title: Acupuncture and electro‐acupuncture for people diagnosed with subacromial pain syndrome: A multicentre randomized trial

abstract: This is the peer-reviewed version of the following article: J. Lewis, J. Sim, and P. Barlas, Acupuncture and electro-acupuncture for people diagnosed with subacromial pain syndrome: A multicentre randomized trial, European Journal of Pain, first published 20 January 2017, available online at doi 10.1002/ejp.1001. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2017 European Pain Federation - EFIC®


title: 3D face recognition using local binary patterns

abstract: It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. In this paper, we develop a novel 3D face recognition algorithm using Local Binary Pattern (LBP) under expression varieties, which is an extension of the LBP operator widely used in ordinary facial analysis. First, to depict the human face more accurately and reduce the effect of facial local distortion for face recognition, a special feature-based 3D face division scheme is proposed. Then, the LBP representation framework for 3D faces is described, and the facial depth and normal information are extracted and encoded by LBP, to reduce the expression effect. For each face region, the statistical histogram is utilized to summarize the facial details, and accordingly three matching strategies are presented to address the recognition task. Finally, the proposed 3D face recognition algorithm is tested on BJUT-3D and FRGC v2.0 databases, achieves promising results, and concludes that it is feasible and valid to apply the LBP representation framework on 3D face recognition.


Integrating Verbal and Nonvebval Input into a Dynamic Response Spoken Dialogue System.


title: Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space

abstract: This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal function approximations. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a Mahalanobis classifier for the detection of anomalous trajectories. Motion trajectories are considered as time series and modelled using orthogonal basis function representations. We have compared three different function approximations --- least squares polynomials, Chebyshev polynomials and Fourier series obtained by Discrete Fourier Transform (DFT). Trajectory clustering is then carried out in the chosen coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self- Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Our proposed techniques are validated on three different datasets --- Australian sign language, hand-labelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for multimedia video surveillance systems are envisaged.


title: Generalisation of threat expectancy increases with time

abstract: ABSTRACTExcessive fear generalisation is a feature characteristic of clinical anxiety and has been linked to its aetiology. Previous animal studies have shown that the mere passage of time increase...


title: Driver Assistance System for Passive Multi-Trailer Vehicles with Haptic Steering Limitations on the Leading Unit

abstract: Driving vehicles with one or more passive trailers has difficulties in both forward and backward motion due to inter-unit collisions, jackknife, and lack of visibility. Consequently, advanced driver assistance systems (ADAS) for multi-trailer combinations can be beneficial to accident avoidance as well as to driver comfort. The ADAS proposed in this paper aims to prevent unsafe steering commands by means of a haptic handwheel. Furthermore, when driving in reverse, the steering-wheel and pedals can be used as if the vehicle was driven from the back of the last trailer with visual aid from a rear-view camera. This solution, which can be implemented in drive-by-wire vehicles with hitch angle sensors, profits from two methods previously developed by the authors: safe steering by applying a curvature limitation to the leading unit, and a virtual tractor concept for backward motion that includes the complex case of set-point propagation through on-axle hitches. The paper addresses system requirements and provides implementation details to tele-operate two different off- and on-axle combinations of a tracked mobile robot pulling and pushing two dissimilar trailers.


title: Design and Implementation of User Context aware Recommendation Engine for Mobile using Bayesian Network, Fuzzy Logic and Rule Base

abstract: Purpose – The purpose of this paper is to propose an intelligent service recommendation model. The paper formulates the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning.Design/methodology/approach – The authors formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning. Bayesian Network is used to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this the paper proposes maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed serv...


title: Interactive Design of Developable Surfaces

abstract: We present a new approach to geometric modeling with developable surfaces and the design of curved-creased origami. We represent developables as splines and express the nonlinear conditions relating to developability and curved folds as quadratic equations. This allows us to utilize a constraint solver, which may be described as energy-guided projection onto the constraint manifold, and which is fast enough for interactive modeling. Further, a combined primal-dual surface representation enables us to robustly and quickly solve approximation problems.


title: Attack-defense trees and two-player binary zero-sum extensive form games are equivalent

abstract: Attack-defense trees are used to describe security weaknesses of a system and possible countermeasures. In this paper, the connection between attack-defense trees and game theory is made explicit. We show that attack-defense trees and binary zero-sum two-player extensive form games have equivalent expressive power when considering satisfiability, in the sense that they can be converted into each other while preserving their outcome and their internal structure.


title: Syntactic structural kernels for natural language interfaces to databases

abstract: A core problem in data mining is to retrieve data in a easy and human friendly way. Automatically translating natural language questions into SQL queries would allow for the design of effective and useful database systems from a user viewpoint. Interesting previous work has been focused on the use of machine learning algorithms for automatically mapping natural language (NL) questions to SQL queries. ::: ::: In this paper, we present many structural kernels and their combinations for inducing the relational semantics between pairs of NL questions and SQL queries. We measure the effectiveness of such kernels by using them in Support Vector Machines to select the queries that correctly answer to NL questions. Experimental results on two different datasets show that our approach is viable and that syntactic information under the form of pairs of syntactic tree fragments (from queries and questions) plays a major role in deriving the relational semantics between the two languages.


title: Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones

abstract: Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.


title: Breast cancer triage CAD for low resource countries using low-cost ultrasound and minimally-trained operators

abstract: We propose an automated breast cancer triage CAD system using machine vision on low-cost, portable ultrasound imaging devices. We demonstrate that the triage CAD software can effectively analyze images captured by minimally-trained operators and output one of three assessments — benign, probably benign (6-month follow-up recommended) and suspicious (biopsy recommended). This system opens up the possibility of offering practical, cost-effective breast cancer diagnosis for symptomatic women in economically developing countries.


title: Post-processing of video sequences using deblocking and adaptive trilateral filtering

abstract: Image and video compression algorithms may yield visual annoying artifacts for limited bit rates. The quality of compressed video signals may be improved by reducing blocking and ringing artifacts. For removing the blocking artifact, a quality measure of a block-based coded image is used to decide filtering modes. Based on filtering modes, the images are segmented into three classes and a specific deblocking filter is applied to each class. An adaptive method for extending the bilateral filter to a trilateral filter is introduced for removing the ringing artifacts. The spread parameters of the trilateral filter are selected adaptively using texture and edge mapping. The analysis of objective and subjective experimental results shows that the proposed algorithm is robustly successful in artifact reduction of low-bit rate H.264 video sequences.


title: Vulnerability Assessment of Cybersecurity for SCADA Systems Using Attack Trees

abstract: By penetrating the SCADA system, an intruder may remotely operate a power system using supervisory control privileges. Hence, cybersecurity has been recognized as a major threat due to the potential intrusion to the online system. This paper proposes a methodology to evaluate the cybersecurity vulnerability using attack trees. The attack tree formulation based on power system control networks is used to evaluate the system, scenario, and leaf vulnerabilities. The measure of vulnerabilities in the power system control framework is determined based on existing cybersecurity conditions before the vulnerability indices are evaluated. After the indices are evaluated, an upper bound is imposed on each scenario vulnerability in order to determine the pivotal attack leaves that require countermeasure improvements. The proposed framework can be extended to security investment analysis.


title: Aspect based Sentiment Analysis using support vector machine classifier

abstract: Sentiment Analysis involves the process of identifying the polarity of opinionated texts. Lots of social networking sites are being used for expressing thoughts and opinions by users to rate products. These user opinionated text is highly unstructured in nature and thus involves the application of various natural language processing techniques. In aspect based sentiment analysis, the various features of a product is identified through the training process. For e.g. the aspects of a camera are picture quality, size, resolution etc. The quantitative analysis of each aspect is done using support vector machine classifier. In most of the previous works, a product review is analysed as a whole rather than considering each aspect of it. Aspect based opinion mining is tedious since the identification of individual features is in itself a challenging task.


title: How Healthcare Technology Shapes Health Literacy? A Systematic Review

abstract: There have been made efforts to develop technologies for low health literate users and evaluate them for effectiveness. We address how technology improves health literacy by identifying salient technical features to support health literacy processes and the specific needs of user populations. A systematic review of 45 published studies on technology to improve health literacy in PubMed, Direct Science, and Google Scholar up to 2014 was performed. Thirteen evaluation studies were reviewed to analyze their effectiveness. Among technological features used in technologies to improve health literacy, multimedia, interactive didactic forms with simple contents, followed by ease of use, are gaining popularity in technology to support health literacy process. Video game and virtual world are effective for young users to be engaged in learning. For seniors, technical features with ease of use like practical assistance are commonly used. This paper shows some technologies' effectiveness and discusses difficulties for the technologies failed to achieve desirable outcomes.


