title: Over 450-GHz ft and fmax InP/InGaAs DHBTs With a Passivation Ledge Fabricated by Utilizing SiN/SiO2 Sidewall Spacers

abstract: This paper describes InP/InGaAs double heterojunction bipolar transistor (HBT) technology that uses SiN/SiO2 sidewall spacers. This technology enables the formation of ledge passivation and narrow base metals by i-line lithography. With this process, HBTs with various emitter sizes and emitter-base (EB) spacings can be fabricated on the same wafer. The impact of the emitter size and EB spacing on the current gain and high-frequency characteristics is investigated. The reduction of the current gain is <;5% even though the emitter width decreases from 0.5 to 0.25 μm. A high current gain of over 40 is maintained even for a 0.25-μm emitter HBT. The HBTs with emitter widths ranging from 0.25 to 0.5 μm also provide peak ft of over 430 GHz. On the other hand, peak fmax greatly increases from 330 to 464 GHz with decreasing emitter width from 0.5 to 0.25 μm. These results indicate that the 0.25-μm emitter HBT with the ledge passivaiton exhibits balanced high-frequency performance (ft = 452 GHz and fmax = 464 GHz), while maintaining a current gain of over 40.


title: M2EC: A multi-tenant resource orchestration in multi-access edge computing systems

abstract: Multi-access Edge Computing (MEC) is envisioned as a key technology in the 5G landscape, able to bring computing capabilities to the edge of the network, closer to the end users. This would help to fulfill the next generation network requirements in terms of low latency and high bandwidth. ETSI has started specifying MEC as an operator-owned system to run third party's applications, which can leverage the benefits inherent to the MEC environment for added value services to the end users. In this paper we explore a novel paradigm for third parties to access the MEC system: renting part of MEC facilities leveraging on the network slicing paradigm to expand the business opportunities for both the system provider and the MEC tenants. We introduce the concept of MEC broker as an entity exposing administration and management capabilities while handling heterogeneous tenant privileges. Our concept is validated by developing an orchestration solution, namely M2EC, to optimally allocate requested resources in compliance with the tenants service level agreements.


title: Smart heart rate monitoring system

abstract: Heart rate that is given by the number of times heart beats per minute is a crucial health parameter that indicates the soundness of the human health. In this project, a real-time heart rate measurement technique called Photo-Plethysmography (PPG) is implemented using simple infrared transmitter and receiver circuit. Arduino Uno board has been used for calculating the heart rate from the fingertip. The obtained heart rate values are initially displayed on a Liquid Crystal Display (LCD) and sent serially to Raspberry Pi which is used as an Internet of Things (IoT) gateway. The values are then sent to the cloud through Message Queuing Telemetry Transport (MQTT) protocol. The designed system updates the user with their heart rate through electronic mail (email), Short Message Service (SMS) and real-time plots and provides name and address of a nearby prominent hospital in case of an emergency.


title: Information and Communication Technology (ICT) as a Means of Enhancing Education in Schools in South Africa: Challenges, Benefits and Recommendations

abstract: This policy brief proposes that the education authorities of the national departments of Basic Education (DBE) and Higher Education and Training (DHET) in South Africa adopt measures that will see the use of Information and Communications Technology (ICT) as a means of enhancing education in South Africa. The South African education system has for a number of years faced immense challenges, which range from mud schools to low pass rates at matric level, high dropout rates, high levels of absenteeism by teachers in schools and, mainly, the poor efficiency and productivity of both teaching and learning in schools. One way in which we could overcome the challenge of low efficiency and productivity of both teaching and learning would be through the use or introduction of ICT in South African schools. Based on previous research carried out in this field within South Africa and Africa at large, this policy brief looks at some of the challenges, benefits and recommendations relating to the use of ICT as a means of enhancing education in schools in South Africa.


title: Age Groups Classification in Social Network Using Deep Learning

abstract: Social networks have a large amount of data available, but often, people do not provide some of their personal data, such as age, gender, and other demographics. Although the sentiment analysis uses such data to develop useful applications in people’s daily lives, there are still failures in this type of analysis, either by the restricted number of words contained in the word dictionaries or because they do not consider the most diverse parameters that can influence the sentiments in a sentence; thus, more reliable results can be obtained, if the users profile information and their writing characteristics are considered. This research suggests that one of the most relevant parameter contained in the user profile is the age group, showing that there are typical behaviors among users of the same age group, specifically, when these users write about the same topic. A detailed analysis with 7000 sentences was performed to determine which characteristics are relevant, such as, the use of punctuation, number of characters, media sharing, topics, among others; and which ones can be disregarded for the age groups classification. Different learning machine algorithms are tested for the classification of the teenager and adult age group, and the deep convolutional neural network had the best performance, reaching a precision of 0.95 in the validation tests. Furthermore, in order to validate the usefulness of the proposed model for classifying age groups, it is implemented into the enhanced sentiment metric (eSM). In the performance validation, subjective tests are performed and the eSM with the proposed model reached a root mean square error and a Pearson correlation coefficient of 0.25 and 0.94, respectively, outperforming the eSM metric, when the age group information is not available.


title: Eye Gaze Tracking for Human Computer Interaction

abstract: With a growing number of computer devices around us, and the increasing time we spend for interacting with such devices, we are strongly interested in finding new interaction methods which ease the use of computers or increase interaction efficiency. Eye tracking seems to be a promising technology to achieve this goal. ::: This thesis researches interaction methods based on eye-tracking technology. After a discussion of the limitations of the eyes regarding accuracy and speed, including a general discussion on Fitts’ law, the thesis follows three different approaches on how to utilize eye tracking for computer input. The first approach researches eye gaze as pointing device in combination with a touch sensor for multimodal input and presents a method using a touch sensitive mouse. The second approach examines people’s ability to perform gestures with the eyes for computer input and the separation of gaze gestures from natural eye movements. The third approach deals with the information inherent in the movement of the eyes and its application to assist the user. The thesis presents a usability tool for recording of interaction and gaze activity. It also describes algorithms for reading detection. ::: All approaches present results based on user studies conducted with prototypes developed for the purpose.


title: A NEW PARADIGM FOR SHIP HULL INSPECTION USING A HOLONOMIC HOVER-CAPABLE AUV

abstract: The MIT Sea Grant AUV Lab, in association with Bluefin Robotics Corporation, has undertaken the task of designing a new autonomous underwater vehicle, a holonomic hover-capable robot capable of performing missions where an inspection capability similar to that of a remotely operated vehicle is the primary goal. One of the primary issues in this mode of operating AUVs is how the robot perceives its environment and thus navigates. The predominant methods for navigating in close proximity to large iron structures, which precludes accurate compass measurements, require the AUV to receive position information updates from an outside source, typically an acoustic LBL or USBL system. The new paradigm we present in this paper divorces the navigation routine from any absolute reference frame; motions are referenced directly to the hull. We argue that this technique offers some substantial benefits over the conventional approaches, and will present the current status of our project.


title: Sparse principal component analysis via regularized low rank matrix approximation

abstract: Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout science and engineering. However, the principal components (PCs) can sometimes be difficult to interpret, because they are linear combinations of all the original variables. To facilitate interpretation, sparse PCA produces modified PCs with sparse loadings, i.e. loadings with very few non-zero elements. In this paper, we propose a new sparse PCA method, namely sparse PCA via regularized SVD (sPCA-rSVD). We use the connection of PCA with singular value decomposition (SVD) of the data matrix and extract the PCs through solving a low rank matrix approximation problem. Regularization penalties are introduced to the corresponding minimization problem to promote sparsity in PC loadings. An efficient iterative algorithm is proposed for computation. Two tuning parameter selection methods are discussed. Some theoretical results are established to justify the use of sPCA-rSVD when only the data covariance matrix is available. In addition, we give a modified definition of variance explained by the sparse PCs. The sPCA-rSVD provides a uniform treatment of both classical multivariate data and high-dimension-low-sample-size (HDLSS) data. Further understanding of sPCA-rSVD and some existing alternatives is gained through simulation studies and real data examples, which suggests that sPCA-rSVD provides competitive results.


