title: DESIR: Decoy-enhanced seamless IP randomization

abstract: Sophisticated adversaries usually initiate their attacks with a reconnaissance phase to discover exploitable vulnerabilities on the targeted networks and systems. To mitigate the effectiveness of persistent reconnaissance attacks, we develop a defensive mechanism that dynamically mutates network topology with a large number of decoys to invalidate the attacker's knowledge from network scanning. We combine the IP randomization technique with decoy techniques and solve two challenges, namely, service availability to legitimate users and service security against unauthorized users. First, our solution can minimize the probability of the real servers being identified and compromised by unauthorized users through deploying a large number of decoy nodes, which change their IP addresses along with the real servers to prolong the scanning time of the attackers. Second, our solution can ensure seamless connection migration so that all existing communication connections between the legitimate users and the servers are always kept alive even after the servers migrate to different IP addresses multiple times. We implement a virtual machine based system prototype and evaluate it using state-of-the-art scanning techniques. Both theoretical analysis and experimental results show that our solution can effectively mitigate network reconnaissance attacks without sacrificing service availability.


title: Active-clamp forward converter with asymmetric transformer turns for reducing transformer DC offset current

abstract: An active-clamp forward (ACF) converter is widely used in low-to-medium power applications because of simple structure and high efficiency. However, a transformer of the ACF converter with double-ended rectifier (DER) has a DC offset current to satisfy the current-second balance for a clamp capacitor. The transformer DC offset current can increase the susceptibility to transformer saturation and a size of transformer to prevent saturation. Moreover, it can increase the core loss of transformer. In this letter, to reduce the DC offset current of transformer in the ACF converter with DER, an ACF converter with asymmetric transformer turns (ATTs) is proposed. Since the proposed converter can have a low transformer DC offset current, the size and the core loss of transformer can be reduced. The operational principle of the proposed converter is analyzed and verified experimentally.


title: The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

abstract: A variety of learning objectives have been recently proposed for latent variable generative models. We show that many of them, including InfoGAN, ALI/BiGAN, ALICE, CycleGAN, VAE, $\beta$-VAE, adversarial autoencoders, AVB, AS-VAE and InfoVAE, etc, are Lagrangian duals of the same primal optimization problem for fixed values of the Lagrange multipliers. Based on this observation, we provide an exhaustive characterization of all the objectives that belong to this class. Next, we propose a dual optimization method where we optimize model parameters as well as the Lagrangian multipliers. This method achieves Pareto near-optimal solutions in terms of optimizing primal objectives and modeling correct distributions.


Big Data Analytics


title: Efficient implementation of a real-time lane departure warning system

abstract: Because of the increasing number of population of vehicles, the road traffic accidents are becoming more and more serious in recent years. Hence, improving driver assistance systems for security has become an important area of research. This paper presents a robust lane detection and tracking system based on monocular vision. We use the Lane Departure Warning (LDW) systems to detect the position of the vehicle with respect to the lane boundary. An algorithm to detect the road lane marking and to control the direction of the vehicle is proposed. Our work consists in establishing a Region Of Interest (ROI) of the road images, data pre-processing using the Gaussian filter and then apply the Canny edge detector to enhance lane boundaries. A method to extract lane boundaries based on color information and image segmentation by using the histogram threshold, Hough transform is proposed, and the current vehicle position is obtained. In such a case, we can decide if the vehicle is doing lane departure used on the vehicle's position, the system sends a warning message to the driver. Our proposed algorithm works accurately with various lighting conditions as well as on different road types.


title: Sensor Data Acquisition and Processing Parameters for Human Activity Classification

abstract: It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes) has an impact on the classification accuracy. For Ambient Assisted Living (AAL), no clear information to select these parameters exists, hence a wide variety and inconsistency across today's literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL) event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA) based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters) and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS), Mean, Signal Magnitude Area (SMA), Signal Vector Magnitude (here SMV), Energy, Entropy, FFTPeak, Standard Deviation (STD). The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve.


title: Learning from facial aging patterns for automatic age estimation

abstract: Age Specific Human-Computer Interaction (ASHCI) has vast potential applications in daily life. However, automatic age estimation technique is still underdeveloped. One of the main reasons is that the aging effects on human faces present several unique characteristics which make age estimation a challenging task that requires non-standard classification approaches. According to the speciality of the facial aging effects, this paper proposes the AGES (AGing pattErn Subspace) method for automatic age estimation. The basic idea is to model the aging pattern, which is defined as a sequence of personal aging face images, by learning a representative subspace. The proper aging pattern for an unseen face image is then determined by the projection in the subspace that can best reconstruct the face image, while the position of the face image in that aging pattern will indicate its age. The AGES method has shown encouraging performance in the comparative experiments either as an age estimator or as an age range estimator.


title: Argumentation Mining in User-Generated Web Discourse

abstract: The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.


title: The challenge of real-time multi-agent systems for enabling IoT and CPS

abstract: Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application domains particularly include three major characteristics: intelligence, autonomy and real-time behavior. Multi-Agent Systems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to comply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from current theories, standards, and technological implementations. In particular, internal agent schedulers, communication middlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.


title: Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization

abstract: Automatic text summarization is a major area of research in the domain of information systems. Most of the methods requires domain knowledge in order to produce a coherent and meaningful summary. In Extractive text summarization, sentences are scored on some features. A large number of feature based scoring methods have been proposed for extractive automatic text summarization by researchers. This paper reviews features for sentence scoring. The results on combinations of various features for scoring are discussed. ROUGE-N is used to evaluate generated summary with abstractive summary of DUC 2002 dataset.


title: CT artifacts: Causes and reduction techniques

abstract: Artifacts are commonly encountered in clinical CT and may obscure or simulate pathology. There are many different types of CT artifacts, including noise, beam hardening, scatter, pseudoenhancement, motion, cone-beam, helical, ring and metal artifacts. We review the cause and appearance of each type of artifact, correct some popular misconceptions and describe modern techniques for artifact reduction. Noise can be reduced using iterative reconstruction or by combining data from multiple scans. This enables lower radiation dose and higher resolution scans. Metal artifacts can also be reduced using iterative reconstruction, resulting in a more accurate diagnosis. Dual- and multi-energy (photon counting) CT can reduce beam hardening and provide better tissue contrast. Methods for reducing noise and out-of-field artifacts may enable ultra-high resolution limited field of view imaging of tumors and other structures.


title: Role of HLA Protein Three-dimensional Conformation Difference in Unrelated Hematopoietic Stem Cell Transplantation

abstract: This study was purposed to investigate the value of Histocheck and HLA-Matchmaker softwares in evaluating influence of HLA protein three dimensional conformation among individuals on outcome of unrelated donor hematopoietic stem cell transplantation(URHSCT).Data of the HLA-A/B/C/DRB1/DQB1 genotypes from 62 cases of URHSCT(HLA-allele 10/10 match 30 cases,9/10 match 32 cases) were input into Histocheck and HLA-Matchmaker softwares respectively.The relationship between the software dissimilar scores and the 1 year overall survival(OS),incidence of aGVHD of Ⅲ-Ⅳ grade and relapse rate was analyzed.The results showed that(1) with increase of the Histocheck scores,incidence of aGVHD of Ⅲ-Ⅳ increased from 0% to 20%(p=0.25),while no or mild aGVHD occurred in 70% cases with the high scores.For the relapsed cases,there was no significant difference between the cases with low scores and with highest scores(relapse rate 20%) except that 9 cases had no relapse in the group with higher score(11-20).(2) the analysis using HLA matchmaker software showed that incidence of aGVHD of Ⅲ-Ⅳ grade increased with the increase of numbers of mismatch Eplets,arranging from 0% to 30%,the incidence of moderate aGVHD reduced(p=0.019),whereas 60% cases in highest scores group had moderate aGVHD.No relapse occurred in the group with higher scores(≥3)(n=10),whereas high relapse rate appeared in the lower score group(20%,p=0.54).It is concluded that the value of Histocheck and HLA-Mtchmaker software for analysing the outcome of URHSCT may be similar despite of different calculating methods;for the certain pair of recipient and donor,correlation of the two score systems with incidence of aGVHD and relapse rate is similar,but with less accuracy;The HLA Matchmaker software appears better than Histocheck software in terms of correlation.


