Functional cartography of complex metabolic networks

High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ‘cartographic representation’ of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.


Dengue fever prediction using K-means clustering algorithm

Dengue fever is a virus infection which is transmitted to humans by mosquitoes that living in tropical and subtropical climates and carries the virus. The dengue viruses occur in 4 serotypes (DENV-1 to DENV-4). A dengue disease ranges from mild febrile disease to severe hemorrhagic fever. Predicting the relationship between the dengue serotypes will surely help the biotechnologists and bioinformaticians to move one step forward to discover antibiotic for dengue. This paper has been focused four stages namely preprocessing, attribute selection, clustering and predicting the dengue fever. R 3.3.2 Tool is used for preprocessing the household of dengue dataset. D win's method has been applied to generate filled dataset by substituting all missing values for nominal and numeric attributes with mode and mean value. Dengue virus can be predicted by applying different data mining techniques. The main goal of research work is to predict the people who are affected by dengue depending upon categorization of age group using K-means clustering algorithm has been implemented.


Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making

Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa, which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.


A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems

Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.


Review on Printed Log Periodic and Yagi MSA

The log periodic antenna and Yagi-Uda antenna are used in the applications where very high directivity is required. They also give very high gain in the range of 17-20dBi. This paper presents a review on various configurations of log periodic and Yagi antennas, their advantages and problems. One problem encountered with Yagi-Uda antenna is relatively less bandwidth. This problem is solved by log periodic antenna which can operate over high bandwidth and providing high gain at the same time. In this paper, a review of various techniques to realize printed Yagi-Uda and log periodic antenna is discussed. They are realized by using different feeding techniques like microstrip feeding, co-axial feeding etc. They are also realized by using modifying the shape of directors and reflectors. The high bandwidth (log periodic antenna) has also been realized by increasing the number of reflectors.


EMG analysis of the scapular muscles during a shoulder rehabilitation program.

The purpose of this study was to determine which exercises most effectively use the scapular muscles. Eight muscles in 9 healthy subjects were studied with indwelling electromyographic electrodes and cinematography while performing 16 exercises. The 8 muscles studied were the upper, middle, and lower trapezius; levator scapula; rhomboids; pectoralis minor; and the middle and lower serratus anterior. Each exercise was divided into arcs of motion and the electromyographic activity was quantified as a percentage of the maximal manual muscle test. The optimal exercises for each muscle were identified based on intensity (greater than 50% maximal manual muscle test) and duration (over at least 3 consecutive arcs of motion) of the muscle activity. Twelve of the exercises qualified as top exercises for all of the muscles. On further analysis, a group of 4 exercises was shown to make up the core of a scapular muscle strengthening program. Those 4 exercises include scaption (scapular plane elevation), rowing, push-up with a plus, and press-up.


Research on autonomous stairs climbing for the shape-shifting robot

In this paper, we present an algorithm for a shape-shifting robot climbing stairs autonomously. The robot has been used several times for search and rescue missions. Because of the special environment of tasks, the robot must have the ability to climb stairs autonomously to help the operators. Due to the characters of robot in "T" configuration, the mathematical model of force is established and the whole process of climbing stairs is analyzed. Grouser-tread hooking, track-stair edge frictional force et al. are all considered during the process of climbing. A description of its dynamic model is also presented which is involved in the controller. The controller is comprised of center control and heading control, which can help the robot climb stairs safely and quickly. The effectiveness of the algorithms proposed are verified by experiment.


A framework based on deep learning and mathematical morphology for cabin door detection in an automated aerobridge docking system

In this paper, a cabin door detection framework based on deep learning and mathematical morphology is proposed. It is applied to an automated docking system for airplane cabin door. This system needs to work under any weather condition like rain, shine, day and night. Limited by the number of videos, just a small dataset based on actual airport operation is established for aerobridge docking process. As the training dataset is small, the trained detector cannot identify all the cabin doors in this dataset. Some of the cabin doors, which are not detected, can be identified with the combination of deep learning and mathematical morphology. Experimental results show that the integration of deep learning and mathematical morphology performs better than the simple deep learning method.


Enhancements in UltraScale CLB Architecture

Each generation of FPGA architecture benefits from optimizations around its technology node and target usage. In this paper, we discuss some of the changes made to the CLB for Xilinx's 20nm UltraScale product family. We motivate those changes and demonstrate better results than previous CLB architectures on a variety of metrics. We show that, in demanding scenarios, logic placed in an UltraScale device requires 16% less wirelength than 7-series. Designs mapped to UltraScale devices also require fewer logic tiles. In this paper, we demonstrate the utilization benefits of the UltraScale CLB attributed to certain CLB enhancements. The enhancements described herein result in an average packing improvement of 3% for the example design suite. We also show that the UltraScale architecture handles aggressive, tighter packing more gracefully than previous generations of FPGA. These significant reductions in wirelength and CLB counts translate directly into power, performance and ease-of-use benefits.


PPDDL 1 . 0 : An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects

We desribe a variation of the planning domain definition language, PDDL, that permits the modeling of probabilistic planning problems with rewards. This language, PPDDL1.0, was used as the input language for the probabilistic track of the 4th International Planning Competition. We provide the complete syntax for PPDDL1.0 and give a semantics of PPDDL1.0 planning problems in terms of Markov decision processes. Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA


Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.


Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.


Single-structure 3-axis lorentz force magnetometer with sub-30 nT/√HZ resolution

This work demonstrates a 3-axis Lorentz force magnetometer for electronic compass purposes. The magnetometer measures magnetic flux in 3 axes using a single structure. With 1 mW power consumption, the sensor achieves sub-30 nT/√Hz resolution in each of the 3 axes. Compared to the 3-axis Hall sensors currently used in smartphones, the 3-axis magnetometer shown here has the advantages of 10× lower noise floor and the ability to be co-fabricated with MEMS inertial sensors.


