title: Generating Consistent Buildings: A Semantic Approach for Integrating Procedural Techniques

abstract: Computer games often take place in extensive virtual worlds, attractive for roaming and exploring. Unfortunately, current virtual cities can strongly hinder this kind of gameplay, since the buildings they feature typically have replicated interiors, or no interiors at all. Procedural content generation is becoming more established, with many techniques for automatically creating specific building elements. However, the integration of these techniques to form complete buildings is still largely unexplored, limiting their application to open game worlds. We propose a novel approach that integrates existing procedural techniques to generate such buildings. With minimal extensions, individual techniques can be coordinated to create buildings with consistently interrelated exteriors and interiors, as in the real world. Our solution offers a framework where various procedural techniques communicate with a moderator, which is responsible for negotiating the placement of building elements, making use of a library of semantic classes and constraints. We demonstrate the applicability of our approach by presenting several examples featuring the integration of a facade shape grammar, two different floor plan layout generation techniques, and furniture placement techniques. We conclude that this approach allows one to preserve the individual qualities of existing procedural techniques, while assisting the consistency maintenance of the generated buildings.


Bayesian Conditional Random Fields


title: Influence of Music on Anxiety Induced by Fear of Heights in Virtual Reality

abstract: Music is a potent mood regulator that can induce relaxation and reduce anxiety in different situations. While several studies demonstrate that certain types of music have a subjective anxiolytic effect, the reported results from physiological responses are less conclusive. Virtual reality allows us to study diverse scenarios of real life under strict experimental control while preserving high ecological validity. We aimed to study the modulating effect of music on the anxiety responses triggered by an immersive virtual reality scenario designed to induce fear of heights. Subjects experienced a virtual scenario depicting an exterior elevator platform ascending and descending the total height of its 350 meters tall supporting structure. Participants were allocated to either a group that experienced the elevator ride with background music or without, in a between-groups design. Furthermore, each group included participants with different degrees of fear of heights, ranging from low to high fear. Recordings of heart rate, galvanic skin response, body balance, and head movements were obtained during the experiments. Subjective anxiety was measured by means of three questionnaires. The scenario produced significant changes in subjective and physiological measures, confirming its efficacy as a stressor. A significant increase in state anxiety was found between pre and post-assessment in the silence group, but not in the music group, indicating that post-stress recovery was faster in the musical group. Results suggest that music can ameliorate the subjective anxiety produced by fear of heights.


Antibody levels to parainfluenza, herpes simplex, varicella-zoster, cytomegalo virus, and measles virus in patients with connective tissue diseases.


title: Stock market prediction

abstract: In a financially volatile market, as the stock market, it is important to have a very precise prediction of a future trend. Because of the financial crisis and scoring profits, it is mandatory to have a secure prediction of the values of the stocks. Predicting a non-linear signal requires advanced algorithms of machine learning. The literature contains studies with different machine learning algorithms such as ANN (artificial neural networks) with different feature selection. The results of this study will show that the algorithm of classification SVM (Support Vector Machines) with the help of feature selection PCA (Principal component analysis) will have the success of making a profit.


title: Analytics for the internet of things

abstract: This paper presents ongoing work on an approach to remotely observe the usage of connected products, analyze collected data and dynamically refine the observation mechanisms for better data. This allows for iteratively working towards the most elaborate, meaningful, and relevant representation of usage behavior in the form of structured and semantically annotated data traces. We show an implementation of the approach in the D'PUIS framework.


title: A 55nm time-domain mixed-signal neuromorphic accelerator with stochastic synapses and embedded reinforcement learning for autonomous micro-robots

abstract: Even as rapid advances are being made in the areas of deep neural networks (DNNs) and convolutional neural networks (CNNs) with most hardware demonstrations geared towards inference in vision-based platforms [1-5], we recognize that true autonomy in intelligent agents will only emerge when such bio-mimetic systems can perform continuous learning through interactions with the environment. Reinforcement learning (RL) presents one such computational paradigm inspired by behaviorist psychology, where autonomous agents take actions in an environment to maximize a notion of cumulative reward. This concept is deeply rooted in the human brain where dopamine mediated neurotransmitters (in the cortex, striatum and thalamus of the brain) have been shown to encourage reward-motivated behavior in all our social interactions (Fig. 7.4.1). In this paper, we present a 690μW (V CC =1.2V) neuromorphic accelerator fabricated in 55nm CMOS, which: (1) inherits unique properties of stochastic neural networks, (2) leverages recent advances in Q-learning as an implementation of RL, and (3) demonstrates energy-efficient time-domain mixed-signal (TD-MS) circuit architectures, to provide autonomy to a mobile, self-driving micro-robot at the edge of the cloud, with possible applications in disaster relief, reconnaissance and personal robotics.


title: High-Precision D-Band FMCW-Radar Sensor Based on a Wideband SiGe-Transceiver MMIC

abstract: In this paper, a miniaturized D-band frequency-modulated continuous-wave (FMCW) radar sensor with 48-GHz bandwidth (32.8%, 122-170 GHz) and a high measurement rate of > 1 kHz for multi-target vibration measurements is presented. The sensor is based on a SiGe transceiver monolithic microwave integrated circuit manufactured via Infineon's B7HF200 bipolar production technology with an fT of 170 GHz and fmax of 250 GHz. Gilbert cell, push-pull, and varactor-based doubler concepts on manufactured chips are compared, and the most promising signal source is embedded into a transceiver chip, which forms the main component of the presented radar sensor. The maximum output power of the system is ≈ -10 dBm and a phase noise of ≈ -80 dBc/Hz is achieved. Measurements are provided to demonstrate the sensor characteristics and show the promising results of FMCW radar in highest precision distance and multi-target vibration measurement applications. Due to the covered wide bandwidth, a range resolution of 5.88 mm is achieved ( -6-dB width, Tukey window). The sensor's distance measurement repeatability is 290 nm (65 nm with 10 × averaging and 0.5-m target distance), and the distance measurement accuracy is m for a target in 65-cm distance moving 1 cm. Additionally, vibration measurement results and range-Doppler plots for advanced multi-target applications are presented.


title: 3D region segmentation using topological persistence

abstract: A ‘region’ is an important concept in interpreting 3D point cloud data since regions may correspond to objects in a scene. To correctly interpret 3D point cloud data, we need to partition the dataset into regions that correspond to objects or parts of an object. In this paper, we present a region growing approach that combines global (topological) and local (color, surface normal) information to segment 3D point cloud data. Using ideas from persistent homology theory, our algorithm grows a simplicial complex representation of the point cloud dataset. At each step in the growth process we compute the zeroth homology group of the complex, which corresponds to the number of connected components, and use color and surface normal statistics to build regions. Lastly, we extract out the segmented regions of the dataset. We show that this method provides a stable segmentation of point cloud data in the presence of noise and poorly sampled data, thus providing advantages over contemporary region-based segmentation techniques.


title: Load Hiding of Household's Power Demand

abstract: With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.


title: Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer

abstract: Deep networks often perform well on the data manifold on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. We propose Manifold Mixup which encourages the network to produce more reasonable and less confident predictions at points with combinations of attributes not seen in the training set. This is accomplished by training on convex combinations of the hidden state representations of data samples. Using this method, we demonstrate improved semi-supervised learning, learning with limited labeled data, and robustness to adversarial examples. Manifold Mixup requires no (significant) additional computation. Analytical experiments on both real data and synthetic data directly support our hypothesis for why the Manifold Mixup method improves results.


title: An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization

abstract: In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models for low-resource languages. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.


title: SafeSpec: Banishing the Spectre of a Meltdown with Leakage-Free Speculation

abstract: Speculative attacks, such as Spectre and Meltdown, target speculative execution to access privileged data and leak it through a side-channel. In this paper, we introduce (SafeSpec), a new model for supporting speculation in a way that is immune to the side-channel leakage by storing side effects of speculative instructions in separate structures until they commit. Additionally, we address the possibility of a covert channel from speculative instructions to committed instructions before these instructions are committed. We develop a cycle accurate model of modified design of an x86-64 processor and show that the performance impact is negligible.


