title: Automated Scenario Generation - Coupling Planning Techniques with Smart Objects

abstract: Serious games allow for adaptive and personalised forms of training; the nature and timing of learning activities can be tailored to the trainee’s needs and interests. Autonomous game-based training requires for the automatic selection of appropriate exercises for an individual trainee. This paper presents a framework for an automated scenario generation system. The underlying notion is that a learning experience is defined by the objects and agents that inhabit the training environment. Our system uses automated planning to assess the behaviour required to achieve the (personalised) training objective. It then generates a scenario by selecting semantically annotated (or 'smart') objects and by assigning goals to the virtual characters. The resulting situations trigger the trainee to execute the desired behaviour. To test the framework, a prototype has been developed to train the First Aid treatment of burns. Experienced instructors evaluated scenarios written by three types of authors: the prototype, first-aid experts, and laymen. The prototype produced scenarios that were at least as good as laymen scenarios. First-aid experts seemed the best scenario writers, although differences were not significant. It is concluded that combining automated planning, smart objects, and virtual agent behaviour, is a promising approach to automated scenario generation.


title: Abstract rendering: out-of-core rendering for information visualization

abstract: As visualization is applied to larger data sets residing in more diverse hardware environments, visualization frameworks need to adapt. Rendering techniques are currently a major limiter since they tend to be built around central processing with all of the geometric data present. This is not a fundamental requirement of information visualization. This paper presents Abstract Rendering (AR), a technique for eliminating the centralization requirement while preserving some forms of interactivity. AR is based on the observation that pixels are fundamentally bins, and that rendering is essentially a binning process on a lattice of bins. By providing a more flexible binning process, the majority of rendering can be done with the geometric information stored out-of-core. Only the bin representations need to reside in memory. This approach enables: (1) rendering on large datasets without requiring large amounts of working memory, (2) novel and useful control over image composition, (3) a direct means of distributing the rendering task across processes, and (4) high-performance interaction techniques on large datasets. This paper introduces AR in a theoretical context, provides an overview of an implementation, and discusses how it has been applied to large-scale data visualization problems.


title: Facial expression recognition from near-infrared video sequences

abstract: Facial expressions can be thought as specific dynamic textures where local appearance and motion information need to be taken into account. We utilize local spatiotemporal operators to describe facial expressions. All current facial expression recognition databases are captured in visible light spectrum. Visual light usually changes with locations, and can also vary with time, which can cause significant variations in image appearance and texture. In this paper, we present a novel research on a dynamic facial expression recognition from near-infrared (NIR) video sequences. NIR imaging is robust with respect to illumination changes. Experiments on a new NIR database show promising and robust results against illumination variations.


title: Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks

abstract: Binary classification is typically achieved by supervised learning methods. Nevertheless, it is also possible using unsupervised schemes. This paper describes a connectionist unsupervised approach to binary classification and compares its performance to that of its supervised counterpart. The approach consists of training an autoassociator to reconstruct the positive class of a domain at the output layer. After training, the autoassociator is used for classification, relying on the idea that if the network generalizes to a novel instance, then this instance must be positive, but that if generalization fails, then the instance must be negative. When tested on three real-world domains, the autoassociator proved more accurate at classification than its supervised counterpart, MLP, on two of these domains and as accurate on the third (Japkowicz, Myers, & Gluck, 1995). The paper seeks to generalize these results and concludes that, in addition to learning a concept in the absence of negative examples, 1) autoassociation is more efficient than MLP in multi-modal domains, and 2) it is more accurate than MLP in multi-modal domains for which the negative class creates a particularly strong need for specialization or the positive class creates a particularly weak need for specialization. In multi-modal domains for which the positive class creates a particularly strong need for specialization, on the other hand, MLP is more accurate than autoassociation.


title: The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation

abstract: Deep reinforcement learning (DRL) demonstrates its potential in learning a model-free navigation policy for robot visual navigation. However, the data-demanding algorithm relies on a large number of navigation trajectories in training. Existing datasets supporting training such robot navigation algorithms consist of either 3D synthetic scenes or reconstructed scenes. Synthetic data suffers from domain gap to the real-world scenes while visual inputs rendered from 3D reconstructed scenes have undesired holes and artifacts. In this paper, we present a new dataset collected in real-world to facilitate the research in DRL based visual navigation. Our dataset includes 3D reconstruction for real-world scenes as well as densely captured real 2D images from the scenes. It provides high-quality visual inputs with real-world scene complexity to the robot at dense grid locations. We further study and benchmark one recent DRL based navigation algorithm and present our attempts and thoughts on improving its generalizability to unseen test targets in the scenes.


title: Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV

abstract: Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.


title: Understanding agile project management methods using Scrum

abstract: Purpose – This paper seeks to define and describe agile project management using the Scrum methodology as a method for more effectively managing and completing projects.Design/methodology/approach – This paper provides a general overview and introduction to the concepts of agile project management and the Scrum methodology in particular.Findings – Agile project management using the Scrum methodology allows project teams to manage digital library projects more effectively by decreasing the amount of overhead dedicated to managing the project. Using an iterative process of continuous review and short‐design time frames, the project team is better able to quickly adapt projects to rapidly evolving environments in which systems will be used.Originality/value – This paper fills a gap in the digital library project management literature by providing an overview of agile project management methods.


title: Construction of neural nets using the radon transform

abstract: The authors present a method for constructing a feedforward neural net implementing an arbitrarily good approximation to any L/sub 2/ function over (-1, 1)/sup n/. The net uses n input nodes, a single hidden layer whose width is determined by the function to be implemented and the allowable mean square error, and a linear output neuron. Error bounds and an example are given for the method. >


title: Advertising Keyword Recommendation based on Supervised Link Prediction in Multi-Relational Network

abstract: In the sponsored search system, advertisers bid on keywords that are related to their products or services to participate in repeated auctions and display their ads on the search result pages. Since there is a huge inventory of possible terms, instructive keyword recommendation is an important component to help advertisers optimize their campaigns and improve ad monetization. In this paper, by constructing a heterogeneous network which contains four types of links between advertisers and keywords based on different data resources and mining complex representations of network structure and task-guided attributes of nodes, we propose an approach to keyword recommendation based on supervised link prediction in multi-relational network. This method can retrieve ample candidates and provide informative ranking scores for recommended list of keywords, experimental results with real sponsored search data validate the effectiveness of the proposed algorithm in producing valuable keywords for advertisers.


title: Dynamic Difficulty Adjustment on MOBA Games

abstract: Abstract This paper addresses the dynamic difficulty adjustment on MOBA games as a way to improve the players entertainment. Although MOBA is currently one of the most played genres around the world, it is known as a game that offer less autonomy, more challenges and consequently more frustration. Due to these characteristics, the use of a mechanism that performs the difficulty balance dynamically seems to be an interesting alternative to minimize and/or avoid that players experience such frustrations. In this sense, this paper presents a dynamic difficulty adjustment mechanism for MOBA games. The main idea is to create a computer controlled opponent that adapts dynamically to the player performance, trying to offer to the player a better game experience. This is done by evaluating the performance of the player using a metric based on some game features and switching the difficulty of the opponent’s artificial intelligence behavior accordingly. Quantitative and qualitative experiments were performed and the results showed that the system is capable of adapting dynamically to the opponent’s skills. In spite of that, the qualitative experiments with users showed that the player’s expertise has a greater influence on the perception of the difficulty level and dynamic adaptation.


title: Cloud based automated irrigation and plant leaf disease detection system using an android application

abstract: Due to drastic climatic changes and scarcity of water, the need for proper and sustainable irrigation methods is of high demand. The water demand for plants varies from place to place with the changes in soil content, texture, climatic factors and so on. The plants need to be irrigated according to their water requirements at that climatic conditions. As like as the water requirements, plant diseases are also a factor that keeps the plants not growing properly. Also, a variety of plant leaf diseases have also been detected nowadays. In this paper, a novel smart irrigation system has been proposed which can control the irrigation automatically using an android mobile application. Apart from this, the photos of plant leaves are captured and are sent to the cloud server, which is further processed and compared with the diseased plant leaf images in the cloud database. Based on the comparison a list of plant diseases suspected are given to the user via the android mobile application.


title: Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning

abstract: This work presents a probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailment: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented as a deterministic Bayesian network. Marginal probabilities are entered as soft evidence in the network and through probabilistic inference become consistent with the discovered knowledge. Our approach offers robust improvements in mean average precision compared to the standard binary relevance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.