CAESAR : Context-Awareness Enabled and Summary-Attentive Reader


title: Intelligent Push Notification for Converged Mobile Computing and Internet of Things

abstract: Push notification is an important approach to distribute interesting information to users timely. With the fast development of mobile devices and mobile applications, push notification is getting more and more popular. The convergence of mobile and IoT also bring new challenges on how the system can handle the mixed push channels designed for M2M communication and human interaction, and enable the effective interaction with both human and IoT devices involved. IoT devices may push notifications of sensor data in a high frequency. To enable push notification for both of mobile devices and IoT devices, the push notification system is required to achieve high throughput to handle the frequent notifications, and support content matching to filter out the undesired notifications. To enable effective push notification with both human and IoT devices involved, the system is required to understand the users' interests for notifications with the IoT devices providing the users' contexts. That is to say users need an intelligent push notification system. In this paper we propose a high performance context-aware push notification system for the converged mobile and IoT messaging. We designed high performance content matching engine as the core to enable efficient message dispatching for push notification according to highly personalized interest to ensure IoT messages to be involved in push notification. A user's interest of notifications is highly related with his context. Therefore, based on the content-matching engine, a framework for efficient context information fusion is built to support various types of context-aware push notification, towards intelligent push notification. Also we designed shared connection scheme to reduce the resource cost. Based on the content-match engine and context-aware features, the proposed push notification service can support both of group push notification and bi-directional push notification. Tests are conducted for performance evaluation.


title: Interacting with eye movements in virtual environments

abstract: Eye movement-based interaction offers the potential of easy, natural, and fast ways of interacting in virtual environments. However, there is little empirical evidence about the advantages or disadvantages of this approach. We developed a new interaction technique for eye movement interaction in a virtual environment and compared it to more conventional 3-D pointing. We conducted an experiment to compare performance of the two interaction types and to assess their impacts on spatial memory of subjects and to explore subjects' satisfaction with the two types of interactions. We found that the eye movement-based interaction was faster than pointing, especially for distant objects. However, subjects' ability to recall spatial information was weaker in the eye condition than the pointing one. Subjects reported equal satisfaction with both types of interactions, despite the technology limitations of current eye tracking equipment.


title: Signals and comonads

abstract: We propose a novel discipline for programming stream functions and for the semantic description of stream manipulation languages based on the observation that both general and causal stream functions can be characterized as coKleisli arrows of comonads. This seems to be a promising application for the old, but very little exploited idea that if monads abstract notions of computation of a value, comonads ought to be useable as an abstraction of notions of value in a context. We also show that causal partial-stream functions can be described in terms of a combination of a comonad and a monad.


title: Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping

abstract: Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In this contribution we propose a batch approach and a novel multi-view frame fusion technique to exploit multiple views for improving the semantic labelling results. The batch approach works offline and is the direct application of an existing single-view method to scene reconstructions with multiple views. The multi-view frame fusion works in an incremental fashion accumulating the single-view results, hence allowing the online multi-view semantic segmentation of single frames and the offline reconstruction of semantic maps. Our experiments show the superiority of the approaches based on our fusion scheme, which leads to a more accurate semantic labelling.


title: ParkLoc: Light-weight Graph-based Vehicular Localization in Parking Garages

abstract: Locating a vehicle indoors (e.g., underground parking garages) has been a difficult problem to tackle, due to the unavailability of GPS and/or Wi-Fi signals. Current GPS-free indoor localization efforts often rely on infrastructure supports such as Wi-Fi or BLE beacons, whereas the smartphone-only proposals mostly require significant data collection and training efforts per garage. In this context, we propose ParkLoc, a novel lightweight smartphone-only solution for vehicular localization in GPS/Wi-Fi-deprived environments such as indoor parking garages. ParkLoc exploits the inherent planar graph structure of the navigable paths in parking facilities, in order to match a vehicle trajectory onto a sub-section of the map, by modeling these as sparse directed graphs. Exploiting an approximate graph matching method, ParkLoc is able to track a vehicle in real-time with a median error of 4.8m and localize a parked vehicle with a median error of 2m from the nearest parking space. Furthermore, ParkLoc adopts the popular GraphSLAM algorithm from robotics research; it learns the map graph from the observed trajectory graphs and a given set of bootstrap (seed) landmark nodes, in a semi-supervised manner. A key benefit of our approach is that ParkLoc works off-the-shelf without any expensive on-site training or sensor data collection per garage. A comprehensive evaluation of ParkLoc through extensive experiments performed in 4 different parking facilities reveals the promising performance of our graph-based approach for both localization and mapping.


title: 3D segmentation of mandible from multisectional CT scans by convolutional neural networks

abstract: Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. In order to address this challenge we propose a convolutional neural network approach for mandible segmentation in CT scans by considering the continuum of anatomical structures through different planes. The proposed convolutional neural network adopts the architecture of the U-Net and then combines the resulting 2D segmentations from three different planes into a 3D segmentation. We implement such a segmentation approach on 11 neck CT scans and then evaluate the performance. We achieve an average dice coefficient of $ 0.89 $ on two testing mandible segmentation. Experimental results show that our proposed approach for mandible segmentation in CT scans exhibits high accuracy.


Deep Learning: Individual Maize Segmentation From Terrestrial Lidar Data Using Faster R-CNN and Regional Growth Algorithms


title: Rapid phenotypic evolution in multidrug-resistant Klebsiella pneumoniae hospital outbreak strains

abstract: Carbapenem-resistant ::: Klebsiella pneumoniae ::: (CRKP) increasingly cause high-mortality outbreaks in hospital settings globally. Following a patient fatality at a hospital in Beijing due to a bla ::: KPC-2-positive CRKP infection, close monitoring was put in place over the course of 14 months to characterize all bla ::: KPC-2-positive CRKP in circulation in the hospital. Whole genome sequences were generated for 100 isolates from bla ::: KPC-2-positive isolates from infected patients, carriers and the hospital environment. Phylogenetic analyses identified a closely related cluster of 82 sequence type 11 (ST11) isolates circulating in the hospital for at least a year prior to admission of the index patient. The majority of inferred transmissions for these isolates involved patients in intensive care units. Whilst the 82 ST11 isolates collected during the surveillance effort all had closely related chromosomes, we observed extensive diversity in their antimicrobial resistance (AMR) phenotypes. We were able to reconstruct the major genomic changes underpinning this variation in AMR profiles, including multiple gains and losses of entire plasmids and recombination events between plasmids, including transposition of bla ::: KPC-2. We also identified specific cases where variation in plasmid copy number correlated with the level of phenotypic resistance to drugs, suggesting that the number of resistance elements carried by a strain may play a role in determining the level of AMR. Our findings highlight the epidemiological value of whole genome sequencing for investigating multi-drug-resistant hospital infections and illustrate that standard typing schemes cannot capture the extraordinarily fast genome evolution of CRKP isolates.


title: Genes associated with Alzheimer’s disease: an overview and current status

abstract: Alzheimer's disease (AD) is a progressive, neurodegenerative disease and the most common form of dementia in elderly people. It is an emerging public health problem that poses a huge societal burden. Linkage analysis was the first milestone in unraveling the mutations in APP, PSEN1, and PSEN2 that cause early-onset AD, followed by the discovery of apolipoprotein E-ε4 allele as the only one genetic risk factor for late-onset AD. Genome-wide association studies have revolutionized genetic research and have identified over 20 genetic loci associated with late-onset AD. Recently, next-generation sequencing technologies have enabled the identification of rare disease variants, including unmasking small mutations with intermediate risk of AD in PLD3, TREM2, UNC5C, AKAP9, and ADAM10. This review provides an overview of the genetic basis of AD and the relationship between these risk genes and the neuropathologic features of AD. An understanding of genetic mechanisms underlying AD pathogenesis and the potentially implicated pathways will lead to the development of novel treatment for this devastating disease.


title: A detailed analysis of the KDD CUP 99 data set

abstract: During the last decade, anomaly detection has attracted the attention of many researchers to overcome the weakness of signature-based IDSs in detecting novel attacks, and KDDCUP'99 is the mostly widely used data set for the evaluation of these systems. Having conducted a statistical analysis on this data set, we found two important issues which highly affects the performance of evaluated systems, and results in a very poor evaluation of anomaly detection approaches. To solve these issues, we have proposed a new data set, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.


