title: A fractal enterprise model and its application for business development

abstract: This paper suggests a new type of enterprise models called fractal enterprise models (FEM), with accompanying methodological support for their design. FEM shows interconnections between the business processes in an enterprise by connecting them to the assets they use and manage. Assets considered in the model could be tangible (buildings, heavy machinery, etc.) and intangible (employees, business process definitions, etc.). A FEM model is built by using two types of patterns called archetypes: a process-assets archetype that connects a process with assets used in it, and an asset-processes archetype that connects an asset with processes aimed to manage this asset (e.g., hiring people, or servicing machinery). Alternating these patterns creates a fractal structure that makes relationships between various parts of the enterprise explicit. FEM can be used for different purposes, including finding a majority of the processes in an enterprise and planning business change or radical transformation. Besides discussing FEM and areas of its usage, the paper presents results from a completed project in order to test the practical usefulness of FEM and its related methodological support.


title: Differential privacy in data publication and analysis

abstract: Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.


title: Cross-modal plasticity: where and how?

abstract: Animal studies have shown that sensory deprivation in one modality can have striking effects on the development of the remaining modalities. Although recent studies of deaf and blind humans have also provided convincing behavioural, electrophysiological and neuroimaging evidence of increased capabilities and altered organization of spared modalities, there is still much debate about the identity of the brain systems that are changed and the mechanisms that mediate these changes. Plastic changes across brain systems and related behaviours vary as a function of the timing and the nature of changes in experience. This specificity must be understood in the context of differences in the maturation rates and timing of the associated critical periods, differences in patterns of transiently existing connections, and differences in molecular factors across brain systems.


title: Description of Interest Regions with Local Binary Patterns

abstract: This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.


title: A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components

abstract: The study and modeling of systems have called the attention of several researchers, who are interested in estimating rules to describe data behavior. However, before proceeding with this estimation, it is necessary to understand the intrinsic features embedded in data. When such features are not correctly analyzed, the model accuracy tends to decrease. A well-known way to perform this analysis is by the study of time series behavior according to their stochastic and deterministic components. Nevertheless, the time series decomposition into these components is not a simple task. In order to address this issue, we conducted a rigorous and well-structured search for scientific papers in different repositories. By analyzing the recovered papers, we drew relevant conclusions such as: which methods are commonly used to decompose time series; the frequency of published papers per year; and the gaps of each method. Moreover, we have also classified the most suitable studies to estimate the determinism and stochastic- ity present in time series. After conducting this study, we concluded the development of methods to decompose time series into stochastic and deterministic components is still an open problem.


title: AppFusion: Interactive Appearance Acquisition Using a Kinect Sensor

abstract: We present an interactive material acquisition system for average users to capture the spatially varying appearance of daily objects. While an object is being scanned, our system estimates its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect sensor, a mirror ball and printed markers. We exploit the Kinect infra-red emitter/receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system.


title: Overview and Comparison of Gate Level Quantum Software Platforms

abstract: Quantum computers are available to use over the cloud, but the recent explosion of quantum software platforms can be overwhelming for those deciding on which to use. In this paper, we provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platforms--Forest (pyQuil), QISKit, ProjectQ, and the Quantum Developer Kit--that enable researchers to use real and simulated quantum devices. Our analysis covers requirements and installation, language syntax through example programs, library support, and quantum simulator capabilities for each platform. For platforms that have quantum computer support, we compare hardware, quantum assembly languages, and quantum compilers. We conclude by covering features of each and briefly mentioning other quantum computing software packages.


title: Enhanced 5G V2X services using sidelink device-to-device communications

abstract: In this paper, we present advances of sidelink (SL) device-to-device (D2D) communications as a key-enabling technology for 5G enhanced vehicular-to-everything (eV2X) communications. We provide an overview about the resource allocation and scheduling of different SL D2D modes under in-coverage and out-of-coverage application scenarios. Moreover, we present the scheduling for SL D2D V2X communications, which relies on semi-persistent scheduling (SPS) as proposed within the 3GPP specification. Simulations were carried out to evaluate the performance in terms of collision probability assuming different values of the key parameters such as resource reselection interval (RRI) and resource selection window. We finally discuss about the open technical challenges for ultra-reliable and low-latency communications as distilled from the 5G V2X use cases introduced in 3GPP Rel.15. A cooperative resource allocation and scheduling solution is also given, where a more detailed version is considered our future work on this topic.


title: A Combined CNN and LSTM Model for Arabic Sentiment Analysis

abstract: Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.


title: Watch and learn: Semi-supervised learning of object detectors from videos

abstract: We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic drift. Our approach does not assume exhaustive labeling of each object instance in any single frame, or any explicit annotation of negative data. Working in such a generic setting allow us to tackle multiple object instances in video, many of which are static. In contrast, existing approaches either do not consider multiple object instances per video, or rely heavily on the motion of the objects present. The experiments demonstrate the effectiveness of our approach by evaluating the automatically labeled data on a variety of metrics like quality, coverage (recall), diversity, and relevance to training an object detector.


title: Decompressive Craniectomy in Diffuse Traumatic Brain Injury

abstract: Patients in the craniectomy group, as compared with those in the standard-care group, had less time with intracranial pressures above the treatment threshold (P<0.001), fewer interventions for increased intracranial pressure (P<0.02 for all comparisons), and fewer days in the intensive care unit (ICU) (P<0.001). However, patients undergoing craniectomy had worse scores on the Extended Glasgow Outcome Scale than those receiving standard care (odds ratio for a worse score in the craniectomy group, 1.84; 95% confidence interval [CI], 1.05 to 3.24; P = 0.03) and a greater risk of an unfavorable outcome (odds ratio, 2.21; 95% CI, 1.14 to 4.26; P = 0.02). Rates of death at 6 months were similar in the craniectomy group (19%) and the standard-care group (18%). Conclusions In adults with severe diffuse traumatic brain injury and refractory intracranial hypertension, early bifrontotemporoparietal decompressive craniectomy decreased intracranial pressure and the length of stay in the ICU but was associated with more unfavorable outcomes. (Funded by the National Health and Medical Research Council of Australia and others; DECRA Australian Clinical Trials Registry number, ACTRN012605000009617.)


title: The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers

abstract: When used in classrooms, personalized learning software allows students to work at their own pace, while freeing up the teacher to spend more time working one-on-one with students. Yet such personalized classrooms also pose unique challenges for teachers, who are tasked with monitoring classes working on divergent activities, and prioritizing help-giving in the face of limited time. This paper reports on the co-design, implementation, and evaluation of a wearable classroom orchestration tool for K-12 teachers: mixed-reality smart glasses that augment teachers' realtime perceptions of their students' learning, metacognition, and behavior, while students work with personalized learning software. The main contributions are: (1) the first exploration of the use of smart glasses to support orchestration of personalized classrooms, yielding design findings that may inform future work on real-time orchestration tools; (2) Replay Enactments: a new prototyping method for real-time orchestration tools; and (3) an in-lab evaluation and classroom pilot using a prototype of teacher smart glasses (Lumilo), with early findings suggesting that Lumilo can direct teachers' time to students who may need it most.


The Interaction Effect of Technostress and Non-Technological Stress on Employees’ Performance


title: Dijkstra's Algorithm On-Line: An Empirical Case Study from Public Railroad Transport

abstract: Traffic information systems are among the most prominent real-world applications of Dijkstra's algorithm for shortest paths. We consider the scenario of a central information server in the realm of public railroad transport on wide-area networks. Such a system has to process a large number of on-line queries for optimal travel connections in real time. In practice, this problem is usually solved by heuristic variations of Dijkstra's algorithm, which do not guarantee an optimal result. We report results from a pilot study, in which we focused on the travel time as the only optimization criterion. In this study, various speed-up techniques for Dijkstra's algorithm were analysed empirically. This analysis was based on the timetable data of all German trains and on a "snapshot" of half a million customer queries.


title: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

abstract: Abstract Remote sensing from airborne and spaceborne platforms provides valuable data for mapping, environmental monitoring, disaster management and civil and military intelligence. However, to explore the full value of these data, the appropriate information has to be extracted and presented in standard format to import it into geo-information systems and thus allow efficient decision processes. The object-oriented approach can contribute to powerful automatic and semi-automatic analysis for most remote sensing applications. Synergetic use to pixel-based or statistical signal processing methods explores the rich information contents. Here, we explain principal strategies of object-oriented analysis, discuss how the combination with fuzzy methods allows implementing expert knowledge and describe a representative example for the proposed workflow from remote sensing imagery to GIS. The strategies are demonstrated using the first object-oriented image analysis software on the market, eCognition, which provides an appropriate link between remote sensing imagery and GIS.


