all the individual decision maker's opinion into one common opinion for rating the importance of criteria and alternatives. Finally, an illustrative example is provided in order to demonstrate its applicability and effectiveness of the proposed approach.


title: Active learning using pre-clustering

abstract: The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary, better performance can be achieved by taking into account the prior data distribution. The main contribution of the paper is a formal framework that incorporates clustering into active learning. The algorithm first constructs a classifier on the set of the cluster representatives, and then propagates the classification decision to the other samples via a local noise model. The proposed model allows to select the most representative samples as well as to avoid repeatedly labeling samples in the same cluster. During the active learning process, the clustering is adjusted using the coarse-to-fine strategy in order to balance between the advantage of large clusters and the accuracy of the data representation. The results of experiments in image databases show a better performance of our algorithm compared to the current methods.


title: Hardware-efficient belief propagation

abstract: Belief propagation (BP) is an effective algorithm for solving energy minimization problems in computer vision. However, it requires enormous memory, bandwidth, and computation because messages are iteratively passed between nodes in the Markov random field (MRF). In this paper, we propose two methods to address this problem. The first method is a message passing scheme called tile-based belief propagation. The key idea of this method is that a message can be well approximated from other faraway ones. We split the MRF into many tiles and perform BP within each one. To preserve the global optimality, we store the outgoing boundary messages of a tile and use them when performing BP in the neighboring tiles. The tile-based BP only requires 1-5% memory and 0.2-1% bandwidth of the ordinary BP. The second method is an O(L) message construction algorithm for the robust functions commonly used for describing the smoothness terms in the energy function. We find that many variables in constructing a message are repetitive; thus these variables can be calculated once and reused many times. The proposed algorithms are suitable for parallel implementations. We design a low-power VLSI circuit for disparity estimation that can construct 440 M messages per second and generate high quality disparity maps in near real-time. We also implement the proposed algorithms on a GPU, which can calculate messages 4 times faster than the sequential O(L) method.


title: Attelia: Reducing user's cognitive load due to interruptive notifications on smart phones

abstract: In today's ubiquitous computing environment where the number of devices, applications and web services are ever increasing, human attention is the new bottleneck in computing. To minimize user cognitive load, we propose Attelia, a novel middleware that identifies breakpoints in user interaction and delivers notifications at these moments. Attelia works in realtime and uses only the mobile devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our evaluation proved the effectiveness of Attelia. A controlled user study showed that notifications at detected breakpoint timing resulted in 46% lower cognitive load compared to randomly-timed notifications. Furthermore, our “in-the-wild” user study with 30 participants for 16 days further validated Attelia's value, with a 33% decrease in cognitive load compared to randomly-timed notifications.


title: A distribution model of functional connectome based on criticality and energy constraints

abstract: The analysis of the network structure of the functional connectivity data constructed from fMRI images provides basic information about functions and features of the brain activity. We focus on the two features which are considered as relevant to the brain activity, the criticality and the constraint regarding energy consumptions. Within a wide variety of complex systems, the critical state occurs associated with a phase transition between distinct phases, random one and order one. Although the hypothesis that human brain activity is also in a state of criticality is supported by some experimental results, it still remains controversial. One issue is that experimental distributions exhibit deviations from the power law predicted by the criticality. Based on the assumption that constraints on brain from the biological costs cause these deviations, we derive a distribution model. The evaluation using the information criteria indicates an advantage of this model in fitting to experimental data compared to other representative distribution models, the truncated power law and the power law. Our findings also suggest that the mechanism underlying this model is closely related to the cost effective behavior in human brain with maximizing the network efficiency for the given network cost.


title: Efficient and Robust Large-Scale Rotation Averaging

abstract: In this paper we address the problem of robust and efficient averaging of relative 3D rotations. Apart from having an interesting geometric structure, robust rotation averaging addresses the need for a good initialization for large scale optimization used in structure-from-motion pipelines. Such pipelines often use unstructured image datasets harvested from the internet thereby requiring an initialization method that is robust to outliers. Our approach works on the Lie group structure of 3D rotations and solves the problem of large-scale robust rotation averaging in two ways. Firstly, we use modern l1 optimizers to carry out robust averaging of relative rotations that is efficient, scalable and robust to outliers. In addition, we also develop a two step method that uses the l1 solution as an initialisation for an iteratively reweighted least squares (IRLS) approach. These methods achieve excellent results on large-scale, real world datasets and significantly outperform existing methods, i.e. the state-of-the-art discrete-continuous optimization method of [3] as well as the Weiszfeld method of [8]. We demonstrate the efficacy of our method on two large scale real world datasets and also provide the results of the two aforementioned methods for comparison.


title: Proxy Graph: Visual Quality Metrics of Big Graph Sampling

abstract: Data sampling has been extensively studied for large scale graph mining. Many analyses and tasks become more efficient when performed on graph samples of much smaller size. The use of proxy objects is common in software engineering for analysis and interaction with heavy objects or systems. In this paper, we coin the term ’proxy graph’ and empirically investigate how well a proxy graph visualization can represent a big graph. Our investigation focuses on proxy graphs obtained by sampling ; this is one of the most common proxy approaches. Despite the plethora of data sampling studies, this is the first evaluation of sampling in the context of graph visualization. For an objective evaluation, we propose a new family of quality metrics for visual quality of proxy graphs. Our experiments cover popular sampling techniques. Our experimental results lead to guidelines for using sampling-based proxy graphs in visualization.


title: Sleep after learning aids memory recall.

abstract: In recent years, the effect of sleep on memory consolidation has received considerable attention. In humans, these studies concentrated mainly on procedural types of memory, which are considered to be hippocampus-independent. Here, we show that sleep also has a persisting effect on hippocampus-dependent declarative memory. In two experiments, we examined high school students’ ability to remember vocabulary. We show that declarative memory is enhanced when sleep follows within a few hours of learning, independent of time of day, and with equal amounts of interference during retention intervals. Sleep deprivation has a detrimental effect on memory, which was significant after a night of recovery sleep. Thus, fatigue accumulating during wake intervals could be ruled out as a confound.


A General Theory of Temporal Connectivity.


title: How much will older adults exercise? A feasibility study of aerobic training combined with resistance training

abstract: Both aerobic training (AT) and resistance training (RT) have multidimensional health benefits for older adults including increased life expectancy and decreased risk of chronic diseases. However, the volume (i.e., frequency*time) of AT combined with RT in which untrained older adults can feasibly and safely participate remains unclear. Thus, our primary objective was to investigate the feasibility and safety of a high-volume exercise program consisting of twice weekly AT combined with twice weekly RT (i.e., four times weekly exercise) on a group of untrained older adults. In addition, we investigated the effects of the program on physical function, aerobic capacity, muscular strength, and explored factors related to participant adherence. We recruited eight inactive older adults (65+ years) to participate in a 6-week, single-group pre-post exercise intervention, consisting of 2 days/week of AT plus 2 days/week of progressive RT for 6 weeks. We recorded program attendance and monitored for adverse events during the course of the program. Participants were tested at both baseline and follow-up on the following: (1) physical function (i.e., timed-up-and-go test (TUG) and short physical performance battery (SPPB)), (2) aerobic capacity (VO2max) using the modified Bruce protocol; and (3) muscular strength on the leg press and lat pull-down. Post intervention, we performed qualitative semi-structured interviews of all participants regarding their experiences in the exercise program. We used these responses to examine themes that may affect continued program adherence to a high-volume exercise program. We recorded an average attendance rate of 83.3% with the lowest attendance for one session being five out of eight participants; no significant adverse events occurred. Significant improvements were observed for SPPB score (1.6; 95% CI: [0.3, 2.9]), VO2max (8.8 ml/kg/min; 95% CI: [2.8, 14.8]), and lat pull-down strength (11.8 lbs; 95% CI: [3.3, 20.2]). Qualitative results revealed two themes that promote older adults’ adherence: (1) convenience of the program and (2) the social benefits of exercise. Our findings suggest untrained older adults can be successful at completing twice weekly AT combined with twice weekly progressive RT; however, these exercise programs should be group-based in order to maintain high adherence.


title: Forecasting S&P 500 index using artificial neural networks and design of experiments

abstract: The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential financial and economical variables along with a feature defined as the number of nodes of the ANN. The results of employing the proposed methodology show that the ANN that uses the most influential features is able to forecast the daily direction of S&P 500 significantly better than the traditional logit model. Furthermore, experimental results of employing the proposed ANN on the trades in a test period indicate that ANN could significantly improve the trading profit as compared with the buy-and-hold strategy.