title: A qualitative meta-synthesis of emergency department staff experiences of violence and aggression

abstract: INTRODUCTION ::: Patient and visitor violence or aggression against healthcare workers in the Emergency Department (ED) is a significant issue worldwide. This review synthesises existing qualitative studies exploring the first-hand experiences of staff working in the ED to provide insight into preventing this issue. ::: ::: ::: METHOD ::: A meta-ethnographic approach was used to review papers. ::: ::: ::: RESULTS ::: Four concepts were identified: 'The inevitability of violence and aggression'; 'Staff judgments about why they face violence and aggression'; 'Managing in isolation'; and 'Wounded heroes'. ::: ::: ::: DISCUSSION ::: Staff resigned themselves to the inevitability of violence and aggression, doing this due to a perceived lack of support from the organisation. Staff made judgements about the reasons for violent incidents which impacted on how they coped and subsequently tolerated the aggressor. Staff often felt isolated when managing violence and aggression. Key recommendations included: Staff training in understanding violence and aggression and clinical supervision. ::: ::: ::: CONCLUSION ::: Violence and aggression in the ED can often be an overwhelming yet inevitable experience for staff. A strong organisational commitment to reducing violence and aggression is imperative.


title: Active query selection for semi-supervised clustering

abstract: Semi-supervised clustering allows a user to specify available prior knowledge about the data to improve the clustering performance. A common way to express this information is in the form of pair-wise constraints. A number of studies have shown that, in general, these constraints improve the resulting data partition. However, the choice of constraints is critical since improperly chosen constraints might actually degrade the clustering performance. We focus on constraint (also known as query) selection for improving the performance of semi-supervised clustering algorithms. We present an active query selection mechanism, where the queries are selected using a min-max criterion. Experimental results on a variety of datasets, using MPCK-means as the underlying semi-clustering algorithm, demonstrate the superior performance of the proposed query selection procedure.


title: Evolutionary Multi-Objective Workflow Scheduling in Cloud

abstract: Cloud computing provides promising platforms for executing large applications with enormous computational resources to offer on demand. In a Cloud model, users are charged based on their usage of resources and the required quality of service (QoS) specifications. Although there are many existing workflow scheduling algorithms in traditional distributed or heterogeneous computing environments, they have difficulties in being directly applied to the Cloud environments since Cloud differs from traditional heterogeneous environments by its service-based resource managing method and pay-per-use pricing strategies. In this paper, we highlight such difficulties, and model the workflow scheduling problem which optimizes both makespan and cost as a Multi-objective Optimization Problem (MOP) for the Cloud environments. We propose an evolutionary multi-objective optimization (EMO)-based algorithm to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform. Novel schemes for problem-specific encoding and population initialization, fitness evaluation and genetic operators are proposed in this algorithm. Extensive experiments on real world workflows and randomly generated workflows show that the schedules produced by our evolutionary algorithm present more stability on most of the workflows with the instance-based IaaS computing and pricing models. The results also show that our algorithm can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases. The conducted experiments are based on the on-demand instance types of Amazon EC2; however, the proposed algorithm are easy to be extended to the resources and pricing models of other IaaS services.


title: Dropout as data augmentation

abstract: Dropout is typically interpreted as bagging a large number of models sharing parameters. We show that using dropout in a network can also be interpreted as a kind of data augmentation in the input space without domain knowledge. We present an approach to projecting the dropout noise within a network back into the input space, thereby generating augmented versions of the training data, and we show that training a deterministic network on the augmented samples yields similar results. Finally, we propose a new dropout noise scheme based on our observations and show that it improves dropout results without adding significant computational cost.


title: Styles and strategies of learning

abstract: Summary. Previous results are reviewed and two series of experiments on learning are described, one carried out in the laboratory, and the other in educational institutions. Both series use ‘conversational’ systems which allow mental activities to be described in terms of dialogue and behaviour. Several types of result are reported: (a) the significance of understanding; (b) the existence of, and variations in, learning strategies; (c) the effect of matching and mismatching a teaching strategy to an individual's learning strategy; (d) the nature and classification of cognitive style; and (e) transfer effects and ‘learning to learn.’ A theoretical basis is developed for a classification of learning styles.


title: Vertical partitioning for database design: a graphical algorithm

abstract: Vertical partitioning is the process of subdividing the attributes of a relation or a record type, creating fragments. Previous approaches have used an iterative binary partitioning method which is based on clustering algorithms and mathematical cost functions. In this paper, however, we propose a new vertical partitioning algorithm using a graphical technique. This algorithm starts from the attribute affinity matrix by considering it as a complete graph. Then, forming a linearly connected spanning tree, it generates all meaningful fragments simultaneously by considering a cycle as a fragment. We show its computational superiority. It provides a cleaner alternative without arbitrary objective functions and provides an improvement over our previous work on vertical partitioning.


Decision-Driven Models with Probabilistic Soft Logic


BackIP : Backpressure Routing in IPv 6-Based Wireless Sensor Networks


title: Pseudo-Projective Dependency Parsing

abstract: In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective dependency structures. We show how a data-driven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transformation techniques to produce non-projective structures. Experiments using data from the Prague Dependency Treebank show that the combined system can handle non-projective constructions with a precision sufficient to yield a significant improvement in overall parsing accuracy. This leads to the best reported performance for robust non-projective parsing of Czech.


title: Unsupervised query segmentation using generative language models and wikipedia

abstract: In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. We use a generative query model to recover a query's underlying concepts that compose its original segmented form. The model's parameters are estimated using an expectation-maximization (EM) algorithm, optimizing the minimum description length objective function on a partial corpus that is specific to the query. To augment this unsupervised learning, we incorporate evidence from Wikipedia. Experiments show that our approach dramatically improves performance over the traditional approach that is based on mutual information, and produces comparable results with a supervised method. In particular, the basic generative language model contributes a 7.4% improvement over the mutual information based method (measured by segment F1 on the Intersection test set). EM optimization further improves the performance by 14.3%. Additional knowledge from Wikipedia provides another improvement of 24.3%, adding up to a total of 46% improvement (from 0.530 to 0.774).


title: Flash Photography for Data-Driven Hidden Scene Recovery

abstract: Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these tasks can be done around corners with consumer cameras. Recent techniques for NLOS imaging using consumer cameras have not been able to both localize and identify the hidden object. We introduce a method that couples traditional geometric understanding and data-driven techniques. To avoid the limitation of large dataset gathering, we train the data-driven models on rendered samples to computationally recover the hidden scene on real data. The method has three independent operating modes: 1) a regression output to localize a hidden object in 2D, 2) an identification output to identify the object type or pose, and 3) a generative network to reconstruct the hidden scene from a new viewpoint. The method is able to localize 12cm wide hidden objects in 2D with 1.7cm accuracy. The method also identifies the hidden object class with 87.7% accuracy (compared to 33.3% random accuracy). This paper also provides an analysis on the distribution of information that encodes the occluded object in the accessible scene. We show that, unlike previously thought, the area that extends beyond the corner is essential for accurate object localization and identification.


title: A model of ERP project implementation

abstract: Problems with the implementation of ERP systems are well documented. Although companies spend millions on ERP packages and the implementation process, there is extensive evidence that they experience considerable problems, particularly during the actual implementation project. This paper presents a project phase model (PPM) of ERP implementation projects that is a synthesis of existing ERP implementation process models and focuses on the implementation project. Two case studies of ERP implementation within the same organization, one unsuccessful and a later successful one, are reported and analysed in order to determine which critical success factors (CSFs) are necessary within each phase of the PPM. The CSFs are drawn from an earlier stage of this research and from recent literature. The PPM is used as a 'lens' for understanding ERP implementation projects, by highlighting the differences between the two cases. We then offer an explanation for these differences, focusing particularly on the successful ca...


title: A prototype real-time intrusion-detection expert system

abstract: The design and implementation of a prototype intrusion-detection expert system (IDES) are described. IDES is based on the concept that an intrusion manifests itself as a departure from expected behavior for a user. The prototype monitors users on a remote system, using audit records that characterize their activities. It adaptively learns the normal behavior of each user and detects and reports anomalous user behavior in real time. >


