title: Idiopathic senile gait disorders are signs of subclinical disease.

abstract: OBJECTIVES ::: To evaluate survival and causes of death in subjects with idiopathic senile gait disorders. ::: ::: ::: DESIGN ::: A population-based longitudinal study. ::: ::: ::: SETTING ::: Survival analysis of the oldest old within the Leiden 85-plus Study. ::: ::: ::: PARTICIPANTS ::: We distinguished three different groups according to their gait: subjects with a normal gait (n = 25), subjects with senile gait disorders (n = 14), and subjects with gait disorders due to known disease (n = 87). The mean age was 90 years in all groups (range 87 to 97 years). ::: ::: ::: MEASUREMENTS ::: The risk of all cause mortality and cardiovascular mortality was estimated over 5 years of follow-up in a Cox-proportional hazards model, adjusted for age and sex. ::: ::: ::: RESULTS ::: Eighty-nine of 126 subjects died during follow-up. Mean survival differed among the three groups (P log-rank = .01). All cause mortality risk was increased in subjects with senile gait disorders compared with subjects with a normal gait (RR = 2.8; 95% CI, 1.1-7.3, P = .03) and was similar to subjects with gait disorders caused by known disease (RR = 1.2; 95% CI: .6-2.5, P = .6). Mortality caused by cardiovascular disease also differed among the three groups (P log-rank = .03). The risk of cardiovascular death in subjects with senile gait disorders was twofold greater than in subjects with a normal gait (RR = 2.1; 95% CI, 0.4-10.3). ::: ::: ::: CONCLUSIONS ::: Senile gait disorders are related to subclinical, perhaps cardiovascular, disease. Senile gait disorders should not be accepted as an inevitable, benign concomitant of the normal aging process.


title: Active materials for adaptive architectural envelopes based on plant adaptation principles

abstract: In this paper, the authors present research into adaptive architectural envelopes that adapt to environmental changes using active materials, as a result of application of biomimetic principles from plants to architecture. Buildings use large amounts of energy in order to maintain their internal comfort, because conventional buildings are designed to provide a static design solution. Most of the current solutions for facades are not designed for optimum adaptation to contextual issues and needs, while biological solutions to adaptation are often complex, multi-functional and highly responsive. We focus on plant adaptations to the environment, as, due to their immobility, they have developed special means of protection against weather changing conditions. Furthermore, recent developments in new technologies are allowing the possibility to transfer these plant adaptation strategies to technical implementation. These technologies include: multi-material 3D printing, advances in materials science and new capabilities in simulation software. Unlike traditional mechanical activation used for dynamic systems in kinetic facades, adaptive architectural envelopes require no complex electronics, sensors, or actuators. The paper proposes a research of the relationship that can be developed between active materials and environmental issues in order to propose innovative and low-tech design strategies to achieve living envelopes according to plant adaptation principles.


title: Human Action Recognition across Datasets by Foreground-Weighted Histogram Decomposition

abstract: This paper attempts to address the problem of recognizing human actions while training and testing on distinct datasets, when test videos are neither labeled nor available during training. In this scenario, learning of a joint vocabulary, or domain transfer techniques are not applicable. We first explore reasons for poor classifier performance when tested on novel datasets, and quantify the effect of scene backgrounds on action representations and recognition. Using only the background features and partitioning of gist feature space, we show that the background scenes in recent datasets are quite discriminative and can be used classify an action with reasonable accuracy. We then propose a new process to obtain a measure of confidence in each pixel of the video being a foreground region, using motion, appearance, and saliency together in a 3D MRF based framework. We also propose multiple ways to exploit the foreground confidence: to improve bag-of-words vocabulary, histogram representation of a video, and a novel histogram decomposition based representation and kernel. We used these foreground confidences to recognize actions trained on one data set and test on a different data set. We have performed extensive experiments on several datasets that improve cross dataset recognition accuracy as compared to baseline methods.


title: Depressive realism: A meta-analytic review

abstract: Abstract The current investigation represents the first meta-analysis of the depressive realism literature. A search of this literature revealed 75 relevant studies representing 7305 participants from across the US and Canada, as well as from England, Spain, and Israel. Results generally indicated a small overall depressive realism effect (Cohen's d = −.07). Overall, however, both dysphoric/depressed individuals ( d = .14) and nondysphoric/nondepressed individuals evidenced a substantial positive bias ( d = .29), with this bias being larger in nondysphoric/nondepressed individuals. Examination of potential moderator variables indicated that studies lacking an objective standard of reality ( d = −.15 versus −.03, for studies possessing such a standard) and that utilize self-report measures to measure symptoms of depression ( d = .16 versus −.04, for studies which utilize structured interviews) were more likely to find depressive realism effects. Methodological paradigm was also found to influence whether results consistent with depressive realism were found ( d 's ranged from −.09 to .14).


title: Cross-Domain Image Retrieval with Attention Modeling

abstract: With the proliferation of e-commerce websites and the ubiquitousness of smart phones, cross-domain image retrieval using images taken by smart phones as queries to search products on e-commerce websites is emerging as a popular application. One challenge of this task is to locate the attention of both the query and database images. In particular, database images, e.g. of fashion products, on e-commerce websites are typically displayed with other accessories, and the images taken by users contain noisy background and large variations in orientation and lighting. Consequently, their attention is difficult to locate. In this paper, we exploit the rich tag information available on the e-commerce websites to locate the attention of database images. For query images, we use each candidate image in the database as the context to locate the query attention. Novel deep convolutional neural network architectures, namely TagYNet and CtxYNet, are proposed to learn the attention weights and then extract effective representations of the images. Experimental results on public datasets confirm that our approaches have significant improvement over the existing methods in terms of the retrieval accuracy and efficiency.


title: Basic Parameters in Speech Processing The Need for Evaluation

abstract: As basic parameters in speech processing we regard pitch, duration, intensity, voice quality, signal to noise ratio, voice activity detection and strength of Lombard effect. Taking in account also adverse conditions the performance of many published algorithms to extract those parameters from the speech signal automatically is not known. A framework based on competitive evaluation is proposed to push algorithmic research and to make progress comparable.


title: Abnormal crowd behavior detection based on social attribute-aware force model

abstract: In this paper, a novel social attribute-aware force model is presented for abnormal crowd pattern detection in video sequences. We take social characteristics of crowd behaviors into account in order to improve the effectiveness of the simulation on the interaction behaviors of the crowd. A quick unsupervised method is proposed to estimate the scene scale. Both the social disorder attribute and congestion attribute are introduced to describe the realistic social behaviors by using statistical context feature. Through the semantic attribute-aware enhancement, we obtain an improved model on the basis of social force. We validate our method in public available datasets for abnormal detection, and the experimental results show promising performance compared with other state of the art methods.


title: Resident and circulating mast cells in propulsative organs of frog Rana temporaria

abstract: Mast cells (MCs) of the "blood" and lymph hearts of the adult frog Rana temporaria were investigated at histochemical and ultrastructural levels. Two populations of MCs were revealed in these propulsative organs: population of resident MCs and population of circulating MCs. It has been shown that the resident cardiac MCs have an oval or elongated form and are located between atrial or ventricular myocytes and under endocardial endothelium. The resident cardiac MCs are situated in connective tissue of epicardium, too. Avascular myocardium of the frog ventricle consists of a spongy network of muscle trabeculae. We revealed circulating MCs in intertrabecular spaces and clefts of the spongy myocardium and in the blood of the main central cavity. Circulating MCs are round in shape and contain a large central nucleus enriched with condensed chromatin. They resemble the lymphocytes, but show cytoplasm filled with granules. These granules ultrastructure is much like that of the granules of the cardiac resident MCs. In the lymph heart, oval and somewhat elongated resident MCs are located in the interstitial space among cross-striated muscle fibers and among smooth muscle cells of tubular (afferent and efferent) valves. Sometimes lymphocyte-like circulating MCs are revealed in the cavity of lymph heart. Circulating MCs are also present in the lymphatics located adjacent to the lymph hearts. In certain parts of the lymphatic walls MCs are in close adhesion to the mesothelial cells lining the lymphatic cavity. Our histochemical investigation revealed that both the resident and circulating MCs of the propulsative organs give a strongly positive reaction with alcian blue, but weakly red with safranin and weakly metachromatic with toluidine blue. The presence of population of circulating MCs in the frog suggests that there are differences in biology of MCs between lower and higher vertebrates.