title: Dissolution of SO3 within a lithium phosphate glass network and structure–property relationships

abstract: The relationship between the structure and properties of glasses with composition 50Li2O.xSO3.(50−x)P2O5 (x=10–30, mol%) has been studied. SO3 can be dissolved into lithium phosphate glasses up to at least 20 mol%, while for glasses containing as much as 30 mol% SO3 it is present as crystalline Li2SO4, as seen by XRD and TEM. 31 P NMR experiments have shown no bonds of the type P–O–S, thus it is thought that Li2SO4 can be homogeneously dissolved within the lithium phosphate glass matrix without direct bonding between phosphate groups and SO4 2− ions. Lithium sulphate enriched regions then become saturated and precipitation of lithium sulphate crystals results. The substitution of P2O5 by SO3 lowers the glass transition temperature and molar volume while increasing the electrical conductivity of the glasses.


title: Cross site scripting (XSS) attack detection using intrustion detection system

abstract: Everyone is now relying on the Internet for our innumerable kind of work; this has increased the opportunity for attackers to corrupt data and make vulnerable. Nowadays diverse kind of attacks is being launched in Cyber Space among which Cross-Site Scripting (Web Application Attack) is amongst top attacks of all time. Proposed work, suggest an outline for a system that can detect Cross-Site Scripting (known as XSS) attack using Intrusion Detection system (IDS). This work focuses on the detection of XSS attack using intrusion detection system. Here attack signature is utilized to detect XSS attack. To test the usefulness and effectiveness of proposed work a proof of concept prototype has been implemented using SNORT IDS. It is observed that proposed system correctly detected XSS attack.


title: On the Performance of Convolutional Neural Networks for Side-Channel Analysis

abstract: In this work, we ask a question whether Convolutional Neural Networks are more suitable for side-channel attacks than some other machine learning techniques and if yes, in what situations. Our results point that Convolutional Neural Networks indeed outperform machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for Convolutional Neural Networks when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similarly or sometimes even better. The experiments with guessing entropy indicate that methods like Random Forest or XGBoost could perform better than Convolutional Neural Networks for the datasets we investigated.


title: Immersive visualizations in a VR Cave environment for the training and enhancement of social skills for children with autism

abstract: Autism is a complex developmental disorder characterized by severe impairment in social, communicative, cognitive and behavioral functioning. Several studies investigated the use of technology and Virtual Reality for social skills training for people with autism with promising and encouraging results (D. Strickland, 1997; Parsons S. & Cobb S., 2011). In addition, it has been demonstrated that Virtual Reality technologies can be used effectively by some people with autism, and that it had helped or could help them in the real world; (S. Parsons, A. Leonard, P. Mitchell, 2006; S. Parsons, P. Mitchell, 2002). The goal of this research is to design and develop an immersive visualization application in a VR CAVE environment for educating children with autism. The main goal of the project is to help children with autism learn and enhance their social skills and behaviours. Specifically, we will investigate whether a VR CAVE environment can be used in an effective way by children with mild autism, and whether children can benefit from that and apply the knowledge in their real life.


title: From Cloud to Fog Computing: A Review and a Conceptual Live VM Migration Framework

abstract: Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined.


title: Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids

abstract: Internet of Things (IoT) has recently emerged as an enabling technology for context-aware and interconnected “smart things.” Those smart things along with advanced power engineering and wireless communication technologies have realized the possibility of next generation electrical grid, smart grid, which allows users to deploy smart meters, monitoring their electric condition in real time. At the same time, increased environmental consciousness is driving electric companies to replace traditional generators with renewable energy sources which are already productive in user’s homes. One of the most incentive ways is for electric companies to institute electricity buying-back schemes to encourage end users to generate more renewable energy. Different from the previous works, we consider renewable energy buying-back schemes with dynamic pricing to achieve the goal of energy efficiency for smart grids. We formulate the dynamic pricing problem as a convex optimization dual problem and propose a day-ahead time-dependent pricing scheme in a distributed manner which provides increased user privacy. The proposed framework seeks to achieve maximum benefits for both users and electric companies. To our best knowledge, this is one of the first attempts to tackle the time-dependent problem for smart grids with consideration of environmental benefits of renewable energy. Numerical results show that our proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.


Econometric Modelling of Markov-Switching Vector Autoregressions using MSVAR for Ox


title: Autonomous predictive driving for blind intersections

abstract: This paper presents a model for safe driving at blind intersections and its integration to a local planner based on a Frenet frame. The model predicts potential moving obstacles from blind intersections to proactively slow down to avoid potential collisions. The derivation of the model is described and its parameters are detailed. The local planner computes smooth trajectories with smooth velocity profiles so that the vehicle can follow the paths without jerk and sudden accelerations resulting in safe and comfortable navigation. Experimental results in simulation and in the real field with an autonomous car, show that the proposed predictive driving framework can reproduce human expert driver's trajectories and velocities when facing blind intersections.


title: Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization

abstract: Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, through our extensive experimentation, we have noticed two major problems in the DKT model. The first problem is that the model fails to reconstruct the observed input. As a result, even when a student performs well on a KC, the prediction of that KC's mastery level decreases instead, and vice versa. Second, the predicted performance for KCs across time-steps is not consistent. This is undesirable and unreasonable because student's performance is expected to transit gradually over time. To address these problems, we introduce regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in prediction. Experiments show that the regularized loss function effectively alleviates the two problems without degrading the original task of DKT.


Putting a Price Tag on Personal Information - A Literature Review


title: Performance evaluation of Latent Dirichlet Allocation in text mining

abstract: This paper introduces three classic models of statistical topic models: Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Indexing (PLSI) and Latent Dirichlet Allocation (LDA). Then a method of text classification based on LDA model is briefly described, which uses LDA model as a text representation method. Each document means a probability distribution of fixed latent topic sets. Next, Support Vector Machine (SVM) is chose as classification algorithm. Finally, the evaluation parameters in classification system of LDA with SVM are higher than other two methods which are LSI with SVM and VSM with SVM, showing a better classification performance.


title: Aperture scanning Fourier ptychographic microscopy

abstract: Fourier ptychographic microscopy (FPM) is implemented through aperture scanning by an LCOS spatial light modulator at the back focal plane of the objective lens. This FPM configuration enables the capturing of the complex scattered field for a 3D sample both in the transmissive mode and the reflective mode. We further show that by combining with the compressive sensing theory, the reconstructed 2D complex scattered field can be used to recover the 3D sample scattering density. This implementation expands the scope of application for FPM and can be beneficial for areas such as tissue imaging and wafer inspection.


title: Towards Benchmarking IaaS and PaaS Clouds for Graph Analytics

abstract: Cloud computing is a new paradigm for using ICT services—only when needed and for as long as needed, and paying only for service actually consumed. Benchmarking the increasingly many cloud services is crucial for market growth and perceived fairness, and for service design and tuning. In this work, we propose a generic architecture for benchmarking cloud services. Motivated by recent demand for data-intensive ICT services, and in particular by processing of large graphs, we adapt the generic architecture to Graphalytics, a benchmark for distributed and GPU-based graph analytics platforms. Graphalytics focuses on the dependence of performance on the input dataset, on the analytics algorithm, and on the provisioned infrastructure. The benchmark provides components for platform configuration, deployment, and monitoring, and has been tested for a variety of platforms. We also propose a new challenge for the process of benchmarking data-intensive services, namely the inclusion of the data-processing algorithm in the system under test; this increases significantly the relevance of benchmarking results, albeit, at the cost of increased benchmarking duration.


title: A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection

abstract: This paper presents and evaluates a deep learning architecture for automated basal cell carcinoma cancer detection that integrates (1) image representation learning, (2) image classification and (3) result interpretability. A novel characteristic of this approach is that it extends the deep learning architecture to also include an interpretable layer that highlights the visual patterns that contribute to discriminate between cancerous and normal tissues patterns, working akin to a digital staining which spotlights image regions important for diagnostic decisions. Experimental evaluation was performed on set of 1,417 images from 308 regions of interest of skin histopathology slides, where the presence of absence of basal cell carcinoma needs to be determined. Different image representation strategies, including bag of features (BOF), canonical (discrete cosine transform (DCT) and Haar-based wavelet transform (Haar)) and proposed learned-from-data representations, were evaluated for comparison. Experimental results show that the representation learned from a large histology image data set has the best overall performance (89.4% in F-measure and 91.4% in balanced accuracy), which represents an improvement of around 7% over canonical representations and 3% over the best equivalent BOF representation.


title: Manual lymph drainage for equine lymphoedema-treatment strategy and therapist training

abstract: Idiopathic primary lymphoedema (‘swollen legs’) and secondary lymphoedema following trauma or infection (e.g. wound oedema or phlegmon/cellulitis) are frequently encountered problems in equine veterinary practice. At present, therapy of these diseases is often based on symptomatic and empirical treatment, using NSAIDs, antibiotics and compression bandages (Fig 1). Less consideration is given to the underlying disturbance of the lymphatic system. In order to understand the susceptibility of horses to lymphoedema better, it must be remembered that the horse is a flight animal, normally in constant movement. Modern methods of keeping horses in box housing, with limited free exercise and concentrated training sessions compromise the lymph system in apparently healthy horses, particularly in the feet (Laue, 1987). Under these conditions, even minor trauma (e.g. skin wounds) can lead to serious oedema. Manual lymph drainage (MLD) is a treatment option applied directly to the lymphatic system. Furthermore, this method does not contravene anti-doping regulations and has no side effects. Although established for decades in human medicine (Foldi et al. 2006), it is only slowly gaining a foothold in veterinary medicine.