Brian: a simulator for spiking neural networks in Python


Model Predictive Control applied to tracking and attitude stabilization of a VTOL quadrotor aircraft


title: A point-based method for animating elastoplastic solids

abstract: In this paper we describe a point-based approach for animating elastoplastic materials. Our primary contribution is a simple method for computing the deformation gradient for each particle in the simulation. The deformation gradient is computed for each particle by finding the affine transformation that best approximates the motion of neighboring particles over a single timestep. These transformations are then composed to compute the total deformation gradient that describes the deformation around a particle over the course of the simulation. Given the deformation gradient we can apply arbitrary constitutive models and compute the resulting elastic forces. Our method has two primary advantages: we do not store or compare to an initial rest configuration and we work directly with the deformation gradient. The first advantage avoids poor numerical conditioning and the second naturally leads to a multiplicative model of deformation appropriate for finite deformations. We demonstrate our approach on a number of examples that exhibit a wide range of material behaviors.


title: gSkeletonClu: Density-Based Network Clustering via Structure-Connected Tree Division or Agglomeration

abstract: Community detection is an important task for mining the structure and function of complex networks. Many pervious approaches are difficult to detect communities with arbitrary size and shape, and are unable to identify hubs and outliers. A recently proposed network clustering algorithm, SCAN, is effective and can overcome this difficulty. However, it depends on a sensitive parameter: minimum similarity threshold $\varepsilon$, but provides no automated way to find it. In this paper, we propose a novel density-based network clustering algorithm, called gSkeletonClu (graph-skeleton based clustering). By projecting a network to its Core-Connected Maximal Spanning Tree (CCMST), the network clustering problem is converted to finding core-connected components in the CCMST. We discover that all possible values of the parameter $\varepsilon$ lie in the edge weights of the corresponding CCMST. By means of tree divisive or agglomerative clustering, our algorithm can find the optimal parameter $\varepsilon$ and detect communities, hubs and outliers in large-scale undirected networks automatically without any user interaction. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu over the baseline methods.


title: A comparative study of methods for estimating query language models with pseudo feedback

abstract: We systematically compare five representative state-of-the-art methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two variants of the relevance language model, two variants of the mixture feedback model, and the divergence minimization estimation method. Our experiment results show that a variant of relevance model and a variant of the mixture model tend to outperform other methods. We further propose several heuristics that are intuitively related to the good retrieval performance of an estimation method, and show that the variations in how these heuristics are implemented in different methods provide a good explanation of many empirical observations.


abstract: In this research, the wafer level Cu/BCB hybrid bonding with TSV for 3D integration by using fly cutting technology is proposed. As we know Cu bump surface is rough by electroplating, and BCB is spin-coated on Cu bump wafer induced high topography. Cu bump surface roughness and Cu/BCB co-planarization are improved by fly cutting to achieve good Cu to Cu and BCB to BCB bonding interface without any large bonding voids at 250°C for 30min, and the result of the bonding strength is evaluated by shear test. TSV fabrication, micro-bumping, hybrid bonding, wafer thinning and backside RDL formation are well developed and integrated to perform the 3D integration platform. Cu/BCB hybrid bonding with TSV for 3D integration is successfully developed and demonstrated in this paper.


Semi-Supervised Disfluency Detection


title: Application of a Hybrid ARIMA and Neural Network Model to Water Quality Time Series Forecasting

abstract: In this paper the water quality forecasting at the Nanjinguan water quality monitoring station of Yangtze River, China, is presented. The time series data used are weekly water quality data obtained directly from Nanjinguan station measurements over the course of five years. In order to forecast water quality, hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANNs) models were developed. The ARIMA models were first used to do water quality forecasting of the time series data and then with the obtained errors ANNs were built taking into account the nonlinear patterns that the ARIMA technique could not capture, in order to reduce potential errors. Once the hybrid models were developed 38 samples out of the data for the station were used to do the water quality forecasting and the results were compared with the ARIMA and the ANNs models worked separately. Statistical error measures such as the root mean square error (RMSE), the mean absolute percentage error (MAPE) and correlation coefficient (R) were calculated to compare the three methods. The results showed that the hybrid models predict the water quality with a higher accuracy than the ARIMA and ANNs models in the examined station.


title: Hyperspectral Light Field Stereo Matching

abstract: In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 x 6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues that rely on BWNCC. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses.


Ethics in international business: multinational approaches to child labor


title: A Real-Time Sliding Mode Control for a Wind Energy System Based on a Doubly Fed Induction Generator

abstract: In this paper, a real time sliding mode control scheme for a variable speed wind turbine that incorporates a doubly feed induction generator is described. In this design, the so-called vector control theory is applied, in order to simplify the system electrical equations. The proposed control scheme involves a low computational cost and therefore can be implemented in real-time applications using a low cost Digital Signal Processor (DSP). The stability analysis of the proposed sliding mode controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. A new experimental platform has been designed and constructed in order to analyze the real-time performance of the proposed controller in a real system. Finally, the experimental validation carried out in the experimental platform shows; on the one hand that the proposed controller provides high-performance dynamic characteristics, and on the other hand that this scheme is robust with respect to the uncertainties that usually appear in the real systems.


title: Adaptation of Hybrid ANN/HMM Models Using Linear Hidden Transformations and Conservative Training

abstract: A technique is proposed for the adaptation of automatic speech recognition systems using Hybrid models combining Artificial Neural Networks with Hidden Markov Models. The application of linear transformations not only to the input features, but also to the outputs of the internal layers is investigated. The motivation is that the outputs of an internal layer represent a projection of the input pattern into a space where it should be easier to learn the classification or transformation expected at the output of the network. A new solution, called Conservative Training, is proposed that compensates for the lack of adaptation samples in certain classes. Supervised adaptation experiments with different corpora and for different adaptation types are described. The results show that the proposed approach always outperforms the use of transformations in the feature space and yields even better results when combined with linear input transformations.


title: Disaster Management Projects Using Wireless Sensor Networks: An Overview

abstract: There are numerous projects dealing with disaster management and emergency response that use wireless sensor networks technologies. Indeed, WSNs offer a good alternative compared to traditional ad hoc networks. Air pollution monitoring, forest fire detection, landslide detection, natural disaster prevention, industrial sense and control applications, dangerous gas leakage, water level monitoring, vibration detection to prevent an earthquake, radiation monitoring are examples of the WSN applications related to disaster management. This paper presents an overview of the recent projects using WSN to collect data in disaster areas.


title: Control of electric vehicles fast charging station supplied by PV/energy storage system/grid

abstract: Currently, electric vehicles (EV) are going through an important emergence, nevertheless, the technology associated to them is still under development. This paper presents a fast charging station (FCS) for EVs supplied by renewable energy with a novel decentralized control of the system. The fast CS is composed of a photovoltaic (PV) system, an energy storage system (ESS), and a connection with the local grid. Thanks to this configuration, the FCS is able to work as a stand-alone system most of the time, with occasional grid support. The proposed control is based on the voltage control of the common medium voltage DC (MVDC) bus, to which all the energy sources are connected. Thus, depending on its voltage, the PV system, the ESS or the grid are used to supply the energy demanded by the EVs. To show the viability of this control, the charging of two EVs is simulated under different operating conditions. Simulation results show the proper operation of the FCS.


title: Suspecting Less and Doing Better: New Insights on Palmprint Identification for Faster and More Accurate Matching

abstract: This paper introduces a generalized palmprint identification framework to unify several state-of-art 2D and 3D palmprint methods. Through this framework, we argue that the methods employing one-to-one matching strategy and binary representation for feature are more effective for palmprint identification. The analysis for the first argument is based on a statistical matching model and is supported by outperforming results on several publicly available 2D palmprpint databases. These two arguments are further evaluated for 3D palmprint matching and used to introduce a new method for encoding 3D palmprint feature. The proposed 3D feature is binary and more efficiently computed. It encodes the 3D shape of palmprint to either convex or concave. The experimental results on two publicly available, from contactless and contact-base 3D palmprint database of 177 and 200 subjects, respectively, outperform the state-of-the-art methods. This paper also provides our palmprint matching algorithm(s) in public domain, unlike the previous work in this area, which will help to further advance research efforts in this area.


title: Privacy enhanced recommender system

abstract: Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data and generate recommendations by processing them under encryption. With this approach, the service provider learns no information on any user’s preferences or the recommendations made. The proposed method is based on homomorphic encryption schemes and secure multiparty computation (MPC) techniques. The overhead of working in the encrypted domain is minimized by packing data as shown in the complexity analysis.