title: Combining Ontology Development Methodologies and Semantic Web Platforms for E-government Domain Ontology Development

abstract: One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-government does not focus on the application of available methodologies and platforms for developing government domain ontologies. Furthermore, only a few of these researches provide detailed guidelines for developing semantic ontology models from a government service domain. This research presents a case study combining an ontology building methodology and two state-of-the-art Semantic Web platforms namely Protege and Java Jena ontology API for semantic ontology development in e-government. Firstly, a framework adopted from the Uschold and King ontology building methodology is employed to build a domain ontology describing the semantic content of a government service domain. Thereafter, UML is used to semi-formally represent the domain ontology. Finally, Protege and Jena API are employed to create the Web Ontology Language (OWL) and Resource Description Framework (RDF) representations of the domain ontology respectively to enable its computer processing. The study aims at: (1) providing egovernment developers, particularly those from the developing world with detailed guidelines for practicing semantic content development in their e-government projects and (2), strengthening the adoption of semantic technologies in e-government. The study would also be of interest to novice Semantic Web developers who might used it as a starting point for further investigations.


title: Individual Differences in Inhibitory Control and Children's Theory of Mind

abstract: This research examined the relation between individual differences in inhibitory control (IC; a central component of executive functioning) and theory-of-mind (ToM) performance in preschool-age children. Across two sessions, 3- and 4-year-old children ( N � 107) were given multitask batteries measuring IC and ToM. Inhibitory control was strongly related to ToM, r � .66, p � .001. This relation remained significant controlling for age, gender, verbal ability, motor sequencing, family size, and performance on pretend-action and mental state control tasks. Inhibitory tasks requiring a novel response in the face of a conflicting prepotent response (Conflict scale) and those requiring the delay of a prepotent response (Delay scale) were significantly related to ToM. The Conflict scale, however, significantly predicted ToM performance over and above the Delay scale and control measures, whereas the Delay scale was not significant in a corresponding analysis. These findings suggest that IC may be a crucial enabling factor for ToM development, possibly affecting both the emergence and expression of mental state knowledge. The implications of the findings for a variety of executive accounts of ToM are discussed.


title: Multipath exploitation in through-the-wall radar imaging using sparse reconstruction

abstract: Multipath exploitation and compressive sensing (CS) have both been applied independently to through-the-wall radar imaging (TWRI). Fast and efficient data acquisition is desired in scenarios where multipath effects cannot be neglected. Hence, we combine the two methods to achieve good image reconstruction in multipath environments from few spatial and frequency measurements. Ghost targets appear in the scene primarily due to specular reflections from interior walls and multiple reflections within the front wall. Assuming knowledge of the room geometry, we can invert the multipath model and eliminate ghosts by means of CS. We develop effective methods for the reconstruction of stationary scenes, which employ a group sparse CS approach. Additionally, we separate the target and wall contributions to the image by a sparse reconstruction approach joining wall and target models, which allows suppression of the ghosts and increased signal-to-clutter ratio (SCR) at the target locations. Effectiveness of the proposed approach is demonstrated using both simulated and real data.


title: Voltage Clamp Circuits for a Power MOSFET PWM Inverter

abstract: Power MOSFETs are fast-switching devices suitable for high-frequency operations. However, high-speed switching and circuit inductance may cause voltage spikes to appear across the device and damage it. Three types of voltage clamp circuits that can be used with power MOSFETs are considered, and their essential features and characteristics are studied. The different modes of operation with these clamp circuits are discussed, and the design equations to determine these parameters in each clamp circuit are presented. The losses in the three clamp circuits are evaluated and compared. One clamp circuit is shown to offer outstanding advantages and is found to be suitable for high-frequency applications such as PWM inverters.


title: Evaluating the effectiveness and efficiency of visual variables for geographic information visualization

abstract: We propose an empirical, perception-based evaluation approach for assessing the effectiveness and efficiency of longstanding cartographic design principles applied to 2D map displays. The approach includes bottom-up visual saliency models that are compared with eye-movement data collected in human-subject experiments on map stimuli embedded in the so-called flicker paradigm. The proposed methods are applied to the assessment of four commonly used visual variables for designing 2D maps: size, color value, color hue, and orientation. The empirical results suggest that the visual variable size is the most efficient (fastest) and most effective (accurate) visual variable to detect change under flicker conditions. The visual variable orientation proved to be the least efficient and effective of the tested visual variables. These empirical results shed new light on the implied ranking of the visual variables that have been proposed over 40 years ago. With the presented approach we hope to provide cartographers, GIScientists and visualization designers a systematic assessment method to develop effective and efficient geovisualization displays.


title: Critical Issues Affecting an ERP Implementation

abstract: Abstract Implementing an ERP causes massive change that needs to be carefully managed to reap the benefits of an ERP solution. Critical issues that must be carefully considered to ensure successful implementation include commitment from top management, reengineering of the existing processes, integration of the ERP with other business information systems, selection and management of consultants and employees, and training of employees on the new system.


title: The semiotic paradigm: implications for tourism research.

abstract: Abstract This paper examines the application of semiotics to tourism studies. The first section of the paper provides the theoretical background by briefly outlining the history, concepts, ontology and epistemology of the semiotic approach. The second section of the paper reviews the applications of semiotics to date in tourism research, focusing mainly on studies of tourism marketing. It highlights the usefulness of this approach for uncovering the systems of signs and the ‘deep structure’ of meaning in tourism marketing discourse. A framework for integrating semiotics with the study of tourism marketing is also introduced. In the final section, this framework is used to identify existing gaps in the research and to suggest directions for future applications of semiotics to tourism research.


title: ROS, Android and cloud robotics: How to make a powerful low cost robot

abstract: With the constant development in the robotics field, many solutions require a high hardware complexity to be executed resulting in an expensive robotic system. Furthermore, these solutions are usually closed box implementations that are hard to be reproduced. Hence, this project proposes a system structure to reduce these difficulties in robotics development. This system consists of a low cost robot with an embedded Android phone in a ROS environment. For testing this implementation, two Android phones, a laptop and a robot with an Arduino microcontroller have been used. The results of the study demonstrated several advantages of using this system, such as incorporating processing and sensing from the smartphone on the robot, having external heavy processing in a Server outside the robot and being able to connect the robot to the cloud. Therefore, using a popular Android device running a Rosjava application can be very useful in robotics applications and development, mainly due to the possibilities provided by this simple system.


AEROBIC SWIMMING TRAINING INCREASES THE ACTIVITY OF ANTIOXIDANT ENzYMES AND THE GLYCOGEN CONTENT IN THE SKELETAL MUSCLE OF RATS


title: Static eccentricity fault detection of the BLDC motor inside the air handler unit (AHU)

abstract: Because of the wide range of application of BLDC (brushless DC) motors, the fault diagnose of BLDC becomes an essential problem. For those BLDC motors in application, rotor faults account for most of the faults in BLDC motors. Static eccentricity and dynamic eccentricity are two of the main faults in the rotor of BLDC motor. In this paper, an improved method detecting static eccentricity is proposed based on the comparison between the positive sequence current and negative sequence current. Comparing to the traditional method which only focuses on the change of the negative sequence of fundamental frequency current, the method proposed in this paper could be more reliable since the positive sequence current and harmonics current are considered as well. A BLDC motor with static eccentricity in AHU (air handler unit) will be tested. The effectiveness and availability are verified in both simulation and experiment results.


title: BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory

abstract: Storing a database (rows and indexes) entirely in non-volatile memory (NVM) potentially enables both high performance and fast recovery. To fully exploit parallelism on modern CPUs, modern main-memory databases use latch-free (lock-free) index structures, e.g. Bw-tree or skip lists. To achieve high performance NVM-resident indexes also need to be latch-free. This paper describes the design of the BzTree, a latch-free B-tree index designed for NVM. The BzTree uses a persistent multi-word compare-and-swap operation (PMwCAS) as a core building block, enabling an index design that has several important advantages compared with competing index structures such as the Bw-tree. First, the BzTree is latch-free yet simple to implement. Second, the BzTree is fast - showing up to 2x higher throughput than the Bw-tree in our experiments. Third, the BzTree does not require any special-purpose recovery code. Recovery is near-instantaneous and only involves rolling back (or forward) any PMwCAS operations that were in-flight during failure. Our end-to-end recovery experiments of BzTree report an average recovery time of 145 μs. Finally, the same BzTree implementation runs seamlessly on both volatile RAM and NVM, which greatly reduces the cost of code maintenance.


title: Design of Type-III controller for dc-dc switch-mode boost converter

abstract: DC-DC power converters have played a key role for different fields applications. Such demanding applications have been required a steady source of voltage irrespective any changes in line and load. The steady-state behaviors of these power-supplies have been defined by the converter topology. But the controller has taken place an important role for maintaining the accurate transient i.e. dynamic performance in dc-dc power-supplies. Some dc-dc power-supplies like boost, buck-boost etc have a right-half-plane zero i.e. there is a problem of nonminimum phase, so it is difficult for the PID controller to exhibit good performance. For this reason a special kind of classical controller i.e. Type-III controller is introduced for obtaining better performance. This paper describes the designing of a Type-III controller for a dc-dc switched-mode boost converter.