FrameBase: Representing N-Ary Relations Using Semantic Frames

Large-scale knowledge graphs such as those in the Linked Data cloud are typically represented as subject-predicate-object triples. However, many facts about the world involve more than two entities. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. They also make it impossible to query the data concisely and without prior knowledge of each individual source. We present FrameBase, a wide-coverage knowledge-base schema that uses linguistic frames to seamlessly represent and query n-ary relations from other knowledge bases, at different levels of granularity connected by logical entailment. It also opens possibilities to draw on natural language processing techniques for querying and data mining.


The Role of Deliberate Practice in the Acquisition of Expert Performance

The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.


Design of Compact Dual Band High Directive Electromagnetic Bandgap (EBG) Resonator Antenna Using Artificial Magnetic Conductor

A compact high directive EBG resonator antenna operating in two frequency bands is described. Two major contributions to this compact design are using single layer superstrate and using artificial surface as ground plane. To obtain only the lower operating frequency band using superstrate layer is enough, but to extract the upper operating frequency band both superstrate layer and artificial surface as ground plane are necessary. Therefore, design of a superstrate to work in two frequency bands is very important. Initially, using appropriate frequency selective surface (FSS) structure with square loop elements, we design an optimum superstrate layer for each frequency band separately to achieve maximum directivity. Also, to design an artificial surface to work in the upper frequency band we use a suitable FSS structure over dielectric layer backed by PEC. Next, by using the idea of FSS structure with double square loop elements we propose FSS structure with modified double square loop elements, so that it operates in both of the desired operating frequency bands simultaneously. Finally, the simulation results for two operating frequency bands are shown to have good agreement with measurements.


Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.


An End-to-End Signal Strength Model for Underwater Optical Communications

In this paper, we present a generic model of signal strength in underwater optical communications. The model includes light sources, detectors, amplifier and detector circuitry, optics, as well as a simple extinction model of the water channel. The end-to-end model provides insights into optimization approaches for underwater optical modems and enables relative pose estimation between underwater optical transmitters and receivers. We instantiate our model to the AquaOptical model by determining its parameters and verifying the model prediction in a suite of pool experiments.


A dynamic bayesian network click model for web search ranking

As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias - urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.


Implementation of SMS based Heartbeat monitoring system using PSoC Microcontroller

The rapidly growing VLSI technology could reduce the cost of the embedded systems considerably which has motivated a new era of low cost personal health care monitoring systems which meeting common man requirements. Such systems are dedicated to one person and provide the diagnostic information through remotely. This paper proposes a PSoC microcontroller and GSM modules we are eliminating the cables attached to patient. The patient has a freedom of doing daily activities and still be under continuous monitoring (very suitable for old age people). PSoC has inbuilt ADC and Programmable Gain amplifier which enabled single chip implementation. The hardware complexity is also simple and reduces cost. The basic principle of this system is to read the bio medical signals from the biomedical sensor modules and perform the tasks of data conversion, sending SMS using GSM, as well as providing the ability of simple preprocessing such as waveform averaging or rectification. GSM based modem interface through AT commands to PSoC. The Microcontroller will be able to receive and send text messages Heart beat is sensed by using a high intensity type LED and LDR. The finger is placed between the LED and LDR. The skin may be illuminated with visible (red) using transmitted or reflected light for detection. The concept of this paper is builds upon the integration of wireless communications into medical applications to revolutionize personal healthcare. Under this concept, patients are no longer bound to a specific healthcare location where they are monitored by medical instruments—wireless communications would not only provide them with safe and accurate monitoring but also the freedom of movement. Remote patient monitoring will not only redefine hospital care but also work, home, and recreational activities. Imagine the home of the future: elderly individuals no longer have to travel to the hospital, a mother stuck at work will be able to receive email updates of her sick child's temperature, a doctor on the go will have the ability to turn on his cell phone to check the status of his patients. The objective of this project is to build a wireless heart beat monitoring system using GSM Technology, which could potentially be an integral part of a suite of personal healthcare appliances for a large-scale remote patient monitoring system. In this paper a low cost Digital Heart rate meter using PSoC Microcontroller is designed, and also this unit is connecting to GSM Modem for send …


Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation

Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are available, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.


MagNet: A Two-Pronged Defense against Adversarial Examples

Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective.
 We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that MagNet is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples.


Delving into Transferable Adversarial Examples and Black-box Attacks

An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, which is a black-box image classification system.


Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently proposed defenses that intentionally combine components designed to work well together. A third defense combines three independent defenses. For all the components of these defenses and the combined defenses themselves, we show that an adaptive adversary can create adversarial examples successfully with low distortion. Thus, our work implies that ensemble of weak defenses is not sufficient to provide strong defense against adversarial examples.


Sentiment Predictability for Stocks

In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.


Control of microglial neurotoxicity by the fractalkine receptor

Microglia, the resident inflammatory cells of the CNS, are the only CNS cells that express the fractalkine receptor (CX3CR1). Using three different in vivo models, we show that CX3CR1 deficiency dysregulates microglial responses, resulting in neurotoxicity. Following peripheral lipopolysaccharide injections, Cx3cr1−/− mice showed cell-autonomous microglial neurotoxicity. In a toxic model of Parkinson disease and a transgenic model of amyotrophic lateral sclerosis, Cx3cr1−/− mice showed more extensive neuronal cell loss than Cx3cr1+ littermate controls. Augmenting CX3CR1 signaling may protect against microglial neurotoxicity, whereas CNS penetration by pharmaceutical CX3CR1 antagonists could increase neuronal vulnerability.


A valid two-item food security questionnaire for screening HIV-1 infected patients in a clinical setting.

OBJECTIVE
To validate a two-item food security questionnaire (FSQ) for use in a clinical setting to screen HIV-1 infected patients for food insecurity.

DESIGN
The present study was a questionnaire-based survey of forty-nine subjects attending an HIV clinic. Subjects completed a two-item questionnaire and a six-item validated FSQ contemporaneously.