title: Image Encryption Using Differential Evolution Approach in Frequency Domain

abstract: This paper presents a new effective method for image encryption which employs magnitude and phase manipulation using Differential Evolution (DE) approach. The novelty of this work lies in deploying the concept of keyed discrete Fourier transform (DFT) followed by DE operations for encryption purpose. To this end, a secret key is shared between both encryption and decryption sides. Firstly two dimensional (2-D) keyed discrete Fourier transform is carried out on the original image to be encrypted. Secondly crossover is performed between two components of the encrypted image, which are selected based on Linear Feedback Shift Register (LFSR) index generator. Similarly, keyed mutation is performed on the real parts of a certain components selected based on LFSR index generator. The LFSR index generator initializes it seed with the shared secret key to ensure the security of the resulting indices. The process shuffles the positions of image pixels. A new image encryption scheme based on the DE approach is developed which is composed with a simple diffusion mechanism. The deciphering process is an invertible process using the same key. The resulting encrypted image is found to be fully distorted, resulting in increasing the robustness of the proposed work. The simulation results validate the proposed image encryption scheme.


title: An enhanced smart card based remote user password authentication scheme

abstract: Smart card based password authentication is one of the simplest and efficient authentication mechanisms to ensure secure communication in insecure network environments. Recently, Chen et al. have pointed out the weaknesses of some password authentication schemes and proposed a robust smart card based remote user password authentication scheme to improve the security. As per their claims, their scheme is efficient and can ensure forward secrecy of the session key. However, we find that Chen et al.'s scheme cannot really ensure forward secrecy, and it cannot detect the wrong password in login phase. Besides, the password change phase of Chen et al.'s scheme is unfriendly and inefficient since the user has to communicate with the server to update his/her password. In this paper, we propose a modified smart card based remote user password authentication scheme to overcome the aforementioned weaknesses. The analysis shows that our proposed scheme is user friendly and more secure than other related schemes.


title: Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

abstract: Incremental learning (il) has received a lot of attention recently, however, the literature lacks a precise problem definition, proper evaluation settings, and metrics tailored specifically for the il problem. One of the main objectives of this work is to fill these gaps so as to provide a common ground for better understanding of il. The main challenge for an il algorithm is to update the classifier whilst preserving existing knowledge. We observe that, in addition to forgetting, a known issue while preserving knowledge, il also suffers from a problem we call intransigence, its inability to update knowledge. We introduce two metrics to quantify forgetting and intransigence that allow us to understand, analyse, and gain better insights into the behaviour of il algorithms. Furthermore, we present RWalk, a generalization of ewc++ (our efficient version of ewc [6]) and Path Integral [25] with a theoretically grounded KL-divergence based perspective. We provide a thorough analysis of various il algorithms on MNIST and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in terms of accuracy, and also provides a better trade-off for forgetting and intransigence.


title: X-Vectors: Robust DNN Embeddings for Speaker Recognition

abstract: In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate between speakers, maps variable-length utterances to fixed-dimensional embeddings that we call x-vectors. Prior studies have found that embeddings leverage large-scale training datasets better than i-vectors. However, it can be challenging to collect substantial quantities of labeled data for training. We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector extractor. However, the x-vector DNN effectively exploits data augmentation, due to its supervised training. As a result, the x-vectors achieve superior performance on the evaluation datasets.


title: Relation of Sympathy and Personal Distress to Prosocial Behavior: A Multimethod Study

abstract: Assessed sympathy and personal distress with facial and physiological indexes (heart rate) as well as self-report indexes and examined the relations of these various indexes to prosocial behavior for children and adults in an easy escape condition. Heart rate deceleration during exposure to the needy others was associated with increased willingness to help. In addition, adults' reports of sympathy, as well as facial sadness and concerned attention, were positively related to their intention to assist. For children, there was some indication that report of positive affect and facial distress were negatively related to prosocial intentions and behavior, whereas facial concern was positively related to the indexes of prosocial behavior. These findings are interpreted as providing additional, convergent support for the notion that sympathy and personal distress are differentially related to prosocial behavior. Over the years, numerous philosophers (e.g., Blum, 1980) and psychologists (e.g., Barnett, 1987; Feshbach, 1978; Hoffman, 1984; Staub, 1978) have argued that empathy and sympathy, denned primarily in affective terms, are important motivators of altruistic behavior. In general, it has been asserted that people who experience emotional reactions consistent with the state of another and who feel other-oriented concern for the other are relatively likely to be motivated to alleviate the other's need or distress.


title: An LLCL Power Filter for Single-Phase Grid-Tied Inverter

abstract: This paper presents a new topology of higher order power filter for grid-tied voltage-source inverters, named the LLCL filter, which inserts a small inductor in the branch loop of the capacitor in the traditional LCL filter to compose a series resonant circuit at the switching frequency. Particularly, it can attenuate the switching-frequency current ripple components much better than an LCL filter, leading to a decrease in the total inductance and volume. Furthermore, by decreasing the inductance of a grid-side inductor, it raises the characteristic resonance frequency, which is beneficial to the inverter system control. The parameter design criteria of the proposed LLCL filter is also introduced. The comparative analysis and discussions regarding the traditional LCL filter and the proposed LLCL filter have been presented and evaluated through experiment on a 1.8-kW-single-phase grid-tied inverter prototype.


title: Design and analysis of an ultra-high efficiency phase shifted full bridge GaN converter

abstract: The paper presents the design and development of an ultra-high efficiency phase shifted full bridge isolated dc-dc converter using Gallium Nitride (GaN) devices. Synchronous rectification along with high efficiency magnetics and use of GaN devices contribute to the improvement in efficiency. In this paper, zero voltage switching is achieved by increasing the magnetizing current in the transformer and hence reduces the switching loss in the converter. The design and development of a 1 kW, 130V to 52V isolated dc-dc phase shifted GaN converter with a switching frequency of 50 kHz is presented in this paper. Furthermore, the design and analysis of high efficiency magnetics are discussed along with the experimental verification of the results. The maximum efficiency of the converter is measured to 98.8% at 80% of full load.


title: Lymphatic abnormalities demonstrated by lymphoscintigraphy after lower limb cellulitis.

abstract: BACKGROUND ::: Cellulitis is a common cause for admission to hospital, and repeated episodes are thought to damage the lymphatic system. Lymphoedema is recognized as a condition predisposing to cellulitis but there are no data to suggest its prevalence among a population presenting to hospital with acute cellulitis. ::: ::: ::: OBJECTIVES ::: To ascertain whether lymphatic abnormalities represent a common problem in patients with lower limb cellulitis. ::: ::: ::: METHODS ::: Patients admitted with cellulitis of the lower limb were invited to undergo clinical examination and lymphoscintigraphy. ::: ::: ::: RESULTS ::: Thirty patients agreed to participate in the study. Fifteen underwent lymphoscintigraphy. Thirteen had abnormal scans indicating impaired lymph drainage (seven patients had clinical lymphoedema). ::: ::: ::: CONCLUSIONS ::: Lymphatic abnormalities represent an important but unrecognized problem in patients with leg cellulitis.


