title: Modeling of reflected and scattered light intensity of ordered mesoporous layers of silica

abstract: Mesoporous materials are characterized by the density of empty or filled pores which modulates their properties, in particular, optical properties. Although pores scatter light, the scattered energy or reflectance spectra of a mesoporous layer show a behaviour that can be attributed mainly to interference created by the interfaces; however, coherent scattering is still present inside the mesoporous layer. In this paper, we show that diffusively reflected light created by the pores depends on their distribution and shape. If they are distributed along a periodic lattice, the structure factor modulates the optical properties and the form factor that describes the geometry of the pores, influences the shape of optical spectrum. We study the influence of the form factor of pores which renders possible a tuning of the optical reflectance spectrum through pore geometry.


title: Internet users' information privacy-protective responses: a taxonomy and a nomological model

abstract: Although Internet users are expected to respond in various ways to privacy threats from online companies, little attention has been paid so far to the complex nature of how users respond to these threats. This paper has two specific goals in its effort to fill this gap in the literature. The first, so that these outcomes can be systematically investigated, is to develop a taxonomy of information privacy-protective responses (IPPR). This taxonomy consists of six types of behavioral responses-refusal, misrepresentation, removal, negative word-of-mouth, complaining directly to online companies, and complaining indirectly to third-party organizations-that are classified into three categories: information provision, private action, and public action. Our second goal is to develop a nomological model with several salient antecedents-concerns for information privacy, perceived justice, and societal benefits from complaining-of IPPR, and to show how the antecedents differentially affect the six types of IPPR. The nomological model is tested with data collected from 523 Internet users. The results indicate that some discernible patterns emerge in the relationships between the antecedents and the three groups of IPPR. These patterns enable researchers to better understand why a certain type of IPPR is similar to or distinct from other types of IPPR. Such an understanding could enable researchers to analyze a variety of behavioral responses to information privacy threats in a fairly systematic manner. Overall, this paper contributes to researchers' theory-building efforts in the area of information privacy by breaking new ground for the study of individuals' responses to information privacy threats.


title: A Regulated Charge Pump for Tunneling Floating-Gate Transistors

abstract: Flash memory is an important component in many embedded system-on-a-chip applications, which drives the need to generate high write/erase voltages in generic CMOS processes. In this paper, we present a regulated, high-voltage charge pump for erasing Flash memory and floating-gate transistors. This 0.069-mm2 charge pump was fabricated in a 0.35 $\mu \text {m}$ standard CMOS process. While operating from a 2.5 V supply, the charge pump generates regulated voltages up to 16 V with a PSRR of 52 dB and an output impedance of 6.8 $\text{k}\Omega $ . To reduce power consumption for use in battery-powered applications, this charge pump uses a variable-frequency regulation technique and a new circuit for minimizing short-circuit current in the clock-generation circuitry; the resulting charge pump is able to erase the charge on floating-gate transistors using only $1.45 \mu J$ .


title: Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported . . .

abstract: In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners' interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multi- dimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in.


title: Analysis And Simulation Of A Rocker-Bogie Exploration Rover

abstract: Rovers will continue to play an important role in planetary exploration. Plans include the use of the rocker-bogie rover configuration. Here, models of the mechanics of this configuration are presented. Methods for solving the inverse kinematics of the system and quasi-static force analysis are described. Also described is a simulation based on the models of the rover’s performance. Experimental results confirm the validity of the models.


title: Review of Ontology Matching Approaches and Challenges

abstract: Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce and data exchange in various domains such as purchase order, health, music and e-commerce. It is performed by ontology matching approaches that find semantic correspondences between ontology entities. In this paper, we review state of the art ontology matching approaches. We describe the approaches according to instance-based, schema-based, instance and schema-based, usage-based, element-level, and structure-level. The analysis of the existing approaches will assist us in revealing some challenges in ontology mapping such as handling ontology matching errors, user involvement and reusing previous match operations. We explain the way of handling the challenges using new strategy in order to increase the performance.


title: A quantum associative memory based on grover’s algorithm

abstract: Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical ideas and introduces a quantum associative memory, a small version of which should be physically realizable in the near future.


title: Privacy: Theory meets Practice on the Map

abstract: In this paper, we propose the first formal privacy analysis of a data anonymization process known as the synthetic data generation, a technique becoming popular in the statistics community. The target application for this work is a mapping program that shows the commuting patterns of the population of the United States. The source data for this application were collected by the U.S. Census Bureau, but due to privacy constraints, they cannot be used directly by the mapping program. Instead, we generate synthetic data that statistically mimic the original data while providing privacy guarantees. We use these synthetic data as a surrogate for the original data. We find that while some existing definitions of privacy are inapplicable to our target application, others are too conservative and render the synthetic data useless since they guard against privacy breaches that are very unlikely. Moreover, the data in our target application is sparse, and none of the existing solutions are tailored to anonymize sparse data. In this paper, we propose solutions to address the above issues.


title: A nine year study of file system and storage benchmarking

abstract: Benchmarking is critical when evaluating performance, but is especially difficult for file and storage systems. Complex interactions between I/O devices, caches, kernel daemons, and other OS components result in behavior that is rather difficult to analyze. Moreover, systems have different features and optimizations, so no single benchmark is always suitable. The large variety of workloads that these systems experience in the real world also adds to this difficulty. In this article we survey 415 file system and storage benchmarks from 106 recent papers. We found that most popular benchmarks are flawed and many research papers do not provide a clear indication of true performance. We provide guidelines that we hope will improve future performance evaluations. To show how some widely used benchmarks can conceal or overemphasize overheads, we conducted a set of experiments. As a specific example, slowing down read operations on ext2 by a factor of 32 resulted in only a 2--5p wall-clock slowdown in a popular compile benchmark. Finally, we discuss future work to improve file system and storage benchmarking.


title: Estimating mobile application energy consumption using program analysis

abstract: Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior.


Evaluation of Visual Object Trackers on Equirectangular Panorama


title: Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification

abstract: The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small.


title: An IoT Approach for Wireless Sensor Networks Applied to e-Health Environmental Monitoring

abstract: This paper presents an Internet of Things (IoT) approach for monitoring temperature and relative humidity applied to product maintenance in hospitals or pharmaceutical entities. Our goal is to integrate a low-cost and scalable network of smart sensors capable of mapping large areas in real-time. In this article, we provide a comprehensive insight into the technologies that compose the IoT architecture: (i) the node layer composed of Wireless Sensor Network (WSN), (ii) the local management layer of the WSN and (iii) the cloud-based layer for enabling remote monitoring. To the date, our IoT system has been working during (8) months in The Hospital Universitario San Ignacio, a 4th level university hospital located in Bogota, Colombia. Here, we present a field report of this work-in-progress system.


title: Using Data to Understand How to Better Design Adaptive Learning

abstract: There is much enthusiasm in higher education about the benefits of adaptive learning and using big data to investigate learning processes to make data-informed educational decisions. The benefits of adaptive learning to achieve personalized learning are obvious. Yet, there lacks evidence-based research to understand how data such as user behavior patterns can be used to design effective adaptive learning systems. The purpose of this study, therefore, is to investigate what behavior patterns learners with different characteristics demonstrate when they interact with an adaptive learning environment. Incoming 1st-year students in a pharmacy professional degree program engaged in an adaptive learning intervention that aimed to provide remedial instruction to better prepare these professional students before they began their formal degree program. We analyzed the participants’ behavior patterns through the usage data to understand how they used the adaptive system based upon their needs and interests. Using both statistical analyses and data visualization techniques, this study found: (1) apart from learners’ cognitive ability, it is important to consider affective factors such as motivation in adaptive learning, (2) lack of alignment among various components in an adaptive system can impact how learners accessed the system and, more importantly, their performance, and (3) visualizations can reveal interesting findings that can be missed otherwise. Such research should provide much needed empirical evidences and useful insights about how the analytics can inform the effective designs of adaptive learning.


title: A critical survey of state-of-the-art image inpainting quality assessment metrics

abstract: Abstract Image inpainting is the process of restoring missing pixels in digital images in a plausible way. Research in image inpainting has received considerable attention in different areas, including restoration of old and damaged documents, removal of undesirable objects, computational photography, retouching applications, etc. The challenge is that the recovery processes themselves introduce noticeable artifacts within and around the restored image regions. As an alternative to subjective evaluation by humans, a number of approaches have been introduced to quantify inpainting processes objectively. Unfortunately, existing objective metrics have their own strengths and weaknesses as they use different criteria. This paper provides a thorough insight into existing metrics related to image inpainting quality assessment, developed during the last few years. The paper provides, under a new framework, a comprehensive description of existing metrics, their strengths, their weaknesses, and a detailed performance analysis on real images from public image inpainting database. The paper also outlines future research directions and applications of inpainting and inpainting-related quality assessment measures.