title: A Digital Phase Leading Filter Current Compensation (PLFCC) Technique for CCM Boost PFC Converter to Improve PF in High Line Voltage and Light Load Conditions

abstract: This paper proposes a digital phase leading filter current compensation (PLFCC) technique for a continuous conduction mode boost power factor correction to improve PF in high line voltage and light load conditions. The proposed technique provides a corrected average inductor current reference and utilizes an enhanced duty ratio feed-forward technique which can cancel the adverse effect of the phase leading currents caused by filter capacitors. Moreover, the proposed PLFCC technique also provides the switching dead-zone in nature so the switching loss can be reduced. Therefore, the proposed PLFCC can significantly improve power quality and can achieve a high efficiency in high line voltage and light load conditions. The principle and analysis of the proposed PLFCC are presented, and performance and feasibility are verified by experimental results from the universal input (90–260 $V_{\rm AC}$ ) and 750 W–400 V output laboratory prototype.


title: Adopting Computer-Based Assessments: The Role of Perceived Value in Classroom Technology Acceptance

abstract: Computer based assessments (CBA) have increasingly become a popular tool for educators to test students’ knowledge of course material because of the many advantages it confers. However, research on its perceived value and satisfaction among students has found mixed results, with some test takers’ attitudes ranging from enthusiasm at being able to complete exams and retrieve test results whenever they want, to others actively disliking its use. As yet, the reasons for the same remain unclear. What is clear is that unmotivated or discontented students’ negative evaluations of CBA could overtime lead to a discontinuance of its usage in classrooms. Understanding the drivers of students’ continued usage of CBA is therefore key to the future use of this technological innovation and the goal of this research. To this end, the study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT)—a model specifically built to understand the adoption of software technology—to the classroom adoption of technology context. Using quantitative survey data from 111 students who were assessed using CBAs, the study examined the role of the UTAUT constructs of performance expectancy, effort expectancy, social influence, and facilitating conditions in predicting students’ continuance intention for CBA. Findings found a direct effect of UTAUT’s core constructs of performance expectancy, social influence, and facilitating conditions on continuance CBA intention. Interestingly, students’ perceived value of CBA partially mediated the effect of these constructs on continuance intention. The results of the study, therefore, point to a single, new, global construct—perceived value of CBA— that predicts whether students prefer classroom technology.


title: A Scalable Heterogeneous Dataflow Architecture For Big Data Analytics Using FPGAs (Abstract Only)

abstract: Due to rapidly expanding data size, there is increasing need for scalable, high-performance, and low-energy frameworks for large- scale data computation. We build a dataflow architecture that harnesses FPGA resources within a distributed analytics platform creating a heterogeneous data analytics framework. This approach leverages the scalability of existing distributed processing environments and provides easy access to custom hardware accelerators for large-scale data analysis. We prototype our framework within the Apache Spark analytics tool running on a CPU-FPGA heterogeneous cluster. As a specific application case study, we have chosen the MapReduce paradigm to implement a multi-purpose, scalable, and customizable RTL accelerator inside the FPGA, capable of incorporating custom High-Level Synthesis (HLS) MapReduce kernels. We demonstrate how a typical MapReduce application can be simply adapted to our distributed framework while retaining the scalability of the Spark platform.


Applying Deep Learning to ICD-9 Multi-label Classification from Medical Records


title: Malayalam text summarization: Minimum spanning tree based graph reduction approach

abstract: In recent days text summarization had tremendous growth in all languages, especially in India regional languages. Yet the performance of such system needs improvement. This paper proposes an extractive Malayalam summarizer which reduces redundancy in summarized content and meaning of sentences are considered for summary generation. A semantic graph is created for entire document and summary generated by reducing graph using minimal spanning tree algorithm.


title: Crowd Behavior Recognition for Video

abstract: Crowd behavior recognition is becoming an important research topic in video surveillance for public places. In this paper, we first discuss the crowd feature selection and extraction and propose a multiple-frame feature point detection and tracking based on the KLT tracker. We state that behavior modelling of crowd is usually coarse compared to that for individuals. Instead of developing general crowd behavior models, we propose to model crowd events for specific end-user scenarios. As a result, a same type of event may be modelled slightly differently from one scenario to another and several models are to be defined. Consequently, fast modelling is required and this is enabled by the use of an extended Scenario Recognition Engine (SRE) in our approach. Crowd event models are defined; particularly, composite events accommodating evidence accumulation allow to increase detection reliability. Tests have been conducted on real surveillance video sequences containing crowd scenes. The crowd tracking algorithm proves to be robust and gives reliable crowd motion vectors. The crowd event detection on real sequences gives reliable results of a few common crowd behaviors by simple dedicated models.


title: An NLP Curator (or: How I Learned to Stop Worrying and Love NLP Pipelines)

abstract: Natural Language Processing continues to grow in popularity in a range of research and commercial applications, yet managing the wide array of potential NLP components remains a difficult problem. This paper describes CURATOR, a NLP management framework designed to address some common problems and inefficiencies associated with building NLP process pipelines; and EDISON, an NLP data structure library in Java that provides streamlined interactions with CURATOR and offers a range of useful supporting functionality.


title: The Forms of Bullying Scale (FBS): Validity and reliability estimates for a measure of bullying victimization and perpetration in adolescence.

abstract: The study of bullying behavior and its consequences for young people depends on valid and reliable measurement of bullying victimization and perpetration. Although numerous self-report bullying-related measures have been developed, robust evidence of their psychometric properties is scant, and several limitations inhibit their applicability. The Forms of Bullying Scale (FBS), with versions to measure bullying victimization (FBS-V) and perpetration (FBS-P), was developed on the basis of existing instruments, for use with 12- to 15-year-old adolescents to economically, yet comprehensively measure both bullying perpetration and victimization. Measurement properties were estimated. Scale validity was tested using data from 2 independent studies of 3,496 Grade 8 and 783 Grade 8-10 students, respectively. Construct validity of scores on the FBS was shown in confirmatory factor analysis. The factor structure was not invariant across gender. Strong associations between the FBS-V and FBS-P and separate single-item bullying items demonstrated adequate concurrent validity. Correlations, in directions as expected with social-emotional outcomes (i.e., depression, anxiety, conduct problems, and peer support), provided robust evidence of convergent and discriminant validity. Responses to the FBS items were found to be valid and concurrently reliable measures of self-reported frequency of bullying victimization and perpetration, as well as being useful to measure involvement in the different forms of bullying behaviors. (PsycINFO Database Record (c) 2013 APA, all rights reserved). Language: en


title: Continuity mapping for multi-chart textures

abstract: It is well known that multi-chart parameterizations introduce seams over meshes, causing serious problems for applications like texture filtering, relief mapping and simulations in the texture domain. Here we present two techniques, collectively known as Continuity Mapping, that together make any multi-chart parameterization seamless: Traveler's Map is used for solving the spatial discontinuities of multi-chart parameterizations in texture space thanks to a bidirectional mapping between areas outside the charts and the corresponding areas inside; and Sewing the Seams addresses the sampling mismatch at chart boundaries using a set of stitching triangles that are not true geometry, but merely evaluated on a perfragment basis to perform consistent linear interpolation between non-adjacent texel values. Continuity Mapping does not require any modification of the artist-provided textures or models, it is fully automatic, and achieves continuity with small memory and computational costs.