title: One-Time Cookies: Preventing Session Hijacking Attacks with Disposable Credentials

abstract: Many web applications are vulnerable to session hijacking attacks due to the insecure use of cookies for session management. The most recommended defense against this threat is to completely replace HTTP with HTTPS. However, this approach presents several challenges (e.g., performance and compatibility concerns) and therefore, has not been widely adopted. In this paper, we propose “One-Time Cookies” (OTC), an HTTP session authentication protocol that is efficient, easy to deploy and resistant to session hijacking. OTC’s security relies on the use of disposable credentials based on a modified hash chain construction. We implemented OTC as a plug-in for the popular WordPress platform and conducted extensive performance analysis using extensions developed for both Firefox and Firefox for mobile browsers. Our experiments demonstrate the ability to maintain session integrity with a throughput improvement of 51% over HTTPS and a performance approximately similar to a cookie-based approach. In so doing, we demonstrate that one-time cookies can significantly improve the security of web sessions with minimal changes to current infrastructure.


title: Conventional physical therapy with lumbar traction; clinical evaluation and magnetic resonance imaging for lumbar disc herniation.

abstract: OBJECTIVE ::: This study measures and compares the outcome of conservative physical therapy with traction, by using magnetic resonance imaging and clinical parameters in patients presenting with low back pain caused by lumbar disc herniation. ::: ::: ::: METHODS ::: A total of 26 patients with LDH (14F, 12M with mean aged 37 +/- 11) were enrolled in this study and 15 sessions (per day on 3 weeks) of physical therapy were applied. That included hot pack, ultrasound, electrotherapy and lumbar traction. Physical examination of the lumbar spine, severity of pain, sleeping order, patient and physician global assessment with visual analogue scale, functional disability by HAQ, Roland Disability Questionnaire, and Modified Oswestry Disability Questionnaire were assessed at baseline and at 4-6 weeks after treatment. Magnetic resonance imaging examinations were carried out before and 4-6 weeks after the treatment ::: ::: ::: RESULTS ::: All patients completed the therapy session. There were significant reductions in pain, sleeping disturbances, patient and physician global assessment and disability scores, and significant increases in lumbar movements between baseline and follow-up periods. There were significant reductions of size of the herniated mass in five patients, and significant increase in 3 patients on magnetic resonance imaging after treatment, but no differences in other patients. ::: ::: ::: CONCLUSIONS ::: This study showed that conventional physical therapies with lumbar traction were effective in the treatment of patient with subacute LDH. These results suggest that clinical improvement is not correlated with the finding of MRI. Patients with LDH should be monitored clinically (Fig. 3, Ref. 18).


Corporate reputat on : Meanin and measurement Corporate reputation : Meaning and measurement


title: Fisher Kernel Temporal Variation-based Relevance Feedback for video retrieval

abstract: We proposed a novel framework for Relevance Feedback based on the Fisher Kernel.The Fisher Kernel representation makes possible to capture temporal variation by using frame-based features.We experiment on a high variety of scenarios and public datasets (genre classification - Blip10000, action recognition - UCF50 / UCF101 and daily activities recognition - ADL) and show the benefits of the proposed approach which outperforms other state of the art approaches.We prove the generalization power of our approach, i.e., the framework is not dependent on a particular type of content descriptors (experiments were made with text, visual and audio features). This paper proposes a novel framework for Relevance Feedback based on the Fisher Kernel (FK). Specifically, we train a Gaussian Mixture Model (GMM) on the top retrieval results (without supervision) and use this to create a FK representation, which is therefore specialized in modelling the most relevant examples. We use the FK representation to explicitly capture temporal variation in video via frame-based features taken at different time intervals. While the GMM is being trained, a user selects from the top examples those which he is looking for. This feedback is used to train a Support Vector Machine on the FK representation, which is then applied to re-rank the top retrieved results. We show that our approach outperforms other state-of-the-art relevance feedback methods. Experiments were carried out on the Blip10000, UCF50, UCF101 and ADL standard datasets using a broad range of multi-modal content descriptors (visual, audio, and text).


title: Board-level solder joint reliability of high performance computers under mechanical loading

abstract: Several changes in high performance computer system design, construction and materials have resulted in potentially greater risk of solder joint failures under mechanical loading. In addition to mechanical/thermal challenges created by the increased volumetric density and power dissipation of components in the system assemblies, computer products increasingly include Pb-free solder and circuit board materials that are often more brittle than their Sn37Pb containing predecessors. Although servers and computer sub-assemblies are unlikely to witness severe impact loading under end-use conditions, high strain-rate events nonetheless occur during board assembly, test, handling and shipment. This paper provides a brief overview of characterization test methods, modeling approaches, and acceptance criteria used to establish component fracture strength, circuit board strain and solder joint reliability of electronic assemblies under various mechanical stress conditions. In addition, key parameters associated with optimization of solder joint brittle fracture strength are identified.


title: Understanding Productivity: Lessons from Longitudinal Microdata

abstract: This paper reviews research that uses longitudinal microdata to document productivity movements and to examine factors behind productivity growth. The research explores the dispersion of productivity across firms and establishments, the persistence of productivity differentials, the consequences of entry and exit, and the contribution of resource reallocation across firms to aggregate productivity growth. The research also reveals important factors correlated with productivity growth, such as managerial ability, technology use, human capital, and regulation. The more advanced literature in the field has begun to address the more difficult questions of the causality between these factors and productivity growth.


title: Tailoring Continuous Word Representations for Dependency Parsing

abstract: Word representations have proven useful for many NLP tasks, e.g., Brown clusters as features in dependency parsing (Koo et al., 2008). In this paper, we investigate the use of continuous word representations as features for dependency parsing. We compare several popular embeddings to Brown clusters, via multiple types of features, in both news and web domains. We find that all embeddings yield significant parsing gains, including some recent ones that can be trained in a fraction of the time of others. Explicitly tailoring the representations for the task leads to further improvements. Moreover, an ensemble of all representations achieves the best results, suggesting their complementarity.


title: Flicker noise upconversion mechanisms in K-band CMOS VCOs

abstract: This paper analyzes the flicker noise upconversion mechanisms in CMOS LC-VCOs, focusing on the bias circuitry and core cross-coupled pair. In contrary to what is widely understood, a current-biased VCO does not necessarily have more upconverted flicker noise compared to a resistor-biased VCO. Resistor biasing obviously does not generate 1/f noise itself, but it allows for a transfer of the core transistors flicker noise to phase noise through AM-PM conversion and the Groszkowski effect. The high impedance of a tail current source improves the phase noise because the amplitude noise is controlled by the bias current source which can be designed with low flicker noise. On the other hand, flicker noise from the cross-coupled pair becomes a main contributor of phase noise at low offset frequency. An analytical expression is proposed to describe such mechanism. To verify the theory, three 25GHz VCOs are taped out in a CMOS 28nm process. The measurement results show that all VCOs have averaged FoM of −187dB at 10MHz offset while the current-biased VCO has 1∼2dB better phase noise/FoM at 1MHz offset due to the better amplitude noise control.


Final Project Report: Cryptoprocessor for Elliptic Curve Digital Signature Algorithm (ECDSA) Team ID: IN00000026


VisTA: Visual Terminology Alignment Tool for Factual Knowledge Aggregation


title: An ISAR Imaging Method Based on MIMO Technique

abstract: With the inverse synthetic aperture radar (ISAR) imaging model, targets should move smoothly during the coherent processing interval (CPI). Since the CPI is quite long, fluctuations of a target's velocity and gesture will deteriorate image quality. This paper presents a multiple-input-multiple-output (MIMO)-ISAR imaging method by combining MIMO techniques and ISAR imaging theory. By using a special M-transmitter N-receiver linear array, a group of M orthogonal phase-code modulation signals with identical bandwidth and center frequency is transmitted. With a matched filter set, every target response corresponding to the orthogonal signals can be isolated at each receiving channel, and range compression is completed simultaneously. Based on phase center approximation theory, the minimum entropy criterion is used to rearrange the echo data after the target's velocity has been estimated, and then, the azimuth imaging will finally finish. The analysis of imaging and simulation results show that the minimum CPI of the MIMO-ISAR imaging method is 1/MN of the conventional ISAR imaging method under the same azimuth-resolution condition. It means that most flying targets can satisfy the condition that targets should move smoothly during CPI; therefore, the applicability and the quality of ISAR imaging will be improved.


title: Query Expansion Using Word Embeddings

abstract: We present a suite of query expansion methods that are based on word embeddings. Using Word2Vec's CBOW embedding approach, applied over the entire corpus on which search is performed, we select terms that are semantically related to the query. Our methods either use the terms to expand the original query or integrate them with the effective pseudo-feedback-based relevance model. In the former case, retrieval performance is significantly better than that of using only the query, and in the latter case the performance is significantly better than that of the relevance model.


title: Conversational Chat Circles: Being All Here Without Having to Hear It All

abstract: Live streaming services are a growing form of social media. Most live streaming platforms allow viewers to communicate with each other and the broadcaster via a text chat. However, interaction in a text chat does not work well with too many users. Existing techniques to make text chat work with a larger number of participants often limit who can participate or how much users can participate. In this paper, we describe a new design for a text chat system that allows more people to participate without overwhelming users with too many messages. Our design strategically limits the number of messages a user sees based on the concept of neighborhoods, and emphasizes important messages through upvoting. We present a study comparing our system to a chat system similar to those found in commercial streaming services. Results of the study indicate that the Conversational Circle system is easier to understand and interact with, while supporting community among viewers and highlighting important content for the streamer.