title: Gut brain axis: diet microbiota interactions and implications for modulation of anxiety and depression

abstract: The human gut microbiome is composed of an enormous number of microorganisms, generally regarded as commensal bacteria. Without this inherent microbial community, we would be unable to digest plant polysaccharides and would have trouble extracting lipids from our diet. Resident gut bacteria are an important contributor to healthy metabolism and there is significant evidence linking gut microbiota and metabolic disorders such as obesity and diabetes. In the past few years, neuroscience research has demonstrated the importance of microbiota in the development of brain systems that are vital to both stress reactivity and stress-related behaviours. Here we review recent literature that examines the impact of diet-induced changes in the microbiota on stress-related behaviours including anxiety and depression.


title: Learning Invariances using the Marginal Likelihood

abstract: Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the predictions to be invariant to transformations on the input that are known to be irrelevant (e.g. translation). Commonly, this is done through data augmentation, where the training set is enlarged by applying hand-crafted transformations to the inputs. We argue that invariances should instead be incorporated in the model structure, and learned using the marginal likelihood, which correctly rewards the reduced complexity of invariant models. We demonstrate this for Gaussian process models, due to the ease with which their marginal likelihood can be estimated. Our main contribution is a variational inference scheme for Gaussian processes containing invariances described by a sampling procedure. We learn the sampling procedure by back-propagating through it to maximise the marginal likelihood.


title: FaThumb: a facet-based interface for mobile search

abstract: In this paper we describe a novel approach for searching large data sets from a mobile phone. Existing interfaces for mobile search require keyword text entry and are not suited for browsing. Our alternative uses a hybrid model to de-emphasize tedious keyword entry in favor of iterative data filtering. We propose navigation and selection of hierarchical metadata (facet navigation), with incremental text entry to further narrow the results. We conducted a formative evaluation to understand the relative advantages of keyword entry versus facet navigation for both browse and search tasks on the phone. We found keyword entry to be more powerful when the name of the search target is known, while facet navigation is otherwise more effective and strongly preferred.


title: A Systematic Review on the Practice of Evaluating Visualization

abstract: We present an assessment of the state and historic development of evaluation practices as reported in papers published at the IEEE Visualization conference. Our goal is to reflect on a meta-level about evaluation in our community through a systematic understanding of the characteristics and goals of presented evaluations. For this purpose we conducted a systematic review of ten years of evaluations in the published papers using and extending a coding scheme previously established by Lam et al. [2012]. The results of our review include an overview of the most common evaluation goals in the community, how they evolved over time, and how they contrast or align to those of the IEEE Information Visualization conference. In particular, we found that evaluations specific to assessing resulting images and algorithm performance are the most prevalent (with consistently 80-90% of all papers since 1997). However, especially over the last six years there is a steady increase in evaluation methods that include participants, either by evaluating their performances and subjective feedback or by evaluating their work practices and their improved analysis and reasoning capabilities using visual tools. Up to 2010, this trend in the IEEE Visualization conference was much more pronounced than in the IEEE Information Visualization conference which only showed an increasing percentage of evaluation through user performance and experience testing. Since 2011, however, also papers in IEEE Information Visualization show such an increase of evaluations of work practices and analysis as well as reasoning using visual tools. Further, we found that generally the studies reporting requirements analyses and domain-specific work practices are too informally reported which hinders cross-comparison and lowers external validity.


title: Learning First-Order Horn Clauses from Web Text

abstract: Even the entire Web corpus does not explicitly answer all questions, yet inference can uncover many implicit answers. But where do inference rules come from? ::: ::: This paper investigates the problem of learning inference rules from Web text in an unsupervised, domain-independent manner. The Sherlock system, described herein, is a first-order learner that acquires over 30,000 Horn clauses from Web text. Sherlock embodies several innovations, including a novel rule scoring function based on Statistical Relevance (Salmon et al., 1971) which is effective on ambiguous, noisy and incomplete Web extractions. Our experiments show that inference over the learned rules discovers three times as many facts (at precision 0.8) as the TextRunner system which merely extracts facts explicitly stated in Web text.


title: Simulated laparoscopy using a head-mounted display vs traditional video monitor: an assessment of performance and muscle fatigue

abstract: BACKGROUND ::: The direction of visual gaze may be an important ergonomic factor that affects operative performance. We designed a study to determine whether a head-mounted display (HMD) worn by the surgeon would improve task performance and/or reduce muscle fatigue during a laparoscopic task when compared to the use of a traditional video monitor display (VMD). ::: ::: ::: METHODS ::: Surgical residents (n = 30) were enrolled in the study. A junior group, consisting of 15 postgraduate year (PGY) = 1 subjects with no previous laparoscopic experience, and a senior group, consisting of 15 PGY 4 and PGY 5 subjects with experience, completed a laparoscopic task that was repeated four times using the Computer Enhanced Laparoscopic Training System (CELTS). Groups alternated between using the HMD with the task placed in a downward frontal position and the VMD with the task at a 30 degrees lateral angle. The CELTS module assessed task completion time, depth perception, path length of instruments, response orientation, motion smoothness; the system then generated an overall score. Electromyography (EMG) was used to record sternocleidomastoid muscle activity. Display preference was surveyed. ::: ::: ::: RESULTS ::: The senior residents performed better than the junior residents overall on all parameters (p < 0.05) except for motion smoothness, where there was no difference. In both groups, the HMD significantly improved motion smoothness when compared to the VMD (p < 0.05). All other parameters were equal. There was less muscle fatigue when using the VMD (p < 0.05). We found that 66% of the junior residents but only 20% of the senior residents preferred the HMD. ::: ::: ::: CONCLUSIONS ::: The CELTS module demonstrated evidence of construct validity by differentiating the performances of junior and senior residents. By aligning the surgeon's visual gaze with the instruments, HMD improved smoothness of motion. Experienced residents preferred the traditional monitor display. Although the VMD produced less muscle fatigue, inexperienced residents preferred the HMD, possibly because of improved smoothness of motion.


title: Understanding inverse document frequency: On theoretical arguments for IDF

abstract: The term‐weighting function known as IDF was proposed in 1972, and has since been extremely widely used, usually as part of a TF*IDF function. It is often described as a heuristic, and many papers have been written (some based on Shannon's Information Theory) seeking to establish some theoretical basis for it. Some of these attempts are reviewed, and it is shown that the Information Theory approaches are problematic, but that there are good theoretical justifications of both IDF and TF*IDF in the traditional probabilistic model of information retrieval.


title: Photovoltaic system fault detection and diagnostics using Laterally Primed Adaptive Resonance Theory neural network

abstract: Cost effective integration of solar photovoltaic (PV) systems requires increased reliability. This can be achieved with a robust fault detection and diagnostic (FDD) tool that automatically discovers faults. This paper introduces the Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network to perform this task. The present work tested the algorithm on actual and synthetic data to assess its potential for wide spread implementation. The tests were conducted on a PV system located in Albuquerque, New Mexico. The system was composed of 14 modules arranged in a configuration that produced a maximum power of 3.7kW. The LAPART algorithm learned system behavior quickly, and detected module level faults with minimal error.


title: Comprehensive review of epidemiology, scope, and impact of spinal pain

abstract: Persistent pain interfering with daily activities is common. Chronic pain has been defined in many ways. Chronic pain syndrome is a separate entity from chronic pain. Chronic pain is defined as, "pain that persists 6 months after an injury and beyond the usual course of an acute disease or a reasonable time for a comparable injury to heal, that is associated with chronic pathologic processes that cause continuous or intermittent pain for months or years, that may continue in the presence or absence of demonstrable pathologies; may not be amenable to routine pain control methods; and healing may never occur." In contrast, chronic pain syndrome has been defined as a complex condition with physical, psychological, emotional, and social components. The prevalence of chronic pain in the adult population ranges from 2% to 40%, with a median point prevalence of 15%. Among chronic pain disorders, pain arising from various structures of the spine constitutes the majority of the problems. The lifetime prevalence of spinal pain has been reported as 54% to 80%. Studies of the prevalence of low back pain and neck pain and its impact in general have shown 23% of patients reporting Grade II to IV low back pain (high pain intensity with disability) versus 15% with neck pain. Further, age related prevalence of persistent pain appears to be much more common in the elderly associated with functional limitations and difficulty in performing daily life activities. Chronic persistent low back and neck pain is seen in 25% to 60% of patients, one-year or longer after the initial episode. Spinal pain is associated with significant economic, societal, and health impact. Estimates and patterns of productivity losses and direct health care expenditures among individuals with back and neck pain in the United States continue to escalate. Recent studies have shown significant increases in the prevalence of various pain problems including low back pain. Frequent use of opioids in managing chronic non-cancer pain has been a major issue for health care in the United States placing a significant strain on the economy with the majority of patients receiving opioids for chronic pain necessitating an increased production of opioids, and escalating costs of opioid use, even with normal intake. The additional costs of misuse, abuse, and addiction are enormous. Comorbidities including psychological and physical conditions and numerous other risk factors are common in spinal pain and add significant complexities to the interventionalist's clinical task. This section of the American Society of Interventional Pain Physicians (ASIPP)/Evidence-Based Medicine (EBM) guidelines evaluates the epidemiology, scope, and impact of spinal pain and its relevance to health care interventions.


