title: ArrayTool: a lightweight profiler to guide array regrouping

abstract: Memory hierarchies in modern computer systems are complex; often, they include multi-level caches and multiple memory controllers on the same chip. Without careful design, programs suffer from unnecessary data movement between caches and memory, degrading performance and increasing energy consumption. Array regrouping can significantly improve data locality by improving spatial reuse of data and reducing cache contention. However, existing techniques for identifying opportunities for array regrouping are lacking in three ways. First, they provide inadequate information to guide regrouping. Second, the cost of monitoring employed by prior tools to identify regrouping opportunities limits the use of these methods in practice. Third, existing metrics for quantifying the benefits of array regrouping can lead to inappropriate transformations that hurt performance. In this paper, we describe ArrayTool — a lightweight profiler that guides array regrouping. Array-Tool has three unique capabilities. First, it focuses attention on arrays with significant access latency. Second, it identifies the feasibility and quantifies the benefits of regrouping arrays with lightweight array-centric profiling. Third, it works on both shared-memory and distributed-memory parallel programs. To illustrate the utility of ArrayTool, we employ it to analyze three benchmarks. Using the guidance it provides, we regroup program arrays, improving performance from 25% to a factor of two.


title: bLSM: a general purpose log structured merge tree

abstract: Data management workloads are increasingly write-intensive and subject to strict latency SLAs. This presents a dilemma: Update in place systems have unmatched latency but poor write throughput. In contrast, existing log structured techniques improve write throughput but sacrifice read performance and exhibit unacceptable latency spikes. We begin by presenting a new performance metric: read fanout, and argue that, with read and write amplification, it better characterizes real-world indexes than approaches such as asymptotic analysis and price/performance. We then present bLSM, a Log Structured Merge (LSM) tree with the advantages of B-Trees and log structured approaches: (1) Unlike existing log structured trees, bLSM has near-optimal read and scan performance, and (2) its new "spring and gear" merge scheduler bounds write latency without impacting throughput or allowing merges to block writes for extended periods of time. It does this by ensuring merges at each level of the tree make steady progress without resorting to techniques that degrade read performance. We use Bloom filters to improve index performance, and find a number of subtleties arise. First, we ensure reads can stop after finding one version of a record. Otherwise, frequently written items would incur multiple B-Tree lookups. Second, many applications check for existing values at insert. Avoiding the seek performed by the check is crucial.


title: Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning

abstract: With the ever-increasing urbanization process, systematically modeling people's activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. ::: ::: To bridge the gap, we present CrossMap, a novel cross-modal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people's activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstruction-based and the other is graph-based. Both strategies capture the correlations among the units by encoding their co-occurrence and neighborhood relationships, and learn low-dimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.


title: Exploratory and higher-order factor analyses of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) adolescent subsample.

abstract: The factor structure of the Wechsler Adult Intelligence Scale–Fourth Edition (WAISIV; Wechsler, 2008a) with the adolescent participants (ages 16–19 years; N 400) in the standardization sample was assessed using exploratory factor analysis, multiple factor extraction criteria, and higher-order exploratory factor analyses. Results from exploratory factor analyses were not included in the WAIS-IV Technical and Interpretation Manual (Wechsler, 2008b) and are necessary for determining convergence or divergence with the reported confirmatory factor analyses. As found with the total WAIS-IV standardization sample (Canivez & Watkins, in press), the present results with the adolescent subsample found all WAIS-IV subtests (10and 15-subtest configurations) were properly associated with their four theoretically proposed first-order factors, but only one factor extraction criterion (standard error of scree) recommended extraction of four factors. Hierarchical exploratory analyses with the Schmid and Leiman (1957) procedure found that the second-order g factor accounted for major portions of total and common variance, while the four first-order factors accounted for small portions of total and common variance. It was concluded that the WAIS-IV provides strong measurement of general intelligence in adolescents and clinical interpretation should be primarily at that level.


XBX Benchmarking Results January 2012


title: Towards measles elimination in Italy : virological surveillance and genotypes trend (2013–2015)

abstract: Abstract In accordance with the goal of the World Health Organization Regional Office for Europe, the Italian National Measles and Rubella Elimination Plan aimed to interrupt indigenous measles transmission in Italy by the end of 2015. However, from 2013 to 2015, Italy experienced high measles burden with 4902 measles cases (49.3% laboratory-confirmed) reported to the enhanced measles surveillance system (cumulative incidence in the triennium reference period: 2.4/100,000 population). The measles elimination goal was not reached. Laboratory surveillance of measles circulating genotypes is performed by the Measles and Rubella National Reference Laboratory (NRL) at the Italian National Institute of Health (Istituto Superiore di Sanita − ISS), in Rome. Samples received from 1 January 2013–31 December 2015 were analysed. Those positive for measles genome by molecular tests were sequenced and phylogenetically analysed. Phylogenetic analysis performed by NRL identified that genotypes D4 and D8 were endemic and co-circulated in 2011–2013: study results show that genotype D4 disappeared during 2013. Sporadic cases were associated to genotype B3 during 2011–2013, which became endemic in Italy during 2014 and co-circulated with D8 until 2015. Sporadic cases were found belonging to genotypes D9 and H1 all over the period in exam. Similar trend has been observed in European WHO Region.


title: A critical evaluation of the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)

abstract: With RPL - the “IPv6 Routing Protocol for Low-power Lossy Networks” - emerging as a Proposed Standard “Request For Comment” (RFC) in the Internet Engineering Task Force (IETF) after a ∼2-year development cycle, this paper presents a critical evaluation of the resulting protocol and its applicability and limits. The paper presents a selection of observations of the protocol characteristics, exposes experiences acquired when producing a prototype implementation of RPL, and presents results obtained from testing this protocol - both in a network simulator, and in real-world experiments on a wireless sensor network testbed. The paper aims at providing a better understanding of possible weaknesses and limits of RPL, notably the possible directions that further protocol developments should explore, in order to address these.


title: Self-excitation systems for synchronous generator with bipolar and unipolar PWM DC-DC converter

abstract: Models of Synchronous Generators with PWM DC-DC converter and Self-Excitation Systems have been designed. Calculations have shown that the unipolar PWM DC-DC converter in some modes shortly excludes from the voltage regulation process. The bipolar PWM converter effectively affects the generator and consequently enhances the system operation speed. The bipolar PWM converter uses the energy recovery in exciter as a short-circuit protection. The unipolar DC-DC converter uses explosive fuse and dumping resistor as a short-circuit protection.


Toward Automated Modeling of Floor Plans


title: Energy Management Strategy for a Hybrid Electric Vehicle Based on Deep Reinforcement Learning

abstract: An energy management strategy (EMS) is important for hybrid electric vehicles (HEVs) since it plays a decisive role on the performance of the vehicle. However, the variation of future driving conditions deeply influences the effectiveness of the EMS. Most existing EMS methods simply follow predefined rules that are not adaptive to different driving conditions online. Therefore, it is useful that the EMS can learn from the environment or driving cycle. In this paper, a deep reinforcement learning (DRL)-based EMS is designed such that it can learn to select actions directly from the states without any prediction or predefined rules. Furthermore, a DRL-based online learning architecture is presented. It is significant for applying the DRL algorithm in HEV energy management under different driving conditions. Simulation experiments have been conducted using MATLAB and Advanced Vehicle Simulator (ADVISOR) co-simulation. Experimental results validate the effectiveness of the DRL-based EMS compared with the rule-based EMS in terms of fuel economy. The online learning architecture is also proved to be effective. The proposed method ensures the optimality, as well as real-time applicability, in HEVs.


title: Order preserving encryption for numeric data

abstract: Encryption is a well established technology for protecting sensitive data. However, once encrypted, data can no longer be easily queried aside from exact matches. We present an order-preserving encryption scheme for numeric data that allows any comparison operation to be directly applied on encrypted data. Query results produced are sound (no false hits) and complete (no false drops). Our scheme handles updates gracefully and new values can be added without requiring changes in the encryption of other values. It allows standard databse indexes to be built over encrypted tables and can easily be integrated with existing database systems. The proposed scheme has been designed to be deployed in application environments in which the intruder can get access to the encrypted database, but does not have prior domain information such as the distribution of values and annot encrypt or decrypt arbitrary values of his choice. The encryption is robust against estimation of the true value in such environments.