title: A Framework for Product Description Classification in E-commerce

abstract: We propose the Hierarchical Product Classification (HPC) framework for the purpose of classifying products using a hierarchical product taxonomy. The framework uses a classification system with multiple classification nodes, each residing on a different level of the taxonomy. The innovative part of the framework stems from the definition of classification recipes that can be used to construct high-quality classifier nodes, using the product descriptions in the most optimal way. These classifier recipes are specifically tailored for the e-commerce domain. The use of these classifier recipes enables flexible classifiers that adjust to the taxonomy depth-specific characteristics of product taxonomies. Furthermore, in order to gain insight into which components are required to perform high quality product classification, we evaluate several feature selection methods and classification techniques in the context of our framework. Based on 3000 product descriptions obtained from Amazon.com, HPC achieves an overall accuracy of 76.80% for product classification. Using 110 categories from CircuitCity.com and Amazon.com, we obtain a precision of 93.61% for mapping the categories to the taxonomy of shopping.com.


title: Towards a Data-driven IoT Software Architecture for Smart City Utilities

abstract: The Internet of Things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart Cities are practical manifestation of IoT, with the goal of efficient, reliable and safe delivery of city utilities like water, power and transport to residents, through their intelligent management. A data-driven IoT Software Platform is essential for realizing manageable and sustainable Smart Utilities, and for novel applications to be developed upon them. Here, we propose such a service-oriented software architecture to address two key operational activities in a Smart Utility -- the IoT fabric for resource management, and the data and application platform for decision making. Our design uses open web standards and evolving network protocols, Cloud and edge resources, and streaming Big Data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus-scale IoT testbed at the Indian Institute of Science (IISc), Bangalore, and present our experiences. Our architecture is scalable to a township or city, while also generalizable to other Smart Utility domains. Our experiences serves as a template for other similar efforts, particularly in emerging markets, and highlights the gaps and opportunities for a data-driven IoT Software architecture for smart cities.


title: State-of-the-Art Report of Visual Analysis for Event Detection in Text Data Streams

abstract: Event detection from text data streams has been a popular research area in the past decade. Recently, the evolution of microblogging and social network services opens up great opportunities for various kinds of knowledge-based intelligence activities which require tracking of real-time events. In a sense, visualizations in combination with analytical processes could be a viable method for such tasks because it can be used to analyze the sheer amounts of text streams. However, data analysts and visualization experts often face grand challenges stemming out of the ill-defined concept of event and various kinds of textual data. As a result, we have few guidelines on how to build successful visual analysis tools that can handle specific event types and diverse textual data sources. Our goal is to take the first step towards answering the question by organizing insights from prior research studies on event detection and visual analysis. In the scope of this report, we summarize the evolution of event detection in combination with visual analysis over the past 14 years and provide an overview of the state-of-the-art methods. Our investigation sheds light on various kinds of research areas that can be the most beneficial to the field of visual text event analytics.


P.: Haskell vs. Ada vs. C++ vs. awk vs.. . . An experiment in software prototyping productivity


title: Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition

abstract: Named Entity Recognition (NER) is one of the most common tasks of the natural language processing. The purpose of NER is to find and classify tokens in text documents into predefined categories called tags, such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others. Although there is a number of approaches have been proposed for this task in Russian language, it still has a substantial potential for the better solutions. In this work, we studied several deep neural network models starting from vanilla Bi-directional Long Short-Term Memory (Bi-LSTM) then supplementing it with Conditional Random Fields (CRF) as well as highway networks and finally adding external word embeddings. All models were evaluated across three datasets: Gareev's dataset, Person-1000, FactRuEval-2016. We found that extension of Bi-LSTM model with CRF significantly increased the quality of predictions. Encoding input tokens with external word embeddings reduced training time and allowed to achieve state of the art for the Russian NER task.


title: Brain-Controlled Wheelchairs: A Robotic Architecture

abstract: Independent mobility is central to being able to perform activities of daily living by oneself. However, power wheelchairs are not an option for many people who, due to severe motor disabilities, are unable to use conventional controls. For some of these people, noninvasive brain-computer interfaces (BCIs) offer a promising solution to this interaction problem.


title: Light-Weight Head Pose Invariant Gaze Tracking

abstract: Unconstrained remote gaze tracking using off-the-shelf cameras is a challenging problem. Recently, promising algorithms for appearance-based gaze estimation using convolutional neural networks (CNN) have been proposed. Improving their robustness to various confounding factors including variable head pose, subject identity, illumination and image quality remain open problems. In this work, we study the effect of variable head pose on machine learning regressors trained to estimate gaze direction. We propose a novel branched CNN architecture that improves the robustness of gaze classifiers to variable head pose, without increasing computational cost. We also present various procedures to effectively train our gaze network including transfer learning from the more closely related task of object viewpoint estimation and from a large high-fidelity synthetic gaze dataset, which enable our ten times faster gaze network to achieve competitive accuracy to its current state-of-the-art direct competitor.


title: Association of Park Size, Distance, and Features With Physical Activity in Neighborhood Parks

abstract: Objectives. We studied whether park size, number of features in the park, and distance to a park from participants’ homes were related to a park being used for physical activity.Methods. We collected observational data on 28 specific features from 33 parks. Adult residents in surrounding areas (n=380) completed 7-day physical activity logs that included the location of their activities. We used logistic regression to examine the relative importance of park size, features, and distance to participants’ homes in predicting whether a park was used for physical activity, with control for perceived neighborhood safety and aesthetics.Results. Parks with more features were more likely to be used for physical activity; size and distance were not significant predictors. Park facilities were more important than were park amenities. Of the park facilities, trails had the strongest relationship with park use for physical activity.Conclusions. Specific park features may have significant implications for park-based phy...


title: Noise and autism spectrum disorder in children: An exploratory survey

abstract: Abstract Background With more students being educated in schools for Autism Spectrum Disorder (ASD) than ever before, architects and interior designers need to consider the environmental features that may be modified to enhance the academic and social success of autistic students in school. Aim This study explored existing empirical research on the impact of noise on children with ASD and provides recommendations regarding design features that can contribute to noise reduction. Methods and procedures A survey, which addressed the impact of architectural design elements on autism-related behavior, was developed for teachers of children with ASD and distributed to three schools. Outcomes and results Most teachers found noise control to be an important issue for students with autism and many observed children using ear defenders. In terms of managing issues related to noise, most teachers agreed that thick or soundproof walls and carpet in the classroom were the most important issues for children with ASD. Conclusions Suggested future research should address architectural considerations for building an acoustically friendly environment for children with autism, identifying patterns of problematic behaviors in response to acoustical features of the built environment of the classroom setting, and ways to manage maladaptive behaviors in acoustically unfriendly environments.


title: An Overview of LPV Systems

abstract: The framework of Linear Parameter Varying (LPV) systems concerns linear dynamical systems whose state-space representations depend on exogenous nonstationary parameters. Since its introduction by Shamma and Athans in 1988 to model gain-scheduling, the LPV paradigm has become a standard formalism in systems and controls, with many papers devoted to analysis, controller synthesis, and system identification of LPV models. This chapter reviews basic concepts and presents a representative selection of analytical approaches for LPV systems.


title: A-DRM: Architecture-aware Distributed Resource Management of Virtualized Clusters

abstract: Virtualization technologies has been widely adopted by large-scale cloud computing platforms. These virtualized systems employ distributed resource management (DRM) to achieve high resource utilization and energy savings by dynamically migrating and consolidating virtual machines. DRM schemes usually use operating-system-level metrics, such as CPU utilization, memory capacity demand and I/O utilization, to detect and balance resource contention. However, they are oblivious to microarchitecture-level resource interference (e.g., memory bandwidth contention between different VMs running on a host), which is currently not exposed to the operating system. We observe that the lack of visibility into microarchitecture-level resource interference significantly impacts the performance of virtualized systems. Motivated by this observation, we propose a novel architecture-aware DRM scheme (ADRM), that takes into account microarchitecture-level resource interference when making migration decisions in a virtualized cluster. ADRM makes use of three core techniques: 1) a profiler to monitor the microarchitecture-level resource usage behavior online for each physical host, 2) a memory bandwidth interference model to assess the interference degree among virtual machines on a host, and 3) a cost-benefit analysis to determine a candidate virtual machine and a host for migration. Real system experiments on thirty randomly selected combinations of applications from the CPU2006, PARSEC, STREAM, NAS Parallel Benchmark suites in a four-host virtualized cluster show that ADRM can improve performance by up to 26.55%, with an average of 9.67%, compared to traditional DRM schemes that lack visibility into microarchitecture-level resource utilization and contention.


title: A New View into the Regulation of Purine Metabolism: The Purinosome.