title: Interactive visualization of volumetric data with WebGL in real-time

abstract: This article presents and discusses the implementation of a direct volume rendering system for the Web, which articulates a large portion of the rendering task in the client machine. By placing the rendering emphasis in the local client, our system takes advantage of its power, while at the same time eliminates processing from unreliable bottlenecks (e.g. network). The system developed articulates in efficient manner the capabilities of the recently released WebGL standard, which makes available the accelerated graphic pipeline (formerly unusable). The dependency on specially customized hardware is eliminated, and yet efficient rendering rates are achieved. The Web increasingly competes against desktop applications in many scenarios, but the graphical demands of some of the applications (e.g. interactive scientific visualization by volume rendering), have impeded their successful settlement in Web scenarios. Performance, scalability, accuracy, security are some of the many challenges that must be solved before visual Web applications popularize. In this publication we discuss both performance and scalability of the volume rendering by WebGL ray-casting in two different but challenging application domains: medical imaging and radar meteorology.


title: Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling

abstract: We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distribution. In contrast to typical analysis, we directly bound the decrease of the primal-dual error (in expectation), without the need to first analyze the dual error. Depending on the choice of the sampling, we obtain efficient serial and mini-batch variants of the method. In the serial case, our bounds match the best known bounds for SDCA (both with uniform and importance sampling). With standard mini-batching, our bounds predict initial data-independent speedup as well as additional data-driven speedup which depends on spectral and sparsity properties of the data.


title: Regression Analysis for ATM Cash Flow Prediction

abstract: One of the most challenging task for a bank is to maintain cash in their ATMs (Automated Teller Machines) so that they can easily serve their customers. To solve this problem, they create a daily estimate for each of their ATM, which can result into “Out of Cash” or “Over Stock” situations. This requires a solution which can resonably predict how much cash inflow would be needed for the next day by examining and learning from past transactional data. We present results for regression techniqes, including using the LSTM model for time-series for the first time to the best of our knowledge, to solve the “Cash Estimation” problem. This would allow banks to adopt to the changing needs of cash according to specific occasions, holidays, etc. The dataset that we would be using is transaction record for past 2.5 years for ATMs situated in a busy district of Karachi, Pakistan. This research will help banks in effectively reducing extra cost error which they bear for maintenance of their cash as well as increasing customer satisfaction.


title: An Evaluation of a Hybrid Occupational Therapy and Supported Employment Program in Japan for Persons With Schizophrenia

abstract: OBJECTIVE. A vocational rehabilitation program (occupational therapy and supported employment) for promoting the return to the community of long-stay persons with schizophrenia was established at a psychiatric hospital in Japan. The purpose of the study was to evaluate the program in terms of hospitalization rates, community tenure, and social functioning with each individual serving as his or her control. METHODS. Fifty-two participants, averaging 8.9 years of hospitalization, participated in the vocational rehabilitation program consisting of 2 to 6 hours of in-hospital occupational therapy for 6 days per week and a postdischarge supported employment component. Seventeen years after the program was established, a retrospective study was conducted to evaluate the impact of the program on hospitalizations, community tenure, and social functioning after participants’ discharge from hospital, using an interrupted time‐series analysis. The postdischarge period was compared with the period from onset of illness to the index discharge on the three outcome variables. RESULTS. After discharge from the hospital, the length of time spent by participants out of the hospital increased, social functioning improved, and risk of hospitalization diminished by 50%. Female participants and those with supportive families spent more time out of the hospital than participants who were male or came from nonsupportive families. CONCLUSION. A combined program of occupational therapy and supported employment was successful in a Japanese psychiatric hospital when implemented with the continuing involvement of a clinical team. Interventions that improve the emotional and housing supports provided to persons with schizophrenia by their families are likely to enhance the outcome of vocational services.


title: A complete translation from SPARQL into efficient SQL

abstract: This paper presents a feature-complete translation from SPARQL, the proposed standard for RDF querying, into efficient SQL. We propose "SQL model"-based algorithms that implement each SPARQL algebra operator via SQL query augmentation, and generate a flat SQL statement for efficient processing by relational database query engines. SPARQL-to-SQL translation presented is feature-complete, since it applies to all SPARQL language features. Finally, we demonstrate the performance and scalability of our method by an extensive evaluation using recent SPARQL benchmark queries, and a benchmark dataset, as well as a real-world photo dataset.


title: RF tracking test system design for closed loop testing of Ku-band antenna

abstract: Ku band antennae are one of the unique features of GSAT11 spacecraft. As the pointing accuracy of the satellite platform is around 0.1°, improved antenna pointing is achieved using the steerable Deployment Pointing Mechanism (DPM), Radio Frequency (RF) Tracking based measurement system and Attitude and Orbit Control Electronics (AOCE) resident controller. Testing of these subsystems in open and closed loop has been carried out for the first time in zero-g environment. This test has been instrumental in evaluating/validating the closed loop performance of all the subsystems along with the characterization of the RF tracking system and its system response. This paper discusses the design approach and development of RF Tracking Test System and improvements carried out therein to enable smooth testing and lessons learnt. Here, AOCE simulator design has been effected for accelerating the product design cycle and in-parallel carrying out the interface testing along with the algorithmic validation and system characterization.


title: User Response Learning for Directly Optimizing Campaign Performance in Display Advertising

abstract: Learning and predicting user responses, such as clicks and conversions, are crucial for many Internet-based businesses including web search, e-commerce, and online advertising. Typically, a user response model is established by optimizing the prediction accuracy, e.g., minimizing the error between the prediction and the ground truth user response. However, in many practical cases, predicting user responses is only part of a rather larger predictive or optimization task, where on one hand, the accuracy of a user response prediction determines the final (expected) utility to be optimized, but on the other hand, its learning may also be influenced from the follow-up stochastic process. It is, thus, of great interest to optimize the entire process as a whole rather than treat them independently or sequentially. In this paper, we take real-time display advertising as an example, where the predicted user's ad click-through rate (CTR) is employed to calculate a bid for an ad impression in the second price auction. We reformulate a common logistic regression CTR model by putting it back into its subsequent bidding context: rather than minimizing the prediction error, the model parameters are learned directly by optimizing campaign profit. The gradient update resulted from our formulations naturally fine-tunes the cases where the market competition is high, leading to a more cost-effective bidding. Our experiments demonstrate that, while maintaining comparable CTR prediction accuracy, our proposed user response learning leads to campaign profit gains as much as 78.2% for offline test and 25.5% for online A/B test over strong baselines.


title: Aspect-Oriented Instrumentation with GCC

abstract: We present the INTERASPECT instrumentation framework for GCC, a widely used compiler infrastructure. The addition of plugin support in the latest release of GCC makes it an attractive platform for runtime instrumentation, as GCC plug-ins can directly add instrumentation by transforming the compiler's intermediate representation. Such transformations, however, require expert knowledge of GCC internals. INTERASPECT addresses this situation by allowing instrumentation plug-ins to be developed using the familiar vocabulary of Aspect-Oriented Programming pointcuts, join points, and advice functions. INTERASPECT also supports powerful customized instrumentation, where specific information about each join point in a pointcut, as well as results of static analysis, can be used to customize the inserted instrumentation. We introduce the INTERASPECT API and present several examples that illustrate how it can be applied to useful runtime verification problems.


title: Maximum torque control for optimal design to reduce cogging torque in spoke type interior permanent magnet synchronous motor

abstract: This paper proposes the optimal design process to effectively reduce the cogging torque considering the torque and efficiency in constant torque region and constant power region by maximum torque control in spoke type interior permanent magnet synchronous motor (IPMSM). For an increase in torque and efficiency in constant power region, the saliency ratio is increased more than initial model. The two points, 2000 rpm (rated speed) in the constant torque region and 4000 rpm (double rated speed) in the constant power region, are chosen for comparing the torque and efficiency with the initial model. The maximum torque control is composed of maximum torque per ampere (MTPA) control in constant torque region and flux weakening (FW) control in constant power region. In the optimal design process, the d- and q- axis inductances at each current angle are extracted by finite element method (FEM) for MTPA and FW control. The FEM is used to calculate the cogging torque. And then, radial basis function (RBF) and genetic algorithm (GA) are used. The results showed an increase in the efficiency and torque compare to the initial model, and also the cogging torque is decreased more than initial model.