title: A new method for decontamination of de novo transcriptomes using a hierarchical clustering algorithm

abstract: Motivation ::: The identification of contaminating sequences in a de novo assembly is challenging because of the absence of information on the target species. For sample types where the target organism is impossible to isolate from its matrix, such as endoparasites, endosymbionts and soil-harvested samples, contamination is unavoidable. A few post-assembly decontamination methods are currently available but are based only on alignments to databases, which can lead to poor decontamination. ::: ::: ::: Results ::: We present a new decontamination method based on a hierarchical clustering algorithm called MCSC. This method uses frequent patterns found in sequences to create clusters. These clusters are then linked to the target species or tagged as contaminants using classic alignment tools. The main advantage of this decontamination method is that it allows sequences to be tagged correctly even if they are unknown or misaligned to a database. ::: ::: ::: Availability and Implementation ::: Scripts and documentation about the MCSC decontamination method are available at https://github.com/Lafond-LapalmeJ/MCSC_Decontamination . ::: ::: ::: Contact ::: : benjamin.mimee@agr.gc.ca. ::: ::: ::: Supplementary information ::: Supplementary data are available at Bioinformatics online.


title: Hierarchical Bayesian Inference and Recursive Regularization for Large-Scale Classification

abstract: In this article, we address open challenges in large-scale classification, focusing on how to effectively leverage the dependency structures (hierarchical or graphical) among class labels, and how to make the inference scalable in jointly optimizing all model parameters. We propose two main approaches, namely the hierarchical Bayesian inference framework and the recursive regularization scheme. The key idea in both approaches is to reinforce the similarity among parameter across the nodes in a hierarchy or network based on the proximity and connectivity of the nodes. For scalability, we develop hierarchical variational inference algorithms and fast dual coordinate descent training procedures with parallelization. In our experiments for classification problems with hundreds of thousands of classes and millions of training instances with terabytes of parameters, the proposed methods show consistent and statistically significant improvements over other competing approaches, and the best results on multiple benchmark datasets for large-scale classification.


title: Principle and Realization of Micromouse Fast Dashing Turning Based onSTM32

abstract: Micromouse is an intelligent maze robot. Affected by the ability of MCU, micromouse based on MCS-51 can not guarantee the performance of micromouse in quick dashing. High speed micromouse servo controller based on STM32 was designed; In order to meet the fast dashing of micromouse in a complicated maze environment, different speed-time curves were used. To realize the fast turning of micromouse in dashing, the turning trajectory was divided into five paths, different control parameters and the navigation sensors were used to track the turning. Experimental results show that five-path tracking method can realize micromouse dashing turning in high speed, which helps to improve the dynamic performance and stability of micromouse, and can also reduce its dashing time.


title: Psychometric properties of the Diabetes Self-Management Questionnaire (DSMQ) in Urdu

abstract: BACKGROUND ::: Numerous study tools on diabetes self-care have been introduced; however, most existing tools do not show expectable and meaningful correlations with patients' glycaemic control. The Diabetes Self-Management Questionnaire (DSMQ) was designed to appraise self-care activities which can predict glycaemic control outcomes. However, this tool has not been validated in Pakistan. Therefore, the aim of this study was to translate and examine the psychometric properties of the Urdu version of DSMQ among type 2 diabetes patients. ::: ::: ::: METHOD ::: Standard forward-backward translation was used to translate the DSMQ into Urdu language. A convenience sample of 130 patients with type 2 diabetes was collected to assess the Urdu version's psychometric properties. Reliability was assessed by Cronbach's coefficient α and validity was assessed using confirmatory factor analysis and criterion-related correlations. ::: ::: ::: RESULTS ::: High internal consistency was found for all DSMQ scales (Sum scale: α = 0.96, Glucose Management: 0.91; Dietary Control: 0.88; Physical Activity: 0.89; Health-Care Use: 0.73). The DSMQ subscales showed significant correlations with HbA1c (Glucose Management: -0.75; Dietary Control: -0.76; Physical Activity: -0.71; Health-Care Use: -0.64; Sum Scale: -0.78; all p < 0.001). However, when associations with HbA1c were assessed in one multiple linear regression model, only Glucose Management and Dietary Control were significantly associated with lower HbA1c values (Beta = -0.42, p = 0.004 and Beta = -0.30, p = 0.028, respectively), while Physical Activity and Health-Care Use were not (p > 0.05). Adequate fit to the data was achieved for single factor model after successively modelling all significant correlations between the items' error terms, with Chi2 = 106.6, df = 84, p = 0.049; TLI = 0.98, CFI = 0.99 and RMSEA = 0.05 (90% CI 0.01-0.07). Whereas a comparatively lower fit indices to data were observed in case of four factor model. ::: ::: ::: CONCLUSION ::: The findings support the Urdu version of the DSMQ as a reliable and valid instrument for assessing self-care activities associated with glycaemic control in type 2 diabetes patients.


title: Pattern induction and matching in music signals

abstract: This paper discusses techniques for pattern induction and matching in musical audio. At all levels of music - harmony, melody, rhythm, and instrumentation - the temporal sequence of events can be subdivided into shorter patterns that are sometimes repeated and transformed. Methods are described for extracting such patterns from musical audio signals (pattern induction) and computationally feasible methods for retrieving similar patterns from a large database of songs (pattern matching).


title: Architecture design and implementation of the increasing radius - List sphere detector algorithm

abstract: A list sphere detector (LSD) is an enhancement of a sphere detector (SD) that can be used to approximate the optimal MAP detector. In this paper, we introduce a novel architecture for the increasing radius (IR)-LSD algorithm, which is based on the Dijkstra's algorithm. The parallelism possibilities are introduced in the presented architecture, which is also scalable for different multiple-input multiple-output (MIMO) systems. The novel architecture is implemented on a Virtex-IV field programmable gate array (FPGA) chip using high-level ANSI C++ language based Catapult C Synthesis tool from Mentor Graphics. The used word lengths, the latency of the design, and the required resources are presented and analyzed for 4 × 4 MIMO system with 16- quadrature amplitude modulation (QAM). The detector implementation achieves a maximum throughput of 12.1Mbps at high signal-to-noise ratio (SNR).


title: Excitation Control Method for a Low Sidelobe SIW Series Slot Array Antenna With 45$^{\circ}$ Linear Polarization

abstract: A sidelobe suppression method for a series slot array antenna which radiates 45° -inclined linear polarization is proposed. Axial displacements are employed to create arbitrary excitation coefficients for individual centered-inclined radiating slots along the center line of a broad wall. To verify the proposed design method, we design two types of center-fed linear slot array antennas with a Dolph-Chebyshev distribution for -20 dB and -26 dB sidelobe levels (SLLs) in the Ka band. Furthermore, a cross-validation process involving an equivalent circuit model analysis and electromagnetic full-wave simulation using CST MWS is utilized. The entire structure of the proposed series slot array antenna is fabricated on printed circuit boards (PCBs), including drilling and chemical etching, to secure advantages of miniaturization and cost reduction. The measured realized gains are 15.17 and 15.95 dBi and SLLs are -18.7 and -22.5 dB respectively for two types of fabricated antennas. It demonstrates the validity of the proposed sidelobe suppression method.


title: Self-Shielded High-Efficiency Yagi-Uda Antennas for 60 GHz Communications

abstract: A high-efficiency self-shielded microstrip-fed Yagi-Uda antenna has been developed for 60 GHz communications. The antenna is built on a Teflon substrate (er = 2.2) with a thickness of 10 mils (0.254 mm). A 7-element design results in a measured S11 of ; 9.5 dBi at 58 - 63 GHz. The antenna shows excellent performance in free space and in the presence of metal-planes used for shielding purposes. A parametric study is done with metal plane heights from 2 mm to 11 mm, and the Yagi-Uda antenna results in a gain >; 12 dBi at 58 - 63 GHz for h = 5 - 8 mm. A 60 GHz four-element switched-beam Yagi-Uda array is also presented with top and bottom shielding planes, and allows for 180° angular coverage with <; 3 dB amplitude variations. This antenna is ideal for inclusion in complex platforms, such as laptops, for point-to-point communication systems, either as a single element or a switched-beam system.


title: PV power output uncertainty in Australia

abstract: Uncertainty in power output of photovoltaic (PV) plants has raised concerns in regard to the interconnection of PV plants to the power systems. Climate conditions vastly affect the uncertainty of output power; however, aggregation of PV plants is potentially able to reduce the uncertainty; hence, the effectiveness of aggregation should be evaluated for climatically diverse locations. In this paper, an analysis has been performed to investigate the uncertainty in Australia by considering seven utility scale plants. A PV predictive model taking into account the PV module temperature is used to predict the output power for the modeled PV plants. Temporal and spatial ramp rate analysis is performed to characterize the level of uncertainty and evaluate the level of reduction in uncertainty through aggregation. Besides, it is validated that 1-min and 5-min ramp rates becomes uncorrelated on the order of 1 km and 20 km, respectively.