title: A Robust and Energy-Efficient Classifier Using Brain-Inspired Hyperdimensional Computing

abstract: The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. Hypervectors are high-dimensional, holographic, and (pseudo)random with independent and identically distributed (i.i.d.) components. They provide for energy-efficient computing while tolerating hardware variation typical of nanoscale fabrics. We describe a hardware architecture for a hypervector-based classifier and demonstrate it with language identification from letter trigrams. The HD classifier is 96.7% accurate, 1.2% lower than a conventional machine learning method, operating with half the energy. Moreover, the HD classifier is able to tolerate 8.8-fold probability of failure of memory cells while maintaining 94% accuracy. This robust behavior with erroneous memory cells can significantly improve energy efficiency.


title: Iris Recognition Based on Combined Feature of GLCM and Wavelet Transform

abstract: Iris biometric is considered to be one of the most efficient and trusted biometric method for authenticating users. This paper presents a method for iris recognition based on hybrid feature set of wavelet and GLCM features, a non-filter based technique, combined with Haar wavelet transform to increase the efficiency of the system. Here we combine frequency domain feature with spatial domain feature to increase overall efficiency of system. Probability neural Network is used to classify the features. Results show that the overall system efficiency is 94% with false rejection rate higher than false acceptance rate. The technique is tested on CASIA Iris database.


title: Book2Vec: Representing Books in Vector Space Without Using the Contents

abstract: This paper presents book2vec, a neural network based embedding approach for creating book representations. In this work, a well-known method from natural language processing domain, namely word2vec, is applied to a dataset of the books read by different users from the Goodreads website. Unlike previous works, we use non-textual features, considering only the book IDs. We represent the books read by each user as a sentence where the books' IDs are the words in the sentences. The results show that this approach can find meaningful representation of the books.


title: GLTM: A Global and Local Word Embedding-Based Topic Model for Short Texts

abstract: Short texts have become a kind of prevalent source of information, and discovering topical information from short text collections is valuable for many applications. Due to the length limitation, conventional topic models based on document-level word co-occurrence information often fail to distill semantically coherent topics from short text collections. On the other hand, word embeddings as a powerful tool have been successfully applied in natural language processing. Word embeddings trained on large corpus are encoded with general semantic and syntactic information of words, and hence they can be leveraged to guide topic modeling for short text collections as supplementary information for sparse co-occurrence patterns. However, word embeddings are trained on large external corpus and the encoded information is not necessarily suitable for training data set of topic models, which is ignored by most existing models. In this article, we propose a novel global and local word embedding-based topic model (GLTM) for short texts. In the GLTM, we train global word embeddings from large external corpus and employ the continuous skip-gram model with negative sampling (SGNS) to obtain local word embeddings. Utilizing both the global and local word embeddings, the GLTM can distill semantic relatedness information between words which can be further leveraged by Gibbs sampler in the inference process to strengthen semantic coherence of topics. Compared with five state-of-the-art short text topic models on four real-world short text collections, the proposed GLTM exhibits the superiority in most cases.


title: Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

abstract: We introduce a new method for time-domain signal processing, based on deep learning neural networks, which has the potential to revolutionize data analysis in engineering and science. To demonstrate how this enables real-time multimessenger astrophysics, we designed two deep convolutional neural networks that can analyze time-series data from observatories including advanced LIGO. The first neural network recognizes the presence of gravitational waves from binary black hole mergers, and the second one estimates the mass of each black hole, given weak signals hidden in extremely noisy time-series inputs. We highlight the advantages offered by this novel method, which outperforms matched-filtering or conventional machine learning techniques, and propose strategies to extend our implementation for simultaneously targeting different classes of gravitational wave sources while ignoring anomalous noise transients. Our results strongly indicate that deep neural networks are highly efficient and versatile tools for directly processing any raw noisy data streams. We also pioneer a new paradigm to accelerate scientific discovery by combining high-performance simulations on traditional supercomputers and artificial intelligence algorithms that exploit innovative hardware architectures such as deep-learning-optimized GPUs. This unique approach immediately provides a natural framework to unify multi-spectrum observations in real-time thus enabling coincident detection campaigns of gravitational waves sources and their electromagnetic counterparts.


title: Autonomous Driving in Reality with Reinforcement Learning and Image Translation

abstract: Supervised learning is widely used in training autonomous driving vehicle. However, it is trained with large amount of supervised labeled data. Reinforcement learning can be trained without abundant labeled data, but we cannot train it in reality because it would involve many unpredictable accidents. Nevertheless, training an agent with good performance in virtual environment is relatively much easier. Because of the huge difference between virtual and real, how to fill the gap between virtual and real is challenging. In this paper, we proposed a novel framework of reinforcement learning with image semantic segmentation network to make the whole model adaptable to reality. The agent is trained in TORCS, a car racing simulator.


Sketchable Histograms of Oriented Gradients for Object Detection


title: On improving deep learning generalization with adaptive sparse connectivity

abstract: Large neural networks are very successful in various tasks. However, with limited data, the generalization capabilities of deep neural networks are also very limited. In this paper, we empirically start showing that intrinsically sparse neural networks with adaptive sparse connectivity, which by design have a strict parameter budget during the training phase, have better generalization capabilities than their fully-connected counterparts. Besides this, we propose a new technique to train these sparse models by combining the Sparse Evolutionary Training (SET) procedure with neurons pruning. Operated on MultiLayer Perceptron (MLP) and tested on 15 datasets, our proposed technique zeros out around 50% of the hidden neurons during training, while having a linear number of parameters to optimize with respect to the number of neurons. The results show a competitive classification and generalization performance.


title: The Role of Hormones in Controlling Vascular Differentiation

abstract: The vascular system in plants is induced and controlled by streams of inductive hormonal signals. Auxin produced in young leaves is the primary controlling hormone in vascular differentiation. Its downward transport pathways, major controlling mechanisms, and sensitivity of cells to auxin are clarified. Cytokinin, from the root cap moves upward, increases the sensitivity to auxin and stimulates cambial cell divisions. Gibberellin produced in mature leaves moves non-polarly and promotes elongation, regulates cambium activity, and induces long fibers and long tracheids. Transgenic plants with elevated bioactive gibberellin concentrations grow rapidly and yield numerous longer fibers and longer tracheids. Centrifugal movement of ethylene from maturing vessels induces the radial vascular rays. In conifer trees, jasmonate, which promotes defense response, is mediated by ethylene and induces traumatic resin ducts. In addition the role of the hormonal signals in regulating gradients of cell size and density, in controlling the type of differentiating vascular element and how they have shaped wood evolution are elucidated.


title: Forage availability versus seasonal diets, as determined by fecal analysis, of dorcas gazelles in Morocco

abstract: Le regime alimentaire des femelles adultes et des mâles adultes territoriaux de gazelles dorcas a ete mesure d'apres des analyses microhistologiques des feces et d'apres la biomasse a 1,20 m du sol, de la vegetation disponible ; cette etude a ete faite aux endroits ou ces animaux se nourrissent pendant une saison, en utilisant des unites de reference, pendant trois saisons, dans une reserve au Maroc


Impact of maceration time on colour-related phenolics, sensory characteristics and volatile composition of mulberry wine


Internal Branding and the Competitive Performance of Private Universities in Ghana


title: Lipedema: a frequently misdiagnosed and misunderstood fatty deposition syndrome.

abstract: PURPOSE ::: To enhance the learner's competence in caring for patients with lipedema through understanding the differential diagnoses, pathophysiology, and treatment/management options. ::: ::: ::: TARGET AUDIENCE ::: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. ::: ::: ::: OBJECTIVES ::: After participating in this educational activity, the participant should be better able to: 1. Differentiate lipedema from other similar diagnoses. 2. Tell patients with lipedema and their caregivers about treatment of this condition. 3. Construct assessments, treatment plans, and management options for patients with lipedema.