title: PQR: Predicting Query Execution Times for Autonomous Workload Management

abstract: Modern enterprise data warehouses have complex workloads that are notoriously difficult to manage. One of the key pieces to managing workloads is an estimate of how long a query will take to execute. An accurate estimate of this query execution time is critical to self managing Enterprise Class Data Warehouses. In this paper we study the problem of predicting the execution time of a query on a loaded data warehouse with a dynamically changing workload. We use a machine learning approach that takes the query plan, combines it with the observed load vector of the system and uses the new vector to predict the execution time of the query. The predictions are made as time ranges. We validate our solution using real databases and real workloads. We show experimentally that our machine learning approach works well. This technology is slated for incorporation into a commercial, enterprise class DBMS.


title: Optimal Selfish Mining Strategies in Bitcoin

abstract: The Bitcoin protocol requires nodes to quickly distribute newly created blocks. Strong nodes can, however, gain higher payoffs by withholding blocks they create and selectively postponing their publication. The existence of such selfish mining attacks was first reported by Eyal and Sirer, who have demonstrated a specific deviation from the standard protocol (a strategy that we name SM1).


title: The LLVM compiler framework and infrastructure tutorial

abstract: The LLVM Compiler Infrastructure (http://llvm.cs. uiuc.edu) is a robust system that is well suited for a wide variety of research and development work. This brief paper introduces the LLVM system and provides pointers to more extensive documentation, complementing the tutorial presented at LCPC.


title: Analyzing the Behavior of Visual Question Answering Models

abstract: Recently, a number of deep-learning based models have been proposed for the task of Visual Question Answering (VQA). The performance of most models is clustered around 60-70%. In this paper we propose systematic methods to analyze the behavior of these models as a first step towards recognizing their strengths and weaknesses, and identifying the most fruitful directions for progress. We analyze two models, one each from two major classes of VQA models -- with-attention and without-attention and show the similarities and differences in the behavior of these models. We also analyze the winning entry of the VQA Challenge 2016. ::: Our behavior analysis reveals that despite recent progress, today's VQA models are "myopic" (tend to fail on sufficiently novel instances), often "jump to conclusions" (converge on a predicted answer after 'listening' to just half the question), and are "stubborn" (do not change their answers across images).


title: Routing and Tracking System for Mobile Vehicles in Large Area

abstract: The paper describes a practical model for routing and tracking with mobile vehicle in a large area outdoor environment based on the Global Positioning System (GPS) and Global System for Mobile Communication (GSM). The supporting devices, GPS module-eMD3620 of AT&S company and GSM modem-GM862 of Telit company, are controlled by a 32bits microcontroller LM3S2965 implemented a new version ARM Cortex M3 core. The system is equipped the Compass sensor-YAS529 of Yamaha company and Accelerator sensor- KXSC72050 of Koinix company to determine moving direction of a vehicle. The device will collect positions of the vehicle via GPS receiver and then sends the data of positions to supervised center by the SMS (Short Message Services) or GPRS (General Package Radio Service) service. The supervised center is composed of a development kit that supports GSM techniques-WMP100 of the Wavecom company. After processing data, the position of the mobile vehicle will be displayed on Google Map.


title: Social Popularity Score: Predicting Numbers of Views, Comments, and Favorites of Social Photos Using Only Annotations

abstract: In this paper, we propose an algorithm to predict the social popularity (i.e., the numbers of views, comments, and favorites) of content on social networking services using only text annotations. Instead of analyzing image/video content, we try to estimate social popularity by a combination of weight vectors obtained from a support vector regression (SVR) and tag frequency. Since our proposed algorithm uses text annotations instead of image/video features, its computational cost is small. As a result, we can estimate social popularity more efficiently than previously proposed methods. Furthermore, tags that significantly affect social popularity can be extracted using our algorithm. Our experiments involved using one million photos on the social networking website Flickr, and the results showed a high correlation between actual social popularity and the determination thereof using our algorithm. Moreover, the proposed algorithm can achieve high classification accuracy with regard to a classification between popular and unpopular content.


title: Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management

abstract: Spyware is a rapidly spreading problem for PC users causing significant impact on system stability and privacy concerns. It attaches to extensibility points in the system to ensure the spyware will be instantiated when the system starts. Users may willingly install free versions of software containing spyware as an alternative to paying for it. Traditional anti-virus techniques are less effective in this scenario because they lack the context to decide if the spyware should be removed.In this paper, we introduce Auto-Start Extensibility Points (ASEPs) as the key concept for modeling the spyware problem. By monitoring and grouping "hooking" operations made to the ASEPs, our Gatekeeper solution complements the traditional signature-based approach and provides a comprehensive framework for spyware management. We present ASEP hooking statistics for 120 real-world spyware programs. We also describe several techniques for discovering new ASEPs to further enhance the effectiveness of our solution.


title: The D* Algorithm for Real-Time Planning of Optimal Traverses.

abstract: Abstract : Finding the lowest-cost path through a graph of states is central to many problems, including route planning for a mobile robot. If arc costs change during the traverse, then the remainder of the path may need to be replanned. Such is the case for a sensor-equipped mobile robot with incomplete or inaccurate information about its environment. As the robot acquires additional information via its sensors, it has the opportunity to revise its plan to reduce the total cost of the traverse. If the prior information is grossly incomplete or inaccurate, the robot may discover useful information in nearly every piece of sensor data. During the replanning process, the robot must either stop and wait for the new path to be computed or continue to move in the wrong direction; therefore, rapid replanning is essential. This paper describes a new algorithm, D*, capable of planning optimal traverses in real-time through focussed state expansion. D* repairs plans quickly by taking advantage of the fact that most arc cost corrections occur in the vicinity of the robot and the path needs only to be replanned out to the robot's current state. D* can be used not only for route planning but for any graph-based cost optimization problem for which arc costs change during the traverse of the solution path.


title: K-band GaN power amplifier design with a harmonic suppression power combiner

abstract: In this paper, a Gallium Nitride (GaN) power amplifier (PA) design implementing a novel power combiner with harmonic suppression is presented. This new combiner recently developed in prior work is denoted as the ‘Wei-Chi’ combiner. The ‘Wei-Chi’ Combiner has an 87% power combining efficiency. The ‘Wei-Chi’ PA produced a measured small-signal peak gain of 9.5 dB. In addition, power measurements showed an output power (Pout) of 2.4W and a peak power added efficiency (PAE) of 42% at 26 GHz.


title: Directions Toward Effective Utilization of Tactile Skin: A Review

abstract: A wide variety of tactile (touch) sensors exist today for robotics and related applications. They make use of various transduction methods, smart materials and engineered structures, complex electronics, and sophisticated data processing. While highly useful in themselves, effective utilization of tactile sensors in robotics applications has been slow to come and largely remains elusive today. This paper surveys the state of the art and the research issues in this area, with the emphasis on effective utilization of tactile sensors in robotic systems. One specific with the use of tactile sensing in robotics is that the sensors have to be spread along the robot body, the way the human skin is-thus dictating varied 3-D spatio-temporal requirements, decentralized and distributed control, and handling of multiple simultaneous tactile contacts. Satisfying these requirements pose challenges to making tactile sensor modality a reality. Overcoming these challenges requires dealing with issues such as sensors placement, electronic/mechanical hardware, methods to access and acquire signals, automatic calibration techniques, and algorithms to process and interpret sensing data in real time. We survey this field from a system perspective, recognizing the fact that the system performance tends to depend on how its various components are put together. It is hoped that the survey will be of use to practitioners designing tactile sensing hardware (whole-body or large-patch sensor coverage), and to researchers working on cognitive robotics involving tactile sensing.


title: Mechanical Programming of Soft Actuators by Varying Fiber Angle

abstract: Abstract In this work we investigate the influence of fiber angle on the deformation of fiber-reinforced soft fluidic actuators and examine the manner in which these actuators extend axially, expand radially and twist about their axis as a function of input pressure. We study the quantitative relationship between fiber angle and actuator deformation by performing finite element simulations for actuators with a range of different fiber angles, and we verify the simulation results by experimentally characterizing the actuators. By combining actuator segments in series, we can achieve combinations of motions tailored to specific tasks. We demonstrate this by using the results of simulations of separate actuators to design a segmented wormlike soft robot capable of propelling itself through a tube and performing an orientation-specific peg insertion task at the end of the tube. Understanding the relationship between fiber angle and pressurization response of these soft fluidic actuators enables rapid explorat...