title: Revealing the vectors of cellular identity with single-cell genomics

abstract: Computational methods for analyzing single-cell data are uncovering new ways of defining cells.


title: The Propagation of Technology Management Taxonomies for Evaluating Investments in Information Systems

abstract: The management of Information Technology (IT) and Information Systems (IS) is considered a complex exercise by academics and practitioners alike. The reason for this is that there are ubiquitous portfolios of tangible and intangible benefits that are offered to an organization following the adoption of IT/IS that, in turn, all need managing to ensure realization. Organizations also have to take into account the direct and often larger indirect costs that are typically associated with IT/IS deployments. To provide managers with a critical insight into the management of new technology, this paper uses a case study research strategy to examine the technology management experiences of a leading U.K. manufacturing organization during its adoption of a vendor-supplied Manufacturing Resource Planning (MRPII) information system. Following the lack of attention given to human and organizational technology management factors while implementing MRPII, the vendor-based information system was later abandoned and deemed a failure. In addressing those technology management factors that were later identified as important, it was found that key employees were able to overcome a number of organizational barriers and develop and implement a bespoke MRPII system that significantly improved the organization's competitive position. Technology management taxonomies that contributed to the failure and later successful implementation of MRPII are identified and discussed. The organization's experiences in solving the problems associated with the implementation of their IS offers a learning opportunity for those companies that are seeking a competitive advantage through technology management.


title: Which model to use for cortical spiking neurons?

abstract: We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.


title: A survey of bio-inspired robotics hands implementation: New directions in dexterous manipulation

abstract: Recently, significant advances have been made in ROBOTICS, ARTIFICIAL INTELLIGENCE and other COGNITIVE related fields, allowing to make much sophisticated biomimetic robotics systems. In addition, enormous number of robots have been designed and assembled, explicitly realize biological oriented behaviors. Towards much skill behaviors and adequate grasping abilities (i.e. ARTICULATION and DEXTEROUS MANIPULATION), a new phase of dexterous hands have been developed recently with biomimetically oriented and bio-inspired functionalities. In this respect, this manuscript brings a detailed survey of biomimetic based dexterous robotics multi-fingered hands. The aim of this survey, is to find out the state of the art on dexterous robotics end-effectors, known in literature as (ROBOTIC HANDS) or (DEXTEROUS MULTI-FINGERED) robot hands. Hence, this review finds such biomimetic approaches using a framework that permits for a common description of biological and technical based hand manipulation behavior. In particular, the manuscript focuses on a number of developments that have been taking place over the past two decades, and some recent developments related to this biomimetic field of research. In conclusions, the study found that, there are rich research efforts in terms of KINEMATICS, DYNAMICS, MODELING and CONTROL methodologies. The survey is also indicating that, the topic of biomimetic inspired robotics systems make significant contributions to robotics hand design, in four main directions for future research. First, they provide a genuine world test of models of biologically inspired hand designs and dexterous manipulation behaviors. Second, they provide novel manipulation articulations and mechanisms available for industrial and domestic uses, most notably in the field of human like hand design and real world applications. Third, this survey has also indicated that, there are quite large number of attempts to acquire biologically inspired hands. These attempts were almost successful, where they exposed more novel ideas for further developments. Such inspirations were directed towards a number of topics related (HAND MECHANICS AND DESIGN), (HAND TACTILE SENSING), (HAND FORCE SENSING), (HAND SOFT ACTUATION) and (HAND CONFIGURATION AND TOPOLOGY). FOURTH, in terms of employing AI related sciences and cognitive thinking, it was also found that, rare and exceptional research attempts were directed towards the employment of biologically inspired thinking, i.e. (AI, BRAIN AND COGNITIVE SCIENCES) for hand upper control and towards much sophisticated dexterous movements. Throughout the study, it has been found there are number of efforts in terms of mechanics and hand designs, tactical sensing, however, for hand soft actuation, it seems this area of research is still far away from having a realistic muscular type fingers and hand movements.


title: Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems

abstract: Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding errors, slot-level errors can have more impact on the overall performance of a dialogue system compared to intent-level errors. In addition, our experiments demonstrate that the reinforcement learning based dialogue system is able to learn when and what to confirm in order to achieve better performance and greater robustness.


Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models


title: The Hippocampal-VTA Loop: Controlling the Entry of Information into Long-Term Memory

abstract: In this article we develop the concept that the hippocampus and the midbrain dopaminergic neurons of the ventral tegmental area (VTA) form a functional loop. Activation of the loop begins when the hippocampus detects newly arrived information that is not already stored in its long-term memory. The resulting novelty signal is conveyed through the subiculum, accumbens, and ventral pallidum to the VTA where it contributes (along with salience and goal information) to the novelty-dependent firing of these cells. In the upward arm of the loop, dopamine (DA) is released within the hippocampus; this produces an enhancement of LTP and learning. These findings support a model whereby the hippocampal-VTA loop regulates the entry of information into long-term memory.


title: Sparse Non-negative Matrix Language Modeling

abstract: We present Sparse Non-negative Matrix (SNM) estimation, a novel probability estimation technique for language modeling that can efficiently incorporate arbitrary features. We evaluate SNM language models on two corpora: the One Billion Word Benchmark and a subset of the LDC English Gigaword corpus. Results show that SNM language models trained with n-gram features are a close match for the well-established Kneser-Ney models. The addition of skip-gram features yields a model that is in the same league as the state-of-the-art recurrent neural network language models, as well as complementary: combining the two modeling techniques yields the best known result on the One Billion Word Benchmark. On the Gigaword corpus further improvements are observed using features that cross sentence boundaries. The computational advantages of SNM estimation over both maximum entropy and neural network estimation are probably its main strength, promising an approach that has large flexibility in combining arbitrary features and yet scales gracefully to large amounts of data.


title: Knowledge management technology and the reproduction of knowledge work practices

abstract: Abstract Organizations seeking ways to manage their knowledge assets are increasingly turning to information technology for solutions. As knowledge management systems are being developed and implemented, it behooves both practitioners and researchers to learn from the successes and failures of more established types of information systems including MIS and DSS. According to the Standish Group, the implementation success rate for these systems runs at around 30%. Many argue that these low success rates are, in part, attributable to technologists’ lack of understanding of the situated work practices of the systems’ user communities. This has lead to increasing calls for research on work practice in the field of Information Systems. Unfortunately, it is not always clear what is meant by work practice. Furthermore, the consideration of work practice outside of its circuit of reproduction can be misleading. By circuits of reproduction we mean the reciprocal relationships through which practice creates and recreates the objectified social structures and conditions in which it occurs. In this paper, we adopt Bourdieu's Theory of Practice to illuminate work practices and their circuits of reproduction. Relying on data that were collected during an eight-month ethnography of knowledge work practices in a US-based, Fortune 500 manufacturing firm, we focus on the situated “gatekeeping” practices of a group of competitive intelligence analysts and explore how their situated practices were at odds with the generalized “gatekeeping” practices embedded in a knowledge management technology whose implementation they themselves were advocating. We argue that their inability to see this incongruence until very late in the pilot implementation is associated with an understanding of their work practices in isolation, i.e. outside of their circuits of reproduction.


title: A brain-computer interface with vibrotactile biofeedback for haptic information

abstract: BackgroundIt has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.MethodsA Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.Results and ConclusionSubjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.


title: Intelligent web crawler

abstract: As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. Web crawlers are one of the most crucial components in search engines and their optimization would have a great effect on improving the searching efficiency. This paper, introduces an intelligent web crawler that uses an ontological engineering concepts for improving its crawling performance. Intelligent crawler estimates the best path for crawling. This is the first crawler that acts intelligently without any relevance feedback or training.


title: A multichannel CSMA MAC protocol with receiver-based channel selection for multihop wireless networks

abstract: We propose a CSMA-based medium access control protocol for multihop wireless networks that uses multiple channels and a dynamic channel selection method. The proposed protocol uses one control channel and N data channels, where N is independent of the number of nodes in the network. The source uses an exchange of control packets on the control channel to decide on the best channel to send the data packet on. Channel selection is based on maximizing the signal-to-interference plus noise ratio at the receiver. We present performance evaluations obtained from simulations that demonstrate the effectiveness of the proposed protocol.