title: A Lyapunov Optimization Approach for Green Cellular Networks with Hybrid Energy Supplies

abstract: Powering cellular networks with renewable energy sources via energy harvesting (EH) have recently been proposed as a promising solution for green networking. However, with intermittent and random energy arrivals, it is challenging to provide satisfactory quality of service (QoS) in EH networks. To enjoy the greenness brought by EH while overcoming the instability of the renewable energy sources, hybrid energy supply (HES) networks that are powered by both EH and the electric grid have emerged as a new paradigm for green communications. In this paper, we will propose new design methodologies for HES green cellular networks with the help of Lyapunov optimization techniques. The network service cost, which addresses both the grid energy consumption and achievable QoS, is adopted as the performance metric, and it is optimized via base station assignment and power control (BAPC). Our main contribution is a low-complexity online algorithm to minimize the long-term average network service cost, namely, the Lyapunov optimization-based BAPC (LBAPC) algorithm. One main advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of channels and EH processes. To determine the network operation, we only need to solve a deterministic per-time slot problem, for which an efficient inner–outer optimization algorithm is proposed. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Finally, sample simulation results are presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.


title: Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

abstract: In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry- and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification scheme's feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics.


title: WatchdogLite: Hardware-Accelerated Compiler-Based Pointer Checking

abstract: Lack of memory safety in C is the root cause of a multitude of serious bugs and security vulnerabilities. Numerous software-only and hardware-based schemes have been proposed to enforce memory safety. Among these approaches, pointer-based checking, which maintains per-pointer metadata in a disjoint metadata space, has been recognized as providing comprehensive memory safety. Software approaches for pointer-based checking have high performance overheads. In contrast, hardware approaches introduce a myriad of hardware structures and widgets to mitigate those performance overheads. This paper proposes WatchdogLite, an ISA extension that provides hardware acceleration for a compiler implementation of pointer-based checking. This division of labor between the compiler and the hardware allows for hardware acceleration while using only preexisting architectural registers. By leveraging the compiler to identify pointers, perform check elimination, and insert the new instructions, this approach attains performance similar to prior hardware-intensive approaches without adding any hardware structures for tracking metadata.


title: Addressing the Challenges of Inquiry-Based Learning through Technology and Curriculum Design

abstract: Inquiry experiences can provide valuable opportunities for students to improve their understanding of both science content and scientific practices. However, the implementation of inquiry learning in classrooms presents a number of significant challenges. We have been exploring these challenges through a program of research on the use of scientific visualization technologies to support inquiry-based learning in the geosciences. In this article, we describe 5 significant challenges to implementing inquiry-based learning and present strategies for addressing them through the design of technology and curriculum. We present a design history covering 4 generations of software and curriculum to show how these challenges arise in classrooms and how the design strategies respond to them.


title: Hierarchical Semi-supervised Classification with Incomplete Class Hierarchies

abstract: In an entity classification task, topic or concept hierarchies are often incomplete. Previous work by Dalvi et al. [12] has showed that in non-hierarchical semi-supervised classification tasks, the presence of such unanticipated classes can cause semantic drift for seeded classes. The Exploratory learning [12] method was proposed to solve this problem; however it is limited to the flat classification task. This paper builds such exploratory learning methods for hierarchical classification tasks. We experimented with subsets of the NELL [8] ontology and text, and HTML table datasets derived from the ClueWeb09 corpus. Our method (OptDAC-ExploreEM) outperforms the existing Exploratory EM method, and its naive extension (DAC-ExploreEM), in terms of seed class F1 on average by 10% and 7% respectively.


Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud


title: Breathing life into shape: capturing, modeling and animating 3D human breathing

abstract: Modeling how the human body deforms during breathing is important for the realistic animation of lifelike 3D avatars. We learn a model of body shape deformations due to breathing for different breathing types and provide simple animation controls to render lifelike breathing regardless of body shape. We capture and align high-resolution 3D scans of 58 human subjects. We compute deviations from each subject's mean shape during breathing, and study the statistics of such shape changes for different genders, body shapes, and breathing types. We use the volume of the registered scans as a proxy for lung volume and learn a novel non-linear model relating volume and breathing type to 3D shape deformations and pose changes. We then augment a SCAPE body model so that body shape is determined by identity, pose, and the parameters of the breathing model. These parameters provide an intuitive interface with which animators can synthesize 3D human avatars with realistic breathing motions. We also develop a novel interface for animating breathing using a spirometer, which measures the changes in breathing volume of a "breath actor".


title: A spatiotemporal model with visual attention for video classification

abstract: High level understanding of sequential visual input is important for safe and stable autonomy, especially in localization and object detection. While traditional object classification and tracking approaches are specifically designed to handle variations in rotation and scale, current state-of-the-art approaches based on deep learning achieve better performance. This paper focuses on developing a spatiotemporal model to handle videos containing moving objects with rotation and scale changes. Built on models that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify sequential data, this work investigates the effectiveness of incorporating attention modules in the CNN stage for video classification. The superiority of the proposed spatiotemporal model is demonstrated on the Moving MNIST dataset augmented with rotation and scaling.


title: Reactive Planning for Assistive Robots

abstract: We consider a vehicle consisting of a robotic walking assistant pushed by a user. The robot can guide the person along a path and suggest a velocity by various means. The vehicle moves in a crowded environment and can detect other pedestrians in the surroundings. We propose a reactive planner that modifies the path in order to avoid pedestrians in the surroundings. The algorithm relies on a very accurate model to predict the motion of each pedestrian, i.e., the headed social force model. The possible trajectories for both the vehicle and the pedestrians are modeled as clothoid curves, which are efficient to manage from the numeric point of view and are very comfortable to follow for the user. Probabilistic techniques are used to account for the variability of the motion of each pedestrian. The path is efficient to generate, is collision free (up to a certain probability), and is comfortable to follow. Simulations and comparisons with a state-of-the-art planner using real data as well as experiments are reported to prove the effectiveness of the method.


title: Design & implementation of a line following robot for irrigation based application

abstract: The agriculture sector is the backbone of an economy which provides the basic ingredients to mankind and raw materials for industrialization. With the increasing number of the population over the world, the demand for agricultural products is also increased. In order to increase the production rate, irrigation technique should be more efficient. The irrigation techniques used till date are not in satisfactory level, especially in a developing country like Bangladesh. This paper has proposed a line follower robot for irrigation based application which may be considered as a cost-effective solution by minimizing water loss as well as an efficient system for irrigation purposes. This proposed system does not require an operator to accomplish its task. This gardening robot is completely portable and is equipped with a microcontroller, an on-board water reservoir, and an attached water pump. The area to be watered by the robot can be any field with plants, placed in a predefined path. It is capable of comparing movable objects and stationary plants to minimize water loss and finally watering them autonomously without any human intervention. The designed robot was tested and it performed nicely.


Reducing the Dimensionality of Data with Neural


title: The Theta-Gamma Neural Code

abstract: Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, we review evidence for the following hypothesis: that the dual oscillations form a code for representing multiple items in an ordered way. This form of coding has been most clearly demonstrated in the hippocampus, where different spatial information is represented in different gamma subcycles of a theta cycle. Other experiments have tested the functional importance of oscillations and their coupling. These involve correlation of oscillatory properties with memory states, correlation with memory performance, and effects of disrupting oscillations on memory. Recent work suggests that this coding scheme coordinates communication between brain regions and is involved in sensory as well as memory processes.


title: Design and implementation of a parallel priority queue on many-core architectures

abstract: An efficient parallel priority queue is at the core of the effort in parallelizing important non-numeric irregular computations such as discrete event simulation scheduling and branch-and-bound algorithms. GPGPUs can provide powerful computing platform for such non-numeric computations if an efficient parallel priority queue implementation is available. In this paper, aiming at fine-grained applications, we develop an efficient parallel heap system employing CUDA. To our knowledge, this is the first parallel priority queue implementation on many-core architectures, thus represents a breakthrough. By allowing wide heap nodes to enable thousands of simultaneous deletions of highest priority items and insertions of new items, and taking full advantage of CUDA's data parallel SIMT architecture, we demonstrate up to 30-fold absolute speedup for relatively fine-grained compute loads compared to optimized sequential priority queue implementation on fast multicores. Compared to this, our optimized multicore parallelization of parallel heap yields only 2–3 fold speedup for such fine-grained loads. This parallelization of a tree-based data structure on GPGPUs provides a roadmap for future parallelizations of other such data structures.


title: Entanglement and its Role in Shor’s Algorithm

abstract: Entanglement has been termed a critical resource for quantum information processing and is thought to be the reason that certain quantum algorithms, such as Shor's factoring algorithm, can achieve exponentially better performance than their classical counterparts. The nature of this resource is still not fully understood: here we use numerical simulation to investigate how entanglement between register qubits varies as Shor's algorithm is run on a quantum computer. The shifting patterns in the entanglement are found to relate to the choice of basis for the quantum Fourier transform.