title: A new rail-to-rail ultra low voltage high speed comparator

abstract: This paper presents a new rail-to-rail input highperformance regenerative comparator suitable for low-voltage low-power applications, e.g. bio-implantable circuits. In this circuit the body of the PMOS transistor is used as comparator's input. A technique is proposed to increase the speed of the circuit. The proposed comparator has a good performance in weak inversion (sub-threshold). Simulations are done in the 0.18-μm CMOS technology with a supply voltage of 0.5 V. The propagation delay time of the second proposed comparator is 16.4 ns, the power dissipation is 20 nW and the power delay product is 0.33 fJ in clock frequency of 5 MHz and input voltage of 500 μV. Also the supply voltage can be decreased to 0.3 V.


title: Creeping Proteins in Microporous Structures: Polymer Brush-Assisted Fabrication of 3D Gradients for Tissue Engineering

abstract: Coupling of rapid prototyping techniques and surface-confined polymerizations allows the fabrication of 3D multidirectional gradients of biomolecules within microporous scaffolds. The compositional gradients can be tailored by polymer-brush-assisted diffusion of protein solutions. This technique allows spatial control over stem cells manipulation within 3D environments


title: Automated Geocoding of Textual Documents: A Survey of Current Approaches

abstract: This survey article describes previous research addressing text-based document geocoding, i.e. the task of predicting the geospatial coordinates of latitude and longitude, that best correspond to an entire document, based on its textual contents. We describe (1) early document geocoding systems that use heuristics over place names mentioned in the text (e.g. names of cities and states), (2) probabilistic language modeling approaches, where generative models are built for different regions in the world (usually considering a discretization based on a rectangular grid) from the words occurring in a set of georeferenced training documents, which are then used to predict per-region probabilities for previously unseen test documents, (3) combinations of different models and heuristics, including clustering procedures, feature selection approaches, and/or language models built from different sources, and (4) recent approaches based on discriminative classification models.


title: Case-based Reasoning for Invoice Analysis and Recognition

abstract: This paper introduces the approach CBRDIA (Case-based Reasoning for Document Invoice Analysis) which uses the principles of case-based reasoning to analyze, recognize and interpret invoices. Two CBR cycles are performed sequentially in CBRDIA. The first one consists in checking whether a similar document has already been processed, which makes the interpretation of the current one easy. The second cycle works if the first one fails. It processes the document by analyzing and interpreting its structuring elements (adresses, amounts, tables, etc) one by one. The CBR cycles allow processing documents from both knonwn or unknown classes. Applied on 923 invoices, CBRDIA reaches a recognition rate of 85,22% for documents of known classes and 74,90% for documents of unknown classes.


title: A dynamic multi-level collaborative filtering method for improved recommendations

abstract: One of the most used approaches for providing recommendations in various online environments such as e-commerce is collaborative filtering. Although, this is a simple method for recommending items or services, accuracy and quality problems still exist. Thus, we propose a dynamic multi-level collaborative filtering method that improves the quality of the recommendations. The proposed method is based on positive and negative adjustments and can be used in different domains that utilize collaborative filtering to increase the quality of the user experience. Furthermore, the effectiveness of the proposed method is shown by providing an extensive experimental evaluation based on three real datasets and by comparisons to alternative methods. We propose a method that improves collaborative filtering recommendations.It may use either a static or a dynamic multi-level approach.It is based on positive and negative adjustments of the users similarity values.Both approaches have been experimentally evaluated using three real datasets.Our approaches produce results of better quality when compared to alternatives.


title: Effectiveness of Botulinum Toxin (Type-A) Administered by the Fixed-Site Dosing Approach Versus the Muscle Area Identification

abstract: Introduction ::: Botulinum toxin is widely used in glabellar musculature. The authors express the need to individualize the approach by means of muscular identification to improve effectiveness. Despite these guidelines, the fixed-point technique is still used.


title: Planning For 5G: A Problem Structuring Approach for Survival in the Telecoms Industry

abstract: This paper examines the application of systemic problem structuring methods to the development of a research strategy in response to the challenges of fifth generation 5G. The paper proposes a methodology for strategic decision making. The key stakeholders, objectives, technologies, and boundaries from existing literature are identified and problem structuring based on hierarchical process modeling is used to explore the dependency of certain features of 5G on specific technologies, giving an indication of the importance of certain technologies over others and thus insight into where to place research effort. The hard technical challenges of 5G are discussed and equally the importance of the soft social and business challenges explored. For context, we explain how 5G will provide a platform for innovations and discuss how new and existing businesses may use this to their advantage. Problem structuring is used to explore how the challenges and opportunities of future wireless systems are related to the process of developing new business models.


title: Analog inference circuits for deep learning

abstract: Deep Machine Learning (DML) algorithms have proven to be highly successful at challenging, high-dimensional learning problems, but their widespread deployment is limited by their heavy computational requirements and the associated power consumption. Analog computational circuits offer the potential for large improvements in power efficiency, but noise, mismatch, and other effects cause deviations from ideal computations. In this paper we describe circuits useful for DML algorithms, including a tunable-width bump circuit and a configurable distance calculator. We also discuss the impacts of computational errors on learning performance. Finally we will describe a complete deep learning engine implemented using current-mode analog circuits and compare its performance to digital equivalents.


title: DWT and LPC based feature extraction methods for isolated word recognition

abstract: In this article, new feature extraction methods, which utilize wavelet decomposition and reduced order linear predictive coding (LPC) coefficients, have been proposed for speech recognition. The coefficients have been derived from the speech frames decomposed using discrete wavelet transform. LPC coefficients derived from subband decomposition (abbreviated as WLPC) of speech frame provide better representation than modeling the frame directly. The WLPC coefficients have been further normalized in cepstrum domain to get new set of features denoted as wavelet subband cepstral mean normalized features. The proposed approaches provide effective (better recognition rate), efficient (reduced feature vector dimension), and noise robust features. The performance of these techniques have been evaluated on the TI-46 isolated word database and own created Marathi digits database in a white noise environment using the continuous density hidden Markov model. The experimental results also show the superiority of the proposed techniques over the conventional methods like linear predictive cepstral coefficients, Mel-frequency cepstral coefficients, spectral subtraction, and cepstral mean normalization in presence of additive white Gaussian noise.


title: Forgetting techniques for stream-based matrix factorization in recommender systems

abstract: Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users’ preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users’ preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information significantly improves predictive power of recommender systems.


title: Data sets and data quality in software engineering

abstract: OBJECTIVE - to assess the extent and types of techniques used to manage quality within software engineering data sets. We consider this a particularly interesting question in the context of initiatives to promote sharing and secondary analysis of data sets. METHOD - we perform a systematic review of available empirical software engineering studies. RESULTS - only 23 out of the many hundreds of studies assessed, explicitly considered data quality. CONCLUSIONS - first, the community needs to consider the quality and appropriateness of the data set being utilised; not all data sets are equal. Second, we need more research into means of identifying, and ideally repairing, noisy cases. Third, it should become routine to use sensitivity analysis to assess conclusion stability with respect to the assumptions that must be made concerning noise levels.


title: Manipulation of Nanson's and Baldwin's Rules

abstract: Nanson's and Baldwin's voting rules select a winner by successively eliminating candidates with low Borda scores. We show that these rules have a number of desirable computational properties. In particular, with unweighted votes, it is NP-hard to manipulate either rule with one manipulator, whilst with weighted votes, it is NP-hard to manipulate either rule with a small number of candidates and a coalition of manipulators. As only a couple of other voting rules are known to be NP-hard to manipulate with a single manipulator, Nan-son's and Baldwin's rules appear to be particularly resistant to manipulation from a theoretical perspective. We also propose a number of approximation methods for manipulating these two rules. Experiments demonstrate that both rules are often difficult to manipulate in practice. These results suggest that elimination style voting rules deserve further study.


Modeling Communicative Purpose with Functional Style: Corpus and Features for German Genre and Register Analysis


title: Intelligent Personal Assistants Based on Internet of Things Approaches

abstract: The Internet has emerged as a key network to make information accessible quickly and easily, revolutionizing how people communicate and interact with the world. The information available on the Internet about a given subject may be extensive, allowing the development of new solutions to solve people's day-to-day problems. One such solution is the proposal of intelligent personal assistants (IPAs), which are software agents that can assist people in many of their daily activities. IPAs are capable of accessing information from databases to guide people through different tasks, deploying a learning mechanism to acquire new information on user performance. IPAs can improve the assistance they offer to users by collecting information autonomously from objects that are available in the surrounding environment. To make this idea feasible, IPAs could be integrated into ubiquitous computing environments in an Internet of Things (IoT) context. Therefore, it is necessary to integrate wireless sensor networks with the Internet properly, considering many different factors, such as the heterogeneity of objects and the diversity of communication protocols and enabling technologies. This approach fulfills the IoT vision. This paper surveys the current state of the art of IoT protocols, IPAs in general, and IPAs based on IoTs.