RESULTS
A strong correlation was found between the two-item and six-item FSQ (rho = 0.895; 95 % CI 0.821, 0.940; P < 0.0001). Cronbach's alpha coefficient was found to be 0.94 and 0.90 for the two-item and six-item FSQ, respectively. The two-item FSQ yielded a sensitivity of 100 % (95 % CI 75, 100) and a specificity of 78 % (95 % CI 61, 90). The negative predictive value was found to be 100 % (95 % CI 88, 100).

CONCLUSIONS
The results of the present study suggest that the two-item FSQ is a valid, reliable and sensitive screening tool of food insecurity in people living with HIV in a clinical setting.


Clinical Information Extraction via Convolutional Neural Network

We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their part-of-speech tags and shape information as features. Then we hire temporal (1D) con-volutional neural network to learn hidden feature representations. Finally, we use Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baselines.


Playable Experiences at AIIDE 2015

This paper describes entries to the third Playable Experiences track to be held at the AIIDE conference. We discuss the five entries accepted to the track for 2015, as well as the ongoing development of the track as part


Affordable Self Driving Cars and Robots with Semantic Segmentation

Image semantic segmentation is of immense interest for self-driving car research. Accurate and efficient segmentation mechanisms are required. We have evaluated Intersection over Union (IoU) metric over Cityscapes and KITTI datasets. We designed baseline softmax regression and maximum likelihood estimation, which performs quite poorly for the image segmentation task. We ran fully convolutional networks (FCN), which performed almost perfectly over the KITTI dataset. We found overfitting problem for the more complex Cityscape dataset. We conducted several experiments with regularization, dropout, data augmentation, image scaling and newer architectures. We are able to successfully mitigate overfitting by data augmentation. We also generated a confusion matrix and conducted error ablative analysis to get a deeper understanding of FCNs.


Genetic process mining: an experimental evaluation

One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework.


Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.


Classification of Brain Cancer using Artificial Neural Network

A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement, morphological operations and feature extraction have been developed for detection of the brain tumor in the MRI images of the cancer affected patients. The extraction of texture features in the detected tumor has been achieved by using Gray Level Co-occurrence Matrix (GLCM). These features are compared with the stored features in the Knowledge Base. Finally a Neuro Fuzzy Classifier has been developed to recognize different types of brain cancers. The whole system has been tested in two phases firstly Learning/Training Phase and secondly Recognition/Testing Phase. The known MRI images of affected brain cancer patients obtained from Radiology Department of Tata Memorial Hospital (TMH) were used to train the system. The unknown samples of brain cancer affected MRI images are also obtained from TMH and were used to test the system. The system was found efficient in classification of these samples and responds any abnormality.


Triple Band Annular Ring Loaded Stacked Circular Patch Microstrip Antenna

In this paper a novel structure of annular ring loaded stacked circular patch microstrip antenna is theoretically analysed to observe various parameters such as return loss, input impedance, gain, directivity and radiation pattern. It is found that antenna possess three band of operation which signify the compactness and multiband operation of antenna. The antenna is resonating at three operating frequencies 1.720, 2.950, 3.060 GHz. The proposed theory is verified by simulation using Ansoft’s HFSS and theoretical results are in good agreement with simulated results. The antenna is useful for multi-services operations such as WLAN, GSM, UMTS, and WiMAX services.


Transfer learning for children's speech recognition

Children's speech processing is more challenging than that of adults due to lacking of large scale children's speech corpora. With the developing of the physical speech organ, high inter speaker and intra speaker variabilities are observed in children's speech. On the other hand, data collection on children is difficult as children usually have short attention span and their language proficiency is limited. In this paper, we propose to improve children's automatic speech recognition performance with transfer learning technique. We compare two transfer learning approaches in enhancing children's speech recognition performance with adults' data. The first method is to perform acoustic model adaptation on the pre-trained adult model. The second is to train acoustic model with deep neural network based multi-task learning approach: the adults' and children's acoustic characteristics are learnt jointly in the shared hidden layers, while the output layers are optimized with different speaker groups. Our experiment results show that both transfer learning approaches are effective in transferring rich phonetic and acoustic information from adults' model to children model. The multi-task learning approach outperforms the acoustic adaptation approach. We further show that the speakers' acoustic characteristics in languages can also benefit the target language under the multi-task learning framework.


Process Mining and Security: Visualization in Database Intrusion Detection

Nowadays, more and more organizations keep their valuable and sensitive data in Database Management Systems (DBMSs). The traditional database security mechanisms such as access control mechanisms, authentication, data encryption technologies do not offer a strong enough protection against the exploitation of vulnerabilities (e.g. intrusions) in DBMSs from insiders. Intrusion detection systems recently proposed in the literature focus on statistical approaches, which are not intuitive. Our research is the first ever effort to use process mining modeling low-level event logs for database intrusion detection. We have proposed a novel approach for visualizing database intrusion detection using process mining techniques. Our experiments showed that intrusion detection visualization will be able to help security officers who might not know deeply the complex system, identify the true positive detection and eliminate the false positive results.


Vision-based obstacle recognition system for automated lawn mower robot development

Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system’s performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.


Radio frequency identification: technologies, applications, and research issues

A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers’ interference, and security issue. Copyright # 2006 John Wiley & Sons, Ltd.


Binarized Genetic Algorithm with Neural Network for Stock Market Prediction

Stock market prediction is a challenging task to predict the upcoming stock values. It is very difficult to predict because of unstable nature of stock. This stock market prices are continuously changing day by day. Many Business Analysts spends more money on this stock market. Some of the persons may lose the money on this stock market because they don't have clear idea about the stock market. Estimate and analyse the stock market accurately then only get more profit. Artificial neural network is a very popular technique for forecast the stock market price, but using this technique to forecast the stock market price up to some extent. So there is a need to improve the accuracy of the system. In this paper, propose a novel system called Binarized Genetic Algorithm with Artificial Neural Network (BGANN) technique to predict and forecast future behavior of individual stocks or the upcoming stock. Binarized Genetic Algorithm is used for optimizing Neural Network weights while training. Comparative analysis shows that BGANN method performance is better compared to the Support Vector Machine (SVM), Neural Network (NN), and Auto Regressive Integrated Moving Average (ARIMA) models.