title: A 1 MHz eGaN FET based 4-switch buck-boost converter for automotive applications

abstract: This paper presents the development of a 1 MHz enhancement mode GaN (eGaN) FET based 4-switch buck-boost converter with bootstrap driver for automotive applications. Conventionally, with Si MOSFETs, it is difficult to optimize the efficiency of a 4-switch buck-boost converter since the high side switches need to switch at high frequency or completely conduct the output/input current in either buck or boost operation mode. GaN FET with much lower Figure of Merit provides the possibility to achieve high efficiency of 4-switch buck-boost converter at MHz switching frequency. This paper investigates the challenges of using bootstrap driver in MHz 4-switch buck-boost converter, and presents associated solutions. A 6–45Vin, 10V/7A, 1 MHz eGaN FET based 4-switch buck-boost converter is built and measured high conversion efficiency confirms the superior performance with GaN FET.


title: Deriving haptic design guidelines from human physiological, psychophysical, and neurological foundations

abstract: We survey the haptics literature and identify conditions under which haptic interaction displays can enhance human perception and performance. Integrating haptic interactions in multimodal systems requires understanding user's sensory, perceptual, and cognitive abilities and limitations. Haptic design guidelines can aid developers of multimodal interactive systems. Haptic interaction relates to all aspects of touch and body movement and the application of these senses to computer interaction. This involves not only sensation and perception, but also motor and cognitive aspects of active movement (that is, self-initiated movement) for which detailed motor plans are created, stored in memory, and compared to receptor feedback from the muscles, joints, and skin.


title: A machine learning methods: Outlier detection in WSN

abstract: Wireless sensor networks are gaining more and more attention these days. They gave us the chance of collecting data from noisy environment. So it becomes possible to obtain precise and continuous monitoring of different phenomenons. However wireless Sensor Network (WSN) is affected by many anomalies that occur due to software or hardware problems. So various protocols are developed in order to detect and localize faults then distinguish the faulty node from the right one. In this paper we are concentrated on a specific type of faults in WSN which is the outlier. We are focus on the classification of data (outlier and normal) using three different methods of machine learning then we compare between them. These methods are validated using real data obtained from motes deployed in an actual living lab.


title: Real-time Ethernet for automotive applications: A solution for future in-car networks

abstract: Data networks of today's automobiles form a complex conglomerate of heterogeneously interconnected components. At the same time, high additional demands for future in-car communication systems are emerging from chassis control, camera based driver assistance and infotainment that cannot be accommodated by established technologies. A new approach towards a flexible highly scalable in-car network is real-time Ethernet. In this paper we discuss the upcoming requirements and argue why current in-car network designs are not suitable for future tasks. By demonstrating how a camera based time-critical driver assistance application can be integrated into a real-time Ethernet based in-car network, we present a typical use case for automotive broadband real-time communication and show application related design and configuration decisions.


title: A SEPARATOR THEOREM FOR PLANAR GRAPHS

abstract: Let G be any n-vertex planar graph. We prove that the vertices of G can be partitioned into three sets A,B,C such that no edge joins a vertex in A with a vertex in B, neither A nor B contains more than 2n/3 vertices, and C contains no more than $2\sqrt{2}\sqrt{2}$ vertices. We exhibit an algorithm which finds such a partition A,B,C in O(n) time.


title: Rules Generation from Natural Language Specification

abstract: In this paper, we present a novel approach of translating natural languages specification to SBVR business rules. The business rules constraint business structure or control behaviour of a business process. In modern business modelling, one of the important phases is writing business rules. Typically, a business rule analyst has to manually write hundreds of business rules in a natural language (NL) and then manually translate NL specification of all the rules in a particular rule language such as SBVR, or OCL, as required. However, the manual translation of NL rule specification to formal representation as SBVR rule is not only difficult, complex and time consuming but also can result in erroneous business rules. In this paper, we propose an automated approach that automatically translates the NL (such as English) specification of business rules to SBVR (Semantic Business Vocabulary and Rules) rules. The major challenge in NL to SBVR translation was complex semantic analysis of English language. We have used a rule based algorithm for robust semantic analysis of English and generate SBVR rules. Automated generation of SBVR based Business rules can help in improved and efficient constrained business aspects in a typical business modelling.


A Graph-based Text Similarity Measure That Employs Named Entity Information


title: Autism, social cognition and superior temporal sulcus

abstract: Results on brain imaging studies have led to a ::: better understanding of the neural circuits involved in social cognition and ::: its implication in autism spectrum disorders(ASD). ::: It has been shown that the superior temporal sulcus(STS)is highly implicated in social processes, from perception of socially relevant ::: information, such as body movements or eye gaze, to more complex social ::: cognition processes. Furthermore, several multimodal brain imaging results ::: point to anatomo-functional abnormalities in the STS in both children and adults with ASD. These results are highly consistent with social impairments ::: in ASD, among which eye gaze perception is particularly relevant. Gaze ::: abnormalities can now be objectively measured using eye-tracking methodology, ::: leading to a better characterization of social perception impairments in autism. Moreover, these gaze abnormalities have been associated with STS abnormalities in ASD. Based on these results, our ::: hypothesis is that anatomo-functional anomalies in the STS occurring early ::: across brain development could constitute the first step in the cascade of ::: neural dysfunction underlying autism. In the present work, we’ll review recent data of STS contribution to normal social cognition and it’s implication in autism.


title: Inhibition of pulmonary cancer progression by epidermal growth factor receptor-targeted transfection with Bcl-2 and survivin siRNAs

abstract: Clinical application of small interfering RNA (siRNA) in cancer therapy has been limited by the lack of an efficient systemic siRNA delivery system. In this report we describe an efficient siRNA delivery system directed to metastasized tumors, especially in the lungs. Anticancer siRNA was condensed in the presence of 9-arginine peptides (9Arg) and then complexed with cationic O,O'-dimyristyl-N-lysyl glutamate liposomes conjugated to antibodies against the epidermal growth factor receptor (EGFR). The ternary complex of optimized anti-EGFR-9Arg-lipoplexes exhibited efficient siRNA transfection of LS174T-Luc cancer cells grown in culture or orthotopically in mouse lungs. Anti-tumor Bcl-2/survivin siRNAs loaded in the anti-EGFR-9Arg-lipoplexes effectively suppressed transcription of their target genes, resulting in an efficient cancer cell death. Repeated intravenous administrations of the anti-EGFR-9Arg-lipoplexes effectively inhibited tumor growth in the mouse lungs and prolonged survival of the mice compared with nontargeted lipoplexes. These results suggest that the ternary complexes of anti-EGFR-9Arg-lipoplexes might have clinical applications in RNA interference cancer therapy.


title: Subjective quality evaluation of the 3GPP EVS codec

abstract: This paper discusses the voice and audio quality characteristics of EVS, the recently standardized 3GPP codec. Comparison to Opus, IETF driven open source codec as well as industry standard voice codecs: 3GPP AMR and AMR-WB, and ITU-T G.718B, G.722.1C and G.719 as well as direct signals at varying bandwidths was made. Voice and audio quality was evaluated with three subjective listening tests containing clean and noisy speech in Finnish language as well as a mixed condition test containing both speech and music intermixed. Nine-scale subjective mean opinion score was calculated for all tested conditions.


title: Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?

abstract: Despite being the appearance-based classifier of choice in recent years, relatively few works have examined how much convolutional neural networks (CNNs) can improve performance on accepted expression recognition benchmarks and, more importantly, examine what it is they actually learn. In this work, not only do we show that CNNs can achieve strong performance, but we also introduce an approach to decipher which portions of the face influence the CNN's predictions. First, we train a zero-bias CNN on facial expression data and achieve, to our knowledge, state-of-the-art performance on two expression recognition benchmarks: the extended Cohn-Kanade (CK+) dataset and the Toronto Face Dataset (TFD). We then qualitatively analyze the network by visualizing the spatial patterns that maximally excite different neurons in the convolutional layers and show how they resemble Facial Action Units (FAUs). Finally, we use the FAU labels provided in the CK+ dataset to verify that the FAUs observed in our filter visualizations indeed align with the subject's facial movements.


title: Digit recognition in a natural scene with skew and slant normalization

abstract: This paper proposes a method to recognize digits in a natural scene, such as telephone numbers on a signboard. Candidate regions of digits are extracted from an image through contrast enhancement, edge extraction, and labeling. Since the target text patterns are in a 3D space, unlike traditional character recognition problems, we have to deal with the image transformation effect due to the orientation in the 3D space and projection. We have to cancel the effect as much as possible before digit recognition. In our method, the image transformation effect is modeled as skew and slant. In the proposed method, simplified Hough transform is used for the skew normalization. After the skew normalization, the remaining effect of image transformation is corrected by circumscribing digit patterns with tilted rectangles and affine transformation. In experiments, we tested a total of 1,332 images of signboards with 11,939 digits. We obtained a digit extraction rate of 99.2% and a correct digit recognition rate of 98.8%.