Themed articles: Virtuality and emotion: Introduction


title: Virtual U: defeating face liveness detection by building virtual models from your public photos

abstract: In this paper, we introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, we show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. Our framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. The synthetic face of the user is displayed on the screen of the VR device, and as the device rotates and translates in the real world, the 3D face moves accordingly. To an observing face authentication system, the depth and motion cues of the display match what would be expected for a human face. ::: ::: We argue that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems: Unless they incorporate other sources of verifiable data, systems relying on color image data and camera motion are prone to attacks via virtual realism. To demonstrate the practical nature of this threat, we conduct thorough experiments using an end-to-end implementation of our approach and show how it undermines the security of several face authentication solutions that include both motion-based and liveness detectors.


title: Tick repellents and acaricides of botanical origin: a green roadmap to control tick-borne diseases?

abstract: Arthropods are dangerous vectors of agents of deadly diseases, which may hit as epidemics or pandemics in the increasing world population of humans and animals. Among them, ticks transmit more pathogen species than any other group of blood-feeding arthropods worldwide. Thus, the effective and eco-friendly control of tick vectors in a constantly changing environment is a crucial challenge. A number of novel routes have been attempted to prevent and control tick-borne diseases, including the development of (i) vaccines against viruses vectored by ticks; (ii) pheromone-based control tools, with special reference to the "lure and kill" techniques; (iii) biological control programmes relying on ticks' natural enemies and pathogens; and (iv) the integrated pest management practices aimed at reducing tick interactions with livestock. However, the extensive employment of acaricides and tick repellents still remains the two most effective and ready-to-use strategies. Unfortunately, the first one is limited by the rapid development of resistance in ticks, as well as by serious environmental concerns. On the other hand, the exploitation of plants as sources of effective tick repellents is often promising. Here, we reviewed current knowledge concerning the effectiveness of plant extracts as acaricides or repellents against tick vectors of public health importance, with special reference to Ixodes ricinus, Ixodes persulcatus, Amblyomma cajennense, Haemaphysalis bispinosa, Haemaphysalis longicornis, Hyalomma anatolicum, Hyalomma marginatum rufipes, Rhipicephalus appendiculatus, Rhipicephalus (Boophilus) microplus, Rhipicephalus pulchellus, Rhipicephalus sanguineus and Rhipicephalus turanicus. Eighty-three plant species from 35 botanical families were selected. The most frequent botanical families exploited as sources of acaricides and repellents against ticks were Asteraceae (15 % of the selected studies), Fabaceae (9 %), Lamiaceae (10 %), Meliaceae (5 %), Solanaceae (6 %) and Verbenaceae (5 %). Regression equation analyses showed that the literature grew by approximately 20 % per year (period: 2005-2015). Lastly, in the final section, insights for future research are discussed. We focused on some caveats for future data collection and analysis. Current critical points mainly deal with (a) not uniform methods used, which prevent proper comparison of the results; (b) inaccurate tested concentrations, frequently 100 % concentration corresponded to the gross extract, where the exact amounts of extracted substances are unknown; and (c) not homogeneous size of tested tick instars and species. Overall, the knowledge summarized in this review may be helpful for comparative screening among extensive numbers of plant-borne preparations, in order to develop newer and safer tick control tools.


title: Design of a Compact Wideband Feed Cluster With Dual-Polarized Sum- and Difference-Patterns Implemented via 3-D Metal Printing

abstract: A compact wideband feed cluster with dual-polarized sum- and difference-patterns is proposed in this paper. By utilizing dual-ridged waveguide orthomode transducers (OMTs) and wideband Magic-Tees, a highly integrated feed cluster with wide operational bandwidth, dual-polarization, and good isolation is developed. The feed cluster consists of five horns with square apertures arranged in a cross. A total of five OMTs and four Magic-Tees are used to construct the necessary comparator network to achieve sum- and difference-patterns in both polarizations. All components and the interface between them are designed with the three-dimensional metal printing in mind. A prototype is fabricated and the measured results show that the isolation between the orthogonal polarizations is better than 50 dB throughout the operational bandwidth of 10.5 to 14.5 GHz. The null-depths of the difference-patterns are better than –30 dB. The proposed feed cluster is practically useful in a variety of satellite-based applications.


title: A System Architecture for Context-Aware Mobile Computing

abstract: Computer applications traditionally expect a static execution environment. However, this precondition is generally not possible for mobile systems, where the world around an application is constantly changing. This thesis explores how to support and also exploit the dynamic configurations and social settings characteristic of mobile systems. More specifically, it advances the following goals: (1) enabling seamless interaction across devices; (2) creating physical spaces that are responsive to users; and (3) and building applications that are aware of the context of their use. Examples of these goals are: continuing in your office a program started at home; using a PDA to control someone else's windowing UI; automatically canceling phone forwarding upon return to your office; having an airport overhead-display highlight the flight information viewers are likely to be interested in; easily locating and using the nearest printer or fax machine; and automatically turning off a PDA's audible e-mail notification when in a meeting. ::: The contribution of this thesis is an architecture to support context-aware computing; that is, application adaptation triggered by such things as the location of use, the collection of nearby people, the presence of accessible devices and other kinds of objects, as well as changes to all these things over time. Three key issues are addressed: (1) the information needs of applications, (2) where applications get various pieces of information and (3) how information can be efficiently distributed. ::: A dynamic environment communication model is introduced as a general mechanism for quickly and efficiently learning about changes occurring in the environment in a fault tolerant manner. For purposes of scalability, multiple dynamic environment servers store user, device, and, for each geographic region, context information. In order to efficiently disseminate information from these components to applications, a dynamic collection of multicast groups is employed. The thesis also describes a demonstration system based on the Xerox PARCTAB, a wireless palmtop computer.


title: Art 2-A: an adaptive resonance algorithm for rapid category learning and recognition

abstract: Abstract This article introduces Adaptive Resonance Theory 2-A (ART 2-A), an efficient algorithm that emulates the self-organizing pattern recognition and hypothesis testing properties of the ART 2 neural network architecture, but at a speed two to three orders of magnitude faster. Analysis and simulations show how the ART 2-A systems correspond to ART 2 dynamics at both the fast-learn limit and at intermediate learning rates. Intermediate learning rates permit fast commitment of category nodes but slow recoding, analogous to properties of word frequency effects, encoding specificity effects, and episodic memory. Better noise tolerance is hereby achieved without a loss of learning stability. The ART 2 and ART 2-A systems are contrasted with the leader algorithm. The speed of ART 2-A makes practical the use of ART 2 modules in large scale neural computation.


title: A Software-Based Sonar Ranging Sensor for Smart Phones

abstract: We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone’s microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.


title: The Balanced Accuracy and Its Posterior Distribution

abstract: Evaluating the performance of a classification algorithm critically requires a measure of the degree to which unseen examples have been identified with their correct class labels. In practice, generalizability is frequently estimated by averaging the accuracies obtained on individual cross-validation folds. This procedure, however, is problematic in two ways. First, it does not allow for the derivation of meaningful confidence intervals. Second, it leads to an optimistic estimate when a biased classifier is tested on an imbalanced dataset. We show that both problems can be overcome by replacing the conventional point estimate of accuracy by an estimate of the posterior distribution of the balanced accuracy.


title: Modulation of Δ6 and Δ5 rat liver microsomal desaturase activities by dexamethasone-induced factor

abstract: This report supports evidence for the existence of a dexamethasone-induced factor that modulates fatty acid desaturase activities. Dexamethasone at a dose of 1 mg/rat produced a significant decrease in microsomal Δ6 and Δ5 desaturation activity 12 h after the injection. Both desaturase activities were depressed by a soluble factor present in the cytosolic fraction of cells, since the supernatant of microsomes separated at 110 000 × g from hormonal-treated rat liver homogenates, added to crude or washed control microsomes, was able to inhibit in vitro linoleic and homo-γ-linolenic conversion to γ-linolenic and arachidonic acids, respectively. The inhibitory factor was loosely bound to microsomes, since it was also present in a soluble fraction obtained after washing crude microsomes from dexamethasone-treated rats with a low-ionic-strength solution. Besides, trypsin digestion deactivates the dexamethasone-induced factor. Therefore, the depressing effect of glucocorticoids on Δ6 and Δ5 desaturation capacity depends on an unchanged protein structure present in the cytosolic fraction of the cell and whose biosynthesis is brought about by hormonal induction.