Offline Signature Verification and Recognition using Neural Network


title: Effects of cue frequency and repetition on prospective memory: An ERP investigation

abstract: Prospective memory involves the formation and completion of delayed intentions and is essential for independent living. In this study (n = 33), event-related potentials (ERPs) were used to systematically evaluate the effects of PM cue frequency (10% versus 30%) and PM cue repetition (high versus low) on ERP modulations. PM cues elicited prospective positivity and frontal positivity but not N300, perhaps due to the semantic nature of the task. Results of this study revealed an interesting interaction between PM cue frequency and PM cue repetition for prospective positivity and frontal positivity, highlighting the importance of taking both factors into account when designing future studies. (C) 2013 Elsevier B.V. All rights reserved.


title: A layered method for determining manga text bubble reading order

abstract: Comic books of all cultures are an active research area as digitizing content for mobile and web is becoming more common. Past research on comics has largely concentrated on text extraction, panel segmentation and document analysis, while the utilisation of the extracted data has had less attention. In this paper we present a method to automatically determine the reading order of Japanese manga text bubbles using only text bubble position and image data. Our method classifies and orders page and text position information on three layers, which are hierarchically sorted to obtain the final ordering. The method is evaluated on a data set of 1769 manga pages with 14726 manually annotated text positions and correct ordering. Evaluation shows the method has over 95% transition accuracies and vastly outperforms a naive implementation.


title: An Approach to the Use of Depth Cameras for Weed Volume Estimation

abstract: The use of depth cameras in precision agriculture is increasing day by day. This type of sensor has been used for the plant structure characterization of several crops. However, the discrimination of small plants, such as weeds, is still a challenge within agricultural fields. Improvements in the new Microsoft Kinect v2 sensor can capture the details of plants. The use of a dual methodology using height selection and RGB (Red, Green, Blue) segmentation can separate crops, weeds, and soil. This paper explores the possibilities of this sensor by using Kinect Fusion algorithms to reconstruct 3D point clouds of weed-infested maize crops under real field conditions. The processed models showed good consistency among the 3D depth images and soil measurements obtained from the actual structural parameters. Maize plants were identified in the samples by height selection of the connected faces and showed a correlation of 0.77 with maize biomass. The lower height of the weeds made RGB recognition necessary to separate them from the soil microrelief of the samples, achieving a good correlation of 0.83 with weed biomass. In addition, weed density showed good correlation with volumetric measurements. The canonical discriminant analysis showed promising results for classification into monocots and dictos. These results suggest that estimating volume using the Kinect methodology can be a highly accurate method for crop status determination and weed detection. It offers several possibilities for the automation of agricultural processes by the construction of a new system integrating these sensors and the development of algorithms to properly process the information provided by them.


title: Secure device-to-device communications over WiFi direct

abstract: D2D communications facilitate proximal devices to directly communicate with each other, bypassing cellular base stations or access points, and bring many benefits such as improvement in both spectral efficiency and energy efficiency. Among existing D2D enabling techniques, the recently released WiFi Direct is one promising protocol that offers high data rate D2D communications in local areas. However, WiFi Direct is susceptible to security threats due to the open access of wireless channels and lack of security infrastructures. In this article, we identify several attacks that challenge WiFi-Direct-based D2D communications. Since pairwise key establishment lies in the area of securing D2D communications, we introduce a short authentication-string-based key agreement protocol and analyze its security performance. We also integrate the SAS-based key agreement protocol into the existing WiFi Direct protocol, and implement it using Android smartphones.


title: Accurate tracking in NLOS environments using integrated IMU and fixed lag smoother

abstract: Multi-sensor data fusion using Inertial Measurement Units (IMUs) is a promising technique for improving the performance of positioning systems. However, the performance of conventional sensor fusion algorithms based on the Kalman Filter (KF) is compromised in indoor environments due to non-line-of-sight (NLOS) propagation. In this paper, we propose a semi-real time tracking algorithm which uses a fixed lag smoother for sensor fusion and achieves high accuracy in NLOS environments. The computational complexity of the algorithm is taken into consideration and is reduced by decreasing the operating rate of the smoother. The performance of the proposed algorithm is validated experimentally using a real indoor positioning platform. It is shown that the 90th percentile positioning error for a pedestrian is reduced by 42% using the proposed semi-real time tracking algorithm with 10 s lag, compared with using a KF-based real time tracking algorithm.


title: Attendance system on Android smartphone

abstract: Attendance system which is currently excist still has weaknesses. The first is the long queues in front of the attendace machine at the time to come to work and leave work. The second is cheating, employees can ask her/his friend to do attendance process. The third is mostly attendance system has not been connected with the payment system in human resouces software or in the finance department. The fourth, employees who work outside the office can not do attendance process. In this paper, we introduced an attendance system based finger print technology and GPS using a smartphone integrated with payment system that will eliminate all the problems above. Our research also based on prediction that in the next few year all the smart phone will have a fingerprint scanner.


title: The New Statistics

abstract: We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data- analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.


title: Current Sharing Control Strategy for IGBTs Connected in Parallel

abstract: This work focuses on current sharing between punch-through insulated gate bipolar transistors (IGBTs) connected in parallel and evaluates the mechanisms that allow overall current balancing. Two different control strategies are presented. These strategies are based on the modification of transistor gate-emitter control voltage VGE by using an active gate driver circuit. The first strategy relies on the calculation of the average value of the current flowing through all parallel-connected IGBTs. The second strategy is proposed by the authors on the basis of a current cross reference control scheme. Finally, the simulation and experimental results of the application of the two current sharing control algorithms are presented.


title: CLOUD COMPUTING AND PRIVACY REGULATIONS : AN EXPLORATORY STUDY ON ISSUES AND IMPLICATIONS

abstract: Cloud computing is a new paradigm in the world of Information Technology Advancement. Considerable amount of cloud computing technology is already being used and developed in various flavors. Cloud Computing affects people, process and technology of the enterprise. In spite of having benefits with Cloud computing paradigm such as efficiency, flexibility, easy set up and overall reduction in IT cost [22], cloud computing paradigm could raise privacy and confidentiality risks. “Not all types of cloud computing raise the same privacy and confidentiality risks. Some believe that much of the computing activity occurring today entirely on computers owned and controlled locally by users will shift to the cloud in the future”[11]. In Cloud computing, users connect to the CLOUD, which appears as a single entity as opposed to the traditional way of connecting to multiple servers located on company premises. Public Private Partnership these days is a usually adopted pattern of governance to meet the diverse needs of their citizens with confidence and providing quality of these services. Cloud Computing Technology can also act as a facilitator between public and private partnership. In such cases there is a possibility that an external party can be involved in providing Cloud Services having partial control over the data storage, processing and transmission of data and privacy regulations become relevant [20]. Cloud computing has significant implications for the privacy of personal information as well as for the confidentiality of business and governmental information. A survey by EDUCAUSE involving 372 of its member institutions revealed that a great proportion of the respondents with use cases that involved cloud-based services reported that data privacy risks and data security risks were among their top barriers to overcome [22]. A principal goal of this paper is to identify privacy and confidentiality issue that may be of interest and concern to cloud computing participants and users [11]. Thus this paper explores to elicit possible issues and regulations in the area of privacy that affect the implementation of Cloud Computing Technologies.


title: Embedded Multi-die Interconnect Bridge (EMIB) -- A High Density, High Bandwidth Packaging Interconnect

abstract: The EMIB dense MCP technology is a new packaging paradigm that provides localized high density interconnects between two or more die on an organic package substrate, opening up new opportunities for heterogeneous on-package integration. This paper provides an overview of EMIB architecture and package capabilities. First, EMIB is compared with other approaches for high density interconnects. Some of the inherent advantages of the technology, such as the ability to cost effectively implement high density interconnects without requiring TSVs, and the ability to support the integration of many large die in an area much greater than the typical reticle size limit are highlighted. Next, the overall EMIB architecture envelope is discussed along with its constituent building blocks, the package construction with the embedded bridge, die to package interconnect features. Next, the EMIB assembly process is described at a high level. Finally, high bandwidth signaling between the die is discussed and the link bandwidth envelope is quantified.


title: Multi-defense Mechanism against DDoS in SDN Based CDNi

abstract: Lately enhancing the capability of network services automatically and dynamically through SDN and CDN/CDNi networks has become a recent topic of research. While, in one hand, these systems can be very beneficial to control and optimize the overall network services that studies the topology, traffic paths, packet handling and such others, on the other hand, the servers in such architectures can also be a potential target for DoS and/or DDoS attacks. We, therefore, propose a mechanism for the SDN based CDNi networks to securely deliver services with a multi-defense strategy against DDoS attacks. Addition of ALTO like servers in such architectures enables mapping a very big network to provide a bird's eye view. We propose an additional marking path map in the ALTO server to trace the request packets. The next defense is a protection switch to protect the main servers. A Management Information Base (MIB) is also proposed in the SDN controller to compare and assess the request traffic coming to the protection switches.


title: Introduction to Evolutionary Computation and Genetic Programming

abstract: In this chapter we describe evolutionary computation (EC) and in particular a sub-branch of it known as genetic programming (GP). We discuss the five most widely used forms of GP: Tree-based, Linear (or Machine code), Grammar-based, Stack-based and Cartesian Graph-based.


title: Shared last-level TLBs for chip multiprocessors

abstract: Translation Lookaside Buffers (TLBs) are critical to processor performance. Much past research has addressed uniprocessor TLBs, lowering access times and miss rates. However, as chip multiprocessors (CMPs) become ubiquitous, TLB design must be re-evaluated. This paper is the first to propose and evaluate shared last-level (SLL) TLBs as an alternative to the commercial norm of private, per-core L2 TLBs. SLL TLBs eliminate 7–79% of system-wide misses for parallel workloads. This is an average of 27% better than conventional private, per-core L2 TLBs, translating to notable runtime gains. SLL TLBs also provide benefits comparable to recently-proposed Inter-Core Cooperative (ICC) TLB prefetchers, but with considerably simpler hardware. Furthermore, unlike these prefetchers, SLL TLBs can aid sequential applications, eliminating 35–95% of the TLB misses for various multiprogrammed combinations of sequential applications. This corresponds to a 21% average increase in TLB miss eliminations compared to private, per-core L2 TLBs. Because of their benefits for parallel and sequential applications, and their readily-implementable hardware, SLL TLBs hold great promise for CMPs.