title: Air and Soul: The Science and Application of Aerosol Therapy

abstract: This paper reviews the history of aerosol therapy; discusses patient, drug, and device factors that can influence the success of aerosol therapy; and identifies trends that will drive the science of aerosol therapy in the future. Aerosol medication is generally less expensive, works more rapidly, and produces fewer side effects than the same drug given systemically. Aerosol therapy has been used for thousands of years by steaming and burning plant material. In the 50 years since the invention of the pressurized metered-dose inhaler, advances in drugs and devices have made aerosols the most commonly used way to deliver therapy for asthma and COPD. The requirements for aerosol therapy depend on the target site of action and the underlying disease. Medication to treat airways disease should deposit on the conducting airways. Effective deposition of airway particles generally requires particle size between 0.5 and 5 μm mass median aerodynamic diameter; however, a smaller particle size neither equates to greater side effects nor greater effectiveness. However, medications like peptides intended for systemic absorption, need to deposit on the alveolar capillary bed. Thus ultrafine particles, a slow inhalation, and relatively normal airways that do not hinder aerosol penetration will optimize systemic delivery. Aerosolized antimicrobials are often used for the treatment of cystic fibrosis or bronchiectasis, and mucoactive agents to promote mucus clearance have been delivered by aerosol. As technology improves, a greater variety of novel medications are being developed for aerosol delivery, including for therapy of pulmonary hypertension, as vaccines, for decreasing dyspnea, to treat airway inflammation, for migraine headache, for nicotine and drug addiction, and ultimately for gene therapy. Common reasons for therapeutic failure of aerosol medications include the use of inactive or depleted medications, inappropriate use of the aerosol device, and, most importantly, poor adherence to prescribed therapy. The respiratory therapist plays a key role in patient education, device selection, and outcomes assessment.


Beyond Identification – High Memory RFID in Aviation


title: Modeling and observing the jitter in ring oscillators implemented in FPGAs

abstract: Random number generators represent one of basic cryptographic primitives used to compose cryptographic protocols. While field programmable gate arrays (FPGAs) are well suited for implementing algorithmic random number generators (pseudo-random number generators), generating fast and secured true random bitstreams inside FPGAs is an open problem. Most of true random number generators in FPGAs employ the timing jitter present in ring oscillator clocks as a source of randomness. The paper analyses the jitter generated in ring oscillators and presents a simple physical model of its sources. The jitter generated in MATLAB in accordance with the proposed model is then used as an input in VHDL simulations. To evaluate the model, we use an embedded technique of jitter measurement. The principle is simulated in VHDL and validated by experiments using different FPGA technologies.


title: A unified view of gradient-based attribution methods for Deep Neural Networks

abstract: Understanding the flow of information in Deep Neural Networks is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, only few attempts to analyze them from a theoretical perspective have been made in the past. In this work we analyze various state-of-the-art attribution methods and prove unexplored connections between them. We also show how some methods can be reformulated and more conveniently implemented. Finally, we perform an empirical evaluation with six attribution methods on a variety of tasks and architectures and discuss their strengths and limitations.


title: Retrieving Web Pages Using Content, Links, URLs and Anchors

abstract: For this year’s web track, we concentrated on the entry page finding task. For the content-only runs, in both the ad-hoc task and the entry page finding task, we used an information retrieval system based on a simple unigram language model. In the Ad hoc task we experimented with alternatieve approaches to smoothing. For the entry page task, we incorporated additional information into the model. The sources of information we used in addition to the document’s content are links, URLs and anchors. We found that almost every approach can improve the results of a content only run. In the end, a very basic approach, using the depth of the path of the URL as a prior, yielded by far the largest improvement over the content only results.


title: Bat algorithm for scheduling workflow applications in cloud

abstract: Workflow is one of the important aspects of cloud computing today. Cloud computing is one of the fastest growing technologies in the world. Workflows can be used in cloud as we use them in grid. Many operations in the cloud are based on workflow execution. Workflow systems are now becoming more complex and for such kind of systems efficient workflow management is important. Workflow scheduling is an important part of workflow management. Scheduling in general is NP-hard problem. To solve such kind of problems exhaustive methods cannot be used. Only non-exhaustive techniques can be used. In this paper we have used a metaheuristic approach called bat algorithm. Bat algorithm is specifically designed for optimizing hard problems. Here, bat algorithm with the help of binary bat algorithm is used for scheduling workflow in a cloud. Specifically the mapping of tasks and resources is done using this method. The optimal resources are selected such that the overall cost of the workflow is minimal.


title: Modeling of losses in substrate integrated waveguide by Boundary Integral-Resonant Mode Expansion method

abstract: This paper presents an efficient technique for the evaluation of different types of losses in substrate integrated waveguide (SIW). This technique is based on the Boundary Integral-Resonant Mode Expansion (BI-RME) method in conjunction with a perturbation approach. This method also permits to derive automatically multimodal and parametric equivalent circuit models of SIW discontinuities, which can be adopted for an efficient design of complex SIW circuits. Moreover, a comparison of losses in different types of planar interconnects (SIW, microstrip, coplanar waveguide) is presented.


title: To offload or not to offload? The bandwidth and energy costs of mobile cloud computing

abstract: The cloud seems to be an excellent companion of mobile systems, to alleviate battery consumption on smartphones and to backup user's data on-the-fly. Indeed, many recent works focus on frameworks that enable mobile computation offloading to software clones of smartphones on the cloud and on designing cloud-based backup systems for the data stored in our devices. Both mobile computation offloading and data backup involve communication between the real devices and the cloud. This communication does certainly not come for free. It costs in terms of bandwidth (the traffic overhead to communicate with the cloud) and in terms of energy (computation and use of network interfaces on the device). In this work we study the fmobile software/data backupseasibility of both mobile computation offloading and mobile software/data backups in real-life scenarios. In our study we assume an architecture where each real device is associated to a software clone on the cloud. We consider two types of clones: The off-clone, whose purpose is to support computation offloading, and the back-clone, which comes to use when a restore of user's data and apps is needed. We give a precise evaluation of the feasibility and costs of both off-clones and back-clones in terms of bandwidth and energy consumption on the real device. We achieve this through measurements done on a real testbed of 11 Android smartphones and an equal number of software clones running on the Amazon EC2 public cloud. The smartphones have been used as the primary mobile by the participants for the whole experiment duration.


title: Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks

abstract: Stereo-pair images obtained from two cameras can be used to compute three-dimensional (3D) world coordinates of a point using triangulation. However, to apply this method, camera calibration parameters for each camera need to be experimentally obtained. Camera calibration is a rigorous experimental procedure in which typically 12 parameters are to be evaluated for each camera. The general camera model is often such that the system becomes nonlinear and requires good initial estimates to converge to a solution. We propose that, for stereo vision applications in which real-world coordinates are to be evaluated, artificial neural networks be used to train the system such that the need for camera calibration is eliminated. The training set for our neural network consists of a variety of stereo-pair images and corresponding 3D world coordinates. We present the results obtained on our prototype mobile robot that employs two cameras as its sole sensors and navigates through simple regular obstacles in a high-con...


title: An Overview and Computer Forensic Challenges in Image Steganography

abstract: The development of powerful imaging tools, editing images for changing their data content is becoming a mark to undertake. Tempering image contents by adding, removing, or copying/moving without leaving a trace or unable to be discovered by the investigation is an issue in the computer forensic world. The protection of information shared on the Internet like images and any other con?dential information is very signi?cant. Nowadays, forensic image investigation tools and techniques objective is to reveal the tempering strategies and restore the firm belief in the reliability of digital media. This paper investigates the challenges of detecting steganography in computer forensics. Open source tools were used to analyze these challenges. The experimental investigation focuses on using steganography applications that use same algorithms to hide information exclusively within an image. The research finding denotes that, if a certain steganography tool A is used to hide some information within a picture, and then tool B which uses the same procedure would not be able to recover the embedded image.


title: Leveraging user libraries to bootstrap collaborative filtering

abstract: We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM's chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. Overall, CSTM is a joint directed probabilistic model of user-item scores (ratings), and the textual side information in the user libraries and the items. Creating a generative description of scores and the text allows CSTM to perform well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many. Experiments on real-world datasets demonstrate CSTM's performance. We further demonstrate its utility in an application for personal recommendations of posters which we deployed at the NIPS 2013 conference.