title: On understanding creative language: The late positive complex and novel metaphor comprehension

abstract: Novel metaphoric sentences have repeatedly evoked larger N400 amplitudes than literal sentences, while investigations of the late positive complex (LPC) have brought inconsistent results, with reports of both increased and reduced amplitudes. In two experiments, we examined novel metaphor comprehension in Polish, using the same set of literal, novel metaphoric, and anomalous sentences. The first aim of the study was to test whether novel metaphors would evoke larger or smaller late positivity complex (LPC) amplitudes compared to literal and anomalous sentences. Some earlier studies have shown that whether increased LPC amplitudes are observed or not may be related to the task participants are asked to perform, with explicit acceptability judgments being more likely to evoke the LPC effect. The second aim of the study was, thus, to test whether the observed LPC pattern would be the same across two tasks, the semantic decision task (Experiment 1) and the reading task (Experiment 2). Our results replicated the N400 effect observed in earlier studies on metaphor in both experiments. Most importantly, a reduction in late positivity to novel metaphors relative to anomalous sentences was found in both experiments. Additionally, this difference was broadly distributed over parietal sites in Experiment 1, and clearly left-lateralized in Experiment 2, which might imply differences in the involvement of recollection and semantic processes. Overall, these results seem to indicate that both conventionality and task demands modulate the LPC pattern.


CoChat: Enabling Bot and Human Collaboration for Task Completion


title: Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters

abstract: The enormous growth of data in a variety of applications has increased the need for high performance data mining based on distributed environments. However, standard data mining toolkits per se do not allow the usage of computing clusters. The success of MapReduce for analyzing large data has raised a general interest in applying this model to other, data intensive applications. Unfortunately current research has not lead to an integration of GUI based data mining toolkits with distributed file system based MapReduce systems. This paper defines novel principles for modeling and design of the user interface, the storage model and the computational model necessary for the integration of such systems. Additionally, it introduces a novel system architecture for interactive GUI based data mining of large data on clusters based on MapReduce that overcomes the limitations of data mining toolkits. As an empirical demonstration we show an implementation based on Weka and Hadoop.


title: A comparative study on temporal database models: A survey

abstract: Time is implicit in most of the database management applications. To handle time varying data for every application using ad hoc methods is a cumbersome task. Temporal database provides an efficient solution, provides a framework for such a problem. This paper briefly introduces temporal databases and surveys the work done in the field of temporal database models. First the paper introduces the conventional database which we use today and how temporal database differs from it. We examine different time domains employed to implement such database and classification of temporal database. Finally, the paper discusses various proposed and implemented temporal models and future work in the field of temporal database.


title: Translocation (8;16) in a patient with acute myelomonocytic leukemia, occurring after treatment with fludarabine for a low-grade non-Hodgkin's lymphoma.

abstract: We describe a 65-year old woman who developed a t(8;16)(p11;p13) positive acute myeloid leukemia (AML)-M4 without a prior myelodysplasia thirty-six months after a low-grade non-Hodgkin's lymphoma treated with alkylating agents (chlorambucil and cyclophosphamide) and fludarabine, a purine analog with a significant activity in lymphoproliferative disorders. The t(8;16)(p11;p13) is present in 0.4% of AML of M4-M5 cytotype. In the present case it was identified by conventional cytogenetics; involvement of the MOZ and CBP genes was demonstrated by fluorescence in situ hybridization, but not by reverse transcription polymerase chain reaction. The patient died of sepsis after the first course of induction chemotherapy. This is the first t(8;16) AML-M4 arising after fludarabine treatment of which the leukemogenic role in our case is very difficult to ascertain. Most t(8;16) t-AML cases had received anthracyclines with or without cyclophosphamide; none was ever administered chlorambucil. Our patient was never given anthracyclines and the cumulative doses of chlorambucil and cyclophosphamide employed were low.


Predictive analytics and data mining


title: Statistical Analysis of Well Fracture Data to Distinguish and Extract Fracture Corridors from Diffuse Fractures

abstract: Recognizing fracture swarms from diffuse fractures, and interpreting them as being related to corridors, thin rock beds or facies, is a critical and often time-consuming step in naturally fractured reservoir (NFR) characterization. On the one hand, it is necessary to decide whether or not visually aggregated fractures are indeed anomalous and should be considered as belonging to a single fracture corridor. On the other hand, user-friendly tools are needed to differentiate fractures within and outside corridors so as to properly take them into account in NFR models. An innovative statistical approach, associated with post-processing tools for automatic and assisted recognition of within-corridor fractures, is presented. Based on the SiZer method (SIgnificant ZERo crossings of derivatives), the statistical identification of fracture swarms relies on statistical hypothesis testing to detect significant fracture density trends along wells at different observation scales. Knowing zones of very likely increasing, stationary or decreasing fracture densities, a post-processing algorithm allows to distinguish fractures that are inside and outside of identified corridors, to mark them distinctively and to generate equivalent data that can be used to characterize and model large scale fracture corridors. The paper presents the approach and illustrates its performances on a real case study.


title: O-band passive mode-locked Raman fiber laser based on Single Wall Carbon Nanotubes as saturable absorbers

abstract: We present a passive mode-locked Raman laser incorporating SWCNT (Single-Wall Carbon Nanotubes) as saturable absorbers operating in O-band. We construct two single mode fiber lasers to generate ultrashort pulses near zero dispersion wavelength fiber. As results, pulse-trains were achieved in two different regimes and we compared the output bandwidth, rate repetition and pulse durations from both lasers. With high accumulated dispersion, nanoseconds square duration pulses were generated from laser cavity. So we study the pulse duration behavior introducing a dispersion shifted fiber (DSF) inside the cavity and verified a strong relation between pulse shortening and DSF length.


title: Content Based Image Retrieval using Advanced Color and Texture Features

abstract: The paper presents an efficient Content Based Image Retrieval (CBIR) system using color and texture. In proposed system, two different feature extraction techniques are employed. A universal content based image retrieval system uses color, texture and shape based feature extraction techniques for better matched images from the database. In proposed CBIR system, color and texture features are used. The texture features were extracted from the query image by applying block wise Discrete Cosine Transforms (DCT) on the entire image and from the retrieved images the color features were extracted by using moments of colors (Mean, Deviation and Skewness) theory. The proposed system has used Corel database of 1000 images. The feature vectors of the query image will then be compared with feature vectors of the database to obtain similar images. Individual and combined vectors using color and texture features were computed and the combined feature vector results were comparatively better. The proposed system provides an efficiency of 60%.


title: A Review of Mobile HCI Research Methods

abstract: This paper examines and reviews research methods applied within the field of mobile human-computer interaction. The purpose is to provide a snapshot of current practice for studying mobile HCI to identify shortcomings in the way research is conducted and to propose opportunities for future approaches. 102 publications on mobile human-computer interaction research were categorized in a matrix relating their research methods and purpose. The matrix revealed a number of significant trends with a clear bias towards building systems and evaluating them only in laboratory settings, if at all. Also, gaps in the distribution of research approaches and purposes were identified; action research, case studies, field studies and basic research being applied very infrequently. Consequently, we argue that the bias towards building systems and a lack of research for understanding design and use limits the development of cumulative knowledge on mobile human computer interaction. This in turn inhibits future development of the research field as a whole.


title: Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

abstract: Detection and tracking of humans in video streams is important for many applications. We present an approach to automatically detect and track multiple, possibly partially occluded humans in a walking or standing pose from a single camera, which may be stationary or moving. A human body is represented as an assembly of body parts. Part detectors are learned by boosting a number of weak classifiers which are based on edgelet features. Responses of part detectors are combined to form a joint likelihood model that includes an analysis of possible occlusions. The combined detection responses and the part detection responses provide the observations used for tracking. Trajectory initialization and termination are both automatic and rely on the confidences computed from the detection responses. An object is tracked by data association and meanshift methods. Our system can track humans with both inter-object and scene occlusions with static or non-static backgrounds. Evaluation results on a number of images and videos and comparisons with some previous methods are given.