Time Series Classification: Decision Forests and SVM on Interval and DTW Features


title: WhyCon: an efficient, marker-based localization system

abstract: We present an open-source marker-based localization system intended as a low-cost easy-to-deploy solution for aerial and swarm robotics. The main advantage of the presented method is its high computational efficiency, which allows its deployment on small robots with limited computational resources. Even on low-end computers, the core component of the system can detect and estimate 3D positions of hundreds of black and white markers at the maximum frame-rate of standard cameras. The method is robust to changing lighting conditions and achieves accuracy in the order of millimeters to centimeters. Due to its reliability, simplicity of use and availability as an open-source ROS module (http://purl.org/robotics/whycon), the system is now used in a number of aerial robotics projects where fast and precise relative localization is required.


title: Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images

abstract: Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature representations are effectively enhanced. The results are further improved by the iterative refinement. Moreover, our method is quite efficient by taking advantage of a fully convolutional network, which is formulated as an end-to-end learning framework of detection and segmentation. Extensive experimental results on a large-scale database corroborated that our method achieved a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.


title: The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved

abstract: Internet of Things (IoT) is an increasingly popular technology that enables physical devices, vehicles, home appliances, etc., to communicate and even inter operate with one another. It has been widely used in industrial production and social applications including smart home, healthcare, and industrial automation. While bringing unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years. There are increasing research works to ease these threats, but many problems remain open. To better understand the essential reasons of new IoT threats and the challenges in current research, this survey first proposes the concept of “IoT features.” Then, we discuss the security and privacy effects of eight IoT features including the threats they cause, existing solutions to threats and research challenges yet to be solved. To help researchers follow the up-to-date works in this field, this paper finally illustrates the developing trend of IoT security research and reveals how IoT features affect existing security research by investigating most existing research works related to IoT security from 2013 to 2017.


title: Political Corruption, Economic Incentive, Educational Resource Input, and the Quality of Human Capital: A Panel Analysis Over Twenty-Five Years for the Fifty U.S. States

abstract: This paper uses panel regression analysis on annual data for the fifty states for the period 1980 to 2004 to estimate the potential impact of political corruption on the quality of human capital, as measured by mean state student performance on scholastic aptitude math and verbal exams. The findings are consistent with the hypothesis that political corruption has a negative effect on the quality of human capital, and, as a result, a detrimental effect on economic growth. In addition, the results of the paper suggest that economic incentive also matters for educational performance, and that, unlike the findings of some earlier studies, input into the educational process, in terms of expenditure per pupil, has a positive effect on educational performance.


title: Information Security Risk Assessment Based on Information Measure and Fuzzy Clustering

abstract: To address the problems of lack of training data and difficult to find optimal value in information security risk assessment, this paper applying a new information measure method and fuzzy clustering in information security risk assessment. The new method quantifies risk factors of all data and the dependence degree of safety with the mutual information computing. Then search optimal points in each degree of risk as original center points of K-means clustering algorithm, and use the K-means clustering algorithm for data classification. This method has less computation, and it can overcome the K-means’s shortcoming of sensitive to initial value and problem of nonlinear and complexity of information security risk assessment. Experimental results show the effectiveness of our method


title: Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis

abstract: Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader's expectation and may involved him/her in irrecoverable cost. Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi-Sugeno-Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. Fuzzy C-Mean clustering implemented for identifying number of rules. Initial membership function of the premise part approximately defined as Gaussian function. TSK parameters tuned by Adaptive Nero-Fuzzy Inference System (ANFIS). Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX). This index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors.


title: Sensory feedback in cockroach locomotion: current knowledge and open questions

abstract: The American cockroach, Periplaneta americana, provides a successful model for the study of legged locomotion. Sensory regulation and the relative importance of sensory feedback vs. central control in animal locomotion are key aspects in our understanding of locomotive behavior. Here we introduce the cockroach model and describe the basic characteristics of the neural generation and control of walking and running in this insect. We further provide a brief overview of some recent studies, including mathematical modeling, which have contributed to our knowledge of sensory control in cockroach locomotion. We focus on two sensory mechanisms and sense organs, those providing information related to loading and unloading of the body and the legs, and leg-movement-related sensory receptors, and present evidence for the instrumental role of these sensory signals in inter-leg locomotion control. We conclude by identifying important open questions and indicate future perspectives.


title: Effects of Pd distribution at free air ball in Pd coated Cu wire

abstract: Bonding wire, one of the bonding materials, has been rapidly changed from Au to Cu due to an increase in gold price. Although Pd coated Cu (Au-Pd-Cu) wires, which comprise the largest market share among Cu bonding wires, show high reliability compared to bare Cu wire in high-humidity reliability, the reliability issue still appears in certain area. In the case of Pd coated Cu wire, studies have reported that the Pd distribution on the free air ball (FAB) surface is an important factor for improving the reliability. This study confirmed that the distribution of FAB could be controlled according to the wire manufacturing conditions, and that the Pd distribution further changed depending on the change of EFO conditions. The study further confirmed that the denser Pd distribution outside bonded ball induces the higher chemical resistance at the interface between bonding pad and the bonded ball, which improves the reliability at high temperature and high humidity after PKG.


title: Children teach a care-receiving robot to promote their learning: field experiments in a classroom for vocabulary learning

abstract: In contrast to conventional teaching agents (including robots) that were designed to play the role of human teachers or caregivers, we propose the opposite scenario in which robots receive instruction or care from children. We hypothesize that by using this care-receiving robot, we may construct a new educational framework whose goal is to promote children's spontaneous learning by teaching through their teaching the robot. In this paper, we describe the introduction of a care-receiving robot into a classroom at an English language school for Japanese children (3--6 years of age) and then conduct an experiment to evaluate if the care-receiving robot can promote their learning using English verbs. The results suggest that the idea of a care-receiving robot is feasible and that the robot can help children learn new English verbs efficiently. In addition, we report on investigations into several forms of teaching performed by children, which were revealed through observations of the children, parent interviews, and other useful knowledge. These can be used to improve the design of care-receiving robots for educational purposes.


title: KillingFusion: Non-rigid 3D Reconstruction without Correspondences

abstract: We introduce a geometry-driven approach for real-time 3D reconstruction of deforming surfaces from a single RGB-D stream without any templates or shape priors. To this end, we tackle the problem of non-rigid registration by level set evolution without explicit correspondence search. Given a pair of signed distance fields (SDFs) representing the shapes of interest, we estimate a dense deformation field that aligns them. It is defined as a displacement vector field of the same resolution as the SDFs and is determined iteratively via variational minimization. To ensure it generates plausible shapes, we propose a novel regularizer that imposes local rigidity by requiring the deformation to be a smooth and approximately Killing vector field, i.e. generating nearly isometric motions. Moreover, we enforce that the level set property of unity gradient magnitude is preserved over iterations. As a result, KillingFusion reliably reconstructs objects that are undergoing topological changes and fast inter-frame motion. In addition to incrementally building a model from scratch, our system can also deform complete surfaces. We demonstrate these capabilities on several public datasets and introduce our own sequences that permit both qualitative and quantitative comparison to related approaches.