Socio-Economic Status and Physical Attractiveness in Mate Selection Choice


Introduction to Dual Decomposition for Inference


title: Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures

abstract: Due to the popularity of Deep Neural Network (DNN) models, we have witnessed extreme-scale DNN models with the continued increase of the scale in terms of depth and width. However, the extremely high memory requirements for them make it difficult to run the training processes on single many-core architectures such as a Graphic Processing Unit (GPU), which compels researchers to use model parallelism over multiple GPUs to make it work. However, model parallelism always brings very heavy additional overhead. Therefore, running an extreme-scale model in a single GPU is urgently required. There still exist several challenges to reduce the memory footprint for extreme-scale deep learning. To address this tough problem, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities for memory reuse at both the intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of the training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that could not previously be run on a single GPU. Experiments show that, compared to the original Caffe, Layrub can cut down the memory usage rate by an average of 58.2% and by up to 98.9%, at the moderate cost of 24.1% higher training execution time on average. Results also show that Layrub outperforms some popular deep learning systems such as GeePS, vDNN, MXNet, and Tensorflow. More importantly, Layrub can tackle extreme-scale deep learning tasks. For example, it makes an extra-deep ResNet with 1,517 layers that can be trained successfully in one GPU with 12GB memory, while other existing deep learning systems cannot.


title: Benchmarking Reinforcement Learning Algorithms on Real-World Robots

abstract: Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks. To carry forward these successes to real-world applications, it is crucial to withhold utilizing the unique advantages of simulations that do not transfer to the real world and experiment directly with physical robots. However, reinforcement learning research with physical robots faces substantial resistance due to the lack of benchmark tasks and supporting source code. In this work, we introduce several reinforcement learning tasks with multiple commercially available robots that present varying levels of learning difficulty, setup, and repeatability. On these tasks, we test the learning performance of off-the-shelf implementations of four reinforcement learning algorithms and analyze sensitivity to their hyper-parameters to determine their readiness for applications in various real-world tasks. Our results show that with a careful setup of the task interface and computations, some of these implementations can be readily applicable to physical robots. We find that state-of-the-art learning algorithms are highly sensitive to their hyper-parameters and their relative ordering does not transfer across tasks, indicating the necessity of re-tuning them for each task for best performance. On the other hand, the best hyper-parameter configuration from one task may often result in effective learning on held-out tasks even with different robots, providing a reasonable default. We make the benchmark tasks publicly available to enhance reproducibility in real-world reinforcement learning.


title: Mobile Food Recognition with an Extreme Deep Tree

abstract: Food recognition is an emerging topic in the field of computer vision. The recent interest of the research community in this area is justified by the rise in popularity of food diary applications, where the users take note of their food intake for self-monitoring or to provide useful statistics to dietitians. However, manually annotating food intake can be a tedious task, thus explaining the need of a system that automatically recognizes food, and possibly its amount, from pictures acquired by mobile devices. In this work we propose an approach to food recognition which combines the strengths of different state-of-the-art classifiers, namely Convolutional Neural Networks, Extreme Learning Machines and Neural Trees. We show that the proposed architecture can achieve good results even with low computational power, as in the case of mobile devices.


title: Colorization of Black-and-White Cartoons

abstract: We introduce a novel colorization framework for old black-and-white cartoons originally produced by a cel or paper based technology. In this case, the dynamic part of the scene is represented by a set of outlined homogeneous regions which superimpose the static background. To reduce a large amount of manual intervention we combine unsupervised image segmentation, background reconstruction, and structural prediction. Our system allows the user to specify the brightness of applied colors unlike the most of previous approaches which operate only with hue and saturation. We also present simple but effective color modulation, composition and dust spot removal techniques able to produce color images in broadcast quality without additional user intervention.


title: Direction matters: hand pose estimation from local surface normals

abstract: We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the tree structured topology of hand from wrist to finger tips. We propose a conditional regression forest, i.e., the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature. At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints. By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations. We also introduce a new efficient approximation to estimate surface normals. We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.


title: Grid-Connected Photovoltaic Systems: An Overview of Recent Research and Emerging PV Converter Technology

abstract: Photovoltaic (PV) energy has grown at an average annual rate of 60% in the last five years, surpassing one third of the cumulative wind energy installed capacity, and is quickly becoming an important part of the energy mix in some regions and power systems. This has been driven by a reduction in the cost of PV modules. This growth has also triggered the evolution of classic PV power converters from conventional singlephase grid-tied inverters to more complex topologies to increase efficiency, power extraction from the modules, and reliability without impacting the cost. This article presents an overview of the existing PV energy conversion systems, addressing the system configuration of different PV plants and the PV converter topologies that have found practical applications for grid-connected systems. In addition, the recent research and emerging PV converter technology are discussed, highlighting their possible advantages compared with the present technology. Solar PV energy conversion systems have had a huge growth from an accumulative total power equal to approximately 1.2 GW in 1992 to 136 GW in 2013 (36 GW during 2013) [1]. This phenomenon has been possible because of several factors all working together to push the PV energy to cope with one important position today (and potentially a fundamental position in the near future). Among these factors are the cost reduction and increase in efficiency of the PV modules, the search for alternative clean energy sources (not based on fossil fuels), increased environmental awareness, and favorable political regulations from local governments (establishing feed-in tariffs designed to accelerate investment in renewable energy technologies). It has become usual to see PV systems installed on the roofs of houses or PV farms next to the roads in the countryside. Grid-connected PV systems account for more than 99% of the PV installed capacity compared to


title: Design & construction of a Vertical Axis Wind Turbine

abstract: A wind turbine is a device that converts kinetic energy from the wind into electrical power. Vertical Axis Wind Turbine (VAWT) is one type of wind turbine where the main rotor shaft is set vertically and it can capture wind from any direction. The aim of this work is to develop a theoretical model for the design and performance of Darrieus type vertical axis wind turbine for small scale energy applications. A small 3 bladed turbine (prototype) is constructed and investigated the performance for low wind velocity. The model is based on NACA 0018 airfoil & light wood is used as blade material. The full scale Vertical Axis Wind Turbine is made for 36 inch height, 24 inch diameter, blade cord length is 3.937 inch & blade height is 24 inch. A 100 watt 24 volt brushless DC motor is used to measure output power. The whirling speed of blade & electric power output for the corresponding speed is measured through Tachometer & Wattmeter. The power curves show the relation between the rotational wind speed of the turbine and the power produced for a range of wind speeds. This approach indicates to develop vertical axis wind turbine with better performance to meet the increasing power demand.


title: Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides

abstract: Because of their continuous and natural motion, fluidically powered soft actuators have shown potential in a range of robotic applications, including prosthetics and orthotics. Despite these advantages, robots using these actuators require stretchable sensors that can be embedded in their bodies for sophisticated functions. Presently, stretchable sensors usually rely on the electrical properties of materials and composites for measuring a signal; many of these sensors suffer from hysteresis, fabrication complexity, chemical safety and environmental instability, and material incompatibility with soft actuators. Many of these issues are solved if the optical properties of materials are used for signal transduction. We report the use of stretchable optical waveguides for strain sensing in a prosthetic hand. These optoelectronic strain sensors are easy to fabricate, are chemically inert, and demonstrate low hysteresis and high precision in their output signals. As a demonstration of their potential, the photonic strain sensors were used as curvature, elongation, and force sensors integrated into a fiber-reinforced soft prosthetic hand. The optoelectronically innervated prosthetic hand was used to conduct various active sensation experiments inspired by the capabilities of a real hand. Our final demonstration used the prosthesis to feel the shape and softness of three tomatoes and select the ripe one.