Observations on the oxidation of homogentisic acid in urine


title: The changing professional role of community nurses

abstract: Community nurses work in diverse community settings to provide primary nursing and health care across the lifespan. Traditionally community nurses meet a continuum-of-health needs that range from the management of specific disease/s to broader community development and public health promotion needs. Health promotion and intervention consciously centre on the client who is viewed holistically; thus, care also considers the social conditions and relationships that affect an individual or a population’s health status. In recent years the community nurse’s role has begun to shift, directing more attention to the provision of disease recovery nursing care for transitioning clients as they move out of the hospital environment and into the community context. Additionally, the community nurse’s role has become more focused on the provision of early intervention measures to prevent exacerbations or complications for clients living with chronic illness/conditions to prevent unnecessary hospital (re)admission. These changes to the community nurse’s role are the product of a complex blend of issues that are discussed in this chapter.


title: Behavioral Economics and Psychology of Incentives

abstract: Monetary incentives can backfire while nonstandard interventions, such as framing, can be effective in influencing behavior. I review the empirical evidence on these two sets of anomalies. Paying for inherently interesting tasks, paying for prosocial behavior, paying too much, paying too little, and providing too many options can all be counterproductive. At the same time, proper design of the decision-making environment can be a potent way to induce certain behaviors. After presenting the empirical evidence, I discuss the relative role of beliefs, preferences, and technology in the anomalous impacts of incentives. I argue that inference, signaling, loss aversion, dynamic inconsistency, and choking are the primary factors that explain the data.


title: Band-Rejected Design of the Printed Open Slot Antenna for WLAN/WiMAX Operation

abstract: The band-rejected designs of the printed open slot antenna for wireless local area network (WLAN)/worldwide interoperability for microwave access (WiMAX) applications are investigated. First, the broadband characteristic (2.3 ~ 6uarr GHz) of open slot antenna design with small size is implemented and measured. Then, inserted a single strip on the broadband antenna is studied and investigated. By inserting a strip on the printed open slot of the broadband antenna, to reject frequency from 3.56 to 4.58 GHz, a dual-broadband design of the open slot antenna with small size is obtained for WLAN applications in the 2.4 GHz (2400-2484 MHz) and 5 GHz (5150-5825 MHz) bands. Furthermore, by inserting two strips of different length on the open slot antenna, a three-band antenna is achieved. By adjusting the dimension of two strips on open slot antenna, a three operating modal frequencies at 2.37 ~ 2.72,3.31 ~ 3.78, and 4.5 ~ 5.97 GHz for WiMAX application have been obtained. Detailed design steps and experimental results for the designs are studied and investigated in this paper.


title: Customer relationship management in retailing: A content analysis of retail trade journals

abstract: Abstract The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement customer relationship management (CRM) in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify CRM strategies used by retail companies. A keyword search within business databases using CRM and CRM identified publications with CRM content. Content analysis was used on articles ( N =149) drawn from Stores, Chain Store Age, Harvard Business Review, and Retail Forward over a 5 year period (2000–2005). Selected articles were stored as text files in QDA Miner, a computerized qualitative analysis tool. Key organization/infrastructure needs emerged focusing on data structure, organizational systems, technology structure, and data accessibility. Retailers goals/objectives and strategies focused on marketing, customer service, understanding customers through data analysis and increasing acquisition and retention through customer loyalty programs. Data mining tools identified supported marketing and customer analysis efforts. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.


title: Extended Life

abstract: This paper reformulates some of the questions raised by extended mind theorists from an enactive, life/mind continuity perspective. Because of its reliance on concepts such as autopoiesis, the enactive approach has been deemed internalist and thus incompatible with the extended mind hypothesis. This paper answers this criticism by showing (1) that the relation between organism and cogniser is not one of co-extension, (2) that cognition is a relational phenomenon and thereby has no location, and (3) that the individuality of a cogniser is inevitably linked with the question of its autonomy, a question ignored by the extended mind hypothesis but for which the enactive approach proposes a precise, operational, albeit non-functionalist answer. The paper raises a pespective of embedded and intersecting forms of autonomous identity generation, some of which correspond to the canonical cases discussed in the extended mind literature, but on the whole of wider generality. In addressing these issues, this paper proposes unbiased, non-species specific definitions of cognition, agency and mediation, thus filling in gaps in the extended mind debates that have led to paradoxical situations and a problematic over-reliance on intutions about what counts as cognitive.


title: Creativity challenges and opportunities in social computing

abstract: There is a convergence in recent theories of creativity that go beyond characteristics and cognitive processes of individuals to recognize the importance of the social construction of creativity. In parallel, there has been a rise in social computing supporting the collaborative construction of knowledge. The panel will discuss the challenges and opportunities from the confluence of these two developments by bringing together the contrasting and controversial perspective of the individual panel members. It will synthesize from different perspectives an analytic framework to understand these new developments, and how to promote rigorous research methods and how to identify the unique challenges in developing evaluation and assessment methods for creativity research.


title: A modular NFA architecture for regular expression matching

abstract: We propose a non-deterministic finite automata (NFA) based architecture for regexp scanners on FPGA, called CES: the Character Class with Constraint Repetition (CCR) based regExp Scanner. CES is designed to realize a new MIN-MAX counting algorithm, which can solve both the character class ambiguity problem and the overlapped matching problem. CES also supports non-regular Perl grammars such as zero-width pattern and back-reference. We propose a CCR-syntax tree and its parsing scheme to map a Perl or POSIX regexp rule to a CES topology. The interconnection patterns, and operational parameters of CCR modules (CCRM), which are the building blocks of CES, can be easily configured by regular memory writes when regexp rules change, without re-synthesis of low-level logic. For implementation, character classes of CCRs are stored in Block RAMs. The MIN-MAX algorithm uses two counters MIN and MAX to resolve the character class ambiguity problem. Two checkpoint counters are employed to implement overlapped matching detection. CES topologies optimized for different types of rules can run in different Partial Reconfigurable Regions (PRR), and can be swapped on the fly by a PRR controller. We developed a tool chain to automate the CES implementation to a Virtex 5 LX110T device. This device can host up to 3000 CCRMs, and run at an estimated throughput of 1.996 Gbps in simulation, and 863 Mbps between a PC and the Virtex 5 board in real tests. The Snort and SpamAssassin rule sets can be parsed and mapped in milliseconds. Once a base CES architecture is synthesized, the physical reconfiguration of a CES on the Virtex 5 LX110T chip can be done in less than a second.


title: A Design for a Fuzzy Logic based Frequency Controller for Efficient wind Farm Operation

abstract: Abstract: Recently wind energy penetration into power systems has increased. Wind power, as a renewable energy source, plays a different role in the power system compared to conventional power generation units. As long as only single and small wind power units are installed in the power system, wind power does not influence power system operation and can easily be integrated. However, when wind power penetration reaches a significantly high level and conventional power production units are substituted, the impact of wind power on the power system becomes noticeable and must be handled. The connection of large wind turbines and wind farms to the grid has a large impact on grid stability. The electrical power system becomes more vulnerable to and dependent on wind energy production, and therefore there is an increased concern about the large wind turbines impact on grid stability. In this work, a new type of fuzzy logic controller for the frequency control of wind farms is proposed and its performance is verified using SimWindFarm toolbox which was developed as part of the Aeolus FP7 project.Keywords: wind power system, energy penetration, fuzzy logic controller, frequency control, simwindfarm toolbox


title: A 4-µW 0.8-V rail-to-rail input/output CMOS fully differential OpAmp

abstract: This paper presents an ultra low power rail-to-rail input/output operational amplifier (OpAmp) designed in a low cost 0.18 µm CMOS technology. In this OpAmp, rail-to-rail input operation is enabled by using complementary input pairs with g m control. To maximize the output swing a rail-to-rail output stage is employed. For low-voltage low-power operation, the operating transistors in the input and output stage are biased in the sub-threshold region. The simulated DC open loop gain is 51 dB, and the slew-rate is 0.04 V/µs with a 10 pF capacitive load connected to each of the amplifier outputs. For the same load, the simulated unity gain frequency is 131 kHz with a 64° phase margin. A common-mode feed-forward circuit (CMFF) increases CMRR, reducing drastically the variations in the output common mode voltage and keeping the DC gain almost constant. In fact, their relative error remains below 1.2 % for a (−20°C, +120°C) temperature span. In addition, the proposed OpAmp is very simple and consumes only 4 µW at 0.8 V supply.