title: Learning Generative Models with Visual Attention

abstract: Attention has long been proposed by psychologists as important for effectively dealing with the enormous sensory stimulus available in the neocortex. Inspired by the visual attention models in computational neuroscience and the need of object-centric data for generative models, we describe for generative learning framework using attentional mechanisms. Attentional mechanisms can propagate signals from region of interest in a scene to an aligned canonical representation, where generative modeling takes place. By ignoring background clutter, generative models can concentrate their resources on the object of interest. Our model is a proper graphical model where the 2D Similarity transformation is a part of the top-down process. A ConvNet is employed to provide good initializations during posterior inference which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our model can robustly attend to face regions of novel test subjects. More importantly, our model can learn generative models of new faces from a novel dataset of large images where the face locations are not known.


title: Formal specification of an information gateway service interface in Estelle

abstract: Abstract This paper introduces a new communication interface, the Information Gateway Service Interface (IGSI) and specifies it in Estelle, one of ISO's standard formal description techniques. IGSI allows a user to select and connect to a variety of information services using a single interactive access device by providing a standard interface for the interworking between communication switches and host computers. Such an interface will result in widening the range of accessible value-added services across communication networks. It will also encourage the development of standard client-server computer supported telecommunications applications in which the communication switch plays the role of the server.


title: An Arduino-based EIS with a logarithmic amplifier for corrosion monitoring

abstract: Electrochemical Impedance Spectroscopy (EIS) is an interesting non-invasive technique to study the corrosion layer of metallic artifacts as well as to test the integrity of protective coatings, however commercial EIS systems are rather expensive devices. This paper describes an extremely low cost realization of an EIS system that is based on a simple commercial Arduino Board whose cost is below 50$. The instrument is able to perform impedance measurements in the range 1 kΩ to 1 GΩ, thanks to a specifically designed front end based on a logarithmic amplifier, and in the frequency range of 0.01 Hz to 100 kHz. The instrument can work as a stand-alone device or connected to a PC. The required current is of about 100 mA, thus, the USB port of a common laptop can power the instrument for several hours without external supply. It is also possible to employ several instruments at the same time for a massive parallel measurement campaign with a negligible cost. The instrument accuracy is of about 2% for impedances in the range of 1 kΩ to 100 MΩ, with uncertainty on the phase of about 2°.


title: Men of Energy and Snap: The Origins and Early Years of the Billings Public Library

abstract: This essay examines the origins and early years of the Parmly Billings Public Library. The author considers the creation of the library in relation to the funding and growth of Billings, Montana. Indeed, libraries are seen as one aspect of a hierarchy of urban growth along with schools and churches. Also considered are the reasons, stated and unstated, that the townspeople wanted a library. Social portraits of the principals involved are provided.


title: From the Internet of Things to the Internet of People

abstract: There's growing interest in developing applications for the Internet of Things. Such applications' main objective is to integrate technology into people's everyday lives, to be of service to them en masse. The form in which this integration is implemented, however, still leaves much room for improvement. Usually, the user must set parameters within the application. When the person's context changes, they have to manually reconfigure the parameters. What was meant to be a commodity in an unforeseen situation then becomes extra noise. This article describes a reference architecture that improves how people are integrated with the IoT, with smartphones doing the connecting. The resulting integration opens the way to new IoT scenarios supporting evolution towards the Internet of People.


title: Investigating QoS of Real-World Web Services

abstract: Quality of service (QoS) is widely employed for describing nonfunctional characteristics of web services. Although QoS of web services has been investigated intensively in the field of service computing, there is a lack of real-world web service QoS data sets for validating various QoS-based techniques and models. To investigate QoS of real-world web services and to provide reusable research data sets for future research, we conduct several large-scale evaluations on real-world web services. First, addresses of 21,358 web services are obtained from the Internet. Then, three large-scale real-world evaluations are conducted. In our evaluations, more than 30 million real-world web service invocations are conducted on web services in more than 80 countries by users from more than 30 counties. Detailed evaluation results are presented in this paper and comprehensive web service QoS data sets are publicly released online.


title: Develop a Multi-Channel Legal Knowledge Service Center with Knowledge Mining Capability

abstract: In the knowledge centric business environment, knowledge management (KM) is the process of creating, accumulating and accessing the values from intangible assets. Owing to the fast development of information technology and the global reach of legal documents, patents, trademarks and copyrights, technical legal services, organizations are challenged to efficiently provide customized services. They must anticipate the various customer needs to increase competitiveness and customer loyalty. In order to efficiently fulfill the client requests, lawyers and patent consultants can provide customer centric services by using contact center applications and data analysis methodologies. Multi-channel service centers that integrate voice, fax, e-mail and web applications are increasingly being used to manage customer interactions, queries, complaints and service requests. A multi-channel legal knowledge service center not only provides greater access but also provides the means to analyze customer interaction data. Data collected through the channels must be transformed into meaningful knowledge to benefit corporations in capturing customer needs and exploring the knowledge development trend. This paper depicts the development of contact center and data analysis methodologies to demonstrate the feasibility and the versatility of legal knowledge service center functions. The aim of this paper is to provide a new knowledge management model for legal service providers to enhance their operation effectiveness and efficiency.


title: Machine Learned Learning Machines

abstract: There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary adaptation and machine learning into one approach. Our focus is on machines that can learn during their lifetime, but instead of equipping them with a machine learning algorithm we aim to let them evolve their ability to learn by themselves. We use evolvable networks of probabilistic and deterministic logic gates, known as Markov Brains, as our computational model organism. The ability of Markov Brains to learn is augmented by a novel adaptive component that can change its computational behavior based on feedback. We show that Markov Brains can indeed evolve to incorporate these feedback gates to improve their adaptability to variable environments. By combining these two methods, we now also implemented a computational model that can be used to study the evolution of learning.


title: Conversion of Natural Language Query to SQL Query

abstract: This paper present an approach to automate the conversion of Natural Language Query to SQL Query effectively. Structured Query Language is a powerful tool for managing data held in a relational database management system. To retrieve or manage data user have to enter the correct SQL Query. But the users who don't have any knowledge about SQL are unable to retrieve the required data. To overcome this we proposed a model in Natural Language Processing for converting the Natural Language Query to SQL query. This helps novice user to get required content without knowing any complex details about SQL. This system can also deal with complex queries. This system is designed for Training and Placement cell officers who work on student database but don't have any knowledge about SQL. In this system, user can also enter the query using speech. System will convert speech into the text format. This query will get transformed to SQL query. System will execute the query and gives output to the user.


title: Multi-Domain and Multi-Task Learning for Human Action Recognition

abstract: Domain-invariant (view-invariant and modality-invariant) feature representation is essential for human action recognition. Moreover, given a discriminative visual representation, it is critical to discover the latent correlations among multiple actions in order to facilitate action modeling. To address these problems, we propose a multi-domain and multi-task learning (MDMTL) method to: 1) extract domain-invariant information for multi-view and multi-modal action representation and 2) explore the relatedness among multiple action categories. Specifically, we present a sparse transfer learning-based method to co-embed multi-domain (multi-view and multi-modality) data into a single common space for discriminative feature learning. Additionally, visual feature learning is incorporated into the multi-task learning framework, with the Frobenius-norm regularization term and the sparse constraint term, for joint task modeling and task relatedness-induced feature learning. To the best of our knowledge, MDMTL is the first supervised framework to jointly realize domain-invariant feature learning and task modeling for multi-domain action recognition. Experiments conducted on the INRIA Xmas Motion Acquisition Sequences data set, the MSR Daily Activity 3D (DailyActivity3D) data set, and the Multi-modal & Multi-view & Interactive data set, which is the most recent and largest multi-view and multi-model action recognition data set, demonstrate the superiority of MDMTL over the state-of-the-art approaches.