Evaluating the Tsunami Risk in California


title: Deep Investigation of Cross-Language Plagiarism Detection Methods

abstract: This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.


title: Masked Autoregressive Flow for Density Estimation

abstract: Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow. This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.


title: The Graph Traversal Pattern

abstract: A graph is a structure composed of a set of vertices (i.e.nodes, dots) connected to one another by a set of edges (i.e.links, lines). The concept of a graph has been around since the late 19$^\text{th}$ century, however, only in recent decades has there been a strong resurgence in both theoretical and applied graph research in mathematics, physics, and computer science. In applied computing, since the late 1960s, the interlinked table structure of the relational database has been the predominant information storage and retrieval model. With the growth of graph/network-based data and the need to efficiently process such data, new data management systems have been developed. In contrast to the index-intensive, set-theoretic operations of relational databases, graph databases make use of index-free, local traversals. This article discusses the graph traversal pattern and its use in computing.


title: Is word error rate a good indicator for spoken language understanding accuracy

abstract: It is a conventional wisdom in the speech community that better speech recognition accuracy is a good indicator for better spoken language understanding accuracy, given a fixed understanding component. The findings in this work reveal that this is not always the case. More important than word error rate reduction, the language model for recognition should be trained to match the optimization objective for understanding. In this work, we applied a spoken language understanding model as the language model in speech recognition. The model was obtained with an example-based learning algorithm that optimized the understanding accuracy. Although the speech recognition word error rate is 46% higher than the trigram model, the overall slot understanding error can be reduced by as much as 17%.


title: A tunable combline bandpass filter using barium strontium titanate interdigital varactors on an alumina substrate

abstract: Barium strontium titanate (BST) has a field-dependent permittivity that enables it to be used as a dielectric in a voltage-tunable capacitor or varactor. A tunable combline bandpass filter was designed and characterized using BST varactors fabricated on a polycrystalline alumina substrate with copper metallization and is 14 mm /spl times/ 14 mm in size. The center frequency of the filter varies from 1.6 to 2.0 GHz with the application of 200 V tuning voltage. A 25% tuning range was achieved using tuning field strength of 300 kV/cm. The zero bias insertion loss was 6.6 dB and this decreased to 4.3 dB at the high bias state. The return loss was better than 10 dB.


title: Wafer-level bonding/stacking technology for 3D integration

abstract: Enhanced transmission speeds, lower power consumption, better performance, and smaller form factors are reported as advantages in many devices and applications when using 3D integration. One core technique for performing 3D interconnection is stacked bonding. In this paper, wafer-level bonding technologies are reviewed and described in detail, including bonding materials and bonding conditions. The corresponding 3D integration technologies and platforms developed world-wide are also organized and addressed.


title: Role of RUNX1 in hematological malignancies.

abstract: RUNX1 is a member of the core-binding factor family of transcription factors and is indispensable for the establishment of definitive hematopoiesis in vertebrates. RUNX1 is one of the most frequently mutated genes in a variety of hematological malignancies. Germ line mutations in RUNX1 cause familial platelet disorder with associated myeloid malignancies. Somatic mutations and chromosomal rearrangements involving RUNX1 are frequently observed in myelodysplastic syndrome and leukemias of myeloid and lymphoid lineages, that is, acute myeloid leukemia, acute lymphoblastic leukemia, and chronic myelomonocytic leukemia. More recent studies suggest that the wild-type RUNX1 is required for growth and survival of certain types of leukemia cells. The purpose of this review is to discuss the current status of our understanding about the role of RUNX1 in hematological malignancies.


title: Device-free gesture tracking using acoustic signals

abstract: Device-free gesture tracking is an enabling HCI mechanism for small wearable devices because fingers are too big to control the GUI elements on such small screens, and it is also an important HCI mechanism for medium-to-large size mobile devices because it allows users to provide input without blocking screen view. In this paper, we propose LLAP, a device-free gesture tracking scheme that can be deployed on existing mobile devices as software, without any hardware modification. We use speakers and microphones that already exist on most mobile devices to perform device-free tracking of a hand/finger. The key idea is to use acoustic phase to get fine-grained movement direction and movement distance measurements. LLAP first extracts the sound signal reflected by the moving hand/finger after removing the background sound signals that are relatively consistent over time. LLAP then measures the phase changes of the sound signals caused by hand/finger movements and then converts the phase changes into the distance of the movement. We implemented and evaluated LLAP using commercial-off-the-shelf mobile phones. For 1-D hand movement and 2-D drawing in the air, LLAP has a tracking accuracy of 3.5 mm and 4.6 mm, respectively. Using gesture traces tracked by LLAP, we can recognize the characters and short words drawn in the air with an accuracy of 92.3% and 91.2%, respectively.


title: An integrated framework for optimizing automatic monitoring systems in large IT infrastructures

abstract: The competitive business climate and the complexity of IT environments dictate efficient and cost-effective service delivery and support of IT services. These are largely achieved by automating routine maintenance procedures, including problem detection, determination and resolution. System monitoring provides an effective and reliable means for problem detection. Coupled with automated ticket creation, it ensures that a degradation of the vital signs, defined by acceptable thresholds or monitoring conditions, is flagged as a problem candidate and sent to supporting personnel as an incident ticket. This paper describes an integrated framework for minimizing false positive tickets and maximizing the monitoring coverage for system faults. In particular, the integrated framework defines monitoring conditions and the optimal corresponding delay times based on an off-line analysis of historical alerts and incident tickets. Potential monitoring conditions are built on a set of predictive rules which are automatically generated by a rule-based learning algorithm with coverage, confidence and rule complexity criteria. These conditions and delay times are propagated as configurations into run-time monitoring systems. Moreover, a part of misconfigured monitoring conditions can be corrected according to false negative tickets that are discovered by another text classification algorithm in this framework. This paper also provides implementation details of a program product that uses this framework and shows some illustrative examples of successful results.


title: Students' perceptions on effective dimensions of interactive learning in a blended learning environment

abstract: This study investigates students’ perceptions of the “effective dimensions of interactive learning” in a hybrid course. A case-study design was used, and 25 students enrolled in “Computer Networks and Communication,” a course at a public university in Turkey, formed the sample of this study. The study lasted for 14 weeks. At the end of the study, interviews were conducted to gather data on the “effective dimensions of interactive learning”. Additionally, computer logs of the students were kept and analyzed to triangulate the interview data. The findings of the study showed that the way instructivist and constructivist elements are blended, the need for metacognitive support, authentic learning activities, collaboration, type and source of motivation, individualized learning, and access to the Internet played important roles in students’ learning in the hybrid course.


title: The quality and qualities of information

abstract: The paper discusses and analyzes the notion of information quality in terms of a pragmatic philosophy of language. It is argued that the notion of information quality is of great importance, and needs to be situated better within a sound philosophy of information to help frame information quality in a broader conceptual light. It is found that much research on information quality conceptualizes information quality as either an inherent property of the information itself, or as an individual mental construct of the users. The notion of information quality is often not situated within a philosophy of information. This paper outlines a conceptual framework in which information is regarded as a semiotic sign, and extends that notion with Paul Grice's pragmatic philosophy of language to provide a conversational notion of information quality that is contextual and tied to the notion of meaning.


title: An Analysis of CSR on Firm Financial Performance in Stakeholder Perspectives

abstract: Sustainable growth can be a source of success for firms. Corporate social responsibility (CSR) is a key tool for sustainable growth. However, should firms invest in CSR without having confidence in the effects and methods of CSR? This study explored the R&D, technology commercialization, and CSR motivation as core competencies that enhance corporate performance through CSR from a normative perspective—the stakeholder’s perspective. The purpose of this study was to investigate both strategic and traditional CSR’s relationships with financial performance based on the confidence in the effectiveness of CSR. Another important objective of this study was to explore management factors that influence strategic CSR. Firms consider R&D and technology commercialization as strategic management factors. Therefore, this study analyzed the influence of these strategic management factors along with CSR motivations, which may influence strategic and traditional CSR.


title: UAV-based Moving Object Detection Based on Sliding-Window Trajectories Analysis

abstract: Moving object detection (or say change detection) for stationary cameras has been well studied by research, however such problem for cameras with general motion still remains challenging. This is because the motion of target is coupled with the motion of camera platform and the scene structure. Moreover, the problem can be further complicated if thermal cameras are used. In this work, we propose an effective moving object detection technique for image sequences captured via a UAV’s thermal camera. Specifically, in each adaptive sliding window of frames, sparse feature trajectories of the thermal images are extracted, followed by a group sparsity constrained low-rank decomposition to detect foreground in the trajectory domain. Due to the unavailability of a standard benchmark dataset for the freely moving thermal camera case, in addition to the VIVID sequence, we include three challenging thermal image sequences that reflects real-world settings for performance evaluation. The results demonstrate that our method works effectively and achieves better performance than many available methods.