title: P.: A Bayesian approach for learning and planning in partially observable Markov decision processes

abstract: Bayesian learning methods have recently been shown to provide an elegant solution to the exploration-exploitation trade-off in reinforcement learning. However most investigations of Bayesian reinforcement learning to date focus on the standard Markov Decision Processes (MDPs). The primary focus of this paper is to extend these ideas to the case of partially observable domains, by introducing the Bayes-Adaptive Partially Observable Markov Decision Processes. This new framework can be used to simultaneously (1) learn a model of the POMDP domain through interaction with the environment, (2) track the state of the system under partial observability, and (3) plan (near-)optimal sequences of actions. An important contribution of this paper is to provide theoretical results showing how the model can be finitely approximated while preserving good learning performance. We present approximate algorithms for belief tracking and planning in this model, as well as empirical results that illustrate how the model estimate and agent's return improve as a function of experience.


title: Rotation Detection in Chest Radiographs Based on Generalized Line Histogram of Rib-Orientations

abstract: We present a generalized line histogram technique to compute global rib-orientation for detecting rotated lungs in chest radiographs. We use linear structuring elements, such as line seed filters, as kernels to convolve with edge images, and extract a set of lines from the posterior rib-cage. After convolving kernels in all possible orientations in the range [0, p), we measure the angle for which the line histogram has maximum magnitude. This measure provides a good approximation of the global chest rib-orientation for each lung. A chest radiograph is said to be upright if the difference between the orientation angles of both lungs with respect to the horizontal axis, is negligible. We validate our method on sets of normal and abnormal images and argue that rib orientation can be used for rotation detection in chest radiographs as aid in quality control during image acquisition, and to discard images from training and testing data sets. In our test, we achieve a maximum accuracy of 90%.


title: Object tracking using firefly algorithm

abstract: Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presented as it is applied to solve tracking problem. A general optimisation-based tracking architecture is proposed and the parameters' sensitivity and adjustment of the FA in tracking system are studied. Experimental results show that the FA-based tracker can robustly track an arbitrary target in various challenging conditions. The authors compare the speed and accuracy of the FA with three typical tracking algorithms including the particle filter, meanshift and particle swarm optimisation. Comparative results show that the FA-based tracker outperforms the other three trackers.


title: Creating Subjective and Objective Sentence Classifiers from Unannotated Texts

abstract: This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification by learning extraction patterns associated with objectivity and creating objective classifiers that achieve substantially higher recall than previous work with comparable precision.


title: MicroRNA profiling: approaches and considerations

abstract: Owing to the important role of microRNAs in gene regulation, profiling repertoires of expressed microRNAs can be informative in basic research and clinical settings. This Review describes the methods that are available for microRNA profiling and considerations for choosing among analytical options.


title: Within-person changes in mindfulness and self-compassion predict enhanced emotional well-being in healthy, but stressed adolescents.

abstract: Abstract Meditation training programs for adolescents are predicated on the assumptions that mindfulness and self-compassion can be directly cultivated, and further, that doing so is beneficial for emotional well-being. Yet, very little research with adolescents has tested these assumptions directly. In the current study, I examined longitudinal relationships between changes in mindfulness and self-compassion and changes in emotional well-being among healthy, but stressed adolescents who participated in five-day, intensive meditation retreats. Immediately before and after the retreats, and then three months later, 132 adolescents ( M age = 16.76 years, 61% female) completed questionnaires measuring mindfulness, self-compassion, and emotional well-being. Repeated measures ANOVA showed adolescents improved in mindfulness, self-compassion, and all indices of emotional well-being immediately following the retreat (Cohen's d = |0.39–1.19|), and many of these improvements were maintained three months later (Cohen's d = |0.04–0.68|). Further, multilevel growth curve analyses with time-varying covariates indicated within-person changes in self-compassion predicted enhanced emotional well-being more consistently than within-person changes in mindfulness. Specifically, increases in self-compassion predicted reductions in perceived stress, rumination, depressive symptoms, and negative affect, and conversely, increases in positive affect and life satisfaction (pseudo- R 2 variance explained = 5.9% and 15.8%, p s


title: Please Do Not Disturb: Managing Interruptions And Task Complexity

abstract: The increasing use of computer-mediated communication (CMC) by knowledge workers in problemsolving environments intensifies the need for empirical evidence on the effects of interruption on users’ problem-solving performance. Email and instant messaging are common desktop and mobile applications that frequently seek the user’s attention via audible and visual cues. This study is concerned with whether the effects of immediate interruptions are more disruptive than negotiated interruptions to users’ problem-solving performance. It recognizes the importance of task complexity in explaining the relationship between the effects of interruption type and users’ performance in the context of problem-solving. An experiment is used to capture how user interruptions in the form of instant messages that demand users’ immediate response and e-mail messages that allow users to negotiate a delayed response could affect users’ problem-solving performance across simple and complex tasks. Results indicate task complexity negatively moderates the effects between immediate and negotiated types of interruptions from CMC on users’ problem-solving performance. Interruptions that demand users’ immediate response were found to degrade users’ efficiency and accuracy for complex tasks, compared to interruptions that allow users to negotiate a delayed response, while the effects between both types of interruptions were not significantly different for simple tasks.


title: Mining high utility episodes in complex event sequences

abstract: Frequent episode mining (FEM) is an interesting research topic in data mining with wide range of applications. However, the traditional framework of FEM treats all events as having the same importance/utility and assumes that a same type of event appears at most once at any time point. These simplifying assumptions do not reflect the characteristics of scenarios in real applications and thus the useful information of episodes in terms of utilities such as profits is lost. Furthermore, most studies on FEM focused on mining episodes in simple event sequences and few considered the scenario of complex event sequences, where different events can occur simultaneously. To address these issues, in this paper, we incorporate the concept of utility into episode mining and address a new problem of mining high utility episodes from complex event sequences, which has not been explored so far. In the proposed framework, the importance/utility of different events is considered and multiple events can appear simultaneously. Several novel features are incorporated into the proposed framework to resolve the challenges raised by this new problem, such as the absence of anti-monotone property and the huge set of candidate episodes. Moreover, an efficient algorithm named UP-Span (Utility ePisodes mining by Spanning prefixes) is proposed for mining high utility episodes with several strategies incorporated for pruning the search space to achieve high efficiency. Experimental results on real and synthetic datasets show that UP-Span has excellent performance and serves as an effective solution to the new problem of mining high utility episodes from complex event sequences.


title: Content driven clustering algorithm combining density and distance functions

abstract: Abstract Density and distance based clustering are two distinct approaches to the same problem. In this contribution, a novel algorithm is presented in order to exploit the benefits of both approaches. This is achieved, not by combining those approaches into a single notion, but by utilizing the advantages of each one, depending on what each step of the algorithm aims to achieve. To be precise, the Window Density Function is utilized to provide regions of high density and hence a region of clusters or a part of a cluster. Affinity Propagation is, consequently, utilized to provide a group of clusters within such a region. Finally, these regions are merged to form actual clusters. The proposed methodology is tested on a variety of synthetic and real-life datasets. The algorithm presented in this contribution outperforms other well-known algorithms, with which it is compared to, in the majority of the datasets used.


title: Avoidance of Social Media Advertising: A Latent Profile Analysis

abstract: Some individuals actively avoid social media advertising, for instance by scrolling over ads or ignoring ads. Therefore, this research aims to identify distinct profiles of individuals avoiding social media advertising. We build upon the advertising avoidance model and take a person-centered approach, using latent profile analysis to identify different profiles of individuals, who avoid social media advertising. We identified three distinct profiles of individuals, differing in their perception and their level of avoidance: unconcerned users, playful avoiding users and goal-oriented users. We contribute by characterizing individuals avoiding SMA, so that companies can use these profiles to derive different strategies how to deal with different profiles.


title: Optimal supply and threshold scaling for subthreshold CMOS circuits

abstract: With technology scaling, power supply and threshold voltage continue to decrease to satisfy high performance and low power requirements. In the past, subthreshold CMOS circuits have been inadequate for high performance applications, but have been used in applications that require ultra low power dissipation. Many applications including medical and wireless applications, require ultra low power dissipation with low-to-moderate performance (10kHz-100MHz). In this work, using BSIM3 models, the performance and energy dissipation of 0.18-/spl mu/m CMOS circuits for the range of V/sub dd/ = 0.1-0.6V and V/sub th/ = 0-0.6V are analyzed to show that subthreshold CMOS circuits can be used in low performance applications. A simple characterization circuit is introduced which can be used to evaluate the performance and energy dissipation for a given process under varying activity. These results are useful in circuit design by giving insight into optimal voltage supply and threshold voltage operation for a given application specification.