title: Enhanced Precoding Design with Adaptive Beam Width for 5G New Radio Systems

abstract: Multiple-Input Multiple-Output (MIMO) is considered as a key technology for next generation wireless cellular systems. Depending on the scenario and propagation environment the optimal beamwidth of the precoding vectors in MIMO systems can be different. Precoding vectors corresponding to narrow beams are most appropriate in several scenarios, as they provide maximum beamforming gain in the desired direction and generate less interference in other directions. However, in some cases the use of only narrow beams for precoding is not optimal. For example, to increase the spatial multiplexing gains and the number of transmitted MIMO layers, the precoding vectors corresponding to wider beams should be also considered. Moreover, for open-loop transmission schemes, the use of precoding with wider beamwidth results in better diversity and, therefore, improved performance. To accommodate the variable beamwidth requirement for MIMO beamforming, this paper proposes an enhanced precoding approach with adaptive beamwidth of the precoding vectors for 5G New Radio (NR) systems. System-level and link- level performance evaluation results show that the proposed precoding vector structure with adaptive beamwidth of the precoding vectors outperforms conventional DFT-based precoding with narrow beamwidth supported in LTE-A systems.


title: GISMO: a Graphical Interactive Student Monitoring Tool for Course Management Systems

abstract: This paper presents GISMO, a graphical interactive student monitor- ing and tracking system tool that extracts tracking data from an on-line course maintained with a Course Management System, and generates graphical repre- sentations that can be explored by course instructors to examine various aspects of distance students. GISMO uses techniques from Information Visualisation to render into an appropriate graphical manner the complex multidimensional stu- dent tracking data provided by the Course Management System. GISMO aims to help instructors to become aware of what is happening in their classes and provide a better support to their learners.


title: A methodology for mapping Instagram hashtags

abstract: While social media research has provided detailed cumulative analyses of selected social media platforms and content, especially Twitter, newer platforms, apps, and visual content have been less extensively studied so far. This paper proposes a methodology for studying Instagram activity, building on established methods for Twitter research by initially examining hashtags, as common structural features to both platforms. In doing so, we outline methodological challenges to studying Instagram, especially in comparison to Twitter. Finally, we address critical questions around ethics and privacy for social media users and researchers alike, setting out key considerations for future social media research.


title: Managing New Product Development and Supply Chain Risks: The Boeing 787 Case

abstract: To stimulate revenue growth and market response, Boeing decided to develop the 787 Dreamliner. The 787 Dreamliner is not only a revolutionary aircraft, but it also utilizes an unconventional supply chain intended to drastically reduce development cost and time. However, despite significant management efforts and capital investment, Boeing is currently facing a series of delays in its schedule for the maiden flight and plane delivery to customers. This paper analyzes Boeing’s rationale for the 787’s unconventional supply chain, describes Boeing’s challenges for managing this supply chain, and highlights some key lessons for other manufacturers to consider when designing their supply chains for new product development.


title: Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony

abstract: For the maximum utilization of solar energy, photovoltaic (PV) power generation systems are operated at the maximum power point (MPP) under varying atmospheric conditions, and MPP tracking (MPPT) is generally achieved using several conventional methods. However, when partial shading occurs in a PV system, the resultant powervoltage (PV) curve exhibits multiple peaks and traditional methods that need not guarantee convergence to true MPP always. This paper proposes an artificial bee colony (ABC) algorithm for global MPP (GMPP) tracking under conditions of in-homogenous insolation. The formulation of the problem, application of the ABC algorithm, and the results are analyzed in this paper. The numerical simulations carried out on two different PV configurations under different shading patterns strongly suggest that the proposed method is far superior to existing MPPT alternatives. Experimental results are also provided to validate the new dispensation.


title: An evaluation of Convolutional Neural Networks for music classification using spectrograms

abstract: Graphical abstractDisplay Omitted HighlightsMusic classification using spectrograms and Convolutional Neural Networks.Compare results with state of the art in Latin Music Database, ISMIR 2004 and African music collection.Assessing complementarity between Convolutional Neural Networks and classifiers built with hand-crafted features. Music genre recognition based on visual representation has been successfully explored over the last years. Classifiers trained with textural descriptors (e.g., Local Binary Patterns, Local Phase Quantization, and Gabor filters) extracted from the spectrograms have achieved state-of-the-art results on several music datasets. In this work, though, we argue that we can go further with the time-frequency analysis through the use of representation learning. To show that, we compare the results obtained with a Convolutional Neural Network (CNN) with the results obtained by using handcrafted features and SVM classifiers. In addition, we have performed experiments fusing the results obtained with learned features and handcrafted features to assess the complementarity between these representations for the music classification task. Experiments were conducted on three music databases with distinct characteristics, specifically a western music collection largely used in research benchmarks (ISMIR 2004 Database), a collection of Latin American music (LMD database), and a collection of field recordings of ethnic African music. Our experiments show that the CNN compares favorably to other classifiers in several scenarios, hence, it is a very interesting alternative for music genre recognition. Considering the African database, the CNN surpassed the handcrafted representations and also the state-of-the-art by a margin. In the case of the LMD database, the combination of CNN and Robust Local Binary Pattern achieved a recognition rate of 92%, which to the best of our knowledge, is the best result (using an artist filter) on this dataset so far. On the ISMIR 2004 dataset, although the CNN did not improve the state of the art, it performed better than the classifiers based individually on other kind of features.


title: Temperature-responsive ionic liquid/water interfaces: relation between hydrophilicity of ions and dynamic phase change

abstract: Phase separation between ionic liquids (ILs) and molecular liquids is of interest physico-chemically, and also has industrial relevance. IL/water mixtures are of great interest in many fields. Unlike static phase separation between IL and water, dynamic shifts of IL/water mixtures between a homogeneous mixture and separate phases have a wide variety of applications. The miscibility of ILs with water generally increases upon heating, and a few ILs undergo a lower critical solution temperature (LCST)-type phase transition with water in which the separated biphases become miscible upon cooling. As the phase transition is controlled by changing the temperature by a few degrees, the LCST-type phase response of IL/water mixtures makes it possible to use ILs as solvents in various energy-saving processes. Since many hydrophilic ILs do not undergo phase separation with water, we aim to determine the necessary conditions under which hydrophobic ILs undergo the phase transition. Based on physico-chemical analysis of many hydrophobic ILs that undergo a phase separation after mixing with water, we find there is a particular range of “hydrophilicity” of these hydrophobic ILs within which the LCST-type phase transition is possible. Accordingly, a hydrophilicity index (HI) of ILs is proposed, in terms of the number of water molecules in the separated IL phase. The HI value proves to be a good indicator of the phase behaviour of IL/water mixtures, as well as their phase transition temperature. Potential application of the LCST-type phase change to the selective extraction of water-soluble proteins is also summarised.


title: Size‐ and shape‐dependent effects of microplastic particles on adult daggerblade grass shrimp (Palaemonetes pugio)

abstract: The incidence of microplastics in marine environments has been increasing over the past several decades. The objective of this study was to characterize the size and shape dependent effects of microplastic particles (spheres, fibers and fragments) on the adult daggerblade grass shrimp, Palaemonetes pugio. Grass shrimp were exposed to 11 sizes of plastic; spheres (30, 35, 59, 75, 83, 116, and 165 µm), fragments (34 and 93 µm), and fibers (34 and 93 µm) at a concentration of 20,00 particles/400 mL (= 50,000 particles/L) for three hours. Following exposure, grass shrimp were monitored for survival, ingested and ventilated microplastics, and residence time. Mortality ranged from 0-55%. Spheres and fragments under 50 microns were not acutely toxicity. Spheres and fragments greater than 50 microns had mortalities ranging from 5-40%. Mortality was significantly higher at 93 µm fibers than other sizes tested (p < 0.001). The shape of the particle had a significant influence on the number of particles ingested by the shrimp (p < 0.001). The residence time of particles in the gut ranged from 27-75 hours with an average at 43.0 ± 13.8 hours. Within the gills, the residence time ranged from 27-45 hours with an average of 36.9 ± 5.4 hours. Results from this study suggest that microplastic particles of various sizes and shapes can be ingested and ventilated by adult daggerblade grass shrimp, resulting in acute toxicity. This article is protected by copyright. All rights reserved


title: Regulatory mechanisms of major depressive disorder risk variants

abstract: Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and a leading cause of disability worldwide. Though recent genome-wide association studies (GWAS) have identified multiple risk variants for MDD, how these variants confer MDD risk remains largely unknown. Here we systematically characterize the regulatory mechanism of MDD risk variants using a functional genomics approach. By integrating chromatin immunoprecipitation sequencing (ChIP-Seq) (from human brain tissues or neuronal cells) and position weight matrix (PWM) data, we identified 34 MDD risk SNPs that disrupt the binding of 15 transcription factors (TFs). We verified the regulatory effect of the TF binding-disrupting SNPs with reporter gene assays, allelic-specific expression analysis, and CRISPR-Cas9-mediated genome editing. Expression quantitative trait loci (eQTL) analysis identified the target genes that might be regulated by these regulatory risk SNPs. Finally, we found that NEGR1 (regulated by the TF binding-disrupting MDD risk SNP rs3101339) was dysregulated in the brains of MDD cases compared with controls, implying that rs3101339 may confer MDD risk by affecting NEGR1 expression. Our findings reveal how genetic variants contribute to MDD risk by affecting TF binding and gene regulation. More importantly, our study identifies the potential MDD causal variants and their target genes, thus providing pivotal candidates for future mechanistic study and drug development.