title: Influence of a video class system on learners' study and learning strategies and their achievement in mathematics

abstract: AbstractThis paper reflects on an investigation to determine the influence of a video class system (VCS) on Grade 9 learners' study and learning strategies as well as achievement in mathematics. The features of effective/expert learning and teaching of school mathematics served as framework for the planning, execution and assessment of the empirical investigation. Results firstly showed no significant changes in the mathematics achievement of either the experimental groups or the control group; secondly, it became evident that teachers involved did not adapt their teaching strategies to accommodate the VCS; thirdly, the results, however, did show that exposure to the VCS significantly improved learners' use of learning and study strategies with regard to attitude, motivation, anxiety, concentration, selection of main ideas, and the use of test strategies. In the South African context with its shortage of properly qualified mathematics teachers, the frequent teachers and learner absenteeism and many learne...


title: Fundamentals of Biomass Pretreatment by Fractionation

abstract: With the rise in global energy demand and environmental concerns about the use of fossil fuels, the need for rapid development of alternative fuels from sustainable, non-food sources is now well acknowledged. The effective utilization of low-cost high-volume agricultural and forest biomass for the production of transportation fuels and bio-based materials will play a vital role in addressing this concern [1]. The processing of lignocellulosic biomass, especially from mixed agricultural and forest sources with varying composition, is currently significantly more challenging than the bioconversion of corn starch or cane sugar to ethanol [1,2]. This is due to the inherent recalcitrance of lignocellulosic biomass to enzymatic and microbial deconstruction, imparted by the partly crystalline nature of cellulose and its close association with hemicellulose and lignin in the plant cell wall [2,3]. Pretreatments that convert raw lignocellulosic biomass to a form amenable to enzymatic degradation are therefore an integral step in the production of bioethanol from this material [4]. Chemical or thermochemical pretreatments act to reduce biomass recalcitrance in various ways. These include hemicellulose removal or degradation, lignin modification and/or delignification, reduction in crystallinity and degree of polymerization of cellulose, and increasing pore volume. Biomass pretreatments are an active focus of industrial and academic research efforts, and various strategies have been developed. Among commonly studied pretreatments, organosolv pretreatment, in which an aqueous organic solvent mixture is used as the pretreatment medium, results in the fractionation of the major biomass components, cellulose, lignin, and hemicellulose into three process streams [5,6]. Cellulose and lignin are recovered as separate solid streams, while hemicelluloses and sugar degradation products such as furfural and hydroxymethylfurfural (HMF) are released as a water-soluble fraction. The combination of ethanol as the solvent and


title: Identification of DNA markers linked to the male sex in dioecious hemp (Cannabis sativa L.)

abstract: A 400-bp RAPD marker generated by a primer of random decamer sequence has been found associated with the male sex phenotype in 14 dioecious cultivars and accessions of hemp (Cannabis sativa L.). The primer OPA8 generates a set of bands, most of which polymorphic among all the individual plants tested, and 1 of which, named OPA8400, present in all male plants and absent in female plants. A screening of 167 plants belonging to different genotypes for the association of the OPA8400 marker with the sex phenotype revealed that only in 3 cases was the 400-bp band was present in plants phenotypically female; on the contrary, in male plants the band was never missing, while in monoecious plants it was never present. Despite this sex-specific association, the sequences corresponding to OPA8400 were present in both staminate and carpellate plants, as revealed by Southern blotting and hybridization with the cloned RAPD band. The RAPD marker was sequenced, and specific primers were constructed. These primers generated, on the same genotypes used for RAPD analysis, a SCAR marker 390 bp in length and male-specific. This SCAR is suitable for a precise, early and rapid identification of male plants during breeding programs of dioecious and monoecious hemp.


title: AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models

abstract: The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated in defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation. Since correlation analyses often involve manual selection of metrics by a domain expert, we introduce AutoSpearman, an automated metric selection approach based on correlation analyses. Our evaluation indicates that AutoSpearman yields the highest consistency of subsets of metrics among training samples and mitigates correlated metrics, while impacting model performance by 1-2%pts. Thus, to automatically mitigate correlated metrics when interpreting defect models, we recommend future studies use AutoSpearman in lieu of commonly-used feature selection techniques.


title: Evaluating React Native and Progressive Web App development using ISO 25010

abstract: The vast supply of different smartphone makes and models, along with their accompanying operating systems, increase the demand for an all-in-one development solution. Quite a few appr ...


title: Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks

abstract: This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout, and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field (MRF) model. Results on various images show that our method outperforms the most recent techniques.


title: Twitter spam detection based on deep learning

abstract: Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.


title: Bearing Fault Model for Induction Motor With Externally Induced Vibration

abstract: This paper investigates the relationship between vibration and current in induction motors operated under external vibrations. Two approaches are usually available to define this relationship. The former is based on airgap variations, while the latter is based on torque perturbation. This paper is focused on the airgap variation model. The ball bearing fault is modeled by contact mechanics. External vibrations often occur in many industrial applications where externally induced vibrations of suitable amplitude cause cyclic radial loading on the machine shaft. The model is validated by experiments, owing to a dedicated test setup, where an external vibration source (shaker) was employed, together with ball bearing alterations in order to decrease the stiffness of the support along the radial direction. To maximize the effects of externally induced vibrations, the frequency chosen was near the flexural resonance of the rotor (determined by finite-element method analysis). The direction of the external vibration is radial with respect to the axis of the electric machine under test. During tests, both stator phase currents and vibration of the machine were sampled. The test setup allowed one to vary the machine speed and load, vibration amplitude, and bearing stiffness (damage level). Radial effects are usually visible only in the case of large failures that result in significant airgap variations, as confirmed by experiments.


Neuronal circuits for fear and anxiety — the missing link


Characterization of the thermolysis products of Nafion membrane: A potential source of perfluorinated compounds in the environment


title: Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

abstract: Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.


title: On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

abstract: Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can be broken down into bias and variance components. While unbiasedness is often cited as a beneficial quality of a model selection criterion, we demonstrate that a low variance is at least as important, as a non-negligible variance introduces the potential for over-fitting in model selection as well as in training the model. While this observation is in hindsight perhaps rather obvious, the degradation in performance due to over-fitting the model selection criterion can be surprisingly large, an observation that appears to have received little attention in the machine learning literature to date. In this paper, we show that the effects of this form of over-fitting are often of comparable magnitude to differences in performance between learning algorithms, and thus cannot be ignored in empirical evaluation. Furthermore, we show that some common performance evaluation practices are susceptible to a form of selection bias as a result of this form of over-fitting and hence are unreliable. We discuss methods to avoid over-fitting in model selection and subsequent selection bias in performance evaluation, which we hope will be incorporated into best practice. While this study concentrates on cross-validation based model selection, the findings are quite general and apply to any model selection practice involving the optimisation of a model selection criterion evaluated over a finite sample of data, including maximisation of the Bayesian evidence and optimisation of performance bounds.


title: Handedness in shearing auxetics creates rigid and compliant structures

abstract: In nature, repeated base units produce handed structures that selectively bond to make rigid or compliant materials. Auxetic tilings are scale-independent frameworks made from repeated unit cells that expand under tension. We discovered how to produce handedness in auxetic unit cells that shear as they expand by changing the symmetries and alignments of auxetic tilings. Using the symmetry and alignment rules that we developed, we made handed shearing auxetics that tile planes, cylinders, and spheres. By compositing the handed shearing auxetics in a manner inspired by keratin and collagen, we produce both compliant structures that expand while twisting and deployable structures that can rigidly lock. This work opens up new possibilities in designing chemical frameworks, medical devices like stents, robotic systems, and deployable engineering structures.


title: Leakiness and creepiness in app space: perceptions of privacy and mobile app use

abstract: Mobile devices are playing an increasingly intimate role in everyday life. However, users can be surprised when informed of the data collection and distribution activities of apps they install. We report on two studies of smartphone users in western European countries, in which users were confronted with app behaviors and their reactions assessed. Users felt their personal space had been violated in "creepy" ways. Using Altman's notions of personal space and territoriality, and Nissenbaum's theory of contextual integrity, we account for these emotional reactions and suggest that they point to important underlying issues, even when users continue using apps they find creepy.


title: Wearable Biomedical Measurement Systems for Assessment of Mental Stress of Combatants in Real Time

abstract: The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the “Coincidente” program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems.


title: Cultural effects on the neural basis of theory of mind.

abstract: Abstract “Theory of mind” has been described as the ability to attribute and understand other people's desires and intentions as distinct from one's own. It has been found to develop as early as between 3 and 4 years old, with precursor abilities possibly developing much earlier. There has been debate about the extent to which the developmental trajectory of theory of mind may differ across cultures or language systems. Although very few neuroimaging studies have directly compared different groups from different culture and language systems, across studies of a number of cultural/language groups have been used to explore the neural correlates of theory of mind. A summary of these findings suggests that there may be both universal and culture or language-specific neural correlates related to theory of mind. These studies, while still preliminary in many ways, illustrate the importance of taking into account the cultural background of participants. Furthermore these results suggest that there may be important cultural influence on theory of mind and the neural correlates associated with this ability.