title: A surface micromachined high directivity GPS patch antenna with a four-leaf clover shape metamaterial slab

abstract: A high gain patch antenna with a four-leaf clover shape metamaterial slab on top is implemented using the combination of multiple ceramic and organic layers for global positioning system (GPS) applications. The four-leaf clover shape metamaterial slab showing a refractive index of zero or close to zero is designed for high gain antenna applications. An effective refractive index of 0.5 is obtained for a designed architecture. As a source antenna, a circularly polarized rectangular patch antenna is implemented with two diagonal corners truncated. The demonstrated architecture not only increases the antenna gain, but also maintains the circular polarization. The antenna size is reduced by using a high dielectric constant ceramic substrate, where the overall antenna size is as small as 25 × 25 × 4 mm3. It is surface mountable and has circular polarization with a center frequency of 1.58 GHz, and a gain of 4.48 dB.


title: Big Data in Pharmaceutical R&D: Creating a Sustainable R&D Engine

abstract: Over the last 20 years, productivity in the pharmaceutical industry has been diminishing because of constantly increasing costs while output has overall been stagnant. Despite many efforts, productivity remains a challenge within the industry. At the same time, healthcare providers quite rightly require better value for money and clear evidence that new drugs are better than the current standard of care, making a complex situation even more complex. With the implementation of ‘Big Data’ initiatives trying to integrate data from disparate data sources and disciplines that are available in life science, the industry has identified a new frontier that might provide the insights needed to turn the ship around and allow the industry to return to sustainable growth.


A New Continuum Robot With Crossed Elastic Strips: Extensible Sections With Only One Actuator per Section


title: Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid

abstract: In this paper, we consider a smart power infrastructure, where several subscribers share a common energy source. Each subscriber is equipped with an energy consumption controller (ECC) unit as part of its smart meter. Each smart meter is connected to not only the power grid but also a communication infrastructure such as a local area network. This allows two-way communication among smart meters. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a novel real-time pricing algorithm for the future smart grid. We focus on the interactions between the smart meters and the energy provider through the exchange of control messages which contain subscribers' energy consumption and the real-time price information. First, we analytically model the subscribers' preferences and their energy consumption patterns in form of carefully selected utility functions based on concepts from microeconomics. Second, we propose a distributed algorithm which automatically manages the interactions among the ECC units at the smart meters and the energy provider. The algorithm finds the optimal energy consumption levels for each subscriber to maximize the aggregate utility of all subscribers in the system in a fair and efficient fashion. Finally, we show that the energy provider can encourage some desirable consumption patterns among the subscribers by means of the proposed real-time pricing interactions. Simulation results confirm that the proposed distributed algorithm can potentially benefit both subscribers and the energy provider.


title: Teacher-Student Relationships and Engagement: Conceptualizing, Measuring, and Improving the Capacity of Classroom Interactions

abstract: Classrooms are complex social systems, and student-teacher relationships and interactions are also complex, multicomponent systems. We posit that the nature and quality of relationship interactions between teachers and students are fundamental to understanding student engagement, can be assessed through standardized observation methods, and can be changed by providing teachers knowledge about developmental processes relevant for classroom interactions and personalized feedback/support about their interactive behaviors and cues. When these supports are provided to teachers’ interactions, student engagement increases. In this chapter, we focus on the theoretical and empirical links between interactions and engagement and present an approach to intervention designed to increase the quality of such interactions and, in turn, increase student engagement and, ultimately, learning and development. Recognizing general principles of development in complex systems, a theory of the classroom as a setting for development, and a theory of change specific to this social setting are the ultimate goals of this work. Engagement, in this context, is both an outcome in its own right and a mediator of impacts that teachers have on student outcomes through their interactions with children and youth. In light of this discussion, we offer suggestions or directions for further research in this area.


title: Ultra-wideband power dividers with good isolation and sharp roll-off skirt

abstract: In this paper, an UWB power dividers based on single-layer microstrip topology is proposed, analyzed and designed. Following the basic idea of the design of UWB bandpass filters, quarter-wavelength short-circuited stubs are placed at the two output ports of a traditional single-stage Wilkinson divider. The expected ultra-wideband power-splitting performance can be realized with good impedance matching at the input port and 3-dB power transmission from the input to the two output ports. As a proper resistor is placed between two output ports, good isolation can be achieved in the overall UWB region. This UWB power divider has several attractive features that include, size-compactness with 0.39lambda2 in area (lambda is the guided wavelength at center frequency), high in-band selectivity, sharp roll-off skirt, less number of resistors only one isolation resistor is required against several resistors and easy for fabrication on single-layer PCB technology. A prototype power divider is then designed and fabricated on the RT/Duroid 6010 substrate with a dielectric constant of 10.8, loss tangent of 0.0023 and thickness of 0.635 mm.


title: Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems

abstract: Given the significant advancement in face detection and recognition techniques for human faces over recent years, we questioned how well would they work for cartoon faces - a domain that remains largely unexplored yet, mainly due to the unavailability of abundant data sets and failure of traditional methods on these. In the present article, we employ various state-of-the-art deep learning frameworks for detecting and recognizing faces of cartoon characters along with proposing a novel approach to cartoon face recognition. For face detection, our work demonstrates the effectiveness of the Multi-task Cascaded Convolutional Network (MTCNN) architecture and contrasts it with other benchmark methods. For face recognition, we present two feature-based techniques: (i) an Inductive transfer approach combining the feature learning capability of the Inception v3 network and feature recognizing capability of Support Vector Machines (SVM), (ii) a proposed Hybrid Convolutional Neural Network (HCNN) based recognition framework trained over a fusion of pixel values and 15 manually located facial key points. All the methods are evaluated on the Cartoon Faces in the Wild (IIIT-CFW) database. We show a detailed analysis of the performance of the models using several metrics over a number of input constraints. Experiments show that the MTCNN based model results in a respective gain of 3.97%, 1.19% and 2.78% on the True positive rate, False positive rate and False negative rate over the state-of-the-art detection method while both the recognition models surpass the state-of-the-art in terms of F-score. The Inception v3+SVM recognition model also establishes a new benchmark F-score of 0.910 on the task of cartoon gender recognition. We also introduce a small sized database containing location coordinates of 15 key points of the cartoon faces belonging to 50 public figures included in the IIIT-CFW database.


title: Automatic Detection of Calibration Grids in Time-of-Flight Images

abstract: It is convenient to calibrate time-of-flight cameras by established methods, using images of a chequerboard pattern. The low resolution of the amplitude image, however, makes it difficult to detect the board reliably. Heuristic detection methods, based on connected image-components, perform very poorly on this data. An alternative, geometrically-principled method is introduced here, based on the Hough transform. The projection of a chequerboard is represented by two pencils of lines, which are identified as oriented clusters in the gradient-data of the image. A projective Hough transform is applied to each of the two clusters, in axis-aligned coordinates. The range of each transform is properly bounded, because the corresponding gradient vectors are approximately parallel. Each of the two transforms contains a series of collinear peaks; one for every line in the given pencil. This pattern is easily detected, by sweeping a dual line through the transform. The proposed Hough-based method is compared to the standard OpenCV detection routine, by application to several hundred time-of-flight images. It is shown that the new method detects significantly more calibration boards, over a greater variety of poses, without any overall loss of accuracy. This conclusion is based on an analysis of both geometric and photometric error.


title: Positive Attitude Toward Math Supports Early Academic Success: Behavioral Evidence and Neurocognitive Mechanisms

abstract: Positive attitude is thought to impact academic achievement and learning in children, but little is known about its underlying neurocognitive mechanisms. Using a large behavioral sample of 240 children, we found that positive attitude toward math uniquely predicted math achievement, even after we accounted for multiple other cognitive-affective factors. We then investigated the neural mechanisms underlying the link between positive attitude and academic achievement in two independent cohorts of children (discovery cohort: n = 47; replication cohort: n = 28) and tested competing hypotheses regarding the differential roles of affective-motivational and learning-memory systems. In both cohorts, we found that positive attitude was associated with increased engagement of the hippocampal learning-memory system. Structural equation modeling further revealed that, in both cohorts, increased hippocampal activity and more frequent use of efficient memory-based strategies mediated the relation between positive attit...


title: Cross-section of option returns and volatility

abstract: We study the cross-section of stock option returns by sorting stocks on the difference between historical realized volatility and at-the-money implied volatility. We find that a zero-cost trading strategy that is long (short) in the portfolio with a large positive (negative) difference between these two volatility measures produces an economically and statistically significant average monthly return. The results are robust to different market conditions, to stock risks-characteristics, to various industry groupings, to option liquidity characteristics, and are not explained by usual risk factor models.


title: Black Hole attack Detection using fuzzy based IDS

abstract: In the past few years, an evolution in the wireless communication has been emerged, along with the evolution a new type with large potential application of wireless network appears, which is the Mobile Ad-Hoc Network (MANET). Black hole attack consider one of the most affected kind on MANET. Therefore, the use of intrusion detection system (IDS) has a major importance in the MANET protection. In this paper, an optimization of a fuzzy based intrusion detection system is proposed which automate the process of producing a fuzzy system by using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the initialization of the FIS and then optimize this initialized system by using Genetic Algorithm (GA). In addition, a normal estimated fuzzy based IDS is introduces to see the effect of the optimization on the system. From this study, it is proven that the optimized proposed IDS perform better that the normal estimated systems.