title: Report on the Development of the Advanced Encryption Standard (AES)

abstract: In 1997, the National Institute of Standards and Technology (NIST) initiated a process to select a symmetric-key encryption algorithm to be used to protect sensitive (unclassified) Federal information in furtherance of NIST’s statutory responsibilities. In 1998, NIST announced the acceptance of fifteen candidate algorithms and requested the assistance of the cryptographic research community in analyzing the candidates. This analysis included an initial examination of the security and efficiency characteristics for each algorithm. NIST reviewed the results of this preliminary research and selected MARS, RC6™, Rijndael, Serpent and Twofish as finalists. Having reviewed further public analysis of the finalists, NIST has decided to propose Rijndael as the Advanced Encryption Standard (AES). The research results and rationale for this selection are documented in this report.


title: A 16 bit linear passive-charge-sharing SAR ADC in 55nm CMOS

abstract: This paper presents a first reported passive-charge-sharing SAR ADC that achieves 16 bit linearity. It is known that on chip passive-charge-sharing suffers from poor linearity due to the unregulated reference voltage during bit trials. The proposed unique ADC architecture and calibration technique addresses the issue of signal dependent reference voltage droop during SAR ADC bit trials and orthogonalize the bit weights to achieve 16bit linearity. In addition, the proposed architecture maximizes SNR by sampling on to the bit cap, the first reported in this type of SAR ADC. Measurement result from a prototype test chip shows +/−0.8 LSB (16-bit level) INL at 1MSPS.


title: Bilateral counterparty risk valuation with stochastic dynamical models and application to Credit Default Swaps

abstract: We introduce the general arbitrage-free valuation framework for counterparty risk adjustments in presence of bilateral default risk, including default of the investor. We illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option, both with zero strike and written on the residual net value of the contract at the relevant default times. We allow for correlation between the default times of the investor, counterparty and underlying portfolio risk factors. We use arbitrage-free stochastic dynamical models. We then specialize our analysis to Credit Default Swaps (CDS) as underlying portfolio, generalizing the work of Brigo and Chourdakis (2008) [5] who deal with unilateral and asymmetric counterparty risk. We introduce stochastic intensity models and a trivariate copula function on the default times exponential variables to model default dependence. Similarly to [5], we find that both default correlation and credit spread volatilities have a relevant and structured impact on the adjustment. Differently from [5], the two parties will now agree on the credit valuation adjustment. We study a case involving British Airways, Lehman Brothers and Royal Dutch Shell, illustrating the bilateral adjustments in concrete crisis situations.


title: Analytical model for the thermal resistance of windings consisting of solid or litz wire

abstract: In this paper a method to calculate the thermal resistance of windings is presented and validated by measurements. Two formulas have been developed for widely used wire types in power electronics being round and litz wire. With the presented approach it is possible to describe the thermal resistance of arbitrary wire arrangements. This analytical approach can either be used in fast forward designs of magnetic devices, respectively can also be integrated in automatized optimization procedures to improve the designs of magnetic components. The calculated results are showing good coincidence with the measured values.


title: Automated problem diagnosis in distributed systems

abstract: Quickly finding the cause of software bugs and performance problems in production environments is a crucial task that currently requires substantial effort of skilled analysts. Our thesis is that many problems can be accurately located with automated techniques that work on unmodified systems and use little application knowledge for diagnosis. This dissertation identifies main obstacles for such diagnosis and presents a three-step approach for addressing them. ::: First, we introduce self-propelled instrumentation, an execution monitoring approach that can be rapidly deployed on demand in a distributed system. We dynamically inject a fragment of code referred to as the agent into one of the application processes. The agent starts propagating through the system, inserting instrumentation ahead of the control flow in the traced process and across process and kernel boundaries. As a result, it obtains the distributed control flow trace of the execution. ::: Second, we introduce a flow-separation algorithm, an approach for identifying concurrent activities in the system. In parallel and distributed environments, applications often perform multiple concurrent activities, possibly on behalf of different users. Traces collected by our agent would interleave events from such activities thus complicating manual examination and automated analysis. Our flow-separation algorithm is able to distinguish events from different activities using little user help. ::: Finally, we introduce an automated root cause analysis approach. We focus on identification of anomalies rather than massive failures, as anomalies are often harder to investigate. Our techniques help the analyst to locate an anomalous flow (e.g., an abnormal request in an e-commerce system or a node of a parallel application) and to identify a function call in that flow that is a likely cause of the anomaly. ::: We evaluated these three steps on a variety of sequential and distributed applications. Our tracing approach proved effective for low-overhead on-demand data collection across the process and kernel boundaries. Manual trace examination enabled us to locate the causes of two performance problems in a multimedia and a GUI application, and a crash in the Linux kernel. Our automated analyses enabled us to find the causes of four problems in the Score and Condor batch scheduling systems.


title: GPFS: A Shared-Disk File System for Large Computing Clusters

abstract: GPFS is IBM's parallel, shared-disk file system for cluster computers, available on the RS/6000 SP parallel supercomputer and on Linux clusters. GPFS is used on many of the largest supercomputers in the world. GPFS was built on many of the ideas that were developed in the academic community over the last several years, particularly distributed locking and recovery technology. To date it has been a matter of conjecture how well these ideas scale. We have had the opportunity to test those limits in the context of a product that runs on the largest systems in existence. While in many cases existing ideas scaled well, new approaches were necessary in many key areas. This paper describes GPFS, and discusses how distributed locking and recovery techniques were extended to scale to large clusters.


A Taxonomy of Consumer Online Shopping Attitudes and Behavior


title: Probabilistic Data Generation for Deduplication and Data Linkage

abstract: In many data mining projects the data to be analysed contains personal information, like names and addresses. Cleaning and pre-processing of such data likely involves deduplication or linkage with other data, which is often challenged by a lack of unique entity identifiers. In recent years there has been an increased research effort in data linkage and deduplication, mainly in the machine learning and database communities. Publicly available test data with known deduplication or linkage status is needed so that new linkage algorithms and techniques can be tested, evaluated and compared. However, publication of data containing personal information is normally impossible due to privacy and confidentiality issues. An alternative is to use artificially created data, which has the advantages that content and error rates can be controlled, and the deduplication or linkage status is known. Controlled experiments can be performed and replicated easily. In this paper we present a freely available data set generator capable of creating data sets containing names, addresses and other personal information.


title: An e-Learning Theoretical Framework

abstract: E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e-learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here [the authors] propose an e-learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services related to e-learning. This article presents an in-depth literature review on those dimensions. The article first presents the related concepts of computer use in learning across time, revealing the emergence of new trends on e-learning. The theoretical framework is a contribution for guiding e-learning studies. The article classifies the stakeholder groups and their relationship with e-learning systems. The framework shows a typology of e-learning systems' services. This theoretical approach integrates learning strategies, technologies and stakeholders.


Location-based Mobile Games


Security Analysis of Wearable Fitness Devices ( Fitbit )


title: Orthogonal Frequency Division Multiplexing With Index Modulation

abstract: In this paper, a novel orthogonal frequency division multiplexing (OFDM) scheme, which is called OFDM with index modulation (OFDM-IM), is proposed for frequency-selective fading channels. In this scheme, inspiring from the recently introduced spatial modulation concept for multiple-input multiple-output (MIMO) channels, the information is conveyed not only by M-ary signal constellations as in classical OFDM, but also by the indices of the subcarriers, which are activated according to the incoming bit stream. Different transceiver structures are proposed and a theoretical error performance analysis is provided for the new scheme. It is shown via computer simulations that the proposed scheme achieves significantly better error performance than classical OFDM due to the information bits carried in the spatial domain by the indices of OFDM subcarriers.


title: Top Rank Supervised Binary Coding for Visual Search

abstract: In recent years, binary coding techniques are becoming increasingly popular because of their high efficiency in handling large-scale computer vision applications. It has been demonstrated that supervised binary coding techniques that leverage supervised information can significantly enhance the coding quality, and hence greatly benefit visual search tasks. Typically, a modern binary coding method seeks to learn a group of coding functions which compress data samples into binary codes. However, few methods pursued the coding functions such that the precision at the top of a ranking list according to Hamming distances of the generated binary codes is optimized. In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information. The core idea is to train the disciplined coding functions, by which the mistakes at the top of a Hamming-distance ranking list are penalized more than those at the bottom. To solve such coding functions, we relax the original discrete optimization objective with a continuous surrogate, and derive a stochastic gradient descent to optimize the surrogate objective. To further reduce the training time cost, we also design an online learning algorithm to optimize the surrogate objective more efficiently. Empirical studies based upon three benchmark image datasets demonstrate that the proposed binary coding approach achieves superior image search accuracy over the state-of-the-arts.