abstract: Other than serving as building blocks for DNA and RNA, purine metabolites provide a cell with the necessary energy and cofactors to promote cell survival and proliferation. A renewed interest in how purine metabolism may fuel cancer progression has uncovered a new perspective into how a cell regulates purine need. Under cellular conditions of high purine demand, the de novo purine biosynthetic enzymes cluster near mitochondria and microtubules to form dynamic multienzyme complexes referred to as 'purinosomes'. In this review, we highlight the purinosome as a novel level of metabolic organization of enzymes in cells, its consequences for regulation of purine metabolism, and the extent that purine metabolism is being targeted for the treatment of cancers.


title: Analysis and design of an active-clamping ZVS isolated inverse-SEPIC converter

abstract: This paper proposes an active-clamping zero-voltage-switching (ZVS) isolated inverse-SEPIC converter. The high voltage spikes when turning off the switches are eliminated. The energies stored in the parasitic elements can be recycled to achieve the ZVS of switches. Therefore the converter efficiency increases substantially, and the circuit cost is also reduced. Detailed analysis and design of this new topology are described. Experimental results are recorded for a prototype converter with a DC input voltage ranging from 130 to 180V, an output voltage of 12 V and an output rated power of 120 W, operating at a switching frequency of 65 kHz. The average active-mode efficiency is above 88 %.


title: The Biological Effects of Childhood Trauma

abstract: Trauma in childhood is a psychosocial, medical, and public policy problem with serious consequences for its victims and for society. Chronic interpersonal violence in children is common worldwide. Developmental traumatology, the systemic investigation of the psychiatric and psychobiological effects of chronic overwhelming stress on the developing child, provides a framework and principles when empirically examining the neurobiological effects of pediatric trauma. This article focuses on peer-reviewed literature on the neurobiological sequelae of childhood trauma in children and in adults with histories of childhood trauma. Language: en


title: Group information guided ICA shows more sensitivity to group differences than dual-regression

abstract: Prior work has reported that brain functional networks can be utilized to differentiate healthy subjects and patients with mental disorder. Group independent component analysis (GICA) is a widely-used data-driven method for extracting brain functional networks from resting-state functional magnetic resonance imaging (fMRI) data of multiple subjects. GICA approaches estimate the group-level independent components first, then back-reconstruct the subject-specific networks and their associated time courses based on the group-level independent components. To estimate the subject-specific networks, previous studies have employed PCA-based, regression-based (e.g. dual regression or spatio-temporal regression (STR)) and group information guided ICA (GIG-ICA) methods, among which dual regression and GIG-ICA can be used to yield the subject-specific networks for additional subjects. However, it is largely unknown which GICA method is more sensitive to subtle group differences between controls and patients. This paper aims to evaluate the efficacy of identifying biomarkers from the subject-specific networks and time courses estimated from STR and GIG-ICA using fMRI data of healthy controls (HCs) and schizophrenia patients (SZs). Regarding the measures from functional network maps, GIG-ICA revealed markedly greater differences between HCs and SZs than STR. Furthermore, the interaction among networks (i.e. functional network connectivity) also showed more group differences using GIG-ICA method, compared to STR. In summary, our work suggests that while both methods provide similar overall conclusions, GIG-ICA, which estimates individually tuned components based on higher order statistics is more sensitive to group differences and biomarker detection than STR.


title: A depth space approach to human-robot collision avoidance

abstract: In this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. The distances are used to generate repulsive vectors that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.


ACES : Automatic Coding Evaluation of Style by Stephanie Rogers Research Project


title: A new quality metric for image fusion

abstract: We present three variants of a new quality metric for image fusion. The interest of our metrics, which are based on an image quality index recently introduced by Wang and Bovik in [Z. Wang et al., March 2002], lies in the fact that they do not require a ground-truth or reference image. We perform several simulations which show that our metrics are compliant with subjective evaluations and can therefore be used to compare different image fusion methods or to find the best parameters for a given fusion algorithm.


title: Design of broadband dual-polarized oval-shaped base station antennas for mobile systems

abstract: This paper proposes a new design of wideband oval-shaped antenna with ±4° dipoles suitable for the base station of mobile communication systems. The designed antennas cover bands of 700–960 MHz (the lower band antenna) and 1700–2700 MHz (the upper band antenna) for cellular 2G/3G/LTE technologies. The design enjoys the advantages of stable directivity and beamwidth within frequency bands and a simple feeding structure with a compact size and low profile. The oval-shaped structure makes the antenna smaller in size than other polygon shapes. The antenna in the lower band has a beamwidth of 85.4°± 1.3° and directivity of 7.2±0.13 dBi with the reflection coefficient ≤ −17 dB and the isolation between the ports ≥ 19 dB while the antenna in the upper band has a beamwidth of 84.15°±3.9° and directivity of 6.94±0.38 dBi with the reflection coefficient ≤ −10.5 dB and the isolation between the ports ≥ 20.5dB. High cross polarization discrimination ratios were also achieved within the desired frequency bands.


title: Barecloud: bare-metal analysis-based evasive malware detection

abstract: The volume and the sophistication of malware are continuously increasing and evolving. Automated dynamic malware analysis is a widely-adopted approach for detecting malicious software. However, many recent malware samples try to evade detection by identifying the presence of the analysis environment itself, and refraining from performing malicious actions. Because of the sophistication of the techniques used by the malware authors, so far the analysis and detection of evasive malware has been largely a manual process. One approach to automatic detection of these evasive malware samples is to execute the same sample in multiple analysis environments, and then compare its behaviors, in the assumption that a deviation in the behavior is evidence of an attempt to evade one or more analysis systems. For this reason, it is important to provide a reference system (often called bare-metal) in which the malware is analyzed without the use of any detectable component. ::: ::: In this paper, we present BareCloud, an automated evasive malware detection system based on bare-metal dynamic malware analysis. Our bare-metal analysis system does not introduce any in-guest monitoring component into the malware execution platform. This makes our approach more transparent and robust against sophisticated evasion techniques. We compare the malware behavior observed in the bare-metal system with other popular malware analysis systems. We introduce a novel approach of hierarchical similarity-based malware behavior comparison to analyze the behavior of a sample in the various analysis systems. Our experiments show that our approach produces better evasion detection results compared to previous methods. BareCloud was able to automatically detect 5,835 evasive malware out of 110,005 recent samples.


title: Mobility-Aware Caching and Computation Offloading in 5G Ultra-Dense Cellular Networks

abstract: Recent trends show that Internet traffic is increasingly dominated by content, which is accompanied by the exponential growth of traffic. To cope with this phenomena, network caching is introduced to utilize the storage capacity of diverse network devices. In this paper, we first summarize four basic caching placement strategies, i.e., local caching, Device-to-Device (D2D) caching, Small cell Base Station (SBS) caching and Macrocell Base Station (MBS) caching. However, studies show that so far, much of the research has ignored the impact of user mobility. Therefore, taking the effect of the user mobility into consideration, we proposes a joint mobility-aware caching and SBS density placement scheme (MS caching). In addition, differences and relationships between caching and computation offloading are discussed. We present a design of a hybrid computation offloading and support it with experimental results, which demonstrate improved performance in terms of energy cost. Finally, we discuss the design of an incentive mechanism by considering network dynamics, differentiated user's quality of experience (QoE) and the heterogeneity of mobile terminals in terms of caching and computing capabilities.


abstract: The recent emergence of Local Binary Patterns (LBP) has led to significant progress in applying texture methods to various computer vision problems and applications. The focus of this research has broadened from 2D textures to 3D textures and spatiotemporal (dynamic) textures. Also, where texture was once utilized for applications such as remote sensing, industrial inspection and biomedical image analysis, the introduction of LBP-based approaches have provided outstanding results in problems relating to face and activity analysis, with future scope for face and facial expression recognition, biometrics, visual surveillance and video analysis. Computer Vision Using Local Binary Patterns provides a detailed description of the LBP methods and their variants both in spatial and spatiotemporal domains. This comprehensive reference also provides an excellent overview as to how texture methods can be utilized for solving different kinds of computer vision and image analysis problems. Source codes of the basic LBP algorithms, demonstrations, some databases and a comprehensive LBP bibliography can be found from an accompanying web site. Topics include: local binary patterns and their variants in spatial and spatiotemporal domains, texture classification and segmentation, description of interest regions, applications in image retrieval and 3D recognition - Recognition and segmentation of dynamic textures, background subtraction, recognition of actions, face analysis using still images and image sequences, visual speech recognition and LBP in various applications. Written by pioneers of LBP, this book is an essential resource for researchers, professional engineers and graduate students in computer vision, image analysis and pattern recognition. The book will also be of interest to all those who work with specific applications of machine vision.


title: A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

abstract: A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.