Modeling visual problem solving as analogical reasoning.

We present a computational model of visual problem solving, designed to solve problems from the Raven's Progressive Matrices intelligence test. The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly. Images are compared via structure mapping, aligning the common relational structure in 2 images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model dynamically rerepresents them to facilitate the comparison. In our analysis, we find that the model matches adult human performance on the Standard Progressive Matrices test, and that problems which are difficult for the model are also difficult for people. Furthermore, we show that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solving, and reasoning more generally, at the highest level. (PsycINFO Database Record


A 71-Gb/s NRZ Modulated 850-nm VCSEL-Based Optical Link

We report error free (BER <; 10-12) operation of a directly non-return-to-zero modulated 850-nm vertical cavity surface-emitting laser (VCSEL) link operating to 71 Gb/s. This is the highest error free modulation rate for a directly modulated laser of any type. The optical link consists of a 130-nm BiCMOS driver IC with two-tap feed-forward equalization, a wide bandwidth 850-nm VCSEL, a surface illuminated GaAs PIN photodiode, and a 130-nm BiCMOS receiver IC.


Minimizing Faulty Executions of Distributed Systems

When troubleshooting buggy executions of distributed systems, developers typically start by manually separating out events that are responsible for triggering the bug (signal) from those that are extraneous (noise). In this paper we present DEMi, a tool for automatically performing this minimization. We apply DEMi to buggy executions of two very different distributed systems, Raft and Spark, and find that it achieves up to 97% reduction of execution length within a small time budget.


Stationary Frame Induction Motor Feed Forward Current Controller With Back EMF Compensation

In this paper, a novel induction motor (IM) stator current controller is proposed based on the stationary frame feed forward controlling structure. The proposed regulator represents an improved variant of the existing stationary and rotational frame IM stator current regulators based on the back EMF compensation. The paper novelty consists of the stationary frame controlling structure based on two feed forward actions that enable asymptotic reference tracking. Namely, the novel stationary frame regulator includes a proportional integral (PI) regulation term combined with a stator resistance voltage drop and back EMF feed forward compensations, with the back EMF compensation based on the stator flux value estimated by means of a programmable low-pass filter. The feedback term is based on a PI controller, with the corresponding parameter tuning procedure outlined in the paper. The simulation runs are included in the paper in order to examine the regulator performance in relation to the factors critical to the IM stator current regulation: variable IM model parameter values, transport delay introduced in the control loop, variable stator frequency, and rotor speed values. Also, the experimental tests are presented and carried out on an experimental setup for different operating conditions typical for an IM drive.


Coping with stress during childhood and adolescence: problems, progress, and potential in theory and research.

Progress and issues in the study of coping with stress during childhood and adolescence are reviewed. Definitions of coping are considered, and the relationship between coping and other aspects of responses to stress (e.g., temperament and stress reactivity) is described. Questionnaire, interview, and observation measures of child and adolescent coping are evaluated with regard to reliability and validity. Studies of the association of coping with symptoms of psychopathology and social and academic competence are reviewed. Initial progress has been made in the conceptualization and measurement of coping, and substantial evidence has accumulated on the association between coping and adjustment. Problems still remain in the conceptualization and measurement of coping in young people, however, and aspects of the development and correlates of coping remain to be identified. An agenda for future research on child-adolescent coping is outlined.


An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

Received Nov 14, 2017 Revised Jan 26, 2018 Accepted Feb 11, 2018 Online Social Networks (OSNs) is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no contentbased preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previously defined web content mining and security techniques. To overcome the issues, Level-level Security Optimization & Content Visualization Algorithm is proposed to avoid the privacy issues during content sharing and data visualization. It adopts level by level privacy based on user requirement in the social network. It evaluates the privacy compatibility in the online social network environment to avoid security complexities. The mechanism divided into three parts namely like online social network platform creation, social network privacy, social network within organizational privacy and network controlling and authentication. Based on the experimental evaluation, a proposed method improves the privacy retrieval accuracy (PRA) 9.13% and reduces content retrieval time (CRT) 7 milliseconds and information loss (IL) 5.33%.


Probability of Random Correspondence for Fingerprints

Individuality of fingerprints can be quantified by computing the probabilistic metrics for measuring the degree of fingerprint individuality. In this paper, we present a novel individuality evaluation approach to estimate the probability of random correspondence (PRC). Three generative models are developed respectively to represent the distribution of fingerprint features: ridge flow, minutiae and minutiae together with ridge points. A mathematical model that computes the PRCs are derived based on the generative models. Three metrics are discussed in this paper: (i) PRC of two samples, (ii) PRC among a random set of n samples (nPRC) and (iii) PRC between a specific sample among n others (specific nPRC). Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the NIST4 database.


Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing

We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.


Applying Model Management to Classical Meta Data Problems

Model management is a new approach to meta data management that offers a higher level programming interface than current techniques. The main abstractions are models (e.g., schemas, interface definitions) and mappings between models. It treats these abstractions as bulk objects and offers such operators as Match, Merge, Diff, Compose, Apply, and ModelGen. This paper extends earlier treatments of these operators and applies them to three classical meta data management problems: schema integration, schema evolution, and round-trip engineering.


Towards a unified architecture for in-RDBMS analytics

The increasing use of statistical data analysis in enterprise applications has created an arms race among database vendors to offer ever more sophisticated in-database analytics. One challenge in this race is that each new statistical technique must be implemented from scratch in the RDBMS, which leads to a lengthy and complex development process. We argue that the root cause for this overhead is the lack of a unified architecture for in-database analytics. Our main contribution in this work is to take a step towards such a unified architecture. A key benefit of our unified architecture is that performance optimizations for analytics techniques can be studied generically instead of an ad hoc, per-technique fashion. In particular, our technical contributions are theoretical and empirical studies of two key factors that we found impact performance: the order data is stored, and parallelization of computations on a single-node multicore RDBMS. We demonstrate the feasibility of our architecture by integrating several popular analytics techniques into two commercial and one open-source RDBMS. Our architecture requires changes to only a few dozen lines of code to integrate a new statistical technique. We then compare our approach with the native analytics tools offered by the commercial RDBMSes on various analytics tasks, and validate that our approach achieves competitive or higher performance, while still achieving the same quality.