title: Malaria Models with Spatial Effects

abstract: In this chapter, we provide a brief review about some recent studies on mathematical modeling of malaria transmission in spatially heterogeneous en- vironments. Deterministic models described by ordinary differential equations and reaction-diffusion equations are used to investigate the spatial spread of malaria be- tween humans and mosquitoes. Selected topics include the importance of modeling spatial heterogeneity, basic models with infective immigrants, multi-patch models, and reaction-diffusion models. The chapter ends with a brief discussion about possi- ble future research directions.


title: Differentiating between light and deep sleep stages using an ambulatory device based on peripheral arterial tonometry

abstract: The objective of this study is to develop and assess an automatic algorithm based on the peripheral arterial tone (PAT) signal to differentiate between light and deep sleep stages. The PAT signal is a measure of the pulsatile arterial volume changes at the finger tip reflecting sympathetic tone variations and is recorded by an ambulatory unattended device, the Watch-PAT100, which has been shown to be capable of detecting wake, NREM and REM sleep. An algorithm to differentiate light from deep sleep was developed using a training set of 49 patients and was validated using a separate set of 44 patients. In both patient sets, Watch-PAT100 data were recorded simultaneously with polysomnography during a full night sleep study. The algorithm is based on 14 features extracted from two time series of PAT amplitudes and inter-pulse periods (IPP). Those features were then further processed to yield a prediction function that determines the likelihood of detecting a deep sleep stage epoch during NREM sleep periods. Overall sensitivity, specificity and agreement of the automatic algorithm to identify standard 30 s epochs of light and deep sleep stages were 66%, 89%, 82% and 65%, 87%, 80% for the training and validation sets, respectively. Together with the already existing algorithms for REM and wake detection we propose a close to full stage detection method based solely on the PAT and actigraphy signals. The automatic sleep stages detection algorithm could be very useful for unattended ambulatory sleep monitoring assessing sleep stages when EEG recordings are not available.


Tempo And Beat Estimation Of Musical Signals


title: Integrated solar energy harvesting and storage

abstract: To explore integrated solar energy harvesting as a power source for low power systems such as wireless sensor nodes, an array of energy scavenging photodiodes based on a passive-pixel architecture for imagers and have been fabricated together with storage capacitors implemented using on-chip interconnect in a 0.35 mum CMOS logic process. Integrated vertical plate capacitors enable dense energy storage without limiting optical efficiency. Measurements show 225 muW/mm2 output power generated by a light intensity of 20k LUX


The first workshop on end-user software engineering


Don’t Judge a Plant by Its Flowers


title: Adversarial Attacks and Defences Competition

abstract: To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.


title: A Compact, Planar, and CPW-Fed Metamaterial-Inspired Dual-Band Antenna

abstract: A novel compact, planar, and coplanar waveguide (CPW)-fed dual-band antenna is designed and proposed using composite metamaterial. Such composite metamaterial consists of an inner split-ring resonator (SRR) and an outer closed-ring resonator (CRR). The composite metamaterial can provide dual-band operation at 2.595-2.654 and 3.185-4.245 GHz with reflection coefficient better than -10 dB by the two resonant modes of SRR and CRR, respectively. A CPW-fed line with trapeziform ground plane and tapered impedance transformer line is employed to improve the impedance matching of the antenna. The uniqueness of this design is that the inner SRR with size much smaller than the resonant wavelength is used for obtaining the lower narrow frequency band, which makes the dual-band antenna very compact. Antenna parameters, including reflection coefficient, radiation pattern, radiation efficiency, and gain, are analyzed with numerical simulation and experimental measurement. Good agreement between the simulation and measurement is observed.


title: Characteristic Analysis of Surface Permanent-Magnet Vernier Motor According to Pole Ratio and Winding Pole Number

abstract: This paper aims to analyze the characteristics of a surface permanent-magnet Vernier motor (SPMVM) according to the pole ratio among the rotor magnet pole pairs, stator teeth, and winding pole number. The SPMVM has the same operational principle as a magnetic gear, and it is operated using the space harmonics of the air-gap flux density due to the magnetomotive force and air-gap permeance. In this paper, first, the design parameters of the SPMVM are specified based on an analytical calculation of the air-gap magnetic flux density. Moreover, various combinations of the rotor magnet pole pairs and stator teeth number are made and compared according to the operational principle of a Vernier motor using a 2-D finite-element method.


title: Analysis of Features Selection and Machine Learning Classifier in Android Malware Detection

abstract: The proliferation of Android-based mobile devices and mobile applications in the market has triggered the malware author to make the mobile devices as the next profitable target. With user are now able to use mobile devices for various purposes such as web browsing, ubiquitous services, online banking, social networking, MMS and etc, more credential information is expose to exploitation. Applying a similar security solution that work in Desktop environment to mobile devices may not be proper as mobile devices have a limited storage, memory, CPU and power consumption. Hence, there is a need to develop a mobile malware detection that can provide an effective solution to defence the mobile user from any malicious threat and at the same time address the limitation of mobile devices environment. Prior to this matter, this research focused on evaluating the best features selection to be used in the best machine-learning classifiers. To find the best combination of both features selection and classifier, five sets of different feature selection are applies to five different machine learning classifiers. The classifier outcome is evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), and Accuracy. The best combination of both features selection and classifier can be used to reduce features selection and at the same time able to classify the infected android application accurately.


title: Code to learn with Scratch? A systematic literature review

abstract: The use of computer programming in K-12 spread into schools worldwide in the 70s and 80s of the last century, but it disappeared from the educational landscape in the early 90s. With the development of visual programming languages such as Scratch, this movement has emerged again in recent years, as teachers at all educational levels and from different disciplines consider that the use of programming enhances learning in many subjects and allows students to develop important skills. The systematic literature review presented in this article aims to summarize the results of recent research using programming with Scratch in subjects not related to computing and communications, as well as studies analyzing the kind of skills students develop while learning to code in this environment. Although the analyzed papers provide promising results regarding the use of programming as an educational resource, this review highlights the need to conduct more empirical research in classrooms, using larger samples of students that allow to obtain clear conclusions about the types of learning that could be enhanced through programming.


title: Big Social Data Analytics for Public Health: Predicting Facebook Post Performance Using Artificial Neural Networks and Deep Learning

abstract: Facebook "post popularity" analysis is fundamental for differentiating between relevant posts and posts with low user engagement and consequently their characteristics. This research study aims at health and care organizations to improve information dissemination on social media platforms by reducing clutter and noise. At the same time, it will help users navigate through vast amount of information in direction of the relevant health and care content. Furthermore, study explores prediction of popularity of healthcare posts on the largest social media platform Facebook. Methodology is presented in this paper to predict user engagement based on eleven characteristics of the post: Post Type, Hour Span, Facebook Wall Category, Level, Country, isHoliday, Season, Created Year, Month, Day of the Week, Time of the Day. Finally, post performance prediction is conducted using Artificial Neural Networks (ANN) and Deep Neural Networks (DNN). Different network topology measures are used to achieve best accuracy prediction followed by examples and discussion on why DNN might not be optimal technique for the given data set.


title: A study of machine learning classifiers for spam detection

abstract: In the present world, there is a need of emails communication but unsolicited emails hamper such communications. The present research emphasises to build a spam classification model with/without the use of ensemble of classifiers methods have been incorporated. Through this study, the aim is to distinguish between ham emails and spam emails by making an efficient and sensitive classification model that gives good accuracy with low false positive rate. Greedy Stepwise feature search method has been incorporated for searching informative feature of the Enron email dataset. The comparison has been done among different machine learning classifiers (such as Bayesian, Naive Bayes, SVM (support vector machine), J48 (decision tree), Bayesian with Adaboost, Naive Bayes with Adaboost). The concerned classifiers are tested and evaluated on metric (such as F-measure (accuracy), False Positive Rate, and training time). By analysing all these aspects in their entirety, it has been found that SVM is the best classifier to be used. It has the high accuracy and the low false positive rate. However, training time of SVM to build the model is high, but as the results on other parameters are positive, the time does not pose such an issue.


title: Latest developments of high-frequency series load resonant inverter type built-in cooktops for induction heated all metallic appliances

abstract: This paper deals with the 1st generation prototype of one-stage boost-half bridge (B-HB) series load resonant (SLR) soft-switching high-frequency (HF) inverter with a lossless snubbing capacitor for a variety of induction heating (IH) appliances. The B-HB SLR HF inverter treated here is based upon a simple dual SLR frequency selection strategy changed automatically in accordance with various metal materials of IH loads. In the first place, the triple SLR frequency (three times of switching frequency) operated B-HB inverter is demonstrated for IH of non-magnetic and low resistivity metallic pans/utensils fabricated by aluminum, copper and multi-layer of aluminum and stainless steel. In the second place, the fundamental resonant frequency (switching frequency) operated B-HB SLR HF inverter for IH is also demonstrated of magnetic and high resistivity metallic pans/utensils fabricated by iron, iron cast and stainless steel. Finally, the principle of operation control, implemental and inherent unique features of the B-HB SLR HF inverter employing automatically dual resonant frequency selection scheme for a variety of IH metallic pans/utensils is described from an experimental point of view, along with its operating performance. This 1st generation HF SLR inverter type built-in IH cooktop with two ranges/three ranges has been put into practice in home energy utilizations in all electricity residential systems.