title: OsHAK1 controls the vegetative growth and panicle fertility of rice by its effect on potassium-mediated sugar metabolism

abstract: Abstract Plant growth and reproduction are both energy-requiring processes; the necessary energy is supplied by the products of photosynthesis. Both the vegetative growth and reproductive success of rice are compromised by the absence of a functional copy of the gene OsHAK1 . Here, a comparison between wild type rice and OsHAK1 knockout mutants not only confirmed the known detrimental effect of the absence of OsHAK1 on root growth, pollen viability and fertility, but also showed that sucrose phosphate synthase activity was lowered, and the sucrose content of the leaves was markedly increased, due to a partial block on the up-loading of sucrose into the phloem. The impaired allocation of sugar to the roots and spikelets caused by the knocking out of OsHAK1 was accompanied by a down-regulation in the leaf sheaths and panicle axes of genes encoding sucrose transporters ( SUT genes), which are active in the phloem, as well as in the roots and spikelets of those encoding monosaccharide transporters ( MST genes), which transport hexose sugars across the plant plasma membrane. The activity of sucrose synthase, acid invertase and neutral invertase in the roots of mutant plants assayed at the tillering stage, and in their spikelets, assayed during grain-filling, was significantly lower than in the equivalent organs of wild type plants. As a result, the supply of total soluble sugar, glucose and fructose to sink organs was reduced, consistent with the effect of the mutation on root growth and panicle fertility. Compared to wild type plants, the mutants accumulated less potassium (K) throughout the plant. The conclusion was that the failure to fully supply the demand of the mutant’s sink organs for assimilate was responsible for its compromised phenotype, and that the deficiency in K uptake induced by the loss of OsHAK1 functionality was responsible for the disruption of sugar metabolism.


title: Active Learning for Wireless IoT Intrusion Detection

abstract: The Internet of Things (IoT) is becoming truly ubiquitous in our everyday lives, but it also faces unique security challenges. Intrusion detection is critical for the security and safety of a wireless IoT network. This article discusses the human-in-theloop active learning approach for wireless intrusion detection. We first present the fundamental challenges against the design of a successful intrusion detection system for a wireless IoT network. We then briefly review the rudimentary concepts of active learning and propose its employment in the diverse applications of wireless intrusion detection. An experimental example is also presented to show the significant performance improvement of the active learning method over the traditional supervised learning approach. While machine learning (ML) techniques have been widely employed for intrusion detection, the application of human-in-the-loop ML that leverages both machine and human intelligence to intrusion detection of IoT is still in its infancy. We hope this article can assist readers in understanding the key concepts of active learning and spur further research in this area.


title: Automated Detection of Reflection Hyperbolas in Complex GPR Images With No A Priori Knowledge on the Medium

abstract: In this paper, we propose an automated detection algorithm for well- and ill-shaped ground-penetrating radar reflection hyperbolas for complex media, calibrated with human recognition principles. The algorithm detects the apex of the hyperbolas by fitting an analytical function of a hyperbola to the profile edge dots detected with a Canny filter. The existence of a hyperbola is determined using a set of carefully chosen criteria calibrated in order to fit the ambiguity zone for the human brain. The inherent misshapedness of field hyperbolas is further considered by defining a buffer zone around the theoretical hyperbola. First, the method was tested in the laboratory over tree roots and PVC pipes and on field images over tree root systems. Both time- and frequency-domain radars were used on-ground. After around 1–3 min of computation time for 10 000 edge dots in a MATLAB environment (single 1.96-GHz processor), the results showed rates of false alarm and nondetection of maximum 20% and 28%, respectively. In comparison with the semiautomated hyperbola detection provided by a commercial software, these rates were lower. Second, we conducted a sensitivity analysis to estimate the validity of the fitting of a hyperbola equation neglecting the object radius. The fitting was close, but the derivation of the relative permittivity from the analytical equation neglecting the radius led to high errors. In conclusion, owing to the low computational time and its good performances, the proposed algorithm is suitable for complex environments.


title: SECRET: On the Feasibility of a Secure, Efficient, and Collaborative Real-Time Web Editor

abstract: Real-time editing tools like Google Docs, Microsoft Office Online, or Etherpad have changed the way of collaboration. Many of these tools are based on Operational Transforms (OT), which guarantee that the views of different clients onto a document remain consistent over time. Usually, documents and operations are exposed to the server in plaintext -- and thus to administrators, governments, and potentially cyber criminals. Therefore, it is highly desirable to work collaboratively on encrypted documents. Previous implementations do not unleash the full potential of this idea: They either require large storage, network, and computation overhead, are not real-time collaborative, or do not take the structure of the document into account. The latter simplifies the approach since only OT algorithms for byte sequences are required, but the resulting ciphertexts are almost four times the size of the corresponding plaintexts. We present SECRET, the first secure, efficient, and collaborative real-time editor. In contrast to all previous works, SECRET is the first tool that (1.) allows the encryption of whole documents or arbitrary sub-parts thereof, (2.) uses a novel combination of tree-based OT with a structure preserving encryption, and (3.) requires only a modern browser without any extra software installation or browser extension. We evaluate our implementation and show that its encryption overhead is three times smaller in comparison to all previous approaches. SECRET can even be used by multiple users in a low-bandwidth scenario. The source code of SECRET is published on GitHub as an open-source project:https://github.com/RUB-NDS/SECRET/


title: Optimal Design of the Electrical Layout of an Offshore Wind Farm Applying Decomposition Strategies

abstract: Electrical layout design is a key element in offshore wind farm planning, with a critical impact on both plant cost and reliability. Offshore Windfarm Layout optimizer (OWL) has been developed to efficiently find optimal electrical layouts in affordable computation times. The tool includes the possibility of HVDC connection and incorporates an approximation of losses, as well as stochasticity in wind inputs and component failures. OWL has been applied to several case studies including Barrow Offshore Wind Farm. OWL produces a significant cost reduction over the actually implemented design, with total realizable savings of EUR 800 k. The optimal layout includes redundant elements and deviates from a symmetrical pattern, therefore showing that a full optimization of the layout, rather than the selection of a pre-defined configuration, is necessary in order to fully capture efficiencies. The model relies on MIP and exploits the structure of the problem via decomposition strategies. Two different approaches have been developed, both resulting in substantial time savings. Benders' decomposition has been further improved by the addition of partially relaxed cuts and the application of scenario aggregation techniques. In addition, the Progressive Contingency Incorporation algorithm proposed by the authors is applied. Computation time savings reach two orders of magnitude.


End-to-end self-assembly of gold nanorods in isopropanol solution: experimental and theoretical studies


title: Compressive Sensing Based Time Domain Synchronous OFDM Transmission for Vehicular Communications

abstract: Time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency and faster synchronization than standard cyclic prefix OFDM (CP-OFDM), but suffers from the difficulty of supporting 256QAM in low-speed vehicular channels with long delay spread and the performance loss over fast time-varying vehicular channels. This paper addresses how to efficiently use the compressive sensing (CS) theory to solve those problems. First, we break through the conventional concept of cancelling the interferences if present, and propose the idea of using the inter-block-interference (IBI)-free region of small size to reconstruct the high-dimensional sparse multipath channel, whereby no interference cancellation is required any more. In this way, without changing the current signal structure of TDS-OFDM at the transmitter, the mutually conditional time-domain channel estimation and frequency-domain data detection in conventional TDS-OFDM receivers can be decoupled. Second, we propose the parameterized channel estimation method based on priori aided compressive sampling matching pursuit (PA-CoSaMP) algorithm to achieve reliable performance over vehicular channels, whereby partial channel priori available in TDS-OFDM is used to improve the performance and reduce the complexity of the classical CoSaMP signal recovery algorithm. Simulation results demonstrate that the proposed scheme can support the 256QAM and gain improved performance over fast fading channels.


title: Optimistic Recovery for Iterative Dataflows in Action

abstract: Over the past years, parallel dataflow systems have been employed for advanced analytics in the field of data mining where many algorithms are iterative. These systems typically provide fault tolerance by periodically checkpointing the algorithm's state and, in case of failure, restoring a consistent state from a checkpoint. In prior work, we presented an optimistic recovery mechanism that in certain cases eliminates the need to checkpoint the intermediate state of an iterative algorithm. In case of failure, our mechanism uses a compensation function to transit the algorithm to a consistent state, from which the execution can continue and successfully converge. Since this recovery mechanism does not checkpoint any state, it achieves optimal failure-free performance while guaranteeing fault tolerance. In this paper, we demonstrate our recovery mechanism with the Apache Flink data processing engine. During our demonstration, attendees will be able to run graph algorithms and trigger failures to observe the algorithms recovering with compensation functions instead of checkpoints.


title: Design of Microstrip Lowpass-Bandpass Diplexer

abstract: The matching circuit design of a lowpass-bandpass diplexer may be a challenge in conventional designs because a wideband (from zero frequency to approximately the cutoff frequency of the lowpass filter) open condition is required to achieve in bandpass channel circuit. This paper proposes a new lowpass-bandpass diplexer that involves using a simple matching design that can easily meet the required matching condition in each channel circuit. The measured results are in good agreement with the simulated responses. For the lowpass channel, the measured in-band insertion loss and cutoff frequency are less than 0.25 dB and approximately 1.5 GHz, respectively. For the bandpass channel, the measured minimal insertion loss, center frequency, and 3 dB fractional bandwidth are approximately 2.42 dB, 2.4 GHz, and 7.6%, respectively. The measured isolation between the two operating bands is better than 35 dB.