title: IEEE 802.11n vs. IEEE 802.15.4: A Study on Communication QoS to Provide Safe FANETs

abstract: Flying Ad hoc Network (FANET) is an infrastructure-less multi-hop radio ad hoc network in which Unmanned Aerial Vehicles (UAVs) and Ground Control Station (GCS) collaborates to forward data traffic. Compared to the standard Mobile Ad hoc NETworks (MANETs), the FANET architecture has some specific features (3D mobility, low UAV density, intermittent network connectivity) that bring challenges to the communication protocol design. Such routing protocol must provide safety by finding an accurate and reliable route between UAVs. This safety can be obtained through the use of agile method during software based routing protocol development (for instance the use of Model Driven Development) by mapping each FANET safety requirement into the routing design process. This process must be completed with a sequential safety validation testing with formal verification tools, standardized simulator (by using real simulation environment) and real-world experiments. In this paper, we considered FANET communication safety by presenting design methodologies and evaluations of FANET routing protocols. We use the LARISSA architecture to guarantee the efficiency and accuracy of the whole system. We also use the model driven development methodology to provide model and code consistency through the use of formal verification tools. To complete the FANET safety validation, OMNeT++ simulations (using real UAVs mobility traces) and real FANET outdoor experiments have been carried out. We confront both results to evaluate routing protocol performances and conclude about its safety consideration.


title: Bifacial effects of engineering tumour cell‐derived exosomes on human natural killer cells

abstract: Extracellular vesicles (EVs) are nano vesicular structures that are secreted by almost all kinds of cells. Exosomes are small EVs derived from endosomes, with a diameter between 30-100nm. Tumour-derived exosomes carry many molecules and factors from tumour cells. These exosomes are recognized and taken up by immunocytes. However, tumour-derived exosomes can not only suppress immune cell functions but also help tumours escape immune surveillance in the tumour microenvironment. The present work investigated the effect of exosomes derived from genetical modified K562 cells (GMK cells), which express IL-15, IL-18 and 4-1BBL (TNFSF9) on their surface. The results showed that these GME exosomes, carrying IL-15, IL-18 and 4-1BBL proteins similar to their host cells, could activate NK cells, increase the cytotoxicity of NK cells on some tumour cells in a short treatment (4h) and promote NK cells proliferation. However, with an extended treatment time (48h), these exosomes could inhibite the cytotoxicity of NK cells by inhibiting activated receptor expression on NK cells. These results indicated the bifacial effects of GMK exosomes on NK cells, which will be helpful to explore the possibility of using transformed exosomes as an anti-tumour immune vaccine or a therapeutic tool in future.


title: FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results

abstract: Abstract : As part of the Face Recognition Technology (FERET) program, the U.S. Army Research Laboratory (ARL) conducted supervised government tests and evaluations of automatic face recognition algorithms. The goal of the tests was to provide an independent method of evaluating algorithms and assessing the state of the art in automatic face recognition. This report describes the design and presents the results of the August 1994 and March 1995 FERET tests. Results for FERET tests administered by ARL between August 1994 and August 1996 are reported.


title: Deepdocclassifier: Document classification with deep Convolutional Neural Network

abstract: This paper presents a deep Convolutional Neural Network (CNN) based approach for document image classification. One of the main requirement of deep CNN architecture is that they need huge number of samples for training. To overcome this problem we adopt a deep CNN which is trained using big image dataset containing millions of samples i.e., ImageNet. The proposed work outperforms both the traditional structure similarity methods and the CNN based approaches proposed earlier. The accuracy of the proposed approach with merely 20 images per class outperforms the state-of-the-art by achieving classification accuracy of 68.25%. The best results on Tobbacoo-3428 dataset show that our proposed method outperforms the state-of-the-art method by a significant margin and achieved a median accuracy of 77.6% with 100 samples per class used for training and validation.


title: Will 5G See its Blind Side? Evolving 5G for Universal Internet Access

abstract: Internet has shown itself to be a catalyst for economic growth and social equity but its potency is thwarted by the fact that the Internet is off limits for the vast majority of human beings. Mobile phones—the fastest growing technology in the world that now reaches around 80% of humanity—can enable universal Internet access if it can resolve coverage problems that have historically plagued previous cellular architectures (2G, 3G, and 4G). These conventional architectures have not been able to sustain universal service provisioning since these architectures depend on having enough users per cell for their economic viability and thus are not well suited to rural areas (which are by definition sparsely populated). The new generation of mobile cellular technology (5G), currently in a formative phase and expected to be finalized around 2020, is aimed at orders of magnitude performance enhancement. 5G offers a clean slate to network designers and can be molded into an architecture also amenable to universal Internet provisioning. Keeping in mind the great social benefits of democratizing Internet and connectivity, we believe that the time is ripe for emphasizing universal Internet provisioning as an important goal on the 5G research agenda. In this paper, we investigate the opportunities and challenges in utilizing 5G for global access to the Internet for all (GAIA). We have also identified the major technical issues involved in a 5G-based GAIA solution and have set up a future research agenda by defining open research problems.


title: A Context-Aware Indoor Air Quality System for Sudden Infant Death Syndrome Prevention

abstract: Context-aware monitoring systems designed for e-Health solutions and ambient assisted living (AAL) play an important role in today’s personalized health-care services. The majority of these systems are intended for the monitoring of patients’ vital signs by means of bio-sensors. At present, there are very few systems that monitor environmental conditions and air quality in the homes of users. A home’s environmental conditions can have a significant influence on the state of the health of its residents. Monitoring the environment is the key to preventing possible diseases caused by conditions that do not favor health. This paper presents a context-aware system that monitors air quality to prevent a specific health problem at home. The aim of this system is to reduce the incidence of the Sudden Infant Death Syndrome, which is triggered mainly by environmental factors. In the conducted case study, the system monitored the state of the neonate and the quality of air while it was asleep. The designed proposal is characterized by its low cost and non-intrusive nature. The results are promising.


title: The global distribution of clinical episodes of Plasmodium falciparum malaria

abstract: A new ‘malaria map’ suggests that the disease is far more common than was thought. There is still no reliable estimate of the global distribution of the public health burden posed by malaria, making it impossible to allocate resources for malaria control on the basis of objective evidence. Snow et al. set out to improve the situation by developing a map of the global distribution of clinical episodes of Plasmodium falciparum malaria incorporating epidemiological, geographic and demographic data. The bottom line is a total of around 500 million cases in 2002, which is 50% higher than the WHO figure. For areas outside Africa the new estimate is twice the WHO figure, reflecting lax reporting procedures in some countries. Interest in mapping the global distribution of malaria is motivated by a need to define populations at risk for appropriate resource allocation1,2 and to provide a robust framework for evaluating its global economic impact3,4. Comparison of older5,6,7 and more recent1,4 malaria maps shows how the disease has been geographically restricted, but it remains entrenched in poor areas of the world with climates suitable for transmission. Here we provide an empirical approach to estimating the number of clinical events caused by Plasmodium falciparum worldwide, by using a combination of epidemiological, geographical and demographic data. We estimate that there were 515 (range 300–660) million episodes of clinical P. falciparum malaria in 2002. These global estimates are up to 50% higher than those reported by the World Health Organization (WHO) and 200% higher for areas outside Africa, reflecting the WHO's reliance upon passive national reporting for these countries. Without an informed understanding of the cartography of malaria risk, the global extent of clinical disease caused by P. falciparum will continue to be underestimated.


title: Improving Semantic Search through Entity-Based Document Ranking

abstract: Traditional keyword-based IR approaches take into account the document context only in a limited manner. In our paper we present a novel document ranking approach based on the semantic relationships between named entities. In the first step we annotate all documents with named entities from a knowledge base (for example people, places and organisations). In the next step these annotations in combination with the relationships from the knowledge base are used to rank documents in order to perform a semantic search. Documents that contain the specific named entity that was searched for as well as other strongly related entities, receive a higher ranking. The inclusion of the document context in the ranking approach achieves a higher precision in the Top-K results.


title: Visual service scape aesthetics and consumer response : a holistic model

abstract: The paper looks at the impact of visual servicescapes at consumer preferences. Using an experimental methodology, we try to understand the imapct of different servicescape aesthetic dimensions on emotional and congnitive responses of customers.


title: The neural basis of decision making.

abstract: The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.