Adoption of e-Payment Systems : A Review of Literature


Citances: Citation Sentences for Semantic Analysis of Bioscience Text


title: A Comparison of Human and Machine Learning-based Accuracy for Valence Classification of Subjects in Video Fragments

abstract: Facial expressions are the primary way to show one’s emotional state. Automatic recognition of these cues from video using software allows for various improvements in human-computer interaction, ranging from improved feedback for recommender systems to automatic labeling of movies according to the emotions they induce. A number of affective display databases have been created to aid development in this field. These datasets are frequently available for academic use [1, 2, 3], use picture or video stimuli and range from highly controlled [1, 2] to more natural settings [3]. We observe that methods using these datasets report accuracy figures that leave room for improvement [5].


title: On the modeling and forecasting of call center arrivals

abstract: We review and discuss the key issues in building statistical models for the call arrival process in telephone call centers, and then we survey and compare various types of models proposed so far. These models are used both for simulation and to forecast incoming call volumes to make staffing decisions and build (or update) work schedules for agents who answer those calls. Commercial software and call center managers usually base their decisions solely on point forecasts, given in the form of mathematical expectations (conditional on current information), but distributional forecasts, which come in the form of (conditional) probability distributions, are generally more useful, in particular in the context of simulation. Building realistic models is not simple, because arrival rates are themselves stochastic, time-dependent, dependent across time periods and across call types, and are often affected by external events. As an illustration, we evaluate the forecasting accuracy of selected models in an empirical study with real-life call center data.


title: “Radio inside my head”: A curious case of early onset “stuck song syndrome” or obsessive-compulsive disorder with predominant musical obsession

abstract: Musical obsessions also called “stuck song syndrome” is a topic of limited research. It is among the most poorly understood and rare phenomenology. There are only a few cases reported across the world which describes it. Subjects usually present with complaints of continuous play of a musical tune in the head which the subject is unable to remove. Because of its similarity in presentation with auditory musical hallucination, it presents a diagnostic dilemma to the clinician. Here, we present the case of a 25-year-old man who presented with this particular phenomenon, and the approach we took to manage it.


title: A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing

abstract: The rapid evolution of real-time multimedia applications requires Quality of Service (QoS) based multicast routing in underlying computer networks. The constrained Steiner Tree, as the underpinning mathematical structure, is a well-known NP-complete problem. In this paper we investigate a variable neighborhood descent (VND) search, a variant of variable neighborhood search, for the delay-constrained least-cost (DCLC) multicast routing problem. The neighborhood structures designed in the VND approaches are based on the idea of path replacement in trees. They are simple, yet effective operators, enabling a flexible search over the solution space of this complex problem with multiple constraints. A large number of simulations demonstrate that our algorithm is highly efficient in solving the DCLC multicast routing problem in terms of the tree cost and execution time. To our knowledge, this is the first study of VND algorithm on the DCLC multicast routing problem. It outperforms other existing algorithms over a range of problem instances.


title: Public transport networks: empirical analysis and modeling

abstract: We use complex network concepts to analyze statistical properties of urban public transport networks (PTN). To this end, we present a comprehensive survey of the statistical properties of PTNs based on the data of fourteen cities of so far unexplored network size. Especially helpful in our analysis are different network representations. Within a comprehensive approach we calculate PTN characteristics in all of these representations and perform a comparative analysis. The standard network characteristics obtained in this way often correspond to features that are of practical importance to a passenger using public traffic in a given city. Specific features are addressed that are unique to PTNs and networks with similar transport functions (such as networks of neurons, cables, pipes, vessels embedded in 2D or 3D space). Based on the empirical survey, we propose a model that albeit being simple enough is capable of reproducing many of the identified PTN properties. A central ingredient of this model is a growth dynamics in terms of routes represented by self-avoiding walks.


Does size matter?


title: Design, modeling and control of omni-directional aerial robot

abstract: We propose a novel multi-rotor flying platform, ODAR (omni-directional aerial robot), which is fully-actuated (i.e., can assume arbitrary motion in SE(3) or generate arbitrary control wrench in se(3)) by six opportunistically distributed rotors, each driven by reversible ESC (electronic speed controller) for bi-directional thrust generation. Due to this omni-directional wrench generation, the ODAR system can realize such powerful behaviors impossible with conventional multi-rotor flying platforms as: 360° photo/video shooting while holding its position for VR scene generation; or resisting sideway gust while keeping its attitude and exerting downward pushing force larger than its own weight for aerial manipulation applications. This paper presents optimal mechanical design, modeling and control, hardware and software implementation, and experimental verification of the ability of the ODAR system to attain those behaviors impossible with the conventional drones as stated above.


title: Parietal cortex and attention

abstract: The parietal lobe forms about 20% of the human cerebral cortex and is divided into two major regions, the somatosensory cortex and the posterior parietal cortex. Posterior parietal cortex, located at the junction of multiple sensory regions, projects to several cortical and subcortical areas and is engaged in a host of cognitive operations. One such operation is selective attention, the process where by the input is filtered and a subset of the information is selected for preferential processing. Recent neuroimaging and neuropsychological studies have provided a more fine-grained understanding of the relationship between brain and behavior in the domain of selective attention.


title: Loneliness and health: Potential mechanisms

abstract: In Study 1, we assessed autonomic activity, salivary cortisol levels, sleep quality, and health behaviors in 89undergraduate students selected based on pretests to be among the top or bottom quintile in feelings of loneliness.In Study 2, we assessed blood pressure, heart rate, salivary cortisol levels, sleep quality, and health behaviors in 25older adults whose loneliness was assessed at the time of testing at their residence.


Using subtitles to deal with Out-of-Domain interactions


title: Smart Home: Integrating Internet of Things with Web Services and Cloud Computing

abstract: Smart Home minimizes user's intervention in monitoring home settings and controlling home appliances. This paper presents an approach to the development of Smart Home applications by integrating Internet of Things (IoT) with Web services and Cloud computing. The approach focuses on: (1) embedding intelligence into sensors and actuators using Arduino platform, (2) networking smart things using Zigbee technology, (3) facilitating interactions with smart things using Cloud services, (4) improving data exchange efficiency using JSON data format. Moreover, we implement three use cases to demonstrate the approach's feasibility and efficiency, i.e., measuring home conditions, monitoring home appliances, and controlling home access.


title: A Comparative Study Ontology Building Tools for Semantic Web Applications

abstract: Ontologies have recently received popularity in the area of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all possible existing ontologies and ontology management tools (Protege 3.4, Apollo, IsaViz & SWOOP) that are freely available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d) market status and penetration. The results of the review in ontologies are analyzed for each application area, such as transport, tourism, personal services, health and social services, natural languages and other HCI-related domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks. Although each tool provides different functionalities, most of the users just use only one, because they are not able to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different ontologies with different development and management tools. The paper is also concerns the detection of commonalities and differences between the examined ontologies, both on the same domain (application area) and among different domains.


Aalborg Universitet Towards Efficient Search for Activity Trajectories


title: Multi-level Modeling for Hybrid Manufacturing Systems Using Arena and MATLAB

abstract: Considering the hybrid nature of manufacturing systems where continuity and discreteness coexist, a hybrid analytic and simulation modeling approach for manufacturing systems using Arena, MATLABpsilas Simulink and Statflow is proposed. According to different simulation demands in different resolution level, the multi-resolution model is divided into three levels, so it can reveal both microscopic continuous characteristics of cutting and global discrete feature of the whole system. ActiveX Automation, VBA technology and excel link are used to implement the communication between Arena and MATLAB. The simulation of an actual case indicates that the method is feasible and effective.


title: Open Source Software Evolution and Its Dynamics

abstract: This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution dynamics (the mechanisms and causes of change and growth). ::: We propose a multipurpose systematic approach to extracting program facts (e.g., function calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. ::: We propose that software evolution can be viewed as Punctuated Equilibrium (i.e., long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spectrograph to visualize file level changes to the implemented system structure. The other approach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. ::: We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events ( e.g., new functional requirements) according to Self-Organized Criticality (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present empirical evidence that SOC occurs in open source software systems.


title: Smart, Deep Copy-Paste

abstract: In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input. Our system naturally resolves both shading and geometric inconsistencies between source and target image, resulting in a merged result image that features the content from the pasted source image, seamlessly pasted into the target context. Our framework is based on a novel training image transformation procedure that allows to train a deep convolutional neural network end-to-end to automatically learn a representation that is suitable for copy-pasting. Our training procedure works with any image dataset without additional information such as labels, and we demonstrate the effectiveness of our system on two popular datasets, high-resolution face images and the more complex Cityscapes dataset. Our technique outperforms the current state of the art on face images, and we show promising results on the Cityscapes dataset, demonstrating that our system generalizes to much higher resolution than the training data.


title: Chronological Age vs. Biological Maturation: Implications for Exercise Programming in Youth

abstract: Biological maturation is associated with significant change to a number of physiological and structural processes throughout childhood and, in particular, adolescence. Mismatched rapid growth in the long bones relative to muscular lengthening may disrupt structure, neuromuscular function, and physical performance. Practitioners who work with school-age youth should be aware of the age-related changes that typically take place during a child's development to ensure that their strength and conditioning programming is as safe and effective as possible for enhancing performance and reducing injury risk. Although there are several methods available to assess biological maturation, practitioners who work with youth can benefit from assessment methods that are available and feasible, and that provide utility in the quantification of the degree and stages of biological maturation that affect motor performance in children and adolescents. This article synthesizes the relevant assessment methods and provides a rationale for understanding usable biological maturation assessment tools that can aid in the development of training program design for youth.