title: Fractal Merkle Tree Representation and Traversal

abstract: Authenticated data structures provide cryptographic proofs that their answers are as accurate as the author intended, even if the data structure is maintained by a remote host. We present techniques for authenticating data structures that represent graphs and collections of geometric objects. In our model, a data structure maintained by a trusted source is mirrored at distributed directories that answer queries and provide proof of correctness. Our work has applications to the authentication of network management systems and geographic information systems.


title: Silhouette-Based 3D Face Shape Recovery

abstract: The creation of realistic 3D face models is still a fundamental problem in computer graphics. In this paper we present a novel method to obtain the 3D shape of an arbitrary human face using a sequence of silhouette images as input. Our face model is a linear combination of eigenheads, which are obtained by a Principal Component Analysis (PCA) of laser-scanned 3D human faces. The coefficients of this linear decomposition are used as our model parameters. We introduce a near-automatic method for reconstructing a 3D face model whose silhouette images match closest to the set of input silhouettes.


title: Depth Creates No Bad Local Minima

abstract: In deep learning, \textit{depth}, as well as \textit{nonlinearity}, create non-convex loss surfaces. Then, does depth alone create bad local minima? In this paper, we prove that without nonlinearity, depth alone does not create bad local minima, although it induces non-convex loss surface. Using this insight, we greatly simplify a recently proposed proof to show that all of the local minima of feedforward deep linear neural networks are global minima. Our theoretical results generalize previous results with fewer assumptions, and this analysis provides a method to show similar results beyond square loss in deep linear models.


title: Hard drive failure prediction using non-parametric statistical methods

abstract: We present a case study of a difficult real-world pattern recognition problem: predicting hard drive failure using attributes monitored internally by individual drives. We compare the performance of support vector machines (SVMs), unsupervised clustering, and non-parametric sta- tistical tests (rank-sum and reverse arrangements). Some- what surprisingly, the rank-sum method outperformed the other methods, including SVMs. We also show the utility of using non-parametric tests for feature set selection.


title: Differential Diagnosis of rSr’ Pattern in Leads V1-V2. Comprehensive Review and Proposed Algorithm

abstract: One of the more frequent dilemmas in ECG interpretation is the differential diagnosis of an rSr' pattern in leads V1 -V2 . We often face this finding in asymptomatic and otherwise healthy individuals and the causes may vary from benign nonpathological variants to severe or life-threatening heart diseases, such as Brugada syndrome or arrhythmogenic right ventricular dysplasia. In other cases, a normal variant of rSr' pattern can be misinterpreted as pathological after the occurrence of certain clinical events such as cardiac arrest or syncope of unknown cause. In this review we analyze in detail all the possible conditions, both benign and pathological that may explain the presence of this electrocardiographic pattern. We also propose a simple electrocardiographic algorithm for differential diagnosis.


title: Spectral Representation for Matching and Recognition

abstract: In this thesis, we aim to use the spectral graph theory to develop a framework to solve the problems of computer vision. The graph spectral methods are concerned with using the eigenvalues and eigenvectors of the adjacency matrix or closely related Laplacian matrix. In this thesis we develop four methods using spectral techniques: (1) We use a Hermitian property matrix for point pattern matching problem; (2) We use coefficients of symmetric polynomials to cluster similar human poses using the skeletal representation acquired from Microsoft Kinect; (3) We use coefficients of the elementary symmetric polynomials to make the direction of the eigenvectors of the proximity matrices consistent with each other for the problem of correspondence matching; (4) We use commute time embedding to construct a 3D shape descriptor for the purpose of 3D shape classification. ::: ::: In Chapter 3 we address the problem of correspondence matching. We extend the Laplacian matrix to the complex domain by constructing a Hermitian property matrix. We construct a Hermitian property matrix from the spatial locations of the 2D feature points extracted from a pair of images and the angular information associated with these feature points. We construct the Hermitian property matrix in a way that reflects the Laplacian matrix. The complex eigenvectors of the Hermitian matrix is then used to find the correspondences between pairs of points across two images. We embed the complex eigenvectors of the Hermitian property matrix in the iterative alignment EM algorithm developed by Carcassoni and Hancock to make it robust to rotation, noise and point-position jitter. Experimental results on both synthetic and real world data have been presented. ::: ::: Chapter 4 develops a clustering method using four different type of feature vectors constructed from the complex coefficients of the elementary symmetric polynomials. These polynomials are computed from the eigenvalues and the complex eigenvectors of a Hermitian property matrix. The feature vectors are embedded into a pattern-space using Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) to cluster similar human poses acquired using the Microsoft Kinect device for Xbox 360. The Hermitian property matrix is constructed from the length of the limbs and the angles subtended by each pair of limbs using the three-dimensional skeletal data produced by the Kinect device. The given skeleton is converted to its equivalent line graph to compute the angles between pairs of limbs. The joints locations are used to compute the limb lengths. ::: ::: In Chapter 5, we describe a method to correct the sign of eigenvectors of the proximity matrix for the problem of correspondence matching. The signs of the eigenvectors of a proximity matrix are not unique and play an important role in computing the correspondences between a set of feature points. We use the coefficients of the elementary symmetric polynomials to make the direction of the eigenvectors of the two proximity matrices consistent with each other. ::: ::: Chapter 6 describes a 3D shape descriptor that is robust to changes in pose and topology. The descriptor is based on the D2 shape descriptor developed by Osada et al, which is essentially the frequency distribution of the Euclidian distance between randomly selected points on the surface of the 3D shape. We use the commute-time distance instead of using the Euclidian distance between randomly selected points. A new and completely unsupervised mesh segmentation algorithm is proposed, which is based on the commute time embedding of the mesh and k-means clustering using the embedded mesh vertices.


title: Power optimization of wind turbines with data mining and evolutionary computation

abstract: A data-driven approach for maximization of the power produced by wind turbines is presented. The power optimization objective is accomplished by computing optimal control settings of wind turbines using data mining and evolutionary strategy algorithms. Data mining algorithms identify a functional mapping between the power output and controllable and non-controllable variables of a wind turbine. An evolutionary strategy algorithm is applied to determine control settings maximizing the power output of a turbine based on the identified model. Computational studies have demonstrated meaningful opportunities to improve the turbine power output by optimizing blade pitch and yaw angle. It is shown that the pitch angle is an important variable in maximizing energy captured from the wind. Power output can be increased by optimization of the pitch angle. The concepts proposed in this paper are illustrated with industrial wind farm data.


title: Security behaviors of smartphone users

abstract: Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings ind...


title: A review of using Augmented Reality in Education from 2011 to 2016

abstract: In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique educational settings. This paper reports a review of literature on augmented reality in educational settings considering the factors include the uses, advantages, features, and effectiveness of augmented reality in educational settings. In total, 55 studies published between 2011 and 2016 in Social Sciences Citation Index database were analyzed. The main findings from this review provide the current state of the art on research in AR in education. Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research in augmented reality for educational settings.


title: Heritage Building Information Modelling (HBIM): A Review of Published Case Studies

abstract: Building Information Modelling (BIM) is gaining popularity worldwide as a system of collaboration and data management for the AEC sectors. However, the usefulness of BIM has largely been seen in the design and construction phases, rather than in post-construction—for example in facilities management or occupancy. As the concept of BIM has matured, there has been increasing interest in applying these concepts and technologies to heritage buildings (HBIM), despite some fundamental differences: heritage buildings usually have a long history of use, re-use and alteration; their management is often related to conservation rather than occupancy; and they are often a social and community resource. Therefore, HBIM is faced with a different set of stakeholders to ‘standard BIM’, which leads us to question the optimistic perceptions of its usefulness when BIM is applied to a historic built environment context. We investigate this question by thinking about how we can characterise HBIM, and what sort of information will give us further insights. Using published case studies as a source of secondary data, we have collected information about specific characteristics, which we analyse and use to discuss the uptake of HBIM, the purpose of HBIM and the role of stakeholders. We conclude that there needs to be a significant change in perception of HBIM by academics and technicians, before it is likely to be adopted by practitioners in the heritage sector.