title: Wide-Coverage Semantic Analysis with Boxer

abstract: Boxer is an open-domain software component for semantic analysis of text, based on Combinatory Categorial Grammar (CCG) and Discourse Representation Theory (DRT). Used together with the CC (b) discourse structure triggered by conditionals, negation or discourse adverbs was overall correctly computed; (c) some measure and time expressions are correctly analysed, others aren't; (d) several shallow analyses are given for lexical phrases that require deep analysis; (e) bridging references and pronouns are not resolved in most cases. Boxer is distributed with the C&C tools and freely available for research purposes.


title: An Integrative and Discriminative Technique for Spoken Utterance Classification

abstract: Traditional methods of spoken utterance classification (SUC) adopt two independently trained phases. In the first phase, an automatic speech recognition (ASR) module returns the most likely sentence for the observed acoustic signal. In the second phase, a semantic classifier transforms the resulting sentence into the most likely semantic class. Since the two phases are isolated from each other, such traditional SUC systems are suboptimal. In this paper, we present a novel integrative and discriminative learning technique for SUC to alleviate this problem, and thereby, reduce the semantic classification error rate (CER). Our approach revolves around the effective use of the N-best lists generated by the ASR module to reduce semantic classification errors. The N-best list sentences are first rescored using all the available knowledge sources. Then, the sentence that is most likely to helps reduce the CER are extracted from the N-best lists as well as those sentences that are most likely to increase the CER. These sentences are used to discriminatively train the language and semantic-classifier models to minimize the overall semantic CER. Our experiments resulted in a reduction of CER from its initial value of 4.92% to 4.04% in the standard ATIS task.


title: CT findings in primary osteosarcoma of the jaw.

abstract: Findings of computed tomography (CT) in five patients with osteosarcoma of the jaw are presented. CT played an important role in the assessment of the tumour extent before the planning of treatment for both primary and recurrent jaw osteosarcoma. Plain CT was capable of demonstrating subtle bony change (cortical reaction and calcification) and medullary tumour extension without interference from other superimposed bones. Tumour vasculature was well represented by thin-slice CT scan with intravenous bolus injection of contrast medium. We emphasize the importance of preoperative CT scanning from both a radiological and surgical point of view when confronted with a tumour of the maxilla or mandible.


title: A Comparative Quantitative Analysis of Contemporary Big Data Clustering Algorithms for Market Segmentation in Hospitality Industry

abstract: The hospitality industry is one of the data-rich industries that receives huge Volumes of data streaming at high Velocity with considerably Variety, Veracity, and Variability. These properties make the data analysis in the hospitality industry a big data problem. Meeting the customers' expectations is a key factor in the hospitality industry to grasp the customers' loyalty. To achieve this goal, marketing professionals in this industry actively look for ways to utilize their data in the best possible manner and advance their data analytic solutions, such as identifying a unique market segmentation clustering and developing a recommendation system. In this paper, we present a comprehensive literature review of existing big data clustering algorithms and their advantages and disadvantages for various use cases. We implement the existing big data clustering algorithms and provide a quantitative comparison of the performance of different clustering algorithms for different scenarios. We also present our insights and recommendations regarding the suitability of different big data clustering algorithms for different use cases. These recommendations will be helpful for hoteliers in selecting the appropriate market segmentation clustering algorithm for different clustering datasets to improve the customer experience and maximize the hotel revenue.


title: Semantics for Big Data access & integration: Improving industrial equipment design through increased data usability

abstract: With the advent of Big Data technologies, organizations can efficiently store and analyze more data than ever before. However, extracting maximal value from this data can be challenging for many reasons. For example, datasets are often not stored using human-understandable terms, making it difficult for a large set of users to benefit from them. Further, given that different types of data may be best stored using different technologies, datasets that are closely related may be stored separately with no explicit linkage. Finally, even within individual data stores, there are often inconsistencies in data representations, whether introduced over time or due to different data producers. These challenges are further compounded by frequent additions to the data, including new raw data as well as results produced by large-scale analytics. Thus, even within a single Big Data environment, it is often the case that multiple rich datasets exist without the means to access them in a unified and cohesive way, often leading to lost value. This paper describes the development of a Big Data management infrastructure with semantic technologies at its core to provide a unified data access layer and a consistent approach to analytic execution. Semantic technologies were used to create domain models describing mutually relevant datasets and the relationships between them, with a graphical user interface to transparently query across datasets using domain-model terms. This prototype system was built for GE Power & Water's Power Generation Products Engineering Division, which has produced over 50TB of gas turbine and component prototype test data to date. The system is expected to result in significant savings in productivity and expenditure.


title: FOREX Rate prediction using Chaos and Quantile Regression Random Forest

abstract: This paper presents a hybrid of chaos modeling and Quantile Regression Random Forest (QRRF) for Foreign Exchange (FOREX) Rate prediction. The exchange rates data of US Dollar (USD) versus Japanese Yen (JPY), British Pound (GBP), and Euro (EUR) are used to test the efficacy of proposed model. Based on the experiments conducted, we conclude that the proposed model yielded accurate predictions compared to Chaos + Quantile Regression (QR), Chaos+Random Forest (RF) and that of Pradeepkumar and Ravi [12] in terms of both Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE).


title: Hippocampal place cells, context, and episodic memory

abstract: Although most observers agree that the hippocampus has a critical role in learning and memory, there remains considerable debate about the precise functional contribution of the hippocampus to these processes. Two of the most influential accounts hold that the pri- mary function of the hippocampus is to generate cognitive maps and to mediate episodic memory processes. The well-documented spatial firing patterns (place fields) of hippocampal neurons in rodents, along with the spatial learning impairments observed with hippocampal damage support the cognitive mapping hypothesis. The amnesia for personally experienced events seen in humans with hippocampal damage and the data of animal models, which show severe memory deficits associated with hippocampal lesions, support the episodic memory account. Although an extensive literature supports each of these hypotheses, a specific contribution of place cells to episodic memory has not been clearly demonstrated. Recent data from our laboratory, together with previous findings, indicate that hippocampal place fields and neuronal responses to task-relevant stimuli are highly sensitive to the context, even when the contexts are defined by abstract task demands rather than the spatial geometry of the environment. On the basis of these findings, it is proposed that place fields reflect a more general context processing function of the hippocampus. Hippocampal context represen- tations could serve to differentiate contexts and prime the relevant memories and behaviors. Since episodic memories, by definition, in- clude information about the time and place where the episode oc- curred, contextual information is a necessary prerequisite for any epi- sodic memory. Thus, place fields contribute importantly to episodic memory as part of the needed context representations. Additionally, recent findings indicate that hippocampal neurons differentiate contexts at progressively finer levels of detail, suggesting a hierarchical coding scheme which, if combined with temporal information, could provide a means of differentiating memory episodes. V C 2006 Wiley-Liss, Inc.


title: Low-complexity color demosaicing algorithm based on integrated gradients

abstract: Color demosaicing is critical for digital cameras, because it converts a Bayer sensor mosaic output to a full color image, which determines the output image quality of the camera. In this work, an efficient decision-based demosaicing method is presented. This method exploits a new edge-sensing measure called integrated gradient (IG) to effectively extract gradient information in both color intensity and color difference domains simultaneously. This measure is reliable and supports full resolution, which allows one to interpolate the missing samples along an appropriate direction and hence directly improves the demosaicing performance. By sharing it in different demosaicing stages to guide the interpolation of various color planes, it guarantees the consistency of the interpolation direction in different color channels and saves the effort required to repeatedly extract gradient information from intermediate interpolation results at different stages. An IG-based green plane enhancement is also proposed to further improve the method's efficiency. Simulation results confirm that the proposed demosaicing method outperforms up-to-date demosaicing methods in terms of output quality at a complexity of around 80 arithmetic operations per pixel.


title: Gut-Brain Cross-Talk in Metabolic Control

abstract: Because human energy metabolism evolved to favor adiposity over leanness, the availability of palatable, easily attainable, and calorically dense foods has led to unprecedented levels of obesity and its associated metabolic co-morbidities that appear resistant to traditional lifestyle interventions. However, recent progress identifying the molecular signaling pathways through which the brain and the gastrointestinal system communicate to govern energy homeostasis, combined with emerging insights on the molecular mechanisms underlying successful bariatric surgery, gives reason to be optimistic that novel precision medicines that mimic, enhance, and/or modulate gut-brain signaling can have unprecedented potential for stopping the obesity and type 2 diabetes pandemics.


title: Computational personality traits assessment: A review

abstract: Computational personality traits assessment is one of an interesting areas in affective computing. It becomes popular because personality identification can be used in many areas and get benefits. Such areas are business, politics, education, social media, medicine, and user interface design. The famous statement "Face is a mirror of the mind" proves that person's appearance depends on the inner aspects of a person. Conversely, Person's behavior and appearance describe the person's personality, so an analyze on appearance and behavior gives knowledge on personality traits. There are varieties of methods have been discovered by researchers to assess personality computationally with various machine learning algorithms. In this paper reviews methods and theories involved in psychological traits assessment and evolution of computational psychological traits assessment with different machine learning algorithms and different feature sets.