title: A fault tolerant, area efficient architecture for Shor's factoring algorithm

abstract: We optimize the area and latency of Shor's factoring while simultaneously improving fault tolerance through: (1) balancing the use of ancilla generators, (2) aggressive optimization of error correction, and (3) tuning the core adder circuits. Our custom CAD flow produces detailed layouts of the physical components and utilizes simulation to analyze circuits in terms of area, latency, and success probability. We introduce a metric, called ADCR, which is the probabilistic equivalent of the classic Area-Delay product. Our error correction optimization can reduce ADCR by order of magnitude or more. Contrary to conventional wisdom, we show that the area of an optimized quantum circuit is not dominated exclusively by error correction. Further, our adder evaluation shows that quantum carry-lookahead adders (QCLA) beat ripple-carry adders in ADCR, despite being larger and more complex. We conclude with what we believe is one of most accurate estimates of the area and latency required for 1024-bit Shor's factorization: 7659 mm2 for the smallest circuit and 6 x 108 seconds for the fastest circuit.


title: The cyclic towers of Hanoi

abstract: The famous Towers of Hanoi puzzle consists of 3 pegs (A, B, C) on one of which (A) are stacked n rings of different sizes, each ring resting on a larger ring. The objective is to move the n rings one by one until they are all stacked on another peg (B) in such a way that no ring is ever placed on a smaller ring; the other peg (C) can be used as workspace. The problem has tong been a favourite iir programming courses as one which admits a concise recursive solution. This solution hinges on the observation that, when the largest ring is moved from A to B, the n 1 remaining rings must all be on peg C. This immediately leads to the recursive procedure


An Architecture for Smart Health Monitoring System Based on Fog Computing


title: Self-Reconfiguring Microservices

abstract: Microservices is an emerging paradigm for the development of distributed systems that, originating from Service-Oriented Architecture, focuses on the small dimension, the loose coupling, and the dynamic topology of services. Microservices are particularly appropriate for the development of distributed systems in the Cloud. However, their dynamic nature calls for suitable techniques for their automatic deployment. In this paper we address this problem and we propose JRO Jolie Redeployment Optimiser, a tool for the automatic and optimised deployment of microservices written in the Jolie language. The tool uses Zephyrus, a state of the art tool that automatically generates a fully detailed Service-Oriented Architecture configuration starting from a partial and abstract description of the target application.


title: An Introduction to Population Protocols

abstract: Population protocols are used as a theoretical model for a collection (or population) of tiny mobile agents that interact with one another to carry out a computation. The agents are identically programmed finite state machines. Input values are initially distributed to the agents, and pairs of agents can exchange state information with other agents when they are close together. The movement pattern of the agents is unpredictable, but subject to some fairness constraints, and computations must eventually converge to the correct output value in any schedule that results from that movement. This framework can be used to model mobile ad hoc networks of tiny devices or collections of molecules undergoing chemical reactions. This chapter surveys results that describe what can be computed in various versions of the population protocol model.


title: TI-TRNG: Technology independent true random number generator

abstract: True random number generators (TRNGs) are needed for a variety of security applications and protocols. The quality (randomness) of TRNGs depends on sensitivity to random noise, environmental conditions, and aging. Random sources of noise improve TRNG quality. In older or more mature technologies, the random sources are limited resulting in low TRNG quality. Prior work has also shown that attackers can manipulate voltage supply and temperature to bias the TRNG output. In this paper, we propose bias detection mechanisms and a technology independent TRNG (TI-TRNG) architecture. The TI-TRNG enhances power supply noise for older technologies and uses a self-calibration mechanism that reduces bias in TRNG output due to aging and attacks. Experiment results on 130nm, 90nm, and 45nm FPGAs demonstrate the quality of random sequences from the TI-TRNG across aging and different environmental conditions.


title: CNN architectures for large-scale audio classification

abstract: Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.


title: Elliptic curve cryptosystem and its applications

abstract: The goal of this research is to develop a basis for utilizing efficient encryption schemes in wireless communications and in devices with low computing power and resources. Elliptic curve cryptography (ECC) fits well for an efficient and secure encryption scheme. It is more efficient than the ubiquitous RSA based schemes because ECC utilizes smaller key sizes for equivalent security. A comparative study of ECC with RSA is made in terms of key size, computational power, size of data files and encrypted files. Also, another aim is to design an API to implement ECC encryption/decryption algorithm.


title: Modeling urban growth using a variable grid cellular automaton

abstract: Constrained cellular automata (CA) are frequently used for modeling land use change and urban growth. In these models land use dynamics are generated by a set of cell state transition rules that incorporate a neighborhood effect. Generally, neighborhoods are relatively small and therefore only a limited amount of spatial information is included. In this study a variable grid CA is implemented to allow incorporation of more spatial information in a computationally efficient way. This approach aggregates land uses at greater distances, in accordance with a hierarchical concept of space. More remote areas are aggregated into consecutively larger areas. Therefore the variable grid CA is capable of simulating regional as well as local dynamics at the same time. The variable grid CA is used here to model urban growth in the Greater Vancouver Regional District (GVRD) between 1996 and 2001. Calibration results are tested for goodness of fit at the cellular level by means of the kappa statistic and for land use patterns by means of cluster size analysis and radial analysis. Kappa results show that the model performs considerably better than a neutral allocation model. Cluster and radial analysis indicate that the model is capable of producing realistic urban growth patterns.


title: D–Q Impedance Based Stability Analysis and Parameter Design of Three-Phase Inverter-Based AC Power Systems

abstract: Small-signal stability is an important concern in three-phase inverter-based ac power systems. The impedance-based approach based on the generalized Nyquist stability criterion (GNC) can analyze the stability related with the medium and high-frequency modes of the systems. However,. the GNC involves the right-half-plane (RHP) pole calculation of return-ratio transfer function matrices, which cannot be avoided for stability analysis of complicated ac power systems. Therefore, it necessitates the detailed internal control information of the inverters, which is not normally available for commercial inverters. To address this issue, this paper introduces the component connection method (CCM) in the frequency domain for stability analysis in the synchronous d–q frame, by proposing a method of deriving the impedance matrix of the connection networks of inverter-based ac power systems. Demonstration on a two-area system and a microgrid shows that: The CCM-enabled approach can avoid the RHP pole calculation of return-ratio matrices and enables the stability analysis by using only the impedances of system components, which could be measured without the need for the internal information. A stability analysis method based on d–q impedances, the CCM, and the determinant-based GNC is also proposed to further simplify the analysis process. Inverter controller parameters can be designed as stability regions in parameter spaces, by repetitively applying the proposed stability analysis method. Simulation and experimental results verify the validity of the proposed stability analysis method and the parameter design approach.


title: A varactor-tuned dual-band slot antenna

abstract: A new technique for designing dual-band reconfigurable slot antennas is introduced. The technique is based on loading a slot antenna with a lumped capacitor (or varactor) at a certain location along the slot. Given a fixed capacitor location along the slot, decreasing the capacitance results in increasing the first and second resonant frequencies of the slot antenna. However, the changes in the resonant frequencies are significantly different for the first and second resonances and, hence, a dual-band antenna with considerable frequency ratio tuning range can be obtained. Based on this technique, an electronically tunable dual-band antenna with a frequency ratio in the range of 1.2-1.65 is designed and fabricated using a single varactor with a capacitance range of 0.5-2.2 pF. The antenna has similar radiation patterns with low cross-polarization levels at both bands and across the entire tunable frequency range.


title: The ENTICE approach to decompose monolithic services into microservices

abstract: Cloud computing has enabled elastic and on-demand service provisioning to achieve more efficient resource utilisation and quicker responses to varying application loads. Virtual machines, the building blocks of clouds, can be created using provider specific templates stored in proprietary repositories, which may lead to provider lock-in and decreased portability. Despite these enabling technologies, large scale service oriented applications are still mostly inelastic. Such applications often use monolithic services that limit the elasticity (e.g., by obstructing the replicability of parts of a monolithic service). Decomposing these services (leading to smaller, more targeted and more modular services) would open towards elasticity, but the decomposition process is mostly manual. This paper introduces a methodology for decomposing monolithic services to several so called microservices. The proposed methodology applies several outcomes of the ENTICE project (namely its image synthesis and optimisation tools). Finally, the paper provides insights on how these outcomes help revitalise past monolithic services, and what techniques are applied to aid future microservice developers.