Applying Software Patterns to Address Interoperability Challenges in Blockchain-based Healthcare Apps


title: Spectral analysis of full field digital mammography data

abstract: The spectral content of mammograms acquired from using a full field digital mammography (FFDM) system are analyzed. Fourier methods are used to show that the FFDM image power spectra obey an inverse power law; in an average sense, the images may be considered as 1/f fields. Two data representations are analyzed and compared (1) the raw data, and (2) the logarithm of the raw data. Two methods are employed to analyze the power spectra (1) a technique based on integrating the Fourier plane with octave ring sectioning developed previously, and (2) an approach based on integrating the Fourier plane using rings of constant width developed for this work. Both methods allow theoretical modeling. Numerical analysis indicates that the effects due to the transformation influence the power spectra measurements in a statistically significant manner in the high frequency range. However, this effect has little influence on the inverse power law estimation for a given image regardless of the data representation or the theoretical analysis approach. The analysis is presented from two points of view (1) each image is treated independently with the results presented as distributions, and (2) for a given representation, the entire image collection is treated as an ensemble with the results presented as expected values. In general, the constant ring width analysis forms the foundation for a spectral comparison method for finding spectral differences, from an image distribution sense, after applying a nonlinear transformation to the data. The work also shows that power law estimation may be influenced due to the presence of noise in the higher frequency range, which is consistent with the known attributes of the detector efficiency. The spectral modeling and inverse power law determinations obtained here are in agreement with that obtained from the analysis of digitized film–screen images presented previously. The form of the power spectrum for a given image is approximately 1/f 2β with β∼1.4–1.5.


title: Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

abstract: We propose an approach to reverberant speech recognition adopting deep learning in the front-end as well as b a c k-e n d o f a r e v e r b e r a n t s p e e c h r e c o g n i t i o n s y s t e m, a n d a n o v e l m e t h o d t o i m p r o v e t h e d e r e v e r b e r a t i o n p e r f o r m a n c e of the front-end network using phone-class information. At the front-end, we adopt a deep autoencoder (DAE) for enhancing the speech feature parameters, and speech recognition is performed in the back-end using DNN-HMM acoustic models trained on multi-condition data. The system was evaluated through the ASR task in the Reverb Challenge 2014. The DNN-HMM system trained on the multi-condition training set achieved a conspicuously higher word accuracy compared to the MLLR-adapted GMM-HMM system trained on the same data. Furthermore, feature enhancement with the deep autoencoder contributed to the improvement of recognition accuracy especially in the more adverse conditions. While the mapping between reverberant and clean speech in DAE-based dereverberation is conventionally conducted only with the acoustic information, we presume the mapping is also dependent on the phone information. Therefore, we propose a new scheme (pDAE), which augments a phone-class feature to the standard acoustic features as input. Two types of the phone-class feature are investigated. One is the hard recognition result of monophones, and the other is a soft representation derived from the posterior outputs of monophone DNN. The augmented feature in either type results in a significant improvement (7–8 % relative) from the standard DAE.


title: A Comparison Between Photogrammetry and Laser Scanning

abstract: A comparison between data acquisition and processing from passive optical sensors and airborne laser scanning is presented. A short overview and the major differences between the two technologies are outlined. Advantages and disadvantages with respect to various aspects are discussed, like sensors, platforms, flight planning, data acquisition conditions, imaging, object reflectance, automation, accuracy, flexibility and maturity, production time and costs. A more detailed comparison is presented with respect to DTM and DSM generation. Strengths of laser scanning with respect to certain applications are outlined. Although airborne laser scanning competes to a certain extent with photogrammetry and will replace it in certain cases, the two technologies are fairly complementary and their integration can lead to more accurate and complete products, and open up new areas of application.


Artificial Intelligence based Battery Power Management for Solar PV And Wind Hybrid Power System


title: Dynamic eye convergence for head-mounted displays improves user performance in virtual environments

abstract: In Virtual Environments (VE), users are often facing tasks that involve direct manipulation of virtual objects at close distances, such as touching, grabbing, placement. In immersive systems that employ head-mounted displays these tasks could be quite challenging, due to lack of convergence of virtual cameras. We present a mechanism that dynamically converges left and right cameras on target objects in VE. This mechanism simulates the natural process that takes place in real life automatically. As a result, the rendering system maintains optimal conditions for stereoscopic viewing of target objects at varying depths, in real time. Building on our previous work, which introduced the eye convergence algorithm [Sherstyuk and State 2010], we developed a Virtual Reality (VR) system and conducted an experimental study on effects of eye convergence in immersive VE. This paper gives the full description of the system, the study design and a detailed analysis of the results obtained.


title: GelJ – a tool for analyzing DNA fingerprint gel images

abstract: DNA fingerprinting is a technique for comparing DNA patterns that has applications in a wide variety of contexts. Several commercial and freely-available tools can be used to analyze DNA fingerprint gel images; however, commercial tools are expensive and usually difficult to use; and, free tools support the basic functionality for DNA fingerprint analysis, but lack some instrumental features to obtain accurate results. In this paper, we present GelJ, a feather-weight, user-friendly, platform-independent, open-source and free tool for analyzing DNA fingerprint gel images. Some of the outstanding features of GelJ are mechanisms for accurate lane- and band-detection, several options for computing migration models, a number of band- and curve-based similarity methods, different techniques for generating dendrograms, comparison of banding patterns from different experiments, and database support. GelJ is an easy to use tool for analyzing DNA fingerprint gel images. It combines the best characteristics of both free and commercial tools: GelJ is light and simple to use (as free programs), but it also includes the necessary features to obtain precise results (as commercial programs). In addition, GelJ incorporates new functionality that is not supported by any other tool.


title: A multi-threaded PIPELINED Web server architecture for SMP/SoC machines

abstract: Design of high performance Web servers has become a recent research thrust to meet the increasing demand of network-based services. In this paper, we propose a new Web server architecture, called multi-threaded PIPELINED Web server, suitable for Symmetric Multi-Processor (SMP) or System-on-Chip (SoC) architectures. The proposed PIPELINED model consists of multiple thread pools, where each thread pool consists of five basic threads and two helper threads. The main advantages of the proposed model are global information sharing by the threads, minimal synchronization overhead due to less number of threads, and non-blocking I/O operations, possible with the helper threads.We have conducted an in-depth performance analysis of the proposed server model along with four prior Web server models (Multi-Process (MP), Multi-Thread (MT), Single-Process Event-Driven (SPED) and Asynchronous Multi-Process Event-Driven (AMPED)) via simulation using six Web server workloads. The experiments are conducted to investigate the impact of various factors such as the memory size, disk speed and numbers of clients. The simulation results indicate that the proposed PIPELINED Web server architecture shows the best performance across all system and workload parameters compared to the MP, MT, SPED and AMPED models. Although the MT and AMPED models show competitive performance with less number of processors, the advantage of the PIPELINED model becomes obvious as the number of processors or clients in an SMP/SoC machine increases. The MP model shows the worst performance in most of the cases. The results indicate that the proposed server architecture can be used in future large-scale SMP/SoC machines to boost system performance.


title: Vietnamese spelling detection and correction using Bi-gram, Minimum Edit Distance, SoundEx algorithms with some additional heuristics

abstract: The spelling checking problem is considered to contain two main phases: the detecting phase and the correcting phase. In this paper, we present a new approach for Vietnamese spelling checking based on Vietnamese characteristics for each phase. Our research approach includes the use of a syllable Bi-gram in combination with parts of speech (POS) to find out suspected syllables. In the correcting phase, we based on minimum edit distance, SoundEx algorithms and some heuristics to build a weight function for assessing suggestion candidates. The training corpus and the test set were collected from e-newspapers.


title: Blood flow fluctuations in cerebral cortex microvessels.

abstract: Studies on the dynamics of the blood flow in cerebral microvessels with the diameter of erythrocyte flow (5-13 micron) carried out by the cinema-TV method showed that the linear velocity of the blood flow in microvessels experienced considerable fluctuations the amplitude of which varied within 10 to 1500 micron/s and the period within 0.2 to 7 s. It was established that variations in linear velocity of the blood flow were most frequently observed during 0.5-1 s. The autocorrelation analysis showed there was periodicity in the blood flow fluctuations in most microvessels which varied within a wide range of 0.3 to 3 s. A linear dependence was found between the amplitude and the period of fluctuations. The observed variations in the blood flow are likely to be caused by the changes in local resistance when leukocytes and erythrocyte aggregates passed through microvessels.