Mathematics of Autonomy: Mathematical Methods for Cyber-Physical-Cognitive Systems


title: Three phase front end active boost rectifier with unity power factor, DC voltage regulation and bi-directional power flow

abstract: In this paper different topologies of three phase rectifier are studied and compared with proposed universal bridge topology. From the universal bridge topology: regulation of dc link voltage, low harmonic distortion of line current, near sinusoidal current waveform, power factor correction, bidirectional power flow is obtained by simulation using Matlab/Simulink software. For that synchronous reference frame theory is used to generate reference current and hysteresis current controller for switching pulse generation.


title: Smartphone-Based Vehicle Telematics: A Ten-Year Anniversary

abstract: Just like it has irrevocably reshaped social life, the fast growth of smartphone ownership is now beginning to revolutionize the driving experience and change how we think about automotive insurance, vehicle safety systems, and traffic research. This paper summarizes the first ten years of research in smartphone-based vehicle telematics, with a focus on user-friendly implementations and the challenges that arise due to the mobility of the smartphone. Notable academic and industrial projects are reviewed, and system aspects related to sensors, energy consumption, cloud computing, vehicular ad hoc networks, and human-machine interfaces are examined. Moreover, we highlight the differences between traditional and smartphonebased automotive navigation, and survey the state-of-the-art in smartphone-based transportation mode classification, driver classification, and road condition monitoring. Future advances are expected to be driven by improvements in sensor technology, evidence of the societal benefits of current implementations, and the establishment of industry standards for sensor fusion and driver assessment


title: Ontology and semantic matching for diabetic food recommendations

abstract: Foods recommendation for diabetes patients is indispensable for controlling blood sugar levels. Currently, the foods preparation is done by a nutrition expert. The patient's dependence on the nutrition experts is very high, thus the selection of foods could not be done independently. The Automation system to determine foods combination for diabetic patients is needed to solve these problems. In this study, the automation system has been designed and implemented. The technologies used in this research are the OWL and SWRL. There are few researches that explore an automation process of foods recommendation for diabetes patients using the technology of OWL and SWRL. Domain knowledge based on Ontology is needed to process foods composition automatically. However, using SWRL and OWL technology is not enough, because the accuracy of the words required. A semantic ontology understanding was added using weighted tree similarity method to specify the composition of foods for diabetic patients. 73% data were able to be correctly predicted by this method.


title: Pathogenic conversion of Foxp3+ T cells into TH17 cells in autoimmune arthritis

abstract: Regulatory T (Treg) cells exhibit substantial phenotypic and functional plasticity. Hiroshi Takayanagi and his colleagues report that in autoimmune arthritis, a subset of Treg cells can lose Foxp3 expression and convert into TH17 cells. This conversion is mediated by synovial fibroblast-derived IL-6, and in vivo, these cells are osteoclastogenic and exacerbate arthritis. These findings suggest that a proportion of pathogenic TH17 cells in autoimmune disease may be derived from Treg cells. Autoimmune diseases often result from an imbalance between regulatory T (Treg) cells and interleukin-17 (IL-17)-producing T helper (TH17) cells; the origin of the latter cells remains largely unknown. Foxp3 is indispensable for the suppressive function of Treg cells, but the stability of Foxp3 has been under debate. Here we show that TH17 cells originating from Foxp3+ T cells have a key role in the pathogenesis of autoimmune arthritis. Under arthritic conditions, CD25loFoxp3+CD4+ T cells lose Foxp3 expression (herein called exFoxp3 cells) and undergo transdifferentiation into TH17 cells. Fate mapping analysis showed that IL-17–expressing exFoxp3 T (exFoxp3 TH17) cells accumulated in inflamed joints. The conversion of Foxp3+CD4+ T cells to TH17 cells was mediated by synovial fibroblast-derived IL-6. These exFoxp3 TH17 cells were more potent osteoclastogenic T cells than were naive CD4+ T cell–derived TH17 cells. Notably, exFoxp3 TH17 cells were characterized by the expression of Sox4, chemokine (C-C motif) receptor 6 (CCR6), chemokine (C-C motif) ligand 20 (CCL20), IL-23 receptor (IL-23R) and receptor activator of NF-κB ligand (RANKL, also called TNFSF11). Adoptive transfer of autoreactive, antigen-experienced CD25loFoxp3+CD4+ T cells into mice followed by secondary immunization with collagen accelerated the onset and increased the severity of arthritis and was associated with the loss of Foxp3 expression in the majority of transferred T cells. We observed IL-17+Foxp3+ T cells in the synovium of subjects with active rheumatoid arthritis (RA), which suggests that plastic Foxp3+ T cells contribute to the pathogenesis of RA. These findings establish the pathological importance of Foxp3 instability in the generation of pathogenic TH17 cells in autoimmunity.


title: A deep learning model for air quality prediction in smart cities

abstract: In recent years, Internet of Things (IoT) concept has become a promising research topic in many areas including industry, commerce and education. Smart cities employ IoT based services and applications to create a sustainable urban life. By using information and communication technologies, IoT enables smart cities to make city stakeholders more aware, interactive and efficient. With the increase in number of IoT based smart city applications, the amount of data produced by these applications is increased tremendously. Governments and city stakeholders take early precautions to process these data and predict future effects to ensure sustainable development. In prediction context, deep learning techniques have been used for several forecasting problems in big data. This inspires us to use deep learning methods for prediction of IoT data. Hence, in this paper, a novel deep learning model is proposed for analyzing IoT smart city data. We propose a novel model based on Long Short Term Memory (LSTM) networks to predict future values of air quality in a smart city. The evaluation results of the proposed model are found to be promising and they show that the model can be used in other smart city prediction problems as well.


title: Proteomic analyses of CSF aimed at biomarker development for pediatric brain tumors

abstract: Primary brain tumors cumulatively represent the most common solid tumors of childhood and are the leading cause of cancer related death in this age group. Traditionally, molecular findings and histological analyses from biopsies of resected tumor tissue have been used for diagnosis and classification of these diseases. However, there is a dearth of useful biomarkers that have been validated and clinically implemented for pediatric brain tumors. Notably, diseases of the central nervous system (CNS) can be assayed through analysis of cerebrospinal fluid (CSF) and as such, CSF represents an appropriate medium to obtain liquid biopsies that can be informative for diagnosis, disease classification and risk stratification. Proteomic profiling of pediatric CNS malignancies has identified putative protein markers of disease, yet few effective biomarkers have been clinically validated or implemented. Advances in protein quantification techniques have made it possible to conduct such investigations rapidly and accurately through proteome-wide analyses. This review summarizes the current literature on proteomics in pediatric neuro-oncology and discusses the implications for clinical applications of proteomics research. We also outline strategies for translating effective CSF proteomic studies into clinical applications to optimize the care of this patient population.


title: Animating human dressing

abstract: Dressing is one of the most common activities in human society. Perfecting the skill of dressing can take an average child three to four years of daily practice. The challenge is primarily due to the combined difficulty of coordinating different body parts and manipulating soft and deformable objects (clothes). We present a technique to synthesize human dressing by controlling a human character to put on an article of simulated clothing. We identify a set of primitive actions which account for the vast majority of motions observed in human dressing. These primitive actions can be assembled into a variety of motion sequences for dressing different garments with different styles. Exploiting both feed-forward and feedback control mechanisms, we develop a dressing controller to handle each of the primitive actions. The controller plans a path to achieve the action goal while making constant adjustments locally based on the current state of the simulated cloth when necessary. We demonstrate that our framework is versatile and able to animate dressing with different clothing types including a jacket, a pair of shorts, a robe, and a vest. Our controller is also robust to different cloth mesh resolutions which can cause the cloth simulator to generate significantly different cloth motions. In addition, we show that the same controller can be extended to assistive dressing.


title: Disclosures about important life events on Facebook: Relationships with stress and quality of life

abstract: Online surveys were used to examine adults' Facebook disclosures.Greater general social media use is related to decreased well-being.Facebook use is unrelated to stress or quality of life.Sharing important, bad health news is related to decreased well-being.Not sharing to protect oneself is linked to decreased well-being. The current study examined the relationship between general perceived levels of stress, quality of life, social networking usage, and disclosing important life events on Facebook in order to better understand the complex relationship between online disclosure and individual well-being. An online survey was completed by adult Facebook users aged 18-70. Results indicate that the more time spent on and the more social network memberships, the higher stress and lower quality of life; Facebook-specific usage was unrelated to either well-being variable. Together, these findings suggest that the current increase in social media variety and usage may be detrimental to user well-being. Users who shared important, bad health news on Facebook had higher stress and lower quality of life than those who did not, with no significant differences for sharing good health news. The more that users did not share important news on Facebook for self-protection and friend unresponsiveness reasons, the greater their stress. The self-protection reason was also negatively related to quality of life. These inconsistent findings can likely be partially explained by the nature of the information that is shared. These findings are discussed in light of disclosure and relationship patterns on social networks.