Finding high-quality web pages using cohesiveness


title: Cloud security is not (just) virtualization security: a short paper

abstract: Cloud infrastructure commonly relies on virtualization. Customers provide their own VMs, and the cloud provider runs them often without knowledge of the guest OSes or their configurations. However, cloud customers also want effective and efficient security for their VMs. Cloud providers offering security-as-a-service based on VM introspection promise the best of both worlds: efficient centralization and effective protection. Since customers can move images from one cloud to another, an effective solution requires learning what guest OS runs in each VM and securing the guest OS without relying on the guest OS functionality or an initially secure guest VM state. We present a solution that is highly scalable in that it (i) centralizes guest protection into a security VM, (ii) supports Linux and Windows operating systems and can be easily extended to support new operating systems, (iii) does not assume any a-priori semantic knowledge of the guest, (iv) does not require any a-priori trust assumptions into any state of the guest VM. While other introspection monitoring solutions exist, to our knowledge none of them monitor guests on the semantic level required to effectively support both white- and black-listing of kernel functions, or allows to start monitoring VMs at any state during run-time, resumed from saved state, and cold-boot without the assumptions of a secure start state for monitoring.


title: INNOVATIVE APPROACH TO IMPROVE HYBRID CRYPTOGRAPHY BY USINGDNA STEGANOGRAPHY

abstract: There exists a big demand for innovative secure electronic communications while the expertise level of attackers increases rapidly and that causes even bigger demands and needs for an extreme secure connection. An ideal security protocol should always be protecting the security of connections in many aspects, and leaves no trapdoor for the attackers. Nowadays, one of the popular cryptography protocols is hybrid cryptosystem that uses private and public key cryptography to change secret message. In available cryptography protocol attackers are always aware of transmission of sensitive data. Even non-interested attackers can get interested to break the ciphertext out of curiosity and challenge, when suddenly catches some scrambled data over the network. First of all, we try to explain the roles of innovative approaches in cryptography. After that we discuss about the disadvantages of public key cryptography to exchange secret key. Furthermore, DNA steganography is explained as an innovative paradigm to diminish the usage of public cryptography to exchange session key. In this protocol, session key between a sender and receiver is hidden by novel DNA data hiding technique. Consequently, the attackers are not aware of transmission of session key through unsecure channel. Finally, the strength point of the DNA steganography is discussed.


title: Computational intelligence in games

abstract: This invention relates to the stoichiometric condensation of substituted phenylacetonitriles with dibasic carboxylic anhydrides. The resulting cyano-keto-acids are obtained in good yield and can be used to prepare biologically active 2-aryl-1,3-cyclohexanediones without elaborate purification.


title: Combining Chat and Task-Based Multimodal Dialogue for More Engaging HRI: A Scalable Method Using Reinforcement Learning

abstract: We develop the first system to combine task-based and chatbot-style dialogue in a multimodal system for Human-Robot Interaction. We show that Reinforcement Learning is beneficial for training dialogue management (DM) in such systems -- providing a scalable method for training from data and/or simulated users. We first train in simulation, and evaluate the benefits of a combined chat/task policy over systems which can only perform chat or task-based conversation. In a real user evaluation, we then show that a trained combined chat/task multimodal dialogue policy results in longer dialogue interactions than a rule-based approach, suggesting that the learned dialogue policy provides a more engaging mixture of chat and task interaction than a rule-based DM method.


title: Data Mining Techniques in Fraud Detection

abstract: The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naive Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models.


title: Simulation in an SDN network scenario using the POX Controller

abstract: Computer networks have evolved due to new trends in the society's needs since their emergence as a way of providing remote access and sharing of computational resources. The architecture inflexibility of the computer networks presents a challenge for researchers, since their experiments can hardly be evaluated in real networks. Thus, in general, tests of new technologies are conducted on network simulators, which imply in a streamline of the reality. The paradigm of Software Defined Networks (SDN) and OpenFlow architecture, offer a way for the implementation of a programmable network architecture, able to be implemented gradually in production networks, which offers the possibility of separating the control mechanisms of the many traffic flows served, so that a scientific experiment can be performed in a real network (adapted for SDN) without interfering with its operation. This paper contextualizes the existing problems in current computer networks, and presents the SDN network as one of the main proposals for the viability of the Internet of the Future. In this context, it is discussed the OpenFlow architecture, which allows the creation of applications for Software Defined Networks. Finally it is presented the network simulator SDN, the Mininet, which implements the OpenFlow interface in a network simulation scenario containing a controller POX with two components, one OpenFlow switch and three nodes. The main objective was to evaluate the communication and bandwidth between nodes.


title: Psychometric Properties of the Persian Version of Spiritual Well-Being Scale in Patients with Acute Myocardial Infarction

abstract: The purpose of this study was to assess the psychometric properties of the Persian version of Spiritual Well-Being Scale (SWBS) in patients with acute myocardial infarction. A multisite, cross-sectional survey was employed to determine the instrument’s reliability (Cronbach’s α and construct reliability) and validity (face, content, and construct). Using systematic sampling of adult outpatients at primary care clinic sites in the Qazvin City, Iran (N = 300), it was found that the Cronbach’s alpha and construct reliability of both factors associated with the SWBS were above 0.7. The construct validity of the scale was determined using exploratory factor analysis. The findings supported two factors: relation with God and relation with life. Further investigation through confirmatory factor analysis (eigenvalues of greater than one) confirmed a third factor construct associated with the SWBS. A total of 50.65 % of the variance were explained by these three factors. The overall findings of the study demonstrated that the SWBS is a valid and reliable instrument that has potential utility in future research and clinical practice settings.


title: Dyadic transfer learning for cross-domain image classification

abstract: Because manual image annotation is both expensive and labor intensive, in practice we often do not have sufficient labeled images to train an effective classifier for the new image classification tasks. Although multiple labeled image data sets are publicly available for a number of computer vision tasks, a simple mixture of them cannot achieve good performance due to the heterogeneous properties and structures between different data sets. In this paper, we propose a novel nonnegative matrix tri-factorization based transfer learning framework, called as Dyadic Knowledge Transfer (DKT) approach, to transfer cross-domain image knowledge for the new computer vision tasks, such as classifications. An efficient iterative algorithm to solve the proposed optimization problem is introduced. We perform the proposed approach on two benchmark image data sets to simulate the real world cross-domain image classification tasks. Promising experimental results demonstrate the effectiveness of the proposed approach.


title: Bone engineering in dog mandible: Coculturing mesenchymal stem cells with endothelial progenitor cells in a composite scaffold containing vascular endothelial growth factor

abstract: We sought to assess the effects of coculturing mesenchymal stem cells (MSCs) and endothelial progenitor cells (EPCs) in the repair of dog mandible bone defects. The cells were delivered in β-tricalcium phosphate scaffolds coated with poly lactic co-glycolic acid microspheres that gradually release vascular endothelial growth factor (VEGF). The complete scaffold and five partial scaffolds were implanted in bilateral mandibular body defects in eight beagles. The scaffolds were examined histologically and morphometrically 8 weeks after implantation. Histologic staining of the decalcified scaffolds demonstrated that bone formation was greatest in the VEGF/MSC scaffold (63.42 ± 1.67), followed by the VEGF/MSC/EPC (47.8 ± 1.87) and MSC/EPC (45.21 ± 1.6) scaffolds, the MSC scaffold (34.59 ± 1.49), the VEGF scaffold (20.03 ± 1.29), and the untreated scaffold (7.24 ± 0.08). Hence, the rate of new bone regeneration was highest in scaffolds containing MSC, either mixed with EPC or incorporating VEGF. Adding both EPC and VEGF with the MSC was not necessary. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 1767-1777, 2017.


title: SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews

abstract: Many location based services, such as FourSquare, Yelp, TripAdvisor, Google Places, etc., allow users to compose reviews or tips on points of interest (POIs), each having a geographical coordinates. These services have accumulated a large amount of such geo-tagged review data, which allows deep analysis of user preferences in POIs. This paper studies two types of user preferences to POIs: topical-region preference and category aware topical-aspect preference. We propose a unified probabilistic model to capture these two preferences simultaneously. In addition, our model is capable of capturing the interaction of different factors, including topical aspect, sentiment, and spatial information. The model can be used in a number of applications, such as POI recommendation and user recommendation, among others. In addition, the model enables us to investigate whether people like an aspect of a POI or whether people like a topical aspect of some type of POIs (e.g., bars) in a region, which offer explanation for recommendations. Experiments on real world datasets show that the model achieves significant improvement in POI recommendation and user recommendation in comparison to the state-of-the-art methods. We also propose an efficient online recommendation algorithm based on our model, which saves up to 90% computation time.