Sequence Based Classification for Predictive Maintenance


ISSN 2229‐5518 Evaluation of Anti Tumor Activity of Curcuma


title: Compact Triband Square-Slot Antenna With Symmetrical L-Strips for WLAN/WiMAX Applications

abstract: A novel triband square-slot antenna with symmetrical L-strips is presented for WLAN and WiMAX applications. The proposed antenna is composed of a square slot, a pair of L-strips, and a monopole radiator. By employing these structures, the antenna can yield three different resonances to cover the desired bands while maintaining small size and simple structure. Based on this concept, a prototype of a triband antenna is designed, fabricated, and tested. The experimental results show the antenna has the impedance bandwidths of 480 MHz (2.34-2.82 GHz), 900 MHz (3.16-4.06 GHz), and 680 MHz (4.69-5.37 GHz), which can cover both WLAN in the 2.4/5.2-GHz bands and WiMAX in the 2.5/3.5-GHz bands.


title: Storage Efficient Substring Searchable Symmetric Encryption

abstract: We address the problem of substring searchable encryption. A single user produces a big stream of data and later on wants to learn the positions in the string that some patterns occur. Although current techniques exploit auxiliary data structures to achieve efficient substring search on the server side, the cost at the user side may be prohibitive. We revisit the work of substring searchable encryption in order to reduce the storage cost of auxiliary data structures. Our solution entails a suffix array based index design, which allows optimal storage cost $O(n)$ with small hidden factor at the size of the string n. Moreover, we implemented our scheme and the state of the art protocol \citeChase to demonstrate the performance advantage of our solution with precise benchmark results.


title: Automatic lane-level map generation for advanced driver assistance systems using low-cost sensors

abstract: Lane-level digital maps can simplify driving tasks for robotic cars as well as enhance performance and reliability for advanced driver assistance systems (ADAS) by providing strong priors about the driving environment. In this paper, we present a system for automatic generation of precise lane-level maps by using conventional low-cost sensors installed in most of current commercial cars. It mainly consists of two modules, i.e. road orthographic image generation and lane graph construction. First, we divide the global map into fixed local segments based on the road network topology. According to the local map segments, we accumulate the bird's eye view images of the road surface by fusing GPS, INS and visual odometry, and subsequently integrate them into synthetic orthographic images with the reference of the local map segments. Furthermore, the information of the driving lanes is extracted from the orthographic images and a large amount of vehicle trajectories, which is used to construct the lane graph of the map based on the lane models we proposed. Such a system can offer increased value as well as promote the automation level for today's commercial cars without being supplemented additional sensors. Experiments show promising results of the automatic map generation of the real-world roads, which substantiated the effectiveness of the proposed approach.


title: Seeing fearful body language rapidly freezes the observer's motor cortex

abstract: Fearful body language is a salient signal alerting the observer to the presence of a potential threat in the surrounding environment. Although detecting potential threats may trigger an immediate reduction of motor output in animals (i.e., freezing behavior), it is unclear at what point in time similar reductions occur in the human motor cortex and whether they originate from excitatory or inhibitory processes. Using single-pulse and paired-pulse transcranial magnetic stimulation (TMS), here we tested the hypothesis that the observer's motor cortex implements extremely fast suppression of motor readiness when seeing emotional bodies - and fearful body expressions in particular. Participants observed pictures of body postures and categorized them as happy, fearful or neutral while receiving TMS over the right or left motor cortex at 100-125msec after picture onset. In three different sessions, we assessed corticospinal excitability, short intracortical inhibition (SICI) and intracortical facilitation (ICF). Independently of the stimulated hemisphere and the time of the stimulation, watching fearful bodies suppressed ICF relative to happy and neutral body expressions. Moreover, happy expressions reduced ICF relative to neutral actions. No changes in corticospinal excitability or SICI were found during the task. These findings show extremely rapid bilateral modulation of the motor cortices when seeing emotional bodies, with stronger suppression of motor readiness when seeing fearful bodies. Our results provide neurophysiological support for the evolutionary notions that emotion perception is inherently linked to action systems and that fear-related cues induce an urgent mobilization of motor reactions. Language: en


title: Tri-band miniature GPS Array with a single-fed CP antenna element

abstract: A single-fed CP stacked patch antenna is proposed to cover all the GPS bands, including E5a/E5b for the Galileo system. The small aperture size (lambda/8 at the L5 band) and the single feeding property make this antenna a promising element for small GPS arrays. The design procedures and antenna performances are presented, and issues related to coupling between array elements are discussed.


title: Theory and Practice of Data Citation

abstract: Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive," where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated data sets. Yet, given a data set, there is no quantitative, consistent, and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded, or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical the why and what and the practical the how angle.


title: Histogram based ghost removal in high dynamic range images

abstract: Multiple images with different exposures are used to produce a high dynamic range image (HDRI). When a user captures an image sequence, the ghost effect caused by moving objects and background change appears in an HDRI. This paper proposes a histogram based ghost removal method, in which object motion and background change between two exposures are detected using multi-level threshoding of the intensity histogram. The detected object motion is used to determine weighting factors in construction of the radiance map. Experiments with several sets of test images with different exposures show that the proposed method gives a better performance than existing methods in terms of visual results and computation time.


title: As Light as your Footsteps: Altering Walking Sounds to Change Perceived Body Weight, Emotional State and Gait

abstract: An ever more sedentary lifestyle is a serious problem in our society. Enhancing people's exercise adherence through technology remains an important research challenge. We propose a novel approach for a system supporting walking that draws from basic findings in neuroscience research. Our shoe-based prototype senses a person's footsteps and alters in real-time the frequency spectra of the sound they produce while walking. The resulting sounds are consistent with those produced by either a lighter or heavier body. Our user study showed that modified walking sounds change one's own perceived body weight and lead to a related gait pattern. In particular, augmenting the high frequencies of the sound leads to the perception of having a thinner body and enhances the motivation for physical activity inducing a more dynamic swing and a shorter heel strike. We here discuss the opportunities and the questions our findings open.


title: The Microsoft Research Sentence Completion Challenge

abstract: Work on modeling semantics in text is progressing quickly, yet currently there are few public datasets which authors can use to measure and compare their systems. This work takes a step towards addressing this issue. We present the MSR Sentence Completion Challenge Data, which consists of 1,040 sentences, each of which has four impostor sentences, in which a single (fixed) word in the original sentence has been replaced by an impostor word with similar occurrence statistics. For each sentence the task is then to determine which of the five choices for that word is the correct one. This dataset was constructed from Project Gutenberg data. Seed sentences were selected from five of Sir Arthur Conan Doyle’s Sherlock Holmes novels, and then imposter words were suggested with the aid of a language model trained on over 500 19th century novels. The language model was used to compute 30 alternative words for a given low frequency word in a sentence, and human judges then picked the 4 best impostor words, based on a set of provided guidelines. Although the data presented here will not be changed, this is still a work in progress, and we plan to add similar datasets based on other sources. This technical report is a living document and will be updated appropriately as new datasets are constructed and new results on existing datasets (for example, using human subjects) are reported.


title: A survey on haptic technologies for mobile augmented reality

abstract: Augmented Reality (AR) and Mobile Augmented Reality (MAR) applications have gained much research and industry attention these days. The mobile nature of MAR applications limits users' interaction capabilities such as inputs, and haptic feedbacks. This survey reviews current research issues in the area of human computer interaction for MAR and haptic devices. The survey first presents human sensing capabilities and their applicability in AR applications. We classify haptic devices into two groups according to the triggered sense: cutaneous/tactile: touch, active surfaces, and mid-air, kinesthetic: manipulandum, grasp, and exoskeleton. Due to the mobile capabilities of MAR applications, we mainly focus our study on wearable haptic devices for each category and their AR possibilities. To conclude, we discuss the future paths that haptic feedbacks should follow for MAR applications and their challenges.


title: DimmWitted: A Study of Main-Memory Statistical Analytics

abstract: We perform the first study of the tradeoff space of access methods and replication to support statistical analytics using first-order methods executed in the main memory of a Non-Uniform Memory Access (NUMA) machine. Statistical analytics systems differ from conventional SQL-analytics in the amount and types of memory incoherence that they can tolerate. Our goal is to understand tradeoffs in accessing the data in row- or column-order and at what granularity one should share the model and data for a statistical task. We study this new tradeoff space and discover that there are tradeoffs between hardware and statistical efficiency. We argue that our tradeoff study may provide valuable information for designers of analytics engines: for each system we consider, our prototype engine can run at least one popular task at least 100× faster. We conduct our study across five architectures using popular models, including SVMs, logistic regression, Gibbs sampling, and neural networks.


title: Development of the standard CubeSat deployer and a CubeSat class PicoSatellite

abstract: Cal Poly students are participating in the development of a new class of picosatellite, the CubeSat. CubeSats are ideal as space development projects for universities around the world. In addition to their significant role in educating space scientists and engineers, CubeSats provide a low-cost platform for testing and space qualification of the next generation of small payloads in space. A key component of the project is the development of a standard CubeSat deployer. This deployer is capable of releasing a number of CubeSats as secondary payloads on a wide range of launchers. The standard deployer requires all CubeSats to conform to common physical requirements, and share a standard deployer interface. CubeSat development time and cost can be significantly reduced by the development of standards that are shared by a large number of spacecraft.