Preparation of 3,5‐Dibromo‐2‐Pyrone From Coumalic Acid


title: An analysis of robust cost functions for CNN in computer-aided diagnosis

abstract: Deep convolutional neural networks (CNNs) have proven to be powerful and flexible tools that advance the state-of-the-art in many fields, e.g. speech recognition, computer vision and medical imaging. Usually deep CNN models employ the logistic (soft-max) loss function in the training process of classification tasks. Recent evidence on a computer vision benchmark data-set indicates that the hinge (SVM) loss might give smaller misclassification errors on the test set compared to the logistic loss (i.e. offer better generality). In this paper, we study and compare four different loss functions for deep CNNs in the context of computer-aided abdominal and mediastinal lymph node detection and diagnosis (CAD) using CT images. Besides the logistic loss, we compare three other CNN losses that have not been previously studied for CAD problems. The experiments confirm that the logistic loss performs the worst among the four losses, and an additional 3% increase in detection rate at 3 false positives/volume can be ob...


title: Planning complex physical tasks for disaster response with a humanoid robot

abstract: Humanoid robots are attempting ever more complex tasks in lieu of humans. Disaster response is a promising area for the use of humanoids due to safety concerns. However, controlling a high DOF humanoid robot to autonomously perform a complex task in unknown and unstructured environments is challenging. In this paper we describe a simulation framework for humanoid grasping and transport tasks that includes dynamics, and is easily ported to a real physical humanoid robot. The system can be used to rapidly prototype humanoid motions and dynamics in simulation, and can then be ported to the physical hardware. Experimental results are presented, both in simulation and physical experiments, with the HUBO humanoid, on a task from the DARPA Robotics Challenge, attaching a fire hose to a hydrant.


title: A.J.: Dynamics systems vs. optimal control — a unifying view

abstract: In the past, computational motor control has been approached from at least two major frameworks: the dynamic systems approach and the viewpoint of optimal control. The dynamic system approach emphasizes motor control as a process of self-organization between an animal and its environment. Nonlinear differential equations that can model entrainment and synchronization behavior are among the most favorable tools of dynamic systems modelers. In contrast, optimal control approaches view motor control as the evolutionary or development result of a nervous system that tries to optimize rather general organizational principles, e.g., energy consumption or accurate task achievement. Optimal control theory is usually employed to develop appropriate theories. Interestingly, there is rather little interaction between dynamic systems and optimal control modelers as the two approaches follow rather different philosophies and are often viewed as diametrically opposing. In this paper, we develop a computational approach to motor control that offers a unifying modeling framework for both dynamic systems and optimal control approaches. In discussions of several behavioral experiments and some theoretical and robotics studies, we demonstrate how our computational ideas allow both the representation of self-organizing processes and the optimization of movement based on reward criteria. Our modeling framework is rather simple and general, and opens opportunities to revisit many previous modeling results from this novel unifying view.


title: Nearest-Neighbor Thermodynamics and NMR of DNA Sequences with Internal A·A, C·C, G·G, and T·T Mismatches†

abstract: Thermodynamic measurements are reported for 51 DNA duplexes with A·A, C·C, G·G, and T·T single mismatches in all possible Watson−Crick contexts. These measurements were used to test the applicability of the nearest-neighbor model and to calculate the 16 unique nearest-neighbor parameters for the 4 single like with like base mismatches next to a Watson−Crick pair. The observed trend in stabilities of mismatches at 37 °C is G·G > T·T ≈ A·A > C·C. The observed stability trend for the closing Watson−Crick pair on the 5‘ side of the mismatch is G·C ≥ C·G ≥ A·T ≥ T·A. The mismatch contribution to duplex stability ranges from −2.22 kcal/mol for GGC·GGC to +2.66 kcal/mol for ACT·ACT. The mismatch nearest-neighbor parameters predict the measured thermodynamics with average deviations of ΔG°37 = 3.3%, ΔH° = 7.4%, ΔS° = 8.1%, and TM = 1.1 °C. The imino proton region of 1-D NMR spectra shows that G·G and T·T mismatches form hydrogen-bonded structures that vary depending on the Watson−Crick context. The data reported ...


title: The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory.

abstract: Temporal construal theory states that distant future situations are construed on a higher level (i.e., using more abstract and central features) than near future situations. Accordingly, the theory suggests that the value associated with the high-level construal is enhanced over delay and that the value associated with the low-level construal is discounted over delay. In goal-directed activities, desirability of the activity's end state represents a high-level construal, whereas the feasibility of attaining this end state represents a low-level construal. Study 1 found that distant future activities were construed on a higher level than near future activities. Studies 2 and 3 showed that decisions regarding distant future activities, compared with decisions regarding near future activities, were more influenced by the desirability of the end state and less influenced by the feasibility of attaining the end state. Study 4 presented students with a real-life choice of academic assignments varying in difficulty (feasibility) and interest (desirability). In choosing a distant future assignment, students placed relatively more weight on the assignment's interest, whereas in choosing a near future assignment, they placed relatively more weight on difficulty. Study 5 found that distant future plans, compared with near future plans, were related to desirability of activities rather than to time constraints. In everyday life, people judge and make decisions about events that will take place either in the relatively near future or in the distant future. For example, one may need to decide whether to take a vacation, give a lecture, or attend a conference a few months in advance or just a few days in advance. This article addresses the question of how temporal distance from an event affects people's decisions regarding that event. We argue that distant future events are construed on a higher level (i.e., by using more central and abstract features of the event) than near future events. Therefore, decisions regarding distant future events are likely to be based on relatively central and


title: Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

abstract: Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.


A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment


title: Economic-based Distributed Resource Management and Scheduling for Grid Computing

abstract: Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications.


title: A Data-Driven Approach to Kinematic Analysis in Running Using Wearable Technology

abstract: Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.


title: Reduction of stray inductance in power electronic modules using basic switching cells

abstract: This paper introduces the concepts of two basic switching cells, P-cell and N-cell, along with their implications in power electronic circuits. The basic switching cells exist in almost every power electronic circuit. To take advantage of these structures, this paper proposes a novel packaging method for power electronics modules. The proposed packaging method uses the basic switching cells as the unit in a module, instead of traditional anti-parallel connection of active switch and diode. This rearrangement can reduce the stray inductance in the current commutation pass; therefore, the performance and reliability of the power device module and the power electronic system can be improved. A conventional phase leg module and a proposed module are modeled. Electromagnetic simulation is carried out to extract the stray inductance from the two modules. Switching behavior under different package parasitics is studied based on Saber simulation.


title: Adaptive redundancy control for systematic erasure code based real time data transmission in Internet

abstract: We propose an adaptive redundancy control scheme for a systematic erasure coding scheme to transmit real time data in the Internet. Since the recovery rate of packet loss depends on the amount of redundancy data, we use the redundancy estimation algorithm which considers the loss burstiness. The Gilbert model is used for modeling the loss process and adjusting the number of redundant packets. Analysis and experimental results show that the proposed scheme is efficient for loss recovery in a consecutive loss environments like the Internet.


title: Self-Driving Car Steering Angle Prediction Based on Image Recognition

abstract: Self-driving vehicles have expanded dramatically over the last few years. Udacity has release a dataset containing, among other data, a set of images with the steering angle captured during driving. The Udacity challenge aimed to predict steering angle based on only the provided images. We explore two different models to perform high quality prediction of steering angles based on images using different deep learning techniques including Transfer Learning, 3D CNN, LSTM and ResNet. If the Udacity challenge was still ongoing, both of our models would have placed in the top ten of all entries.


title: A Theory of Problematic Internet Use and Psychosocial Well-Being

abstract: The model introduced and tested in the current study suggests that lonely and depressed individuals may develop a preference for online social interaction, which, in turn, leads to negative outcomes associated with their Internet use. Participants completed measures of preference for online social interaction, depression, loneliness, problematic Internet use, and negative outcomes resulting from their Internet use.Results indicated that psychosocial health predicted levels of preference for online social interaction, which, in turn, predicted negative outcomes associated with problematic Internet use.In addition, the results indicated that the influence of psychosocial distress on negative outcomes due to Internet use is mediated by preference for online socialization and other symptoms of problematic Internet use.The results support the current hypothesis that that individuals’ preference for online, rather than face-to-face, social interaction plays an important role in the development of negative consequences associated with problematic Internet use.