title: Effect of Brief Mindfulness Intervention on Tolerance and Distress of Pain Induced by Cold-Pressor Task

abstract: A number of studies have demonstrated that short-term meditation intervention can lead to greater tolerance and lower pain or distress ratings of experimentally induced pain. However, few attempts have been made to examine the effects of short-term mindfulness-based intervention on the tolerance and distress of pain, when delivered in a therapist-free form. The present research explored the effect of brief mindfulness intervention using pre-recorded instruction on pain experimentally induced by the cold-pressor task. The effects of the mindfulness strategy, the distraction strategy and spontaneous strategy, all through the instructions of pre-recorded voices, were compared. The subjects were drawn from healthy college students and randomly assigned to the aforementioned three groups. Our results showed that compared with using spontaneous strategies, the mindfulness intervention significantly improved the participants' pain tolerance and reduced their immersion distress. The distraction strategy also significantly improved the participants' pain tolerance. However, it did not have a significant effect on the participants' level of distress during the immersion period. Our results suggest that brief mindfulness intervention without a therapist's personal involvement is capable of helping people cope with pain induced by the cold-pressor task. Copyright © 2012 John Wiley & Sons, Ltd.


title: A spectro-temporal modulation index (STMI) for assessment of speech intelligibility

abstract: Abstract We present a biologically motivated method for assessing the intelligibility of speech recorded or transmitted under various types of distortions. The method employs an auditory model to analyze the effects of noise, reverberations, and other distortions on the joint spectro-temporal modulations present in speech, and on the ability of a channel to transmit these modulations. The effects are summarized by a spectro-temporal modulation index (STMI). The index is validated by comparing its predictions to those of the classical STI and to error rates reported by human subjects listening to speech contaminated with combined noise and reverberation. We further demonstrate that the STMI can handle difficult and nonlinear distortions such as phase-jitter and shifts, to which the STI is not sensitive.


Granular Computing on Binary Relations ii: Rough set representations and belief functions


title: Training with Confusion for Fine-Grained Visual Classification

abstract: Research in Fine-Grained Visual Classification has focused on tackling the variations in pose, lighting, and viewpoint using sophisticated localization and segmentation techniques, and the usage of robust texture features to improve performance. In this work, we look at the fundamental optimization of neural network training for fine-grained classification tasks with minimal inter-class variance, and attempt to learn features with increased generalization to prevent overfitting. We introduce Training-with-Confusion, an optimization procedure for fine-grained classification tasks that regularizes training by introducing confusion in activations. Our method can be generalized to any fine-tuning task; it is robust to the presence of small training sets and label noise; and adds no overhead to the prediction time. We find that Training-with-Confusion improves the state-of-the-art on all major fine-grained classification datasets.


title: AMIFS: adaptive feature selection by using mutual information

abstract: An adaptive feature selection method based on mutual information, called AMIFS, is presented. AMIFS is an enhancement over Battiti's MIFS and MIFS-U methods. In AMIFS the tradeoff between eliminating irrelevance or redundancy is controlled adoptively, instead of using a fixed parameter. The mutual information is computed by discrete probabilities in the case of discrete features or by using an extended version of Fraser's algorithm in the case of continuous features. The performance of AMIFS is compared with that of MIFS and MIFS-U on artificial and benchmark datasets. The simulation results show that AMIFS outperforms both MIFS and MIFS-U, specially for high-dimensional data with many irrelevant and/or redundant features.


title: A simple method for quantitative calcium imaging in unperturbed developing neurons

abstract: Calcium imaging has been widely used to address questions of neuronal function and development. To gain deeper insights into the actions of calcium as a second messenger, but also to measure synaptic function, it is necessary to quantify the level of calcium at rest and during calcium transients. While quantification of calcium levels is straightforward when using ratiometric calcium indicators, these dyes have several draw-backs due to their short wavelength excitation spectra, such as light scattering and cytotoxicity. In contrast, many single-wavelength indicators exhibit superior photostability, low phototoxicity, extended dynamic ranges and very high signal to noise ratios. However, quantifying calcium levels in unperturbed neurons has not been performed with these indicators. Here, we explore a new approach for determining the calcium concentration at rest as well as calcium rises during evoked and spontaneous neuronal activity in unperturbed developing neurons using a single-wavelength calcium indicator. We show that measuring the maximal fluorescence at the end of an imaging experiment allows determining calcium levels with high resolution. Specifically, we assessed the limits of calcium measurements with a CCD camera in small neuronal processes and found that even in small diameter dendrites and spines the intracellular calcium concentration and its changes can be estimated accurately. This approach may not only allow mapping patterns of neuronal activity quantitatively with the resolution of single synapses and a few tens of milliseconds, but also facilitate investigating the role of calcium as a second messenger.


title: Multimodal Relational Tensor Network for Sentiment and Emotion Classification

abstract: Understanding Affect from video segments has brought researchers from the language, audio and video domains together. Most of the current multimodal research in this area deals with various techniques to fuse the modalities, and mostly treat the segments of a video independently. Motivated by the work of (Zadeh et al., 2017) and (Poria et al., 2017), we present our architecture, Relational Tensor Network, where we use the inter-modal interactions within a segment (intra-segment) and also consider the sequence of segments in a video to model the inter-segment inter-modal interactions. We also generate rich representations of text and audio modalities by leveraging richer audio and linguistic context alongwith fusing fine-grained knowledge based polarity scores from text. We present the results of our model on CMU-MOSEI dataset and show that our model outperforms many baselines and state of the art methods for sentiment classification and emotion recognition.


title: Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing

abstract: Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. Aiming at provisioning flexible on-demand mobile-edge cloud service, in this paper we propose a comprehensive framework consisting of a resource-efficient computation offloading mechanism for users and a joint communication and computation (JCC) resource allocation mechanism for network operator. Specifically, we first study the resource-efficient computation offloading problem for a user, in order to reduce user's resource occupation by determining its optimal communication and computation resource profile with minimum resource occupation and meanwhile satisfying the QoS constraint. We then tackle the critical problem of user admission control for JCC resource allocation, in order to properly select the set of users for resource demand satisfaction. We show the admission control problem is NP-hard, and hence develop an efficient approximation solution of a low complexity by carefully designing the user ranking criteria and rigourously derive its performance guarantee. To prevent the manipulation that some users may untruthfully report their valuations in acquiring mobile-edge cloud service, we further resort to the powerful tool of critical value approach to design truthful pricing scheme for JCC resource allocation. Extensive performance evaluation demonstrates that the proposed schemes can achieve superior performance for on-demand mobile-edge cloud computing.


title: Max-SINR ISI/ICI-Shaping Multicarrier Communication Over the Doubly Dispersive Channel

abstract: For communication over doubly dispersive channels, we consider the design of multicarrier modulation (MCM) schemes based on time-frequency shifts of prototype pulses. We consider the case where the receiver knows the channel state and the transmitter knows the channel statistics (e.g., delay spread and Doppler spread) but not the channel state. Previous work has examined MCM pulses designed for suppression of inter-symbol/inter-carrier interference (ISI/ICI) subject to orthogonal or biorthogonal constraints. In doubly dispersive channels, however, complete suppression of ISI/ICI is impossible, and the ISI/ICI pattern generated by these (bi)orthogonal schemes can be difficult to equalize, especially when operating at high bandwidth efficiency. We propose a different approach to MCM pulse design, whereby a limited expanse of ISI/ICI is tolerated in modulation/demodulation and treated near-optimally by a downstream equalizer. Specifically, we propose MCM pulse designs that maximize a signal-to-interference-plus-noise ratio (SINR) which suppresses ISI/ICI outside a target pattern. In addition, we propose two low-complexity turbo equalizers, based on minimum mean-squared error and maximum likelihood criteria, respectively, that leverage the structure of the target ISI/ICI pattern. The resulting system exhibits an excellent combination of low complexity, low bit-error rate, and high spectral efficiency.


title: Error and uncertainty in modeling and simulation

abstract: Abstract This article develops a general framework for identifying error and uncertainty in computational simulations that deal with the numerical solution of a set of partial differential equations (PDEs). A comprehensive, new view of the general phases of modeling and simulation is proposed, consisting of the following phases: conceptual modeling of the physical system, mathematical modeling of the conceptual model, discretization and algorithm selection for the mathematical model, computer programming of the discrete model, numerical solution of the computer program model, and representation of the numerical solution. Our view incorporates the modeling and simulation phases that are recognized in the systems engineering and operations research communities, but it adds phases that are specific to the numerical solution of PDEs. In each of these phases, general sources of uncertainty, both aleatory and epistemic, and error are identified. Our general framework is applicable to any numerical discretization procedure for solving ODEs or PDEs. To demonstrate this framework, we describe a system-level example: the flight of an unguided, rocket-boosted, aircraft-launched missile. This example is discussed in detail at each of the six phases of modeling and simulation. Two alternative models of the flight dynamics are considered, along with aleatory uncertainty of the initial mass of the missile and epistemic uncertainty in the thrust of the rocket motor. We also investigate the interaction of modeling uncertainties and numerical integration error in the solution of the ordinary differential equations for the flight dynamics.