A MultiView Dense Point Cloud Generation Algorithm Based on Low-Altitude Remote Sensing Images


title: The Church-Turing thesis: Consensus and opposition

abstract: Many years ago, I wrote [7]: ::: ::: It is truly remarkable (Godel ...speaks of a kind of miracle) that it has proved possible to give a precise mathematical characterization of the class of processes that can be carried out by purely machanical means. It is in fact the possibility of such a characterization that underlies the ubiquitous applicability of digital computers. In addition it has made it possible to prove the algorithmic unsolvability of important problems, has provided a key tool in mathematical logic, has made available an array of fundamental models in theoretical computer science, and has been the basis of a rich new branch of mathemtics. ::: ::: A few years later I wrote [8]: ::: ::: The subject ...is Alan Turing's discovery of the universal (or all-purpose) digital computer as a mathematical abstraction. ...We will try to show how this very abstract work helped to lead Turing and John von Neumann to the modern concept of the electronic computer.


title: Learning Dense Correspondence via 3D-Guided Cycle Consistency

abstract: Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground-truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4-cycle. We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and realto-synthetic correspondences that are cycle-consistent with the ground-truth. At test time, no CAD models are required. We demonstrate that our end-to-end trained ConvNet supervised by cycle-consistency outperforms stateof-the-art pairwise matching methods in correspondencerelated tasks.


title: A Logical Framework for Behaviour Reasoning and Assistance in a Smart Home

abstract: Smart Homes (SH) have emerged as a realistic intelligent assistive environment capable of providing assistive living for the elderly and the disabled. Nevertheless, it still remains a challenge to assist the inhabitants of a SH in performing the “right” action(s) at the “right” time in the “right” place. To address this challenge, this paper introduces a novel logical framework for cognitive behavioural modelling, reasoning and assistance based on a highly developed logical theory of actions - the Event Calculus. Cognitive models go beyond data-centric behavioural models in that they govern an inhabitant’s behaviour by reasoning about its knowledge, actions and environmental events. In our work we outline the theoretical foundation of such an approach and describe cognitive modelling of SH. We discuss the reasoning capabilities and algorithms of the cognitive SH model and present the details of the various tasks it can support. A system architecture is proposed to illustrate the use of the framework in facilitating assistive living. We demonstrate the perceived effectiveness of the approach through presentation of its operation in the context of a real world daily activity scenario.


title: Flexible screen-printed piezoelectric P(VDF-TrFE) copolymer microgenerators for energy harvesting

abstract: Fabrication and characterization of multilayered screenprinted P(VDF-TrFE) piezoelectric microgenerators is reported. Micrometer-sized films were deposited on a flexible polymer substrate with evaporated gold-bottom electrodes, followed by deposition of PEDOT:PSS electrodes. Electrical characterization showed excellent ferroelectric properties and open-circuit voltage was measured as a function of applied strain. Generated energy is calculated and discussed in terms of energy harvesting applications. Maximum open-circuit voltage of 10 V and corresponding energy density of 275 µJ/cm3 are achieved.


title: Molecular polarity in tropomyosin-troponin T co-crystals

abstract: New features of the structure and interactions of troponin T and tropomyosin have been revealed by electron microscopy of so-called double-diamond co-crystals. These co-crystals were formed using rabbit alpha2 tropomyosin complexed with troponin T from either skeletal or cardiac muscle, which have different lengths in the amino-terminal region, as well as a bacterially expressed skeletal muscle troponin T fragment of 190 residues that lacks the amino-terminal region. Differences in the images of the co-crystals have allowed us to establish the polarities of both the troponin T subunit and tropomyosin in the projected lattice. Moreover, in agreement with their sequences, the amino-terminal region of a bovine cardiac muscle troponin T isoform appears to be longer than that from the rabbit skeletal muscle troponin T isoform and to span more of the amino terminus of tropomyosin at the head-to-tail filament joints. Images of crystals tilted relative to the electron beam also reveal the supercoiling of the tropomyosin filaments in this lattice. Based on these results, a three-dimensional model of the double-diamond lattice has been constructed.


title: Malik F. Saleh Information Security Maturity Model

abstract: To ensure security, it is important to build-in security in both the planning and the design phases and adapt a security architecture which makes sure that regular and security related tasks, are deployed correctly. Security requirements must be linked to the business goals. We identified four domains that affect security at an organization namely, organization governance, organizational culture, the architecture of the systems, and service management. In order to identify and explore the strength and weaknesses of particular organization’s security, a wide range model has been developed. This model is proposed as an information security maturity model (ISMM) and it is intended as a tool to evaluate the ability of organizations to meet the objectives of security.


title: A critical review of the psychophysiology of driver fatigue

abstract: Driver fatigue is a major cause of road accidents and has implications for road safety. This review discusses the concepts of fatigue and provides a summary on psychophysiological associations with driver fatigue. A variety of psychophysiological parameters have been used in previous research as indicators of fatigue, with electroencephalography perhaps being the most promising. Most research found changes in theta and delta activity to be strongly linked to transition to fatigue. Therefore, monitoring electroencephalography during driver fatigue may be a promising variable for use in fatigue countermeasure devices. The review also identified anxiety and mood states as factors that may possibly affect driver fatigue. Furthermore, personality and temperament may also influence fatigue. Given the above, understanding the psychology of fatigue may lead to better fatigue management. The findings from this review are discussed in the light of directions for future studies and for the development of fatigue countermeasures.


title: A novel dual frequency rectenna for high efficiency wireless power transmission at 2.45 and 5.8 GHz

abstract: A dual frequency printed dipole rectenna has been developed at 2.45 and 5.8 GHz: (ISM bands) for the wireless power transmission. For operating at dual band, a new dual frequency uniplanar printed dipole antenna and a novel coplanar stripline (CPS) filter are developed. A device nonlinear analysis is used to select the diode with low junction capacitance to reduce the effect of frequency dependence. The measured conversion efficiencies achieved at free space are 84.4 and. 82.7% at 2.45 and 5.8 GHz, respectively. The measured results agree very well with the theoretical analysis.


title: Generating Handwritten Chinese Characters using CycleGAN

abstract: Handwriting of Chinese has long been an important skill in East Asia. However, automatic generation of handwritten Chinese characters poses a great challenge due to the large number of characters. Various machine learning techniques have been used to recognize Chinese characters, but few works have studied the handwritten Chinese character generation problem, especially with unpaired training data. In this work, we formulate the Chinese handwritten character generation as a problem that learns a mapping from an existing printed font to a personalized handwritten style. We further propose DenseNet CycleGAN to generate Chinese handwritten characters. Our method is applied not only to commonly used Chinese characters but also to calligraphy work with aesthetic values. Furthermore, we propose content accuracy and style discrepancy as the evaluation metrics to assess the quality of the handwritten characters generated. We then use our proposed metrics to evaluate the generated characters from CASIA dataset as well as our newly introduced Lanting calligraphy dataset.


title: Assumptions of Asian American Similarity: The Case of Filipino and Chinese American Students

abstract: In social work, the cultural beliefs and values of a group are believed to have significant implications for service delivery. The perception of culture as a critical factor in social work practice emerged in the 1970s. Most of the early literature on ethnic and racial minority culture was anecdotal. And the profession has continued to rely on nonempirical literature to understand and guide its practice with ethnic and racial minority clients. When empirical research on ethnic minority groups has been conducted, it has tended to follow a conventional model of race that subsumes numerous ethnic groups into one of four racial categories: Asian, black, Hispanic, and white. For example, culturally distinct ethnic groups such as Cambodians, Filipinos, Japanese, and Pakistanis are lumped into one racial category, Asian Americans. A conventional model assumes that membership in a racial group is analogous with shared cultural values, beliefs, and attitudes. For example, when differences in social services utilization are found among racial categories, they are attributed to culture rather than race. Thus, a direct empirical examination of cultural beliefs and attitudes often uses racial categories that serve as proxies for cultural factors (Agbayani-Siewert, Takeuchi, & Pangan, 1999). For example, mental health agencies might associate low rates of Asian American service utilization with cultural concepts such as loss of face, but the validity of this assumption has not been empirically tested. Each racial category has within-group cultural differences that may conceal more than they inform (Takeuchi, Uehara, & Maramba, 1999). For example, Asian American groups are distinguished from one another by such characteristics as language, cultural values and beliefs, history, acculturation, place of birth, socioeconomic status, and age. An alternative to the conventional approach is an elaborated model that examines the values, beliefs, and attitudes shaped by culture and includes social factors such as race. An elaborated model is well-suited for studying the Asian American population, which includes 25 subgroups (Uba, 1994) and is the fastest growing racial population in the United States. Between 1980 and 1990 the Asian American population increased by 95 percent compared with a 53 percent increase among Hispanics (Hing, 1996)). Between 1990 and 2000 the rage of growth of Asian Americans was 71.9 percent (Ong, 2003). The National Committee for Research on Census Data concluded that "practically or theoretically, it makes little sense to lump together Americans of Asian origin ... as if they were a single entity" (Barringer, Gardner, & Levin, 1993, p. 2). This study postdates that important ethnic cultural differences exist within racial categories by comparing the gender attitudes of white, Hispanic, Filipino, and Chinese college students. A conventional model would predict differences between white students and students in the three other ethnic groups. Conversely, an elaborated model permits a study of differences and similarities among students in the two Asian ethnic groups and among white students and ethnic minority students. Filipino and Chinese Americans Filipino and Chinese Americans are the two largest ethnic groups in the Asian American racial category. Both groups are largely new immigrant communities acculturating to a new society, but cultural and historical differences exist that affect acculturation. Filipino culture and society is an admixture of numerous influences, most notably American, Spanish, and Chinese. The history of the Philippines is characterized by almost 400 years of colonized rule under the Spanish, followed by American occupation that began in 1899 (Agoncillo, 1990). In contrast, although China has experienced invasions and attempts at domination, there has never been a sustained period of Western rule. Euro-American colonialism has had and continues to have a profound effect on Filipinos, including children in the Philippines and the United States (Lott, 1980; Rimonte, 1997; Santos, 1983). …