title: Hierarchical Cache Consistency in a WAN

abstract: This paper explores ways to provide improved consistency for Internet applications that scale to millions of clients. We make four contributions. First, we identify how workloads affect the scalability of cache consistency algorithms. Second, we define two primitive mechanisms, split and join, for growing and shrinking consistency hierarchies, and we present a simple mechanism for implementing them. Third, we describe and evaluate policies for using split and join to address the fault tolerance and performance challenges of consistency hierarchies. Fourth, using synthetic workload and trace-based simulation, we compare various algorithms for maintaining strong consistency in a range of hierarchy configurations. Our results indicate that a promising configuration for providing strong consistency in a WAN is a two-level consistency hierarchy where servers and proxies work to maintain consistency for data cached at clients. Specifically, by adapting to clients' access patterns, two-level hierarchies reduce the read latency for demanding workloads without introducing excessive overhead for nondemanding workloads. Also, they can improve scalability by orders of magnitude. Furthermore, this configuration is easy to deploy by augmenting proxies, and it allows invalidation messages to traverse firewalls.


title: Data Management in the Cloud: Limitations and Opportunities

abstract: Recently the cloud computing paradigm has been receiving significant excitement and attention in the media and blogosphere. To some, cloud computing seems to be little more than a marketing umbrella, encompassing topics such as distributed computing, grid computing, utility computing, and softwareas-a-service, that have already received significant research focus and commercial implementation. Nonetheless, there exist an increasing number of large companies that are offering cloud computing infrastructure products and services that do not entirely resemble the visions of these individual compo-


title: Utilizing query change for session search

abstract: Session search is the Information Retrieval (IR) task that performs document retrieval for a search session. During a session, a user constantly modifies queries in order to find relevant documents that fulfill the information need. This paper proposes a novel query change retrieval model (QCM), which utilizes syntactic editing changes between adjacent queries as well as the relationship between query change and previously retrieved documents to enhance session search. We propose to model session search as a Markov Decision Process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent's actions are query changes that we observe and the search agent's actions are proposed in this paper. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and 2012.


Feature-Rich Networks for Knowledge Base Completion


title: A model-based approach to test automation for context-aware mobile applications

abstract: Current testing tools for mobile applications do not provide sufficient support for context-aware application testing. In addition to regular input vectors (e.g. touch events, text entry) context parameters must be considered (e.g. accelerometer data interpreted as shake gestures, GPS location data, etc.). A multitude of possible application faults resulting from these additional context parameters requires an appropriately selected set of test cases. In this paper, we propose a model-based approach to improve the testing of context-aware mobile applications by deducing test cases from design-time system models. Using a custom-built version of the calabash-android testing framework enhanced by an arbitrary context parameter facility, our approach to test case generation and automated execution is validated on a context-aware mobile application.


title: Radio-telepathy: extracting a secret key from an unauthenticated wireless channel

abstract: Securing communications requires the establishment of cryptographic keys, which is challenging in mobile scenarios where a key management infrastructure is not always present. In this paper, we present a protocol that allows two users to establish a common cryptographic key by exploiting special properties of the wireless channel: the underlying channel response between any two parties is unique and decorrelates rapidly in space. The established key can then be used to support security services (such as encryption) between two users. Our algorithm uses level-crossings and quantization to extract bits from correlated stochastic processes. The resulting protocol resists cryptanalysis by an eavesdropping adversary and a spoofing attack by an active adversary without requiring an authenticated channel, as is typically assumed in prior information-theoretic key establishment schemes. We evaluate our algorithm through theoretical and numerical studies, and provide validation through two complementary experimental studies. First, we use an 802.11 development platform with customized logic that extracts raw channel impulse response data from the preamble of a format-compliant 802.11a packet. We show that it is possible to practically achieve key establishment rates of ~ 1 bit/sec in a real, indoor wireless environment. To illustrate the generality of our method, we show that our approach is equally applicable to per-packet coarse signal strength measurements using off-the-shelf 802.11 hardware.


title: Antipsychotics, Delirium, and Acute Respiratory Distress Syndrome: What Is the Link?

abstract: Acute respiratory distress syndrome (ARDS) is an acute inflammatory process that impairs the ability of the lungs to oxygenate and ultimately leads to respiratory failure. Patients who develop ARDS often have prolonged and complicated hospital courses putting them at risk for intensive care unit (ICU) delirium. Patients with ICU delirium often need chemical sedation, mechanical ventilation, prolonged duration of ICU and hospital stays, long-term cognitive impairment, and increased mortality. In a patient with ARDS, ICU delirium further complicates the hospital course and increases the risk of morbidly and mortality. Antipsychotics are prescribed to decrease the severity and duration of ICU delirium, thus potentially decreasing their risk of morbidity and mortality. Antipsychotics, however, are associated with many adverse effects including respiratory failure. Given the long-term sequelae associated with the development of ICU delirium and the risks associated with antipsychotic use, clinicians must weigh the risks and benefits of antipsychotic use. The aim of this review is to investigate the interrelationship between ARDS, delirium, and antipsychotic use. In addition to discussing relevant studies evaluating antipsychotics for the prevention and treatment of delirium, we will investigate safety concerns with the use of antipsychotics, especially as they relate to ARDS. Using the data compiled in this review, clinicians can make an informed decision about the use of antipsychotics for the prevention or treatment of delirium, with special consideration for their patients with ARDS. Future studies are needed to critically evaluate antipsychotic timing, dose, and duration for the prevention and treatment of ICU delirium and specifically evaluate impact in special populations, particularly patients with ARDS. ::: ::: This article is protected by copyright. All rights reserved.


title: Exploiting Investors Social Network for Stock Prediction in China's Market

abstract: Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail investors are prone to be influenced by news or other social media, psychological and behavioral features extracted from social media platforms are thought to well predict stock price movements in the China's market. Recent advances in the investor social network in China enables the extraction of such features from web-scale data. In this paper, on the basis of tweets from Xueqiu, a popular Chinese Twitter-like social platform specialized for investors, we analyze features with regard to collective sentiment and perception on stock relatedness and predict stock price movements by employing nonlinear models. The features of interest prove to be effective in our experiments.


title: Object Tracking Method Based on SURF

abstract: Abstract An object tracking algorithm based on SURF is presented in this paper. Interest points are detected by SURF detector in reference region which is located in the first frame manually. In the following sequences, the SURF feature points are extracted in a larger window which is selected as test region. In the matching stage, we calculate the Euclidean distance between the descriptor vectors of interest points in the test image and ones in the reference image. The proposed algorithm is implemented on an embedded platform with TI's DM6446 high performance processor. The experimental results show that our system implements a real-time tracking with robustness against appearance variations, scale change, and cluttered scenes.


title: Parent and Family Impact of Autism Spectrum Disorders: A Review and Proposed Model for Intervention Evaluation

abstract: Raising a child with an autism spectrum disorder (ASD) can be an overwhelming experience for parents and families. The pervasive and severe deficits often present in children with ASD are associated with a plethora of difficulties in caregivers, including decreased parenting efficacy, increased parenting stress, and an increase in mental and physical health problems compared with parents of both typically developing children and children with other developmental disorders. In addition to significant financial strain and time pressures, high rates of divorce and lower overall family well-being highlight the burden that having a child with an ASD can place on families. These parent and family effects reciprocally and negatively impact the diagnosed child and can even serve to diminish the positive effects of intervention. However, most interventions for ASD are evaluated only in terms of child outcomes, ignoring parent and family factors that may have an influence on both the immediate and long-term effects of therapy. It cannot be assumed that even significant improvements in the diagnosed child will ameliorate the parent and family distress already present, especially as the time and expense of intervention can add further family disruption. Thus, a new model of intervention evaluation is proposed, which incorporates these factors and better captures the transactional nature of these relationships.


title: Semantic pattern mining for text mining

abstract: Pattern mining is a fundamental topic in data mining area. Many pattern mining techniques, such as closed and maximal pattern mining have been proposed for different applications. However, when calculating the frequency of a pattern, the existing techniques treat each word equally. For example, although the word ‘pie’ in ‘I love eating pie.’ is quite different from ‘pie’ in ‘american pie’, ‘pie’ in ‘american pie’ will still be added up to the counts of ‘pie’ when calculating its frequency. Therefore, this paper aims to overcome the drawback to find the valid patterns tailored to text mining. We will approach pattern mining from a different perspective and introduce a novel problem of frequent semantic pattern mining. We then propose an algorithm to solve this problem via suffix array sorting. The algorithm can be implemented to run in linear time. Compared with traditional pattern representations, our results show the semantic patterns extracted are more than 13% compact. Also, classifier built on these features is no less or more powerful.