title: Clockwork Convnets for Video Semantic Segmentation

abstract: Recent years have seen tremendous progress in still-image segmentation; however the na\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets.


title: Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks

abstract: This paper describes bidirectional recurrent mixture density networks, which can model multi-modal distributions of the type P(xt|y1T) and P(xt|x1, x2,..., xt-1, y1T) without any explicit assumptions about the use of context. These expressions occur frequently in pattern recognition problems with sequential data, for example in speech recognition. Experiments show that the proposed generative models give a higher likelihood on test data compared to a traditional modeling approach, indicating that they can summarize the statistical properties of the data better.


title: Local Alignments for Fine-Grained Categorization

abstract: The aim of this paper is fine-grained categorization without human interaction. Different from prior work, which relies on detectors for specific object parts, we propose to localize distinctive details by roughly aligning the objects using just the overall shape. Then, one may proceed to the classification by examining the corresponding regions of the alignments. More specifically, the alignments are used to transfer part annotations from training images to unseen images (supervised alignment), or to blindly yet consistently segment the object in a number of regions (unsupervised alignment). We further argue that for the distinction of sub-classes, distribution-based features like color Fisher vectors are better suited for describing localized appearance of fine-grained categories than popular matching oriented shape-sensitive features, like HOG. They allow capturing the subtle local differences between subclasses, while at the same time being robust to misalignments between distinctive details. We evaluate the local alignments on the CUB-2011 and on the Stanford Dogs datasets, composed of 200 and 120, visually very hard to distinguish bird and dog species. In our experiments we study and show the benefit of the color Fisher vector parameterization, the influence of the alignment partitioning, and the significance of object segmentation on fine-grained categorization. We, furthermore, show that by using object detectors as voters to generate object confidence saliency maps, we arrive at fully unsupervised, yet highly accurate fine-grained categorization. The proposed local alignments set a new state-of-the-art on both the fine-grained birds and dogs datasets, even without any human intervention. What is more, the local alignments reveal what appearance details are most decisive per fine-grained object category.


title: Hand3D: Hand Pose Estimation using 3D Neural Network

abstract: We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image. Different from previous works that mostly run on 2D depth image domain and require intermediate or post process to bring in the supervision from 3D space, we convert the depth map to a 3D volumetric representation, and feed it into a 3D convolutional neural network(CNN) to directly produce the pose in 3D requiring no further process. Our system does not require the ground truth reference point for initialization, and our network architecture naturally integrates both local feature and global context in 3D space. To increase the coverage of the hand pose space of the training data, we render synthetic depth image by transferring hand pose from existing real image datasets. We evaluation our algorithm on two public benchmarks and achieve the state-of-the-art performance. The synthetic hand pose dataset will be available.


title: Measurement of Cognitive Load in HCI Systems Using EEG Power Spectrum: An Experimental Study

abstract: Abstract Ubiquitous computing involving complex interactive systems are leading to increase in cognitive load in day to day activities. Apart from causing stress and mental exhaustion, increase in cognitive load is likely to cause expensive human errors in case of critical tasks. Though subjective measures of cognitive load are in use in HCI, there is a need to explore non-invasive and non-intrusive physiological measures of cognitive load. This paper discusses the theory of cognitive load, identifies EEG frequency bands likely to capture the cognitive load, discusses brain locations related to the cognitive load based on literature, proposes a methodology to measure cognitive load using EEG power spectrum and verifies the framework by an experimental study.


Analysis with Deeply Learned Distributed Representations of Variable Length Texts


title: Elastic Reliability Optimization Through Peer-to-Peer Checkpointing in Cloud Computing

abstract: Modern day data centers coordinate hundreds of thousands of heterogeneous tasks and aim at delivering highly reliable cloud computing services. Although offering equal reliability to all users benefits everyone at the same time, users may find such an approach either inadequate or too expensive to fit their individual requirements, which may vary dramatically. In this paper, we propose a novel method for providing elastic reliability optimization in cloud computing. Our scheme makes use of peer-to-peer checkpointing and allows user reliability levels to be jointly optimized based on an assessment of their individual requirements and total available resources in the data center. We show that the joint optimization can be efficiently solved by a distributed algorithm using dual decomposition. The solution improves resource utilization and presents an additional source of revenue to data center operators. Our validation results suggest a significant improvement of reliability over existing schemes.


title: The structure of images

abstract: In practice the relevant details of images exist only over a restricted range of scale. Hence it is important to study the dependence of image structure on the level of resolution. It seems clear enough that visual perception treats images on several levels of resolution simultaneously and that this fact must be important for the study of perception. However, no applicable mathematically formulated theory to deal with such problems appears to exist. In this paper it is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied. The structure of this family is governed by the well known diffusion equation (a parabolic, linear, partial differential equation of the second order). As such the structure fits into existing theories that treat the front end of the visual system as a continuous stack of homogeneous layers, characterized by iterated local processing schemes. When resolution is decreased the images becomes less articulated because the extrem ("light and dark blobs") disappear one after the other. This erosion of structure is a simple process that is similar in every case. As a result any image can be described as a juxtaposed and nested set of light and dark blobs, wherein each blob has a limited range of resolution in which it manifests itself. The structure of the family of derived images permits a derivation of the sampling density required to sample the image at multiple scales of resolution.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Designing and simulation of full adder cell using FINFET technique

abstract: This paper proposes a 1-Bit full adder cell using Double Gate FINFET (Fin Shaped Field Effect Transistor) at 45nm CMOS technology. The intention of this paper is to reduce leakage power and leakage current of 1-bit Full Adder while maintaining the competitive performance with few transistors are used (transistors count 10). A new high performance 1-bit Full Adder based on new logic approach is presented in this paper. Double Gate FINFET technique which decreases the process variation on 1-bit Full Adder is presented in this paper; the key of Double Gate FINFET technique is applied on 1-bit Full Adder is to reduce the operating power, leakage power and leakage current. We investigate the use of Double Gate FINFET technology provides low leakage and high performance operation by utilizing high speed and low thresh hold voltage transistors for logic cells. Which show that it is particularly effective in sub threshold circuits and can eliminate performance variations with Low power. A 20ns access time and frequency 0.05GHz provide 45nm CMOS process technology with 0.7V power supply is employed to carry out 1-bit Full Adder.


The early growth and fall frost damage of poplar clones in Latvia.


title: Prevalence of and factors associated with burnout among health care professionals in Arab countries: a systematic review

abstract: Burnout among healthcare professionals is one of the key challenges affecting health care practice and quality of care. This systematic review aims to (1) estimate the prevalence of burnout among health care professionals (HCP) in Arab countries; and (2) explore individual and work-related factors associated with burnout in this population. Multiple electronic databases were searched for studies published in English or Arabic from January 1980 to November 2014 assessing burnout (using the Maslach Burnout Inventory; MBI) amongst health care professionals (HCP) in Arab countries. Nineteen studies (N = 4108; 49.3% females) conducted on HCP in Bahrain, Egypt, Jordan, Lebanon, Palestine, Saudi Arabia and Yemen were included in this review. There was a wide range of prevalence estimates for the three MBI subscales, high Emotional Exhaustion (20.0–81.0%), high Depersonalization (9.2–80.0%), and low Personal Accomplishment (13.3–85.8%). Gender, nationality, service duration, working hours, and shift patterns were all significantly associated with burnout. Within the constraints of the study and the range of quality papers available, our review revealed moderate-to-high estimates of self-reported burnout among HCP in Arab countries that are similar to prevalence estimates in non-Arabic speaking westernized developed countries. In order to develop culturally appropriate interventions, further research using longitudinal designs is needed to confirm the risk factors for burnout in specific HCP settings and specialties in Arab countries.


title: Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

abstract: Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.