Combining Service and Learning. A Resource Book for Community and Public Service. Volume II.


title: Compact Wideband Circularly Polarized Patch Antenna Array Using Metasurface

abstract: This letter proposes a compact wideband circularly polarized (CP) antenna array, which is a set of 2 × 2 metasurface-based CP patch antennas fed by a sequential-phase (SP) network. The single element is composed of a truncated corner square patch sandwiched between the ground plane and the metasurface of a lattice of 4 × 4 periodic metal plates. These metasurface-based antennas are incorporated with the SP network of a sequentially rotated series-parallel feed to achieve wideband operation. The radiation pattern and operational bandwidth in the high-frequency region were improved by reducing the spacing between the driven patches while maintaining the structure and overall size of the metasurface. The final design, with an overall size of 64 × 64 × 2.34 mm3 (approximately 1.26λo × 1.26λo × 0.046λo at 5.9 GHz), was fabricated and measured. The antenna array has a measured | $S_{11}$ | < −10-dB bandwidth of 4.40–8.00 GHz (58.06%), a 3-dB axial ratio bandwidth of 4.75–7.25 GHz (41.67%), a 3-dB gain bandwidth of 4.8–7.0 GHz (37.3%), and a peak gain of 12.08 dBic at 6.0 GHz. In addition, the antenna array yielded a broadside, left-hand CP radiation with a symmetrical profile, low sidelobe level, and high radiation efficiency.


title: Synthetic Aperture Radar Ship Detection Using Haar-Like Features

abstract: The detection of ships at sea is a complex task made more so by adverse weather conditions, lack of night visibility and large areas of concern. Synthetic Aperture Radar imagery with large swaths can provide the needed coverage at a reduced resolution. The development of ship detection methods that can effectively detect ships despite the reduced image resolution is an important area of research. A novel ship detection method is introduced that makes use of a standard Constant False Alarm Rate prescreening step followed by a cascade classifier ship discriminator. Ships are identified using Haar-like features using AdaBoost training on the classifier with an accuracy of 89.38% and false alarm rate of 1.47 x 10-8 across a large swath Sentinel-1 and RADARSAT-2 newly created SAR dataset.


title: Optimization of the Settings of Multiphase Induction Heating System

abstract: This paper deals with the setting parameter optimization procedure for a multi-phase induction heating system considering transverse flux heating. This system is able to achieve uniform static heating of different thin/size metal pieces without movable inductor parts, yokes or magnetic screens. The goal is reached by the predetermination of the induced power density distribution using an optimization procedure that leads to the required inductor supplying currents. The purpose of the paper is to describe the optimization program with the different solution obtained and to show that some compromise must be done between the accuracy of the temperature profile and the energy consumption.


title: Prefetching on Storage Servers through Mining Access Patterns on Blocks

abstract: Distributed file systems have been widely deployed as back-end storage systems to offer I/O services for parallel/distributed applications that process large amounts of data. Data prefetching in distributed file systems is a well-known optimization technique which can mask both network and disk latency and consequently boost I/O performance. Traditionally, data prefetching is initiated by the client file systems, however, conventional prefetching schemes are not well suited for client machines that have limited memory and computing capacity. To offer an efficient prefetching approach for resource-limited client machines, this paper proposes a novel server-side prefetching mechanism. Specifically, we propose to piggyback client identification to I/O requests so that server side block access history can be put into context. On the server side, we utilize the horizontal visibility graph technique to transform per-client time series of block access sequences into a connected graph for which we employ Tarjan’s algorithm to disclose cut points in the connected graph. We express these patterns with feature tuples and we propose the X -step pattern matching algorithm to find a matching access pattern (i.e., a feature tuple) for a given block access history. Experimental results indicate that our newly proposed prefetching mechanism can ease client machines and their applications from the process of data prefetching, boosting client performance accordingly, and that it yields an attractive increase in data throughput as well.


title: Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers

abstract: As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive aggregate information from these repositories without revealing information about individual data instances. Though the differential privacy model provides a framework to analyze such mechanisms for databases belonging to a single party, this framework has not yet been considered in a multi-party setting. In this paper, we propose a privacy-preserving protocol for composing a differentially private aggregate classifier using classifiers trained locally by separate mutually untrusting parties. The protocol allows these parties to interact with an untrusted curator to construct additive shares of a perturbed aggregate classifier. We also present a detailed theoretical analysis containing a proof of differential privacy of the perturbed aggregate classifier and a bound on the excess risk introduced by the perturbation. We verify the bound with an experimental evaluation on a real dataset.


title: A Novel High-Gain Dual-Band Antenna for RFID Reader Applications

abstract: A novel high-gain dual-band antenna for radio frequency identification (RFID) reader applications covering free ISM bands of 2.45 and 5.8 GHz is presented in this letter. The antenna is composed of a U-shaped copper strip with unequal arm, a printed rectangular ring, and the ground plane. A good impedance bandwidth of 120 MHz has been achieved from 2.37 to 2.49 GHz for the lower band, while the upper band covers 420 MHz (from 5.55 to 5.97 GHz). The U-shaped feeding strip excites the rectangular ring effectively by providing a good impedance matching with over 85% of total antenna efficiency in both the frequency bands and also 9.56 and 10.17 dBi gain in the lower and higher frequency band, respectively.


The Discrete Cosine Transform ( DCT ) : Theory and Application 1


title: Areview of process fault detection and diagnosis Part II: Qualitative models and search strategies

abstract: In this part of the paper, we review qualitative model representations and search strategies used in fault diagnostic systems. Qualitative models are usually developed based on some fundamental understanding of the physics and chemistry of the process. Various forms of qualitative models such as causal models and abstraction hierarchies are discussed. The relative advantages and disadvantages of these representations are highlighted. In terms of search strategies, we broadly classify them as topographic and symptomatic search techniques. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Various forms of topographic and symptomatic search strategies are discussed.


title: Stemming Indonesian

abstract: Stemming words to (usually) remove suffixes has applications in text search, machine translation, document summarisation, and text classification. For example, English stemming reduces the words "computer", "computing", "computation", and "computability" to their common morphological root, "comput-". In text search, this permits a search for "computers" to find documents containing all words with the stem "comput-". In the Indonesian language, stemming is of crucial importance: words have prefixes, suffixes, infixes, and confixes that make matching related words difficult. In this paper, we investigate the performance of five Indonesian stemming algorithms through a user study. Our results show that, with the availability of a reasonable dictionary, the unpublished algorithm of Nazief and Adriani correctly stems around 93% of word occurrences to the correct root word. With the improvements we propose, this almost reaches 95%. We conclude that stemming for Indonesian should be performed using our modified Nazief and Adriani approach.


title: A Triangle Bending Constraint Model for Position-Based Dynamics

abstract: We present a novel bending model and constraint creation method for position-based dynamics. Our new bending model is introduced as an alternative to the current state-of-the-art dihedral bending model. Our model is motivated by geometric principles and operates on virtual triangles. It has the same cheap computational cost as the stick constraint model but with higher simulation quality and faster convergence than the dihedral bending model. Along with the model a new global bending parameter is introduced to control the curvature deformation at high precision compared to the traditional stiffness constant. Further, we propose a new constraint creation method that we believe is well suited for the triangle bending model and less affected by the underlying mesh tessellation.


title: Generative Knowledge Transfer for Neural Language Models

abstract: In this paper, we propose a generative knowledge transfer technique that trains an RNN based language model (student network) using text and output probabilities generated from a previously trained RNN (teacher network). The text generation can be conducted by either the teacher or the student network. We can also improve the performance by taking the ensemble of soft labels obtained from multiple teacher networks. This method can be used for privacy conscious language model adaptation because no user data is directly used for training. Especially, when the soft labels of multiple devices are aggregated via a trusted third party, we can expect very strong privacy protection.


title: Classification and Retrieval of Digital Pathology Scans: A New Dataset

abstract: In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000$\times$1000 (0.5mm$\times$0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch-and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80\% for CNN.