Acridine orange stain.


title: Air painting with Corel Painter Freestyle and the leap motion controller: a revolutionary new way to paint!

abstract: The feel of air painting is like none other! It is like being a dancer, conductor and magician all wrapped up in one. As legendary science fiction writer and visionary, Arthur C. Clarke, famously said, "any sufficiently advanced technology is indistinguishable from magic". Air painting is indeed magical! Imagine being able to control every aspect of the painting process through only gesture and movement of your fingers in the air, and being able to paint with up to ten simultaneous brush strokes controlled by the movement of your fingers and thumbs on both hands. This is now a reality with the combination of the revolutionary new Leap Motion Controller providing 3-D motion-generated input data to the Corel Painter Freestyle application. It is the powerful synergy of these two complementary technologies applied to fine art observational painting that is the focus of this talk.


title: AI-based Game Design Patterns

abstract: This paper proposes a model for designing games around Artificial Intelligence (AI). AI-based games put AI in the foreground of the player experience rather than in a supporting role as is often the case in many commercial games. We analyze the use of AI in a number of existing games and identify design patterns for AI in games. We propose a generative ideation technique to combine a design pattern with an AI technique or capacity to make new AI-based games. Finally, we demonstrate this technique through two examples of AI-based game prototypes created using these patterns.


title: Color Invariance

abstract: This paper presents the measurement of colored object reflectance, under different, general assumptions regarding the imaging conditions. We exploit the Gaussian scale-space paradigm for color images to define a framework for the robust measurement of object reflectance from color images. Object reflectance is derived from a physical reflectance model based on the Kubelka-Munk theory for colorant layers. Illumination and geometrical invariant properties are derived from the reflectance model. Invariance and discriminative power of the color invariants is experimentally investigated, showing the invariants to be successful in discounting shadow, illumination, highlights, and noise. Extensive experiments show the different invariants to be highly discriminative, while maintaining invariance properties. The presented framework for color measurement is well-founded in the physics of color as well as in measurement science. Hence, the proposed invariants are considered more adequate for the measurement of invariant color features than existing methods.


title: Is HIV infection a risk factor for complications of surgery

abstract: Background: The literature is inconsistent as to whether HIV-infected patients have higher rates of surgical complication than do HIV-uninfected patients. This inconsistency reflects the failure to control for confoundingvariables in many of the previous studies. Methods: A retrospective cohort study of records of HIV-infected individuals who underwent surgical procedures between 1990 and 1995 was matched with the records of HIV-uninfected control patients. We performed a logistic regression analysis to determine the independent effects of HIV infection and other potential risk factors for surgical complications. Results: The crude rates of death and infectious and hematologic complications were higher among HIV-infected patients than among uninfected patients. Although the crude risk of having any complication was higher among the HIV-infected (odds ratio [OR]=2.47, p=0.015), the adjusted risk was not (OR=0.72 [p<0.613]). Variables significantly associated with complications were American Society of Anesthesiology (ASA) risk class (OR=2.7), age (OR=1.06 per year), and weight (OR=0.96 per kg). Conclusions: HIV sero-status was not found to be an independent risk factor for complications of surgery. The most important risk factor for complication of surgery in HIV-infected patients is ASA risk class.


title: Solving Atari Games Using Fractals And Entropy

abstract: In this paper, we introduce a novel MCTS based approach that is derived from the laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC), allows us to create an agent that takes intelligent actions in both continuous and discrete environments while providing control over every aspect of the agent behavior. Results show that FMC is several orders of magnitude more efficient than similar techniques, such as MCTS, in the Atari games tested.


title: FIVE-INPUT MAJORITY GATE, A NEW DEVICE FOR QUANTUM-DOT CELLULAR AUTOMATA

abstract: Science and Research Branch of IAU, Tehran, IranQuantum-dot Cellular Automata (QCA) is one of the most attractive technologies for computing atnano-scale. The principle logic element in QCA is majority gate. In this paper, a novel design for5-input majority gate is presented. A 5-input majority gate study has been proposed; however thisstudy has changed the scheme of basic QCA cells. The new proposed device reduces cell countsand area and uses conventional form of QCA cells. Accuracy of this design is proven by applyingsome simple physical substantiation and QCADesigner tool is used for verifying majority circuitlayout and functionality. Furthermore, a QCA Full-Adder is constructed using the new proposeddesign. Simulation results demonstrate that the proposed design of majority gates and Full-Adderresulted in signiﬁcant improvements in designing logical circuits.


title: Driving behavior analysis with smartphones: insights from a controlled field study

abstract: We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.


Building a Control-flow Graph from Scheduled Assembly Code


title: Graphical models for optimal power flow

abstract: Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithm for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for"smart grid"applications like control of distributed energy resources. We evaluate our technique numerically on several benchmark networks and show that practical OPF problems can be solved effectively using this approach.


title: Classical planning with simulators: results on the Atari video games

abstract: The Atari 2600 games supported in the Arcade Learning Environment [Bellemare et al., 2013] all feature a known initial (RAM) state and actions that have deterministic effects. Classical planners, however, cannot be used off-the-shelf as there is no compact PDDL-model of the games, and action effects and goals are not known a priori. Indeed, there are no explicit goals, and the planner must select actions on-line while interacting with a simulator that returns successor states and rewards. None of this precludes the use of blind lookahead algorithms for action selection like breadth-first search or Dijkstra's yet such methods are not effective over large state spaces. We thus turn to a different class of planning methods introduced recently that have been shown to be effective for solving large planning problems but which do not require prior knowledge of state transitions, costs (rewards) or goals. The empirical results over 54 Atari games show that the simplest such algorithm performs at the level of UCT, the state-of-the-art planning method in this domain, and suggest the potential of width-based methods for planning with simulators when factored, compact action models are not available.


title: A computer vision based web application for tracking soccer players

abstract: Soccer is a sport where everyone that is involved with it make all the efforts aiming for excellence. Not only the players need to show their skills on the pitch but also the coach, and the remaining staff, need to have their own tools so that they can perform at higher levels. Footdata is a project to build a new web application product for soccer (football), which integrates two fundamental components of this sport’s world: the social and the professional. While the former is an enhanced social platform for soccer professionals and fans, the later can be considered as a Soccer Resource Planning, featuring a system for acquisition and processing information to meet all the soccer management needs. In this paper we focus only in a specific module of the professional component. We will describe the section of the web application that allows to analyse movements and tactics of the players using images directly taken from the pitch or from videos, we will show that it is possible to draw players and ball movements in a web application and detect if those movements occur during a game.


title: Listening to music and physiological and psychological functioning: The mediating role of emotion regulation and stress reactivity

abstract: Music listening has been suggested to have short-term beneficial effects. The aim of this study was to investigate the association and potential mediating mechanisms between various aspects of habitual music-listening behaviour and physiological and psychological functioning. An internet-based survey was conducted in university students, measuring habitual music-listening behaviour, emotion regulation, stress reactivity, as well as physiological and psychological functioning. A total of 1230 individuals (mean = 24.89 ± 5.34 years, 55.3% women) completed the questionnaire. Quantitative aspects of habitual music-listening behaviour, i.e. average duration of music listening and subjective relevance of music, were not associated with physiological and psychological functioning. In contrast, qualitative aspects, i.e. reasons for listening (especially ‘reducing loneliness and aggression’, and ‘arousing or intensifying specific emotions’) were significantly related to physiological and psychological functioning ...


title: Vectorized Radviz and Its Application to Multiple Cluster Datasets

abstract: Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.