Normalization of real-time quantitative reverse transcription-pcr data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets


title: Checksum-Aware Fuzzing Combined with Dynamic Taint Analysis and Symbolic Execution

abstract: Fuzz testing has proven successful in finding security vulnerabilities in large programs. However, traditional fuzz testing tools have a well-known common drawback: they are ineffective if most generated inputs are rejected at the early stage of program running, especially when target programs employ checksum mechanisms to verify the integrity of inputs. This article presents TaintScope, an automatic fuzzing system using dynamic taint analysis and symbolic execution techniques, to tackle the above problem. TaintScope has several novel features: (1) TaintScope is a checksum-aware fuzzing tool. It can identify checksum fields in inputs, accurately locate checksum-based integrity checks by using branch profiling techniques, and bypass such checks via control flow alteration. Furthermore, it can fix checksum values in generated inputs using combined concrete and symbolic execution techniques. (2) TaintScope is a taint-based fuzzing tool working at the x86 binary level. Based on fine-grained dynamic taint tracing, TaintScope identifies the “hot bytes” in a well-formed input that are used in security-sensitive operations (e.g., invoking system/library calls), and then focuses on modifying such bytes with random or boundary values. (3) TaintScope is also a symbolic-execution-based fuzzing tool. It can symbolically evaluate a trace, reason about all possible values that can execute the trace, and then detect potential vulnerabilities on the trace. We evaluate TaintScope on a number of large real-world applications. Experimental results show that TaintScope can accurately locate the checksum checks in programs and dramatically improve the effectiveness of fuzz testing. TaintScope has already found 30 previously unknown vulnerabilities in several widely used applications, including Adobe Acrobat, Flash Player, Google Picasa, and Microsoft Paint. Most of these severe vulnerabilities have been confirmed by Secunia and oCERT, and assigned CVE identifiers (such as CVE-2009-1882, CVE-2009-2688). Vendor patches have been released or are in preparation based on our reports.


title: Rule-based ontological knowledge base for monitoring partners across supply networks

abstract: This study demonstrates how ontology combined with semantic rules can be used for searching complete business partners in a supply network. Rather than being fully self-sufficient, individual enterprises are often part of a supply chain. Involving partners and understanding the importance of their activities is essential for enterprise agility. The question is how far-reaching and in what capacity a supply network is needed. Partner tracing becomes more difficult if search tasks involve potential partners or conforms to future production planning. This study utilized Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) technologies to develop supply network ontology and a problem-solving ontology, respectively. The three objectives of the study are creating a conceptual knowledge model for describing supply partners and their relationships, developing semantic rules as problem-solving methods for partner tracing in response to research questions and gathering experimental facts for evaluating knowledge-intensive designs. An example in the solar power industry is used to explain the ontological knowledge design and its uses. The experiments in this study showed that ontological approaches can effectively manage intricate and dynamic partnerships among supply networks. This design is also scalable in both domain ontology and task ontology for solving more complex problems.


title: Investigating student interactions with tutorial dialogues in EER-Tutor

abstract: Eye-movement tracking and student-system interaction logs provide different types of information which can be used as a potential source of real-time adaptation in learning environments. By analysing student interactions with an intelligent tutoring system (ITS), we can identify sub-optimal behaviour such as not paying attention to important interface components. On the basis of such findings, ITSs can be enhanced to be proactive, rather than reactive, to users’ actions. Tutorial dialogues are one of the teaching strategies used in ITSs which has been shown empirically to significantly improve learning. Enhanced entity-relationship (EER)-Tutor is a constraint-based ITS that teaches conceptual database design. This paper presents the preliminary results of a project that investigates how students interact with the tutorial dialogues in EER-Tutor using both eye-gaze data and student-system interaction logs. Our findings indicate that advanced students are selective of the interface areas they visually focus on, whereas novices waste time by paying attention to interface areas that are inappropriate for the task at hand. Novices are also unaware that they require help with the tutorial dialogues. Furthermore, we have demonstrated that the student’s prior knowledge, the problem complexity and the percentage of the dialogue’s prompts that are answered correctly are factors that can be used to predict future errors. The findings from our study can be used to further enhance EER-Tutor in order to support learning better, including real-time classification of students into novices and advanced students in order to adapt system feedback and interventions.


title: Modified Projected Space Clustering Model on Weather Data to Predict Climate of Next Season

abstract: Objectives: Four seasons of Indian weather are interdependent. Prediction of seasonal weather supports many fields to work successfully. The objective of proposed model to work on Weather Pattern identification in Initial phase of prediction which has to include unequal weight of attributes. Methods: The projected space clustering model is suitable to handle the non-sequence patterns of data set. The existing projected space clustering eliminates the least weighted attribute. The framework suggested in this paper incorporate modified projected space cluster which work on complete set of attributes to form pattern wise clusters which is dynamic in number for each season. Next part of framework is seasonal weather prediction using ANN, works on dynamic set of clusters. Findings: Dynamic nature of clusters formed in modified projected space clustering completely eliminates the error rate arise because of fixed number of cluster. The extreme events patterns formed as a separate clusters are not eliminated as outline. The result of these clusters gives the study report of each season, like the changes of climate pattern, the frequency of extreme event and weather prediction of next season. Application/ Improvement: The modified projected space clustering work well on unequal complete set of attributes to form a cluster of different pattern. For each duration numbers of clusters are dynamic based on the pattern variation in climate data.


title: VREX: Virtual reality education expansion could help to improve the class experience (VREX platform and community for VR based education)

abstract: This paper proposed an innovative education platform-VREX (Virtual Reality based Education eXpansion), with combination of online and offline, to improve the curriculum building and teaching experience. VREX is based on Virtual Reality (VR) and we believe VR can revolutionize the education ecosystem. With some trials, we found VR can be used to promote curriculum effectiveness in an immersive environment so that students can have intuitive sense to understand some abstract knowledge, which is always hard for teachers to describe. We have tried to transfer slides into VR scenes, for the students to learn knowledge in a rather real but totally virtual world. The main contributions were made: (1) VREX build an open and immersion virtual O2O classroom with internet and VR devices so that real classrooms might be used in a different way in the future. (2) VREX provides a distributed mode for students to experience an interactive learning process at anytime, anywhere and any-frequency. (3) VREX can be used to support education in different disciplines, from K-12 to Universities, and we provided some practical cases, like ‘Marine Life’ to show creatures in deep sea, which provides immersive experience to makes students feel they were there. Finally, the feasibility and advantage of VREX are proved by the actual statistical data in the 3rd season 2017.


title: The Frobenius anatomy of word meanings I: subject and object relative pronouns

abstract: This paper develops a compositional vector-based semantics of subject and object relative pronouns within a categorical framework. Frobenius algebras are used to formalise the operations required to model the semantics of relative pronouns, including passing information between the relative clause and the modified noun phrase, as well as copying, combining, and discarding parts of the relative clause. We develop two instantiations of the abstract semantics, one based on a truth-theoretic approach and one based on corpus statistics.


title: Bayesian Support Vector Regression for traffic speed prediction with error bars

abstract: Traffic prediction algorithms can help improve the performance of Intelligent Transportation Systems (ITS). To this end, ITS require algorithms with high prediction accuracy. For more robust performance, the traffic systems also require a measure of uncertainty associated with prediction data. Data driven algorithms such as Support Vector Regression (SVR) perform traffic prediction with overall high accuracy. However, they do not provide any information about the associated uncertainty. The prediction error can only be calculated once field data becomes available. Consequently, the applications which use prediction data, remain vulnerable to variations in prediction error. To overcome this issue, we propose Bayesian Support Vector Regression (BSVR). BSVR provides error bars along with the predicted traffic states. We perform sensitivity and specificity analysis to evaluate the efficiency of BSVR in anticipating variations in prediction error. We perform multi-horizon prediction and analyze the performance of BSVR for expressways as well as general road segments.