title: Measuring Information Security Awareness - A West Africa Gold Mining Environment Case

abstract: AngloGold Ashanti is an international gold mining company that has recently implemented an information security awareness program worldwide at all of their operations. Following the implementation, there was a normal business need to evaluate and measure the success and effectiveness of the program. A measuring tool that can be applied globally and that addressed AngloGold Ashanti’s unique requirements was developed and applied at the mining sites located in the West Africa region. The objective of this paper is, firstly, to give a brief overview on the measuring tool developed and, secondly to report on the application and results in the West Africa region.


title: Intellectual capital disclosure in Malaysia

abstract: This study investigates intellectual capital (IC) disclosure in Malaysian public listed companies. Specifically, this research examines the relationship between IC disclosure and companies' profitability, productivity, and firm size. We measure the IC disclosure using the IC disclosure index and I C disclosure frequency. The sample includes 255 firm-year observations from 2006-2008. The results from our content analysis confirm those of prior research that the IC disclosure has been increasing over time. The study finds that human capital is the most reported intellectual capital disclosure. In addition, we also find that firm size positively contribute to the disclosure of IC. The result of this study added to the body of literature by providing evidence on the relationship between IC disclosure and firms' characteristics.


title: Information Theory and the IrisCode

abstract: Iris recognition has legendary resistance to false matches, and the tools of information theory can help to explain why. The concept of entropy is fundamental to understanding biometric collision avoidance. This paper analyses the bit sequences of IrisCodes computed both from real iris images and from synthetic white noise iris images, whose pixel values are random and uncorrelated. The capacity of the IrisCode as a channel is found to be 0.566 bits per bit encoded, of which 0.469 bits of entropy per bit is encoded from natural iris images. The difference between these two rates reflects the existence of anatomical correlations within a natural iris, and the remaining gap from one full bit of entropy per bit encoded reflects the correlations in both phase and amplitude introduced by the Gabor wavelets underlying the IrisCode. A simple two-state hidden Markov model is shown to emulate exactly the statistics of bit sequences generated both from natural and white noise iris images, including their imposter distributions, and may be useful for generating large synthetic IrisCode databases.


title: Design of Novel Reconfigurable Reflectarrays With Single-Bit Phase Resolution for Ku-Band Satellite Antenna Applications

abstract: Reconfigurable reflectarray antennas operating in Ku-band are presented in this paper. First, a novel multilayer unit-cell based on polarization turning concept is proposed to achieve the single-bit phase shift required for reconfigurable reflectarray applications. The principle of the unit-cell is discussed using the current model and the space match condition, along with simulations to corroborate the design and performance criteria. Then, an offset-fed configuration is developed to verify performance of the unit-cell in antenna application, and its polarization transformation property is elaborated. Finally, an offset-fed reflectarray with $10 \times 10$ elements is developed and fabricated. The dual-polarized antenna utilizes the control code matrices to accomplish a wide angle beam-scanning. A full-wave analysis is applied to the reflectarray, and detailed results are presented and discussed. This electronically steerable reflectarray antenna has significant potential for satellite applications, due to its wide operating band, simple control and beam-scanning capability.


Dynamic Virtual Address Range Adjustment for Intra-Level Privilege Separation on ARM


title: Controllable Electrochemical Synthesis of Copper Sulfides as Sodium-Ion Battery Anodes with Superior Rate Capability and Ultralong Cycle Life.

abstract: Sodium-ion batteries (SIBs) are prospective alternative to lithium-ion batteries for large-scale energy-storage applications, owing to the abundant resources of sodium. Metal sulfides are deemed to be promising anode materials for SIBs due to their low-cost and eco-friendliness. Herein, for the first time, series of copper sulfides (Cu2S, Cu7S4, and Cu7KS4) are controllably synthesized via a facile electrochemical route in KCl-NaCl-Na2S molten salts. The as-prepared Cu2S with micron-sized flakes structure is first investigated as anode of SIBs, which delivers a capacity of 430 mAh g-1 with a high initial Coulombic efficiency of 84.9% at a current density of 100 mA g-1. Moreover, the Cu2S anode demonstrates superior capability (337 mAh g-1 at 20 A g-1, corresponding to 50 C) and ultralong cycle performance (88.2% of capacity retention after 5000 cycles at 5 A g-1, corresponding to 0.0024% of fade rate per cycle). Meanwhile, the pseudocapacitance contribution and robust porous structure in situ formed during cycling endow the Cu2S anodes with outstanding rate capability and enhanced cyclic performance, which are revealed by kinetics analysis and ex situ characterization.


title: The immediate effects of traditional Thai massage on heart rate variability and stress-related parameters in patients with back pain associated with myofascial trigger points.

abstract: The purpose of this study was to investigate the immediate effects of traditional Thai massage (TTM) on stress-related parameters including heart rate variability (HRV), anxiety, muscle tension, pain intensity, pressure pain threshold, and body flexibility in patients with back pain associated with myofascial trigger points. Thirty-six patients were randomly allocated to receive a 30-min session of either TTM or control (rest on bed) for one session. Results indicated that TTM was associated with significant increases in HRV (increased total power frequency (TPF) and high frequency (HF)), pressure pain threshold (PPT) and body flexibility (p<0.05) and significant decreases in self-reported pain intensity, anxiety and muscle tension (p<0.001). For all outcomes, similar changes were not observed in the control group. The adjusted post-test mean values for TPF, HF, PPT and body flexibility were significantly higher in the TTM group when compared with the control group (p<0.01) and the values for pain intensity, anxiety and muscle tension were significantly lower. We conclude that TTM can increase HRV and improve stress-related parameters in this patient population.


title: Physical Design Obfuscation of Hardware: A Comprehensive Investigation of Device- and Logic-Level Techniques

abstract: The threat of hardware reverse engineering is a growing concern for a large number of applications. A main defense strategy against reverse engineering is hardware obfuscation. In this paper, we investigate physical obfuscation techniques, which perform alterations of circuit elements that are difficult or impossible for an adversary to observe. The examples of such stealthy manipulations are changes in the doping concentrations or dielectric manipulations. An attacker will, thus, extract a netlist, which does not correspond to the logic function of the device-under-attack. This approach of camouflaging has garnered recent attention in the literature. In this paper, we expound on this promising direction to conduct a systematic end-to-end study of the VLSI design process to find multiple ways to obfuscate a circuit for hardware security. This paper makes three major contributions. First, we provide a categorization of the available physical obfuscation techniques as it pertains to various design stages. There is a large and multidimensional design space for introducing obfuscated elements and mechanisms, and the proposed taxonomy is helpful for a systematic treatment. Second, we provide a review of the methods that have been proposed or in use. Third, we present recent and new device and logic-level techniques for design obfuscation. For each technique considered, we discuss feasibility of the approach and assess likelihood of its detection. Then we turn our focus to open research questions, and conclude with suggestions for future research directions.


title: Phone recognition with the mean-covariance restricted Boltzmann machine

abstract: Straightforward application of Deep Belief Nets (DBNs) to acoustic modeling produces a rich distributed representation of speech data that is useful for recognition and yields impressive results on the speaker-independent TIMIT phone recognition task. However, the first-layer Gaussian-Bernoulli Restricted Boltzmann Machine (GRBM) has an important limitation, shared with mixtures of diagonal-covariance Gaussians: GRBMs treat different components of the acoustic input vector as conditionally independent given the hidden state. The mean-covariance restricted Boltzmann machine (mcRBM), first introduced for modeling natural images, is a much more representationally efficient and powerful way of modeling the covariance structure of speech data. Every configuration of the precision units of the mcRBM specifies a different precision matrix for the conditional distribution over the acoustic space. In this work, we use the mcRBM to learn features of speech data that serve as input into a standard DBN. The mcRBM features combined with DBNs allow us to achieve a phone error rate of 20.5%, which is superior to all published results on speaker-independent TIMIT to date.


title: Towards a Taxonomy of Intrusion-Detection Systems

abstract: Intrusion-detection systems aim at detecting attacks against computer systems and networks, or against information systems in general, as it is difficult to provide provably secure information systems and maintain them in such a secure state for their entire lifetime and for every utilization. Sometimes, legacy or operational constraints do not even allow a fully secure information system to be realized at all. Therefore, the task of intrusion-detection systems is to monitor the usage of such systems and to detect the apparition of insecure states. They detect attempts and active misuse by legitimate users of the information systems or external parties to abuse their privileges or exploit security vulnerabilities. In this paper, we introduce a taxonomy of intrusion-detection systems that highlights the various aspects of this area. This taxonomy defines families of intrusion-detection systems according to their properties. It is illustrated by numerous examples from past and current projects.