Active Learning


TEMPERATURE-INDUCED VARIABILITY IN PEAS


title: Causal Inference Under Network Interference: A Framework for Experiments on Social Networks

abstract: No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect the outcomes of other units, a phenomenon known as interference. This thesis develops a causal framework and inference methodology for experiments where interference takes place on a network of influence (i.e. network interference). In this framework, the network potential outcomes serve as the key quantity and flexible building blocks for causal estimands that represent a variety of primary, peer, and total treatment effects. These causal estimands are estimated via principled Bayesian imputation of missing outcomes. The theory on the unconfoundedness assumptions leading to simplified imputation highlights the importance of including relevant network covariates in the potential outcome model. Additionally, experimental designs that result in balanced covariates and sizes across treatment exposure groups further improve the causal estimate, especially by mitigating potential outcome model mis-specification. The true potential outcome model is not typically known in real-world experiments, so the best practice is to account for interference and confounding network covariates through both balanced designs and model-based imputation. A full factorial simulated experiment is formulated to demonstrate this principle by comparing performance across different randomization schemes during the design phase and estimators during the analysis phase, under varying network topology and true potential outcome models. Overall, this thesis asserts that interference is not just a nuisance for analysis but rather an opportunity for quantifying and leveraging peer effects in real-world experiments.


title: Enhanced Word Representations for Bridging Anaphora Resolution

abstract: Most current models of word representations(e.g.,GloVe) have successfully captured fine-grained semantics. However, semantic similarity exhibited in these word embeddings is not suitable for resolving bridging anaphora, which requires the knowledge of associative similarity (i.e., relatedness) instead of semantic similarity information between synonyms or hypernyms. We create word embeddings (embeddings_PP) to capture such relatedness by exploring the syntactic structure of noun phrases. We demonstrate that using embeddings_PP alone achieves around 30% of accuracy for bridging anaphora resolution on the ISNotes corpus. Furthermore, we achieve a substantial gain over the state-of-the-art system (Hou et al., 2013) for bridging antecedent selection.


title: CoT: Cooperative Training for Generative Modeling.

abstract: We propose Cooperative Training (CoT) for training generative models that measure a tractable density function for target data. CoT coordinately trains a generator $G$ and an auxiliary predictive mediator $M$. The training target of $M$ is to estimate a mixture density of the learned distribution $G$ and the target distribution $P$, and that of $G$ is to minimize the Jensen-Shannon divergence estimated through $M$. CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE. This low-variance algorithm is theoretically proved to be unbiased for both generative and predictive tasks. We also theoretically and empirically show the superiority of CoT over most previous algorithms, in terms of generative quality and diversity, predictive generalization ability and computational cost.


title: Learning Probabilistic Models of Link Structure

abstract: Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models (PRMs) embraces the object-relational nature of structured data by capturing probabilistic interactions between attributes of related entities. In this paper, we extend this framework by modeling interactions between the attributes and the link structure itself. An advantage of our approach is a unified generative model for both content and relational structure. We propose two mechanisms for representing a probabilistic distribution over link structures: reference uncertainty and existence uncertainty. We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict link structure and, moreover, the observed link structure can be used to provide better predictions for the attributes in the model.


Phosphor Deposits of β-Sialon:Eu2+ Mixed with SnO2 Nanoparticles Fabricated by the Electrophoretic Deposition (EPD) Process


Global Estimates of the Prevalence and Incidence of Four Curable Sexually Transmitted Infections in 2012 Based on Systematic Review and Global Reporting


Rethinking the Camera Pipeline for Computer Vision


title: Saliency-aware geodesic video object segmentation

abstract: We introduce an unsupervised, geodesic distance based, salient video object segmentation method. Unlike traditional methods, our method incorporates saliency as prior for object via the computation of robust geodesic measurement. We consider two discriminative visual features: spatial edges and temporal motion boundaries as indicators of foreground object locations. We first generate framewise spatiotemporal saliency maps using geodesic distance from these indicators. Building on the observation that foreground areas are surrounded by the regions with high spatiotemporal edge values, geodesic distance provides an initial estimation for foreground and background. Then, high-quality saliency results are produced via the geodesic distances to background regions in the subsequent frames. Through the resulting saliency maps, we build global appearance models for foreground and background. By imposing motion continuity, we establish a dynamic location model for each frame. Finally, the spatiotemporal saliency maps, appearance models and dynamic location models are combined into an energy minimization framework to attain both spatially and temporally coherent object segmentation. Extensive quantitative and qualitative experiments on benchmark video dataset demonstrate the superiority of the proposed method over the state-of-the-art algorithms.


title: Mobile offloading in the wild: Findings and lessons learned through a real-life experiment with a new cloud-aware system

abstract: Mobile-cloud offloading mechanisms delegate heavy mobile computation to the cloud. In real life use, the energy tradeoff of computing the task locally or sending the input data and the code of the task to the cloud is often negative, especially with popular communication intensive jobs like social- networking, gaming, and emailing. We design and build a working implementation of CDroid, a system that tightly couples the device OS to its cloud counterpart. The cloud-side handles data traffic through the device efficiently and, at the same time, caches code and data optimally for possible future offloading. In our system, when offloading decision takes place, input and code are likely to be already on the cloud. CDroid makes mobile cloud offloading more practical enabling offloading of lightweight jobs and communication intensive apps. Our experiments with real users in everyday life show excellent results in terms of energy savings and user experience.


title: Autonomous Underwater Vehicles : Trends and Transformations

abstract: Three examples of inter-agency cooperation utilizing current generation, individual Autonomous Underwater Vehicles (AUVs) are described consistent with recent recommendations of the U.S. Commission on Ocean Policy. The first steps in transforming individual AUVs into adaptive, networked systems are underway. To realize an affordable and deployable system, a network-class AUV must be designed with cost–size constraints not necessarily applied in developing solo AUVs. Vehicle types are suggested based on function and ocean operating regime: surface layer, interior and bottom layer. Implications for platform, navigation and control subsystems are explored and practical formulations for autonomy and intelligence are postulated for comparing performance and judging behavior. Laws and conventions governing intelligent maritime navigation are reviewed and an autonomous controller with conventional collision avoidance behavior is described. Network-class cost constraints can be achieved through economies of scale. Productivity and efficiency in AUV manufacturing will increase if constructive competition is maintained. Constructive strategies include interface and operating standards. Professional societies and industry trade groups have a leadership role to play in establishing public, open standards. cations are described at many conferences and in an expanding literature of journal publications. Griffiths (2003) provides recent developments in AUV design, construction and operation. A number of commercial manufacturers have emerged to supply the growing market. Clearly, individual AUVs are evolving into useful tools that extend current measurement methods. Three examples involving current generation, individual AUVs will serve to illustrate trends in inter-agency cooperation utilizing this technology. Following these examples, we examine factors that will transform current measurement methods. Network externalities associated with interagency cooperation will play a role in driving this transformation. Recently the Navy joined with the U. S. Environmental Protection Agency, the Narragansett Bay Estuary Program, and the Autonomous Undersea Systems Institute to demonstrate the effectiveness of using a Solar Powered Autonomous Underwater Vehicle (SAUV II) to measure dissolved oxgyen concentrations in Greenwich Bay, Rhode Island. Utilization of an AUV to rapidly move con


title: A Hybrid Password Authentication Scheme Based on Shape and Text

abstract: Text-based password authentication scheme tends to be more vulnerable to attacks such as shoulder surfing or a hidden camera. To overcome the vulnerabilities of traditional methods, visual or graphical password schemes have been developed as possible alternative solutions to text-based password schemes. Since it also has some drawbacks to simply adopt graphical password authentication, schemes using graphic and text have been developed. In this paper, a hybrid password authentication scheme based on shapes and texts is proposed. Shapes of strokes are used in the grid as the original passwords and users can log in with textual passwords via traditional input device. The method provides strong resistance to shoulder surfing or a hidden camera。Moreover, the scheme has high scalability and flexibility to enhance the authentication process security. The analysis of the security level of this approach is also discussed.


title: UAV as a Reliable Wingman: A Flight Demonstration

abstract: In this brief, we present the results from a flight experiment demonstrating two significant advances in software enabled control: optimization-based control using real-time trajectory generation and logical programming environments for formal analysis of control software. Our demonstration platform consisted of a human-piloted F-15 jet flying together with an autonomous T-33 jet. We describe the behavior of the system in two scenarios. In the first, nominal state communications were present and the autonomous aircraft maintained formation as the human pilot flew maneuvers. In the second, we imposed the loss of high-rate communications and demonstrated an autonomous safe "lost wingman" procedure to increase separation and reacquire contact. The flight demonstration included both a nominal formation flight component and an execution of the lost wingman scenario.