Sketch of Big Data Real-Time Analytics Model

Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Realtime Big Data analysis based on cloud computing approach is our future research direction.


Turning the Page : The Impact of Choice Closure on Satisfaction

This paper introduces the concept of choice closure, defined as the psychological process by which consumers come to perceive a decision to be resolved and complete, and focus on the decision outcome. Choice closure inhibits consumers who have already made a choice from reverting to the decision process and engaging in potentially unfavorable comparisons between the chosen and the forgone options. In four studies, we show that physical acts of closure—such as turning one’s back on, covering, and turning the page on the rejected alternatives—facilitate choice closure in the context of difficult decisions involving large choice sets, and that choice closure results in greater satisfaction.


Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding

There has been significant recent interest towards achievin g highly efficient deep neural network architectures. A promising paradigm for ach ieving this is the concept ofevolutionary deep intelligence , which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural n etworks over successive generations. An important aspect of evolutionary deep inte lligence is the genetic encoding scheme used to mimic heredity, which can have a sign ificant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genet ic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of ima ge classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art perf ormance while having network architectures that are not only significantly more e ffici nt (with a∼125fold decrease in synapses for MNIST) compared to the origina l a cestor network, but also tailored for GPU-accelerated machine learning app lications.


Understanding and Predicting Graded Search Satisfaction

Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.


Speculations Concerning the First Ultraintelligent Machine


AR typing interface for mobile devices

We propose a new user interface system for mobile devices. By using augmented reality (AR) technology, the system overlays virtual objects on real images captured by a camera attached to the back of a mobile device, and the user can operate the mobile device by manipulating the virtual objects with his/her hand in the space behind the mobile device. This system allows the user to operate the device in a wide three-dimensional space and to select small objects easily. Also, the AR technology provides the user with a sense of reality in operating the device. We developed a typing application using our system and verified the effectiveness by user studies. The results showed that more than half of the subjects felt that the operation area of the proposed system is larger than that of a smartphone and that both AR and unfixed key-plane are effective for improving typing speed.


Comparison of Three Different CNN Architectures for Age Classification

As one of the powerful tools of machine learning, Convolutional Neural Network (CNN) architectures are used tosolve complex problems like image recognition, video analysisand natural language processing. In this paper, three differentCNN architectures for age classification using face images arecompared. The Morph dataset containing over 55k images isused in experiments and success of a 6-layer CNN and 2 variantsof ResNet with different depths are compared. The images in thedataset are divided into 6 different age classes. While 80% of theimages are used in training of the networks, the rest of the 20% isused for testing. The performance of the networks are comparedaccording to two different criteria namely, the ability to makethe estimation pointing the exact age classes of test images andthe ability to make the estimation pointing the exact age classesor at most neighboring classes of the images. According to theperformance results obtained, with 6-layer network, it is possibleto estimate the exact or neighboring classes of the images withless than 5% error. It is shown that for a 6 class age classificationproblem 6-layer network is more successful than the deeperResNet counterparts since 6-layer network is less susceptible tooverfitting for this problem.


Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks

Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10±0.015 on the held-out test data (mean ± standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.


NEVESIM: event-driven neural simulation framework with a Python interface

NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies.


Autonomous Positioning of Eye Surgical Robot Using the Tool Shadow and Kalman Filtering

Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon’s coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions—namely, a micropipette, its shadow, and the eye ground—using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.


Open circuit fault detection in PWM voltage source inverter for PMSM drive system

In this paper, a simple and low-cost open-circuit fault detection and identification method for a pulse-width modulated (PWM) voltage-source inverter (VSI) employing a permanent magnet synchronous motor is proposed. An open-circuit fault of a power switch in the PWM VSI changes the corresponding terminal voltage and introduces the voltage distortions to each phase voltage. The proposed open-circuit fault diagnosis method employs the model reference adaptive system techniques and requires no additional sensors or electrical devices to detect the fault condition and identify the faulty switch. The proposed method has the features of fast diagnosis time, simple structure, and being easily inserted to the existing control algorithms as a subroutine without major modifications. The simulations and experiments are carried out and the results show the effectiveness of the proposed method.


A Time Series Classification Method for Behaviour-Based Dropout Prediction

Students' dropout rate is a key metric in online and open distance learning courses. We propose a time-series classification method to construct data based on students' behaviour and activities on a number of online distance learning modules. Further, we propose a dropout prediction model based on the time series forest (TSF) classification algorithm. The proposed predictive model is based on interaction data and is independent of learning objectives and subject domains. The model enables prediction of dropout rates without the requirement for pedagogical experts. Results show that the prediction accuracy on two selected datasets increases as the portion of data used in the model grows. However, a reasonable prediction accuracy of 0.84 is possible with only 5% of the dataset processed. As a result, early prediction can help instructors design interventions to encourage course completion before a student falls too far behind.


Weakly-supervised text-to-speech alignment confidence measure

This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.


Designing an augmented reality board game with children: the battleboard 3D experience

This demo shows BattleBoard 3D which is an Augmented Reality (AR) based game prototype featuring the use of LEGO for the physical and digital pieces. Design concepts, the physical setting and, user interface for the game is illustrated and described. Based on qualitative studies of children playing the game we illustrate design issues for AR board games.