title: Extending English ACE 2005 Corpus Annotation with Ground-truth Links to Wikipedia

abstract: This paper describes an on-going annotation effort which aims at adding a manual annotation layer connecting an existing annotated corpus such as the English ACE-2005 Corpus to Wikipedia. The annotation layer is intended for the evaluation of accuracy of linking to Wikipedia in the framework of a coreference resolution system.


title: Developmental mechanisms channeling cortical evolution

abstract: Increase in the area and neuron number of the cerebral cortex over evolutionary time systematically changes its computational properties. One of the fundamental developmental mechanisms generating the cortex is a conserved rostrocaudal gradient in duration of neuron production, coupled with distinct asymmetries in the patterns of axon extension and synaptogenesis on the same axis. A small set of conserved sensorimotor areas with well-defined thalamic input anchors the rostrocaudal axis. These core mechanisms organize the cortex into two contrasting topographic zones, while systematically amplifying hierarchical organization on the rostrocaudal axis in larger brains. Recent work has shown that variation in ‘cognitive control’ in multiple species correlates best with absolute brain size, and this may be the behavioral outcome of this progressive organizational change.


title: Security in Software Defined Networks: A Survey

abstract: Software defined networking (SDN) decouples the network control and data planes. The network intelligence and state are logically centralized and the underlying network infrastructure is abstracted from applications. SDN enhances network security by means of global visibility of the network state where a conflict can be easily resolved from the logically centralized control plane. Hence, the SDN architecture empowers networks to actively monitor traffic and diagnose threats to facilitates network forensics, security policy alteration, and security service insertion. The separation of the control and data planes, however, opens security challenges, such as man-in-the middle attacks, denial of service (DoS) attacks, and saturation attacks. In this paper, we analyze security threats to application, control, and data planes of SDN. The security platforms that secure each of the planes are described followed by various security approaches for network-wide security in SDN. SDN security is analyzed according to security dimensions of the ITU-T recommendation, as well as, by the costs of security solutions. In a nutshell, this paper highlights the present and future security challenges in SDN and future directions for secure SDN.


title: Switchable Printed Yagi-Uda Antenna with Pattern Reconfiguration

abstract: A switchable Yagi-Uda antenna prototype with radiation pattern reconfiguration is presented in this letter. The proposed reconfigurable antenna is based on the concept of switching between the reflector and director of a Yagi-Uda antenna using a radio frequency PIN diode. As a result, the minimum/maximum radiation can be steered towards desired signals or away from interfering signals in opposite directions. The measured 10 dB impedance bandwidth and gain are 210 MHz (7%) and 8.02 dBi at 3 GHz, respectively. Details of the antenna design and its performance are described and empirically analyzed.


title: Ketogenic diet benefits body composition and well-being but not performance in a pilot case study of New Zealand endurance athletes

abstract: Background ::: Low-carbohydrate, high-fat and ketogenic diets are increasingly adopted by athletes for body composition and sports performance enhancements. However, as yet, there is no consensus on their efficacy in improving performance. There is also no comprehensive literature on athletes’ experiences while undertaking this diet. The purpose of this pilot work was two-fold: i. to examine the effects of a non-calorie controlled ketogenic diet on body composition and performance outcomes of endurance athletes, and ii. to evaluate the athletes’ experiences of the ketogenic diet during the 10-week intervention.


title: Orthogonal polynomials for complex Gaussian processes

abstract: Power amplifiers are the major source of nonlinearity in communications systems. Such nonlinearity causes spectral regrowth as well as in-band distortion, which leads to adjacent channel interference and increased bit error rate. Polynomials are often used to model the nonlinear power amplifier or its predistortion linearizer. In this paper, we present a novel set of orthogonal polynomials for baseband Gaussian input to replace the conventional polynomials and show how they alleviate the numerical instability problem associated with the conventional polynomials. The orthogonal polynomials also provide an intuitive means of spectral regrowth analysis.


Monoaural Audio Source Separation Using Variational Autoencoders


title: A brain-controlled switch for asynchronous control applications

abstract: Asynchronous control applications are an important class of application that has not received much attention from the brain-computer interface (BCI) community. This work provides a design for an asynchronous BCI switch and performs the first extensive evaluation of an asynchronous device in attentive, spontaneous electroencephalographic (EEG) signals. The switch design [named the low-frequency asynchronous switch design (LF-ASD)] is based on a new feature set related to imaginary movements in the 1-4 Hz frequency range. This new feature set was identified from a unique analysis of EEG using a bi-scale wavelet. Offline evaluations of a prototype switch demonstrated hit (true positive) rates in the range of 38%-81% with corresponding false positive rates in the range of 0.3%-11.6%. The performance of the LF-ASD was contrasted with two other ASDs: one based on mu-power features and another based on the outlier processing method (OPM) algorithm. The minimum mean error rates for the LF-ASD were shown to be significantly lower than either of these other two switch designs.


title: Deep Convex Net: A Scalable Architecture for Speech Pattern Classification.

abstract: We recently developed context-dependent DNN-HMM (DeepNeural-Net/Hidden-Markov-Model) for large-vocabulary speech recognition. While achieving impressive recognition error rate reduction, we face the insurmountable problem of scalability in dealing with virtually unlimited amount of training data available nowadays. To overcome the scalability challenge, we have designed the deep convex network (DCN) architecture. The learning problem in DCN is convex within each module. Additional structure-exploited fine tuning further improves the quality of DCN. The full learning in DCN is batch-mode based instead of stochastic, naturally lending it amenable to parallel training that can be distributed over many machines. Experimental results on both MNIST and TIMIT tasks evaluated thus far demonstrate superior performance of DCN over the DBN (Deep Belief Network) counterpart that forms the basis of the DNN. The superiority is reflected not only in training scalability and CPU-only computation, but more importantly in classification accuracy in both tasks.


title: Addressing Deafness and Hidden Terninal Problem in Directional Antenna based Wireless Multi-Hop Networks

abstract: We address deafness and directional hidden terminal problem that occur when MAC protocols are designed for directional antenna based wireless multi-hop networks. Deafness occurs when the transmitter fails to communicate to its intended receiver, because the receiver's antenna is oriented in a different direction. The directional hidden terminal problem occurs when the transmitter fails to hear a prior RTS/CTS exchange between another pair of nodes and cause collision by initiating a transmission to the receiver of the ongoing communication. Though directional antennas offer better spatial reuse, these problems can have a serious impact on network performance. In this paper, we study various scenarios in which these problems can occur and design a MAC protocol that solves them comprehensively using only a single channel and single radio interface. Current solutions in literature either do not address these issues comprehensively or use more than one radio/channel to solve them. We evaluate our protocol using detailed simulation studies. Simulation results indicate that our protocol can effectively address deafness and directional hidden terminal problem and increase network performance.


title: Reduced Graphene Oxide-Based Piezoelectric Nanogenerator With Water Excitation

abstract: Generation of electrical power using mechanical energies that are readily available in the environment has become indispensible to provide source energy to all the electronic devices that are used in day-to-day lives. In this study, reduced graphene oxide (RGO)/poly methyl methacralyte (PMMA)-based hybrid film has been employed for nanogeneration. The thin films were characterized using atomic force microscopy, field emission scanning electron microscopy, and X-ray photoelectron spectroscopy. The mechanical force exerted due to falling water on the device has been converted to electrical current by the RGO/PMMA based nanogenerators. The magnitude of generated current varied with volume of water dropped on the device. The roles of amount of functional groups present in RGO, metal stripes as bottom electrode and the spacing between the fingers have been explored. It was demonstrated that the device can also operate in selfpowered mode, i.e., without applying any bias. It is believed that this study would help in development of highly efficient RGO-based nanogenerator in a cost effective manner.


title: A new RPR fairness algorithm based on deficit round robin scheduling algorithm

abstract: Resilient packet ring (RPR) is a new kind of metropolitan area network technology which has a fast automatic protection switching ability. Fairness algorithm is a key technology of RPR. Current fairness algorithms cannot get high bandwidth utilization and suffer from permanent oscillations under unbalanced conditions. A new fairness algorithm based on deficit round robin (FBDRR) is presented. Its ideal model, based on the GPS (generalized processor sharing) scheduling algorithm, is proved to be RIAS (ring ingress aggregated with spatial reuse) fair. Analysis and simulations prove that FBDRR can solve the oscillation problems, allocate bandwidth fairly and converge very quickly.