title: SQLPhi: A SQL-Based Database Engine for Intel Xeon Phi Coprocessors

abstract: Relational Database Management Systems (RDBMSes) have been widely used for decades and millions of Structured Query Language (SQL) queries are processed every day. However, the row-based design and the strict structure of conventional RDBMSes make it extremely difficult to scale to the petabyte level, to take advantage of the parallelism offered by advanced hardware, and to handle unstructured data. These inherent weaknesses of existing RDBMSes have motivated us to design a novel database architecture that can leverage the powerful processing capabilities and advanced parallel features of modern hardware like GPGPUs, APUs, and the Xeon Phi. In this paper, we design a SQL-based database engine that can execute SQL queries on the Intel Many Integrated Core (MIC) architecture. Our design is specifically focused on the data decomposition of databases and the parallel execution of SQL queries. We have implemented our database engine in C++ and have tested it on the Intel Xeon Phi 5110 coprocessor using synthetic data sets. Our experiments show that when running on Xeon Phi, the execution of a SQL query on eight million records can achieve a speedup of over 2000%. We additionally observe that the overhead of data transfer from the host memory to the memory of the Xeon Phi dwarfs this speedup.


title: Unique Presentation of Hematometrocolpos Mimicking Cauda Equina Syndrome: Severe Back Pain and Urinary Incontinence in an Adolescent Girl.

abstract: BACKGROUND ::: Imperforate hymen with hematometrocolpos in adolescent females is a rare pediatric condition. Classical presentation includes abdominal pain or a pelvic mass in female patients with primary amenorrhea. Atypical complaints and reluctance among emergency physicians to perform genital examination in the emergency department or the pediatric emergency department (PED) may delay correct diagnosis. ::: ::: ::: CASE REPORT ::: We report a unique, cauda equina syndrome-like presentation of hematometrocolpos secondary to imperforate hymen in a 13-year old, previously healthy girl with primary amenorrhea. In the PED, the unusual clinical presentation of severe back pain and urinary incontinence initially mimicked cauda equina syndrome and led to delayed correct diagnosis. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: The novelty of this case is a cauda equina-like presentation of imperforate hymen secondary to hematocolpos. This report illustrates the highly variable clinical presentation of this rare gynecological pediatric entity. It underlines the importance of considering this rare condition in the differential diagnosis of severe upper or lower back pain alongside voiding abnormalities including urinary retention and incontinence in adolescent females with primary amenorrhea. Above all, the importance of performing a thorough history and genital examination in this subgroup early in the investigation process in the PED emerges from this case. Essentially, excellent clinical judgment and genital examination by the emergency physician may minimize unnecessary radiological investigations and ultimately, accelerate correct diagnosis and expedite appropriate surgical treatment. However, not only pediatric and adult emergency physicians, but also pediatricians and general practitioners should be aware of this entity and its diverse clinical presentation.


title: Biologically oriented preparation technique (BOPT) for implant-supported fixed prostheses

abstract: A patient of 58 years of age without medical problems came to the clinic due to missing teeth in the upper posterior region and to change the partial fixed prosthesis in the upper anterior area. Proposed treatment: surgical phase of three conical shape tapering implants with prosthetic platform in occlusal direction with mechanize collar tissue level with fixtures to place implant-supported metal-ceramic restorations. In the anterior area, a zirconium oxide fixed partial prosthesis was vertical preparation of the tooth's. When preparing teeth to receive fixed prostheses, the definition and shape of finish lines has been a subject of endless discussion, modification, and change ever since the beginnings of restorative prosthetic dentistry. The BOPT technique (biologically oriented preparation technique) was first described in the context of tooth-supported restorations but has recently been applied to dental implants with the aim of ensuring healthy peri-implant tissue and creating the possibility of modeling the peri-implant sulcus by modifying prosthetic emergence profiles. Vertical preparation of teeth and abutments without finish line on implants is a technique which was found to be adequate for ensuring the remodeling and stability of peri-implant tissues. Key words:Peri-implant tissue health, shoulderless abutments.


title: Big Data Semantics in Industry 4.0

abstract: The Industry 4.0 is a vision that includes connecting more intensively physical systems with their virtual counterparts in computers. This computerization of manufacturing will bring many advantages, including allowing data gathering, integration and analysis in the scale not seen earlier. In this paper we describe our Semantic Big Data Historian that is intended to handle large volumes of heterogeneous data gathered from distributed data sources. We describe the approach and implementation with a special focus on using Semantic Web technologies for integrating the data.


title: In Defense of Single-column Networks for Crowd Counting

abstract: Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering. However, it is essentially a challenging task because of the greatly varied sizes of objects, coupled with severe occlusions and vague appearance of extremely small individuals. Existing methods heavily rely on multi-column learning architectures to extract multi-scale features, which however suffer from heavy computational cost, especially undesired for crowd counting. In this paper, we propose the single-column counting network (SCNet) for efficient crowd counting without relying on multi-column networks. SCNet consists of residual fusion modules (RFMs) for multi-scale feature extraction, a pyramid pooling module (PPM) for information fusion, and a sub-pixel convolutional module (SPCM) followed by a bilinear upsampling layer for resolution recovery. Those proposed modules enable our SCNet to fully capture multi-scale features in a compact single-column architecture and estimate high-resolution density map in an efficient way. In addition, we provide a principled paradigm for density map generation and data augmentation for training, which shows further improved performance. Extensive experiments on three benchmark datasets show that our SCNet delivers new state-of-the-art performance and surpasses previous methods by large margins, which demonstrates the great effectiveness of SCNet as a single-column network for crowd counting.


title: A fully dynamic algorithm for distributed shortest paths

abstract: We propose a fully dynamic distributed algorithm for the all-pairs shortest paths problem on general networks with positive real edge weights. If Δσ is the number of pairs of nodes changing the distance after a single edge modification σ (insert, delete, weight decrease, or weight increase) then the message complexity of the proposed algorithm is O(nΔσ) in the worst case, where n is the number of nodes of the network. If Δσ = o(n2), this is better than recomputing everything from scratch after each edge modification. Up to now only a result of Ramarao and Venkatesan was known, stating that the problem of updating shortest paths in a dynamic distributed environment is as hard as that of computing shortest paths.


title: Trends in crypto-currencies and blockchain technologies: A monetary theory and regulation perspective

abstract: The internet era has generated a requirement for low-cost, anonymous and rapidly verifiable transactions to be used for online barter, and fast settling money has emerged as a consequence. For the most part, electronic money (e-money) has fulfilled this role, but the last few years have seen two new types of money emerge — centralized virtual currencies, usually for the purpose of transacting in social and gaming economies, and cryptocurrencies, which aim to eliminate the need for financial intermediaries by offering direct peer-to-peer (P2P) online payments. We describe the historical context that led to the development of these currencies and some modern and recent trends in their uptake, in terms of both usage in the real economy and as investment products. As these currencies are purely digital constructs, with no government or local authority backing, we discuss them in the context of monetary theory, in order to determine how they may be valued under each. Finally, we provide an overview of the state of regulatory readiness in terms of dealing with transactions in these currencies in various regions of the world.


Test-retest reliability and internal consistency of the Human Aspects of Information Security Questionnaire (HAIS-Q)


title: A reconfigurable low-noise dynamic comparator with offset calibration in 90nm CMOS

abstract: This paper presents a reconfigurable, low offset, low noise and high speed dynamic clocked-comparator for medium to high resolution Analog to Digital Converters (ADCs). The proposed comparator reduces the input referred noise by half and shows a better output driving capability when compared with the previous work. The offset, noise and power consumption can be controlled by a clock delay which allows simple reconfiguration. Moreover, the proposed offset calibration technique improves the offset voltage from 11.6mV to 533μV at 1 sigma. A prototype of the comparator is implemented in 90nm 1P8M CMOS with experimental results showing 320μV input referred noise at 1.5GHz with 1.2V supply.


title: Automating the translation of assertions using natural language processing techniques

abstract: In order to verify natural language assertions from a specification automatically, they need to be translated into formal representations. This process is error-prone and can lead to a product that does not meet the initial intentions.We automate this process by first partitioning all assertions into subsets based on sentence similarity and then providing a translation template for each subset which must be completed by the designer. Since many assertions are described by similar sentences, the number of manual translation steps can be decreased significantly. We evaluated our approach by translating English constraint sentences from an industrial specification into SystemVerilog assertions.