title: Finding Focus in the Blur of Moving-Target Techniques

abstract: Protecting critical systems and assets against cyberattacks is an ever more difficult challenge that strongly favors attackers. Whereas defenders must protect a large, diverse set of cybersystems containing an unknown number of vulnerabilities of various types, attackers need only find one or a few exploitable vulnerabilities to mount a successful attack. One promising approach that can shift the balance in the defenders' favor is to create uncertainty for attackers by dynamically changing system properties in what is called a cyber moving target (MT). MT techniques seek to randomize system components to reduce the likelihood of a successful attack, add dynamics to a system to reduce the lifetime of an attack, and diversify otherwise homogeneous collections of systems to limit the damage of a large-scale attack. In this article, the authors review the five dominant domains of MT techniques available today as research prototypes and commercial solutions. They present the techniques' strengths and weaknesses and make recommendations for future research that will improve current capabilities.


title: Nutrition Knowledge, Attitude and Practice of College Sportsmen

abstract: Purpose : Nutrition is an important component of any physical fitness program. The main dietary goal for active individuals is to obtain adequate nutrition to optimize health fitness and to increase sports performance. The present study aims to assess the nutrition knowledge, attitude and practice among the selected athletes. Methods: Athletes from five different private colleges situated in Salem District, Tamilnadu, India were selected. A total number of 102 athletes, 32 sportsmen belong to Volleyball discipline, 25 belongs to weightlifter discipline and 45 belong to runners discipline in sports. All the selected athletes were including in the study. The Knowledge, Attitude and Practice (KAP) questionnaire contained ten questions about nutrition knowledge, nine questions about attitudes, and ten questions about dietary practice were collected from the selected athletes. Dietary composition of the sportsmen is also assessed. The collected data was coded and used for evaluation. Results: Results about KAP revealed that 42 per cent of the volleyball players had good nutritional knowledge (60 - 69per cent) compared to weight lifters (43per cent) who had satisfactory (50 - 59per cent) knowledge about nutrition. Twenty nine per cent of the runners had very good (70 - 79per cent) knowledge about nutrition. Regarding food consumption pattern intake of cereals, other vegetables and milk was found to be less compared to the RDA for the athletes. Among the three disciplines sports persons, the mean nutrient intake of the runners is high compared to volleyball and weight lifters. Conclusion: The sports disciplines strongly affected the nutrition knowledge, attitudes and practices of sportsmen. The overall scores indicate that most sportsmen had good knowledge of nutrition and supplements.


title: A Field Guide to Forward-Backward Splitting with a FASTA Implementation

abstract: Non-differentiable and constrained optimization play a key role in machine learning, signal and image processing, communications, and beyond. For high-dimensional minimization problems involving large datasets or many unknowns, the forward-backward splitting method provides a simple, practical solver. Despite its apparently simplicity, the performance of the forward-backward splitting is highly sensitive to implementation details. ::: This article is an introductory review of forward-backward splitting with a special emphasis on practical implementation concerns. Issues like stepsize selection, acceleration, stopping conditions, and initialization are considered. Numerical experiments are used to compare the effectiveness of different approaches. ::: Many variations of forward-backward splitting are implemented in the solver FASTA (short for Fast Adaptive Shrinkage/Thresholding Algorithm). FASTA provides a simple interface for applying forward-backward splitting to a broad range of problems.


title: Anti-jamming communication in cognitive radio networks with unknown channel statistics

abstract: Recently, many opportunistic spectrum sensing and access protocols have been proposed for cognitive radio networks (CRNs). For achieving optimized spectrum usage, existing solutions model the spectrum sensing and access problem as a partially observed Markov decision process (POMDP) and assume that the information states and/or the primary users' (PUs) traffic statistics are known a priori to the secondary users (SUs). While theoretically sound, these existing approaches may not be effective in practice due to two main concerns. First, the assumptions they made are not practical, as before the communication starts, PUs' traffic statistics may not be readily available to the SUs. Secondly and more seriously, existing approaches are extremely vulnerable to malicious jamming attacks. A cognitive attacker can always jam the channels to be accessed by leveraging the same statistic information and stochastic dynamic decision making process that the SUs would follow. To address the above concerns, we formulate the problem of anti-jamming multichannel access in CRNs and solve it as a non-stochastic multi-armed bandit (NS-MAB) problem, where the secondary sender and receiver adaptively choose their arms (i.e., sending and receiving channels) to operate. The proposed protocol enables them to hop to the same set of channels with high probability in the presence of jamming. We analytically show the convergence of the learning algorithms, i.e., the performance difference between the secondary sende and receiver's optimal strategies is no more than O(20k/√e √Tn ln n). Extensive simulations are conducted to validate the theoretical analysis and show that the proposed protocol is highly resilient to various jamming attacks.


title: The Impact of Electronic Word of Mouth on Consumers ’ Purchasing Decisions

abstract: substantial growth in online social networks has vastly expanded the potential impact of electronic word of mouth (eWOM) on consumer purchasing decisions. A critical literature review exposed that there is limited research on the impact of online consumer reviews on online purchasing decisions of Saudi Arabian consumers. This research reports on results of a study on the effects of online reviews on Saudi citizens' online purchasing decisions. The results show that Saudi Internet shoppers are very much influenced by eWOM, and that a larger percentage of them are dependent on such online forums when making decisions to purchase products through the Internet. General Terms E-commerce, online shopping reviews. Keywordsword of mouth; eWOM; online WOM; online consumer reviews; Saudi Arabia.


title: Redundancy effect in multimedia learning: A closer look

abstract: The generally accepted assumption by most multimedia researchers is that learning is inhibited when on-screen text and narration containing the same information is presented simultaneously, rather than on-screen text or narration alone. This is known as the verbal redundancy effect. Are there situations where the reverse is true? This research was designed to investigate the reverse redundancy effect for non-native English speakers learning English reading comprehension, where two instructional modes were used the redundant mode and the modality mode. In the redundant mode, static pictures and audio narration were presented with synchronized redundant on-screen text. In the modality mode, only static pictures and audio were presented. In both modes, learners were allowed to control the pacing of the lessons. Participants were 209 Yemeni learners in their first year of tertiary education. Examination of text comprehension scores indicated that those learners who were exposed to the redundancy mode performed significantly better than learners in the modality mode. They were also significantly more motivated than their counterparts in the modality mode. This finding has added an important modification to the redundancy effect. That is the reverse redundancy effect is true for multimedia learning of English as a foreign language for students where textual information was foreign to them. In such situations, the redundant synchronized on-screen text did not impede learning; rather it reduced the cognitive load and thereby enhanced learning.


title: Quadruplet Network with One-Shot Learning for Visual Tracking

abstract: In the same vein of discriminative one-shot learning, Siamese networks allow recognizing an object from a single exemplar with the same class label. However, they do not take advantage of the underlying structure of the data and the relationship among the multitude of samples as they only rely on the pairs of instances for training. In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation. We design a shared network with four branches that receive a multi-tuple of instances as inputs and are connected by a novel loss function consisting of pair loss and triplet loss. According to the similarity metric, we select the most similar and the most dissimilar instances as the positive and negative inputs of triplet loss from each multi-tuple. We show that this scheme improves the training performance. Furthermore, we introduce a new weight layer to automatically select suitable combination weights, which will avoid the conflict between triplet and pair loss leading to worse performance. We evaluate our quadruplet framework by model-free tracking-by-detection of objects from a single initial exemplar in several visual object tracking benchmarks. Our extensive experimental analysis demonstrates that our tracker achieves superior performance with a real-time processing speed of 78 frames/s. Our source code is available.


title: A Soft Robotic Wearable Wrist Device for Kinesthetic Haptic Feedback

abstract: Advances in soft robotics provide a unique approach for delivering haptic feedback to a user by a soft wearable device. Such devices can apply forces directly on the human joints, while still maintaining the safety and flexibility necessary for use in close proximity to the human body. To take advantage of these properties, we present a new haptic wrist device using pressure-driven soft actuators called reverse pneumatic artificial muscles (rPAMs) mounted on four sides of the wrist. These actuators are originally pre-strained and release compressive stress under pressure, applying a safe torque around the wrist joints while being compact and portable, representing the first soft haptic device capable of real-time feedback. To demonstrate the functional utility of this device, we created a virtual path-following task, wherein the user employs the motion of their wrist to control their embodied agent. We used the haptic wrist device to assist the user in following the path and study their performance with and without haptic feedback in multiple scenarios. Our results quantify the effect of wearable soft robotic haptic feedback on user performance. Specifically, we observed that our haptic feedback system improved the performance of users following complicated paths in a statistically significant manner, but did not show improvement for simple linear paths. Based on our findings, we anticipate broader applications of wearable soft robotic haptic devices towards intuitive user interactions with robots, computers, and other users.