Research and implementation of RSA algorithm for encryption and decryption

Cryptographic technique is one of the principal means to protect information security. Not only has it to ensure the information confidential, but also provides digital signature, authentication, secret sub-storage, system security and other functions. Therefore, the encryption and decryption solution can ensure the confidentiality of the information, as well as the integrity of information and certainty, to prevent information from tampering, forgery and counterfeiting. Encryption and decryption algorithm's security depends on the algorithm while the internal structure of the rigor of mathematics, it also depends on the key confidentiality. Key in the encryption algorithm has a pivotal position, once the key was leaked, it means that anyone can be in the encryption system to encrypt and decrypt information, it means the encryption algorithm is useless. Therefore, what kind of data you choose to be a key, how to distribute the private key, and how to save both data transmission keys are very important issues in the encryption and decryption algorithm. This paper proposed an implementation of a complete and practical RSA encrypt/decrypt solution based on the study of RSA public key algorithm. In addition, the encrypt procedure and code implementation is provided in details.


Personalized machine learning for robot perception of affect and engagement in autism therapy

Robots have the potential to facilitate future therapies for children on the autism spectrum. However, existing robots are limited in their ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Their inference challenge is made even harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in children with autism, we used the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children’s affective states and engagement during robot-assisted autism therapy. Instead of using the traditional one-size-fits-all ML approach, we personalized our framework to each child using their contextual information (demographics and behavioral assessment scores) and individual characteristics. We evaluated this framework on a multimodal (audio, video, and autonomic physiology) data set of 35 children (ages 3 to 13) with autism, from two cultures (Asia and Europe), and achieved an average agreement (intraclass correlation) of ~60% with human experts in the estimation of affect and engagement, also outperforming nonpersonalized ML solutions. These results demonstrate the feasibility of robot perception of affect and engagement in children with autism and have implications for the design of future autism therapies.


Estimating alertness from the EEG power spectrum

In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.


The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets

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Resuming the discussion of AMSTAR: What can (should) be made better?

BACKGROUND
Evidence syntheses, and in particular systematic reviews (SRs), have become one of the cornerstones of evidence-based health care. The Assessment of Multiple Systematic Reviews (AMSTAR) tool has become the most widely used tool for investigating the methodological quality of SRs and is currently undergoing revision. The objective of this paper is to present insights, challenges and potential solutions from the point of view of a group of assessors, while referring to earlier methodological discussions and debates with respect to AMSTAR.

DISCUSSION
One major drawback of AMSTAR is that it relies heavily on reporting quality rather than on methodological quality. This can be found in several items. Furthermore, it should be acknowledged that there are now new methods and procedures that did not exist when AMSTAR was developed. For example, the note to item 1 should now refer to the International Prospective Register of Ongoing Systematic Reviews (PROSPERO). Furthermore, item 3 should consider the definition of hand-searching, as the process of reviewing conference proceedings using the search function (e.g. in Microsoft Word or in a PDF file) does not meet the definition set out by the Cochrane Collaboration. Moreover, methods for assessing the quality of the body of evidence have evolved since AMSTAR was developed and should be incorporated into a revised AMSTAR tool. Potential solutions are presented for each AMSTAR item with the aim of allowing a more thorough assessment of SRs. As the AMSTAR tool is currently undergoing further development, our paper hopes to add to preceding discussions and papers regarding this tool and stimulate further discussion.


Pregnancy-related gigantomastia. Case report

The term gigantomastia has been used to describe breast enlargement to extreme size, with sloughing, haemorrhage and infection. The condition is rare and a case of pregnancy-related gigantomastia is reported.


Disconnecting outcomes and evaluations: the role of negotiator focus.

Three experiments explored the role of negotiator focus in disconnecting negotiated outcomes and evaluations. Negotiators who focused on their target prices, the ideal outcome they could obtain, achieved objectively superior outcomes compared with negotiators who focused on their lower bound (e.g., reservation price). Those negotiators who focused on their targets, however, were less satisfied with their objectively superior outcomes. In the final experiment, when negotiators were reminded of their lower bound after the negotiation, the satisfaction of those negotiators who had focused on their target prices was increased, with outcomes and evaluations becoming connected rather than disconnected. The possible negative effects of setting high goals and the temporal dimensions of the disconnection and reconnection between outcomes and evaluations are discussed.


Random Features for Large-Scale Kernel Machines

To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. Our randomized features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shift-invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms that use these features outperform state-of-the-art large-scale kernel machines.


A Practical Bayesian Framework for Backpropagation Networks

A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible (1) objective comparisons between solutions using alternative network architectures, (2) objective stopping rules for network pruning or growing procedures, (3) objective choice of magnitude and type of weight decay terms or additive regularizers (for penalizing large weights, etc.), (4) a measure of the effective number of well-determined parameters in a model, (5) quantified estimates of the error bars on network parameters and on network output, and (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian "evidence" automatically embodies "Occam's razor," penalizing overflexible and overcomplex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalization ability and the Bayesian evidence is obtained.


The Effects of Intervention Using a Robot on the Levels of Compliance of Four Children with Autism to Requests Given by T heir Mothers

The Effects of Intervention Using a Robot on the Levels of Compliance of Four Children with Autism to Requests Given by Their Mothers Holly J. Nelson Department of Communication Disorders Master of Science The current study presents the use of a humanoid robot to facilitate compliant behaviors to two types of directives in four children with autism. The children participated in a three-month intervention program that incorporated core components of the SCERTS model in order to facilitate social communication (Prizant, Wetherby, Rubin, & Laurent, 2003). Treatment sessions were comprised of 40 minutes of traditional treatment and 10 minutes of interaction with a humanoid robot. Preand post-intervention assessment were conducted, each involving a 5 minute interaction with the child’s mother, in which they were presented with directives in the form of physical manipulation and verbal requests accompanied by a gesture or model. These preand post-intervention sessions were recorded, analyzed, and coded for compliant and noncompliant behavior to the directives, as well as any eye contact, language, or reciprocal action that accompanied their behavior. The overall results were variable, revealing that two participants made notable gains, one child remained consistent, and another participant showed a decrease in compliant behavior in the post-intervention sessions. Further research should be conducted to include a longer period of baseline and intervention, more systematic identification of the most effective probes for the child, and documentation of the child’s physical and emotional state.


Adaptation and coaching of periodic motion primitives through physical and visual interaction

In this paper we propose and evaluate a control system to (1) learn and (2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodicmotion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contactwith a given surface,maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period ofmotion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive. By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach. The complete system thus allows not only a transfer ofmotion, but a transfer ofmotionwithmatching correspondences, i.e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot. Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching. © 2015 Elsevier B.V. All rights reserved. ⇤ Corresponding author. E-mail addresses: andrej.gams@ijs.si (A. Gams), tadej.petric@ijs.si (T. Petri£), martin.do@kit.edu (M. Do), bojan.nemec@ijs.si (B. Nemec), xmorimo@atr.jp (J. Morimoto), asfour@kit.edu (T. Asfour), ales.ude@ijs.si (A. Ude).


A Soldier Health Monitoring System for Military Applications

With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.


High-Conversion-Ratio Bidirectional DC–DC Converter With Coupled Inductor

In this paper, a high-conversion-ratio bidirectional dc-dc converter with coupled inductor is proposed. In the boost mode, two capacitors are parallel charged and series discharged by the coupled inductor. Thus, high step-up voltage gain can be achieved with an appropriate duty ratio. The voltage stress on the main switch is reduced by a passive clamp circuit. Therefore, the low resistance RDS(ON) of the main switch can be adopted to reduce conduction loss. In the buck mode, two capacitors are series charged and parallel discharged by the coupled inductor. The bidirectional converter can have high step-down gain. Aside from that, all of the switches achieve zero voltage-switching turn-on, and the switching loss can be improved. Due to two active clamp circuits, the energy of the leakage inductor of the coupled inductor is recycled. The efficiency can be further improved. The operating principle and the steady-state analyses of the voltage gain are discussed. Finally, a 24-V-input-voltage, 400-V-output-voltage, and 200-W-output-power prototype circuit is implemented in the laboratory to verify the performance.


Safeguarding 5G wireless communication networks using physical layer security

The fifth generation (5G) network will serve as a key enabler in meeting the continuously increasing demands for future wireless applications, including an ultra-high data rate, an ultrawide radio coverage, an ultra-large number of devices, and an ultra-low latency. This article examines security, a pivotal issue in the 5G network where wireless transmissions are inherently vulnerable to security breaches. Specifically, we focus on physical layer security, which safeguards data confidentiality by exploiting the intrinsic randomness of the communications medium and reaping the benefits offered by the disruptive technologies to 5G. Among various technologies, the three most promising ones are discussed: heterogenous networks, massive multiple-input multiple-output, and millimeter wave. On the basis of the key principles of each technology, we identify the rich opportunities and the outstanding challenges that security designers must tackle. Such an identification is expected to decisively advance the understanding of future physical layer security.


Deblurring Face Images with Exemplars

The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.


MorusDB: a resource for mulberry genomics and genome biology

Mulberry is an important cultivated plant that has received the attention of biologists interested in sericulture and plant-insect interaction. Morus notabilis, a wild mulberry species with a minimal chromosome number is an ideal material for whole-genome sequencing and assembly. The genome and transcriptome of M. notabilis were sequenced and analyzed. In this article, a web-based and open-access database, the Morus Genome Database (MorusDB), was developed to enable easy-to-access and data mining. The MorusDB provides an integrated data source and an easy accession of mulberry large-scale genomic sequencing and assembly, predicted genes and functional annotations, expressed sequence tags (ESTs), transposable elements (TEs), Gene Ontology (GO) terms, horizontal gene transfers between mulberry and silkworm and ortholog and paralog groups. Transcriptome sequencing data for M. notabilis root, leaf, bark, winter bud and male flower can also be searched and downloaded. Furthermore, MorusDB provides an analytical workbench with some built-in tools and pipelines, such as BLAST, Search GO, Mulberry GO and Mulberry GBrowse, to facilitate genomic studies and comparative genomics. The MorusDB provides important genomic resources for scientists working with mulberry and other Moraceae species, which include many important fruit crops. Designed as a basic platform and accompanied by the SilkDB, MorusDB strives to be a comprehensive platform for the silkworm-mulberry interaction studies. Database URL: http://morus.swu.edu.cn/morusdb.


Guidelines for haptic interpersonal communication applications: an exploration of foot interaction styles

A new method for researching haptic interaction styles is presented, based on a layered interaction model and a classification of existing devices. The method is illustrated by designing a new foot interaction device. The aim of which is to enhance non-verbal communication over a computer network. A layered protocols interaction model allows to consider all aspects of the haptic communication process: the intention to perform an action, limitations of the human body, and specifications of the communication device and the network. We demonstrate how this model can be used to derive design-guidelines by analyzing and classifying existing communication devices. By designing and evaluating a foot interaction device, we not only demonstrate that feet are suited for personal, concealed communication over a network, but also show the added value of the design-guidelines. Results of user tests provide clues for designing stimuli for foot interaction and indicate applications of foot communication devices.


20.7 A 13.8µW binaural dual-microphone digital ANSI S1.11 filter bank for hearing aids with zero-short-circuit-current logic in 65nm CMOS

This paper presents an ANSI S1.11 1/3-octave filter-bank chip for binaural hearing aids with two microphones per ear. Binaural multimicrophone systems significantly suppress noise interference and preserve interaural time cues at the cost of significantly higher computational and power requirements than monophonic single-microphone systems. With clock rates around the 1MHz mark, these systems are ideal candidates for low-power implementation through charge-recovery design. At such low clock frequencies, however, charge-recovery logic suffers from short-circuit currents that limit its theoretical energy efficiency [1]. The chip described in this paper is designed in 65nm CMOS using a new charge-recovery logic, called zero-short-circuit-current (ZSCC) logic, that drastically reduces short-circuit current. It processes 4 input streams at 1.75MHz with a charge recovery rate of 92%, achieving 9.7× lower power per input compared with the 40nm monophonic single-input chip that represents the published state of the art [2].


Credit scoring using support vector machines with direct search for parameters selection

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.


Capability Model for Open Data: An Empirical Analysis

Creating superior competitiveness is central to open data organization's survivability in the fast changing and competitive open data market. In their quest to develop and increase competitiveness and survivability, many of these organizations are moving towards developing open data capabilities. Research-based knowledge on open data capabilities and how they relate to each other remains sparse, however, with most of the open data literature focusing on social and economic value of open data, not capabilities required. By exploring the related literature on business and organizational capabilities and linking the findings to the empirical evidence collected through the survey of 49 open data organizations around the world, this study develops an open data capability model. The model emerged from our deductive research process improves both theoretical and practical understanding of open data capabilities and their relationships required to help increase competitiveness and survivability of these types of organizations.


Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods

Diabetic retinopathy is a complication of diabetes that is caused by changes in the blood vessels of the retina. The symptoms can blur or distort the patient's vision and are a main cause of blindness. Exudates are one of the primary signs of diabetic retinopathy. Detection of exudates by ophthalmologists normally requires pupil dilation using a chemical solution which takes time and affects patients. This paper investigates and proposes a set of optimally adjusted morphological operators to be used for exudate detection on diabetic retinopathy patients' non-dilated pupil and low-contrast images. These automatically detected exudates are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. The results are successful and the sensitivity and specificity for our exudate detection is 80% and 99.5%, respectively.


On-Line Flatness Measurement in the Steelmaking Industry

Shape is a key characteristic to determine the quality of outgoing flat-rolled products in the steel industry. It is greatly influenced by flatness, a feature to describe how the surface of a rolled product approaches a plane. Flatness is of the utmost importance in steelmaking, since it is used by most downstream processes and customers for the acceptance or rejection of rolled products. Flatness sensors compute flatness measurements based on comparing the length of several longitudinal fibers of the surface of the product under inspection. Two main different approaches are commonly used. On the one hand, most mechanical sensors measure the tensile stress across the width of the rolled product, while manufacturing and estimating the fiber lengths from this stress. On the other hand, optical sensors measure the length of the fibers by means of light patterns projected onto the product surface. In this paper, we review the techniques and the main sensors used in the steelmaking industry to measure and quantify flatness defects in steel plates, sheets and strips. Most of these techniques and sensors can be used in other industries involving rolling mills or continuous production lines, such as aluminum, copper and paper, to name a few. Encompassed in the special issue, State-of-the-Art Sensors Technology in Spain 2013, this paper also reviews the most important flatness sensors designed and developed for the steelmaking industry in Spain.


An OpenNaaS Based SDN Framework for Dynamic QoS Control

Network Service Providers should offer provisioning services with guaranteed Quality of Service (QoS), specifically adapted to the characteristics of the applications running on their network. In this paper we propose the Network Control Layer (NCL), a software framework solution based on Software Defined Networks (SDN), OpenFlow and Network as a Service (NaaS) paradigms. It addresses a major innovation area in the field of network control and management providing on-demand end-to-end network provisioning services with guaranteed QoS, based on the specific requirements of on-top running interactive applications. The NCL implementation is based on the OpenNaaS framework, and it includes mechanisms for network status monitoring and SDN switches configuration based on the interactive applications' QoS network requirements. We demonstrate NCL's utility in the context of control plane models making use of a practical use case.


A Feature-Based Approach for Group-Wise Grid Map Registration

For autonomous vehicles and advanced driver assistance systems, information on the actual state of the environment is fundamental for localization and mapping tasks. Localization benefits from multiple observations of the same location at different times as these may provide important information on static and mobile objects. For efficient mapping, the environment may be explored in parallel. For these purposes, multiple observations represented by grid maps have to be aligned into one mutual frame. This paper addresses the problem of group-wise grid map registration using an image processing approach. For registration, a rotational-invariant descriptor is proposed in order to provide the correspondences of points of interest in radar-based occupancy grid maps. As pairwise registration of multiple grid maps suffers from bias, this paper proposes a graph-based approach for robust registration of multiple grid maps. This will facilitate highly accurate range sensor maps for the aforementioned purposes. Large-scale experiments show the benefit of the proposed methods and compare it to state-of-the-art algorithms on radar measurements.


Very Small Ultra-Wide-Band MMIC Magic T and Applications to Combiners and Dividers

An FET-sized 1-18 GHz monolithic active magic T (1 W hybrid) is proposed. It unifies two different dividers, electrically isolated from each other, in a novel GaAs FET electrode configuration, viz. the LUFET concept. Its characteristics and experiment results are presented. Applications of the magic T to miniature wide-band RF signal processing. . .


The big five personality traits: psychological entities or statistical constructs?

The present study employed multivariate genetic item-level analyses to examine the ontology and the genetic and environmental etiology of the Big Five personality dimensions, as measured by the NEO Five Factor Inventory (NEO-FFI) [Costa and McCrae, Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO-FFI) professional manual, 1992; Hoekstra et al., NEO personality questionnaires NEO-PI-R, NEO-FFI: manual, 1996]. Common and independent pathway model comparison was used to test whether the five personality dimensions fully mediate the genetic and environmental effects on the items, as would be expected under the realist interpretation of the Big Five. In addition, the dimensionalities of the latent genetic and environmental structures were examined. Item scores of a population-based sample of 7,900 adult twins (including 2,805 complete twin pairs; 1,528 MZ and 1,277 DZ) on the Dutch version of the NEO-FFI were analyzed. Although both the genetic and the environmental covariance components display a 5-factor structure, applications of common and independent pathway modeling showed that they do not comply with the collinearity constraints entailed in the common pathway model. Implications for the substantive interpretation of the Big Five are discussed.


Relationship between the cayley-dickson fourier transform and the hartley transform of multidimensional real signals

The paper shows relations between two different frequency representations of n-dimensional signals (n = 1, 2, 3): the Cayley-Dickson Fourier transform and the Hartley transform. The formulas relating the n-D complex Fourier transform and the n-D Hartley transform are presented. New formulas relating the Quaternion and Octonion Fourier transforms and respectively the 2-D and 3-D Hartley transforms are developed. The paper is illustrated with examples of Hartley transforms of 1-D, 2-D and 3-D Gaussian signals.