title: Theoretical framework for physiological profiling using sensors and Big Data Analytics

abstract: In today's world, everybody has limited time and generally do rash driving to reach at destination. So intentionally or unintentionally many crashes occurred and generally get mentally upset which leads to more crime or health disturbance. Due to work pressure and unavoidable environment, some changes in hormones have been occurred. This paper is based on analysis of physiological profile of drivers with the help of smart phone based application. Sensors will collect the real time in vehicle data which would be analyzed with the help of Big Data Analytics. Driver's behavior is affected by number of factors. This paper will discuss about that number of factors which affects the nature of a person. We examine an algorithm to check whether the behavior of driver is normal or abnormal. This will help the administration to analyze the chances of crashes or crimes that may occurred due to abnormal behavior and to some extent, things can be controlled.


title: Marital interaction and satisfaction: A longitudinal view

abstract: Two longitudinal studies of marital interaction were conducted using observational coding of couples attempting to resolve a high-conflict issue. We found that a different pattern of results predicts concurrent marital satisfaction than predicts change in marital satisfaction over 3 years. Results suggest that some marital interaction patterns, such as disagreement and anger exchanges, which have usually been considered harmful to a marriage, may not be harmful in the long run. These patterns were found to relate to unhappiness and negative interaction at home concurrently, but they were predictive of improvement in marital satisfaction longitudinally. However, three interaction patterns were identified as dysfunctional in terms of longitudinal deterioration: defensiveness (which includes whining), stubbomess. and withdrawal from interaction. Hypotheses about gender differences in roles for the maintenance of marital satisfaction are presented.


title: Health Cloud: An Enabler for Healthcare Transformation

abstract: An unprecedented volume of data is being generated in healthcare and life sciences, ranging across medical records, claims, lab data, genomics data, medical images, emerging exogenous data, and knowledge. Much of this data is moving to the cloud. In this paper, we describe examples of how the data from systems of record, exogenous data sources and knowledge sources can be combined at cloud scale and speed to create industry-transforming insights to improve health outcomes. We then describe a cloud architecture and building blocks that enable these solutions, and the compliance aspects that are critical to healthcare solutions. Finally, we outline a realization of this architecture and outline further research topics in this domain.


title: Aerial manipulator for structure inspection by contact from the underside

abstract: This paper presents an aerial manipulator consisting of a multirotor equipped with a robotic multi-link arm attached to the top of the multirotor body. This setup has strong potentialities for inspection of structures, since the arm is able to safely touching the structure surface with a sensor while flying, taking measurements from the underside for example in bridges. The paper presents the dynamic model of the system and the derivation of a nonlinear controller, which is tested in simulation. First flight experiments with prototype of the system are also presented.


title: UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

abstract: Abstract Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior understanding. Existing performance evaluations of MOT methods usually separate the tracking step from the detection step by using one single predefined setting of object detection for comparisons. In this work, we propose a new University at Albany DEtection and TRACking (UA-DETRAC) dataset for comprehensive performance evaluation of MOT systems especially on detectors. The UA-DETRAC benchmark dataset consists of 100 challenging videos captured from real-world traffic scenes (over 140,000 frames with rich annotations, including illumination, vehicle type, occlusion, truncation ratio, and vehicle bounding boxes) for multi-object detection and tracking. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and tracking methods. Our analysis shows the complex effects of detection accuracy on MOT system performance. Based on these observations, we propose effective and informative evaluation metrics for MOT systems that consider the effect of object detection for comprehensive performance analysis.


title: Blount disease.

abstract: Two clinically distinct forms of Blount disease (early-onset and late-onset), based on whether the lower-limb deformity develops before or after the age of four years, have been described. Although the etiology of Blount disease may be multifactorial, the strong association with childhood obesity suggests a mechanical basis. A comprehensive analysis of multiplanar deformities in the lower extremity reveals tibial varus, procurvatum, and internal torsion along with limb shortening. Additionally, distal femoral varus is commonly noted in the late-onset form. When a patient has early-onset disease, a realignment tibial osteotomy before the age of four years decreases the risk of recurrent deformity. Gradual correction with distraction osteogenesis is an effective means of achieving an accurate multiplanar correction, especially in patients with late-onset disease.


title: An Integrated Biometric-Based Security Framework Using Wavelet-Domain HMM in Wireless Body Area Networks (WBAN)

abstract: In this paper, we proposed an integrated biometric-based security framework for wireless body area networks, which takes advantage of biometric features shared by body sensors deployed at different positions of a person's body. The data communications among these sensors are secured via the proposed authentication and selective encryption schemes that only require low computational power and less resources (e.g., battery and bandwidth). Specifically, a wavelet-domain Hidden Markov Model (HMM) classification is utilized by considering the non-Gaussian statistics of ECG signals for accurate authentication. In addition, the biometric information such as ECG parameters is selected as the biometric key for the encryption in the framework. Our experimental results demonstrated that the proposed approach can achieve more accurate authentication performance without extra requirements of key distribution and strict time synchronization.


title: A low latency, loss tolerant architecture and protocol for wide area group communication

abstract: Group communication systems are proven tools upon which to build fault-tolerant systems. As the demands for fault-tolerance increase and more applications require reliable distributed computing over wide area networks, wide area group communication systems are becoming very useful. However, building a wide area group communication system is a challenge. This paper presents the design of the transport protocols of the spread wide area group communication system. We focus on two aspects of the system. First, the value of using overlay networks for application level group communication services. Second, the requirements and design of effective low latency link protocols used to construct wide area group communication. We support our claims with the results of live experiments conducted over the Internet.


title: Neighborhood formation and anomaly detection in bipartite graphs

abstract: Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.


title: Online mobile Micro-Task Allocation in spatial crowdsourcing

abstract: With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Most existing studies focus on offline scenarios, where all the spatiotemporal information of micro-tasks and crowd workers is given. However, they are impractical since micro-tasks and crowd workers in real applications appear dynamically and their spatiotemporal information cannot be known in advance. In this paper, to address the shortcomings of existing offline approaches, we first identify a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem. We first extend the state-of-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in real world. Thus, we consider the average performance of online algorithms, a.k.a online random order model.We propose a two-phase-based framework, based on which we present the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model. To improve its efficiency, we further design the TGOA-Greedy algorithm following the framework, which runs faster than the TGOA algorithm but has lower competitive ratio of 1 over 8. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.


title: A multi-objective optimal mobile robot path planning based on whale optimization algorithm

abstract: Due to the complex physical constraints in working space of robot, optimization for mobile robot operations satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for optimal mobile robot path planning based on Whale optimization algorithm (WOA) is proposed. In the proposed method, two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one. The positions of the target and the obstacles in the environment are considered the fitness for the solution in WOA. The position of the globally best whale in each iteration is selected, and reached by the robot in sequence. In addition, the robot processor updates its information during the motion, and the environment is partially unknown for the robot due to the limit of the detection range of its sensors. Series of simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles, and the proposed method may be the alternative method of optimization for robot planning.


title: Anonymity and security in delay tolerant networks

abstract: A delay tolerant network (DTN) is a store and forward network where end-to-end connectivity is not assumed and where opportunistic links between nodes are used to transfer data. An emerging application of DTNs are rural area DTNs, which provide Internet connectivity to rural areas in developing regions using conventional transportation mediums, like buses. Potential applications of these rural area DTNs are e-governance, telemedicine and citizen journalism. Therefore, security and privacy are critical for DTNs. Traditional cryptographic techniques based on PKI-certified public keys assume continuous network access, which makes these techniques inapplicable to DTNs. We present the first anonymous communication solution for DTNs and introduce a new anonymous authentication protocol as a part of it. Furthermore, we present a security infrastructure for DTNs to provide efficient secure communication based on identity-based cryptography. We show that our solutions have better performance than existing security infrastructures for DTNs.


title: Pallas: Semantic-Aware Checking for Finding Deep Bugs in Fast Path

abstract: Software optimization is constantly a serious concern for developing high-performance systems. To accelerate the workflow execution of a specific functionality, software developers usually define and implement a fast path to speed up the critical and commonly executed functions in the workflow. However, producing a bug-free fast path is nontrivial. Our study on the Linux kernel discloses that a committed fast path can have up to 19 follow-up patches for bug fixing, and most of them are deep semantic bugs, which are difficult to be pinpointed by existing bug-finding tools. In this paper, we present such a new category of software bugs based on our fast-path bug study across various system software including virtual memory manager, file systems, network, and device drivers. We investigate their root causes and identify five error-prone aspects in a fast path: path state, trigger condition, path output, fault handling, and assistant data structure. We find that many of the deep bugs can be prevented by applying static analysis incorporating simple semantic information. We extract a set of rules based on our findings and build a toolkit PALLAS to check fast-path bugs. The evaluation results show that PALLAS can effectively reveal fast-path bugs in a variety of systems including Linux kernel, mobile operating system, software-defined networking system, and web browser.