Hybrid adaptation for detecting skin in color images


title: Assessing Jitter in Sensor Time Series from Android Mobile Devices

abstract: This paper is motivated by work on a real-time Android navigation system using accelerometer, gyroscope, and camera data. We observed that our attempts at combining data from these different sensors yielded time series with jitter, i.e., inaccurate timestamps, that negatively affected program correctness and reliability. Thus, the aim of this paper is to help Android developers anticipate, assess, and understand problems with data quality in sensor-dependent applications. Our analysis focuses on jitter in timestamps obtained from Android's SensorManager and CameraManager services. We demonstrate the presence and severity of jitter obtained through these services and explain the characteristics of Android that originate jitter, resulting in a taxonomy of the various sources of jitter. Finally, we discuss the results of a quantitative analysis of jitter performed on three Android mobile devices (HTC One M8, Google Nexus 5, and Asus Zenfone 2) and provide recommendations on how Android developers can design sensing applications that will behave correctly across the majority of mobile devices.


title: Integrating Security into Agile Development Methods

abstract: Software developers can use agile software development methods to build secure information systems. Current agile methods have few (if any) explicit security features. While several discrete security methods (such as checklists and management standards) can supplement agile methods, few of these integrate seamlessly into other software development methods. Because of the severe constraints imposed by agile methods, these discrete security techniques integrate very poorly into agile approaches. This article demonstrates how the security features can be integrated into agile methods.


title: The Doherty power amplifier

abstract: In this article, we show that the Doherty amplifier is capable of delivering the stringent requirements of the base station power amplifiers. We explain the operation principles, including both linearity and efficiency improvements, and the basic circuit configuration of the amplifier. Advanced design methods to operate across wide bandwidth and improve the linearity are also described. For verification, the Doherty amplifier is implemented using laterally diffused metal oxide semiconductor (LDMOS) transistors and measured using a WCDMA 4FA signal. These results show that the Doherty amplifier is a promising candidate for base station power amplifiers with wide bandwidth, high efficiency, and linearity


title: A Mixed Complementarity Model of Hydrothermal Electricity Competition in the Western United States

abstract: This paper presents a modeling framework for analyzing competition between multiple firms that each possess a mixture of hydroelectric and thermal generation resources. Based upon the concept of a Cournot oligopoly with a competitive fringe, the model characterizes the Cournot equilibrium conditions of a multiperiod hydrothermal scheduling problem. Using data from the western United States electricity market, this framework is implemented as a mixed linear complementarity model. The results show that some firms may find it profitable to allocate considerably more hydro production to off-peak periods then they would under perfect competition. This strategy is a marked contrast to the optimal hydroschedules that would arise if no firms were acting strategically. These results highlight the need to explicitly consider profit-maximizing behavior when examining the impact of regulatory and environmental policies on electricity market outcomes.


title: PAC-inspired Option Discovery in Lifelong Reinforcement Learning

abstract: A key goal of AI is to create lifelong learning agents that can leverage prior experience to improve performance on later tasks. In reinforcement-learning problems, one way to summarize prior experience for future use is through options, which are temporally extended actions (subpolicies) for how to behave. Options can then be used to potentially accelerate learning in new reinforcement learning tasks. In this work, we provide the first formal analysis of the sample complexity, a measure of learning speed, of reinforcement learning with options. This analysis helps shed light on some interesting prior empirical results on when and how options may accelerate learning. We then quantify the benefit of options in reducing sample complexity of a lifelong learning agent. Finally, the new theoretical insights inspire a novel option-discovery algorithm that aims at minimizing overall sample complexity in lifelong reinforcement learning.


title: ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA

abstract: Long Short-Term Memory (LSTM) is widely used in speech recognition. In order to achieve higher prediction accuracy, machine learning scientists have built increasingly larger models. Such large model is both computation intensive and memory intensive. Deploying such bulky model results in high power consumption and leads to a high total cost of ownership (TCO) of a data center. To speedup the prediction and make it energy efficient, we first propose a load-balance-aware pruning method that can compress the LSTM model size by 20x (10x from pruning and 2x from quantization) with negligible loss of the prediction accuracy. The pruned model is friendly for parallel processing. Next, we propose a scheduler that encodes and partitions the compressed model to multiple PEs for parallelism and schedule the complicated LSTM data flow. Finally, we design the hardware architecture, named Efficient Speech Recognition Engine (ESE) that works directly on the sparse LSTM model. Implemented on Xilinx KU060 FPGA running at 200MHz, ESE has a performance of 282 GOPS working directly on the sparse LSTM network, corresponding to 2.52 TOPS on the dense one, and processes a full LSTM for speech recognition with a power dissipation of 41 Watts. Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations. It achieves 40x and 11.5x higher energy efficiency compared with the CPU and GPU respectively.


title: A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

abstract: In recent years, virtual learning environments are gaining more and more momentum, considering both the technologies deployed in their support and the sheer number of terminals directly or indirectly interacting with them. This essentially means that every day, more and more smart devices play an active role in this exemplary Web of Things scenario. This digital revolution, affecting education, appears clearly intertwined with the earliest forecasts of the Internet of Things, envisioning around 50 billions heterogeneous devices and gadgets to be active by 2020, considering also the deployment of the fog computing paradigm, which moves part of the computational power to the edge of the network. Moreover, these interconnected objects are expected to produce more and more significant streams of data, themselves generated at unprecedented rates, sometimes to be analyzed almost in real time. Concerning educational environments, this translates to a new type of big data stream, which can be labeled as educational big data streams. Here, pieces of information coming from different sources (such as communications between students and instructors, as well as students’ tests, etc.) require accurate analysis and mining techniques in order to retrieve fruitful and well-timed insights from them. This article presents an overview of the current state of the art of virtual learning environments and their limitations; then, it explains the main ideas behind the paradigms of big data streams and of fog computing, in order to introduce an e-learning architecture integrating both of them. Such an action aims to enhance the ability of virtual learning environments to be closer to the needs of all the actors in an educational scenario, as demonstrated by a preliminary implementation of the envisioned architecture. We believe that the proposed big stream and fog-based educational framework may pave the way towards a better understanding of students’ educational behaviors and foster new research directions in the field.


title: Push Attack: Binding Virtual and Real Identities Using Mobile Push Notifications

abstract: Popular mobile apps use push notifications extensively to offer an “always connected” experience to their users. Social networking apps use them as a real-time channel to notify users about new private messages or new social interactions (e.g., friendship request, tagging, etc.). Despite the cryptography used to protect these communication channels, the strict temporal binding between the actions that trigger the notifications and the reception of the notification messages in the mobile device may represent a privacy issue. In this work, we present the push notification attack designed to bind the physical owners of mobile devices with their virtual identities, even if pseudonyms are used. In an online attack, an active attacker triggers a push notification and captures the notification packets that transit in the network. In an offline attack, a passive attacker correlates the social network activity of a user with the received push notification. The push notification attack bypasses the standard ways of protecting user privacy based on the network layer by operating at the application level. It requires no additional software on the victim’s mobile device.


title: Improving Comprehension of Measurements Using Concrete Re-expression Strategies

abstract: It can be difficult to understand physical measurements (e.g., 28 lb, 600 gallons) that appear in news stories, data reports, and other documents. We develop tools that automatically re-express unfamiliar measurements using the measurements of familiar objects. Our work makes three contributions: (1) we identify effectiveness criteria for objects used in concrete measurement re-expressions; (2) we operationalize these criteria in a scalable method for mining a large dataset of concrete familiar objects with their physical dimensions from Amazon and Wikipedia; and (3) we develop automated concrete re-expression tools that implement three common re-expression strategies (adding familiar context, reunitization and proportional analogy) as energy minimization algorithms. Crowdsourced evaluations of our tools indicate that people find news articles with re-expressions more helpful and re- expressions help them to better estimate new measurements.


title: LSTM: A Search Space Odyssey

abstract: Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful functional ANalysis Of VAriance framework. In total, we summarize the results of 5400 experimental runs ( $\approx 15$ years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture significantly, and demonstrate the forget gate and the output activation function to be its most critical components. We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.