title: Low voltage ride through (LVRT) ability of DFIG based wind energy conversion system-I

abstract: Doubly Fed Induction Generators (DFIGs) are nowadays extensively used in variable speed wind power plants. Doubly fed induction generators (DFIG) offer many advantages such as reduced converter rating, low cost and reduced losses with an improved efficiency, easy implementation of power factor correction schemes, variable speed operation and four quadrants active and reactive power control capabilities. Due to variable speed operation total energy output is much more in case of DFIG based WECS so capacity utilization factor is improved and cost of per unit energy is reduced. But the main disadvantage of DFIG is that it is very sensitive to grid disturbance/fault, especially for the voltage dip. Since the doubly-fed induction generator (DFIG) has been widely used in wind energy conversion, the low voltage ride through (LVRT) technology of the DFIG has been investigated extensively in recent times. This paper focuses on the Asymmetrical fault ride-through capability of doubly fed induction generator (DFIG) based WECSs. The paper also provides an overview on the interaction between variable-speed DFIG based WECSs and the power system subjected to disturbances. The dynamic behaviour of DFIG wind turbines during Asymmetrical grid faults is simulated and assessed.


title: RESEARCH AND PRACTICE Evaluating the Impact of Menu Labeling on Food Choices

abstract: OBJECTIVES ::: We assessed the impact of restaurant menu calorie labels on food choices and intake. ::: ::: ::: METHODS ::: Participants in a study dinner (n=303) were randomly assigned to either (1) a menu without calorie labels (no calorie labels), (2) a menu with calorie labels (calorie labels), or (3) a menu with calorie labels and a label stating the recommended daily caloric intake for an average adult (calorie labels plus information). Food choices and intake during and after the study dinner were measured. ::: ::: ::: RESULTS ::: Participants in both calorie label conditions ordered fewer calories than those in the no calorie labels condition. When calorie label conditions were combined, that group consumed 14% fewer calories than the no calorie labels group. Individuals in the calorie labels condition consumed more calories after the study dinner than those in both other conditions. When calories consumed during and after the study dinner were combined, participants in the calorie labels plus information group consumed an average of 250 fewer calories than those in the other groups. ::: ::: ::: CONCLUSIONS ::: Calorie labels on restaurant menus impacted food choices and intake; adding a recommended daily caloric requirement label increased this effect, suggesting menu label legislation should require such a label. Future research should evaluate menu labeling's impact on children's food choices and consumption.


title: Gecko adhesion: evolutionary nanotechnology

abstract: If geckos had not evolved, it is possible that humans would never have invented adhesive nanostructures. Geckos use millions of adhesive setae on their toes to climb vertical surfaces at speeds of over 1 m s−1. Climbing presents a significant challenge for an adhesive in requiring both strong attachment and easy rapid removal. Conventional pressure-sensitive adhesives (PSAs) are either strong and difficult to remove (e.g. duct tape) or weak and easy to remove (e.g. sticky notes). The gecko adhesive differs dramatically from conventional adhesives. Conventional PSAs are soft viscoelastic polymers that degrade, foul, self-adhere and attach accidentally to inappropriate surfaces. In contrast, gecko toes bear angled arrays of branched, hair-like setae formed from stiff, hydrophobic keratin that act as a bed of angled springs with similar effective elastic modulus to that of PSAs. Setae are self-cleaning and maintain function for months during repeated use in dirty conditions. Setae are an anisotropic ‘frictional adhesive’ in that adhesion requires maintenance of a proximally directed shear load, enabling either a tough bond or spontaneous detachment. Gecko-like synthetic adhesives may become the glue of the future—and perhaps the screw of the future as well.


title: Misinformation spreading on Facebook

abstract: Social media are pervaded by unsubstantiated or untruthful rumors, that contribute to the alarming phenomenon of misinformation. The widespread presence of a heterogeneous mass of information sources may affect the mechanisms behind the formation of public opinion. Such a scenario is a florid environment for digital wildfires when combined with functional illiteracy, information overload, and confirmation bias. In this essay, we focus on a collection of works aiming at providing quantitative evidence about the cognitive determinants behind misinformation and rumor spreading. We account for users’ behavior with respect to two distinct narratives: (a) conspiracy and (b) scientific information sources. In particular, we analyze Facebook data on a time span of 5 years in both the Italian and the US context, and measure users’ response to (1) information consistent with one’s narrative, (2) troll contents, and (3) dissenting information e.g., debunking attempts. Our findings suggest that users tend to (a) join polarized communities sharing a common narrative (echo chambers), (b) acquire information confirming their beliefs (confirmation bias) even if containing false claims, and (c) ignore dissenting information.


title: Effect of Treatment with Gibberellin, Gibberellin Biosynthesis Inhibitor, and Auxin on Steviol Glycoside Content in Stevia rebaudiana Bertoni

abstract: Steviol glycosides (SGs) are natural compounds present in Stevia rebaudiana (Bertoni) Bertoni, which are approximately 200–400 times sweeter than sucrose. A biosynthetic pathway of SGs shares a biosynthetic pathway of gibberellin (GA) through a formation of ent-kaurenoic acid. Previous studies have demonstrated that SG-related genes are upregulated by exogenous GA treatment. However, the transcription level of UGT85C2, which is involved in a glycosylation step to steviol from steviolmonoside and suggests a rate-limiting step in the SGs biosynthesis, remains controversial in GA treatments. To have a detailed understanding of the relationship between SGs and GAs, a concentration gradient of exogenous GA3, GA inhibitor daminozide (DAM), and naphthaleneacetic acid (NAA) was used for treatment by the leaf infiltration method. Results showed that transcription level of UGT85C2 and the total number of stevioside and rebaudioside-A induced by GA3, DAM, or NAA treatment were significantly upregulated compared to an untreated group. These results suggest that the GA biosynthetic pathway affects the number of SGs and that hormonal crosstalk occurs between GA and auxin, which changes the number of SGs in S. rebaudiana. Controlling the GA biosynthesis could be considered to modulate the yield of SGs.


title: Joint disease mapping of cervical and male oropharyngeal cancer incidence in blacks and whites in South Carolina.

abstract: Human papillomavirus (HPV) infection is an established causal agent for cervical cancer and a subset of oropharyngeal cancers. It is hypothesized that orogenital transmission results in oral cavity infection. In this paper we explore the geographical association between cervical and male oropharyngeal cancer incidence in blacks and whites in South Carolina using Bayesian joint disease mapping models fit to publicly available data. Our results suggest weak evidence for county-level association between the diseases, and different patterns of joint disease behavior for blacks and whites.


title: Recognition of Human Iris Using Accurate Iris Map

abstract: Personal identification based on Biometrics technology is a trend in future. Iris Recognition is regarded as a high accuracy verification technology when compared to traditional approaches. In real-time Iris Recognition, Iris Localization is very important step for Iris Recognition. So, the Iris Masks plays an important role in iris recognition. This Iris Mask indicates which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. If the Iris Mask is inaccurate, it will decrease its performance in Iris Recognition system. So, we use learning- based algorithms to estimate accurate Iris Masks from Iris Images, which propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Index Terms: Gaussian mixture models, iris mask, iris recognition, iris occlusion estimation, simulated annealing.


title: Robot-Mediated Interviews: Do Robots Possess Advantages over Human Interviewers When Talking to Children with Special Needs?

abstract: Children that have a disability are up to four times more likely to be a victim of abuse than typically developing children. However, the number of cases that result in prosecution is relatively low. One of the factors influencing this low prosecution rate is communication difficulties. Our previous research has shown that typically developing children respond to a robotic interviewer very similar compared to a human interviewer. In this paper we conduct a follow up study investigating the possibility of Robot-Mediated Interviews with children that have various special needs. In a case study we investigated how 5 children with special needs aged 9 to 11 responded to the humanoid robot KASPAR compared to a human in an interview scenario. The measures used in this study include duration analysis of responses, detailed analysis of transcribed data, questionnaire responses and data from engagement coding. The main questions in the interviews varied in difficulty and focused on the theme of animals and pets. The results from quantitative data analysis reveal that the children interacted with KASPAR in a very similar manner to how they interacted with the human interviewer, providing both interviewers with similar information and amounts of information regardless of question difficulty. However qualitative analysis suggests that some children may have been more engaged with the robotic interviewer.


title: G.O.T.H.A.M. - mesh network management and visualization for batman-adv

abstract: The mesh network has propagated rapidly, because of the Internet. All over the world, many people cannot use the Internet, because the Internet cable is not installed. So many mesh network communities try to set up the mesh network nodes and manage it. By its effort, there are more than 50 thousand mesh nodes in the world. However, there are no enough people who can make the mesh network and can manage it. By this reason, we develop open source GOTHAM. It has three main part. There are “Self-Organizing”, “Visualization” and “Content-Centric”. G.O.T.H.A.M is designed for people who don't know the network. So, it focuses simplicity, familiarity and automaticity. In this paper we explain original G.O.T.H.A.M and new G.O.T.H.A.M with Hazelcast including three functions. And also, we describe the program which runs with GOTHAM. All of the software in this paper is available on Github.