title: Canary: A Scheduling Architecture for High Performance Cloud Computing

abstract: We present Canary, a scheduling architecture that allows high performance analytics workloads to scale out to run on thousands of cores. Canary is motivated by the observation that a central scheduler is a bottleneck for high performance codes: a handful of multicore workers can execute tasks faster than a controller can schedule them. ::: The key insight in Canary is to reverse the responsibilities between controllers and workers. Rather than dispatch tasks to workers, which then fetch data as necessary, in Canary the controller assigns data partitions to workers, which then spawn and schedule tasks locally. ::: We evaluate three benchmark applications in Canary on up to 64 servers and 1,152 cores on Amazon EC2. Canary achieves up to 9-90X speedup over Spark and up to 4X speedup over GraphX, a highly optimized graph analytics engine. While current centralized schedulers can schedule 2,500 tasks/second, each Canary worker can schedule 136,000 tasks/second per core and experiments show this scales out linearly, with 64 workers scheduling over 120 million tasks per second, allowing Canary to support optimized jobs running on thousands of cores.


title: Concurrently Wireless Charging Sensor Networks with Efficient Scheduling

abstract: Wireless charging technology is considered as a promising solution to address the energy limitation problem for wireless sensor networks (WSNs). In scenarios where the deployed chargers are static, we generally require a number of chargers to work simultaneously. However, due to the radio interference among different wireless chargers, scheduling these chargers is generally necessary. This scheduling problem is challenging since each charger's charging utility cannot be calculated independently due to the nonlinear superposition charging effect caused by radio interference. In this paper, based on the concurrent charging model, we formulate the concurrent charging scheduling problem (CCSP) with the objective of quickly fully charging all the sensor nodes. After proving the NP-hardness of CCSP, we propose two efficient greedy algorithms, and give the approximation ratio of one of them. Both the two greedy algorithms’ performances are very close to that of a well-designed genetic algorithm (GA) which performs almost as well as a brute force algorithm at small network and charger scale. However, the running time of the two greedy algorithms is far lower than that of the GA. We conduct extensive simulations and specially implemented a testbed for wireless chargers. The results verified the good performance of the proposed algorithms.


title: Cognitive and affective effects of seductive details in multimedia learning

abstract: Cognitive and affective processes explain contradictory seductive details research.Seductive details and modality of text were varied in a multimedia instruction.A detrimental seductive details effect was found under high load text conditions.Seductive details caused motivational compensatory effect under narration condition.Moderated mediation analyses confirm situational interest to mediate the effect. The present study integrates cognitive and affective aspects of media processing in order to make an argument for reexamining the current cold cognition perspective in multimedia research in favor of a more integrative perspective. The Cognitive-Affective-Theory-of-Learning-with-Media (CATLM) assumes that students need to become motivated to make full use of their cognitive resources. Therefore, and even though seductive details (sds) are additional interesting but unnecessary pieces of information that do not conform with the coherence principle, their possible motivational role should not be dismissed. Using a 2i?3-experimental design, participants (N=123) were asked to learn about biology with multimedia instruction that manipulated modality (text vs. narration) and presence of seductive details (no-sds vs. textual-sds vs. narrated-sds). Results of variance analyses show a modality effect. In addition, moderated mediation analyses with the moderator modality and mediator situational interest confirm the affective mediation assumption with the following two conditional effects. A direct detrimental effect of seductive details on learning performance under the text-condition and an indirect compensatory effect under the narration-condition were shown.


title: A scalable multi-level feature extraction technique to detect malicious executables

abstract: We present a scalable and multi-level feature extraction technique to detect malicious executables. We propose a novel combination of three different kinds of features at different levels of abstraction. These are binary n-grams, assembly instruction sequences, and Dynamic Link Library (DLL) function calls; extracted from binary executables, disassembled executables, and executable headers, respectively. We also propose an efficient and scalable feature extraction technique, and apply this technique on a large corpus of real benign and malicious executables. The above mentioned features are extracted from the corpus data and a classifier is trained, which achieves high accuracy and low false positive rate in detecting malicious executables. Our approach is knowledge-based because of several reasons. First, we apply the knowledge obtained from the binary n-gram features to extract assembly instruction sequences using our Assembly Feature Retrieval algorithm. Second, we apply the statistical knowledge obtained during feature extraction to select the best features, and to build a classification model. Our model is compared against other feature-based approaches for malicious code detection, and found to be more efficient in terms of detection accuracy and false alarm rate.


title: An Open Source Simulator for IEEE1588 Time Synchronization over 802.11 Networks

abstract: As a key technique in distributed systems, time synchronization plays an important role in time sensitive modern industrial wireless local area networks (WLANs). Based on time packet exchange technology, the IEEE 1588 precise time protocol (PTP) achieves high precise and low power consumption time synchronization in wired Ethernet. In WLANs, however, it is difficult for the wireless sensor nodes to acquire accurate timestamps due to their limited resources. Moreover, compared to wired Ethernet, WLANs suffer more from wireless channel sharing, fading and packet collisions, which leads to overwhelming transmission delay jitters. Therefore, analytic solution to PTP's performance in WLANs is very difficult and it is of importance to evaluate PTP's performance by realistic simulation. In this paper, based on the open source OMNeT++ simulation engine, we present the simulator we developed for PTP time synchronization in 802.11 WLANs. The behavior of the PTP time synchronization, the simulation results and the factors that affect the PTP performance are presented and evaluated.


title: Terahertz Imaging Radar With Inverse Aperture Synthesis Techniques: System Structure, Signal Processing, and Experiment Results

abstract: The combination of the all solid-state terahertz (THz) technology and synthetic aperture radar imaging technique leads to small imaging sensors of high resolution. In this paper, we present an active frequency-modulated continuous-wave THz imaging radar system with inverse aperture synthesis technique to image objects in centimeter-scale resolution in two dimensions. Its high-range resolution is achieved through the use of broadband sweep signal, whose frequency ranges from 336.6 to 343.8 GHz, and the 1-D aperture synthesis enables the improvement of the cross-range resolution. In order to optimize the range resolution, a nonlinearity calibration approach is presented to solve the signal distortion. To verify the imaging performance of the THz radar, the inverse synthetic aperture radar experiments are performed and the imaging results obtained by the 2-D fast Fourier transform method and back-projection algorithm, respectively, show that the THz radar can achieve high resolution in the range and azimuth dimensions.


title: A Systematic Review of Healthcare Applications for Smartphones

abstract: Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.


title: Improving OCR performance with background image elimination

abstract: one critical procedure in OCR is to detect text characters from a document image. However, some documents might come with embedded background images which often mislead the algorithms of character detection. For example, small dots or sharp edges from the background image are often bound-boxed as characters and passed to the next stage of the OCR pipeline, which causes an error chain. Motivated by this observation, we present a novel and cost-effective image preprocessing method to accomplish the task. We first enhance the document images before OCR by utilizing the brightness and chromaticity as contrast parameters. Then we convert color images to gray and threshold it. This way, background images can be removed effectively without losing the quality of text characters. The method was tested using Tesseract (an open source OCR engine) and compared with two commercial OCR software ABBYY Finereader and HANWANG (OCR software for Chinese characters). The experimental results show that the recognition accuracies are improved significantly after removing background images.


title: Local conditioning in Bayesian networks

abstract: Abstract Local conditioning (LC) is an exact algorithm for computing probability in Bayesian networks, developed as an extension of Kim and Pearl's algorithm for singly-connected networks. A list of variables associated to each node guarantees that only the nodes inside a loop are conditioned on the variable which breaks it. The main advantage of this algorithm is that it computes the probability directly on the original network instead of building a cluster tree, and this can save time when debugging a model and when the sparsity of evidence allows a pruning of the network. The algorithm is also advantageous when some families in the network interact through AND/OR gates. A parallel implementation of the algorithm with a processor for each node is possible even in the case of multiply-connected networks.