title: Design and analysis of a compact Substrate Integrated Waveguide bandpass filter for Ku band applications

abstract: The Substrate Integrated Waveguide (SIW) filter is widely used in various RF communications to receive only the desired frequency with least delay. However, the design of SIW filters having high efficiency, compact size and low cost is still a design challenge. In this research work, a compact SIW bandpass filter with tapered via transition and multiple ‘U’ shaped slots is proposed for the Ku band applications. The proposed filter uses a tapered via transition to provide a smooth transition from microstrip line (planar structure) to SIW (waveguide structure) with minimal reflection which results in better S 11 performance and wider bandwidth. The stopband performance of the proposed filter is improved significantly by introducing four numbers of ‘U’ shaped slots in the SIW structure which introduces transmission zeros in the upper stopband. The simulated results obtained from HFSS v.14 shows that the proposed filter has improved performance parameters such as low reflection, high isolation, minimal group delay, etc which make it suitable for Ku band applications.


title: A wide-scan printed planar K-band microwave lens

abstract: Microwave lenses are an attractive option for switched beam antenna systems because several beams can share a single aperture. Of particular interest are printed planar lenses due to their light weight, low volume, and ease of fabrication. Several researchers have developed such lenses using two different methods. The first method is to connect the antennas on the transmit and receive side with delay lines to obtain the appropriate phase response (McGrath, D.T., 1986; Popovic, D. and Popovic, Z., 2002; Pozar, D.M., 1996). The second, the antenna-filter-antenna (AFA) method, is to couple the antennas resonantly on the transmit and receive side forming a bandpass filter response and to scale the unit cells to obtain the desired phase response (Abbaspour-Tamijani, A. et al., IEEE AP-S Symp. Dig., p.675-8, 2004). In a switched beam system, it is often desired to have a wide scan angle. A K-band lens is presented with at least /spl plusmn/50/spl deg/ scanning in both the E-plane and the H-plane and no grating lobes using the AFA concept.


title: Origin of the Cytoplasmic pH Changes during Anaerobic Stress in Higher Plant Cells. Carbon-13 and Phosphorous-31 Nuclear Magnetic Resonance Studies

abstract: We tested the contribution of nucleoside triphosphate (NTP) hydrolysis, ethanol, and organic acid syntheses, and H + -pump ATPases activity in the acidosis of anoxic sycamore ( Acer pseudoplatanus ) plant cells. Culture cells were chosen to alter NTP pools and fermentation with specific nutrient media (phosphate [Pi]-deprived and adenine- or glycerol-supplied). In vivo 31 P- and 13 C-nuclear magnetic resonance (NMR) spectroscopy was utilized to noninvasively measure intracellular pHs, Pi, phosphomonoesters, nucleotides, lactate, and ethanol. Following the onset of anoxia, cytoplasmic (cyt) pH (7.5) decreased to 6.8 within 4 to 5 min, whereas vacuolar pH (5.7) and external pH (6.5) remained stable. The NTP pool simultaneously decreased from 210 to −1 cell wet weight, whereas nuceloside diphosphate, nucleoside monophosphate, and cyt pH increased correspondingly. The initial cytoplasmic acidification was at a minimum in Pi-deprived cells containing little NTP, and at a maximum in adenine-incubated cells showing the highest NTP concentration. Our data show that the release of H + ions accompanying the Pi-liberating hydrolysis of NTP was the principal cause of the initial cyt pH drop and that this cytoplasmic acidosis was not overcome by H + extrusion. After 15 min of anoxia, a partial cyt-pH recovery observed in cells supplied with Glc, but not with glycerol, was attributed to the H + -consuming ATP synthesis accompanying ethanolic fermentation. Following re-oxygenation, the cyt pH recovered its initial value (7.5) within 2 to 3 min, whereas external pH decreased abruptly. We suggest that the H + -pumping ATPase located in the plasma membrane was blocked in anoxia and quickly reactivated after re-oxygenation.


title: Survey over image thresholding techniques and quantitative performance evaluation

abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)


title: Posterior fixation of thoracolumbar burst fracture: short-segment pedicle fixation versus long-segment instrumentation.

abstract: OBJECTIVE ::: The treatment of thoracolumbar burst fracture is a controversial issue. Short-segment (SS) pedicle fixation has become a popular treatment option. However, there are several studies regarding the high rate of failure. The aim of this prospective study was to compare SS versus long-segment (LS) instrumentation. ::: ::: ::: METHODS ::: For this purpose, 18 consecutive patients were assigned to two groups. Group 1 included nine patients treated by SS pedicle fixation, whereas group 2 included nine patients treated by LS instrumentation. SS instrumentation was pedicle fixation one level above and below the fractured vertebra. LS instrumentation was hook fixation (claw hooks attached to second upper vertebra and infralaminar hooks attached to first upper vertebra) above and pedicle fixation (pedicle screws attached to first and second lower vertebrae) below the fractured vertebra. ::: ::: ::: RESULTS ::: As a result, measurements of local kyphosis, sagittal index, and anterior vertebral height compression showed that the LS group had a better outcome at final follow-up (P < 0.05). Also, the SS group had a 55% failure rate, whereas the LS group had prolonged operative time and increased blood loss. However, there was no difference between the two groups according to Low Back Outcome Score. ::: ::: ::: CONCLUSIONS ::: In conclusion, radiographic parameters demonstrated that LS instrumentation is a more effective management of thoracolumbar burst fractures. Nevertheless, clinical outcome was the same between the two groups. However, our conclusions were based on posterior-only surgery. Anterior column support would negate the need for LS fixation. Also, SS would have been more successful if two above and two below pedicle screws were used.


title: Machine Learning Solution Methods for Multistage Stochastic Programming

abstract: This thesis investigates the following question: Can supervised learning techniques be successfully used for finding better solutions to multistage stochastic programs? A similar question had already been posed in the context of reinforcement learning, and had led to algorithmic and conceptual advances in the field of approximate value function methods over the years (Lagoudakis and Parr, 2003; Ernst, Geurts, and Wehenkel, 2005; Langford and Zadrozny, 2005; Antos, Munos, and Szepesvari, 2008). This thesis identifies several ways to exploit the combination “multistage stochastic programming/supervised learning” for sequential decision making under uncertainty. Multistage stochastic programming is essentially the extension of stochastic programming (Dantzig, 1955; Beale, 1955) to several recourse stages. After an introduction to multistage stochastic programming and a summary of existing approximation approaches based on scenario trees, this thesis mainly focusses on the use of supervised learning for building decision policies from scenario-tree approximations. Two ways of exploiting learned policies in the context of the practical issues posed by the multistage stochastic programming framework are explored: the fast evaluation of performance guarantees for a given approximation, and the selection of good scenario trees. The computational efficiency of the approach allows novel investigations relative to the construction of scenario trees, from which novel insights, solution approaches and algorithms are derived. For instance, we generate and select scenario trees with random branching structures for problems over large planning horizons. Our experiments on the empirical performances of learned policies, compared to golden-standard policies, suggest that the combination of stochastic programming and machine learning techniques could also constitute a method per se for sequential decision making under uncertainty, inasmuch as learned policies are simple to use, and come with performance guarantees that can actually be quite good. Finally, limitations of approaches that build an explicit model to represent an optimal solution mapping are studied in a simple parametric programming setting, and various insights regarding this issue are obtained.


title: Sumblr: continuous summarization of evolving tweet streams

abstract: With the explosive growth of microblogging services, short-text messages (also known as tweets) are being created and shared at an unprecedented rate. Tweets in its raw form can be incredibly informative, but also overwhelming. For both end-users and data analysts it is a nightmare to plow through millions of tweets which contain enormous noises and redundancies. In this paper, we study continuous tweet summarization as a solution to address this problem. While traditional document summarization methods focus on static and small-scale data, we aim to deal with dynamic, quickly arriving, and large-scale tweet streams. We propose a novel prototype called Sumblr (SUMmarization By stream cLusteRing) for tweet streams. We first propose an online tweet stream clustering algorithm to cluster tweets and maintain distilled statistics called Tweet Cluster Vectors. Then we develop a TCV-Rank summarization technique for generating online summaries and historical summaries of arbitrary time durations. Finally, we describe a topic evolvement detection method, which consumes online and historical summaries to produce timelines automatically from tweet streams. Our experiments on large-scale real tweets demonstrate the efficiency and effectiveness of our approach.


title: Data Mining the Data Processing Technologies for Inventory Management

abstract: This research applied various data mining approaches to investigate the innovations of data processing technologies for inventory management based on the database of the United States Patent and Trademark Office. The first objective of data mining in this study is to find the core technologies by evaluating patent citation matrix and patent strength. This information can help companies to choose suitable tools through the understanding of the most essential innovations. A total of 63 core technologies were identified from 949 patents under the US patent class of 705/28. Besides, a network of patent development paths was also derived to illustrate the correlations of core advancements. Finally, this study adopted the method of nonhierarchical clustering analysis to identify key groups of technologies through the symmetrical matrix of relative correlation strength. Enterprise can refer the findings of clustering to recognize the trend and characteristics of data processing technologies for their strategic technology management.