Staple : Complementary Learners for Real-Time Tracking Supplementary material


title: Iot based intelligent poultry management system using Linux embedded system

abstract: This work aims to provide details on how to build an automated Environment Controlled Poultry Management System (ECPMS) using low cost commodity hardware and open source software. An all-inclusive system was developed using RaspberryPi-3, used as a Linux embedded processing unit and ArduinoMega board for interfacing with array of sensors. The system has been systematically investigated for different physical parameters associated with effective poultry management which includes temperature, humidity, light intensity and air quality. It was found that the system not only monitors these parameters, but also regulates these parameters effectively. The framework was observed to be very useful for farmers as they could easily access and control the system remotely using their handheld mobile devices. The system reduces human intervention, saves time, optimizes resource utilization and increases poultry production.


title: A Fast-Switching Frequency Synthesizer for UWB Applications

abstract: A fast-switching frequency synthesizer is presented for ultra-wideband MB-OFDM applications. This synthesizer generates three frequency tones with 528 MHz spacing. By using single-sideband mixers with Q-enhancement bandpass filters and switched-cascode multiplexers, the sideband rejection is less than -30dB at least and the frequency switching time is less than 7ns. The chip has been fabricated in 0.18mum CMOS process and the area is 1.32mm2. It consumed 70mW from a 1.8V supply


title: Making {USB} Great Again with {USBFILTER}

abstract: USB provides ubiquitous plug-and-play connectivity for a wide range of devices. However, the complex nature of USB obscures the true functionality of devices from the user, and operating systems blindly trust any physically-attached device. This has led to a number of attacks, ranging from hidden keyboards to network adapters, that rely on the user being unable to identify all of the functions attached to the host. In this paper, we present USBFILTER, which provides the first packet-level access control for USB and can prevent unauthorized interfaces from successfully connecting to the host operating system. USBFILTER can trace individual USB packets back to their respective processes and block unauthorized access to any device. By instrumenting the host’s USB stack between the device drivers and the USB controller, our system is able to filter packets at a granularity that previous works cannot — at the lowest possible level in the operating system. USBFILTER is not only able to block or permit specific device interfaces; it can also restrict interfaces to a particular application (e.g., only Skype can access my webcam). Furthermore, our experimental analysis shows that USBFILTER introduces a negligible (3-10μs) increase in latency while providing mediation of all USB packets on the host. Our system provides a level of granularity and extensibility that reduces the uncertainty of USB connectivity and ensures unauthorized devices are unable to communicate with the host.


title: Technological Frames of Reference in Software Acquisition Decisions: Results of a Multiple Case Study

abstract: Packaged software has gained importance across organizations. While literature has studied the adoption and implementation of packaged software extensively, research on software acquisition has been limited. Especially, scholars have called for more research from a sociological point of view. Software acquisition projects are complex endeavors during which multiple stakeholders and perspectives interact. With this study, we strive to illustrate social interactions in software acquisition decisions through the theoretical lens of technological frames of reference. We conducted a multiple case study with 15 experts from IT, business, and procurement. We find evidence for distinct technological frames across departments that are combined during the software acquisition process, ultimately resulting in common understanding and consensus. Furthermore, we identify eight salient framing effects that facilitate this dynamic alignment of frames. Our results allow for an extension of technological frames of reference theory and support decision makers in optimizing their software acquisition decisions and processes.


title: Feasibility Investigation of Low Cost Substrate Integrated Waveguide (SIW) Directional Couplers

abstract: In this paper, the feasibility of Substrate Integrated Waveguide (SIW) couplers, fabricated using single-layer TACONIC RF-35 dielectric substrate is investigated. The couplers have been produced employing a standard PCB process. The choice of the TACONIC RF-35 substrate as alternative to other conventional materials is motivated by its lower cost and high dielectric constant, allowing the reduction of the device size. The coupler requirements are 90-degree phase shift between the output and the coupled ports and frequency bandwidth from about 10.5GHz to 12.5GHz. The design and optimization of the couplers have been performed by using the software CST Microwave Studio c ∞ . Eight difierent coupler conflgurations have been designed and compared. The better three couplers have been fabricated and characterized. The proposed SIW directional couplers could be integrated within more complex planar circuits or utilized as stand-alone devices, because of their compact size. They exhibit good performance and could be employed in communication applications as broadcast signal distribution and as key elements for the construction of other microwave devices and systems.


title: Space Vector Modulation of Multi-level and Multi-module Converters for High Power Applications

abstract: This thesis presents and investigates Space Vector Modulation (SVM) switching strategies for (i) a multi-level Diode-Clamped Converter (DCC) and (ii) a multi-module Voltage-Sourced Converter (VSC) system in which each module is a conventional two-level VSC. Although the SVM strategies are general and applicable for n-level DCC and n-module VSC systems, this text only concentrates on five-level DCC and four-module VSC systems.For a five-level DCC, a computationally efficient SVM algorithm is proposed. The algorithm, that is based on a classifier Neural Network (NN), reduces the computational time for the SVM realization. Therefore, adequate saving of processor execution time, in each sampling period of SVM, is provided to carry out other functions, e.g. the calculations required for DC-capacitor voltage balancing task. The thesis also proposes a DC-capacitor voltagebalancing strategy to counteract the voltage drift phenomenon of (i) a passive-front-end five-level DCC, and (ii) a back-to-backconnected five-level DCC system. The proposed balancing strategy, that is based on augmenting the proposed SVM algorithm, takes advantage of the redundant switching states to minimize a quadratic cost function associated with voltage deviations of theDC-capacitors. The salient features of the proposed balancing strategy are (i) online calculation of SVM to select the bestswitching states, (ii) minimization of switching frequency, (iii) minimization of the THD content of the AC-side voltage, and (iv) no requirement for additional power circuitry.For a four-module VSC system a sequential sampling SVM strategy is proposed. The proposed strategy (i) provides harmonic cancellation/minimization at the net AC-side voltage of the multi-module VSC system, and (ii) offers a low switching frequencyfor each VSC module.Technical feasibility of the proposed SVM strategies for a five-level DCC and a four-module VSC system, as a STATCOM and aback-to-back HVDC system, are investigated and presented. The studies are conducted in the time-domain, in the PSCAD/EMTDCsoftware environment.


title: A superpixel segmentation algorithm with region correlation saliency analysis for video pedestrian detection

abstract: Most pedestrian detection algorithms only provide the object region instead of the actual body segmentation in video. For reducing the large number of redundant information and extracting a clear contour and texture feature of an up-right person, a superpixel segmentation algorithm with region correlation saliency analysis is proposed from coarse to fine cutting without any prior information. This algorithm cuts single pedestrian target automatically and obtains strict features of human object regardless of whether or not the camera is moving. It exploits saliency analysis to find location and compute region energy in superpixel image to achieve a body segmentation. Experimental results indicate that the proposed approach detects pedestrian efficiently in the complex background environment in both indoor and outdoor videos with precise object cutting boundaries.


title: Affect- and Cognition-Based Trust as Foundations for Interpersonal Cooperation in Organizations

abstract: This study addressed the nature and functioning of relationships of interpersonal trust among managers and professionals in organizations, the factors influencing trust's development, and the implications of trust for behavior and performance. Theoretical foundations were drawn from the sociological literature on trust and the social-psychological literature on trust in close relationships. An initial test of the proposed theoretical framework was conducted in a field setting with 194 managers and professionals.


title: Anycast vs. DDoS: Evaluating the November 2015 Root DNS Event

abstract: Distributed Denial-of-Service (DDoS) attacks continue to be a major threat on the Internet today. DDoS attacks overwhelm target services with requests or other traffic, causing requests from legitimate users to be shut out. A common defense against DDoS is to replicate a service in multiple physical locations/sites. If all sites announce a common prefix, BGP will associate users around the Internet with a nearby site, defining the catchment of that site. Anycast defends against DDoS both by increasing aggregate capacity across many sites, and allowing each site's catchment to contain attack traffic, leaving other sites unaffected. IP anycast is widely used by commercial CDNs and for essential infrastructure such as DNS, but there is little evaluation of anycast under stress. This paper provides the first evaluation of several IP anycast services under stress with public data. Our subject is the Internet's Root Domain Name Service, made up of 13 independently designed services ("letters", 11 with IP anycast) running at more than 500 sites. Many of these services were stressed by sustained traffic at 100× normal load on Nov. 30 and Dec. 1, 2015. We use public data for most of our analysis to examine how different services respond to stress, and identify two policies: sites may absorb attack traffic, containing the damage but reducing service to some users, or they may withdraw routes to shift both good and bad traffic to other sites. We study how these deployment policies resulted in different levels of service to different users during the events. We also show evidence of collateral damage on other services located near the attacks.