title: A Variational Method for Scene Flow Estimation from Stereo Sequences

abstract: This paper presents a method for scene flow estimation from a calibrated stereo image sequence. The scene flow contains the 3-D displacement field of scene points, so that the 2-D optical flow can be seen as a projection of the scene flow onto the images. We propose to recover the scene flow by coupling the optical flow estimation in both cameras with dense stereo matching between the images, thus reducing the number of unknowns per image point. The use of a variational framework allows us to properly handle discontinuities in the observed surfaces and in the 3-D displacement field. Moreover our approach handles occlusions both for the optical flow and the stereo. We obtain a partial differential equations system coupling both the optical flow and the stereo, which is numerically solved using an original multi- resolution algorithm. Whereas previous variational methods were estimating the 3-D reconstruction at time t and the scene flow separately, our method jointly estimates both in a single optimization. We present numerical results on synthetic data with ground truth information, and we also compare the accuracy of the scene flow projected in one camera with a state-of-the-art single-camera optical flow computation method. Results are also presented on a real stereo sequence with large motion and stereo discontinuities. Source code and sample data are available for the evaluation of the algorithm.


title: Tourism and Mobile Technology

abstract: While tourism presents considerable potential for the use of new mobile technologies, we currently have little understanding of how tourists organise their activities or of the problems they face. This paper presents an ethnographic study of city tourists' practices that draws out a number of implications for designing tourist technology. We describe how tourists work together in groups, collaborate around maps and guidebooks, and both 'pre-' and 'post-visit' places. Implications are drawn for three types of tourist technology: systems that explicitly support how tourists co-ordinate, electronic guidebooks and maps, and electronic tour guide applications. We discuss applications of these findings, including the Travelblog, which supports building travel-based web pages while on holiday.


title: Stock Trading with Random Forests, Trend Detection Tests and Force Index Volume Indicators

abstract: The goal of this paper is to investigate if the strong machine learning technique is able to retrieve information from past prices and predict price movements and future trends. The architecture of the system with the on-line adaptation ability to non-stationary two dimensional mixed Black-Scholes Markov time series model is presented. The methodology of investment strategies performance verification is also proposed.


title: Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process

abstract: Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. Inthis paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model to incorporate the time delay aspect of influence diffusion in social networks. We show that time-critical influence maximization under the time-delayed IC model maintains desired properties such as submodularity, which allows a greedy algorithm to achieve an approximation ratio of 1 - 1/e, to circumvent the NP-hardness of the problem. To overcome the inefficiency of the approximation algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures, while the second one converts the problem to one in the original IC model and then applies existing fast heuristics to it. Our simulation results demonstrate that our heuristics achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing algorithms that disregard the deadline constraint and delays in diffusion.


title: Experimental fault-tolerant control of a PMSM drive

abstract: The paper describes the study and experimental verification of remedial strategies against failures occurring in the inverter power devices of a PM synchronous motor drive. The basic idea for designing a fault-tolerant drive consists in incorporating a fourth leg into the inverter stage, which has the same topology and capabilities of the other conventional ones. This represents a minimal redundancy that, when appropriately connected and controlled, allows the drive to face a power device fault condition with smooth torque. The proposed control strategy also fits interior permanent magnet motor drives, which can operate even above the base speed, where a proper flux-weakening current control is adopted.


title: Influence of High-Frequency Mechanical Peening on the Fatigue Life of Stainless Steel Joints in Corrosive Environment

abstract: In the recent times, several post-processing techniques are proposed to improve the fatigue strength of the welded joints. Methods like high-frequency mechanical peening (HFMP) effectively enhances the fatigue corrosion characteristics of the welded metals and alloys in the corrosive environments also. The improvement of fatigue strength by HFMP is based on the relieving residual stresses and severe plastic deformation. This paper investigates the corrosion damage life of the AISI 316 welded stainless steel joints operated in the sea climate. The HFMP strengthened and unstrengthened samples are placed in the salt spray chamber for 1400 h. From the metallographic studies, it is observed that the HFMP strengthening has a significant effect on the fatigue strength at 2 × 106 cycles and increases fatigue strength by 45% before treating with salt spray solution. After soaking in salt spray chamber, HFMP does not exhibit a significant improvement in fatigue cyclic life and corrosion resistance.


title: Balanced Bandpass Filters Using Center-Loaded Half-Wavelength Resonators

abstract: This paper presents a balanced filter that is designed by using the center-loaded half-wavelength resonator. To demonstrate the design ideas, the differential- and common-mode equivalent half circuits are built and studied. The proposed technique is novel in the way that the common-mode suppression can be made independent of the differential-mode response in the balanced filter, making use of a center-loaded half-wavelength resonator. This is very important to simplify the design and tuning processes of the balanced filters. Making use of this feature, the resonator is not only utilized to design the balanced single-band bandpass filter but also the balanced dual-band bandpass one, possibly being the first to be presented. To demonstrate the design ideas, one single-band and two dual-band balanced filters are presented. The experimental results agree well with the theoretical predictions and simulations.


title: Mobile based home automation using Internet of Things(IoT)

abstract: Availability of high speed mobile networks like 3G and Long Term Evolution(LTE) coupled with cheaper and accessible smart phones, mobile industry has seen a tremendous growth in terms of providing various services and applications at the finger tips of the citizens. Internet of Things(IoT) is one of the promising technologies which can be used for connecting, controlling and managing intelligent objects which are connected to Internet through an IP address. Applications ranging from smart governance, smart education, smart agriculture, smart health care, smart homes etc can use IoT for effective delivery of services with out manual intervention in a more effective manner. This paper discusses about IoT and how it can be used for realizing smart home automation using a micro-controller based Arduino board and Android mobile app. In this paper, two prototypes namely home automation using Bluetooth in an indoor environment and home automation using Ethernet in an outdoor environment are presented.


title: Hash Ranking With Weighted Asymmetric Distance for Image Search

abstract: Image search can be viewed as a problem of large-scale approximate nearest neighbor (ANN) search in image feature space. Hash ranking methods have been widely used for ANN search because of their two benefits: less memory usage and high search efficiency. Generally, the hash ranking methods face two problems: binary encoding and binary code ranking. This paper focuses on the latter. In existing work, the ranking of binary hash codes is usually implemented based on Hamming distance or asymmetric distance. Hamming distance easily leads to confusing ranking when different candidate points share the same Hamming distance to the query point. Therefore, recent work prefers the asymmetric distance to Hamming distance. When computing asymmetric distance, it is necessary to give reasonable query-independent values. These values are usually approximated by average values of sample candidate points in existing methods. However, when the distribution of candidate points is not uniform, average values are meaningless, leading to wrong ranking results. To address this problem, we propose two kinds of weighted asymmetric distance algorithms, namely, the Otsu threshold based algorithm (WoRank) and the score calculation based algorithm (WsRank) in this paper. The processes of these two proposed algorithms are similar, consisting of two steps. In the first step, we compute the query-independent values on each bit in accordance with corresponding distribution of candidate points to reduce the approximation error. In the second step, we compute bitwise weights in consideration of each bit's discriminative power to further improve the retrieval accuracy. The differences between WoRank and WsRank are the computation methods of query-independent values and bitwise weights. To evaluate the proposed algorithms, we conduct a large number of experiments on four well-known datasets, namely, SIFT, CIFAR-10, MNIST, and NUS-WIDE. The results show that the proposed algorithms can achieve up to 22% performance gains over Hamming distance based ranking and 13% over the existing asymmetric distance based ranking. We also find WoRank is suitable for feature dataset (SIFT), while WsRank is suitable for image datasets.


title: Compact ridge waveguide filters using non-resonating nodes

abstract: A ridge waveguide filter based on the non-resonating node (NRN) concept is proposed. A new coupling mechanism, suitable for this realization is introduced. The proposed coupling mechanism alleviates the need to use other complicated structures and simplify the design process significantly. Rigorous mode matching method (MMM) is used to model the filter. The filter response is further verified using another numerical technique. A prototype was built and tested. Measurement results agree well with simulation.


title: A method for registration of 3-D shapes

abstract: The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >