title: A novel neural network ensemble architecture for time series forecasting

abstract: We propose a novel homogeneous neural network ensemble approach called Generalized Regression Neural Network (GEFTS-GRNN) Ensemble for Forecasting Time Series, which is a concatenation of existing machine learning algorithms. GEFTS uses a dynamic nonlinear weighting system wherein the outputs from several base-level GRNNs are combined using a combiner GRNN to produce the final output. We compare GEFTS with the 11 most used algorithms on 30 real datasets. The proposed algorithm appears to be more powerful than existing ones. Unlike conventional algorithms, GEFTS is effective in forecasting time series with seasonal patterns.


title: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

abstract: In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.


title: Enhancing remote sensing education with GeoBrain cyberinfrastructure

abstract: Modern geoscience and remote sensing (RS) are increasingly data-intensive. Cyberinfrastructure (CI) plays an essential role in data-intensive geoscience and RS research and education. GeoBrain is one of NASA's significant investments in CI for facilitating data-intensive Earth science (ES) and RS research and education. The GeoBrain CI, which makes large data and computing resources interoperable and sharable over the Web by adopting and developing the latest Web service, geospatial interoperability and related information technologies, provides an innovative approach to data-intensive geospatial computing. This paper addresses the GeoBrain solution to major issues in data-intensive ES/RS education and illustrates how use of GeoBrain could greatly enhance ES/RS education.


title: Plant Mucilages. VIII. Isolation and Characterization of a Mucous Polysaccharide, "Bletilla-glucomannan, "from Bletilla striata Tubers

abstract: A mucous polysaccharide, named Bletilla-glucomannan, has been isolated from the tubers of Bletilla striata REICHENBACH fil. It was homogeneous on glass-fiber paper electrophoresis and by ultracentrifugal analysis. The component sugars of it were D-mannose and D-glucose, and the molar ratio of them was 3 : 1. The molecular weight was estimated at 182000 by the measurement of osmotic pressure. Periodate oxidation and partial acid hydrolysis of it suggested that the polysaccharide is mainly composed of β-1→4 linked aldohexopyranose residues and it contains six aldohexose units per one end group on the average.


title: A UWB based Localization System for Indoor Robot Navigation

abstract: For robots to become more popular for domestic applications, the short comings of current indoor navigation technologies have to be overcome. In this paper, we propose the use of UWB-IR for indoor robot navigation. Various parts of an actual implementation of a UWB-IR based robot navigation system such as system architecture, RF sub-system design, antennas and localization algorithms are discussed. It is shown that by properly addressing the various issues, a localization error of less than 25 cm can be achieved at all points within a realistic indoor localization space.


title: Cannabinoid CB1 and CB2 Receptor Signaling and Bias

abstract: An agonist that acts through a single receptor can activate numerous signaling pathways. Recent studies have suggested that different ligands can differentially activate these pathways by stabilizing a limited range of receptor conformations, which in turn preferentially drive different downstream signaling cascades. This concept, termed "biased signaling" represents an exciting therapeutic opportunity to target specific pathways that elicit only desired effects, while avoiding undesired effects mediated by different signaling cascades. The cannabinoid receptors CB1 and CB2 each activate multiple pathways, and evidence is emerging for bias within these pathways. This review will summarize the current evidence for biased signaling through cannabinoid receptor subtypes CB1 and CB2.


Enriching learning experience in work-integrated education through weblog and e-portfolio


title: Adaptive Clustering Segmentation Based on Pixel Color

abstract: On the basis of the two limitations of cluster algorithm, an adaptive algorithm to accurately segment the color images is proposed.At first, by analysis of color's distribution in an image, the center of pixels' Hue can be found. Taking these hues as main hues, the initial cluster centers are made of the most far adjacent pixels. Then pixels are clustered by the Algorithm Using Color and Spatial Information. During the alternation, usage of color and geometrical measure assure the algorithm fast and accurate. At last,the pixels are quantized according to the clustering results. In the algorithm, the value of k is established by the algorithm itself, and it changes in the alternation automatically. The clustering results are consisted of the object areas. Experiments show the method can effectively achieve the desired color image segmentation.


title: PhytAMP: a database dedicated to antimicrobial plant peptides

abstract: Plants produce small cysteine-rich antimicrobial peptides as an innate defense against pathogens. Based on amino acid sequence homology, these peptides were classified mostly as α-defensins, thionins, lipid transfer proteins, cyclotides, snakins and hevein-like. Although many antimicrobial plant peptides are now well characterized, much information is still missing or is unavailable to potential users. The compilation of such information in one centralized resource, such as a database would therefore facilitate the study of the potential these peptide structures represent, for example, as alternatives in response to increasing antibiotic resistance or for increasing plant resistance to pathogens by genetic engineering. To achieve this goal, we developed a new database, PhytAMP, which contains valuable information on antimicrobial plant peptides, including taxonomic, microbiological and physicochemical data. Information is very easy to extract from this database and allows rapid prediction of structure/function relationships and target organisms and hence better exploitation of plant peptide biological activities in both the pharmaceutical and agricultural sectors. PhytAMP may be accessed free of charge at http://phytamp.pfba-lab.org.


title: An introduction to migraine: from ancient treatment to functional pharmacology and antimigraine therapy.

abstract: Migraine treatment has evolved from the realms of the supernatural into the scientific arena, but it seems still controversial whether migraine is primarily a vascular or a neurological dysfunction. Irrespective of this controversy, the levels of serotonin (5-hydroxytryptamine; 5-HT), a vasoconstrictor and a central neurotransmitter, seem to decrease during migraine (with associated carotid vasodilatation) whereas an i.v. infusion of 5-HT can abort migraine. In fact, 5-HT as well as ergotamine, dihydroergotamine and other antimigraine agents invariably produce vasoconstriction in the external carotid circulation. The last decade has witnessed the advent of sumatriptan and second generation triptans (e.g. zolmitriptan, rizatriptan, naratriptan), which belong to a new class of drugs, now known as 5-HT1B/1D/1F receptor agonists. Compared to sumatriptan, the second-generation triptans have a higher oral bioavailability and longer plasma half-life. In line with the vascular and neurogenic theories of migraine, all triptans produce selective carotid vasoconstriction (via 5-HT1B receptors) and presynaptic inhibition of the trigeminovascular inflammatory responses implicated in migraine (via 5-HT1D/5-ht1F receptors). Moreover, selective agonists at 5-HT1D (PNU-142633) and 5-ht1F (LY344864) receptors inhibit the trigeminovascular system without producing vasoconstriction. Nevertheless, PNU-142633 proved to be ineffective in the acute treatment of migraine, whilst LY344864 did show some efficacy when used in doses which interact with 5-HT1B receptors. Finally, although the triptans are effective antimigraine agents producing selective cranial vasoconstriction, efforts are being made to develop other effective antimigraine alternatives acting via the direct blockade of vasodilator mechanisms (e.g. antagonists at CGRP receptors, antagonists at 5-HT7 receptors, inhibitors of nitric oxide biosynthesis, etc). These alternatives will hopefully lead to fewer side-effects.


title: Why Web 2.0 is Good for Learning and for Research: Principles and Prototypes

abstract: The term "Web 2.0" is used to describe applications that distinguish themselves from previous generations of software by a number of principles. Existing work shows that Web 2.0 applications can be successfully exploited for technology-enhance learning. However, in-depth analyses of the relationship between Web 2.0 technology on the one hand and teaching and learning on the other hand are still rare. In this article, we will analyze the technological principles of the Web 2.0 and describe their pedagogical implications on learning. We will furthermore show that Web 2.0 is not only well suited for learning but also for research on learning: the wealth of services that is available and their openness regarding API and data allow to assemble prototypes of technology-supported learning applications in amazingly small amount of time. These prototypes can be used to evaluate research hypotheses quickly. We will present two example prototypes and discuss the lessons we learned from building and using these prototypes.


title: Improving Deep Neural Network with Multiple Parametric Exponential Linear Units

abstract: Activation function is crucial to the recent successes of deep neural networks. In this paper, we first propose a new activation function, Multiple Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the rectified and exponential linear units. As the generalized form, MPELU shares the advantages of Parametric Rectified Linear Unit (PReLU) and Exponential Linear Unit (ELU), leading to better classification performance and convergence property. In addition, weight initialization is very important to train very deep networks. The existing methods laid a solid foundation for networks using rectified linear units but not for exponential linear units. This paper complements the current theory and extends it to the wider range. Specifically, we put forward a way of initialization, enabling training of very deep networks using exponential linear units. Experiments demonstrate that the proposed initialization not only helps the training process but leads to better generalization performance. Finally, utilizing the proposed activation function and initialization, we present a deep MPELU residual architecture that achieves state-of-the-art performance on the CIFAR-10/100 datasets. The code is available at this https URL


title: A SimpleModel of Negotiation for Cooperative Updates onDatabase Schema Components

abstract: Modern applications involving information systems often require the cooperation of several distinct users, and many models of such cooperation have arisen over the years. One way to model such situations is via a cooperative update on a database; that is, an update for which no single user has the necessary access rights, so that several users, each with distinct rights, must cooperate to achieve the desired goal. However, cooperative update mandates new ways of modelling and extending certain fundamentals of database systems. In this paper, such extensions are explored, using database schema components as the underlying model. The main contribution is an effective three-stage process for inter-component negotiation.


title: Circuit and Coil Design for In-Vitro Magnetic Neural Stimulation Systems

abstract: Magnetic stimulation of neural tissue is an attractive technology because neural excitation may be affected without requiring implantation of electrodes. Pulsed discharge circuits are typically implemented for clinical magnetic stimulation systems. However, pulsed discharge systems can confound in-vitro experimentation. As an alternative to pulsed discharge circuits, we present a circuit to deliver asymmetric current pulses for generation of the magnetic field. We scaled the system down by using ferrite cores for the excitation coil. The scaled system allows observation using electrophysiological techniques and preparations not commonly used for investigation of magnetic stimulation. The design was refined using a comprehensive set of design equations. Circuit modeling and simulation demonstrate that the proposed system is effective for stimulating neural tissue with electric-field gradients generated by time-varying magnetic fields. System performance is verified through electrical test.


title: A real-time lane detection and tracking algorithm

abstract: Aimed at the problem of lane detection in intelligent vehicle safety aided system, fusing multiple features of the lane, we propose a real-time lane detection and tracking algorithm based on Gabor filter and edge extraction respectively. It can be suitable for fast lane detection on the structured road under the multi-lane and multi-scene. Firstly, in the initial lane detection module, the Gabor filter is used to enhance the characteristics of the lane, and the lane is detected under the restriction of polar angle range by the Hough transform, then using the geometric relationship between the lane and the vehicle to judge the current lane. Secondly, referring the tracking detection module, the lane location of the prior frame is used to establish the dynamic region of interest. The edge of lane is extracted by using the different edge detection operator under the different illumination conditions. Next, the lane is tracked by the least squares fitting in this region. Experimental results indicate that the proposed algorithm is real-time and efficient, and enhance the adaptability for the changing environment of road scene.


title: IoT-based health monitoring via LoRaWAN

abstract: In this paper, we present a new loT-based health monitoring approach in which collected medical sensor data is sent to an analysis module via low-cost, low-power and secure communication links provided by a LoRaWAN network infrastructure. We mainly focus on monitoring blood pressure, glucose and temperature in rural areas where cellular network coverage is either absent or does not allow data transmission. The main objective is to reduce the burden of long trips for people living in these areas to visit healthcare facilities, while minimizing the communication cost. Several experiments have been conducted to evaluate the area covered by the LoRa network and the power consumption of our system. The results indicate that the average area covered is around 33 km2 when the LoRa Gateway is placed outdoor on a 12 meter altitude. Moreover, the results demonstrate that the power consumption of our monitoring system is at least ten times lower than other long range cellular solutions, such as GPRS/3G/4G.


title: HyperSentry: enabling stealthy in-context measurement of hypervisor integrity

abstract: This paper presents HyperSentry, a novel framework to enable integrity measurement of a running hypervisor (or any other highest privileged software layer on a system). Unlike existing solutions for protecting privileged software, HyperSentry does not introduce a higher privileged software layer below the integrity measurement target, which could start another race with malicious attackers in obtaining the highest privilege in the system. Instead, HyperSentry introduces a software component that is properly isolated from the hypervisor to enable stealthy and in-context measurement of the runtime integrity of the hypervisor. While stealthiness is necessary to ensure that a compromised hypervisor does not have a chance to hide the attack traces upon detecting an up-coming measurement, in-context measurement is necessary to retrieve all the needed inputs for a successful integrity measurement. HyperSentry uses an out-of-band channel (e.g., Intelligent Platform Management Interface (IPMI), which is commonly available on server platforms) to trigger the stealthy measurement, and adopts the System Management Mode (SMM) to protect its base code and critical data. A key contribution of HyperSentry is the set of novel techniques that overcome SMM's limitation, providing an integrity measurement agent with (1) the same contextual information available to the hypervisor, (2) completely protected execution, and (3) attestation to its output. To evaluate HyperSentry, we implement a prototype of the framework along with an integrity measurement agent for the Xen hypervisor. Our experimental evaluation shows that HyperSentry is a low-overhead practical solution for real world systems.


title: Superimposition of 3 D cone-beam CT models of orthognathic surgery patients

abstract: Objectives: To evaluate the registration of 3D models from cone-beam CT (CBCT) images taken before and after orthognathic surgery for the assessment of mandibular anatomy and position. Methods: CBCT scans were taken before and after orthognathic surgery for ten patients with various malocclusions undergoing maxillary surgery only. 3D models were constructed from the CBCT images utilizing semi-automatic segmentation and manual editing. The cranial base was used to register 3D models of pre- and post-surgery scans (1 week). After registration, a novel tool allowed the visual and quantitative assessment of post-operative changes via 2D overlays of superimposed models and 3D coloured displacement maps. Results: 3D changes in mandibular rami position after surgical procedures were clearly illustrated by the 3D colour-coded maps. The average displacement of all surfaces was 0.77 mm (SD ¼ 0.17 mm), at the posterior border 0.78 mm (SD ¼ 0.25 mm), and at the condyle 0.70 mm (SD ¼ 0.07 mm). These displacements were close to the image spatial resolution of 0.60 mm. The average interobserver differences were negligible. The range of the interobserver errors for the average of all mandibular rami surface distances was 0.02 mm (SD ¼ 0.01 mm). Conclusion: Our results suggest this method provides a valid and reproducible assessment of craniofacial structures for patients undergoing orthognathic surgery. This technique may be used to identify different patterns of ramus and condylar remodelling following orthognathic surgery. Dentomaxillofacial Radiology (2005) 34, 369‐375. doi: 10.1259/dmfr/17102411


title: Morphological Analysis And Generation For Arabic Dialects

abstract: We present Magead, a morphological analyzer and generator for the Arabic language family. Our work is novel in that it explicitly addresses the need for processing the morphology of the dialects. Magead provides an analysis to a root+pattern representation, it has separate phonological and orthographic representations, and it allows for combining morphemes from different dialects.


title: The Geometry Of Algorithms With Orthogonality Constraints

abstract: In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal processing. In addition to the new algorithms, we show how the geometrical framework gives penetrating new insights allowing us to create, understand, and compare algorithms. The theory proposed here provides a taxonomy for numerical linear algebra algorithms that provide a top level mathematical view of previously unrelated algorithms. It is our hope that developers of new algorithms and perturbation theories will benefit from the theory, methods, and examples in this paper.


title: Tajweed checking system to support recitation

abstract: Al-Quran recitation has a set of rules known as tajweed to ensure proper pronunciation, readings and interpretations of the Al-Quran. It has been traditionally taught by ustad or experienced religious teachers. Generally, these teachers will listen to the students recitation and highlight their mistakes. However, the traditional method which requires the present of these skilled teachers has limitation to support self-learning environment. For this reason, we propose tajweed rule checking system using speech recognition technology to help students to learn and revise proper Al-Quran recitation by themself. The proposed system is capable to recognize and point out the mismatch between the students recitation with the recitation made by the experienced teachers stored in a database. The system adopts the MFCC algorithm for feature extraction and HMM for feature classification.


title: SeaLinx: a multi-instance protocol stack architecture for underwater networking

abstract: Advancement in acoustic modem technology has gradually brought underwater network into real life. Despite high cost of sea testing, there have been initial realization effort, such as Sea-Web, Aqua-Net, SUNSET, etc. As the main developers of Aqua-Net, through extensive testing experiences at different scales, we have recognized shortcomings of Aqua-Net and decided to design and implement the next generation of it. In this paper, we introduce SeaLinx as a new protocol stack architecture for underwater networking which (i) enables users to exploit their hardware more efficiently by allowing applications to run simultaneously on a modem, and (ii) provide better support for cross-layer communication.


title: Distilling Task Knowledge from How-To Communities

abstract: Knowledge graphs have become a fundamental asset for search engines. A fair amount of user queries seek information on problem-solving tasks such as building a fence or repairing a bicycle. However, knowledge graphs completely lack this kind of how-to knowledge. This paper presents a method for automatically constructing a formal knowledge base on tasks and task-solving steps, by tapping the contents of online communities such as WikiHow. We employ Open-IE techniques to extract noisy candidates for tasks, steps and the required tools and other items. For cleaning and properly organizing this data, we devise embedding-based clustering techniques. The resulting knowledge base, HowToKB, includes a hierarchical taxonomy of disambiguated tasks, temporal orders of sub-tasks, and attributes for involved items. A comprehensive evaluation of HowToKB shows high accuracy. As an extrinsic use case, we evaluate automatically searching related YouTube videos for HowToKB tasks.


title: Compact 3-D-Printed Circularly Polarized Antenna for Handheld UHF RFID Readers

abstract: In this letter, the aptness of the combination of three-dimensional (3-D) printing and radio frequency identification (RFID) is faced by presenting a compact, low-profile, and cost-effective circularly polarized antenna for handheld UHF RFID reader. The radiating element has been realized through a circular array of four inverted-F monopoles, where the array elements are excited with a 90° phase offset through a microstrip feeding network, realized in 3-D printing technology as well. Taking advantage from low losses and moldability of the 3-D printing materials joint to a specific design strategy, the proposed antenna realizes an improved gain and an appreciable size reduction if compared with the state of the art.


Evolutionary Approach to Machine Learning and Deep Neural Networks


title: The Warrington Recognition Memory Test for Words as a Measure of Response Bias: Total Score and Response Time Cutoffs Developed on “Real World” Credible and Noncredible Subjects

abstract: Several studies have examined the usefulness of the Warrington Recognition Memory Test―Words as a measure to detect suspect effort, although samples have generally been small and/or comprised of simulators rather than "real world" credible and noncredible patients. The current study examined the Warrington Recognition Memory Test―Words total score and response time of "real world" noncredible patients (as determined by motive to feign, failure on ≥2 independent measures of response bias, low cognitive scores inconsistent with normal ADLs; n = 190) versus credible patients (as determined by no motive to feign, failure of ≤1 measure of response bias; n = 124) derived from an archival database of individuals from the Harbor-UCLA Medical Center, Department of Psychiatry, Outpatient Neuropsychology Service, and the private practice of the second author. Noncredible patients obtained significantly lower total scores and longer times to complete the task. A total correct cutoff of ≤42 was found to have excellent specificity (91.9%) and sensitivity (88.9%), whereas a time cutoff of >207" was associated with 65.5% sensitivity at 90.7% specificity, and when the time cut-score was used in combination with the total score cutoff, an additional 5% of the noncredible participants were captured, raising overall sensitivity to 93.7% (at 87.1% specificity). Thus, the Warrington Recognition Memory Test―Words, although not originally created for the purposes of measuring suspect effort, appears to be an excellent measure for detecting response bias on neuropsychological testing.


title: Developing a Prediction Model for Stock Analysis

abstract: Prediction in the stock market is challenging and complicated for investors. Many researches have performed to sense the future market movements. In the stock market, social media data have high impact today than ever. In this work, various prediction algorithms are analyzed to build a prediction model. The prediction model will be based on monthly prediction and daily prediction to forecast the next day market price. This model estimates the open value of the next day in the market. Sentiment Analysis needs to identify and extract sentiments from each individual in the social media. The correlation between the sentiments and the stock value is to be determined. A comparative study of these three algorithms: Multiple Linear Regression, Support Vector Machine and Artificial Neural Network are done. The stock price is predicted by sentiment analysis with the best forecasting algorithm.


title: Histone H2B Monoubiquitination Functions Cooperatively with FACT to Regulate Elongation by RNA Polymerase II

abstract: Over the past years, a large number of histone posttranslational modifications have been described, some of which function to attain a repressed chromatin structure, while others facilitate activation by allowing access of regulators to DNA. Histone H2B monoubiquitination is a mark associated with transcriptional activity. Using a highly reconstituted chromatin-transcription system incorporating the inducible RARβ2 promoter, we find that the establishment of H2B monoubiquitination by RNF20/40 and UbcH6 is dependent on the transcription elongation regulator complex PAF, the histone chaperone FACT, and transcription. H2B monoubiquitination facilitates FACT function, thereby stimulating transcript elongation and the generation of longer transcripts. These in vitro analyses and corroborating in vivo experiments demonstrate that elongation by RNA polymerase II through the nucleosomal barrier is minimally dependent upon (1) FACT and (2) the recruitment of PAF and the H2B monoubiquitination machinery.


title: Experience-dependent structural plasticity in the adult human brain

abstract: Contrary to assumptions that changes in brain networks are possible only during crucial periods of development, research in the past decade has supported the idea of a permanently plastic brain. Novel experience, altered afferent input due to environmental changes and learning new skills are now recognized as modulators of brain function and underlying neuroanatomic circuitry. Given findings in experiments with animals and the recent discovery of increases in gray and white matter in the adult human brain as a result of learning, the old concept of cognitive reserve, that is the ability to reinforce brain volume in crucial areas and thus provide a greater threshold for age-dependent deficits, has been reinforced. The challenge we face is to unravel the exact nature of the dynamic structural alterations and, ultimately, to be able to use this knowledge for disease management. Understanding normative changes in brain structure that occur as a result of environmental changes and demands is pivotal to understanding the characteristic ability of the brain to adapt.


title: Eye Tracking Methodology: Theory and Practice

abstract: to the Human Visual System (HVS).- Visual Attention.- Neurological Substrate of the HVS.- Visual Psychophysics.- Taxonomy and Models of Eye Movements.- Eye Tracking Systems.- Eye Tracking Techniques.- Head-Mounted System Hardware Installation.- Head-Mounted System Software Development.- Head-Mounted System Calibration.- Table-Mounted System Hardware Installation.- Table-Mounted System Software Development.- Table-Mounted System Calibration.- Eye Movement Analysis.- Eye Tracking Methodology.- Experimental Design.- Suggested Empirical Guidelines.- Case Studies.- Eye Tracking Applications.- Diversity and Types of Eye Tracking Applications.- Neuroscience and Psychology.- Industrial Engineering and Human Factors.- Marketing/Advertising.- Computer Science.- Conclusion.


title: The Evolution of Knowledge Systems: Narrative Knowledge versus Scientific Knowledge

abstract: This paper discusses two types of knowledge that are generated by cultural anthropology, narrative knowledge and scientific knowledge. The argument of this article is that narrative knowledge an older form of knowledge that dates to the time when the brain was evolving its capacity for cultural communication, and that scientific knowledge is more recent and depends on writing and other more elaborate technologies for encoding cultural information outside of the human brain. The article explores the various ways in which narrative knowledge


title: Path loss models for 5G millimeter wave propagation channels in urban microcells

abstract: Measurements for future outdoor cellular systems at 28 GHz and 38 GHz were conducted in urban microcellular environments in New York City and Austin, Texas, respectively. Measurements in both line-of-sight and non-line-of-sight scenarios used multiple combinations of steerable transmit and receive antennas (e.g. 24.5 dBi horn antennas with 10.9° half power beamwidths at 28 GHz, 25 dBi horn antennas with 7.8° half power beamwidths at 38 GHz, and 13.3 dBi horn antennas with 24.7° half power beamwidths at 38 GHz) at different transmit antenna heights. Based on the measured data, we present path loss models suitable for the development of fifth generation (5G) standards that show the distance dependency of received power. In this paper, path loss is expressed in easy-to-use formulas as the sum of a distant dependent path loss factor, a floating intercept, and a shadowing factor that minimizes the mean square error fit to the empirical data. The new models are compared with previous models that were limited to using a close-in free space reference distance. Here, we illustrate the differences of the two modeling approaches, and show that a floating intercept model reduces the shadow factors by several dB and offers smaller path loss exponents while simultaneously providing a better fit to the empirical data. The upshot of these new path loss models is that coverage is actually better than first suggested by work in [1], [7] and [8].


title: Simultaneously Fitting and Segmenting Multiple-Structure Data with Outliers

abstract: We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Ordered Scale Estimator (IKOSE). IKOSE can accurately estimate the scale of inliers for heavily corrupted multiple-structure data and is of interest by itself since it can be used in other robust estimators. In addition to IKOSE, our framework includes several original elements based on the weighting, clustering, and fusing of hypotheses. AKSWH can provide accurate estimates of the number of model instances and the parameters and the scale of each model instance simultaneously. We demonstrate good performance in practical applications such as line fitting, circle fitting, range image segmentation, homography estimation, and two--view-based motion segmentation, using both synthetic data and real images.


title: Connectionist language modeling for large vocabulary continuous speech recognition

abstract: This paper describes ongoing work on a new approach for language modeling for large vocabulary continuous speech recognition. Almost all state.. o. f-the-art systems use statistical n-gram language models estimated on text corpora. One principle problem with such language models is the fact that many of the n-grams are never observed even in very large training corpora, and therefore it is common to back-off to a lower-order model. In this paper we propose to address this problem by carrying out the estimation task in a continuous space, enabling a smooth interpolation of the probabilities. A neural network is used to learn the projection of the words onto a continuous space and to estimate the n-gram probabilities. The connectionist language model is being evaluated on the DARPA HUB5 conversational telephone speech recognition task and preliminary results show consistent improvements in both perplexity and word error rate.


title: A novel driver fatigue monitoring using optical imaging of face on safe driving system

abstract: One of the global main goal of the safety driving system is protecting the driver, passenger(s), car, and surrounding environment against accident which are caused by external and internal factors. Driver fatigue, one of the major internal factors, is a leading reason of vehicle breakdown according to a survey done by National Highway Traffic Safety Administration (NHTSA). Thus, it is necessary to build driver fatigue monitoring system. We, then, propose a technique based on optical imaging through digital camera that installed on the car dashboard. The camera detects and tracks the driver face. From the driver face, we can apply non-contact photoplesthymography (PPG) in order to get multiple physiological signals such as brainwave, cardiac and respiration pulses. Those physiological signals can be utilized to measure fatigue level. Alteration in facial features like eyes, mouth, and head, can be used to observe the driver fatigue as well. We propose to use supervised descent method (SDM) with scale-invariant feature transform (SIFT) to excerpt information from the facial features. To classify the fatigue level from those multiple parameters, support vector machine (SVM) will be implemented.


title: Valorization of mining waste from Ouenza iron ore mine (eastern Algeria)

abstract: The present article is devoted to the development of a hematite-poor ore mine in Ouenza, which does not meet the steelmaker's requirements. Significant volumes are stored at the pithead of the mine, and the reserves are estimated at over 100 million tones. This enormous quantity of mining waste occupies an important space and poses a real threat to the environment as well as for the mining city of Ouenza. In order to solve these socio-economic and environmental problems, a sustainable development and a better quality of life for inhabitants of this region is needed. For this, representative samples were taken at the level of the dumps. Taking into account the natural characteristics of the stock namely; mineralogical composition, iron content, particle size of the rock mass, as well as the release mesh of iron minerals from the gangue. Firstly, tests are conducted on the recovery by radiometric separation of iron-rich pieces and graded. Then the rest of the ore was subjected to mechanical preparation followed by enrichment, which will be the subject of another study. The research is conducted on samples to determine the optimal parameters of the g-rays absorption tested by radiometry; these parameters were the velocity of the conveyor belt and the time of exposure to g-rays. The obtained results by this valorization process are very significant: iron content 53.5% and 8.3% recovery.


Ontology Summit 2017 communiqué – AI, learning, reasoning and ontologies


title: Learning Attentional Communication for Multi-Agent Cooperation

abstract: Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision making from globally shared information. Therefore, communication barely helps, and could even impair the learning of multi-agent cooperation. Predefined communication architectures, on the other hand, restrict communication among agents and thus restrain potential cooperation. To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrate shared information for cooperative decision making. Our model leads to efficient and effective communication for large-scale multi-agent cooperation. Empirically, we show the strength of our model in a variety of cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.


title: Social Responsibility and Strategic Management: Toward an Enterprise Strategy Classification

abstract: Corporate social responsibility is incorporated into strategic management at the enterprise strategy level. This paper delineates the domain of enterprise strategy by focusing on how well a firm's social performance matches its competencies and stakeholders rather than on the "quantity" of a firm's social responsibility. Enterprise strategy is defined and a classification of enterprise strategies is set forth.


title: A new design of XOR-XNOR gates for low power application

abstract: XOR and XNOR gate plays an important role in digital systems including arithmetic and encryption circuits. This paper proposes a combination of XOR-XNOR gate using 6-transistors for low power applications. Comparison between a best existing XOR-XNOR have been done by simulating the proposed and other design using 65nm CMOS technology in Cadence environment. The simulation results demonstrate the delay, power consumption and power-delay product (PDP) at different supply voltages ranging from 0.6V to 1.2V. The results show that the proposed design has lower power dissipation and has a full voltage swing.


title: Design of Diet Recommendation System for Healthcare Service Based on User Information

abstract: In the recent aging society, studies on health care services have been actively conducted to provide quality services to medical consumers in wire and wireless environments. However, there are some problems in these health care services due to the lack of personalized service and the uniformed way in services. For solving these issues, studies on customized services in medical markets have been processed. However, because a diet recommendation service is only focused on the personal disease information, it is difficult to provide specific customized services to users. This study provides a customized diet recommendation service for preventing and managing coronary heart disease in health care services. This service provides a customized diet to customers by considering the basic information, vital sign, family history of diseases, food preferences according to seasons and intakes for the customers who are concerning about the coronary heart disease. The users who receive this service can use a customized diet service differed from the conventional service and that supports continuous services and helps changes in customers living habits.


A Constituent-Centric Neural Architecture for Reading Comprehension.


title: Embedded System for Motion Control of an Omnidirectional Mobile Robot

abstract: In this paper, an embedded system for motion control of omnidirectional mobile robots is presented. An omnidirectional mobile robot is a type of holonomic robots. It can move simultaneously and independently in translation and rotation. The RoboCup small-size league, a robotic soccer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-tuned proportional–integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobile robot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high-level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion of four-wheeled omnidirectional mobile robots.


title: Learning Discriminative Features with Multiple Granularities for Person Re-Identification

abstract: The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method has robustly achieved state-of-the-art performances and outperformed any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.


title: Diggit: Automated code review via software repository mining

abstract: We present Diggit, a tool to automatically generate code review comments, offering design guidance on prospective changes, based on insights gained from mining historical changes in source code repositories. We describe how the tool was built and tuned for use in practice as we integrated Diggit into the working processes of an industrial development team. We focus on the developer experience, the constraints that had to be met in adapting academic research to produce a tool that was useful to developers, and the effectiveness of the results in practice.


The Case For Heterogeneous HTAP


title: MALWARE DETECTION BASED ON STRUCTURAL AND BEHAVIOURAL FEATURES OF API CALLS

abstract: In this paper, we propose a five-step approach to detect obfuscated malware by investigating the structural and behavioural features of API calls. We have developed a fully automated system to disassemble and extract API call features effectively from executables. Using n-gram statistical analysis of binary content, we are able to classify if an executable file is malicious or benign. Our experimental results with a dataset of 242 malwares and 72 benign files have shown a promising accuracy of 96.5% for the unigram model. We also provide a preliminary analysis by our approach using support vector machine (SVM) and by varying n-values from 1 to 5, we have analysed the performance that include accuracy, false positives and false negatives. By applying SVM, we propose to train the classifier and derive an optimum n-gram model for detecting both known and unknown malware efficiently.


title: Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context

abstract: A small subset of machine learning algorithms, mostly inductive learning based, applied to the KDD 1999 Cup intrusion detection dataset resulted in dismal performance for user-to-root and remote-to-local attack categories as reported in the recent literature. The uncertainty to explore if other machine learning algorithms can demonstrate better performance compared to the ones already employed constitutes the motivation for the study reported herein. Specifically, exploration of if certain algorithms perform better for certain attack classes and consequently, if a multi-expert classifier design can deliver desired performance measure is of high interest. This paper evaluates performance of a comprehensive set of pattern recognition and machine learning algorithms on four attack categories as found in the KDD 1999 Cup intrusion detection dataset. Results of simulation study implemented to that effect indicated that certain classification algorithms perform better for certain attack categories: a specific algorithm specialized for a given attack category . Consequently, a multi-classifier model, where a specific detection algorithm is associated with an attack category for which it is the most promising, was built. Empirical results obtained through simulation indicate that noticeable performance improvement was achieved for probing, denial of service, and user-to-root


title: Creativity and innovation for corporate happiness management

abstract: In the system in which we are currently inserted, happiness and work seem to be completely exclusive words and without any possibility of association. The following study intends to show that it is possible to find a balance between happiness and work (Csikszentmihalyi in Gestao qualificada: a conexao entre felicidade e negocio, Bookman, Porto Alegre, 2004), by diagnosing the level of happiness shown by people in their working environment’s, proposing solutions to enhance happiness and productive in certain enterprise’s staff (Tidd and Bessant in Innovation and entrepreneurship, Wiley, London, 2007; Gestao da Inovacao, Bookman, Porto Alegre, 2008). It is necessary, hence, an analysis beyond the happy-unhappy dichotomy when observing the different happiness levels presented in the routine and in the working hours, based on the interactions between creativity and innovation (Amabile in KEYS to creativity and innovation: user’s guide, Center For Creative Leadership, Greensboro, 2010; Sawyer in Explaining creativity: the science of human innovation, 2nd edn, Oxford University Press, Cambridge, 2012; West and Richter in Handbook of organizational creativity, 1st edn, Taylor and Francis, New York, 2008). Furthermore, this article shows that it is essential to combine the present’s happiness to a long-term project, an optimistic vision for the future (James and Drown in Handbook of organizational creativity, 1st edn, Elsevier, San Diego, 2012). To evaluate the work and a person’s life, this article develops a Multi Criteria Model of Work Organization and Evaluation and a Map of the Corporate Happiness Levels as a conscious path through workplace, collaboration in the enterprise, the marketplace and society, up to the personal and social life of a subject (Kamel in Artesao da minha propria felicidade, 1st edn. E-papers Servicos Editoriais Ltda, Rio de Janeiro, 2007). It offers ten practical recommendations to raise corporation rates of happiness. Accordingly, this paper proves its relevance by offering reference material to professionals and enterprises who search changes in its current personal management policy, willing to move towards a society with increasingly fulfilled professionals, happy and productive in their own employment.


title: Misbehavior Detection Based on Support Vector Machine and Dempster-Shafer Theory of Evidence in VANETs

abstract: Vehicular ad hoc networks (VANETs) support safety and comfortable driving through frequent information exchange among intelligent vehicles. As an open access environment, VANETs are vulnerable to security threats, such as electronic attack and privacy disclosure. In this paper, we propose a misbehavior detection mechanism based on a support vector machine (SVM) and Dempster–Shafer theory (DST) of evidence to resist false message attack and message suppression attack. The proposed mechanism includes data trust model and vehicle trust model. The data trust model uses an SVM-based classifier to detect false messages based on message content and vehicle attributes. The vehicle trust model consists of a local vehicle trust module and a trust authority (TA) vehicle trust module. The local vehicle trust module uses another SVM-based classifier to evaluate whether the vehicle is credible based on the behavior of the vehicle in terms of message propagation. Then, the TA vehicle trust module uses DST to aggregate multiple trust assessment reports about the same vehicle and derives a comprehensive trust value. Simulation results show that Gaussian kernel best fits our models compared with other functions. In addition, the true positive rate of our data trust model is higher than the model based on back propagation neural network. Moreover, our two models are more robust than basic majority voting, weighted voting, and Bayesian inference in terms of true positive rate under various scenarios.


title: Constructing Experience: Event Models from Perception to Action

abstract: Mental representations of everyday experience are rich, structured, and multimodal. In this article we consider the adaptive pressures that led to human construction of such representations, arguing that structured event representations enable cognitive systems to more effectively predict the trajectory of naturalistic everyday activity. We propose an account of how cortical systems and the hippocampus (HPC) interact to construct, maintain, and update event representations. This analysis throws light on recent research on story comprehension, event segmentation, episodic memory, and action planning. It also suggests how the growing science base can be deployed to diagnose impairments in event perception and memory, and to improve memory for everyday events.


title: FBMC-based air interface for 5G mobile: Challenges and proposed solutions

abstract: Scenarios envisaged for mobile communication systems beyond 2020 imply that future applications and services will impose highly diverse requirements on the system design. To appropriately respond to these requirements while ensuring an efficient usage of available spectrum and system resources, the air interface of 5G system is expected to provide much more flexibility compared to today's systems. Filterbank based multi-carrier (FBMC) may be considered a potential enabler for that purpose, as it provides excellent waveform properties with additional degrees of freedom for the system design, thanks to the use of prototype filters that may be adjusted to meet desired system constraints. This paper elaborates on the challenges that still need to be solved to render FBMC an eligible candidate as fundamental technology for 5G mobile radio and presents some first approaches towards practical solutions.


title: Implementing Edge Detection for Medical Diagnosis of a Bone in Matlab

abstract: Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Edge detection is a basic and important subject in computer vision and image processing In this Paper we discuss several Digital Image Processing Techniques applied in edge feature extraction. Firstly, Linear filtering of Image is done is used to remove noises from the image collected. Secondly, some edge detection operators such as Sobel, Log edge detection, canny edge detection are analyzed and then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the canny operator can obtain better edge feature. Finally, Edge detection is applied to find crack in a bone of a hand. After experimentation, edge detection method proposed in this paper is Feasible.


title: A Recommender System for Ancient Books, Pamphlets and Paintings in Ritsumeikan Art Research Center Database

abstract: In this paper, we develop a recommender system based on the Ritsumeikan Art Research Center (ARC) database, that stores a large number of the digital versions of ancient Japanese books, pamphlets, and paintings. We provide an easier way for novices to get access to this digital archive that was mostly used by experts before and for foreigners to know about Japanese arts and cultures. This recommender system is built as a web page. Users can select items of interest and then be provided with the most interesting items predicted by the recommender system. This system uses the restricted Boltzmann machine (RBM) for collaborative filtering to predict the most interesting items.


title: Disentangling Factors of Variation via Generative Entangling

abstract: Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the multiplicative interactions emulates the entangling of factors of variation. Inference in the model can be seen as disentangling these generative factors. Unlike previous attempts at disentangling latent factors, the proposed model is trained using no supervised information regarding the latent factors. We apply our model to the task of facial expression classification.


title: Static tokens: using dataflow to automate concurrent pipeline synthesis

abstract: We describe a new intermediate compiler representation, static token form, that is suitable for dataflow-style synthesis of high-level asynchronous specifications. Static token form transforms variables into tokens, and simplifies the pipelining of asynchronous computations according to their dataflow graphs. We present a compiler framework that automates this synthesis method, and show how it can be applied to pipelined asynchronous FPGA architectures.


Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service


title: MGAN: Training Generative Adversarial Nets with Multiple Generators

abstract: We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture Generative Adversarial Nets (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators.


Overview of State-of-the-Art Algorithms for Stack-Based High-Dynamic Range (HDR) Imaging


title: A Study of the Similarities of Entity Embeddings Learned from Different Aspects of a Knowledge Base for Item Recommendations

abstract: The recent development of deep learning approaches provides a convenient way to learn entity embeddings from different aspects such as texts and a homogeneous or heterogeneous graph encoded in a knowledge base such as DBpedia. However, it is unclear to what extent domain-specific entity embeddings learned from different aspects of a knowledge base reflect their similarities, and the potential of leveraging those similarities for item recommendations in a specific domain has not been explored. In this work, we investigate domain-specific entity embeddings learned from different aspects of DBpedia with state-of-the-art embedding approaches, and the recommendation performance based on the similarities of these embeddings. The experimental results on two real-word datasets show that recommender systems based on the similarities of entity embeddings learned from a homogeneous graph via the dbo:wikiPageWikiLink property provides the best performance compared to the ones learned from other aspects.


title: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

abstract: Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize. We demonstrate that a trained agent fails completely when facing small visual changes, and that fine-tuning---the common transfer learning paradigm---fails to adapt to these changes, to the extent that it is faster to re-train the model from scratch. We show that by separating the visual transfer task from the control policy we achieve substantially better sample efficiency and transfer behavior, allowing an agent trained on the source task to transfer well to the target tasks. The visual mapping from the target to the source domain is performed using unaligned GANs, resulting in a control policy that can be further improved using imitation learning from imperfect demonstrations. We demonstrate the approach on synthetic visual variants of the Breakout game, as well as on transfer between subsequent levels of Road Fighter, a Nintendo car-driving game. A visualization of our approach can be seen in this https URL and this https URL .


title: A Miniaturized Electromagnetic Generator With Planar Coils and Its Energy Harvest Circuit

abstract: This study presents design, analysis and experiment of a miniaturized rotary generator in size of 10 times 10 times 2 mm3 and its compact energy harvest circuit chip. The designed generator consists of patterned planar copper coils and a multipolar hard magnet ring made of NdFeB. To perform modeling, a harmonic-like magnetic field model along the circumferential path of each magnetic pole is assumed with the assistance from measured peak magnetic flux densities. This is followed by the application of Faraday's law to predict generated electromotive forces (EMFs) in terms of the relative rotational speed between the magnet ring and coils. The genetic algorithm (GA) is next applied to optimize the critical dimensions of the miniaturized generator. The theoretical model of this power microgenerator is evaluated and compared with experimental results, and it is found that the analytical simulation shows a good agreement with the experimental results. The optimized generator offers 4.5 V and 7.23 mW in root mean square (rms) at 10 000 r/min. With microgenerator successfully fabricated, a novel energy harvest circuit employing Dickson charge pump is designed and fabricated via the 0.35-mum process offered by National Chip Implementation Center (CIC) of Taiwan. This charge pump circuit owns the merit of almost-zero thresholds of employed metal-oxide-semiconductor (MOS) transistors, enabling the conversion of low-power alternating current (ac) signals by the microgenerator to direct current (dc) ones.


title: Exploring the Limits of Language Modeling

abstract: In this work we explore recent advances in Recurrent Neural Networks for large scale Language Modeling, a task central to language understanding. We extend current models to deal with two key challenges present in this task: corpora and vocabulary sizes, and complex, long term structure of language. We perform an exhaustive study on techniques such as character Convolutional Neural Networks or Long-Short Term Memory, on the One Billion Word Benchmark. Our best single model significantly improves state-of-the-art perplexity from 51.3 down to 30.0 (whilst reducing the number of parameters by a factor of 20), while an ensemble of models sets a new record by improving perplexity from 41.0 down to 23.7. We also release these models for the NLP and ML community to study and improve upon.


title: Routing in Neighborhood Area Networks: A survey in the context of AMI communications

abstract: Smart Grid is the modern infrastructure of the electric grid, which has the objective to improve efficiency, reliability, and security. This is achieved through the control automation of the transmission and distribution lines, the enhancement of metering technologies, the implementation of renewable energy sources, and new energy management techniques. The growing demand of energy, changes in global weather, problems in the storing and distribution, and the need to implement more efficient metering systems are some of the factors that influence the transit toward a more complex and robust electric grid. A fundamental component of the Smart Grid is an Advanced Metering Infrastructure (AMI), which provides a two-way communication flow between utilities and meters at the customer side. In this survey, we outline the main features of this new infrastructure, including a classification of communication technologies and routing protocols employed in the Neighborhood Area Network domain. We introduce a set of metrics for the AMI network (such as scalability, interoperability, latency, security, and quality of service), and present a full analysis and comparison of AMI-related routing protocols and technologies. Open issues related to wireless and wired technologies, as well as routing for the neighborhood area network domain are also provided.


title: Enteral formula feeds interfere with phenytoin absorption

abstract: Two children treated for status epilepticus had low plasma phenytoin levels during enteral feeding, despite adequate oral dosage. The target range (10-20 mg/l) was achieved when feeds were stopped, or when intravenous phenytoin was used. Enteral feeding may interfere with the absorption of oral phenytoin thereby compromising seizure control.


title: A Survey of Energy Efficiency in Buildings and Microgrids using Networking Technologies

abstract: Intelligent buildings and microgrids are important parts of the future smart grid. The adoption and development process of the intelligent buildings has been slow. There are multiple technical and non-technical reasons. However, two recent trends have accelerated the research and application of the technologies related to this area. First, skyrocketing energy price and the global need for reducing fossil oil consumption for environmental sustainability combined with the fact that buildings are a significant source of energy consumption, making buildings intelligent and energy efficient will have huge impacts on the total CO ::: 2 ::: emission and hence global sustainability. Second, rapid popularity and maturation of mobile smart phone technology and Internet technologies like cloud computing enable smart phone holders to be aware of their energy consumption and participate in controlling and running their buildings with seamless Internet connections. Cloud computing enables active interactions between the consumer-side (buildings) and the provider-side (smart grids). Hence, combining energy efficiency and networking perspectives, in this paper, we investigate the key research topics through a broad survey on the latest developments in intelligent buildings and our vision of microgrids formed by such buildings. Our aim is to draw an overall picture of the current research and potential future applications. Moreover, we further summarize and discuss in detail a series of key issues and trends that can potentially motivate and impact the adoption and development of the intelligent building and microgrid technologies in the near future.


title: A 77-GHz CMOS FMCW Frequency Synthesizer With Reconfigurable Chirps

abstract: This paper presents a 77-GHz CMOS frequency-modulated continuous-wave (FMCW) frequency synthesizer with the capability of reconfigurable chirps. The frequency-sweep range and sweep time of the chirp signals can be reconfigured every cycle such that the frequency-hopping random chirp signal can be realized for an FMCW radar transceiver. The frequency synthesizer adopts the fractional-N phase-locked-loop technique and is fully integrated in TSMC 65-nm digital CMOS technology. The silicon area of the synthesizer is 0.65 mm × 0.45 mm and it consumes 51.3 mW of power. The measured output phase noise of the synthesizer is -85.1 dBc/Hz at 1-MHz offset and the root-mean-square modulation frequency error is smaller than 73 kHz.


Biogas for agriculture


title: A Carrier-Storage-Enhanced Superjunction IGBT With Ultralow Loss and On-State Voltage

abstract: A carrier-storage-enhanced superjunction (SJ) insulated gate bipolar transistor (CSE-SJ-IGBT) is proposed and investigated. In the CSE-SJ-IGBT, the p-pillar is connected to the Emitter via one diode or two series diodes, which helps to raise the hole quasi-Fermi potential of the p-pillar at the on-state so as to enhance the carrier-storage effect in the drift region (n-pillar and p-pillar) and reduce the on-state voltage ( ${V} _{\textbf {CE(sat)}}$ ). Moreover, the CSE-SJ-IGBT also can turn off rapidly as the conventional SJ-IGBT. Simulation results show that, under turn-off loss ( $\text{E}_{off}$ ) of 5 mJ/cm2, ${V} _{\textbf {CE(sat)}}$ of the 1.45-kV rated CSE-SJ-IGBT is as low as 1.07 V, which is 35% and 40% lower than that of the conventional SJ-IGBT and the field stop IGBT, respectively. Besides, the CSE-SJ-IGBT also offers large design freedom for obtaining excellent $\text{E}_{off}$ - ${V} _{\textbf {CE(sat)}}$ tradeoff.


EXISTENCE OF SOLUTIONS FOR A FRACTIONAL ELLIPTIC PROBLEM WITH CRITICAL SOBOLEV-HARDY NONLINEARITIES IN R


title: Filtering Variational Objectives

abstract: When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.


title: Arabic Tokenization System

abstract: Tokenization is a necessary and non-trivial step in natural language processing. In the case of Arabic, where a single word can comprise up to four independent tokens, morphological knowledge needs to be incorporated into the tokenizer. In this paper we describe a rule-based tokenizer that handles tokenization as a full-rounded process with a preprocessing stage (white space normalizer), and a post-processing stage (token filter). We also show how it handles multiword expressions, and how ambiguity is resolved.


title: A study of polar codes for MLC NAND flash memories

abstract: The increasing density of NAND flash memories makes data more prone to errors due to severe process vari- ations and disturbance. The urgency to improve NAND flash reliability encourages searching for optimal channel coding methods. This paper reports our efforts towards a read channel for flash memories using polar coding. Our contributions in- clude the solutions to several challenges raised when applying polar codes to NAND flash memories in practice. We propose efficient schemes for shortening both non-systematic and sys- tematic polar codes, making polar codewords be easily adapted to flash page of any size. We demonstrate that the decoding performance of the shortened polar codes and LDPC codes are comparable using the data obtained by our NAND flash characterization platform. We show the feasibility of a prac- tical adaptive decoding framework where it is not necessary to construct new polar codes for different channel parameters. Experimental results show that the decoding performance ap- proaches the optimized performance where different codes are constructed for different channel conditions. To the best of our knowledge, this work is the first study of polar codes for error correction in flash memories.


title: On multi-view feature learning

abstract: Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single image. We provide an analysis of multi-view feature learning, which shows that hidden variables encode transformations by detecting rotation angles in the eigenspaces shared among multiple image warps. Our analysis helps explain recent experimental results showing that transformation-specific features emerge when training complex cell models on videos. Our analysis also shows that transformation-invariant features can emerge as a by-product of learning representations of transformations.


title: The one-trial learning controversy and its aftermath: remembering Rock (1957).

abstract: In 1957, Ghana obtained its independence, the Soviet Union launched Sputnik 2, the Wham-0 Company produced the first Frisbee, and the Ford Motor Company produced the Edsel (on what the company proclaimed was E-Day a takeoff on D-Day 13 years earlier). The Edsel was going to make a splash, the Ford marketing people thought, and it did—just not the kind desired. ::: ::: Meanwhile, in the world of experimental psychology, the American Journal of Psychology launched a rocket of its own, an article by Irvin Rock titled “The Role of Repetition in Associative Learning” in that same year. Rock declared independence from old ways of thinking about learning as a function of repetition, and his article received a warm reaction. Depending on one’s point of view, it soared like Sputnik, fluttered like a Frisbee, or flopped like the Edsel (a car whose production became synonymous with failure for the next 30 years). Nonetheless, Google Scholar reported (in February 2012) that Rock’s article has been cited 200 times, so it made a splash of some sort in the ocean of psychology. ::: ::: The aim of our article, which is appearing in the 125th anniversary volume of this journal, is to tell the story of Rock’s article, its prequel and its sequel, and to say a few things about the man himself. We provide a retrospective look at the controversy and its aftermath. Rock’s article asked a critical question—one still unanswered today—although its methods have been severely criticized. Nonetheless, the article and others on the same issue (Estes, 1960) ignited a line of research that continues to this day. Although not many current articles cite the 1957 article, some owe a debt to it. ::: ::: No issue so gripped American psychologists in the 20th century as the nature of learning. From the discoveries of Pavlov and Thorndike early in the century, to the dominance of behaviorism in midcentury, to the cognitive revolution late in the century, learning was a central (perhaps the central) topic. All psychology students in the midcentury learned about work of the great learning theorists. Besides Pavlov and Thorndike, there were the often conflicting ideas of Guthrie, Hull and Spence, Skinner, Tolman, and Estes, to name just some of the leading figures. Most of the debates revolved around animal learning and conditioning paradigms that featured mice, rats, cats, dogs, and many other creatures that learned responses through classical and operant conditioning techniques. A rich tradition of researchers studying human learning and memory thrived too, although their enterprise was often overshadowed by (and borrowed ideas from) the animal work. Bower and Hilgard (1981) provided an excellent overview of these various traditions in both animal learning and human verbal learning (see also Deese & Hulse, 1967, for a portrait of these fields in their heyday). ::: ::: The central fact in the study of learning is the learning curve, a graph showing performance (plotted on the ordinate) as a function of the number of trials on some task (plotted on the abscissa). If a researcher aggregates over many subjects learning to perform a task over many trials, the outcome is a beautifully regular, negatively accelerated curve (see Figure 1 for an example from paired-associate learning). The task for the theorist is to explain why learning is always faster on the first few trials of a task and then shows diminishing returns later in practice (even if performance is far from ceiling). Why do learning curves go up as they do? Our story about Rock’s (1957) article starts with this question. ::: ::: ::: ::: FIGURE 1 ::: ::: Learning curves in two paired-associate procedures. Subjects learned pairs composed of nonsense shapes as stimuli and 2-digit numbers as responses to the criterion of one perfect trial. One group of subjects learned by using the anticipation method (the ...


title: Force of Habit and Information Systems Usage: Theory and Initial Validation

abstract: Over the last two decades, information systems (IS) research has primarily focused on people’s conscious (intentional) behavior when trying to explain and predict IS usage. Consequently, almost no research has investigated the potential importance of subconscious (automatic) behaviors, also known as habits. This study represents a first step toward validating the idea that one can add explanatory power to a behavioral model such as Ajzen’s [1985] theory of planned behavior (TPB) by including the habit construct. We conducted a two-stage questionnaire-based survey involving two different groups of students who had access to a sophisticated internet-based communication tool (IBCT). These data were used to test a behavioral model integrating theoretical constructs of TPB and a relevant subset of Triandis’ [1980] behavioral framework. Our findings highlight the importance of considering both conscious (intentions) and subconscious (habits) factors in explaining usage behavior. Furthermore, we share our observations about antecedents of IBCT usage in the educational context. Implications for practice and research are discussed.


title: Effects of emotional arousal in the cerebral hemispheres: a study of oscillatory brain activity and event-related potentials

abstract: Abstract Objective : The present study aimed at examining the time course and topography of oscillatory brain activity and event-related potentials (ERPs) in response to laterally presented affective pictures. Methods : Electroencephalography was recorded from 129 electrodes in 10 healthy university students during presentation of pictures from the international affective picture system. Frequency measures and ERPs were obtained for pleasant, neutral, and unpleasant pictures. Results : In accordance with previous reports, a modulation of the late positive ERP wave at parietal recording sites was found as a function of emotional arousal. Early mid gamma band activity (GBA; 30–45 Hz) at 80 ms post-stimulus was enhanced in response to aversive stimuli only, whereas the higher GBA (46–65 Hz) at 500 ms showed an enhancement of arousing, compared to neutral pictures. ERP and late gamma effects showed a pronounced right-hemisphere preponderance, but differed in terms of topographical distribution. Conclusions : Late gamma activity may represent a correlate of widespread cortical networks processing different aspects of emotionally arousing visual objects. In contrast, differences between affective categories in early gamma activity might reflect fast detection of aversive stimulus features.


title: Seamless texture mapping of subdivision surfaces by model pelting and texture blending

abstract: Subdivision surfaces solve numerous problems related to the geometry of character and animation models. However, unlike on parametrised surfaces there is no natural choice of texture coordinates on subdivision surfaces. Existing algorithms for generating texture coordinates on non-parametrised surfaces often find solutions that are locally acceptable but globally are unsuitable for use by artists wishing to paint textures. In addition, for topological reasons there is not necessarily any choice of assignment of texture coordinates to control points that can satisfactorily be interpolated over the entire surface. We introduce a technique, pelting , for finding both optimal and intuitive texture mapping over almost all of an entire subdivision surface and then show how to combine multiple texture mappings together to produce a seamless result.


title: Synthesis of a New Class of High-Molecular-Weight Soluble Poly(amino acid)s by Oxidative Polymerization of Polyfunctional Macromolecules

abstract: Oxidative coupling of phenol-containing macromolecules, polyglutamine and polyasparagine derivatives, has been examined to produce a new class of poly(amino acid)s. Under the conditions applied, the Fe-salen-catalyzed oxidative coupling of the polymers proceed homogeneously, yielding exclusively soluble polymers of high molecular weight, avoiding the formation of insoluble gels.


title: Improving additive manufacturing by image processing and robotic milling

abstract: This paper describes an improvement of Additive Manufacturing (AM) to process products with complex inner and outer geometries such as turbine blades by using a combination of AM, optical measuring techniques and robotics. One problem of AM is the rough surface caused by the layer-by-layer 3D printing with granular material, so the geometries need to be post-processed with milling techniques. To overcome this problem, we implement an inline quality control management to post-process several manufactured layers by a integrated imaging and robotic techniques, especially while the geometries are still accessible. The produced surfaces are measured by an inline stereo vision system and are compared with the real 3D CAD model data. Detected differences are translated to movements of a robot arm which then follows the contours to smoothen the surfaces with a milling tool. We show our current state on the 3D imaging, on developing the driver for the deployed robot arm and on a 3D simulation using the Robot Operating System (ROS).


title: Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

abstract: As face recognition applications progress from constrained sensing and cooperative subjects scenarios (e.g., driver’s license and passport photos) to unconstrained scenarios with uncooperative subjects (e.g., video surveillance), new challenges are encountered. These challenges are due to variations in ambient illumination, image resolution, background clutter, facial pose, expression, and occlusion. In forensic investigations where the goal is to identify a person of interest, often based on low quality face images and videos, we need to utilize whatever source of information is available about the person. This could include one or more video tracks, multiple still images captured by bystanders (using, for example, their mobile phones), 3-D face models constructed from image(s) and video(s), and verbal descriptions of the subject provided by witnesses. These verbal descriptions can be used to generate a face sketch and provide ancillary information about the person of interest (e.g., gender, race, and age). While traditional face matching methods generally take a single media (i.e., a still face image, video track, or face sketch) as input, this paper considers using the entire gamut of media as a probe to generate a single candidate list for the person of interest. We show that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3-D face models, and incorporation of quality measures for fusion and video frame selection.


title: Turkomatic: automatic recursive task and workflow design for mechanical turk

abstract: On today's human computation systems, designing tasks and workflows is a difficult and labor-intensive process. Can workers from the crowd be used to help plan workflows? We explore this question with Turkomatic, a new interface to microwork platforms that uses crowd workers to help plan workflows for complex tasks. Turkomatic uses a general-purpose divide-and-conquer algorithm to solve arbitrary natural-language requests posed by end users. The interface includes a novel real-time visual workflow editor that enables requesters to observe and edit workflows while the tasks are being completed. Crowd verification of work and the division of labor among members of the crowd can be handled automatically by Turkomatic, which substantially simplifies the process of using human computation systems. These features enable a novel means of interaction with crowds of online workers to support successful execution of complex work.


Pr epr in t Supporting Application Deployment and Management in Fog Computing


title: Weighted partition consensus via kernels

abstract: The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods.


title: Reduction of DC-link current harmonics for three-phase VSI over wide power factor range using single-carrier-comparison discontinuous PWM

abstract: This paper proposes a novel discontinuous PWM (DPWM) in order to reduce dc-link current harmonics of a voltage source inverter (VSI) over wide load-power-factor range. This modulation method, which requires only one carrier, contributes to a lifetime extension of smoothing capacitors in a motor drive system. Furthermore, by realizing this modulation method with only one carrier, a high cost hardware such as a field-programmable gate array (FPGA) is not necessary. By adding an offset to voltage references of conventional DPWM, and shifting two unclamped voltage references in every half control period, the dc-link current harmonics are reduced even when load power factor changes. It is clarified by experiments that the proposed DPWM reduces the dc-link current harmonics by 18.4% at most at modulation index of 0.705 and load power factor of 0.819.


title: Energy-Regenerative Braking Control of Electric Vehicles Using Three-Phase Brushless Direct-Current Motors

abstract: Regenerative braking provides an effective way of extending the driving range of battery powered electric vehicles (EVs). This paper analyzes the equivalent power circuit and operation principles of an EV using regenerative braking control technology. During the braking period, the switching sequence of the power converter is controlled to inverse the output torque of the three-phase brushless direct-current (DC) motor, so that the braking energy can be returned to the battery. Compared with the presented methods, this technology can achieve several goals: energy recovery, electric braking, ultra-quiet braking and extending the driving range. Merits and drawbacks of different braking control strategy are further elaborated. State-space model of the EVs under energy-regenerative braking operation is established, considering that parameter variations are unavoidable due to temperature change, measured error, un-modeled dynamics, external disturbance and time-varying system parameters, a sliding mode robust controller (SMRC) is designed and implemented. Phase current and DC-link voltage are selected as the state variables, respectively. The corresponding control law is also provided. The proposed control scheme is compared with a conventional proportional-integral (PI) controller. A laboratory EV for experiment is setup to verify the proposed scheme. Experimental results show that the drive range of EVs can be improved about 17% using the proposed controller with energy-regeneration control.


title: Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection

abstract: We propose a principled probabilistic formulation of object saliency as a sampling problem. This novel formulation allows us to learn, from a large corpus of unlabelled images, which patches of an image are of the greatest interest and most likely to correspond to an object. We then sample the object saliency map to propose object locations. We show that using only a single object location proposal per image, we are able to correctly select an object in over 42% of the images in the Pascal VOC 2007 dataset, substantially outperforming existing approaches. Furthermore, we show that our object proposal can be used as a simple unsupervised approach to the weakly supervised annotation problem. Our simple unsupervised approach to annotating objects of interest in images achieves a higher annotation accuracy than most weakly supervised approaches.


title: K-Nearest Neighbor Classification Using Anatomized Data

abstract: This paper analyzes k nearest neighbor classification with training data anonymized using anatomy. Anatomy preserves all data values, but introduces uncertainty in the mapping between identifying and sensitive values. We first study the theoretical effect of the anatomized training data on the k nearest neighbor error rate bounds, nearest neighbor convergence rate, and Bayesian error. We then validate the derived bounds empirically. We show that 1) Learning from anatomized data approaches the limits of learning through the unprotected data (although requiring larger training data), and 2) nearest neighbor using anatomized data outperforms nearest neighbor on generalization-based anonymization.


ANALYSIS THE SPEED CONTROL OF BLDC MOTOR DRIVE USING SENSORS MS


title: Development of a PTP Movement of a Industrial Robot via MATLAB by deriving its Kinematics and Integration in an Offline Programming Tool

abstract: This paper explains the development, testing and implementation of a remote control PTP movement for the KUKA KR C4 controller using MATLAB RoBO-2L toolbox. The toolbox is an implementation of a MATLAB interface for a cross-platform offline programming by utilizing the Ethernet UDP/IP communication protocol. Following an engineering approach to solve the PTP (point to point) movement, the forward kinematics has been implemented using the D-H notation. Besides the inverse kinematics has been derived by means of the kinematic decoupling method. A prototype model for the robot was designed using the mechanical design software Solidworks in order to simulate the actual robot. The model was later then imported into the general-purpose robot simulator V-REP. By this means, the calculation was verified before applying it to the real robot. Furthermore, a GUI has been implemented to simplify dealing with the MATLAB toolbox. This GUI shows the current axis and cartesian position of the robot; in addition, it enables the user to enter the desired cartesian position. The field tests for the application of the PTP movement have shown a great reliability among different kind of arbitrary positions and orientations. The work then was applied on the KUKA KR C4 controller, using a KR 6 R900 sixx as real industrial robot.


Enhancement of Encoder and Attention Using Target Monolingual Corpora in Neural Machine Translation


Chapter X : Educational Data Mining and Learning Analytics


title: Calculation of average coding efficiency based on subjective quality scores

abstract: The Bjontegaard model is widely used to calculate the coding efficiency between different codecs. However, this model might not be an accurate predictor of the true coding efficiency as it relies on PSNR measurements. Therefore, in this paper, we propose a model to calculate the average coding efficiency based on subjective quality scores, i.e., mean opinion scores (MOS). We call this approach Subjective Comparison of ENcoders based on fItted Curves (SCENIC). To consider the intrinsic nature of bounded rating scales, a logistic function is used to fit the rate-distortion (R-D) values. The average MOS and bit rate differences are computed between the fitted R-D curves. The statistical property of subjective scores is considered to estimate corresponding confidence intervals on the calculated average MOS and bit rate differences. The proposed model is expected to report more realistic coding efficiency as PSNR is not always correlated with perceived visual quality.


title: Algorithm 812: BPOLY: An object-oriented library of numerical algorithms for polynomials in Bernstein form

abstract: The design, implementation, and testing of a C++ software library for univriate polynomials in Bernstein form is described. By invoking the class environment and operator overloading, each polynomial in an expression is interpreted as an object compatible with the arithmetic operations and other common functions (subdivision, degree, elevation, differentiation and integration, compoistion, greatest common divisor, real-root solving, etc.) for polynomials in Bernstein form. The library allows compact and intuitive implementation of lengthy manipulation of Bernstein-form polynomials, which often arise in computer graphics and computer-aided design and manufacturing applications. A series of empirical tests indicates that the library functions are typically very accurate and reliable, even for polynomials of surprisingly high degree.


Solving Nonlinear Algebraic Systems Using Artificial Neural Networks


WHAT AFFECTS THE ADVERTISING SHARING BEHAVIOR AMONG MOBILE SNS USERS? THE RELATIONSHIPS BETWEEN SOCIAL CAPITAL, OUTCOME EXPECTATIONS AND PREVENTION PRIDE


title: Kinematic Control of Omni-directional Robots for Time-optimal Movement between Two Configurations

abstract: The main goal of this study is to investigate the time-optimal control problem of an omni-directional mobile robot between two configurations. In the proposed method, this problem is formulated and solved as a constrained nonlinear programming (NLP) one. During the optimization process, the count of control steps is fixed initially and the sampling period is treated as a variable to be determined. The goal is to minimize the sampling period such that it is below a specific minimum value, which is set in advance considering the accuracy of discretization. To generate initial feasible solutions of the NLP problem, a systematic approach is also proposed. Since different initial feasible solutions can be generated, the optimization process of the NLP problem can be started from many different points to find the optimal solution. To show the feasibility of the proposed method, simulation and experimental results are included for illustration.


title: Conference on Emerging Ubiquitous Systems and Pervasive Networks ( EUSPN-2013 ) Guidelines for Internet of Things deployment approaches – The Thing Commandments

abstract: Abstract The Internet of Things (IoT) describes the interconnection of objects (or Things) for various purposes including identification, communication, sensing, and data collection. “Things” in this context range from traditional computing devices like Personal Computers (PC) to general household objects embedded with capabilities for sensing and/or communication through the use of technologies such as Radio Frequency Identification (RFID). This conceptual paper, from a philosophical viewpoint, introduces an initial set of guiding principles - also referred to in the paper as commandments - that can be applied by all the stakeholders involved in the IoT during its introduction, deployment and thereafter.


title: Normal-Force Control for an In-Pipe Robot According to the Inclination of Pipelines

abstract: To move freely, in-pipe robots must be able to adapt to the various geometric changes of pipes. Previously, we described an in-pipe robot that can adapt to changes in diameter and curvature of center curves. This robot is able to estimate the forces exerted on the inner surface of the pipes and balance its posture inside the pipe using angular sensors attached to its rotational joints. In this paper, a method is proposed to estimate the relative attitude between the robot's main body and the pipe using the angular sensors attached to a pantograph mechanism. The use of angular sensors makes the structure of the robot simpler and more effective than the use of force or vision sensors because the normal forces and attitude can be estimated from measured angle information. This geometric estimation of attitude relative to the pipes enables the robot to recognize the inclination of the pipes. The PAROYS-II robot can control its normal force according to the variation in pipe inclination. Thus, the proposed method could reduce power consumption and stress on the robot's parts. The algorithm has been validated by multiple experiments.


title: Weakly supervised object detector learning with model drift detection

abstract: A conventional approach to learning object detectors uses fully supervised learning techniques which assumes that a training image set with manual annotation of object bounding boxes are provided. The manual annotation of objects in large image sets is tedious and unreliable. Therefore, a weakly supervised learning approach is desirable, where the training set needs only binary labels regarding whether an image contains the target object class. In the weakly supervised approach a detector is used to iteratively annotate the training set and learn the object model. We present a novel weakly supervised learning framework for learning an object detector. Our framework incorporates a new initial annotation model to start the iterative learning of a detector and a model drift detection method that is able to detect and stop the iterative learning when the detector starts to drift away from the objects of interest. We demonstrate the effectiveness of our approach on the challenging PASCAL 2007 dataset.