title: Gender dysphoria and gender change in androgen insensitivity or micropenis.

abstract: This review article answers three questions relevant to the medical management and care of individuals born with complete androgen insensitivity syndrome (CAIS), partial androgen insensitivity syndrome (PAIS), or a micropenis: (1) Do any of these individuals reassign themselves from their initial gender assignment? (2) Do more reassign than the ones who do not? (3) Is there evidence of gender dysphoria in those who do not self-initiate reassignment? Reviewed were all articles on CAIS, PAIS, and micropenis cited in K. J. Zucker (1999) plus articles published through 2004. There were no documented cases of gender change in individuals with CAIS (N= 156 females) or micropenis (N= 89: 79 males, 10 females). Nine (9.1%) out of 99 individuals with PAIS changed gender. Thus, self-initiated gender reassignment was rare. Gender dysphoria also appears to be a rare occurrence. The best predictor of adult gender identity in CAIS, PAIS, and micropenis is initial gender assignment.


title: Recommendations for Developing Technologies that Encourage Reading Practices Among Children in Families with Low-literate Adults

abstract: Learning to read is a holistic process that requires social connections and individual resources. When a child lacks the necessary tools for literacy development, his or her education suffers as a result. This paper investigates the extent of how low-literacy impacts reading behaviors in adults and children who read together from an interaction design and research perspective. The value of exposure to reading early and often is explored with regards to how a novel technology including digital interaction has the potential to increase interest and spur reading behavior change. We observed children and their low-literate, adult caregivers reading a physical book and an electronic e-book application on a tablet. Then we had the pairings participate in co-design sessions to develop new technologies for developing readers. The findings from the research and subsequent design sessions informed a new design and recommendations for an interactive and immersive reading experience, created with and for low-literate adults and children.


title: A Neural Probabilistic Language Model

abstract: A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.


title: Research on Optimization Design of Topology and Size of the Body Structure for New Energy Vehicle

abstract: In this paper, the author researches on the optimization design of topology and size of the body structure for new energy vehicle. Based on the topology optimization results, the wire frame model of the body-in-white is established to optimize its size, so that it can satisfy the bending stiffness and torsional stiffness and verify it. The results of topology optimization examples show that the topology optimization method based on variable density method can obtain the optimal body-in-white structure of a new energy vehicle without defining the domain. It can satisfy the bending and torsional stiffness requirements of the body structure and lighten the body weight at the same time. The experiment result shows the proposed method can improve the overall performance for the system.


From IT Addiction to Discontinued Use: A Cognitive Dissonance Perspective


title: Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method

abstract: This research work exposes the novel approach of analysis at existing works based on machine vision system for the identification of the visual symptoms of Cotton crop diseases, from RGB images. Diseases regions of cotton crops are revealed in digital pictures, Which were amended and segmented. In this work Proposed Enhanced PSO feature selection method adopts Skew divergence method and user features like Edge, Color, Texture variances to extract the features. Set of features was extracted from each of them. The extracted feature was input to the SVM, Back propagation neural network (BPN), Fuzzy with Edge CYMK color feature and GA feature selection. Tests were performed to identify the best classification model. It has been hypothesized that from the given characteristics of the images, there should be a subset of features more informative of the image domain. To test this hypothesis, three classification models were assessed via cross validation. To Evaluate its efficiency of six types of diseases have been accurately classified like Bacterial Blight, Fusariumwilt, Leaf Blight, Root rot, Micro Nurient, Verticilium Wilt. The Experimental results obtained show that the robust feature vector set which is an Enhancement of a feature extraction method (EPSO) has been afforded the performance assessment of this system.


title: Wide-band compact antenna feed for multi-beam satellite communications

abstract: This paper presents the design of a wide-band compact antenna feed for dual-band single circular polarization. The feed chain system is based on a recently proposed configuration that uses a diplexer and a bi-phase polarizer conveniently joined by a single mode waveguide. This solution leads to compact low-cost feed designs well suited for multi-beam antennas with a single feed per beam in the context of satellite communications. A new design with enhanced performance is presented with simulated results of axial ratio below 0.2 dB across 18.2–20.2 GHz and 28–30 GHz. Moreover, the impact of the finite return loss of the high efficiency horn is studied and a compensated high-performance feed is presented.


title: Multi-Scale Convolutional Neural Networks for Time Series Classification

abstract: Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls short of classification accuracy and efficiency. Traditional approaches typically involve extracting discriminative features from the original time series using dynamic time warping (DTW) or shapelet transformation, based on which an off-the-shelf classifier can be applied. These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance. Plus, most existing methods fail to take into account the fact that time series often have features at different time scales. To address these problems, we propose a novel end-to-end neural network model, Multi-Scale Convolutional Neural Networks (MCNN), which incorporates feature extraction and classification in a single framework. Leveraging a novel multi-branch layer and learnable convolutional layers, MCNN automatically extracts features at different scales and frequencies, leading to superior feature representation. MCNN is also computationally efficient, as it naturally leverages GPU computing. We conduct comprehensive empirical evaluation with various existing methods on a large number of benchmark datasets, and show that MCNN advances the state-of-the-art by achieving superior accuracy performance than other leading methods.


An End to End Model for Automatic Music Generation: Combining Deep Raw and Symbolic Audio Networks


title: A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra

abstract: We present a new pivot-based algorithm which can be used with minor modification for the enumeration of the facets of the convex hull of a set of points, or for the enumeration of the vertices of an arrangement or of a convex polyhedron, in arbitrary dimension. The algorithm has the following properties:(a)Virtually no additional storage is required beyond the input data.(b)The output list produced is free of duplicates.(c)The algorithm is extremely simple, requires no data structures, and handles all degenerate cases.(d)The running time is output sensitive for nondegenerate inputs.(e)The algorithm is easy to parallelize efficiently. ::: ::: For example, the algorithm finds thev vertices of a polyhedron inRd defined by a nondegenerate system ofn inequalities (or, dually, thev facets of the convex hull ofn points inRd, where each facet contains exactlyd given points) in timeO(ndv) andO(nd) space. Thev vertices in a simple arrangement ofn hyperplanes inRd can be found inO(n2dv) time andO(nd) space complexity. The algorithm is based on inverting finite pivot algorithms for linear programming.


Why Do Larks Perform Better At School Than Owls? The Mediating Effect Of Conscientiousness


Network Ingress Filtering: Defeating Denial of Service Attacks which employ IP Source Address Spoofing


title: Multitaper MFCC Features for Acoustic Stress Recognition from Speech

abstract: Ameliorating the performances of speech recognition ::: system is a challenging problem interesting recent researchers. ::: In this paper, we compare two extraction methods of ::: Mel Frequency Cepstral Coefficients used to represent stressed ::: speech utterances in order to obtain best performances. The ::: first method known as traditional is based on single window ::: (taper) generally the Hamming window and the second one ::: is a novel technique developed with multitapers instead of a ::: single taper. The extracted features are then classified using the ::: multiclass Support Vector Machines. Experimental results on the ::: SUSAS database have shown that the multitaper MFCC features ::: outperform the conventional MFCCs.


title: An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

abstract: In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.


title: A multi-objective auto-tuning framework for parallel codes

abstract: In this paper we introduce a multi-objective auto-tuning framework comprising compiler and runtime components. Focusing on individual code regions, our compiler uses a novel search technique to compute a set of optimal solutions, which are encoded into a multi-versioned executable. This enables the runtime system to choose specifically tuned code versions when dynamically adjusting to changing circumstances. We demonstrate our method by tuning loop tiling in cache-sensitive parallel programs, optimizing for both runtime and efficiency. Our static optimizer finds solutions matching or surpassing those determined by exhaustively sampling the search space on a regular grid, while using less than 4% of the computational effort on average. Additionally, we show that parallelism-aware multi-versioning approaches like our own gain a performance improvement of up to 70% over solutions tuned for only one specific number of threads.


title: Comparative effectiveness of adjunctive bevacizumab for advanced lung cancer: the cancer research network experience.