title: A New Multilevel Converter Topology With Reduced Number of Power Electronic Components

abstract: In this paper, a new topology for cascaded multilevel converter based on submultilevel converter units and full-bridge converters is proposed. The proposed topology significantly reduces the number of dc voltage sources, switches, IGBTs, and power diodes as the number of output voltage levels increases. Also, an algorithm to determine dc voltage sources magnitudes is proposed. To synthesize maximum levels at the output voltage, the proposed topology is optimized for various objectives, such as the minimization of the number of switches, gate driver circuits and capacitors, and blocking voltage on switches. The analytical analyses of the power losses of the proposed converter are also presented. The operation and performance of the proposed multilevel converter have been evaluated with the experimental results of a single-phase 125-level prototype converter.


title: Radio frequency identification (RFID)

abstract: First conceived in 1948, Radio Frequency Identification (RFID) has taken many years for the technology to mature to the point where it is sufficiently affordable and reliable for widespread use. From Electronic Article Surveillance (EAS) for article (mainly clothing) security to more sophisticated uses, RFID is seen by some as the inevitable replacement for bar codes. With increasing use comes increasing concern on privacy and security. Clearly there is considerable work to be undertaken before RFID becomes as pervasive as bar codes although the tempo of change is increasing rapidly. ing rapidly.


title: LC-RNN: A Deep Learning Model for Traffic Speed Prediction

abstract: Traffic speed prediction is known as an important but challenging problem. In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions. It takes advantage of both RNN and CNN models by a rational integration of them, so as to learn more meaningful time-series patterns that can adapt to the traffic dynamics of surrounding areas. Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms seven well-known existing methods.


title: Deep Content-User Embedding Model for Music Recommendation

abstract: Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborative filtering and content-based filtering modules are independently trained. The end-to-end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet. In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content. We evaluate the model on music recommendation and music auto-tagging tasks. The results show that the proposed model significantly outperforms the previous work. We also discuss various directions to improve the proposed model further.


title: Hindi POS Tagger Using Naive Stemming : Harnessing Morphological Information Without Extensive Linguistic Knowledge

abstract: Part of Speech tagging for Indian Languages in general and Hindi in particular is not a very widely explored territory. There have been many attempts at developing a good POS tagger for Hindi, but the morphological complexity of the language makes it a hard nut to crack. Some of the best taggers available for Indian Languages employ hybrids of machine learning or stochastic methods and linguistic knowledge. Though, the results achieved using such methods are good, there practicability for other inflective Indian Languages is reduced due to their heavy dependence on linguistic knowledge. Even though taggers can achieve very good results if provided good morphological information, the cost of creating these resources renders such methods impractical. In this paper, we present a simple HMM based POS tagger, which employs a naive(longest suffix matching) stemmer as a pre-processor to achieve reasonably good accuracy of 93.12%. This method does not require any linguistic resource apart from a list of possible suffixes for the language. This list can be easily created using existing machine learning techniques. The aim of this method is to demonstrate that even without employing tools like morphological analyzer or resources like a pre-compiled structured lexicon, it is possible to harness the morphological richness of Indian Languages.


title: n-Valued Refined Neutrosophic Logic and Its Applications to Physics

abstract: In this paper we present a short history of logics: from particular cases of 2-symbol or numerical valued logic to the general case of n-symbol or numerical valued logic. We show generalizations of 2-valued Boolean logic to fuzzy logic, also from the Kleene and Lukasiewicz 3-symbol valued logics or Belnap 4-symbol valued logic to the most general n-symbol or numerical valued refined neutrosophic logic. Two classes of neutrosophic norm (n-norm) and neutrosophic conorm (n-conorm) are defined. Examples of applications of neutrosophic logic to physics are listed in the last section. Similar generalizations can be done for n-Valued Refined Neutrosophic Set, and respectively n- Valued Refined Neutrosopjhic Probability.


title: ValueCharts: Analyzing Linear Models Expressing Preferences and Evaluations

abstract: In this paper we propose ValueCharts, a set of visualizations and interactive techniques intended to support decision-makers in inspecting linear models of preferences and evaluation. Linear models are popular decision-making tools for individuals, groups and organizations. In Decision Analysis, they help the decision-maker analyze preferential choices under conflicting objectives. In Economics and the Social Sciences, similar models are devised to rank entities according to an evaluative index of interest. The fundamental goal of building models expressing preferences and evaluations is to help the decision-maker organize all the information relevant to a decision into a structure that can be effectively analyzed. However, as models and their domain of application grow in complexity, model analysis can become a very challenging task. We claim that ValueCharts will make the inspection and application of these models more natural and effective. We support our claim by showing how ValueCharts effectively enable a set of basic tasks that we argue are at the core of analyzing and understanding linear models of preferences and evaluation.


title: A Compact Design of High-Power Spurious-Free Low-Pass Waveguide Filter

abstract: In this letter, a new technique is proposed for the design of a compact high-power low-pass rectangular waveguide filter with a wide spurious-free frequency behavior. Specifically, the new filter is intended for the suppression of the fundamental mode over a wide band in much higher power applications than the classical corrugated filter with the same frequency specifications. Moreover, the filter length is dramatically reduced when compared to alternative techniques previously considered.


A New Surgical Correction of Cryptotia: A New Flap Design and Switched Double Banner Flap


Ultra-Lightweight Cryptography for Low-Cost RFID Tags: Hummingbird Algorithm and Protocol


Participation of women in business enterprises and its implications on gender: a case of banana traders in Mbeya


title: Influencing factors of adolescent internet addiction

abstract: Addiction is one of major social issues in modern life. With the rapid popularization and the widespread use of the Internet, Internet addiction, as a new form of social addiction, aroused increasing attention from society. This paper attempts to build an analytical framework with the independent variables of adolescents' individual characteristics, differences in the real society and the degree of Internet using, and the dependent variables of the determination and tendency of the addiction to analyze and card the factors influencing adolescent Internet addiction from the structure-agent perspective, and based on that, it proposes basic countermeasures on the social control of adolescent Internet addiction.


title: A neutrosophic approach to image segmentation based on watershed method

abstract: Neutrosophy studies the origin, nature, scope of neutralities, and their interactions with different ideational spectra. It is a new philosophy to extend the fuzzy logic and is the basis of neutrosophic logic, neutrosophic probability, neutrosophic set, and neutrosophic statistics. Image segmentation is a key step for image processing, pattern recognition, computer vision. Many existing methods for image description, classification, and recognition highly depend on the segmentation results. In this paper, neutrosophy is applied to image processing by defining a neutrosophic domain, which is described by three subsets T, I, and F. Then we employ watershed algorithm to perform segmentation of the image in the neutrosophic domain. The experiments show that the proposed method can get better results comparing with that obtained by the existing methods.


The WebNLG Challenge: Generating Text from DBPedia Data


title: Predictive Haptic Feedback for Safe Lateral Control of Teleoperated Road Vehicles in Urban Areas

abstract: Lateral control of teleoperated vehicles is a significant challenge for human operators while driving within dense urban scenarios. This paper presents the general system design of a novel haptic assistance system for safe lateral control of these vehicles. The proposed system is based on a direct control structure to keep the human operator as the main decision maker in human- machine cooperating steering tasks. The haptic system uses a two-stage prediction on a LiDAR occupancy grid. The first prediction stage compensates the communication time delay with which the actual states of the teleoperated vehicle as well as other dynamic objects in the environment are predicted. For the second prediction stage, an extension of the look-ahead approach is proposed, which predicts side collisions and generates a smooth haptic feedback at the steering wheel of the operator workstation. The resulted haptic assistance system supports the human operator in a generic way at all challenging scenarios without the need for road information. The results of the test drives conducted by experienced operators show the capability of the developed system to assist the human operator at preventing side collisions as well as stabilizing the lateral control of teleoperated vehicles.


title: Robot-centric Activity Recognition from First-Person RGB-D Videos

abstract: We present a framework and algorithm to analyze first person RGBD videos captured from the robot while physically interacting with humans. Specifically, we explore reactions and interactions of persons facing a mobile robot from a robot centric view. This new perspective offers social awareness to the robots, enabling interesting applications. As far as we know, there is no public 3D dataset for this problem. Therefore, we record two multi-modal first-person RGBD datasets that reflect the setting we are analyzing. We use a humanoid and a non-humanoid robot equipped with a Kinect. Notably, the videos contain a high percentage of ego-motion due to the robot self-exploration as well as its reactions to the persons' interactions. We show that separating the descriptors extracted from ego-motion and independent motion areas, and using them both, allows us to achieve superior recognition results. Experiments show that our algorithm recognizes the activities effectively and outperforms other state-of-the-art methods on related tasks.