title: Land-surface exchange in a chemically-reactive system; surface fluxes of HNO3, HCl and NH3

abstract: Abstract Vertical gradients from 0.25 to 2 m of NH 3 , HNO 3 and HCl and associated aerosol components have been measured in the field above various surfaces in eastern England. The data have been examined to identify the effect, if any, of chemical reaction processes upon the observed vertical profiles. It is concluded that chemical transformations are too slow to influence concentration gradients and thus the surface exchange process. Assuming chemically conservative behaviour, deposition velocities for HNO 3 and HCl have been calculated; these lie within the range 0.4–7.7 cms −1 and 0.4–6.9 cms −1 for HNO 3 and HCl, respectively. Estimation of resistances to deposition indicates a negligible surface resistance for both species. Fluxes of ammonia were predominantly upward from the ground with a mean value of 0.031 μg m −2 s −1 which is consistent both with an emission inventory of the U.K. and with the measured atmospheric concentration of NH 3 .


title: Life as an Infant: Shedding Light on Erikson, Vygotsky and Piaget

abstract: This paper explores three theories on child development, focusing specifically on infants. These include Erik Erikson's Psychosocial theory, Lev Vygotsky's Social Culture theory, and Jean Piaget's Cognitive Development theory. This paper examines each theory and provides a real-life example in support of each theory from my observations at the McMillan's Child Care Center. This paper was originally submitted for a service-learning Human Development psychology class.


Speech Enhancement Using Bayesian Wavenet.


title: Item recommendation on monotonic behavior chains

abstract: 'Explicit' and 'implicit' feedback in recommender systems have been studied for many years, as two relatively isolated areas. However many real-world systems involve a spectrum of both implicit and explicit signals, ranging from clicks and purchases, to ratings and reviews. A natural question is whether implicit signals (which are dense but noisy) might help to predict explicit signals (which are sparse but reliable), or vice versa. Thus in this paper, we propose an item recommendation framework which jointly models this full spectrum of interactions. Our main observation is that in many settings, feedback signals exhibit monotonic dependency structures, i.e., any signal necessarily implies the presence of a weaker (or more implicit) signal (a 'review' action implies a 'purchase' action, which implies a 'click' action, etc.). We refer to these structures as 'monotonic behavior chains,' for which we develop new algorithms that exploit these dependencies. Using several new and existing datasets that exhibit a variety of feedback types, we demonstrate the quantitative performance of our approaches. We also perform qualitative analysis to uncover the relationships between different stages of implicit vs. explicit signals.


title: Four-directional pixel-wise polarization CMOS image sensor using air-gap wire grid on 2.5-μm back-illuminated pixels

abstract: Polarization information is useful in highly functional imaging. This paper presents a four-directional pixel-wise polarization CMOS image sensor using an air-gap wire grid on 2.5-μm back-illuminated pixels. The fabricated air-gap wire grid polarizer achieved a transmittance of 63.3 % and an extinction ratio of 85 at 550 nm, outperforming conventional polarization sensors. The pixel-wise polarizers fabricated with the wafer process on back-illuminated image sensors exhibit good oblique-incidence characteristics, even with small polarization pixels of 2.5 μm. The proposed image sensor realizes mega-pixel various fusion-imaging applications, such as surface reflection reduction, highly accurate depth mapping, and condition-robust surveillance.


title: Detecting web page structure for adaptive viewing on small form factor devices

abstract: Mobile devices have already been widely used to access the Web. However, because most available web pages are designed for desktop PC in mind, it is inconvenient to browse these large web pages on a mobile device with a small screen. In this paper, we propose a new browsing convention to facilitate navigation and reading on a small-form-factor device. A web page is organized into a two level hierarchy with a thumbnail representation at the top level for providing a global view and index to a set of sub-pages at the bottom level for detail information. A page adaptation technique is also developed to analyze the structure of an existing web page and split it into small and logically related units that fit into the screen of a mobile device. For a web page not suitable for splitting, auto-positioning or scrolling-by-block is used to assist the browsing as an alterative. Our experimental results show that our proposed browsing convention and developed page adaptation scheme greatly improve the user's browsing experiences on a device with a small display.


title: Augmented Reality versus Virtual Reality for 3D Object Manipulation

abstract: Virtual Reality (VR) Head-Mounted Displays (HMDs) are on the verge of becoming commodity hardware available to the average user and feasible to use as a tool for 3D work. Some HMDs include front-facing cameras, enabling Augmented Reality (AR) functionality. Apart from avoiding collisions with the environment, interaction with virtual objects may also be affected by seeing the real environment. However, whether these effects are positive or negative has not yet been studied extensively. For most tasks it is unknown whether AR has any advantage over VR. In this work we present the results of a user study in which we compared user performance measured in task completion time on a 9 degrees of freedom object selection and transformation task performed either in AR or VR, both with a 3D input device and a mouse. Our results show faster task completion time in AR over VR. When using a 3D input device, a purely VR environment increased task completion time by 22.5 percent on average compared to AR ( ${p} ). Surprisingly, a similar effect occurred when using a mouse: users were about 17.3 percent slower in VR than in AR ( ${p} ). Mouse and 3D input device produced similar task completion times in each condition (AR or VR) respectively. We further found no differences in reported comfort.


An OLAP Tool Based on the Bitmap Join Index


title: Compact planar Yagi-Uda antenna with improved characteristics

abstract: In this paper, two different designs of 3-element planar Yagi-Uda antenna based on the shape of driven and reflector elements are studied. To feed the driven element of Yagi-Uda antenna, a tapered microstrip balun is designed, which converts unbalanced coaxial feed to balanced microstrip line feed. Measured results of both the antennas are compared and it is shown that a planar Yagi-Uda antenna using tapered shaped dipole elements is more compact and has better characteristics in terms of bandwidth, front to back ratio and gain than that using normal rectangular shaped dipoles. For Yagi-Uda antenna using tapered dipoles, measured bandwidth is 18.6% for return loss less than 10 dB. Maximum gain is 6.4 dBi with less than 1 dB variation over the bandwidth. Measurement shows a peak front to back ratio of 30 dB.


title: Issues in Stacked Generalization

abstract: Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a 'black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones. ::: ::: We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms for classification tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.


title: Online complete coverage path planning for mobile robots based on linked spiral paths using constrained inverse distance transform

abstract: This paper presents a sensor-based online coverage path planning algorithm guaranteeing a complete coverage of unstructured planar environments by a mobile robot. The proposed complete coverage algorithm abstracts the environment as a union of robot-sized cells and then uses a spiral filling rule. It can be largely classified as an approximate cellular decomposition approach as defined by Choset. In this paper, we first propose a special map coordinate assignment scheme based on active wall-finding using the history of sensor readings, which can drastically reduce the number of turns on the generated coverage path. Next, we develop an efficient path planner to link the simple spiral paths using the constrained inverse distance transform that we introduced the first time. This planner selects the next target cell which is at the minimal path length away from the current cell among the remaining non-contiguous uncovered cells while at the same time, finding the path to this target to save both the memory and time which are important concern in embedded robotics. Experiments on both simulated and real cleaning robots demonstrate the practical efficiency and robustness of the proposed algorithm.


title: Low-Power Near-Threshold 10T SRAM Bit Cells With Enhanced Data-Independent Read Port Leakage for Array Augmentation in 32-nm CMOS

abstract: The conventional six-transistor static random access memory (SRAM) cell allows high density and fast differential sensing but suffers from half-select and read-disturb issues. Although the conventional eight-transistor SRAM cell solves the read-disturb issue, it still suffers from low array efficiency due to deterioration of read bit-line (RBL) swing and $\text{I}_{\mathbf {on}}/\text{I}_{\mathbf {off}}$ ratio with increase in the number of cells per column. Previous approaches to solve these issues have been afflicted by low performance, data-dependent leakage, large area, and high energy per access. Therefore, in this paper, we present three iterations of SRAM bit cells with nMOS-only based read ports aimed to greatly reduce data-dependent read port leakage to enable 1k cells/RBL, improve read performance, and reduce area and power over conventional and 10T cell-based works. We compare the proposed work with other works by recording metrics from the simulation of a 128-kb SRAM constructed with divided-wordline-decoding architecture and a 32-bit word size. Apart from large improvements observed over conventional cells, up to 100-mV improvement in read-access performance, up to 19.8% saving in energy per access, and up to 19.5% saving in the area are also observed over other 10T cells, thereby enlarging the design and application gamut for memory designers in low-power sensors and battery-enabled devices.