abstract: Introduction: Bevacizumab plus carboplatin-paclitaxel (BCP) chemotherapy has Food and Drug Administration approval for advanced nonsquamous, non–small-cell lung cancer based upon improved survival in a clinical trial. However, subgroup analyses of this and other studies have suggested variable results by age and gender. Methods: Using data from four health maintenance organizations (HMOs) belonging to the Cancer Research Network, 1605 HMO nonsquamous, non-small-cell lung cancer patients aged younger than 21 years, diagnosed 2002-2010, who received carboplatin-paclitaxel (CP), with and without bevacizumab for first-line treatment of stage IIIB/IV disease were identified. Patients were categorized into three groups based on year of diagnosis and regimen during 120 days postdiagnosis: (1) diagnosed 2005–2010 and received BCP; (2) 2005–2010, CP (CP2005), and (3) 2002–2004, CP (CP2002). Survival differences between groups were estimated using Cox proportional hazard models with several propensity score adjustments for demographic, comorbidity, and tumor characteristics. Multivariable subanalyses were also estimated. Results: Median survival was 12.3 months (interquartile range [IQR], 6.0–29.1) for BCP patients versus 8.8 months (IQR, 3.7–21.3) for CP2005 patients and 7.5 months (IQR, 3.8–15.6) for CP2002 patients. In the propensity score–adjusted models, BCP demonstrated a significant survival benefit with a hazard ratio of BCP relative to CP2005 and CP2002 patients of 0.79 (95% confidence interval [CI], 0.66–0.94) and 0.63 (95% CI, 0.52–0.75), respectively. In the multivariable-adjusted subanalyses, relative to the CP2005 cohort, the BCP hazard ratios for patients age less than 65 years, age 65 years old or older, and females were 0.78 (95% CI, 0.62–1.00), 0.74 (95% CI, 0.54–1.00), and 0.77 (95% CI, 0.58–1.00). Conclusions: In this community-based, comparative effectiveness analysis, we found an overall survival benefit for adults receiving BCP compared with CP.


title: 2010 A survey of Named Entity Recognition in English and other Indian Languages

abstract: In this paper, a survey is done on various approaches used to recognize name entity in various Indian languages. Firstly, the introduction is given about the work done in the NER task. Then a survey is given about the work done in recognition of name entities in English and other foreign languages like Spanish, Chinese etc. In English language, lots of work has been done in this field, where capitalization is a major clue for making rules. Secondly, a survey is given regarding the work done in Indian Languages. As Punjabi is one of the Indian languages and also the official language of Punjab. In next part, survey is given on Punjabi Language regarding what work is done and what work is


title: Design of a laser navigation system for substation inspection robot

abstract: Aimed at the deficiency of the magnetic guide and RFID parking system used by substation inspection robot now, a laser navigation method is introduced, and a prototype laser navigation system is designed based on NAV200. The whole structure of the navigation system is presented; the data processing and navigation control algorithm are also described in detail. In order to verify the repetitive precision of the system, an experimental test has been carried out in a 500KV outdoor substation. Experimental results show that the laser navigation system works well, and the repetitive precision of navigation control is precise enough to help the robot fulfill inspection tasks.


title: Contextual Bandits in a Collaborative Environment

abstract: Contextual bandit algorithms provide principled online learning solutions to find optimal trade-offs between exploration and exploitation with companion side-information. They have been extensively used in many important practical scenarios, such as display advertising and content recommendation. A common practice estimates the unknown bandit parameters pertaining to each user independently. This unfortunately ignores dependency among users and thus leads to suboptimal solutions, especially for the applications that have strong social components. In this paper, we develop a collaborative contextual bandit algorithm, in which the adjacency graph among users is leveraged to share context and payoffs among neighboring users while online updating. We rigorously prove an improved upper regret bound of the proposed collaborative bandit algorithm comparing to conventional independent bandit algorithms. Extensive experiments on both synthetic and three large-scale real-world datasets verified the improvement of our proposed algorithm against several state-of-the-art contextual bandit algorithms.


title: Heterosexuals' Attitudes Toward Transgender People: Findings from a National Probability Sample of U.S. Adults

abstract: Using data from a national probability sample of heterosexual U.S. adults (N = 2,281), the present study describes the distribution and correlates of men’s and women’s attitudes toward transgender people. Feeling thermometer ratings of transgender people were strongly correlated with attitudes toward gay men, lesbians, and bisexuals, but were significantly less favorable. Attitudes toward transgender people were more negative among heterosexual men than women. Negative attitudes were associated with endorsement of a binary conception of gender; higher levels of psychological authoritarianism, political conservatism, and anti-egalitarianism, and (for women) religiosity; and lack of personal contact with sexual minorities. In regression analysis, sexual prejudice accounted for much of the variance in transgender attitudes, but respondent gender, educational level, authoritarianism, anti-egalitarianism, and (for women) religiosity remained significant predictors with sexual prejudice statistically controlled. Implications and directions for future research on attitudes toward transgender people are discussed.


title: Integrated Control Strategy for Grid Connected Photovoltaic Array, Battery Storage and Supercapacitors

abstract: Upon various renewable based power generation, Solar Photovoltaic (PV) technology is continually developing nowadays. However, the major drawback of this technology is the intermittent issue due to the rapid fluctuations in solar insolation level. Energy storage systems such as batteries and super capacitor banks are used to reduce the above problem. There is a requirement of an integrated controller strategy in order to achieve a power generation unit which includes power share among those energy storage units in between with solar PV array. Using a hybrid system of a battery bank and a super-capacitor bank over using a battery bank alone, helps to increase the lifetime of the batteries. Integrated control strategy is the common method used to control the solar PV array with energy storage units in order to supply the required islanded demand and supply excess energy to the grid while maintaining the DC-link stability through the synchronized control of DC-DC power electronic converters. This paper presents an integrated control strategy for grid connected PV array together with a battery bank and a super-capacitor bank. This further state mathematical proof for the sizing of storage devices. Performance of the integrated control strategy was evaluated using a practical experiment by developing customized DC-DC power electronic converters.


title: Design for new type of main insulation of 35kV electric power transformer based on electric field analysis

abstract: The reason of main insulation structure breakdown based on liquid and solid discharge theory is analyzed. The new design schemes of main insulation structure for 35 kV electric power transformer are presented based on electric field analysis. FEM is introduced to calculate the electric filed of new type of main insulation structure. The electric field distribution, maximum and position of electric field intensity are shown. A way of placing angle ring above high voltage is used to improve the electric field distribution. The electric field distribution for new structure is more uniform than the old main insulation structure.


title: An Anchoring and Adjustment Model of Purchase Quantity Decisions

abstract: How do consumers decide how many units to buy? Whereas prior research on individual consumers' purchases has focused primarily on purchase incidence and brand choice, the authors focus on the psychological process behind the purchase quantity decision. The authors propose that a simple anchoring and adjustment model describes how consumers make purchase quantity decisions and suggests how point-of-purchase promotions can increase sales. Two field experiments and two lab studies show that anchor-based promotions - presented as multiple-unit prices, purchase quantity limits, and suggestive selling - can increase purchase quantities. The final study shows that consumers who retrieve internal anchors can counter these anchor-based promotions depending on whether increases in unit sales reflect increased category consumption, brand switching, variety switching, store switching, or stockpiling.


title: Photometric Stereo Using Internet Images

abstract: Photometric stereo using unorganized Internet images is very challenging, because the input images are captured under unknown general illuminations, with uncontrolled cameras. We propose to solve this difficult problem by a simple yet effective approach that makes use of a coarse shape prior. The shape prior is obtained from multi-view stereo and will be useful in twofold: resolving the shape-light ambiguity in uncalibrated photometric stereo and guiding the estimated normals to produce the high quality 3D surface. By assuming the surface albedo is not highly contrasted, we also propose a novel linear approximation of the nonlinear camera responses with our normal estimation algorithm. We evaluate our method using synthetic data and demonstrate the surface improvement on real data over multi-view stereo results.


Training Multilayer Perceptrons with the Extende Kalman Algorithm


title: Topological Data Analysis of Biological Aggregation Models

abstract: We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.


Impact of Aquatic Exercise Program on Muscle Tone in Spastic Hemiplegic Children with Cerebral Palsy


title: Emotion Recognition in Spontaneous Speech Using GMMs

abstract: Automatic detection of emotions has been evaluated using standard Mel-frequency Cepstral Coefficients, MFCCs, and a variant, MFCC-low, calculated between 20 and 300 Hz, in order to model pitch. Also plain pitch features have been used. These acoustic features have all been modeled by Gaussian mixture models, GMMs, on the frame level. The method has been tested on two different corpora and languages; Swedish voice controlled telephone services and English meetings. The results indicate that using GMMs on the frame level is a feasible technique for emotion classification. The two MFCC methods have similar performance, and MFCC-low outperforms the pitch features. Combining the three classifiers significantly improves performance.