title: Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction

abstract: Agricultural research has been profited by technical advances such as automation, data mining. Today ,data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively a young research field. The large amounts of data that are nowadays virtually harvested along with the crops have to be analyzed and should be used to their full extent. This research aims at analysis of soil dataset using data mining techniques. It focuses on classification of soil using various algorithms available. Another important purpose is to predict untested attributes using regression technique, and implementation of automated soil sample classification.


title: Enabling Smart Data: Noise filtering in Big Data classification

abstract: In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem.


title: Mammalian orthoreovirus infection is enhanced in cells pre-treated with sodium arsenite

abstract: Following reovirus infection, cells activate stress responses that repress canonical cellular translation as a mechanism to limit production of progeny virions. This includes the formation of stress granules (SG) that sequester translationally-stalled cellular transcripts, translation initiation factors, ribosomal proteins, and RNA binding proteins until conditions improve and translation can resume. Work by others suggests that these cellular stress responses, which are part of the integrated stress response, may benefit rather than repress reovirus replication. In agreement with this, we report that stressing cells prior to infection with sodium arsenite (SA), a robust inducer of SG and activator of eIF2α kinases, enhanced viral protein expression, percent infectivity and viral titer in SA-treated cells compared to untreated cells. SA-mediated enhancement of reovirus replication was not strain-specific, but was cell-type specific. While pre-treatment of cells with SA offered the greatest enhancement, treatment of infected cultures as late as 4 h post infection resulted in an increase in the percent of cells infected. SA activates the HRI kinase, which phosphorylates eIF2α and subsequently induces SG formation. Other stresses, such as heat shock (HS) and osmotic shock also activate HRI. Heat shock of cells prior to reovirus infection readily induced SG in greater than 85% of cells. Although HS pre-treatment had no effect on the percentage of infected cells or viral yield, it did enhance viral protein expression. These data suggest that SA pre-treatment perturbs the cell in a way that is beneficial for reovirus and that neither HRI activation nor SG induction is sufficient for reovirus infection enhancement. SIGNIFICANCE All viruses rely on the host translational machinery for the synthesis of viral proteins. In response to viral infection, cells activate the integrated stress response resulting in the phosphorylation of eIF2α and translation shutoff. Despite this, reovirus replicates to reduced titers in the absence of this response. In this work, we report that sodium arsenite activation of the integrated stress response prior to virus inoculation enhances virus infectivity, protein expression and titer. Together, these data suggest that modulation of conserved cellular stress responses can alter reovirus replication.


title: Yoga for hypertension: a systematic review of randomized clinical trials.

abstract: OBJECTIVES ::: To critically evaluate the effectiveness of yoga as a treatment of hypertension. ::: ::: ::: METHODS ::: Seventeen databases were searched from their inceptions to January 2014. Randomized clinical trials (RCTs) were included, if they evaluated yoga against any type of control in patients with any form of arterial hypertension. Risk of bias was estimated using the Cochrane criteria. Three independent reviewers performed the selection of studies, data extraction, and quality assessments. ::: ::: ::: RESULTS ::: Seventeen trials met the inclusion criteria. Only two RCTs were of acceptable methodological quality. Eleven RCTs suggested that yoga leads to a significantly greater reduction in systolic blood pressure (SBP) compared to various forms of pharmacotherapy, breath awareness or reading, health education, no treatment (NT), or usual care (UC). Eight RCTs suggested that yoga leads to a significantly greater reduction in diastolic blood pressure (DBP) or night-time DBP compared to pharmacotherapy, NT, or UC. Five RCTs indicated that yoga had no effect on SBP compared to dietary modification (DIM), enhanced UC, passive relaxation (PR), or physical exercises (PE). Eight RCTs indicated that yoga had no effect on DBP compared to DIM, enhanced UC, pharmacotherapy, NT, PE, PR, or breath awareness or reading. One RCT did not report between-group comparisons. ::: ::: ::: CONCLUSION ::: The evidence for the effectiveness of yoga as a treatment of hypertension is encouraging but inconclusive. Further, more rigorous trials seem warranted.


title: Graph Models for Knowledge Representation and Reasoning for Contemporary and Emerging Needs – A survey

abstract: Reasoning is the fundamental capability which requires knowledge. Various graph models have proven to be very valuable in knowledge representation and reasoning. Recently, explosive data generation and accumulation capabilities have paved way for Big Data and Data Intensive Systems. Knowledge Representation and Reasoning with large and growing data is extremely challenging but crucial for businesses to predict trends and support decision making. Any contemporary, reasonably complex knowledge based system will have to consider this onslaught of data, to use appropriate and sufficient reasoning for semantic processing of information by machines. This paper surveys graph based knowledge representation and reasoning, various graph models such as Conceptual Graphs, Concept Graphs, Semantic Networks, Inference Graphs and Causal Bayesian Networks used for representation and reasoning, common and recent research uses of these graph models, typically in Big Data environment, and the near future needs and challenges for graph based KRR in computing systems. Observations are presented in a table, highlighting suitability of the surveyed graph models for contemporary scenarios.


title: A Review on the Performance of Object Detection Algorithm

abstract: This paper represents multiple Object recognitions technology in the field of computer vision for finding and identifying multiple objects in a image or video sequence.It is basically highlight only those objects which are needed. Algorithmic descriptions of recognition task are implemented on machines which is an complex job. Thus multiple object recognition techniques need to be developed which are less intricate and well-organized The multiple object detection is a very important application of image processing. the literature survey has shown that the most of existing methods have ignored the poor quality images like image with noise or poor intensity . Noise means those signals which are not required .Also the most of the existing work for multiple object detection has neglected the use of hybrid technique. The overall goal of this research work is to propose an efficient and simple multiple object detection using the fuzzy and transition region based image segmentation


title: 3D PRINTING OF ANISOTROPIC METAMATERIALS

abstract: Material properties in radio frequency and microwave regimes are limited due to the lack of molecular resonances at these frequencies. Metamaterials are an attractive means to realize a prescribed permittivity or permeability function, but these are often prohibitively lossy due to the use of ine-cient metallic resonators. All-dielectric metamaterials ofier excellent potential to overcome these losses, but they provide a much weaker interaction with an applied wave. Much design freedom can be realized from all-dielectric structures if their dispersion and anisotropy are cleverly engineered. This, however, leads to structures with very complex geometries that cannot be manufactured by conventional techniques. In this work, artiflcially anisotropic metamaterials are designed and then manufactured by 3D printing. The efiective material properties are measured in the lab and agree well with model predictions.


title: A survey of image-based Arabic sign language recognition

abstract: Sign language is the native language of deaf and hearing impaired people which they prefer to use on their daily life. Few interpreters are available to facilitate communication between deaf and vocal people. However, this is neither practical nor possible for all situations. Advances in information technology encouraged the development of systems that can facilitate the automatic translation between sign language and spoken language, and thus removing barriers facing the integration of deaf people in the society. A lot of research has been carried on the development of systems that translate sign languages into spoken words and the reverse. However, only recently systems translating between Arabic sign language and spoken language have been developed. Many signs of the Arabic sign language are reflection of the environment (White color in Arabic sign language is a finger pointing to the chest of the signer as the tradition for male is to wear white color dress). Several review papers have been published on the automatic recognition of other sign languages. This paper represents the first attempt to review systems and methods for the image based automatic recognition of the Arabic sign language. It reviews most published papers and discusses a variety of recognition methods. Additionally, the paper highlights the main challenges characterizing the Arabic sign language as well as potential future research directions in this area.


title: A Powerful Genetic Algorithm for Traveling Salesman Problem

abstract: This paper presents a powerful genetic algorithm(GA) to solve the traveling salesman problem (TSP). To construct a powerful GA, I use edge swapping(ES) with a local search procedure to determine good combinations of building blocks of parent solutions for generating even better offspring solutions. Experimental results on well studied TSP benchmarks demonstrate that the proposed GA is competitive in finding very high quality solutions on instances with up to 16,862 cities.


title: RDF in the clouds: a survey

abstract: The Resource Description Framework (RDF) pioneered by the W3C is increasingly being adopted to model data in a variety of scenarios, in particular data to be published or exchanged on the Web. Managing large volumes of RDF data is challenging, due to the sheer size, the heterogeneity, and the further complexity brought by RDF reasoning. To tackle the size challenge, distributed storage architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications for the scalability, fault-tolerance, and elasticity feature it provides, enabling the easy deployment of distributed and parallel architectures. In this article, we survey RDF data management architectures and systems designed for a cloud environment, and more generally, those large-scale RDF data management systems that can be easily deployed therein. We first give the necessary background, then describe the existing systems and proposals in this area, and classify them according to dimensions related to their capabilities and implementation techniques. The survey ends with a discussion of open problems and perspectives.


Design and Implementation of Astable Multivibrator for Different Applications in Communication System Rajender Kumar