title: Low-Temperature Data Remanence Attacks Against Intrinsic SRAM PUFs

abstract: In this work, we present the first systematic study of data remanence effects on an intrinsic Static Random Access Memory Physical Unclonable Function (SRAM PUF) implemented on a commercial off-the-shelf (COTS) device in the temperature range between –110 degrees Celsius and –40 degrees Celsius. Based on our experimental results, we propose a new type of attack against intrinsic SRAM PUFs, which takes advantage of data remanence effects exhibited due to low temperatures. We demonstrate that this attack is highly resistant to memory erasure techniques and can be used to manipulate the cryptographic keys produced by the SRAM PUF. Finally, we also discuss and assess potential countermeasures against the attack we propose.


title: Authorship analysis: identifying the author of a program

abstract: Authorship analysis on computer software is a difficult problem. In this paper we explore the classification of programmers' style, and try to find a set of characteristics that remain constant for a significant portion of the programs that this programmer might produce. Our goal is to show that it is possible to identify the author of a program by examining programming style characteristics. Within a closed environment, the results of this paper support the conclusion that, for a specific set of programmers, it is possible to identify the author of any individual program. Also, based on previous work and our observations during the experiments described herein, we believe that the probability of finding two programmers who share exactly those same characteristics should be very small.


title: Adding Gradient Noise Improves Learning for Very Deep Networks

abstract: Deep feedforward and recurrent networks have achieved impressive results in many perception and language processing applications. Recently, more complex architectures such as Neural Turing Machines and Memory Networks have been proposed for tasks including question answering and general computation, creating a new set of optimization challenges. In this paper, we explore the low-overhead and easy-to-implement optimization technique of adding annealed Gaussian noise to the gradient, which we find surprisingly effective when training these very deep architectures. Unlike classical weight noise, gradient noise injection is complementary to advanced stochastic optimization algorithms such as Adam and AdaGrad. The technique not only helps to avoid overfitting, but also can result in lower training loss. We see consistent improvements in performance across an array of complex models, including state-of-the-art deep networks for question answering and algorithm learning. We observe that this optimization strategy allows a fully-connected 20-layer deep network to escape a bad initialization with standard stochastic gradient descent. We encourage further application of this technique to additional modern neural architectures.


title: Graphene-Flakes Printed Wideband Elliptical Dipole Antenna for Low-Cost Wireless Communications Applications

abstract: This letter presents the design, manufacturing, and operational performance of a graphene-flakes-based screen-printed wideband elliptical dipole antenna operating from 2 up to 5 GHz for low-cost wireless communications applications. To investigate radio frequency (RF) conductivity of the printed graphene, a coplanar waveguide (CPW) test structure was designed, fabricated, and tested in the frequency range from 1 to 20 GHz. Antenna and CPW were screen-printed on Kapton substrates using a graphene paste formulated with a graphene-to-binder ratio of 1:2. A combination of thermal treatment and subsequent compression rolling is utilized to further decrease the sheet resistance for printed graphene structures, ultimately reaching 4 Ω/□ at 10- μ m thicknesses. For the graphene-flakes printed antenna, an antenna efficiency of 60% is obtained. The measured maximum antenna gain is 2.3 dBi at 4.8 GHz. Thus, the graphene-flakes printed antenna adds a total loss of only 3.1 dB to an RF link when compared to the same structure screen-printed for reference with a commercial silver ink. This shows that the electrical performance of screen-printed graphene flakes, which also does not degrade after repeated bending, is suitable for realizing low-cost wearable RF wireless communication devices.


title: Learning subjective adjectives from corpora

abstract: Subjectivity tagging is distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarity and gradability (Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone.


title: iMiner: Mining Inventory Data for Intelligent Management

abstract: Inventory management refers to tracing inventory levels, orders and sales of a retailing business. In the current retailing market, a tremendous amount of data regarding stocked goods (items) in an inventory will be generated everyday. Due to the increasing volume of transaction data and the correlated relations of items, it is often a non-trivial task to efficiently and effectively manage stocked goods. In this demo, we present an intelligent system, called iMiner, to ease the management of enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data, and incorporate the latest advances of data mining technologies into the system to perform the tasks of inventory management, e.g., forecasting inventory, detecting abnormal items, and analyzing inventory aging. Since 2014, iMiner has been deployed as the major inventory management platform of ChangHong Electric Co., Ltd, one of the world's largest TV selling companies in China.


title: A Theory of Generative ConvNet

abstract: We show that a generative random field model, which we call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multicategory classification and assuming one of the categories is a base category generated by a reference distribution. If we further assume that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then we obtain a generative ConvNet model that is unique among energy-based models: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process. The Langevin dynamics for sampling the generative ConvNet is driven by the reconstruction error of this autoencoder. The contrastive divergence learning of the generative ConvNet reconstructs the training images by the auto-encoder. The maximum likelihood learning algorithm can synthesize realistic natural image patterns.


title: Total vertex craniopagus with crossed venous drainage: case report of successful surgical separation

abstract: Case report ::: Twin boys joined at the head in a total vertex configuration were born in Egypt in June 2001. At 12 months, they were transported to Dallas for evaluation and eventual surgical separation. In Dallas, a large multidisciplinary team of health care providers from two pediatric hospitals was assembled to care for the boys. Extensive radiographic evaluation revealed that the twins had essentially separate, well-formed brains, each with regions of schizencephaly. Each child’s left cerebral hemisphere drained most of the venous blood to the right jugular system of the other. A detailed assessment of the foreseeable risks of surgical separation was then estimated and presented to the parents, as well as to the ethics committee of the two institutions. The decision was then made to proceed with separation. Surgical planning included the construction of multiple polymer models, and the design and construction of customized head holders and an operating table. Prior to separation a series of preparatory operations were performed to expand the scalp, as well as the fascia lata for dural grafting. At the age of 28 months, the twins were successfully separated during a 33-h operation. No attempt was made to reconstruct the dural venous sinuses. Scalp closure was adequate, requiring a split-thickness skin graft on one boy.


title: The subcutaneous fat compartments in relation to aesthetically important facial folds and rhytides.

abstract: The ideal treatment of the nasolabial fold, the tear trough, the labiomandibular fold and the mentolabial sulcus is still discussed controversially. The detailed topographical anatomy of the fat compartments may clarify the anatomy of facial folds and may offer valuable information for choosing the adequate treatment modality. Nine non-fixed cadaver heads in the age range between 72 and 89 years (five female and four male) were investigated. Computed tomographic scans were performed after injection of a radiographic contrast medium directly into the fat compartments surrounding prominent facial folds. The data were analysed after multiplanar image reconstruction. The fat compartments surrounding the facial folds could be defined in each subject. Different arrangement patterns of the fat compartments around the facial rhytides were found. The nasolabial fold, the tear trough and the labiomandibular fold represent an anatomical border between adjacent fat compartments. By contrast, the glabellar fold and the labiomental sulcus have no direct relation to the boundaries of facial fat. Deep fat, underlying a facial rhytide, was identified underneath the nasolabial crease and the labiomental sulcus. In conclusion, an improvement by a compartment-specific volume augmentation of the nasolabial fold, the tear trough and the labiomandibular fold is limited by existing boundaries that extend into the skin. In the area of the nasolabial fold and the mentolabial sulcus, deep fat exists which can be used for augmentation and subsequent elevation of the folds. The treatment of the tear trough deformity appears anatomically the most challenging area since the superficial and deep fat compartments are separated by an osseo-cutaneous barrier, the orbicularis retaining ligament. In severe cases, a surgical treatment should be considered. By contrast, the glabellar fold shows the most simple anatomical architecture. The fold lies above one subcutaneous fat compartment that can be used for augmentation.


title: Information-flow and data-flow analysis of while-programs

abstract: Until recently, information-flow analysis has been used primarily to verify that information transmission between program variables cannot violate security requirements. Here, the notion of information flow is explored as an aid to program development and validation. Information-flow relations are presented for while-programs, which identify those program statements whose execution may cause information to be transmitted from or to particular input, internal, or output values. It is shown with examples how these flow relations can be helpful in writing, testing, and updating programs; they also usefully extend the class of errors which can be detected automatically in the “static analysis” of a program.


title: Surface integrated waveguide fed Antipodal Fermi-Linear Tapered Slot Antenna at 28 GHz

abstract: To obtain wide bandwidth and high gain at millimeter wave frequency, Antipodal Fermi-Linear Tapered Slot Antenna (AFLTSA) is proposed. The proposed antenna achieves wide bandwidth from 23 to 33 GHz as well as flat gain of 14.5 dBi over wide operating frequency band. It achieves the side lobe level of -8 dB in H Plane and -16 dB in E plane. Proposed design also achieves the front-to-back ratio of -20 dB and symmetric pattern.